Stock_Recommendation_System.ipynb

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   "source": [
    "### GROUP MEMBER:\n",
    "**1. Van Le:** vanle@buffalo.edu - CSE 487\n",
    "\n",
    "**2. Maria Anthony:** mariniv@buffalo.edu - CSE 587\n",
    "\n",
    "**3. Anushka:** atiwari4@buffalo.edu - CSE 587"
   ]
  },
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   "source": [
    "# STOCK-BASED RECOMMENDATION SYSTEM "
   ]
  },
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   "source": [
    "# PHASE I - EDA"
   ]
  },
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   "source": [
    "## 1. Install and import necessary libraries:"
   ]
  },
  {
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   "source": [
    "!pip install pmdarima\n",
    "!pip install xgboost\n",
    "! pip install graph-based-clustering \n",
    "!pip install git+https://github.com/huggingface/transformers\n",
    "!pip install transformers\n",
    "!pip install tensorflow_probability==0.12.2\n",
    "!pip install contractions\n",
    "!pip install emoji\n",
    "!pip install emoticon_fix\n",
    "!pip install -U accelerate\n",
    "!pip install -U spacy==3.*\n",
    "!python -m spacy download en_core_web_sm\n",
    "!python -m spacy info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef061e05-6784-43dd-943c-c97ea8b6aefe",
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   "outputs": [],
   "source": [
    "import os, sys\n",
    "import requests\n",
    "import glob\n",
    "import datetime\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import string\n",
    "import re\n",
    "import ta\n",
    "import itertools\n",
    "import spacy\n",
    "import emoji\n",
    "import collections\n",
    "from pyspark.sql import SparkSession\n",
    "from datetime import datetime, date\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from statsmodels.tsa.stattools import adfuller \n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from pmdarima import auto_arima\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import RandomForestRegressor, StackingRegressor\n",
    "from sklearn.svm import SVR, LinearSVR\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics.pairwise import cosine_distances\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error, confusion_matrix\n",
    "from sklearn.metrics.pairwise import linear_kernel, cosine_similarity\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from pandas.plotting import lag_plot\n",
    "# from tensorflow.keras.models import Sequential\n",
    "# from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
    "import xgboost as xgb\n",
    "from xgboost import plot_importance, plot_tree\n",
    "import contractions\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize, PunktSentenceTokenizer\n",
    "from nltk.stem import PorterStemmer, SnowballStemmer, LancasterStemmer, WordNetLemmatizer\n",
    "from bs4 import BeautifulSoup\n",
    "nltk.download('stopwords', quiet=True)\n",
    "nltk.download('wordnet', quiet=True)\n",
    "nltk.download('punkt', quiet=True)\n",
    "# nltk.download('omw-1.4')\n",
    "from emoticon_fix import emoticon_fix\n",
    "from scipy.sparse import coo_matrix\n",
    "# Use a pipeline as a high-level helper\n",
    "# from transformers import pipeline\n",
    "# pipe_sentiment = pipeline(\"text-classification\", model=\"mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis\")"
   ]
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   "source": [
    "## 2. Data Acquisition and Preparation:"
   ]
  },
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   "source": [
    "### 2.1. NASDAQ Data Retrieval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ea7d55f-8e99-4daf-90d8-0067499a0b76",
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   "source": [
    "# Read cvs file to Dataframe \n",
    "ticker_df = pd.read_csv(\"//Users//marianivethaantonypushparaj//Downloads//nasdaq_ticker_symbols.csv\").reset_index(drop=True)\n",
    "ticker_df.head()"
   ]
  },
  {
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   "id": "dfe132e0-8c9a-4b0a-a150-8df83d1b2336",
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   "source": [
    "### 2.2. Historical Stocks Data"
   ]
  },
  {
   "cell_type": "code",
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   "id": "14e51cfc-779d-4c12-889d-85e1ecd112fb",
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   "source": [
    "# Define the directory where your CSV files are located\n",
    "folder_path = 'Dataset//Stocks'\n",
    "\n",
    "# Initialize an empty DataFrame to store the concatenated data\n",
    "stock_df = []\n",
    "\n",
    "# Loop through all files in the directory\n",
    "for filename in os.listdir(folder_path):\n",
    "    if filename.endswith(\".us.txt\"):\n",
    "        file_path = os.path.join(folder_path, filename)\n",
    "        df = pd.read_csv(file_path, header=None, names=[\"Date\", \"Open\", \"High\", \"Low\", \"Close\", \"Volume\", \"OpenInt\"])\n",
    "        \n",
    "        # Extract the stock symbol from the file name\n",
    "        stock_ticker = filename.split(\".\")[0].upper()\n",
    "        \n",
    "        # Add a \"stock\" column with the stock symbol\n",
    "        df['Ticker'] = stock_ticker\n",
    "        \n",
    "        # Drop the first row \n",
    "        df = df.iloc[1:]\n",
    "        \n",
    "        # Append the DataFrame to the concatenated_data DataFrame\n",
    "        stock_df.append(df)\n",
    "        print(\"Finished processing\", filename)\n",
    "\n",
    "# Save the concatenated data to a new CSV file\n",
    "stock_df = pd.concat(stock_df, ignore_index=True)"
   ]
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    "# Sort the DataFrame based on the 'Ticker' column\n",
    "stock_df.sort_values(by='Ticker', inplace=True)\n",
    "\n",
    "# Save the sorted data to a new CSV file\n",
    "# stock_df.to_csv('//Users//vanle//Downloads//stock_sorted.csv', index=False)\n",
    "\n",
    "merged_df = pd.read_csv(\"//Users//marianivethaantonypushparaj//Downloads//stock_sorted.csv\").reset_index(drop=True)\n",
    "\n",
    "# Merge 2 dataframes using an inner join on the 'Ticker' column\n",
    "merged_df = pd.merge(merged_df, ticker_df, left_on='Ticker', right_on='Symbol', how='inner')\n",
    "\n",
    "# Drop the duplicate 'Ticker' column \n",
    "merged_df.drop(columns=['Symbol'], inplace=True)\n",
    "\n",
    "# Drop the 'Volume_x' column\n",
    "merged_df.drop(columns=['Volume_y'], inplace=True)\n",
    "\n",
    "# Rename the 'Volume_y' column to 'Volume'\n",
    "merged_df.rename(columns={'Volume_x': 'Volume'}, inplace=True)\n",
    "merged_df"
   ]
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   "id": "95bc1195-ad97-480a-8a51-e1c4a018e56d",
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   "source": [
    "### 2.3. Exploratory Data Analysis"
   ]
  },
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   "source": [
    "#### 2.3.1. Data Cleaning"
   ]
  },
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   "source": [
    "# Check the Uniqueness of 'OpenInt' Column\n",
    "merged_df['OpenInt'].nunique()\n",
    "\n",
    "# Drop 'OpenInt' and 'IPO Year' column since it has no contribution in our data\n",
    "merged_df = merged_df.drop(['OpenInt'], axis=1)\n",
    "\n",
    "# Remove duplicate rows\n",
    "merged_df=merged_df.drop_duplicates(subset=['Open', 'High', 'Low', 'Close','Volume'], keep='last')\n",
    "\n",
    "# Convert 'Date' column to datetime format\n",
    "merged_df['Date'] = pd.to_datetime(merged_df['Date'])\n",
    "\n",
    "# Check for missing values\n",
    "missing_values = merged_df.isnull().sum()\n",
    "print(\"Missing Values:\\n\", missing_values)\n",
    "\n",
    "# Fill NaN values in these column: 'Country', 'Sector', 'Industry'  with 'Other'\n",
    "merged_df[['Country', 'Sector', 'Industry']] = merged_df[['Country', 'Sector', 'Industry']].fillna('Other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c15f354a-d482-4ca6-a1cb-21f9a45aa41f",
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   "source": [
    "# Check Data Types of Each Column\n",
    "merged_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b3a7a2d-16f7-4ae8-8f54-693bb12d25fa",
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   "source": [
    "# Convert 'Open', 'High', 'Low', 'Close', 'Last Sale' columns to float\n",
    "merged_df['Open'] = merged_df['Open'].astype(str).str.replace('$', '').astype(float)\n",
    "merged_df['High'] = merged_df['High'].astype(str).str.replace('$', '').astype(float)\n",
    "merged_df['Low'] = merged_df['Low'].astype(str).str.replace('$', '').astype(float)\n",
    "merged_df['Close'] = merged_df['Close'].astype(str).str.replace('$', '').astype(float)\n",
    "merged_df['Last Sale'] = merged_df['Last Sale'].astype(str).str.replace('$', '').astype(float)\n",
    "\n",
    "# Convert 'Volume' column to integer (remove commas)\n",
    "merged_df['Volume'] = merged_df['Volume'].astype(str).str.replace(',', '').astype(int)\n",
    "\n",
    "# Convert '% Change' column to float, remove '%' and handle missing values\n",
    "merged_df['% Change'] = merged_df['% Change'].astype(str).str.replace('%', '')\n",
    "merged_df['% Change'] = pd.to_numeric(merged_df['% Change'], errors='coerce')\n",
    "\n",
    "# Arranging the companies based on Date\n",
    "merged_df = merged_df.sort_values(['Ticker','Date']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1650542-e38c-4480-9beb-8139ec95fc0e",
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   "outputs": [],
   "source": [
    "# Calculate Percentage Change in 'Volume'\n",
    "merged_df['Volume change'] = merged_df['Volume'].pct_change() * 100\n",
    "\n",
    "# Finding largest values of volume column\n",
    "comp_volume = merged_df.groupby('Name')['Volume'].sum()\n",
    "comp_volume.nlargest(10)\n",
    "comp_volume = pd.DataFrame(comp_volume)\n",
    "comp_volume = comp_volume.reset_index()\n",
    "\n",
    "# Dropping the data of those companies because if it shows infinity or o\n",
    "for i in range(0,len(comp_volume)):\n",
    "    if comp_volume['Volume'][i]=='inf' or comp_volume['Volume'][i]<=0:\n",
    "        merged_df=merged_df.drop(merged_df[merged_df['Name'] == comp_volume['Name'][i]].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23b707a7-6db7-439f-9d26-21b35afa36e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking if any outliers in Open,High,Low and Close as price cant be negative or 0\n",
    "merged_df = merged_df.drop(merged_df[merged_df['Open'] <=0].index)\n",
    "merged_df = merged_df.drop(merged_df[merged_df['High'] <=0].index)\n",
    "merged_df = merged_df.drop(merged_df[merged_df['Low'] <=0].index)\n",
    "merged_df = merged_df.drop(merged_df[merged_df['Close'] <=0].index)\n",
    "merged_df = merged_df.drop(merged_df[merged_df['Last Sale'] <=0].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "339b0807-2a72-4613-b291-d5cec27e727f",
   "metadata": {
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   "source": [
    "#### 2.3.2. EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5e39ef-e93e-4dce-84dc-8a0e8e92b0d6",
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   "source": [
    "##### Sector and Industry Breakdown of Company Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dd47a1c-061a-41b7-ba24-6fabf61d7157",
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   "source": [
    "# Sector and Industry Analysis\n",
    "sector_counts = ticker_df['Sector'].value_counts()\n",
    "industry_counts = ticker_df['Industry'].value_counts()\n",
    "\n",
    "# Plot the histogram of company sectors and industries \n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "sector_counts.plot(kind='bar')\n",
    "plt.title('Fig 1. Number of Companies by Sector', fontsize=15)\n",
    "plt.ylabel('Sector counts')\n",
    "plt.xlabel('Industries')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "industry_counts.head(10).plot(kind='bar')\n",
    "plt.title('Fig 2. Top 10 Industries by Company Count', fontsize=15)\n",
    "plt.xlabel('Industries')\n",
    "plt.ylabel('No. of industries based on count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b327937b-e44f-4465-acb8-aecca75811bc",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Relative contribution of each country in stock markets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaf24b49-445b-4507-aa4f-191d6a1f4ee1",
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   "source": [
    "# Country Analysis\n",
    "# Count the occurrences of each country in 'Country'\n",
    "country_counts = ticker_df['Country'].value_counts().head(10)\n",
    "\n",
    "# Plot a pie chart of the top 10 countries\n",
    "plt.figure(figsize=(12, 10))\n",
    "country_counts.plot(kind='pie', autopct='%1.2f%%')\n",
    "plt.title('Fig 3. Distribution of Top 10 Countries with many companies', fontsize=15)\n",
    "plt.ylabel('Country', fontsize=14)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b4cb78c-29d0-4bf6-bbca-054d4ae2bb08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot a pie chart of the countries excluding the top 10\n",
    "plt.figure(figsize=(12,10))\n",
    "drop_catgs = ['United States','Canada','China','Israel','United Kingdom','Bermuda','Singapore','Hong Kong','Cayman Islands','Greece']\n",
    "country_counts1=(merged_df['Country'].value_counts()).drop(drop_catgs)\n",
    "country_counts1.plot(kind='pie')\n",
    "plt.title('Fig 4. Distribution of Companies by Country without Top 10 countries', fontsize=15)\n",
    "plt.ylabel('Country', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57a66db-265e-4d4f-a4ec-a65d4403670c",
   "metadata": {
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    "tags": []
   },
   "source": [
    "##### Identify relationships between these variables using correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b042e695-8e45-4959-99a3-07b2f407e4c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correlation Matrix\n",
    "plt.figure(figsize=(16, 10))\n",
    "plt.title('Fig 5. Correlation matrix of historical and current market information', fontsize=15)\n",
    "correlation_matrix = merged_df.corr()\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0861b4-75ab-4c33-9270-cbfb623d2d73",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Exploring Market Cap Variation and Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27969f9b-c2fc-4105-9de0-ef536df609a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Market Cap by Sectors\n",
    "plt.figure(figsize=(20, 6))\n",
    "palette = sns.color_palette('tab10')\n",
    "sns.boxplot(x='Sector', y='Market Cap', data=merged_df, palette=palette)\n",
    "plt.title('Fig 6. Market Cap by Sector', fontsize=15)\n",
    "plt.xlabel('Sector', fontsize=14)\n",
    "plt.ylabel('Market Cap', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1078bae-1abc-451b-9dba-4b5f582f8934",
   "metadata": {
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    "tags": []
   },
   "source": [
    "##### Analyze market cap quartiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22a6d91a-aeb6-4867-986c-3da504b6f715",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the number of quartiles\n",
    "num_quantiles = 4 \n",
    "\n",
    "# Calculate the quartile values for market cap\n",
    "quartiles = merged_df['Market Cap'].quantile([0, 0.25, 0.5, 0.75, 1])\n",
    "\n",
    "# Create masks for each quartile\n",
    "mask_1st_quartile = (merged_df['Market Cap'] >= quartiles[0]) & (merged_df['Market Cap'] <= quartiles[0.25])\n",
    "mask_2nd_quartile = (merged_df['Market Cap'] > quartiles[0.25]) & (merged_df['Market Cap'] <= quartiles[0.5])\n",
    "mask_3rd_quartile = (merged_df['Market Cap'] > quartiles[0.5]) & (merged_df['Market Cap'] <= quartiles[0.75])\n",
    "mask_4th_quartile = (merged_df['Market Cap'] > quartiles[0.75])\n",
    "\n",
    "# Create DataFrames for each quartile\n",
    "quartile_1 = merged_df[mask_1st_quartile]\n",
    "quartile_2 = merged_df[mask_2nd_quartile]\n",
    "quartile_3 = merged_df[mask_3rd_quartile]\n",
    "quartile_4 = merged_df[mask_4th_quartile]\n",
    "\n",
    "# Descriptive statistic for market cap quartiles\n",
    "quartile_1.describe()\n",
    "quartile_2.describe()\n",
    "quartile_3.describe()\n",
    "quartile_4.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96163961-2949-4385-9133-6fd9b185589e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Understanding Trading Volume Dynamics by Sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb974d09-b156-4491-8899-c1cb01ece0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trading Volume Analysis\n",
    "plt.figure(figsize=(20, 6))\n",
    "palette = sns.color_palette(\"husl\", 8)\n",
    "sns.boxplot(x='Sector', y='Volume', data=merged_df)\n",
    "plt.title('Fig 7. Trading Volume by Sector', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93ca9db8-66ca-4f7a-aa5c-5525ef30e137",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Calculate trading volume statistics by sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "330984cd-a6e5-4e0d-b249-b3b3a95b8f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate trading volume statistics by sector\n",
    "volume_stats = merged_df.groupby('Sector')['Volume'].describe()\n",
    "print(volume_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc45938e-ce8c-420b-ab33-55b04bda5f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a box plot to visualize trading volume distribution by sector\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.boxplot(x='Sector', y='Volume', data=merged_df, order=volume_stats.index, palette='Set3')\n",
    "plt.title('Fig 8. Trading Volume Distribution by Sector', fontsize=15)\n",
    "plt.xlabel('Sector', fontsize=14)\n",
    "plt.ylabel('Trading Volume', fontsize=14)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7445c1bc-0f38-44d1-8ccf-b52c023ca1fe",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Correlation matrix to analyze relationships between trading volume and other variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "407c0324-bd4f-49f4-b140-e49da2d32492",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correlation matrix to analyze relationships between trading volume and other variables\n",
    "correlation_matrix = merged_df.corr()\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(correlation_matrix[['Volume']], annot=True, cmap='coolwarm')\n",
    "plt.title('Fig 9. Correlation of Trading Volume with Other Variables', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0defe539-f8bd-4c17-b56e-2303d3087a8b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Time-series plot for sample stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1584236-b207-4fc3-8cf3-169a9710e5d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter the data for AAPL stock\n",
    "aapl_df = merged_df[merged_df['Ticker'] == 'AAPL']\n",
    "# 'Date' as the index \n",
    "aapl_df.set_index('Date', inplace=True)\n",
    "# Plotting the closing prices\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.set_style('ticks')\n",
    "sns.lineplot(data=aapl_df, x='Date', y='Close', color='firebrick')\n",
    "sns.despine()\n",
    "plt.title('Fig 10. The AAPL Stock Close Price Over Time', size='x-large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b430cf41-edc0-42c0-883a-73064f7e9336",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Finding top 5 companies performance stocks based on last sale value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "523bcb89-0e01-4967-9e8c-ca1e51ce7f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the names of the top 5 companies based on 'Last Sale' values \n",
    "top5 = merged_df['Last Sale'].unique()\n",
    "top5_price = sorted(top5, reverse=True)[:5]\n",
    "top5_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7ddffd8-5519-4c1f-9ba5-8ba84f825ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append top 5 companies to the top_companies list\n",
    "top_companies = []\n",
    "top_companies.append(merged_df[merged_df['Last Sale'] == 5888.47]['Name'])\n",
    "top_companies.append(merged_df[merged_df['Last Sale'] == 3780.66]['Name'])\n",
    "top_companies.append(merged_df[merged_df['Last Sale'] == 2530.76]['Name'])\n",
    "top_companies.append(merged_df[merged_df['Last Sale'] == 1870.47]['Name'])\n",
    "top_companies.append(merged_df[merged_df['Last Sale'] == 1866.01]['Name'])\n",
    "top_companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33249be6-fc8f-4d8d-ac62-653cfc2c0d1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot top 5 companies stock prices\n",
    "Top1_df = merged_df[merged_df['Name'] == 'NVR Inc. Common Stock']\n",
    "Top2_df = merged_df[merged_df['Name'] == 'Seaboard Corporation Common Stock']\n",
    "Top3_df = merged_df[merged_df['Name'] == 'AutoZone Inc. Common Stock']\n",
    "Top4_df = merged_df[merged_df['Name'] == 'Texas Pacific Land Corporation Common Stock']\n",
    "Top5_df = merged_df[merged_df['Name'] == 'Chipotle Mexican Grill Inc. Common Stock']\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "sns.set_style(\"ticks\")\n",
    "sns.lineplot(data=Top1_df,x=\"Date\",y='Close',color='firebrick',label='NVR Inc. Common Stock')\n",
    "sns.lineplot(data=Top2_df,x=\"Date\",y='Close',color='k',label='Seaboard Corporation Common Stock')\n",
    "sns.lineplot(data=Top3_df,x=\"Date\",y='Close',color='y',label='AutoZone Inc. Common Stock')\n",
    "sns.lineplot(data=Top4_df,x=\"Date\",y='Close',color='purple',label='Texas Pacific Land Corporation Common Stock')\n",
    "sns.lineplot(data=Top5_df,x=\"Date\",y='Close',color='g',label='Chipotle Mexican Grill Inc. Common Stock')\n",
    "sns.despine()\n",
    "plt.title(\"Fig 11. The Stock Price Graph for Top 5 biggest Last sale companies all over the world\",size='x-large',color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b79618-b7dc-41fb-a9ca-f25861cb917e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### Finding the worst 5 companies performance stock based on last sale value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "797df121-2aae-4e98-a4a8-e1be01c6ba98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find worst performance companies based on sales\n",
    "worst5= merged_df['Last Sale'].unique()\n",
    "worst_price = sorted(worst5)[:5]\n",
    "worst_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1e70f52-46ec-43a8-8f42-bf9820540ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "low_perf = []\n",
    "low_perf.append(merged_df[merged_df['Last Sale'] == 0.01]['Name'])\n",
    "low_perf.append(merged_df[merged_df['Last Sale'] == 0.0401]['Name'])\n",
    "low_perf.append(merged_df[merged_df['Last Sale'] == 0.05]['Name'])\n",
    "low_perf.append(merged_df[merged_df['Last Sale'] == 0.0588]['Name'])\n",
    "low_perf.append(merged_df[merged_df['Last Sale'] == 0.09]['Name'])\n",
    "low_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0452f29-b672-41be-a0bd-449f0173734f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the worst companies \n",
    "Low1_df = merged_df[merged_df['Name'] == 'Global Partner Acquisition Corp II Warrant']\n",
    "Low2_df = merged_df[merged_df['Name'] == 'Crixus BH3 Acquisition Company Warrants']\n",
    "Low3_df = merged_df[merged_df['Name'] == 'Rose Hill Acquisition Corporation Warrant']\n",
    "Low4_df = merged_df[merged_df['Name'] == 'Oxbridge Re Holdings Limited Warrant expiring']\n",
    "Low5_df = merged_df[merged_df['Name'] == 'Polished Inc. Common Stock']\n",
    "\n",
    "plt.figure(figsize=(14,5))\n",
    "sns.set_style(\"ticks\")\n",
    "sns.lineplot(data=Low1_df,x=\"Date\",y='Close',color='firebrick',label='Global Partner Acquisition Corp II Warrant')\n",
    "sns.lineplot(data=Low2_df,x=\"Date\",y='Close',color='k',label='Crixus BH3 Acquisition Company Warrants')\n",
    "sns.lineplot(data=Low3_df,x=\"Date\",y='Close',color='y',label='Rose Hill Acquisition Corporation Warrant')\n",
    "sns.lineplot(data=Low4_df,x=\"Date\",y='Close',color='purple',label='Oxbridge Re Holdings Limited Warrant expiring')\n",
    "sns.lineplot(data=Low5_df,x=\"Date\",y='Close',color='g',label='Polished Inc. Common Stock')\n",
    "sns.despine()\n",
    "plt.title(\"Fig 12. The Stock Price Graph for Least 5 Last sale value companies all over the world\",size='x-large',color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad4ee45a-39dd-4a7f-ac01-2d0f7661f5b7",
   "metadata": {},
   "source": [
    "We can find that the dates for the first 4 and the last are unmatched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26aa1b45-2a16-47c8-bb57-b0122cf0aecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "Least1_df = merged_df[merged_df['Name'] == 'Global Partner Acquisition Corp II Warrant']\n",
    "Least2_df = merged_df[merged_df['Name'] == 'Crixus BH3 Acquisition Company Warrants']\n",
    "Least3_df = merged_df[merged_df['Name'] == 'Rose Hill Acquisition Corporation Warrant']\n",
    "Least4_df = merged_df[merged_df['Name'] == 'Oxbridge Re Holdings Limited Warrant expiring']\n",
    "\n",
    "plt.figure(figsize=(14,5))\n",
    "sns.set_style(\"ticks\")\n",
    "sns.lineplot(data=Least2_df,x=\"Date\",y='Close',color='k',label='Crixus BH3 Acquisition Company Warrants')\n",
    "sns.lineplot(data=Least3_df,x=\"Date\",y='Close',color='y',label='Rose Hill Acquisition Corporation Warrant')\n",
    "sns.lineplot(data=Least4_df,x=\"Date\",y='Close',color='purple',label='Oxbridge Re Holdings Limited Warrant expiring')\n",
    "sns.lineplot(data=Least1_df,x=\"Date\",y='Close',color='g',label='Global Partner Acquisition Corp II Warrant')\n",
    "sns.despine()\n",
    "plt.title(\"The Stock Price Graph for Least 4 Last sale value companies all over the world\",size='x-large',color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f29877c-1f39-4437-8862-58487796cd65",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3. Feature Selection and Engineer:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11496340-cbeb-4fe8-835f-90eb1a6c99fd",
   "metadata": {},
   "source": [
    " The most common way used to predict stock price movement is technical analysis. It forecasts the development of stock prices through an analysis of historical market data to identify the movement direction of stock prices and turning points in the time series. Different types of technical in- dicators, such as stochastic oscillator, moving averages, and relative strength index (RSI), are used in prediction models, and the effectiveness of these input features for future stock market forecasting is studied."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c963cfd1-44c8-4a99-b5fc-eb432fb52cc4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 3.1. Market Indicators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d5ec9a-a6c2-4c7c-9e0c-4ddecd76ef27",
   "metadata": {},
   "source": [
    "Apart from price features and volume, we choose technical indicators that give an indication of the trend, momentum, or volatility of the stock which also includes candlestick patterns, and strategy features. And, we derived them from raw data to feed them to the models.\n",
    "\n",
    "**1. Overlap Indicators:** \n",
    "- Simple Moving Average (SMA)\n",
    "- Exponential Moving Average (EMA)\n",
    "- Kaufman's Adaptive Moving Average (KAMA)\n",
    "\n",
    "**2. Momentum indicators:** \n",
    "- Average Directional Movement Index (ADX)\n",
    "- Moving Average Convergence/Divergence (MACD)\n",
    "- Money Flow Index\n",
    "- Momentum\n",
    "- Relative Strength Index (RSI)\n",
    "- Stochastic oscillator\n",
    "- Rate of Change (ROC)\n",
    "\n",
    "**3. Volume Indicators:** \n",
    "- Chaikin A/D Line (AD)\n",
    "- Chaikin Oscillator\n",
    "- On Balance Volume (OBV)\n",
    "\n",
    "**4. Volatility Indicators:** \n",
    "- Average True Range (ATR) \n",
    "- Normalized Average True Range\n",
    "- Bollinger Bands\n",
    "- Ichimoku Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bbdf3fd-4842-415d-a317-51810d91e4d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the window values for all technical indicators\n",
    "window=14\n",
    "\n",
    "# Calculate Simple Moving Average (SMA)\n",
    "sma = ta.trend.SMAIndicator(merged_df['Close'], window=window, fillna=True)\n",
    "merged_df['SMA'] = sma.sma_indicator()\n",
    "\n",
    "# Calculate Kaufman's Adaptive Moving Average (KAMA)\n",
    "kama = ta.momentum.KAMAIndicator(merged_df['Close'], window=window, pow1=2, pow2=30, fillna=True)\n",
    "merged_df['KAMA'] = kama.kama()\n",
    "\n",
    "# Calculate Exponential Moving Average (EMA)\n",
    "ema = ta.trend.EMAIndicator(merged_df['Close'], window=window, fillna=True)\n",
    "merged_df['EMA'] = ema.ema_indicator()\n",
    "\n",
    "# Calculate the Directional Movement Index (ADX)\n",
    "adx = ta.trend.ADXIndicator(merged_df['High'], merged_df['Low'], merged_df['Close'], window, fillna=True)\n",
    "merged_df['ADX'] = adx.adx()\n",
    "\n",
    "# Calculate Moving Average Convergence/Divergence (MACD)\n",
    "fast_window = 12  \n",
    "slow_window = 26  \n",
    "signal_window = 9  \n",
    "macd = ta.trend.MACD(merged_df['Close'], fast_window, slow_window, signal_window)\n",
    "merged_df['MACD'] = macd.macd()  \n",
    "merged_df['MACD Signal Line'] = macd.macd_signal()   \n",
    "\n",
    "# # Calculate Money Flow Index (MFI)\n",
    "mfi = ta.momentum.MFIIndicator(merged_df['High'], merged_df['Low'], merged_df['Close'], merged_df['Volume'], window=window, fillna=False)\n",
    "merged_df['MFI'] = mfi\n",
    "\n",
    "# Calculate MomentumIndicator\n",
    "momentum = ta.momentum.MomentumIndicator(merged_df['Close'], window=window, fillna=True)\n",
    "merged_df['Momentum'] = momentum.momentum()\n",
    "merged_df['Momentum_Signal'] = momentum.momentum_diff()\n",
    "\n",
    "# Calculate Relative Strength Index (RSI)\n",
    "rsi = RSIIndicator(merged_df['Close'], window=window, fillna=True)\n",
    "merged_df['RSI'] = rsi.rsi()\n",
    "\n",
    "# Calculate Stochastic Oscillator\n",
    "stoch = StochasticOscillator(merged_df['High'], merged_df['Low'], merged_df['Close'], window=window)\n",
    "merged_df['%K'] = stoch.stoch()\n",
    "merged_df['%D'] = stoch.stoch_signal()\n",
    "\n",
    "# Calculate Chaikin A/D Line (AD)\n",
    "chaikin_ad = ta.volume.ChaikinADIndicator(high=merged_df['High'],low=merged_df['Low'], close=merged_df['Close'], volume=merged_df['Volume'])\n",
    "merged_df['Chaikin_AD'] = chaikin_ad.ad()\n",
    "merged_df['Chaikin_ADOsc'] = chaikin_ad.ad_osc()\n",
    "\n",
    "# Calculate On-Balance Volume (OBV)\n",
    "obv = OnBalanceVolumeIndicator(merged_df['Close'], merged_df['Volume'])\n",
    "merged_df['OBV'] = obv.on_balance_volume()\n",
    "\n",
    "# Calculate Ichimoku Cloud\n",
    "ichimoku = ta.trend.IchimokuIndicator(high=merged_df['High'],\n",
    "                                      low=merged_df['Low'],\n",
    "                                      close=merged_df['Close'],\n",
    "                                      window1=9, window2=26, window3=52) \n",
    "merged_df['tenkan_sen'] = ichimoku.ichimoku_a()\n",
    "merged_df['kijun_sen'] = ichimoku.ichimoku_b()\n",
    "merged_df['senkou_span_a'] = ichimoku.ichimoku_a()\n",
    "merged_df['senkou_span_b'] = ichimoku.ichimoku_b()\n",
    "\n",
    "# Calculate Rate of Change (ROC)\n",
    "roc = ROCIndicator(merged_df['Close'], window=window)\n",
    "merged_df['ROC'] = roc.roc()\n",
    "\n",
    "# Calculate Average True Range (ATR)\n",
    "atr = AverageTrueRange(merged_df['High'], merged_df['Low'], merged_df['Close'], window=window)\n",
    "merged_df['ATR'] = atr.average_true_range()\n",
    "\n",
    "# Calculate Normalized Average True Range \n",
    "merged_df['Normalized_ATR'] = (merged_df['ATR'] / merged_df['Close']) * 100\n",
    "\n",
    "# Calculate Bollinger Bands\n",
    "bollinger_bands = ta.volatility.BollingerBands(merged_df['Close'], window=window, window_dev=2)\n",
    "merged_df['Bollinger_Bands_Middle'] = bollinger_bands.bollinger_mavg()\n",
    "merged_df['Bollinger_Bands_Upper'] = bollinger_bands.bollinger_hband()\n",
    "merged_df['Bollinger_Bands_Lower'] = bollinger_bands.bollinger_lband()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ed596dc-18a9-4c7d-b108-b2058742188e",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = merged_df.dropna()\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65cc335-60ba-4ed1-8b6a-cb3e3393fb5a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 3.2. PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f183f985-b1a5-4631-80c3-47dd6c5c8acf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the columns for PCA\n",
    "columns_for_pca = ['Open', 'High', 'Low', 'Close', 'Volume', 'Market Cap']\n",
    "data_for_pca = merged_df[columns_for_pca]\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(data_for_pca)\n",
    "# Create a PCA model with 10 components \n",
    "num_components = 10\n",
    "pca = PCA(n_components=num_components)\n",
    "pca.fit(scaled_data)\n",
    "pca_result = pca.transform(scaled_data)\n",
    "\n",
    "# Create a DataFrame to store the PCA results\n",
    "pca_df = pd.DataFrame(data=pca_result, columns=[f'PC{i + 1}' for i in range(num_components)])\n",
    "# Concatenate the PCA results with `merged_df`\n",
    "final_df = pd.concat([merged_df, pca_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08b3f635-1560-46c2-abe8-feaa5f087b06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Explained variance ratio to understand the importance of each component.\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "print(\"Explained Variance Ratio:\", explained_variance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10a502aa-68e5-462b-b298-67f104e4bdac",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# PHASE II  -  MODEL DEVELOPMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea2f4cc0-c614-4b58-a6b9-839843874ca0",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Read the csv file we have from the Phase I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3db48aa0-d003-4bd3-b77c-000e738abb09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Name</th>\n",
       "      <th>Last Sale</th>\n",
       "      <th>...</th>\n",
       "      <th>Senkou_SpanA</th>\n",
       "      <th>Senkou_SpanB</th>\n",
       "      <th>Momentum</th>\n",
       "      <th>%K</th>\n",
       "      <th>%D</th>\n",
       "      <th>Chaikin_AD</th>\n",
       "      <th>ROC</th>\n",
       "      <th>ATR</th>\n",
       "      <th>Normalized_ATR</th>\n",
       "      <th>OBV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1774</td>\n",
       "      <td>2000-03-10</td>\n",
       "      <td>103.280</td>\n",
       "      <td>104.560</td>\n",
       "      <td>94.505</td>\n",
       "      <td>95.852</td>\n",
       "      <td>2627257</td>\n",
       "      <td>A</td>\n",
       "      <td>Agilent Technologies Inc. Common Stock</td>\n",
       "      <td>109.73</td>\n",
       "      <td>...</td>\n",
       "      <td>46.44025</td>\n",
       "      <td>40.4375</td>\n",
       "      <td>32.566</td>\n",
       "      <td>72.194339</td>\n",
       "      <td>79.998143</td>\n",
       "      <td>8.672989e+06</td>\n",
       "      <td>51.458458</td>\n",
       "      <td>9.762857</td>\n",
       "      <td>10.185345</td>\n",
       "      <td>20272305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1717</td>\n",
       "      <td>2000-03-13</td>\n",
       "      <td>87.798</td>\n",
       "      <td>93.793</td>\n",
       "      <td>87.417</td>\n",
       "      <td>88.603</td>\n",
       "      <td>2783398</td>\n",
       "      <td>A</td>\n",
       "      <td>Agilent Technologies Inc. Common Stock</td>\n",
       "      <td>109.73</td>\n",
       "      <td>...</td>\n",
       "      <td>47.35700</td>\n",
       "      <td>40.4375</td>\n",
       "      <td>26.837</td>\n",
       "      <td>56.299320</td>\n",
       "      <td>64.246830</td>\n",
       "      <td>6.925071e+06</td>\n",
       "      <td>43.449471</td>\n",
       "      <td>10.009500</td>\n",
       "      <td>11.297022</td>\n",
       "      <td>17488907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1716</td>\n",
       "      <td>2000-03-14</td>\n",
       "      <td>89.782</td>\n",
       "      <td>91.807</td>\n",
       "      <td>80.243</td>\n",
       "      <td>81.935</td>\n",
       "      <td>3733120</td>\n",
       "      <td>A</td>\n",
       "      <td>Agilent Technologies Inc. Common Stock</td>\n",
       "      <td>109.73</td>\n",
       "      <td>...</td>\n",
       "      <td>47.35700</td>\n",
       "      <td>40.5025</td>\n",
       "      <td>15.106</td>\n",
       "      <td>36.268816</td>\n",
       "      <td>46.284068</td>\n",
       "      <td>4.284382e+06</td>\n",
       "      <td>22.603959</td>\n",
       "      <td>10.401500</td>\n",
       "      <td>12.694819</td>\n",
       "      <td>13755787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1715</td>\n",
       "      <td>2000-03-15</td>\n",
       "      <td>86.486</td>\n",
       "      <td>86.486</td>\n",
       "      <td>76.448</td>\n",
       "      <td>77.122</td>\n",
       "      <td>3227960</td>\n",
       "      <td>A</td>\n",
       "      <td>Agilent Technologies Inc. Common Stock</td>\n",
       "      <td>109.73</td>\n",
       "      <td>...</td>\n",
       "      <td>47.73650</td>\n",
       "      <td>40.5025</td>\n",
       "      <td>5.058</td>\n",
       "      <td>25.088274</td>\n",
       "      <td>30.678545</td>\n",
       "      <td>1.489904e+06</td>\n",
       "      <td>7.018761</td>\n",
       "      <td>10.325786</td>\n",
       "      <td>13.388898</td>\n",
       "      <td>10527827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1714</td>\n",
       "      <td>2000-03-16</td>\n",
       "      <td>78.309</td>\n",
       "      <td>83.034</td>\n",
       "      <td>75.604</td>\n",
       "      <td>79.695</td>\n",
       "      <td>2934795</td>\n",
       "      <td>A</td>\n",
       "      <td>Agilent Technologies Inc. Common Stock</td>\n",
       "      <td>109.73</td>\n",
       "      <td>...</td>\n",
       "      <td>48.47600</td>\n",
       "      <td>41.2420</td>\n",
       "      <td>6.707</td>\n",
       "      <td>31.065322</td>\n",
       "      <td>28.076798</td>\n",
       "      <td>1.786938e+06</td>\n",
       "      <td>9.189182</td>\n",
       "      <td>10.341571</td>\n",
       "      <td>12.976437</td>\n",
       "      <td>13462622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        Date     Open     High     Low   Close   Volume Ticker  \\\n",
       "0   1774  2000-03-10  103.280  104.560  94.505  95.852  2627257      A   \n",
       "1   1717  2000-03-13   87.798   93.793  87.417  88.603  2783398      A   \n",
       "2   1716  2000-03-14   89.782   91.807  80.243  81.935  3733120      A   \n",
       "3   1715  2000-03-15   86.486   86.486  76.448  77.122  3227960      A   \n",
       "4   1714  2000-03-16   78.309   83.034  75.604  79.695  2934795      A   \n",
       "\n",
       "                                     Name  Last Sale  ...  Senkou_SpanA  \\\n",
       "0  Agilent Technologies Inc. Common Stock     109.73  ...      46.44025   \n",
       "1  Agilent Technologies Inc. Common Stock     109.73  ...      47.35700   \n",
       "2  Agilent Technologies Inc. Common Stock     109.73  ...      47.35700   \n",
       "3  Agilent Technologies Inc. Common Stock     109.73  ...      47.73650   \n",
       "4  Agilent Technologies Inc. Common Stock     109.73  ...      48.47600   \n",
       "\n",
       "   Senkou_SpanB  Momentum         %K         %D    Chaikin_AD        ROC  \\\n",
       "0       40.4375    32.566  72.194339  79.998143  8.672989e+06  51.458458   \n",
       "1       40.4375    26.837  56.299320  64.246830  6.925071e+06  43.449471   \n",
       "2       40.5025    15.106  36.268816  46.284068  4.284382e+06  22.603959   \n",
       "3       40.5025     5.058  25.088274  30.678545  1.489904e+06   7.018761   \n",
       "4       41.2420     6.707  31.065322  28.076798  1.786938e+06   9.189182   \n",
       "\n",
       "         ATR  Normalized_ATR       OBV  \n",
       "0   9.762857       10.185345  20272305  \n",
       "1  10.009500       11.297022  17488907  \n",
       "2  10.401500       12.694819  13755787  \n",
       "3  10.325786       13.388898  10527827  \n",
       "4  10.341571       12.976437  13462622  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = pd.read_csv(\"stock-feature.csv\")\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "177b7ee2-3998-437e-a20a-3217f363a32a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Name</th>\n",
       "      <th>Last Sale</th>\n",
       "      <th>Net Change</th>\n",
       "      <th>...</th>\n",
       "      <th>Senkou_SpanA</th>\n",
       "      <th>Senkou_SpanB</th>\n",
       "      <th>Momentum</th>\n",
       "      <th>%K</th>\n",
       "      <th>%D</th>\n",
       "      <th>Chaikin_AD</th>\n",
       "      <th>ROC</th>\n",
       "      <th>ATR</th>\n",
       "      <th>Normalized_ATR</th>\n",
       "      <th>OBV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>45.621</td>\n",
       "      <td>46.4360</td>\n",
       "      <td>45.4340</td>\n",
       "      <td>46.178</td>\n",
       "      <td>1751589</td>\n",
       "      <td>A</td>\n",
       "      <td>Agilent Technologies Inc. Common Stock</td>\n",
       "      <td>109.73</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>...</td>\n",
       "      <td>45.18325</td>\n",
       "      <td>45.19350</td>\n",
       "      <td>0.480</td>\n",
       "      <td>81.035599</td>\n",
       "      <td>51.132686</td>\n",
       "      <td>6.295452e+08</td>\n",
       "      <td>1.050374</td>\n",
       "      <td>0.681714</td>\n",
       "      <td>1.476275</td>\n",
       "      <td>397388270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>46.615</td>\n",
       "      <td>47.0630</td>\n",
       "      <td>46.5020</td>\n",
       "      <td>46.784</td>\n",
       "      <td>1833563</td>\n",
       "      <td>A</td>\n",
       "      <td>Agilent Technologies Inc. Common Stock</td>\n",
       "      <td>109.73</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>...</td>\n",
       "      <td>45.18325</td>\n",
       "      <td>45.19350</td>\n",
       "      <td>0.839</td>\n",
       "      <td>86.944314</td>\n",
       "      <td>83.989957</td>\n",
       "      <td>6.295550e+08</td>\n",
       "      <td>1.826096</td>\n",
       "      <td>0.711714</td>\n",
       "      <td>1.521277</td>\n",
       "      <td>399221833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>46.735</td>\n",
       "      <td>46.7540</td>\n",
       "      <td>46.0440</td>\n",
       "      <td>46.228</td>\n",
       "      <td>1513918</td>\n",
       "      <td>A</td>\n",
       "      <td>Agilent Technologies Inc. Common Stock</td>\n",
       "      <td>109.73</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>...</td>\n",
       "      <td>45.13600</td>\n",
       "      <td>45.19350</td>\n",
       "      <td>0.530</td>\n",
       "      <td>60.926533</td>\n",
       "      <td>73.935423</td>\n",
       "      <td>6.288258e+08</td>\n",
       "      <td>1.159788</td>\n",
       "      <td>0.708571</td>\n",
       "      <td>1.532775</td>\n",
       "      <td>397707915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>46.318</td>\n",
       "      <td>47.7470</td>\n",
       "      <td>46.2480</td>\n",
       "      <td>47.668</td>\n",
       "      <td>2902875</td>\n",
       "      <td>A</td>\n",
       "      <td>Agilent Technologies Inc. Common Stock</td>\n",
       "      <td>109.73</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>...</td>\n",
       "      <td>45.12150</td>\n",
       "      <td>45.19350</td>\n",
       "      <td>1.554</td>\n",
       "      <td>97.199575</td>\n",
       "      <td>79.063054</td>\n",
       "      <td>6.314227e+08</td>\n",
       "      <td>3.369909</td>\n",
       "      <td>0.756929</td>\n",
       "      <td>1.587918</td>\n",
       "      <td>400610790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>47.688</td>\n",
       "      <td>48.2290</td>\n",
       "      <td>47.5830</td>\n",
       "      <td>47.817</td>\n",
       "      <td>2592719</td>\n",
       "      <td>A</td>\n",
       "      <td>Agilent Technologies Inc. Common Stock</td>\n",
       "      <td>109.73</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>...</td>\n",
       "      <td>44.72550</td>\n",
       "      <td>45.19350</td>\n",
       "      <td>2.346</td>\n",
       "      <td>87.526491</td>\n",
       "      <td>92.363033</td>\n",
       "      <td>6.307083e+08</td>\n",
       "      <td>5.159332</td>\n",
       "      <td>0.724571</td>\n",
       "      <td>1.515301</td>\n",
       "      <td>403203509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386561</th>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>10.420</td>\n",
       "      <td>11.5400</td>\n",
       "      <td>10.4200</td>\n",
       "      <td>11.190</td>\n",
       "      <td>977948</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>Zynerba Pharmaceuticals Inc. Common Stock</td>\n",
       "      <td>1.34</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>8.50250</td>\n",
       "      <td>12.56000</td>\n",
       "      <td>1.870</td>\n",
       "      <td>85.714286</td>\n",
       "      <td>84.190476</td>\n",
       "      <td>4.268797e+11</td>\n",
       "      <td>20.064378</td>\n",
       "      <td>0.563071</td>\n",
       "      <td>5.031916</td>\n",
       "      <td>472000190549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386562</th>\n",
       "      <td>2017-11-07</td>\n",
       "      <td>11.300</td>\n",
       "      <td>11.4200</td>\n",
       "      <td>10.6700</td>\n",
       "      <td>10.830</td>\n",
       "      <td>451210</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>Zynerba Pharmaceuticals Inc. Common Stock</td>\n",
       "      <td>1.34</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>8.54000</td>\n",
       "      <td>12.55500</td>\n",
       "      <td>1.440</td>\n",
       "      <td>68.584071</td>\n",
       "      <td>77.149178</td>\n",
       "      <td>4.268794e+11</td>\n",
       "      <td>15.335463</td>\n",
       "      <td>0.588071</td>\n",
       "      <td>5.430022</td>\n",
       "      <td>471999739339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386563</th>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>10.700</td>\n",
       "      <td>11.0600</td>\n",
       "      <td>10.3500</td>\n",
       "      <td>10.900</td>\n",
       "      <td>336449</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>Zynerba Pharmaceuticals Inc. Common Stock</td>\n",
       "      <td>1.34</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>8.54000</td>\n",
       "      <td>12.28500</td>\n",
       "      <td>1.240</td>\n",
       "      <td>70.093458</td>\n",
       "      <td>69.338764</td>\n",
       "      <td>4.268796e+11</td>\n",
       "      <td>12.836439</td>\n",
       "      <td>0.609500</td>\n",
       "      <td>5.591743</td>\n",
       "      <td>472000075788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386564</th>\n",
       "      <td>2017-11-09</td>\n",
       "      <td>11.000</td>\n",
       "      <td>11.8563</td>\n",
       "      <td>10.9700</td>\n",
       "      <td>11.600</td>\n",
       "      <td>463067</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>Zynerba Pharmaceuticals Inc. Common Stock</td>\n",
       "      <td>1.34</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>8.54000</td>\n",
       "      <td>12.12500</td>\n",
       "      <td>1.870</td>\n",
       "      <td>89.565607</td>\n",
       "      <td>79.829532</td>\n",
       "      <td>4.268798e+11</td>\n",
       "      <td>19.218911</td>\n",
       "      <td>0.652450</td>\n",
       "      <td>5.624569</td>\n",
       "      <td>472000538855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386565</th>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>11.680</td>\n",
       "      <td>13.1500</td>\n",
       "      <td>11.3043</td>\n",
       "      <td>12.460</td>\n",
       "      <td>885587</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>Zynerba Pharmaceuticals Inc. Common Stock</td>\n",
       "      <td>1.34</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>8.54000</td>\n",
       "      <td>12.10995</td>\n",
       "      <td>2.560</td>\n",
       "      <td>81.600000</td>\n",
       "      <td>85.582803</td>\n",
       "      <td>4.268800e+11</td>\n",
       "      <td>25.858586</td>\n",
       "      <td>0.754286</td>\n",
       "      <td>6.053657</td>\n",
       "      <td>472001424442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>386566 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date    Open     High      Low   Close   Volume Ticker  \\\n",
       "0      2017-01-03  45.621  46.4360  45.4340  46.178  1751589      A   \n",
       "1      2017-01-04  46.615  47.0630  46.5020  46.784  1833563      A   \n",
       "2      2017-01-05  46.735  46.7540  46.0440  46.228  1513918      A   \n",
       "3      2017-01-06  46.318  47.7470  46.2480  47.668  2902875      A   \n",
       "4      2017-01-09  47.688  48.2290  47.5830  47.817  2592719      A   \n",
       "...           ...     ...      ...      ...     ...      ...    ...   \n",
       "386561 2017-11-06  10.420  11.5400  10.4200  11.190   977948   ZYNE   \n",
       "386562 2017-11-07  11.300  11.4200  10.6700  10.830   451210   ZYNE   \n",
       "386563 2017-11-08  10.700  11.0600  10.3500  10.900   336449   ZYNE   \n",
       "386564 2017-11-09  11.000  11.8563  10.9700  11.600   463067   ZYNE   \n",
       "386565 2017-11-10  11.680  13.1500  11.3043  12.460   885587   ZYNE   \n",
       "\n",
       "                                             Name  Last Sale  Net Change  ...  \\\n",
       "0          Agilent Technologies Inc. Common Stock     109.73       -2.74  ...   \n",
       "1          Agilent Technologies Inc. Common Stock     109.73       -2.74  ...   \n",
       "2          Agilent Technologies Inc. Common Stock     109.73       -2.74  ...   \n",
       "3          Agilent Technologies Inc. Common Stock     109.73       -2.74  ...   \n",
       "4          Agilent Technologies Inc. Common Stock     109.73       -2.74  ...   \n",
       "...                                           ...        ...         ...  ...   \n",
       "386561  Zynerba Pharmaceuticals Inc. Common Stock       1.34       -0.01  ...   \n",
       "386562  Zynerba Pharmaceuticals Inc. Common Stock       1.34       -0.01  ...   \n",
       "386563  Zynerba Pharmaceuticals Inc. Common Stock       1.34       -0.01  ...   \n",
       "386564  Zynerba Pharmaceuticals Inc. Common Stock       1.34       -0.01  ...   \n",
       "386565  Zynerba Pharmaceuticals Inc. Common Stock       1.34       -0.01  ...   \n",
       "\n",
       "        Senkou_SpanA  Senkou_SpanB Momentum         %K         %D  \\\n",
       "0           45.18325      45.19350    0.480  81.035599  51.132686   \n",
       "1           45.18325      45.19350    0.839  86.944314  83.989957   \n",
       "2           45.13600      45.19350    0.530  60.926533  73.935423   \n",
       "3           45.12150      45.19350    1.554  97.199575  79.063054   \n",
       "4           44.72550      45.19350    2.346  87.526491  92.363033   \n",
       "...              ...           ...      ...        ...        ...   \n",
       "386561       8.50250      12.56000    1.870  85.714286  84.190476   \n",
       "386562       8.54000      12.55500    1.440  68.584071  77.149178   \n",
       "386563       8.54000      12.28500    1.240  70.093458  69.338764   \n",
       "386564       8.54000      12.12500    1.870  89.565607  79.829532   \n",
       "386565       8.54000      12.10995    2.560  81.600000  85.582803   \n",
       "\n",
       "          Chaikin_AD        ROC       ATR  Normalized_ATR           OBV  \n",
       "0       6.295452e+08   1.050374  0.681714        1.476275     397388270  \n",
       "1       6.295550e+08   1.826096  0.711714        1.521277     399221833  \n",
       "2       6.288258e+08   1.159788  0.708571        1.532775     397707915  \n",
       "3       6.314227e+08   3.369909  0.756929        1.587918     400610790  \n",
       "4       6.307083e+08   5.159332  0.724571        1.515301     403203509  \n",
       "...              ...        ...       ...             ...           ...  \n",
       "386561  4.268797e+11  20.064378  0.563071        5.031916  472000190549  \n",
       "386562  4.268794e+11  15.335463  0.588071        5.430022  471999739339  \n",
       "386563  4.268796e+11  12.836439  0.609500        5.591743  472000075788  \n",
       "386564  4.268798e+11  19.218911  0.652450        5.624569  472000538855  \n",
       "386565  4.268800e+11  25.858586  0.754286        6.053657  472001424442  \n",
       "\n",
       "[386566 rows x 40 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the 'Date' column in merged_df to datetime format \n",
    "merged_df['Date'] = pd.to_datetime(merged_df['Date'])\n",
    "\n",
    "# Filter the DataFrame for the years of 2017\n",
    "start_date = pd.to_datetime('2017-01-01')\n",
    "end_date = merged_df['Date'].max()\n",
    "filtered_df = merged_df[(merged_df['Date'] >= start_date) & (merged_df['Date'] <= end_date)]\n",
    "filtered_df = filtered_df.reset_index(drop=False)\n",
    "filtered_df = filtered_df.drop(['level_0', 'index'], axis=1)\n",
    "filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "232ea025-e683-4ab5-b773-fd6405ce0321",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Date',\n",
       " 'Open',\n",
       " 'High',\n",
       " 'Low',\n",
       " 'Close',\n",
       " 'Volume',\n",
       " 'Ticker',\n",
       " 'Name',\n",
       " 'Last Sale',\n",
       " 'Net Change',\n",
       " '% Change',\n",
       " 'Market Cap',\n",
       " 'Country',\n",
       " 'IPO Year',\n",
       " 'Sector',\n",
       " 'Industry',\n",
       " 'Volume change',\n",
       " 'SMA',\n",
       " 'EMA',\n",
       " 'ADX',\n",
       " 'MACD',\n",
       " 'MACD Signal Line',\n",
       " 'RSI',\n",
       " 'Bollinger_Bands_Middle',\n",
       " 'Bollinger_Bands_Upper',\n",
       " 'Bollinger_Bands_Lower',\n",
       " 'KAMA',\n",
       " 'MFI',\n",
       " 'Tenkan_Sen',\n",
       " 'Kijun_Sen',\n",
       " 'Senkou_SpanA',\n",
       " 'Senkou_SpanB',\n",
       " 'Momentum',\n",
       " '%K',\n",
       " '%D',\n",
       " 'Chaikin_AD',\n",
       " 'ROC',\n",
       " 'ATR',\n",
       " 'Normalized_ATR',\n",
       " 'OBV']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve and list all column names of `filtered_df` \n",
    "list(filtered_df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "956fd175-4a04-450e-a8d4-3ad83fae6a8e",
   "metadata": {},
   "source": [
    "Considering our dataset contains over 4 million data rows, each comprising 30 features that encompass historical stock prices and technical indicators, we aim to extensively implement data scaling and examine the varying effects of different scaling techniques on dimension reduction via PCA. The transformed data from PCA will be supply as input features to several stock price prediction models, the integration of dimensionality reduction may enhance the accuracy of the forecasting model and gives more accurate prediction for the stock price in the future [2,3]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656b50dd-320b-45e0-a2f3-f993521b5786",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 4. Stock Price Prediction Model Development:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71511003-795e-459d-8387-2c6e811adab8",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 4.1. Comparision Scaling Techniques on PCA-based model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b9894e95-e918-462a-a7ca-9c6a992f19c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select columns related to financial indicators from filtered_df for PCA\n",
    "columns_for_pca = ['Open','High','Low','Volume','Market Cap','% Change',\n",
    "                   'SMA','EMA','ADX','MACD','MACD Signal Line','RSI',\n",
    "                   'Bollinger_Bands_Middle','Bollinger_Bands_Upper','Bollinger_Bands_Lower',\n",
    "                   'KAMA','MFI','Tenkan_Sen','Kijun_Sen','Senkou_SpanA','Senkou_SpanB','Momentum',\n",
    "                   '%K','%D','Chaikin_AD','ROC','ATR','Normalized_ATR','OBV']\n",
    "\n",
    "data_for_pca = filtered_df[columns_for_pca]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1419c5be-111a-4ef2-b7fa-818bf5449f52",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SOt2Oy6yMVo2+nNO/BYiIrSKipyvBmZXgAxdWJZKulPTFFuX75H0E72xbR+U68jlJGzSVz84NwAkdjqdUw1PSP0r6kaRHJD0h6U+SPiFppU7FWjduQNaEpKOAE4H/BtYGdgReDVwtaeVuxmZmZmYddxbwAUlqKv8AcH5ELO18SGbcAxzYeCFpG2C1wa5sqA+ESHoNcCOwANgmItYGDgAmAqOH8r3rzA3IGpC0FvAF4L8i4v8i4vmImA+8l9SIfL+kYyVdIukiSYsl/V7SGwrr2FjSjyX9TdI9kj5WmHaspIslnZOXvU3SxE5vp41sxbM7klaTdLakxyTNlfSpFl2uts1HCZ/Ieb9qF8K2EU7SKpJOlnR/fpwsaZU87TpJ++XnO+Uj4Xvm1++UNLuLoVv9/QxYD3hbo0DSusDewDm95WWznJevLbw+S9KX8/NJkhbmOvhhSQ9I2lfSnpLulLRI0mcKy75C0nRJd0t6NO9brDdE22/VdC5wcOH1VOCc4gyS9pL0B0lPSlog6djCtMZZw0Mk3Qdc2/wGkvbL+wxb95Nzv85/H5e0RNJbWsT7BeB3EfGJiHgAICLuiIh/i4jH8/v9KJ/Vf0LSryVtVYjlLEnflXR13oe+TtKrB/qh1Y0bkPXwz8CqwE+KhRGxBLgCeFcu2gf4Eekfyg+Bn0l6paRXAD8H/giMA3YBPi5pt8Lq3g1cCKwDXAZ8e6g2xqyEY4AJwGak/H5/i3neC+wObAq8HpjWodjMij5L6hGyLfAGYAfgc3nadcCk/PztwF+AnQuvr+tUkDb8RMTTwMUsu7P+XuDPwH70npcDtRFpH2Qc8Hng+6Q6eXtS4/XzkjbL834M2JeU5xsDjwHfGeT7Wj3NBNaStEXuAvo+4LymeZ4i5e06wF7AYZL2bZpnZ2ALoLiviqQPknrkvTMibqXvnHt7/rtORKwZETe0iPedwCX9bNMVwObAhsDvgfObph8EfAnYAJjdYvqw4wZkPWwAPNJLd5QH8nSAWRFxSUQ8D3yDVOHvCLwJeFVEfDEinouIv5D+AUwprOe3ETEjIl4gHT16A2bt9zNJjzcewKm9zPde4CsR8VhELAROaTHPKRFxf0QsIh0g2XZIIjbr20HAFyPi4Yj4G+lo9gfytOtYtsF4fOH1zrgBaSvubOAASY0uggfnsr7ycqCeB47L+xYXkvY5/iciFkfEbcBtpIN4AB8GPhsRCyPiWeBYYP+h7oZoldM4C/ku0gGNvxYnRkRPRMyJiBcj4k/ABbxcNzYcGxFP5QMlDR8nXco1KSLm5bIVzbn1SfvSvYqIM3O+N9b/BklrF2b5RUT8Ok//LPAWSZuUfP9acgOyHh4BNujlxzA2T4fUfxuAiHgRWEg6GvNqYOOmHffPAGMK63mw8PzvwKqu8G0I7BsR6zQewH/2Mt/GFPK56XlDc86u2Z4QzQZkY+Dewut7cxnADcA/ShpDOsBxDrCJ0gATO/By9yqzQYmI3wJ/A/bJZwHfROqB1FdeDtSj+eAyQGNn/qHC9Kd5uf59NfDTwr7GXOAFlt3fsOHvXODfSD2DzmmeKOnNkn6VL6t6AvgIL58MaWj1f/+/ge/kA8sNK5pzj5L2pVuStJKkE3IX2SeB+XlSMd7i/vcSYBGD/73VghuQ9XAD8Czwr8VCSWsAewDX5KJNCtNeAYwH7icl9j3FHfeIGB0Re3YkerOBe4CUvw3D+kie1dr9pB2Yhn/IZUTE34FZwBHArRHxHPA74BPA3RHxCGYr7hzS2Z4PAFdFxEP0kZct/B1YvfB6oxWIZQGwR9P+xqoR8dd+l7RhIyLuJQ2msydNl19lPyRdLrVJHrTmu0DzYFDRYrldgc81ri3P+sq5Vuto9ktSl+/e/BvpErF3kgaxnJDLi/EW97/XJF1K1tvvbVhwA7IGIuIJUveTb0naPV/XOIF0veNC0pEegO0l/Ws+c/hxUqNzJnAT8KSkTysNTrJSvvD4TR3fGLNyLgaOlrSupHHAR7sdkFn2SkmrNh6krlefk/SqfGbx8yx7vc91pPxtdFftaXpttqLOIe3c/gep+yr0n5dFs4F/y/sGu7N8V8KB+C5wXGMQkfz++6zA+qy+DgHeERFPtZg2GlgUEc9I2oHUSCvjNtLYB9+R9O5c1lfO/Q14kTSeQm+OAf5Z0tckbZTX8VpJ5yndKm80aX/6UdKBlq+0WMeeSgOlrUy6FvLGiGh1BnXYcAOyJiLiq6Rup18HnuTlIYd3yX2uAS4lXaz8GOlI5L/mEVtfAP6F1IXqHlKX1x+QjqSYVdEXSQdH7iEdHbyEVIGbddsMUpe9xmNV4BbgT8Ac0gALXy7Mfx1pB+TXvbw2WyF5VPbfAWuQzupAysG+8rLoCNI+wuOkayd/tgLh/E+O4SpJi0kHsd+8AuuzmoqIuyPill4m/yfwxZwjnycdNC673j+SRhr+vqQ96CPnci+Q44DrcxfXHVvFCbyFdGbxttyl9sek389i0gGae0nXcd6e19/sh6SG6CLS4FIHld2eulJEmbO7VnV5COTXRkSr0SrNak3SYcCUiFiRI+NmZmZmbSPpLGBhRAx2lONa8hlIM6scSWMlvTXf3+l1wFHAT7sdl5mZmdlI5wakmVXRysD3SN1HriV1z+7tlh9mVkH5mro/SLo8v14v32z7rvx33cK8R0uaJ+mO4j2KJW0vaU6edook5fJVJF2Uy2/M4wI0lpma3+MuSVM7uMk2jDh/zXpXugHpH1K1RcSx7r7amnO3fiLi3ojYOiLWiIhxEXFUHsFyxHH+Wo0dQRpSv2E6cE1EbE4aPXw6gKQtSfcl3oo0QMapSjcgBzgNOJR0E+/N83RIA3Q8FhGvBb5JurE4ktYjXYv0ZtKtUo4p/kbMBsD5a/2KiGkjrfsqDOwMpH9IVlfOXasz56/VjqTxwF6kAdsa9uHlUULPBvYtlF8YEc9GxD3APGAHSWOBtSLihkgDNpzTtExjXZcAu+QDI7sBV0fEooh4DLial/PdrBTnr1nfSt0ovvBDOo50/ypIyT8pPz+bNDT5pyn8kIB7JDV+SPPJP6S8zsYP6Yq8zLF5XZcA327+IeVlGj+kC3qLdYMNNogJEya0nPbUU0+xxhprlNnkjqliTDB84nruuecYPXo0G220EXfddVfjDFYlcxd6z98qfR9ViWUkxFGn/K1b3dtKXeKE6se6zjrrsNFGG/HUU0+9Y8GCBY3cHRMRDwBExAOSNszl41h2ZMOFuez5/Ly5vLHMgryupUojJ65fLG+xTK/qUPcOleG+jYPZvkb+vvjii++48847K52/w6HuLXLM7TNr1qxHIuJVQ7HuUg1I4GTgU6Shxxsq+UOaMGECt9zSetTgnp4eJk2a1NfiHVfFmGD4xLX//vvzv//7vyxevJjJkyc/nYsrmbvQe/5W6fuoSiwjIY465W/d6t5W6hInVDvWyy+/nBkzZnDqqady8sknc+SRRz7dzyLNNxCHdAPw3soHu8yybyodSjozz5gxY/j617++3DxLlixhzTXXbLX4sDHct3Gg23fDDTcwc+ZMjjzySGbPnl3J/C2Tu1DP79Yxt8/kyZPvHap199uAlLQ38HBEzJI0qcQ6u/5D6unpaRnYkiVLep3WLVWMCYZHXDfccAPPP/88ixcvZvbs2WUWqcROTKvtq9L3UZVYhnscdcjfOte9rdQlTqh2rBdccAFXXXUVP/nJT3j22WcBRks6D3hI0th84GMs8HBeZCGwSWEV44H7c/n4FuXFZRZKGkW6r/GiXD6paZmeVnFGxOnA6QATJ06MVg3yKjfU22W4b+NAt+/KK69k1qxZTJs2jWeeeQYqmL9lchfq+d065noocwbyrcC7Je1JumHyWv4htU8VY4LhEVcd/glA/XZiqhLLcI+jDvlb57q3lbrECdWOtRhXPgO5OCLeL+lrwFTghPz30jzbZcAPJX0D2Jh0ne5NEfGCpMVKN/++ETgY+FZhmanADcD+wLUREZKuBL5SuGZ3V+DoodtaG26OP/54jj/+eCD9ziZPnuz8NWvSbwMyIo4mJ28+A/nJuv6Q5vz1CaZN/8Vy5fNP2Guwq7QKG07/BJy7I4/z14ahE4CLJR0C3AccABARt0m6GLgdWAocHhEv5GUOA84CViNdt3tFLj8DODdf67uINIAUEbFI0peAm/N8X2xcyzsYzl0rcP6aZWWvgWyldj8ks8y5a3Xm/LXa2HbbbSGNSklEPArs0mq+iDiONFBfc/ktwNYtyp8h536LaWcCZw42ZrOGfDbd+WvWZEANyIjoIXdj8g/J6sT/BKzOnL9mZmZWFQO5D6SZmZmZmZmNYG5AmpmZmZmZWSluQJqZmZmZmVkpbkCamZmZmZlZKW5AmpmZmZmZWSluQJqZmZmZmVkpbkCamZmZmZlZKW5AmpmZmZmZWSluQJqZmZmZmVkpbkCamZmZmZlZKW5AmpmZmZmZWSluQJqZmZmZmVkpbkCamZmZmZlZKW5AmpmZmZmZWSluQJqZmZmZmVkpbkCamZmZmZlZKW5AmpmZmZmZWSluQJqZmZmZmVkpbkCamZmZmZlZKW5AmpmZmZmZWSluQJqZmZmZmVkpbkCamZmZmZlZKW5AmpmZmZmZWSluQJqZmZmZmVkpbkCamZmZmZlZKW5AmpmZmZmZWSluQJqZmZmZmVkpbkCamZmZmZlZKW5AmplV0IIFC5g8eTJbbLEFW221FcCGAJLWk3S1pLvy33Uby0g6WtI8SXdI2q1Qvr2kOXnaKZKUy1eRdFEuv1HShMIyU/N73CVpasc23GqvmLvTpk0D567ViOtes/7124CUtImkX0maK+k2SUfkcv+QrNL8T8DqbNSoUZx00knMnTuXmTNnAmwoaUtgOnBNRGwOXJNfk6dNAbYCdgdOlbRSXt1pwKHA5vmxey4/BHgsIl4LfBM4Ma9rPeAY4M3ADsAxxd+JWV+KuXvqqaeCc9dqxHWvWf/KnIFcChwVEVsAOwKH+4dkdeB/AlZnY8eOZbvttgNg9OjRAE8D44B9gLPzbGcD++bn+wAXRsSzEXEPMA/YQdJYYK2IuCEiAjinaZnGui4BdskHR3YDro6IRRHxGHA1L+e8WZ+Kubv66quDc9dqxHWvWf/6bUBGxAMR8fv8fDEwF/+QrAb8T8CGi/nz5wOsDtwIjImIByDVz+Qz66TcXlBYbGEuG5efN5cvs0xELAWeANbvY11mA/Lggw+Cc9dqynWvWWujBjJz7p73Rlr8kCQVf0gzC4s1kv95Sv6QJA3ohyTpUNLZIcaMGUNPT0/L+MesBkdts3S58t7m74QlS5Z09f17M9zi6msnppu5C+Xyt0q5W5XcGClxPP300xxxxBEACyLiydx7upVWE6KP8sEu8/Ib1rjubaUqOVVGHWJ9+umn+dznPgcVzF2oX907VOqQSytisNvnurc76piPdYx5RZVuQEpaE/gx8PGq/ZAi4nTgdICJEyfGpEmTWgb2rfMv5aQ5y2/y/INaz98JPT099BZvNw2nuJYsWcLOO+8MFfwnAOXyt0q5W5XcGAlxPP/88+y999585CMf4aijjno8Fz8kaWw++DEWeDiXLwQ2KSw+Hrg/l49vUV5cZqGkUcDawKJcPqlpmZ7m+Opc97ZSlZwqo+qxNnJ311135bTTTns8F1cmd6F+de9QqXourajBbJ/r3u6pYz7WMeYVVWoUVkmvJDUez4+In+Tih/IPiDb+kGjxQ2q1LrNSnn/+efbbbz8OOugggMdzsXPXKi8iOOSQQ9hiiy34xCc+UZx0GdAYlGkqcGmhfEoe2GlT0rW6N+Wz7Ysl7Zi7Vx/ctExjXfsD1+Zu2lcCu0paN1+7u2suM+tXMXff+973Fic5d63yXPea9a/MKKwCzgDmRsQ3CpP8Q7JK8z8Bq7Prr7+ec889l2uvvZZtt90WYEtJewInAO+SdBfwrvyaiLgNuBi4Hfg/4PCIeCGv7jDgB6Treu8GrsjlZwDrS5oHfII8oFRELAK+BNycH1/MZWb9Kubuhz70IXDuWo247jXrX5kurG8FPgDMkTQ7l32G9MO5WNIhwH3AAZB+SJIaP6SlLP9DOgtYjfQjKv6Qzs0/pEWkkTCJiEWSGj8k8A/JBqDxT2CbbbZp9U/AuWuVttNOO5GORSSSbo+IGfnlLq2WiYjjgONalN8CbN2i/Bly/reYdiZw5sAjt5GumLs9PT1MnjzZuWu14brXrH/9NiAj4re0vp4L/EOyCvM/ATMzMzOz9ip1DaSZmZmZmZmZG5BmZmZmZmZWihuQZmZmZmZmVoobkGZmZmZmZlaKG5BmZmZmZmZWihuQZmZmZmZmVoobkGZmZmZmZlaKG5BmZmZmZmZWihuQZmZmZmZmVoobkGZmZmZmZlaKG5BmZmZmZmZWihuQZmZmZmZmVoobkGZmZmZmZlaKG5BmZmZmZmZWihuQZmZmZmZmVoobkGZmZmZmZlaKG5BmZmZmZmZWihuQZmZmZmZmVoobkGZmZmZmZlaKG5BmZmZmZmZWihuQZmZmZmZmVoobkGZmZmZmZlaKG5BmZmZmZmZWihuQZmZmZmZmVoobkGZmZmZmZlaKG5BmZmZmZmZWihuQZmZmZmZmVoobkGZmZmZmZlZKLRqQknaXdIekeZKmdzses7Kcu1Znzl+rK+eu1Znz16qu8g1ISSsB3wH2ALYEDpS0ZXejMuufc9fqzPlrdeXctTpz/lodVL4BCewAzIuIv0TEc8CFwD5djsmsDOeu1Znz1+rKuWt15vy1yqtDA3IcsKDwemEuM6s6567VmfPX6sq5a3Xm/LXKG9XtAEpQi7JYZgbpUODQ/HKJpDt6WdcGwCPLvcGJKxTfimoZUwUMx7he3c5ASug3d6F0/lYpd6uSGyMtjsrlb83r3laqklNl1CXWDahg7kIt696hUpdcGqwV3b7K5e8wrHuL6piPVY15yHK3Dg3IhcAmhdfjgfuLM0TE6cDp/a1I0i0RMbG94a2YKsYEjqtN+s1dKJe/VdruqsTiOIbcsK57W6lLnFCfWHOcEzr8tsOy7h0qw30ba7h9I67uLXLM9VCHLqw3A5tL2lTSysAU4LIux2RWhnPX6sz5a3Xl3LU6c/5a5VX+DGRELJX0UeBKYCXgzIi4rcthmfXLuWt15vy1unLuWp05f60OKt+ABIiIGcCMNqyq39P9XVDFmMBxtcUwzd2qxOI4htgwzd++1CVOqE+sXYlzBObuihju21i77Rvh+euYa0ARy11XbmZmZmZmZracOlwDaWZmZmZmZhUwIhqQknaXdIekeZKmD8H6N5H0K0lzJd0m6Yhcfqykv0qanR97FpY5Osdzh6TdCuXbS5qTp50iSbl8FUkX5fIbJU0oGdv8vL7Zkm7JZetJulrSXfnvup2MS9LrCp/JbElPSvp4FT6vKmp3/lYtX6uQo87JodHu3C35nr3ld1frvT7iXUnSHyRdXvE415F0iaQ/58/2LVWNtV26kb+dJOlMSQ9LurXbsQyF3uqCkaCOuVu3fBzJ+QVARAzrB+kC5LuBzYCVgT8CW7b5PcYC2+Xno4E7gS2BY4FPtph/yxzHKsCmOb6V8rSbgLeQ7gN0BbBHLv9P4Lv5+RTgopKxzQc2aCr7KjA9P58OnNjpuJq+nwdJ96rp+udVtcdQ5G/V8rVqOeqcrG7urmB+V6bea4r3E8APgcu7nfv9xHk28KH8fGVgnarGWuf87fA2vh3YDri127EM0fa1rAu6HVcHtruWuVu3fByp+dV4jIQzkDsA8yLiLxHxHHAhsE873yAiHoiI3+fni4G5wLg+FtkHuDAino2Ie4B5wA6SxgJrRcQNkTLyHGDfwjJn5+eXALs0jtwOQnFdZze9R6fj2gW4OyLu7Sfebn5e3dT2/K1JvnYzR52T7THkdW8rfeR3leo9ACSNB/YCflAormKca5F27s4AiIjnIuLxKsbaRl3J306KiF8Di7odx1AZxP+64aKWuVu3fBzB+QWMjC6s44AFhdcLGcIvOHe7eSNwYy76qKQ/5VPzje49vcU0Lj9vFetLy0TEUuAJYP0SIQVwlaRZkg7NZWMi4oG8rgeADbsQV8MU4ILC625/XlUzpPlbkXytWo46J9ujo3VvK035XaV6r+Fk4FPAi4WyKsa5GfA34H9zd9sfSFqjorG2S9fz19qnxf+64cy522EjLL+AkdGAbHUEc0iGnpW0JvBj4OMR8SRwGvAaYFvgAeCkfmLqK9bBbsdbI2I7YA/gcElv72PeTsaF0g1y3w38KBdV4fOqmiHbjgrla2Vy1DnZVl3d3hb53eusLcqG/LuUtDfwcETMKrtIL+/ZiZwbRepadlpEvBF4itRltTfD4fdRtXhskAZQFwwXzt0OGoH5BYyMBuRCYJPC6/HA/e1+E0mvJCXQ+RHxE4CIeCgiXoiIF4Hvk7oV9BXTwvy8VawvLSNpFLA2JU71R8T9+e/DwE9zDA/lrkTkvw93Oq5sD+D3EfFQjrHrn1cFDUn+VilfK5ajzsn26Ujd20qr/KY69V7DW4F3S5pP6mL2DknnVTDOxnoWRkTj6PolpAZlFWNtl67lr7VPL3XBcOfc7ZARml/AyGhA3gxsLmnTfHZhCnBZO98gX6dxBjA3Ir5RKB9bmO09QGNkqcuAKXnUuU2BzYGbchegxZJ2zOs8GLi0sMzU/Hx/4Np8DUlfca0haXTjObBrjqG4rqlN7zHkcRUcSKGrYLc/r4pqe/5WKV8rmKPOyfYZ8rq3ld7ym+rUewBExNERMT4iJpA+m2sj4v1VizPH+iCwQNLrctEuwO1VjLWNupK/1j591AXDnXO3A0ZwfiVRgZF8hvoB7EkaHelu4LNDsP6dSN0D/gTMzo89gXOBObn8MmBsYZnP5njuII9Cl8snknZS7wa+DSiXr0rqVjePNIrdZiXi2ow0+tYfgdsa2066ruQa4K78d71OxpWXWx14FFi7UNbVz6uqj3bnb5XytUo56pysfu6uYH53vd7rI+ZJvDwKayXjJHXjviV/rj8D1q1qrHXO3w5v3wWkrvnPk85aHdLtmNq8fS3rgm7H1aFtr13u1i0fR3J+RcRLFbeZmZmZmZlZn0ZCF1YzMzMzMzNrAzcgzczMzMzMrBQ3IM3MzMzMzKwUNyDNzMzMzMysFDcgzczMzGxYkXSmpIcl3Vpi3n+Q9CtJf5D0J0l7diJGG9kGmKNvl/R7SUsl7V8o31bSDZJuy7n7vqGNOnED0szMzMyGm7OA3UvO+zng4oh4I+m+iacOVVBmBWdRPkfvA6YBP2wq/ztwcERsldd1sqR12hRfr9yANDMzM7NhJSJ+DSwqlkl6jaT/kzRL0m8k/VNjdmCt/Hxt4P4Ohmoj1EByNCLmR8SfgBeb1nFnRNyVn98PPAy8aqhjdwNyGJA0SdLCbsdhZmZmVmGnA/8VEdsDn+TlM43HAu/P+1IzgP/qTnhmveZovyTtAKwM3D1Esb3EDcgOkXSypMdyP+VxhfKDJP1PieV3kDRD0uOSFkm6SdIHhzZqs/aStJOk30l6Iufx9ZLeJGmapJD0jab5983lZzWVryFpiaQZHd0AG7EkzZf0dM67xuPbzl2rE0k9eV9klULZWZKek7Q4P26VdLyktQvz/FcuX7lQ9vF8zeCoTm/HYEhaE/hn4EeSZgPfA8bmyQcCZ0XEeGBP4FxJ3kfusFzPPidpg6by2bk+nVAoOzaX7dBiPb3uM+eTLi8W6vGFki6W9KYh38B+9JOj/S07FjgX+GBEvNjf/CvKP44OyMm9PbAR8Fvg6Fy+Nunowuf7Wf4twLXAdcBrgfWBw4A9hi5qs/aStBZwOfAtYD1gHPAF4Nk8y93A+5p2Rg4G7myxuv3zcrvmStOsE/4lItYsPD6ay527Vnl55/ttpO6a726a/NWIGE3q+vZBYEfgeklr5OnfAR4HPpvXtRmp/j4kIpYOefDt8Qrg8YjYtvDYIk87BLgYICJuAFYFNuhlPTa07iE16AGQtA2wWnEGSQI+QOr+ObVpWpl95vsjYk1gNCnX/wz8RtIu7d6YAeorR3uV969+AXwuImYOeZS4AdkpmwK/jYhngWuAzXL5ccDXIuKJfpb/GnB2RJwYEY9EMisi3lucSdJRSqM5PVA8Oylpr3yU8ElJCyQdW5g2IR/BmSrpPkmPSPpsYfpqks7ORyznSvpUsbuspI0l/VjS3yTdI+ljg/2QbNj7R4CIuCAiXoiIpyPiqtynH+BBYA6wG4Ck9UhH4i5rsa6pwHeBPwEHDXnkZn1z7lodHAzMJA3cMbXVDBHxTETcTGpgrk9qTJLPaBwCHCnp9cD3gVMj4vcdiLstIuJJ4B5JB0BqhEh6Q558H7BLLt+C1ID8W1cCtXNJudowFTinaZ63ARsDRwBTimfGKbnPDJCnLYyIzwM/AE5s65YMUD852lLe9p8C50TEjzoQJuAGZKfcBrxN0mqkCuo2SROB10VE82hKy5C0OvAW4JJ+3mMj0oXf40iV/HckrZunPUX6Ma4D7AUcJmnfpuV3Al6X4/t8rkABjgEmkBq97wLeX4jtFcDPgT/m990F+Lik3fqJ1UamO4EX8gGJPQr5WXQOL//jmAJcystnKIE03DowCTg/P4r/aMy6xblrVXcwL+febpLG9DZjRCwGribtqDfK7gCOJ53dGU86A1lZki4AbgBel7spHkI6aHOIpD+S9s32ybMfBfxHLr8AmBYR0Y24jZnAWpK2kLQS8D7gvKZ5ppL2Py/Kr/eGAe0zt/ITYLvCWfchN5AcVbrcZyFwAPA9Sbfl1bwXeDswLXf1nS1p26GOvRb91usuIm6V9GPSj+LPwOGknYsP5TN2+wMLgMMj4vGmxdclNfQf6Odtnge+mLuSzJC0hNQgnBkRPYX5/pQTdmfgZ4XyL0TE08Afc9K+AZhLSszDIuIx4DFJp5AuNgd4E/CqiPhifv0XSd8n7Txd2U+8NsJExJOSdgI+TTp6vZHSdWD/UZjtp8A3c/fug0n/1Ju7ah8M/Ckibpf0OPBVSW+MiD8M+UbYSPczScXuev9NqnvBuWsVluveV5NuVfGIpLuBfwO+2cdi95Muvyn6DfAV4HsR8cyQBNsmEXFgL5OWu21CRNwOvHVoI7IBaJyFvI603/zXxoTcSDyAdOuK5yVdQmpQ/oTy+8yt3A+IdLLlqRUJvqwB5ujNpAM3zeXnsXwDe8j5DGSHRMQ3I+INEfE+0tGU35A+/0NJZ+7mAtNbLPoYacje/q6VebTpOoS/A2sCSHqz0g1y/ybpCeAjLN+3/8FWy5K6CCwoTCs+fzWwsdJFyo/nHaLPAL0e1bSRLSLmRsS0PFDB1qT8Orkw/WlyP35gg4i4vsVqGkfRG0NWX0cv3bHM2mzfiFin8Ph+Y4Jz1ypuKnBVRDySX/+Q/nNvHIVbDOSuct8jXcf+0XwdpNlQOJd0gGMay3dffQ+wlDRaLqQ6dQ9Jr6L8PnMr40jXBz8+iGVHHDcgOyx3Gfkw8EXSDvSfIuJ54Gbg9c3zR8TfSae391uBt/0h6VqcTSJibdL1Nyq57AMse8Rjk8LzBcA9TTtUoyNizxWI1UaIiPgz6VqcrZsmnUM6e3Nu8zKS/hnYHDha0oOSHgTeDByomowEaMOac9cqJ18+815g50LuHQm8obfrq5RGg3wn6WB3w/8j3WPuCNJ+xPeGNHAbsSLiXtJgOnuSziwWTSWd5Lgv5/KPgFcCB67gPvN7gN9HREfOPtadG5Cd9w3gmJzk9wBvyhX1JOAvvSzzKVLf5v+WtD6ApDdIurDke44GFkXEM0ojwv7bAOK9mLTDs67S7Uc+Wph2E/CkpE/nwXZWkrS1KjAUslWPpH9SGuhpfH69CWmkteYRw64jXW/7rRarmUq6LmdLYNv82BpYHY9KbN3n3LUq2hd4gWVzbwtS43CZ63AlrSJpe9IlLo8B/5vL3wB8DPiPfG3gscAE+XZiNnQOAd7R1KBrjLexNy/n8htIg980zqiX3mfOg9SMk3QM8CFSLzorwQ3IDpI0GVgnIn4KEBE3kbo8LQAmAye0Wi4ifge8Iz/+ImkR6UajZe8j9p/AFyUtJt0y5OIBhP1FYCGpsftL0oXJz+a4XgD+hfQDvgd4hDSK1dqtVmQj3mLSGZcbJT1FajjeSjpj85I8Kto1EbGoWC5pVdJR9G9FxIOFxz2kMz7uCmhD7eda9j6QPy1OdO5aRU0F/jci7ivmH/Bt0oAdo4BP5X2ERaQz6bOAf46Ip/JAJmcAx0XEPHipy/Z/AF/razAes8GKiLsj4pam4rcBs/MI7sVcPgV4vaStS+4zb5zHCllC6gG4DTApIq4a6u0aLuRBpmwgJB0GTImInbsdi5mZmZmZdZbPQFqfJI2V9FZJr5D0OtLZop/2t5yZmZmZmQ0/vnDf+tMYdW1T0shUFwKndjMgMzMzMzPrDp+BtD5FxL0RsXVErBER4yLiqIh4rttxDUQe3OcPki7Pr9eTdLWku/LfdQvzHi1pnqQ7JO1WKN9e0pw87RRJyuWrSLool98oaUJhman5Pe6S5GucbFCcv2Zmnee616x3bkDaSHAE6T6bDdOBayJic+Ca/BpJWwJTgK1IN3E9NQ8eAHAa6Z6dm+dH4yavhwCPRcRrSTdkPjGvaz3gGNKgMTsAxxT/2ZgNgPPXzKzzXPea9cINSBvWlG4ZsRdpdNiGfYCz8/OzSUOcN8ovjIhn8+iI84AdJI0F1oqIG/Lw5ec0LdNY1yXALvkI427A1RGxKCIeIw3f3/jHYVaK89fMrPNc95r1bdhdA7nBBhvEhAkTXnr91FNPscYaa3QvoEGqY9xVjHmdddZho4024sUXX3zHnXfe2eh6OyYiHgCIiAckbZjLx7HsPQkX5rLn8/Pm8sYyC/K6lkp6Ali/WN5imV41529DFT/bdhiO29XObapT/vaWu+DvuU6GYrtmzZr1SES8qq0rbbO+8rfTqppbVYxrqGIaLnXvUKtKTjiO1nEMZd077BqQEyZM4JZbXr5tTE9PD5MmTepeQINUx7irFvPll1/OjBkzOPXUU+np6WHy5MlP97OIWpRFH+WDXWbZN5UOJXVxYcyYMXz9619fbp4lS5aw5pprtlq81objdrVrm2644QZmzpzJkUceyezZsznyyCMrl79lchf8PdfJUGzX5MmT723rCodA875DN1Xtf2lDFeMaipjqsO9Qtu4dalWpBx1H6ziGsu4ddg1Is4brr7+eyy67jBkzZvDMM88AjJZ0HvCQpLH5COJY4OG8yEJgk8IqxgP35/LxLcqLyyyUNApYm3Qj5oXApKZlelrFGRGnk25yy8SJE6PVP8Mq/uNuh+G4Xe3apiuvvJJZs2Yxbdq0yuZvmdwFf891Mly3y6ysOuw7lK17h1pV6gvH0fk43IDsognTf9HrtLN27/4p8Lo7/vjjOf744wEaRxEXR8T7JX0NmAqckP9emhe5DPihpG8AG5MueL8pIl6QtFjSjsCNwMHAtwrLTAVuAPYHro2IkHQl8JXCxe+7AkcPdlvm/PUJprXIl/kn7DXYVVrFOX/Nho++/t/7d1Atw6nubZfe8tf7qiOXG5A2Ep0AXCzpEOA+4ACAiLhN0sXA7cBS4PCIeCEvcxhwFrAacEV+AJwBnCtpHuno4ZS8rkWSvgTcnOf7YkQsGuoNsxHB+Wtm1nmue80yNyBtRMin8ucBRMSjwC6t5ouI44DjWpTfAmzdovwZ8j+RFtPOBM4cbMxmDc5fM7POc91r1pobkGZmZmZt0FdXVTOz4cL3gTQzMzMzM7NS3IA0MzMzMzOzUvptQEraRNKvJM2VdJukI3L5epKulnRX/rtuYZmjJc2TdIek3Qrl20uak6edIkm5fBVJF+XyGyVNKCwzNb/HXZKmtnXrzczMzMzMrLQyZyCXAkdFxBbAjsDhkrYEpgPXRMTmwDX5NXnaFGArYHfgVEkr5XWdRrrx6eb5sXsuPwR4LCJeC3wTODGvaz3gGODNwA7AMcWGqpmZmZmZmXVOvw3IiHggIn6fny8G5gLjgH2As/NsZwP75uf7ABdGxLMRcQ9p9Kod8k1X14qIGyIigHOalmms6xJgl3x2cjfg6ohYFBGPAVfzcqPTzMzMzMzMOmhA10DmrqVvJN0QdUxEPACpkQlsmGcbBywoLLYwl43Lz5vLl1kmIpYCTwDr97EuMzMzMzMz67DSt/GQtCbwY+DjEfFkvnyx5awtyqKP8sEuU4ztUFLXWMaMGUNPT89L05YsWbLM6yo5apulvU6rcty9qWPMZmZmZmZWXqkGpKRXkhqP50fET3LxQ5LGRsQDuXvqw7l8IbBJYfHxwP25fHyL8uIyCyWNAtYGFuXySU3L9DTHFxGnA6cDTJw4MfKNXwHo6emh+LpKpvVxv6izdl+jsnH3psqftZmZWRU13zvyqG2WMm36L5h/wl5disjMrG9lRmEVcAYwNyK+UZh0GdAYFXUqcGmhfEoeWXVT0mA5N+Vurosl7ZjXeXDTMo117Q9cm6+TvBLYVdK6efCcXXOZmZmZmZmZdViZM5BvBT4AzJE0O5d9BjgBuFjSIcB9wAEAEXGbpIuB20kjuB4eES/k5Q4DzgJWA67ID0gN1HMlzSOdeZyS17VI0peAm/N8X4yIRYPbVDMzMzMzM1sR/TYgI+K3tL4WEWCXXpY5DjiuRfktwNYtyp8hN0BbTDsTOLO/OM3MzMzMzGxoDWgUVjMzMzMzMxu53IA0MzOzjpE0X9IcSbMl3ZLL1pN0taS78t91C/MfLWmepDsk7VYo3z6vZ56kU/L4CuQxGC7K5TfmW5CZmVmbuAFpZmZmnTY5IraNiIn59XTgmojYHLgmv0bSlqRxEbYCdgdOlbRSXuY00i28Ns+P3XP5IcBjEfFa4JvAiR3YHjOzEcMNSDMzM+u2fYCz8/OzgX0L5RdGxLMRcQ8wD9gh3z5srYi4IY/afk7TMo11XQLs0jg7aWZmK84NSDMzM+ukAK6SNEvSoblsTL7dF/nvhrl8HLCgsOzCXDYuP28uX2aZiFgKPAGsPwTbYWY2IpW5jYeZmZlZu7w1Iu6XtCFwtaQ/9zFvqzOH0Ud5X8ssu+LUeD0UYMyYMfT09PQZdBlHbbN0hdcxZrW0nnbE005LlixxTGYGuAFpZmZmHRQR9+e/D0v6KbAD8JCksRHxQO6e+nCefSGwSWHx8cD9uXx8i/LiMgsljQLWJt1jujmO04HTASZOnBiTJk1a4W2bNv0XK7yOo7ZZyklzRjH/oBWPp516enpox2fUTlWMyWwkcBdWMzMz6whJa0ga3XgO7ArcClwGTM2zTQUuzc8vA6bkkVU3JQ2Wc1Pu5rpY0o75+saDm5ZprGt/4Np8naSZmbWBz0CamZlZp4wBfprHtBkF/DAi/k/SzcDFkg4B7gMOAIiI2yRdDNwOLAUOj4gX8roOA84CVgOuyA+AM4BzJc0jnXmc0okNMzMbKdyANDMzs46IiL8Ab2hR/iiwSy/LHAcc16L8FmDrFuXPkBugZmbWfm5AmpmZmVXMhF6up5x/wl4djsTMbFm+BtLMzMzMzMxKcQPSzMzMzMzMSnED0szMzMzMzEpxA9LMzMzMzMxKcQPSzMzMzMzMSnED0szMzMzMzEpxA9KGrQULFjB58mS22GILttpqK4ANASStJ+lqSXflv+s2lpF0tKR5ku6QtFuhfHtJc/K0U5Tvgi1pFUkX5fIbJU0oLDM1v8ddkqZ2bMNtWHD+mpl1nutes/65AWnD1qhRozjppJOYO3cuM2fOBNhQ0pbAdOCaiNgcuCa/Jk+bAmwF7A6cKmmlvLrTgEOBzfNj91x+CPBYRLwW+CZwYl7XesAxwJuBHYBjiv9szPrj/DUz6zzXvWb9G9XtAMyGytixYxk7diwAo0ePBngaGAfsA0zKs50N9ACfzuUXRsSzwD2S5gE7SJoPrBURNwBIOgfYF7giL3NsXtclwLfzEcbdgKsjYlFe5mrSP44LhmhzbZhx/ppV14Tpv+h2CDZEXPea9c8NSBsR5s+fD7A6cCMwJiIeAIiIByRtmGcbB8wsLLYwlz2fnzeXN5ZZkNe1VNITwPrF8hbLLEPSoaQjlIwZM4aenp7l5hmzGhy1zdLlylvNWydLliyp/TY0G4ptevDBB6GC+Vsmd2F45u9wzF0YvttlNhhV3XcoW/e2S6v6G6pTXziOzsfhBqQNe0uWLGG//fYDWBART+ZLEFppNSH6KB/sMssWRpwOnA4wceLEmDRp0nLzfOv8SzlpzvI/1/kHLT9vnfT09NBqe+us3du0ZMkSdt55Z6hg/pbJXRie+TsccxeG73aZDVSV9x3K1r3tMq2XM+5n7b5GJeqLqtRbIykONyBtWHv++efZb7/9OOigg/j973//eC5+SNLYfARxLPBwLl8IbFJYfDxwfy4f36K8uMxCSaOAtYFFuXxS0zI97douGxmcv2bWrLfus/NP2KvDkQxfrnvN+tbvIDqSzpT0sKRbC2UeicoqLyI45JBD2GKLLfjEJz5RnHQZ0MinqcClhfIpOSc3JV3wflPusrJY0o45bw9uWqaxrv2BayMigCuBXSWtm38fu+Yys1Kcv2Zmnee616x/Zc5AngV8GzinUNYYieoESdPz6083jUS1MfBLSf8YES/w8khUM4EZpIuCr6AwEpWkKaSRqN5XGIlqIun0/SxJl0XEYyu60TYyXH/99Zx77rlss802bLvttgBbStoTOAG4WNIhwH3AAQARcZuki4HbgaXA4Tl3AQ4j/RZWI+XtFbn8DODcfNH8IlL+ExGLJH0JuDnP98XGRfFmZTh/zcw6z3WvWf/6bUBGxK+LZwUzj0RllbfTTjuRDuglkm6PiBn55S6tlomI44DjWpTfAmzdovwZ8j+RFtPOBM4ceORmzl8zs25w3WvWv8HeB3KZkajIN1ml99GjxlFyJCpgwKNYmpmZmZmZ2dBr9yA6XRnFsq/hjKsypG4rvQ2LDNWOuzd1jNnMzMzMzMobbAOyUiNR9TWccVWG1G2lt2GRoTpDIw9ElT9rMzPrPkmbkMZU2Ah4ETg9Iv5H0rHAfwB/y7N+ptFtUNLRpPESXgA+FhFX5vLtefn6shnAERERklbJ77E98CjwvoiY35ENNDMbAQbbhdUjUZmZmdlALQWOiogtgB2Bw/MAfADfjIht86PReCwOzrc7cKqklfL8jcH5Ns+P3XP5S4PzAd8kDc5nZmZt0u8ZSEkXkM4EbiBpIWlkVI9EZWZmZgOSDyg3xlBYLGkufY9v0LbB+aI4MoqZmQ1amVFYD+xlkkeiMjMzs0HJI7y/EbgReCvwUUkHA7eQzlI+Rmpcziws1hhQ73lKDs4nqTE43yNDtjFmZiNIuwfRMTMzM+uTpDWBHwMfj4gnJZ0GfIk0WN6XgJOAf6e9g/M1x9DrAHz96WsQvBU1ZrXBrf9b51/asnybcWuvaEhANQfKq2JMZiOBG5BmZmbWMZJeSWo8nh8RPwGIiIcK078PXJ5ftnNwvmX0NQBff/oaBG9FHbXNUk6a077ds/kHTWrLeqo4UF4VYzIbCQY7iI6ZmZnZgOSB9M4A5kbENwrlYwuzvQe4NT9v5+B8ZmbWBj4DaWZmZp3yVuADwBxJs3PZZ4ADJW1L6mo6H/gwtHdwPjMzaw83IM3MzKwjIuK3tL5GcUYfy7RtcD4zM1txbkBW1Jy/PtHyGov5J+zVhWjMzMzMzMzcgOyICUN4sb2ZmZmZmVmneBAdMzMzMzMzK8VnIM3MzMyGqd56QfmSGDMbLJ+BNDMzMzMzs1LcgDQzMzMzM7NS3IA0MzMzMzOzUtyANDMzMzMzs1I8iI6ZmZnZCOPBdcxssHwG0szMzMzMzEpxA9LMzMzMzMxKcQPSzMzMzMzMSnED0szMzMzMzErxIDpmZmZmBvQ+uM5R2yxlUmdDMbOK8hlIMzMzMzMzK8UNSDMzMzMzMyvFXVjNzMzMWuitO+dI5XtHmhnU5AykpN0l3SFpnqTp3Y7HrCznrtWZ89fqyrlrdeb8taqr/BlISSsB3wHeBSwEbpZ0WUTc3t3IzPrm3LU6c/5aXTl3O89nJtvH+Wt1UPkGJLADMC8i/gIg6UJgH6ByP6ROdHVxJV0rtcldsxacv1ZXzt2K8D7LoDh/rfLq0IAcBywovF4IvLlLsVSWK+lKcu5anTl/ra6cuxXnfZY+OX+t8urQgFSLslhmBulQ4ND8comkOwqTNwAeGaLYhszH2hS3TmxDMOVV/bN+dYffr9/chX7zt6HlZ9vh73coVD1nBmOotqly+Vsyd2F45u9wzF0Ymu2qXO7CgPK3o9r1/7/dOhHXIOqETnxWlcvfquTu5BMrk6uOY1mNOIYsd+vQgFwIbFJ4PR64vzhDRJwOnN5qYUm3RMTEoQtvaNQx7jrGPMT6zV3oO38bhutnOxy3axht0wrVvUXD6DN5yXDcJhg229W2urcbqvodVDGuKsbUBm2re4daVT5/x9H5OOowCuvNwOaSNpW0MjAFuKzLMZmV4dy1OnP+Wl05d63OnL9WeZU/AxkRSyV9FLgSWAk4MyJu63JYZv1y7lqdOX+trpy7VmfOX6uDyjcgASJiBjBjkIt3/RT/INUx7jrGPKRWMHeLhutnOxy3a9hsk/O3T8Nxm2CYbFcbc7cbqvodVDGuKsa0wmqUv1X5/B3HsoY8DkUsd125mZmZmZmZ2XLqcA2kmZmZmZmZVcCwbUBK+pqkP0v6k6SfSlqnMO1oSfMk3SFpty6GuRxJu+e45kma3u14eiNpE0m/kjRX0m2Sjsjl60m6WtJd+e+63Y617qqeE4PJhd5+g5K2lzQnTztFknL5KpIuyuU3SprQoW1bSdIfJF0+XLapk5y7zl3rWx85eKykv0qanR97djiu+fm7ny3pllzW1f/vkl5X+DxmS3pS0se7/VmNRENdt3eibh5ALENWlw4wjnUkXaLUtpkr6S3digWAiBiWD2BXYFR+fiJwYn6+JfBHYBVgU+BuYKVux5tjWynHsxmwco5zy27H1UusY4Ht8vPRwJ35s/0qMD2XT2987n4M35wYaC709RsEbgLeQroP1hXAHrn8P4Hv5udTgIs6tG2fAH4IXJ5f136bnLvO3bpu03B89JGDxwKf7GJc84ENmsoq8/891y8Pku5z19XPaqQ9OlG3d6JuHkAsQ1aXDjCOs4EP5ecrA+t0K5aIGL5nICPiqohYml/OJN1HB2Af4MKIeDYi7gHmATt0I8YWdgDmRcRfIuI54EJSvJUTEQ9ExO/z88XAXGAcKd6z82xnA/t2JcDho/I5MYhcaPkblDQWWCsibohUy53TtExjXZcAuwz6qFlJksYDewE/KBTXeps6zLn78jLOXWupjxysoir9f98FuDsi7u1iDCPVkNftHaqb+9WBurRsHGsBbwfOAIiI5yLi8W7E0jBsG5BN/p3UyoaUgAsK0xZSncq6yrH1KndfeiNwIzAmIh6AVAEAG3YxtOGgVjlRMhd626Zx+Xlz+TLL5ANDTwDrD8lGvOxk4FPAi4Wyum9TJzl3m5Zx7lpfmnIQ4KNKl+Gc2enuokAAV0maJenQXFal/+9TgAsKr7v5WY00Ha3bh7BuLuNkhrYuLWsz4G/A/+butD+QtEaXYgFq3oCU9EtJt7Z47FOY57PAUuD8RlGLVVVlKNoqx9aSpDWBHwMfj4gnux3PMFSbnBhALvS2TX1ta0c/B0l7Aw9HxKyyi7Qoq9Q2dUFtts+5u5xKbdNI0CIHTwNeA2wLPACc1OGQ3hoR2wF7AIdLenuH379XklYG3g38KBd1+7MaaTr2+x/iurm/9+5EXVrWKGA74LSIeCPwFKnLajdieSmg2oqId/Y1XdJUYG9gl3yqFlJre5PCbOOB+4cmwgGrcmzLkfRK0g/7/Ij4SS5+SNLYiHggnyp/uHsRDgu1yIkB5kJv27SQl7uaF8uLyyyUNApYG1g0JBuTvBV4dx6MYVVgLUnnUe9t6jTn7rLLOHetpVY5GBEPFaZ/H7i8kzFFxP3578OSfkrqtliV/+97AL9vfEbd/qxGoI7U7R2om/vTibq0rIXAwoho9E64hNSA7EYsQM3PQPZF0u7Ap4F3R8TfC5MuA6YojSK3KbA56YLSKrgZ2FzSpvkI2xRSvJWTr3c5A5gbEd8oTLoMmJqfTwUu7XRsw0zlc2IQudDyN5i7XyyWtGNe58FNyzTWtT9wbeGgUNtFxNERMT4iJpA+82sj4v113qYucO4uvy7nri2jtxzMO4MN7wFu7WBMa0ga3XhOGpTwVqrz//1ACt1Xu/lZjVBDXrd3qG7uU4fq0lIi4kFggaTX5aJdgNu7EUsxqGH5IF0wugCYnR/fLUz7LGlEojsY5OhDQxj3nqTRpu4GPtvtePqIcyfSae8/FT7jPUnXwVwD3JX/rtftWOv+qHpODCYXevsNAhNJ//zvBr4NKJevSuquNI90wGezDm7fJF4efW1YbJNz17lb120abo8+cvBcYE4uvwwY28GYNiON4PhH4LbGb7evHOpgbKsDjwJrF8q69lmN1MdQ1+2dqJsHGM+Q1KUDjGFb4Jb8mfwMWLdbsUTES/8MzMzMzMzMzPo0bLuwmpmZmZmZWXu5AWlmZmZmZmaluAFpZmZmZmZmpbgBaWZmZmZmZqW4AWlmZmZmZmaluAFpZmZmZmZmpbgBaWZmZmZmZqW4ATmMSPqupP/Xgfc5VtJ5g1iuI/GZWfVJmibpt4XXIem1+fmIryuKn8cQv88SSZv1Mm2Z76jF9B5JHyoz73DkHO5bp3LYBsf52zfnb9/cgGwiab6k5yRt0FQ+OyfThKbyY3P5Di3WtYOkGZIel7RI0k2SPpinTZL0Yv7nvUTSQkkXS3pTP/EdIunPkhZLekjSLySNBoiIj0TEl1b4Q1gBebsWtppWhfisOvJv7emc/w9KOkvSmnnaeEk/lvSIpCckzZE0LU+bkH9zo7q6AQYs9z0+luukTVZknVWsK3J+Ppe3c7GkWZJ27nZcreR6OCT9pKn8Dbm8p1EWEWtGxF86HmSFOIerp7cDEvm7emc3Yqoq52/19LUvPFy4AdnaPcCBjReStgFWa55JkoAPAIuAqU3T3gJcC1wHvBZYHzgM2KMw2/0RsSYwGtgR+DPwG0m7tAoq/1C+AhwYEaOBLYCLB7eJZpXwL/k3sC3wRuDoXH4usAB4Nem3czDwUDcCtFIa3+NY0vf0rS7HMyiSVupnlq/m7VwbOA34SYlluuVvwD9LWr9QNhW4s0vxVJ1z2FqqycFK56+1NFT56wZka+eSdlgbpgLntJjvbcDGwBHAFEkrF6Z9DTg7Ik6MiEcimRUR721eSZ62MCI+D/wAOLGXuN4E3BARf8jLLYqIsyNiMbx0dObLjZklfUrSA5Lul/Shpu4JZ0n6Tj5StVjSjZJeU1j2fyQtkPRkPsrztn4/tX4U42scnZF0lKSHc5wfLMy7iqSvS7ovn2n9rqTlGvE2PETEg8CVpIYkpFw/KyKeioilEfGHiLiiawFaKRHxDHAJsCWApLUlnSPpb5LulfQ5Sf3+3xlgXbG+pJ/nuupmSV/Wst2y/knS1Uq9QO6Q9N6m9zlNqafIU8Dkktv5IvBDYD1gTF7XayRdK+lRpTPn50tap/Be8yV9UtKflM6qXyRp1cL0/y7U1//e9HnsKen2XFf/VdInS4T5HPAzYEpex0rAe4Hzm9Zd/L+wvqTL8md5E/CapnnfpdQD5glJ3wbU25v39blXmXO4Ujncp7zt382fzWJJ10l6dWF6SPqYpL/k7fla8buT9O+S5iqdtbuyxbKHS7oLuGtFY+0U52/181fSFkrd/x+XdJukd+fyTXPZK/LrH0h6uLDceZI+np+vLemMHO9f82e+Up42TdL1kr4paRFw7GBj7YsbkK3NBNbKX/JKwPuAVtf8TQV+DlyUX+8NIGl14C2kH/FA/QTYTtIaLabdCOwm6QuS3ippld5WIml34BPAO0lnQFud5j8Q+AKwLjAPOK4w7WbSzvx6pB/pj4o/tDbZiHQUaRxwCPAdSevmaScC/5hjeG2e5/Ntfn+rCEnjSWfn5+WimaR8mCLpH7oXmQ1ErvveR/r+IB0FXxvYjFQHHQx8sPXSfeqrrvgO8FSeZyqF3iC5Hr2aVIdtSKrzTpW0VWHd/0aq+0YDpa7hy/8XDib1VmmcGRdwPOmg4hbAJiz/j/u9wO7ApsDrgWl5fbsDnwTeBWxOqreLzgA+nHuebE3q3VLGObx8MHQ34Dbg/j7m/w7wDOksxr/nBznGDYAfA58DNgDuBt7aaiUlP/dKcg5XLof7cxDwJVJOzqbpAAnwHmAisB2wDzmnJe0LfAb4V+BVwG+AC5qW3Rd4M7kxVgfO32rnr6RXktoNV5E+j/8Czpf0uoi4B3iS1BsL0kmqJZK2yK/fTurVCHA2sJS0f/xGYFfgQ4W3ejPwl/wexX379okIPwoPYD4pcT5HSsTdSck/CghgQp5vddIXvW9+/T3g0vx8XJ73n/p4n0nAwhbl/5SXHdfLcnuQku9xYAnwDWClPO0s4Mv5+ZnA8YXlXpvX+9rCvD8oTN8T+HMf8T4GvCE/PxY4byDb1SK+ScDTwKjC9IdJXXlFqoxeU5j2FuCebueHH+175N/aEmBxzs1rgHXytHWBE0g7vC+QdgzelKdNyPOP6lbsfrT8Hh8n/UO7H9gGWAl4FtiyMO+HgZ78fBrw28K05vqpTF2xEvA88LrCtC831kvakfpNU7zfA44pvM85JbfzLFLj6vH89xngoD7m3xf4Q9Pn9P7C668C383PzwROKEz7x6bP47782a1VMtaX6mHS2ZPXAReSdrY/1PgOip974bP8p8K0rxQ+y4OBmYVpAhYCH2r+Pvv73Kv2cA5XMoeX+WybYnhnYXsuLExbk/T/YpPC97F7Yfp/Atfk51cAhxSmvQL4O/DqwrLv6HZuOn9rm7+TaL2P/zbgQeAVhbILgGPz83NJJ382Au7IMX6E1OB9POfpmPy9rlZYx4HArwrf631DnXc+A9m7c0lHRabRuvvqe0g/1Bn59fnAHpJeRWpsvUg6ijtQjcbn460mRsQVEfEvpDOD++T4PtRi1o1J15A1LGgxz4OF538nVb4A5K4Kc/Np/sdJR542oL0ejYilLWJ4FamBPiufzn8c+L9cbsPLvpGO6E0iHTzZACAiHouI6RGxFamynA38TFKvXeasq/aNiHWAVYCPko6SjgdWBu4tzHcvqY4bqL7qilH0Xte9Gnhzox7JdclBpH/Orebvz9fzdq5GOqvxNUl7AEjaUNKFuTvRk6ReK811Zm91bnN9XfzMAPYjHeS7V6mb3lsGEPO5pO9kMvDTPuZr9VkW41gmxkh7Kr19dmU+96pxDlcrh5cCr2xR/kpSg6WhmJNLSGNSbNxqeo6pMe3VwP8UPtNFpIMi43pZtuqcv9XK395sDCyI1AW3+F6N7+Q60v7Q24FfAz2kM8c7kxriL5I+01cCDxQ+0++RzjY2DHnuugHZi4i4l3RqfE9St9JmU0mJd5+kB4Efkb7QAyPi78ANpIQbqPcAv4+Ip/qJ78WIuIZ0Gn3rFrM8QKo8GkqPyKV0veOnSaf6180/1ifo43qXNnuEdLRrq4hYJz/WjnThtA1DEXEd6eji11tMeySXb0w6cGIVFREvRMRPSGcBdiTt6L26MMs/AH9t41v+jbSj2VtdtwC4rlCPrBNp1NHDimEP9E0juRW4HtgrFx+f1/X6iFgLeD/l68wHmuJeptt2RNwcEfuQdhB+xsAGTzuXdOZlRv7f1JvGZ9lbHMvEmA/m9PZ/pcznXknO4crk8H3APxQPGubumRuy7M59MSfXJP2PuL/V9BxTY9oCUpfE4ue6WkT8rhh2PzFWjvO3Mvnbm/uBTbTsdajF7+Q60lnKSfn5b0mXCuzMy91XF5DOQG5Q+EzXygfcXwp5kPGV5gZk3w4hdWFYpjEnaRywC+max23z4w2k6/am5tk+BUxTuih3/bzcGyRd2PwmSsZJOoZ0NvEzrYKRtE++JmzdvMwOpKSa2WL2i4EP5us4V2dg1w+OJlUIfwNGSfo8sNYAlkfSqk2P0o3PfITl+8A3JW2Y1zdO0m4DicFq52TgXZK2lXSipK0ljVK6Tc1hwLyIeLS7IVpfcr20D6kL8q2keug4SaOVBqj4BK2vJx+UiHiBdIDvWEmrS/onlh0A7XLgHyV9QNIr8+NNhWtKBi2/106kbtaQ6s0lwOP5f8R/D2B1F5P+X2yZ6+tjCu+zsqSDJK0dEc+TLp14oeyKI11XszPw2X7ma/4st2TZ0cV/AWwl6V+VRvX7GL2fURyyz32oOYcrk8M3krooTs/7EGuQLmu4hWUbkHtK2klpEMMvATdGRPHsy3/nfaZNSAMeNsas+C5wtPK1eEqDkhwwgO2tJOdvZfK3sewy+8LATaRLtD6VP4tJwL+QLjEgIu4inUB5P/DriHiSdI3nfuQGZEQ8QLqG8iRJa0l6hdIAQjsPYHtXmBuQfYiIuyPilhaTPgDMjoirIuLBxgM4BXi9pK3zUax35MdflEZCOp2Xu7wCbCxpCSnhbyb1WZ8UEVf1EtJjwH+QrmlpnJ7/WkQ0XzROpBErTwF+RRqY5IY86dkSm34l6fqAO0kV9TMM7HT4ONIPoPh4TZ9LLO/TpLhnKnVF+CXpOh4bpiLib6Tu4v+P1IX5p6Su3H8hHUF9d9eCs/78PNdlT5Iu2J8aEbeRBgh4ivQd/pY0kMKZbX7vj5K62D9IOtt2AbmeizRC9a6kkUjvz/OcSOrmNRifUroH2VOkf+D/S+o6BGlAsu1IvTV+QeueKy3l+vpkUo+SeSw/QMMHgPm5LvwIaeeitIj4bUT0NXhOw0dJPWseJPUI+N/COh4BDiDtxD9KGmji+l7er92feyc4hyuUwxHxLOnM0iTStbZ/IfVCeW/uPt3wQ9LO/iJge1L3yKJLgVmkyyB+QRoMhYj4KelzvDDHdCvL3matbpy/FcrfrNW+8CakfZk9SL3tTgUOjog/F5a7jtRl+L7CawF/KMxzMKl78u2ktsElDO6yuUHTsr9DG67y0Z5bgVVi2X7sZmbDhqQTgY0iYmq/M5tVkHO4HElnkQYq+Vwv0wPYPCLmtZpuQ8P5OzL4DOQwJuk9+dT7uqQjPj9349HMhhOle4y9Pnfd2oF06UFfg8WYVYpz2OrM+TsyuQE5vH2YdB3j3aT+2pUfwMDMbIBGk7oqPUW6juUkUre1AVO6qfOSFo/mbnFdJ+kzvcR6RbdjswFzDjuH68z5OxLzN8rfZ2YlUv/by/Pr9Uj3R7wr/123MO/RpD7EdwC7Fcq3B+bkaafwchfaVUgXNs8jXTg9obDM1Pwed5H6dA/pfU388MMPP6r0cN3rR10fzl0/6vxw/vrhR++PgZyBPAKYW3g9nXRD1s1JNwCfDqA0ctsUYCtgd+BUSSvlZU4DDiVdfL95ng7pdPdjEfFa4Juk7pZIWo90cfSbgR2AY3J3TDOzkcJ1r9WVc9fqzPlr1otSDUhJ40mjYf2gULwPcHZ+fjawb6H8woh4NtLw4fOAHSSNBdaKiBsiIkijLe7bYl2XALtIErAbcHVELIqIx0hHfBo/PjOzYc11r9WVc9fqzPlr1rdRJec7mXRfw9GFsjGR7kVCRDygfL8+0rC1xfsSLsxlz+fnzeWNZRbkdS2V9ASwfrG8xTItbbDBBjFhwoRSG/XUU0+xxhprlJq3Chzvipk1a9YjEfGqbsfRl97yt2qfZSeMxG2GZbd7nXXWYaONNuLFF198x5133vlcnqV2de9w/C6H4zZB+7arTrkLw6PurVOsUO1465S/A9nvLary598uI30bh3K/t98GpKS9gYcjYpbSDS/7XaRFWfRRPthlijEeSuoiwJgxY/j6179eIkxYsmQJa665Zql5q8DxrpjJkyff2/9c3TVhwgRuuWX5W4/29PQwadKkzgfURSNxm+Hl7b788suZMWMGp556Kj09PUyePPnpfhatbN1btbqgHYbjNkF7tuuGG25g5syZHHnkkcyePZsjjzyycrkL5fK3Tt9znWKF6sZbh/wd7H5vUVU//3Ya6ds4lPu9Zc5AvhV4t6Q9gVWBtSSdBzwkaWw+CjMWeDjPv5B0o8yG8aSbhy7Mz5vLi8sslDSKdEPSRbl8UtMyPc0BRsTpwOkAEydOjLI7nXXbQXW8ZiPH9ddfz2WXXcaMGTN45plnAEbXte4djnXBcNwmaM92XXnllcyaNYtp06ZVNnehXP7W6XuuU6xQ3XjrkL+D3e8tqurn307exqHT7zWQEXF0RIyPiAmki4SvjYj3A5eRRooi/20M2XsZMEXSKpI2JV00fFM+7b9Y0o65n/fBTcs01rV/fo8ArgR2lbRuvoh411w2KBOm/2KZx5y/PsGE6b8Y7OrMrA2af5eNx0h3/PHHs3DhQubPn8+FF14IsLiudW+jrvV3PDI4d63OhlP+9qa4D+y8tsEoew1kKycAF0s6BLgPOAAgIm6TdDFwO7AUODwiXsjLHAacBawGXJEfAGcA50qaRzoCMyWva5GkLwE35/m+GBGLViBms2XkkdJuAf4aEXvnEdAuAiYA84H35gvZkXQ0aeS0F4CPRcSVuXx7Xs7rGcARERGSViFdNL898CjwvoiY37GNq7k5f32CaQP8hzb/hL2GKJpKcd1rdeXctTpz/pplA2pARkQP+VR6RDwK7NLLfMcBx7UovwXYukX5M+QfYotpZwJnDiROswFoDNO9Vn7dGKb7BEnT8+tPNw3TvTHwS0n/mP9JNIbpnklqQO5O+ifx0jDdkqaQhul+X+c2zYaL3D1lHrjutXpx7lqdOX/NWhvIfSDNhpUuDtNtZmZmZlZLbkDaSHYy6fY0LxbKlhmmGygO091qaO1xlBymG2gM021mZmZmVksrcg2kWW11+fY0zbEsMxx3T0/PcgstWbKkZflwcNQ2S1uWj1mt92m9GQ6f0XD+rs3MzKz+3IC0kaqbt6dZxnAbSn6gehso56htlnLSnIFVUfMPmtSGiLprOH/XZmZmVn/uwmojUpdvT2NmZmZmVks+A2m2rCEfptvMzMzMrK7cgLQRrxu3p7Gh0duNkEfI/SHNzMzMhpy7sJqZmZmZmVkpbkCamZmZmZlZKW5AmpmZmZmZWSm+BtLMOqK36xPNzMzMrD58BtLMzMzMzMxKcQPSzMzMzMzMSnED0szMzMzMzEpxA9LMzMzMzMxK8SA6Zjbs9TaAz/wT9upwJGZmZmb15jOQZmZmZmZmVoobkGZmZmZmZlaKG5BmZmZmZmZWihuQZmZmZmZmVooH0TGztuptwBozMzMzqz+fgTQzMzMzM7NS3IA0MzMzMzOzUtyANDMzMzMzs1LcgDQzMzMzM7NS3IA0MzMzMzOzUtyANDMzMzMzs1LcgDQzMzMzM7NSfB9IMxuxertn5fwT9upwJGZmZmb10O8ZSEmbSPqVpLmSbpN0RC5fT9LVku7Kf9ctLHO0pHmS7pC0W6F8e0lz8rRTJCmXryLpolx+o6QJhWWm5ve4S9LUtm69mVlFLViwgMmTJ7PFFluw1VZbAWwIrnut+py7VmfOX7P+lenCuhQ4KiK2AHYEDpe0JTAduCYiNgeuya/J06YAWwG7A6dKWimv6zTgUGDz/Ng9lx8CPBYRrwW+CZyY17UecAzwZmAH4JjiD9bMbLgaNWoUJ510EnPnzmXmzJkAG7rutTpw7lqdOX/N+tdvAzIiHoiI3+fni4G5wDhgH+DsPNvZwL75+T7AhRHxbETcA8wDdpA0FlgrIm6IiADOaVqmsa5LgF3yUZrdgKsjYlFEPAZczcs/PjOzYWvs2LFst912AIwePRrgaVz3Wg04d63OnL9m/RvQNZD5FPsbgRuBMRHxAKRGpqQN82zjgJmFxRbmsufz8+byxjIL8rqWSnoCWL9Y3mIZs0GTtAmpMt8IeBE4PSL+Jx/9uwiYAMwH3psrcSQdTTpq+ALwsYi4MpdvD5wFrAbMAI6IiJC0Sn6P7YFHgfdFxPwObeKQ6+36QWu/+fPnA6yO616rGeeu1Znz16y10g1ISWsCPwY+HhFP5m7cLWdtURZ9lA92mWJsh5K6CDBmzBh6enpaBnbUNkuXeT1mtVTW2/xVs2TJktrECpWPt9E1+/eSRgOzJF0NTCN1UTlB0nRSF5VPN3VR2Rj4paR/jIgXeLmLykxSA3J34AoKXVQkTSF1UXlfR7fSam/JkiXst99+AAvqWvc26tpmFa4f+lXx+m3Q2rldTz/9NEcccQRUMHehXP7WKXfrlpNVj7fK+Vu27u3NUdssbZnbVf4+BqPqOdYO3drGUg1ISa8kNR7Pj4if5OKHJI3NR2HGAg/n8oXAJoXFxwP35/LxLcqLyyyUNApYG1iUyyc1LdPTHF9EnA6cDjBx4sSYNGlS8ywATGs6Y3LUNks5ac4o5h/Uev6q6enpobdtq6Iqx5uPIjaOJC6WVOyaPSnPdjYp3z5NoYsKcI+kRheV+eQuKgCSGl1UrsjLHJvXdQnwbUnKXVnM+vX888+z3377cdBBB/H73//+8Vxcu7r3W+dfyklzlv93U5e6t5Uq128rol3b9fzzz7P33nvzkY98hKOOOurxXFyZ3IVy+Vun3K1bTlY53qrnb9m6tzfTpv/ipX3goirm9Yqoco61S7e2scworALOAOZGxDcKky4DGqNDTQUuLZRPySNMbUq6aPimvMO+WNKOeZ0HNy3TWNf+wLV5J/tKYFdJ6+aLiHfNZWZt01fXbPLoa/TerWQcJbuoAI0uKmb9iggOOeQQtthiCz7xiU8UJ7nutUpz7lqdOX/N+lfmDORbgQ8AcyTNzmWfAU4ALpZ0CHAfcABARNwm6WLgdlI3wcNzNz+Aw3j5WrEr8gNSA/XcfFZnEamrIBGxSNKXgJvzfF+MiEWD21Sz5XWpa3ZzDP12RaliN4xW3braqbeuY53Qzc+68V3PmTOHc889l80224zLL78cYEtJe+K61yru+uuv59xzz2WbbbZh2223Beeu1Yjz16x//TYgI+K3tN4RBtill2WOA45rUX4LsHWL8mfIP8QW084EzuwvTrOB6mLX7GWU6YpSxW4YzV3C261V95pO6WY3nsZ3PWnSJP7rv/7rpXJJt0fEjPzSda9V1k477USxp75z1+rE+WvWvzL3gTQbdrrcNdvMzMzMrJa6c3jfrPu61jXbzMzMzKyu3IC0EanbXbOt2nq7x+X8E/bqcCRmZmZm1eIurGZmZmZmZlaKG5BmZmZmZmZWihuQZmZmZmZmVoqvgTSzPvV2PaCZmZmZjTw+A2lmZmZmZmaluAFpZmZmZmZmpbgBaWZmZmZmZqW4AWlmZmZmZmaluAFpZmZmZmZmpbgBaWZmZmZmZqX4Nh5mZiX1dkuT+Sfs1eFIzMzMzLrDZyDNzMzMzMysFJ+BNDOg97NrZmZmZmYNPgNpZmZmZmZmpbgBaWZmZmZmZqW4AWlmZmZmZmaluAFpZmZmZmZmpbgBaWZmZmZmZqV4FFYzsxXU1wi2vkekmZmZDSduQJqNIL5Vh5mZmZmtCHdhNTMzMzMzs1LcgDQzMzMzM7NS3IA0MzMzMzOzUtyANDMzMzMzs1I8iI7ZMOTBcqqjt+/Co7OamZlZHfkMpJmZmZmZmZVSiwakpN0l3SFpnqTp3Y7HrCznrtWZ89fqyrlrdeb8taqrfBdWSSsB3wHeBSwEbpZ0WUTc3t3IzPrWidx1V9X66u27O2v3NTocSWuue62unLtWZ85fq4PKNyCBHYB5EfEXAEkXAvsA/iFZ1bUtd+f89QmmubFoneW61+rKuWt15vy1yqtDA3IcsKDweiHw5uIMkg4FDs0vl0i6o8yKPwYbAI/oxHaE2REbAI90O4gBqFq8r+7w+/Wbu1A6f6v2WQ65j43AbQaYfGKv2125/B1A3dtym2pU97YyXPNzKLarcrkLK1b3VjR365aTdYm3cvk72P3eolb/Yyua1yuiLjm2IvraxiHL3To0INWiLJZ5EXE6cPqAVyzdEhETBxtYpzne2uk3d6Fc/o7Ez3IkbjNUarvbVvdWaJvaZjhuEwyb7RqRdW+dYoX6xdtBQ7bfu8ybjIDP39s4dOowiM5CYJPC6/HA/V2KxWwgnLtWZ85fqyvnrtWZ89cqrw4NyJuBzSVtKmllYApwWZdjMivDuWt15vy1unLuWp05f63yKt+FNSKWSvoocCWwEnBmRNzWptWv0On/LnC8NdLm3B2Jn+VI3GaoyHY7f/s1HLcJhsF2jeDcrVOsUL94O2KI93uLRsLn720cIopY7rIAMzMzMzMzs+XUoQurmZmZmZmZVYAbkGZmZmZmZlbKiG1AStpd0h2S5kma3qUYNpH0K0lzJd0m6Yhcvp6kqyXdlf+uW1jm6BzzHZJ2K5RvL2lOnnaKpFbDQLcr7pUk/UHS5XWIt+6qkKudIGl+zonZkm7JZb3mVh1JOlPSw5JuLZQN+PdTF1XP3U7UwZJWkXRRLr9R0oQObduQ1dPd2qZO60b+1jEnnWv1U/W6uaiOv4nBqtVvKSJG3IN0UfLdwGbAysAfgS27EMdYYLv8fDRwJ7Al8FVgei6fDpyYn2+ZY10F2DRvw0p52k3AW0j3D7oC2GMI4/4E8EPg8vy60vHW+VGVXO3Qts4HNmgqa5lbdX0Abwe2A27tbxv7+v3U4VGH3O1EHQz8J/Dd/HwKcFGHtm3I6ulubdNIyN865qRzrV6PbuX2CsRbu9/ECmxrbX5LXU+MLn1BbwGuLLw+Gji6AnFdCrwLuAMYm8vGAne0ipM0Qtdb8jx/LpQfCHxviGIcD1wDvKOQ4JWNt+6PqubqEG3rfJZvQLbMrTo/gAks24Ac0O+n2/EPYDtrl7tDUQcXvzfSyOePkAewG8LtGNJ6uhvb1IVcqET+Vj0nnWv1e1Qlt1cg/kr/JlZgu2r1WxqpXVjHAQsKrxfmsq7Jp5LfCNwIjImIBwDy3w3zbL3FPS4/by4fCicDnwJeLJRVOd66q1yuDqEArpI0S9Khuay33BpOBvr7qYtaxT+EdfBLy0TEUuAJYP0h2YiXnczQ1tPd2KZO63r+1iQnT8a5Vjddz+3BqslvYrBOpka/pZHagGx1vV10PIpM0prAj4GPR8STfc3aoiz6KG8rSXsDD0fErLKLtCjrWLzDxEj6rN4aEdsBewCHS3p7twPqsrp/97WJf4jr4I5+Dh2qp2vz3a6Arm5jHXLSuVZbtfxM6/CbGKw6/pZGagNyIbBJ4fV44P5uBCLplaQfxPkR8ZNc/JCksXn6WODhXN5b3Avz8+bydnsr8G5J84ELgXdIOq/C8Q4HlcnVoRYR9+e/DwM/BXag99waTgb6+6mLWsTfgTr4pWUkjQLWBha1f0te0ol6utPb1A1dy98a5aRzrZ5qUTcX1eg3MVi1+y2N1AbkzcDmkjaVtDLpYtLLOh1EHhnpDGBuRHyjMOkyYGp+PpXU37tRPiWPpLQpsDlwUz6tvVjSjnmdBxeWaZuIODoixkfEBNJndm1EvL+q8Q4TlcjVoSZpDUmjG8+BXYFb6T23hpMB/X66EN9gVT53O1QHF9e1P6neHLIj2x2qpzu6TV3SlfytU04612qr8nVzUZ1+E4NVy9/SUF8UWtUHsCdpJKe7gc92KYadSKeP/wTMzo89SX2SrwHuyn/XKyzz2RzzHRRGLgUmkna47wa+zdAP0jCJly/yrXy8dX5UIVc7sI2bkUYU+yNwW2M7+8qtOj6AC4AHgOdJRwMPGczvpy6PquduJ+pgYFXgR8A80gGAzTq4fUNST3dzm4Z7/tY1J51r9XpUvW5uirWWv4kV2N5a/JYaKzUzMzMzMzPr00jtwmpmZmZmZmYD5AakmZmZmZmZleIGpJmZmZmZmZXiBqSZmZmZmZmV4gakmZmZmZmZleIGpJmZmZmZmZXiBqSZmZlZzUj6B0lLJK2UX18haWp/y5lVgaRpkn7b7ThscNyArDhJ8yW9s/B6iqTHJO2cX0+SFJI+1bTchFz++6byDSQ9J2l+i/fqyeteZYg2xwx4Ka+fk7RBU/nsnLcTJJ2V51lSeLyvsPw7W6/drH0kHSvpvG7HYSNXb/sBwKYRsWZEvAAQEXtExNlDHMshkv4sabGkhyT9QtLooXxP6z5JO0n6naQnJC2SdL2kN3U7roHo9jbk/yUhaYdOvedQcgOyRvKRxe8Ae0XEdbl4KrAo/21lDUlbF17/G3BPi3VPAN4GBPDudsVs1od7gAMbLyRtA6zWNM9X8w5S43FRRyO0Wmk62PCipKcLrw/qdnxlSBov6ceSHsk7OnMkTet2XFYNvewHdOq9dwa+AhwYEaOBLYCLOxmDdZ6ktYDLgW8B6wHjgC8Az3YzroHo9jZIEvAB+t5frxU3IGtC0qHAScBuEfG7XLY6sD9wOLC5pIktFj2XZZP1YOCcFvMdDMwEzmKYJLdV3rmkvGuYSuvcNCuleLABuA/4l0LZ+d2Or6RzgQXAq4H1Sb+Rh7oakVVC835AoafRqDy9R9KH8vNlzpz3Mu+X8lmYxZKuau4R0sKbgBsi4g8AEbEoIs6OiMV5natI+rqk+/LZye9KWi1PmyRpoaSjJD0s6QFJH2z3Z2RD4h8BIuKCiHghIp6OiKsi4k8Akv5d0tzcg+1KSa9uLJhz7iOS7srTv5MbU8uR9DVJv5W0tqSNJV2WzxTOk/QfhfnOkvTlwutJkhau4DZMy7+Fb+UDd3+WtEvhPT6Yt3GxpL9I+nDz+/eT228DNgaOAKZIWrmfeCvPDch6OAz4ErBLRNxSKN8PWAL8CLiSZXfGG84jJetKkrYARgM3tpjvYOD8/NhN0pg2xm/WykxgLUlbKF3D8z5Svpq1laRXSJou6W5Jj0q6WNJ6eVpjx3pq3vF9RNJne1nPKyVdkM8QrtyGnYpW3gScFRFPRcTSiPhDRFxRWO+OSt2wHpf0R0mTCtMG0yiweuhtP2BF/BvwQWBDYGXgk/3MfyNp/+ALkt6q5S93OZG0o74t8FrSWZ7PF6ZvBKydyw8BviNp3RXdCBtydwIvSDpb0h7F70zSvsBngH8FXgX8Brigafm9SfXaG4D3ArsVJ+b6+fvA64FdI+KJvI6FpEbX/sBXig26dm5DwZuBvwAbAMcAP2n8nwAeztuxFuk3801J2xWW7S+3pwI/Bxq9qPZegW2pBDcg6+FdpJ3tOU3lU4GL8vUPPwQOlPTKpnkWAncA76SXMzySdiId7b44ImYBd5P+sZgNtcZZyHcBfwb+2jT9k3lH+XFJj3Q8OhsuPgbsC+xM2iF5jNQNsGgn4HXALsDn8wG3l+QzKT8jdXl6b0Q8x4rvVLQyM883RdI/NMUwDvgF8GVSN6xPAj+W9KrCbANtFFg99LYfsCL+NyLujIinSV1Rt+1r5oj4DamhsB0pDx+V9I18gFrAfwBH5jOTi0ndXacUVvE88MWIeD4iZpAOgL+ujdtjQyAiniTVjwF8H/hbPjs4BvgwcHxEzI2IpaTvfNviWUjghIh4PCLuA37Fsnn2SlJjcT1Sj5G/S9okv9+nI+KZiJgN/IDUBXQotqHhYeDknJ8Xkfad98rL/yIi7o7kOuAq0lnFhl5zW6m34AHADyPieeAShkFPPzcg6+EjpKN6P2ic+s8/sMmkM4YAlwKrkpO9yTnANNL1Zq3O8EwFroqIxg76DxkGyW21cC5ph3carbuvfj0i1skPn0mxwfow8NmIWBgRzwLHAvsrd+fLvpC7Nf0R+CPpaHnDWsD/kQ6ufbAwaMmgdyr6cADpKP7/A+5RGliqMdDD+4EZETEjIl6MiKuBW4A9C8sPqFFgtbHcfkAbPFh4/ndgzf4WiIgrIuJfSDv8+5Dq7g+Rzj6tDsxqHPQj/WaKBzcezY2MAb2ndV9uIE6LiPHA1qQDcSeTTj78T+E7XwSIdNCsoa88ey0pj76QD8qR1904CNFwb9M627kNDX+NiGh6z40B8lnLmUpdah8n1bnFfZK+cvs9wFJgRn59PrBH04G/2nEDsh4eJh0Vfxtwai77AOn7+7mkB0mn3VeldTfWH5Maln+JiHuLE/JR9fcCO0t6MK/rSOANkt6w/KrM2ifn4z2kyvgnXQ7Hhq9XAz8t7OTMBV4Aikef+9rJ2ZHUveqE4g7GCu5UtBQRj0XE9IjYKsc3G/hZbjS8GjigcFb+cdJR9bElt8Pqq9V+QF+eIjXoGjZqZzD5AMY1wLWknfFHgKeBrQoH/dbO1yPbMBIRfyaNl7E16XrtDxe+83UiYrXGWB0lzCX1mLhCUuPg2v3Aelp2dN9/4OUeSiuc203b0DCu6eDMPwD3567aPwa+DoyJiHVIjcGyB3Kmkurh+/I+9o9IZ14P7HOpinMDsiYi4n7gHcDukr5Jaih+gXR0ufHYD9hL0vpNyz6Vl/1Qi1XvS9qR2rKwni1IR8BbNUbN2u0Q4B05T82GwgJgj6adnFUjornLdG+uAo4Hrml0eWrDTkW/cq+Qr5OOgq+Xt+Pcpu1YIyJOaNd7WnW12A/oy2zg7Ur3ilwbOHpF31/SPrlr9bpKdiB1C58ZES+SugZ+U9KGef5xknbra51WfZL+KV/LPT6/3oTU+JkJfBc4WtJWedrakg4YyPoj4gLSdZS/lPSaiFgA/A44XtKqkl5P2k9o9LibDewpaT1JGwEfX8FtaNgQ+JjSte4HkPaFZ5AuBVgF+BuwVNIewK5lti1fdrAL6VKHbfPjDaTrhWvd088NyBrJP6p3AB8ldYP6TkQ8WHhcBsyjxVGNiLglIu5usdqppC5P9xXXBXwbOKipi5dZ2+UugO0aFMKsle8CxzWuy5H0Kkn7DGQFEfFVUvf+a/LANIPeqeiLpBMlbS1pVD4CfxgwLyIeJV2C8C+SdsvXna2qNFjP+BV9X6uHwn7A/qSDGr3NdzVpwI4/AbNItzBYUY+RrnO8C3iSlI9fi5dHOP40aR9kpqQngV/iaxyHg8WkAWZulPQUqdF1K3BURPyU1Bi6MH/ntwJ7DPQNIt2/9IvAtUq3lTsQmEA6G/lT4Jic05AuffkjMJ90cK/M7b163YbCPDcCm5POph8H7B8Rj+autB8jXRLwGOmym8tKbtoHgNmRRnwt7mOfArxey95mr1a0bHdfMzOz+pM0H/hQRPxS0itIR6k/TDqb9zBpALLP5J2Ve4BXNrqbSuoBzouIH0g6FnhtRLw/T/sy6WjyO0g7OZ8nNSR/TuqWNC8iPqc0Oup5+Xqb5WLqI+5vAbuTuqU+Tdqp+e+ImJunvxn4KrANqffITcBhEXFfMe4877T8fjsN5jO0epH0a+AHEeHbIZkNgOvKgXMD0szMzKzG8kiPdwIHRhot1cxKcgNy4NyF1czMzKym8jWHDwLXAb9dwXUdJGlJi8dtbQnWbIjk631b5e4SNd0SyVZc6QZkvt7iD5Iuz6/Xk3S1pLvy3+KNRY+WNE/SHcULqCVtL2lOnnZKY7QjSatIuiiX35i7FDWWmZrf4y5Jtb7g1MxsoFz3Dj+SbutlJ+egbsfWTs7dzoiIhyNirYg4KFawW1lEnB8Ra7Z4bNWueOvC+VsveSyPVrm7ZqR7UPa17Fk++zgwAzkDeQRpuN2G6cA1EbE5cE1+jaQtSTeO3Yp0HcepklbKy5wGHEq6SHXzPB3S6EqPRcRrgW+SLshF0nrAMaQLX3cAjlH/N2E2MxtOXPcOMxGxVS87Oef3v3StOHetzpy/Zr0o1YBUGuFtL+AHheJ9gLPz87NJt4NolF8YEc9GxD2kEbl2kDQWWCsibshHyM5pWqaxrkuAXfJRmt2AqyNiUUQ8BlzNyz8+M7NhzXWv1ZVz1+rM+WvWt7K3aDgZ+BRQvKnnmIh4ACAiHlC+7w8wjmXvq7Iwlz2fnzeXN5ZZkNe1VNITwPrF8hbLtLTBBhvEhAkTWk576qmnWGONNfpavPK8DYM3a9asRyLiVR1/4wHoLX+Hw/c+UCNxm2HZ7V5nnXXYaKONePHFF99x5513Ppdncd3bJlWNC+ofW51yF+pZ91Y5Nqh2fP3FVqf8rWPdO9RG6nbDsts+lPu9/TYgJe0NPBwRs5SGJe93kRZl0Uf5YJcpxngoqYsAY8aM4etf/3rLwJYsWcKaa67ZclpdeBsGb/Lkyfd2/E0HaMKECdxyy/K3ROzp6WHSpEmdD6iLRuI2w8vbffnllzNjxgxOPfVUenp6mDx58tP9LOq6d4CqGhfUO7YbbriBmTNncuSRRzJ79myOPPLIyuUulMvfOn8P3Vbl+PqKrQ75W/e6d6iN1O2GZbd9KPd7y5yBfCvwbkl7AqsCa0k6D3hI0th8FGYs6b5akI6WbFJYfjzpRqAL8/Pm8uIyC5VuXL82sCiXT2papqc5wIg4HTgdYOLEidHbTudw2CH1NpiNDNdffz2XXXYZM2bM4JlnngEY7bq3vaoaF9Q7tiuvvJJZs2Yxbdq0yuYulMvfOn8P3Vbl+PqKrQ75W/e6d6iN1O2Gzm17vw3IiDgaOBogn4H8ZES8X9LXgKnACfnvpXmRy4AfSvoG6YbNmwM3RcQLkhZL2pF0Y+SDgW8VlpkK3ADsD1wbESHpSuArhQuId23EMhhz/voE06b/Yrny+SfsNdhVmtkQ6e33CiPjN3v88cdz/PHHAzTOQC523Wt14Ny1OnP+mvWv7DWQrZwAXCzpEOA+4ACAiLhN0sXA7cBS4PCIeCEvcxhwFrAacEV+AJwBnCtpHukIzJS8rkWSvgTcnOf7YkQsWoGYzWwYmDCCG5a47rX6cu5anTl/zbIBNSAjood8Kj0iHgV26WW+44DjWpTfAmzdovwZ8g+xxbQzgTMHEqeZ2XCSu6PMA9e9Vi/OXasz569ZawO5D6SZmZmZmZmNYG5AmpmZmZmZWSluQNqIJmklSX+QdHl+vZ6kqyXdlf+uW5j3aEnzJN0habdC+faS5uRpp+SbASNpFUkX5fIbJU3o+AaamZmZmbXRigyiYzYcHAHMBdbKr6cD10TECZKm59eflrQl6SL3rUijrP1S0j/mC+VPI92PaSYwA9iddKH8IcBjEfFaSVOAE4H3dW7T6qG3AXGO2qbDgZiZmZlZv3wG0kYsSeOBvYAfFIr3Ac7Oz88G9i2UXxgRz0bEPaSL6nfI94JaKyJuiIgAzmlaprGuS4BdGmcnzczMzMzqyGcgbSQ7GfgUMLpQNiYiHgDINwveMJePI51hbFiYy57Pz5vLG8ssyOtaKukJYH3gkfZuhjWM8Nt7mJmZmQ05NyBtRJK0N/BwRMySNKnMIi3Koo/yvpZpjuVQUhdYxowZQ09Pz3ILLVmypGX5cHDUNktblo9ZrfdpA1Wnz244f9dmZmZWf25A2kj1VuDdkvYEVgXWknQe8JCksfns41jg4Tz/QmCTwvLjgftz+fgW5cVlFkoaBaxNumHwMiLidOB0gIkTJ0a+79Qyenp6aFU+HEzr9RrIpZw0pz1V1PyDJrVlPZ0wnL9rMzMzqz9fA2kjUkQcHRHjI2ICaXCcayPi/cBlwNQ821Tg0vz8MmBKHll1U2Bz4Kbc3XWxpB3z9Y0HNy3TWNf++T2WOwNpZmZmZlYXPgNptqwTgIslHQLcBxwAEBG3SboYuB1YChyeR2AFOAw4C1iNNPrqFbn8DOBcSfNIZx6ndGojzMzMzMyGghuQNuJFRA/Qk58/CuzSy3zHAce1KL8F2LpF+TPkBqiZmZmZ2XDgLqxmZmZmZmZWis9AmllH9HaLDTMzMzOrD5+BNDMzMzMzs1LcgDQzMzMzM7NS3IA0MzMzMzOzUnwNpJkNe71dfzn/hL06HImZmZlZvfkMpJmZmZmZmZXiBqSZmZmZmZmV4gakmZmZmZmZleIGpJmZmZmZmZXiBqSZmZmZmZmV4gakmZmZmZmZleLbeJhZW/V2ywwzMzMzqz+fgTQzMzMzM7NS3IA0MzMzMzOzUtyANDMzMzMzs1LcgDQzMzMzM7NS3IA0MzMzMzOzUvptQEraRNKvJM2VdJukI3L5epKulnRX/rtuYZmjJc2TdIek3Qrl20uak6edIkm5fBVJF+XyGyVNKCwzNb/HXZKmtnXrzcwqasGCBUyePJktttiCrbbaCmBDcN1r1efctTpz/pr1r8wZyKXAURGxBbAjcLikLYHpwDURsTlwTX5NnjYF2ArYHThV0kp5XacBhwKb58fuufwQ4LGIeC3wTeDEvK71gGOANwM7AMcUf7BmZitiwvRftHxUwahRozjppJOYO3cuM2fOBNjQda/VgXPX6sz5a9a/fhuQEfFARPw+P18MzAXGAfsAZ+fZzgb2zc/3AS6MiGcj4h5gHrCDpLHAWhFxQ0QEcE7TMo11XQLsko/S7AZcHRGLIuIx4Gpe/vGZmQ1bY8eOZbvttgNg9OjRAE/jutdqwLlrdeb8NevfgK6BzKfY3wjcCIyJiAcgNTLJp/hJP7IFhcUW5rJx+Xlz+TLLRMRS4Alg/T7WZWY2YsyfPx9gdVz3Ws04d63OnL9mrY0qO6OkNYEfAx+PiCdzN+6Ws7Yoiz7KB7tMMbZDSV0EGDNmDD09PS0DG7MaHLXN0uXKe5u/ipYsWVKreFsZDttg1ilLlixhv/32A1jgure9qlwXDYfYnn76aY444gioYO5Cufytau5CtXMEqh1fmdiqnL91r3uHWpVzb6h1attLNSAlvZLUeDw/In6Six+SNDYiHsin6R/O5QuBTQqLjwfuz+XjW5QXl1koaRSwNrAol09qWqanOb6IOB04HWDixIkxadKk5lkA+Nb5l3LSnOU3ef5Breevop6eHnrbvrqowjZI2oTUnWQj/n979x4vV1XfffzzlZvIPVBCTNCAohKgIqSIijbII4RLhVaQWJSkTaVYLQjpo4k+LVZLBRUExRsVmohAQNSScmlEMFUo11A0hIgEOJJAJEK4JAiawO/5Y68hOycz5+w5Z87M3jPf9+s1rzOz9uWsNfObPXuttdfa8BJwYUScn8YfXAGMB/qA96fLSJA0i2zcwovAKRExP6XvD8wGtgSuA06NiJC0Rfof+wNPAsdHRF+bimhdYO3atbzvfe/jhBNO4O677346JfvY2yJlOBY1UvW8rV27lqOOOoqTTz6ZGTNmPJ2SSxO7UCx+yxq7UO4YgXLnb7C8lT1+q37sHWlljr2R1q6yF5mFVcBFwJKIODe3aB5Qmx1qKnB1Ln1KmmFqN7JBw3ek7v7Vkg5M+zyx3za1fR0L3JSuF58PHCpphzSI+NCUZjZcHZscqluUeQKabhARTJ8+nT333JPTTz89v8jHXis1x65VmePXbHBFeiDfAXwIWCTpnpT2KeAs4EpJ04FHgOMAImKxpCuB+8hO0j8aES+m7T7C+p6a69MDsgrqJZKWkrXATEn7WiXpc8Cdab3PRsSqoRXVbL10YK+NZVgtKT851KS02hyylr9PkhskDzycYvUASX2kQfIAkmqD5K9P23wm7esq4AJJSj8SZgO65ZZbuOSSS9hnn33Yd999ASZIOgIfe63kHLtWZY5fs8ENWoGMiJupf002wCENtjkTOLNO+l3A3nXSXyB9Eessuxi4eLB8mg3VQJNDScoPkr8tt1ltYPtaCg6Sl1QbJP/EyJTEuslBBx1Evq1B0n0RcV166WOvlZZj16rM8Ws2uMKT6Jh1ow5NDtU/D4MOhi/jgPB6A/NbqdHg/3bo5Htdxs/azMzMrMYVSOtZHZwcagNFBsOXcUD4tBEe7zhjn3V1B/+3QycnGCjjZ21mZmZW09R9IM26RYcnhzIzMzMzqyT3QFqv6tjkUGZmZmZmVeUKpPWkTk8OZWZmZmZWRb6E1czMzMzMzApxD6SZWT/jG0wQ1HfWkW3OiZmZmVm5uAfSzMzMzMzMCnEF0szMzMzMzArxJaxmNqBGl3OamZmZWe9xD6SZmZmZmZkV4gqkmZmZmZmZFeIKpJmZmZmZmRXiCqSZmZmZmZkV4gqkmZmZmZmZFeIKpJmZmZmZmRXiCqSZmZmZmZkV4vtAmpkV1OiemH1nHdnmnJiZmZl1hiuQZgY0rhyZmZmZmdX4ElYzMzMzMzMrxBVIMzMzMzMzK8QVSDMzMzMzMyvEFUgzMzMzMzMrxBVIMzMzMzMzK8QVSDMzMzMzMyvEt/Ew6yG+VcfIGOh99T0izczMrJu4B9LMzMzMzMwKcQXSzMzMzMzMCnEF0szMzMzMzAqpxBhISZOB84FNgG9HxFkdzpJZIZ2KXY91LI9Gn0UVxkb62GtV5di1KnP8WtmVvgdS0ibA14DDgQnAByRN6GyuzAbn2LUqc/xaVTl2rcocv1YFVeiBPABYGhEPAUiaCxwN3NfRXJkNbsRj1z2N1dXos5s9eas256QhH3utqhy7VmWOXyu9KlQgxwLLcq+XA2/tUF7MmtGy2F306DNMc2XR2svHXqsqx65VmePXSq8KFUjVSYsNVpBOAk5KL9dIur/BvnYCntjoH5w9rPy1W90yVEynyvDaNv+/QWMXCsdvN3zuTTmlB8sMcPDZDctduvjtgmNvmWOsm/JWutiF4R17SxC7UO4YgXLnr5m8lS5+u+DYO9LKHHsjLV/2EYvdKlQglwO75l6PAx7LrxARFwIXDrYjSXdFxMTWZq+9XIZKGTR2oVj89tB79rJeLDOUqtxdf+wta77AeRumnjj2ljlvUO78lTlv9MCxd6T1armhfWUv/SQ6wJ3AHpJ2k7Q5MAWY1+E8mRXh2LUqc/xaVTl2rcocv1Z6pe+BjIh1kj4GzCebzvjiiFjc4WyZDcqxa1Xm+LWqcuxalTl+rQpKX4EEiIjrgOtasKtBu/srwGWoEMfusPRimaFE5e6B+C1rvsB5G5YeiF0od96g3Pkrc956JX5HUq+WG9pUdkVsNK7czMzMzMzMbCNVGANpZmZmZmZmJdATFUhJkyXdL2mppJklyM+ukn4iaYmkxZJOTemjJN0g6YH0d4fcNrNS/u+XdFgufX9Ji9Kyr0hSSt9C0hUp/XZJ40eoLJtI+l9J11S1DGVXtvgdKZL6UhzcI+mulNYwnqpI0sWSVkq6N5fW9HemKtoRu2U/npb1GClpe0lXSfpleu/eVpa8lYXj9+X9ljKG0/aO4zraEbvtoCbPC6r8+apF5wdDKaukqel/PCBpaqEMR0RXP8gGID8I7A5sDvwcmNDhPI0B9kvPtwF+BUwAvgDMTOkzgbPT8wkp31sAu6XybJKW3QG8jey+QdcDh6f0vwO+mZ5PAa4YobKcDlwGXJNeV64MZX6UMX5HsKx9wE790urGU1UfwLuA/YB7ByvjQN+ZKjzaFbtlP56W9RgJzAH+Jj3fHNi+LHkrw8PxW/4Ydhx3NnbbVJY+Cp4XVP3zpUXnB82WFRgFPJT+7pCe7zBofjsdHG34QN4GzM+9ngXM6nS++uXxauA9wP3AmJQ2Bri/Xp7JZuZ6W1rnl7n0DwDfyq+Tnm9KdlNRtTjf44AbgXez/oelUmUo+6MK8dvCsvax8Q9F3Xiq8gMY3+8HoqnvTKfz30Q5OxK7ZTqelvUYCWwLPNx/3TLkrSwPx2+5Y9hxXL7YHaGy9FHwvKAbPl+GeX4wlLLm10nLvgV8YLC89sIlrGOBZbnXy1NaKaQu5LcAtwOjI2IFQPq7c1qtURnGpuf90zfYJiLWAc8AO7Y4++cBnwBeyqVVrQxlV+r4bbEAfiRpoaSTUlqjeOomzX5nqqLt+S/h8fQ8ynmM3B34LfDv6dLEb0vaqiR5KwvHb+Y8yhnD4DhupOq/HXnNnBd04+fbjrIOKV56oQKpOmnR9lzUIWlr4PvAxyPi2YFWrZMWA6QPtE1LSDoKWBkRC4tu0iA/HStDRfTSe/COiNgPOBz4qKR3dTpDHVb1z76t+S/b8bTkx8hNyS6V+kZEvAV4juzyqDLkrSx6On5Tnsocw+A4bqSKeW6kmfOCXvl8obVlHdJ70AsVyOXArrnX44DHOpSXl0najOzH4tKI+EFKflzSmLR8DLAypTcqw/L0vH/6BttI2hTYDljVwiK8A3ivpD5gLvBuSd+tWBmqoJTxOxIi4rH0dyXwQ+AAGsdTN2n2O1MVbct/SY+nZT5GLgeWR8Tt6fVVZCfiZchbWfR6/EK5Y7i2reN4Y1X/7XhZk+cF3fj5tqOsQ4qXXqhA3gnsIWk3SZuTDRyd18kMpRmRLgKWRMS5uUXzgKnp+VSysRC19ClpBqXdgD2AO1J39mpJB6Z9nthvm9q+jgVuinRxcytExKyIGBcR48ne05si4oNVKkNFlC5+R4KkrSRtU3sOHArcS+N46iZNfWc6kL+hakvslvV4WuZjZET8Blgm6Y0p6RDgvjLkrUR6On6h3DGc8uc4rq8rzhuGcF7QjZ9vO8o6HzhU0g7KZnk9NKUNbLBBkt3wAI4gm9nsQeDTJcjPQWTdw78A7kmPI8iuRb4ReCD9HZXb5tMp//eTZlRK6RPJvlAPAheQBv8CrwS+BywlO+ncfQTLM4n1g+srWYYyP8oWvyNUxt3JZhT7ObC4Vs6B4qmKD+ByYAWwlqzVb/pQvjNVebQjdqtwPC3jMRLYF7grvW//QTb7XinyVpaH47fcMew47mzstqEMTZ8XVPnzpUXnB0MpK/DXKX0p8FdF8lvbqZmZmZmZmdmAeuESVjMzMzMzM2sBVyDNzMzMzMysEFcgzczMzMzMrBBXIM3MzMzMzKwQVyDNzMzMzMysEFcgzczMzMzMrBBXIEtC0jRJN3c6H2ZmZmZmZo24AjkEkg6S9D+SnpG0StItkv6k0/lqRqfKIGm2pD9IWiNptaSFkv50pP+vNUdSX/qcduqXfo+kkDS+Q1nbSIqpf+l0Pqy3SDpP0lOSbpU0Npd+gqTzc68/I+m7uddjJf1S0lckqd35Nmsydtem3+rVkn4l6QJJYzqTc6uy/sfCJrc9QdKPcq9D0usbrLtG0u5DzacV4wpkkyRtC1wDfBUYBYwF/hn4fSfz1YwSlOELEbE1sB3wDeAHkjZp0/+24h4GPlB7IWkfYMvOZcesHCQdAOwP7ALcDMxK6dsB/wD8U4PtXgv8FJgXEadERLQnx2aZIcTuFRGxDdm5wp+n7Ra6EmmNSPpLSXelitwKSddLOmg4+4yISyPi0ILrbh0RDw3n/wEo85Ck++osWyDphdSw8mzqDJkpaYvh/t+qcAWyeW8AiIjLI+LFiHg+In4UEb8AkPTXkpak1r356YSBtCwknSzpgbT8a41aoCV9UdLNkraT9GpJ81JP4VJJH86tt0Hvi6RJkpYPswzTUo/kV1MP5S8lHZL7H3+Vyrg6fbn+tv//lzRD0sp08PirepmIiJeAy8h+mEYPkmdrv0uAE3OvpwLfqb1IsfkdSb+V9GtJ/0/SK9KyWgx9WdLTKU7entKXpdiYmtvXFpK+JOkRSY9L+qakLdOyhjEl6STgBOAT6cfqP1P6Bq2T+e9Jbn+fyO3vGElHpBb2VZI+NWLvqnWD3YCbI+L3wI1ArbX7TOCLEfFM/w0kvY6s8nhZRHyibTk121DTsQsQEWsjYjFwPPBbYEY7MmvVIul04DzgX8nO614DfB04uoPZGqp3ATsDu6v+FXofS40rY8i+D1OA6xqd13cbVyCb9yvgRUlzJB0uaYfaAknHAJ8C/gL4I+BnwOX9tj8K+BPgzcD7gcPyCyW9QtK/AX8MHJoO5pcDy4FXA8cC/5qv0LWyDDlvBR4CdgLOIOslHJWWrUzl2Bb4K+DLkvbLbbsLWe/iWGA68LV6/0NZr+OJZD1djw+jPDYybgO2lbRn+qyOB/KXn3yV7HPeHfhTss8y31jwVuAXwI5kDQVzyWL/9cAHgQskbZ3WPZusYWPftHwsG7aE142piLgQuJTUqx0Rf1awbLsAr8z9n39LedofeCfwT/IlMNbYYuCdqZHjEGCxpInAGyPisjrr705WefxWRPxjG/Np1l+zsbuBiHgRuJrsOGn2MmW92J8FPhoRP4iI51LDw39GxP9Nq22eGp5XS6rFXm37mZIeTMvuk/TnuWUN5wlRNiRrmaSD0+uXG5BT4/HXJF2b9nt7aswrYipZrF+XnteVyrkAeC/wNuDIgvuvNFcgmxQRzwIHAUF20vnb1Ds4Gvhb4PMRsSQi1pG1wOyrXC8kcFZEPB0RjwA/ITthrtmMrLI4CviziPidpF3T//tkRLwQEfcA3wY+NEJlqFkJnJe+/FcA95O+FBFxbUQ8GJn/Bn7Ehj8ma4HPpm2vA9YAb8wt/wdJTwPPkbVU/WP6UbLyqfVCvgf4JfBoSq9VKGdFxOqI6APOYcO4fDgi/j19tlcAu5LFxe8j4kfAH4DXp9a6DwOnRcSqiFhN9t2ZktvXYDHVrLXAmRGxlqxiuxNwfirLYrKTrD8exv6ti0XEvcD3yRpZXkPWAHI+cIqkUyT9VNKlkrZPm+wNbEX2PTDrmCHEbj2PkZ2nmOW9jaxh9ocDrPNest/c7YF5wAW5ZQ+SnUtuRzas6rsa5FJpSYeRnTe/LyJ+0mC1D6T97QAsJettH5CkV5F12FyaHlMkbT7QNum8/i56pHHFFcghSBXEaRExjuzE4NVkFaHXAuenS/aeBlYBIuvlqPlN7vnvgK1zr19P1s3/zxHxh5T2aqB2Ul3z6377bGUZah7tNz7n12kdUq/lbelSv6eBI8hOwGueTBXomv7l/FJEbE82nm4i8EVJhw+nPDZiLgH+EphG7vJVss97c7K4qOkfl/le5ecBIqJ/2tZkvfWvIhtXU/vu/FdKrxksppr1ZK7R4vkG+R3O/q3LRcSXI+LNEXE8WWPKz8h+U08i69lZAsxMq88DLgZu6tegaNZ2TcZuPWPJzm/M8nYEnuj3W93fzRFxXfr9vYTsajwAIuJ7EfFYRLyUOi4eAA4YYF/HARcCR0TEHQOs94OIuCPl61I27Lhp5C/I5gX5EdmcIZtSrGexZxpXXIEcpoj4JTCbrBK2DPjbiNg+99gyIv6n4O6WkF0CeL2kWu/KY8AoSdvk1nsN63uCniM7+a7ZZZhlqBnb7zru1wCPKRsg/H3gS8DoVBG8jqyi3Oz/jdQaegs90uVfNRHxa7JLjI8AfpBb9ARZL17+ZDgfl814gqzCtlfue7NdZBMtFcpmnbTfMczvhVkRuatPPkt2DP1F6tm+k1wvdkScTnYicpNyM1+adUrR2O23zSuAPyOrdJrlPQnsJGnTAdbp34nyytr6kk5UNtN7rSF5bzbsnOjv48CVEbFokHwN1HHTyNS073VpvPAPGOAy1pyeaVxxBbJJkt6UJvMYl17vStY9fhvwTWCWpL3Ssu0kHdfM/iPicrJxlD+W9LqIWAb8D/B5Sa+U9MdkY8AuTZvcAxwhaZSkXci+UMMpQ83OZJe0bJbKsCdZRXFzYAuyQfTrUs9hoZmxGuWF7HLaxUPdh4246cC7I+K5XNqLwJXAmZK2Sb0qp7PhGMlC0mRK/0Y2lnZnePlWB4cNvOXLHmf9RBA19wB/KWkTSZPJxmiajYRzgTMi4ndkjS1/ksb2TiIbR573MeAm4MZ+QwbMOqFw7KZzgT3JLhfcJW1rlncr8AJwTLMbpnOIfyM7Ru6YOifuZeDOieOAYyR9vNn/N0hexgHvBj4o6TeSfkN2OesR6ndrs37b7Uo2j0JPNK64Atm81WSTg9wu6TmySte9wIyI+CHZeIK5kp5N6U1fmhkRc8haBG9Sdr+9DwDjyXojf0h2wL8hrX4J8HOgj6yrvcgYm4ZlyK1zO7AHWe/QmcCxEfFkupT2FLLKw1NklzfOa7KItRkzn0t5/nfgW03uw9okjXe9q86ivyfrAX+IbDr4y8gu0xuKT5KNTbgtfXd+TPExjhcBE1Kr5X+ktFPJWsmfJpul9T/qbmk2DGnShu3TsZ90GdW1ZFejHAyclV8/DQv4W+AOskbCgVrXzUZME7F7vKQ1ZMfSeWS9TPtHxGNtz7SVWmSTPv4T2SR3x0h6VWp4OFzSFwbZfCuyq4l+C9ls/2x4VVw9j5Fdcn2KpL8bZvbzPkQ22eQbyS533Zdskr/l5G5tVpPK+adkE+7cQdbZ0vUUvg2V9SNpGvA3ETGs+/aYmZmZWe+QdAJwGtmVa6uBhWQdEYcCr4+ID6b1xpP1fG8WEesknQl8BHiJbM6F/YFLIuLb/c9LJQWwR0QslbQbsAD4XFo3v2w2sDwi/l/abhLw3TT/R6P8/xL4WkR8tV/6J4D3R8RESQuAA8mG8kDWAH4VcE5EvDCkN65iXIG0jbgCaWZmZmZm9Qw00NUqTNJrgPsaLJ6Qphs2MzMzMzMrrPAYyDQZxf9Kuia9HiXpBkkPpL875NadJWmppPvzE2FI2l/SorTsK7VZPiVtIemKlH576taubTM1/Y8HJBWZAcnI7kcT2Y3V6z0GrDxGxOxu6n107JqZmZkZgKR3prk4Nnp0Om9V0cwkOqeS3WaiZiZwY0TsAdyYXiNpAtkNwPcCJgNfl7RJ2uYbZPcZ2iM9Jqf06cBTEfF64MtkE9EgaRRwBtmELwcAZ+RP9s0KcuyamZmZGRHxs0adLJ3OW1UUqkCmKW2PBL6dSz4amJOez2H9tL1HA3Mj4vcR8TDZwNIDJI0Bto2IW9NMdN/pt01tX1cBh6QensOAGyJiVUQ8BdzA+hN3s0E5ds3MzMzMWqfoGMjzgE8A+ZvZj46IFQARsULp/m1kN9HM309weUpbm573T69tsyzta52kZ4Ad8+l1tqlrp512ivHjx9dd9txzz7HVVlsNtHlX68Xyb7/99uyyyy689NJL7/7Vr371h5RcytiFgeO3E6oYM92a54ULFz4REX/Upiw1zcfeDbnM65U9dqFx/Ppz7B1F4zddmXQX8GhEHJWuOLqC7HZrfWQzdT6V1p1FdqXSi8ApETE/pe8PzAa2JLvtw6kREZK2YP0MpE8Cx0dE30D57vXYdTkbG8lj76AVSElHASsjYmGa/nbQTeqkxQDpQ90mn8eTyC4vZPTo0XzpS1+qm7E1a9aw9da92zvda+W/9dZbue222zjttNO45557OO20054fZJO2xy4Uj99OqGLMdGueDz744F+3KTtDMn78eO66q97tQmHBggVMmjSpvRnqMJd5PUmljl1oHL/+HHtHE/FbGxazbXpdGxZzlqSZ6fUn+w2LeTXZvV/fEBEvsn5YzG1kFcjJwPXkhsVImkI2LOb4gfLd67HrcjY2ksfeIj2Q7wDeK+kI4JXAtpK+CzwuaUzqwRkDrEzrLwd2zW0/juxmn8vT8/7p+W2WS9oU2A5YldIn9dtmQf8MRsSFwIUAEydOjEZvcK8EWSO9Vv758+ezcOFCpk2bxgsvvACwTdliF4rHbydUMWacZzMzGwm5YTFnAqen5KNZ/3s/h+y3/pPkhsUAD0uqDYvpIw2LSfusDYu5Pm3zmbSvq4ALJCl8zz0rmUHHQEbErIgYFxHjyVpSbko3AZ0H1GaWnApcnZ7PA6ak2Sl3I5tw5I50yeBqSQemMWIn9tumtq9j0/8IYD5wqKQd0gQkh6Y0s0F9/vOfZ/ny5fT19TF37lyA1Y5dMzMzG6LzyIZ0vZRL22BYDJAfFlNvKMtYCg6LAWrDYsxKZTj3gTwLuFLSdOAR4DiAiFgs6UqyexCuAz6auusBPsL6a76vTw+Ai4BLUuvMKrKKKhGxStLngDvTep+NiFVDzfCiR59h2sxrN0rvO+vIoe7SqqlysWvFjJ95LTP2WbfR99zfcbPWGV/ndxRg9uTuH4dk5dIoFgcy1N+DDg/p6p+XDYa+LFiwYKON1qxZUzd9IIsefaZu+j5jt2tqP+00lHJWUdnK2VQFMiIWkC7Di4gngUMarHcmWfd+//S7gL3rpL9AOomvs+xi4OJm8mnWX7o8cCk4ds3MzKxpnRzStYEiQ1+GMjSiXicLQN8Jze2nnXplCEjZyjmcHkgzMzPrUkPp3THrVhExC5gFkHog/yEiPijpi2RDWc5i42Exl0k6l2wSndqwmBclrZZ0IHA72bCYr+a2mQrcyobDYsxKxRVIMzMzM7OhGfFhMWZl4wqkmZlZD3NPo1lzOjGky6xMXIE0MzMzs9JyI4dZuQx6Gw8zMzMzMzMzcAXSzMzMzMzMCvIlrGZmZj3AlwGamVkruAfSzMzMzMzMCnEF0szMzMzMzApxBdLMzMzMzMwK8RhIMzOzLuKxjlZVjl2zanAPpJlZhUjaVdJPJC2RtFjSqSl9lKQbJD2Q/u6Q22aWpKWS7pd0WC59f0mL0rKvSFJK30LSFSn9dknj215QMzMzKyX3QJqZVcs6YEZE3C1pG2ChpBuAacCNEXGWpJnATOCTkiYAU4C9gFcDP5b0hoh4EfgGcBJwG3AdMBm4HpgOPBURr5c0BTgbOL6tpTSzruWeRrNqcw+kmVmFRMSKiLg7PV8NLAHGAkcDc9Jqc4Bj0vOjgbkR8fuIeBhYChwgaQywbUTcGhEBfKffNrV9XQUcUuudNDMzs97mHkgzs4pKl5a+BbgdGB0RKyCrZEraOa02lqyHsWZ5SlubnvdPr22zLO1rnaRngB2BJ0amJNasqvbgSNqVrLFiF+Al4MKIOF/SKOAKYDzQB7w/Ip5K28wi6xV/ETglIuan9P2B2cCWZD3op0ZESNoi/Y/9gSeB4yOir01FNLMGBjpu9Z11ZBtzYsPlCqSZWQVJ2hr4PvDxiHh2gA7CegtigPSBtumfh5PILoFl9OjRLFiwoG4G1qxZ03BZtxrpMs/YZ92I7XuoCpbZl2CbWVdqVEHuxsqxK5BmZhUjaTOyyuOlEfGDlPy4pDGp93EMsDKlLwd2zW0+DngspY+rk57fZrmkTYHtgFX98xERFwIXAkycODEmTZpUN78LFiyg0bJu1aoyN26xL9/P9+zJWw1a5tRLXuspXy0pfwl2beM5wALgk+QuwQYellS7BLuPdAk2gKTaJdjXp20+k/Z1FXCBJKVLta2NqtpTbmYDK98vkJmZNZTGIl4ELImIc3OL5gFTgbPS36tz6ZdJOpesB2cP4I6IeFHSakkHkl0CeyLw1X77uhU4FrjJJ98jqxdPtDt9CXaRHnT3ng9PGXvKfaWE2fC5AmlmVi3vAD4ELJJ0T0r7FFnF8UpJ04FHgOMAImKxpCuB+8guH/xouvwP4COsH0N2fXpAVkG9JPX2rCK7hNCsZcpwCXaRHnT3ng/PtBI2jPSdMKluei9+1mZD5QqkmVmFRMTN1D9BBjikwTZnAmfWSb8L2LtO+gukCqhZq5XlEmxrnV7sQTfrZa5AmpmZtdiiR58pZe9Lp/kSbDOz6nMF0szMzNqlqy/BHkpPXKdmaGyU19mTt2p6GzPrLa5AmplZqTQ7FXovTZ1edb4Ee2OOXzOrGlcgzcysEprt/ehkb8mMfTr2r61LtCp+m62I+vJrMxuMK5BmZtYRvhzObOT5e2bD5V5y688VSDMzG1Hu0TAzM+serkCamZmZNcG9embWy17R6QyYmZmZmZlZNbgH0szMzMzMSsfjL8tp0B5ISbtK+omkJZIWSzo1pY+SdIOkB9LfHXLbzJK0VNL9kg7Lpe8vaVFa9pV0Q2EkbSHpipR+u6TxuW2mpv/xgKSpLS29dbVly5Zx8MEHs+eee7LXXnsB7AyOXTMzMzOzoSrSA7kOmBERd0vaBlgo6QZgGnBjRJwlaSYwE/ikpAlkN+3dC3g18GNJb0g3/v0GcBJwG3AdMJnsxr/Tgaci4vWSpgBnA8dLGgWcAUwEIv3veRHxVKveAOtem266Keeccw777bcfq1evZtttt905xec0HLtmZmZWkKRdge8AuwAvARdGxPnp9/4KYDzQB7y/9lsvaRbZecKLwCkRMT+l7w/MBrYkO6c4NSJC0hbpf+wPPAkcHxF9bSpiR3lccbUM2gMZESsi4u70fDWwBBgLHA3MSavNAY5Jz48G5kbE7yPiYWApcICkMcC2EXFrRATZFyS/TW1fVwGHpB6ew4AbImJV+jLeQHbibjaoMWPGsN9++wGwzTbbADyPY9fMzMyaV+tQ2RM4EPhoanieSdYovQdwY3pNv0bpycDXJW2S9lVrlN4jPWrnBy83SgNfJmuUNiudpsZApsvz3gLcDoyOiBWQVTIl7ZxWG0vWS1OzPKWtTc/7p9e2WZb2tU7SM8CO+fQ625gV1tfXB/AqHLtmZmY9q1FP1+zJWw24XTpvqJ07rJaU71CZlFabAywAPkmuURp4WFKtUbqP1CgNIKnWKH192uYzaV9XARdIUmq8rgz3JhZT5fGdhSuQkrYGvg98PCKeTUPA6q5aJy0GSB/qNvm8nUTWksPo0aNZsGBB3YyN3hJm7LNuo/RG63ebNWvW9ExZ855//nlOPfVUgGVli10oHr+dULWYmbHPurrf87KXoWrvs5lZL+tAh8oTI1MSa4durFAXqkBK2oys8nhpRPwgJT8uaUz6sowBVqb05cCuuc3HAY+l9HF10vPbLJe0KbAdsCqlT+q3zYL++YuIC4ELASZOnBiTJk3qvwoAX730as5ZtHGR+06ov363WbBgAY3em261du1ajjrqKE4++WRmzJjxdEouTexC8fjthKrFzLSZ1zJjn3Ubfc/L/h2v2vtsZtarOtSh0j8PgzY8D6Vhsl4ny0Aa7b/Z/QxF7X+XqQG2VeVu1ec5kgatQKbxXBcBSyLi3NyiecBU4Kz09+pc+mWSziWbiGQP4I6IeFHSakkHkrXYnAh8td++bgWOBW5Kg4nnA/+amyXzUGDWkEtrPSUimD59OnvuuSenn346M2bMqC1y7JqZmVlTOtihsoEiDc9DaZic1mRPWaPG2Wb3MxS1/12mBthWlbve+1qmckKxHsh3AB8CFkm6J6V9iuzk+0pJ04FHgOMAImKxpCuB+8gGHH80zWIJ8BHWzzp1fXpAVkG9JF0fvops0DERsUrS54A703qfjYiNvkhm9dxyyy1ccskl7LPPPuy7774AEyQdgWPXzMzMmtDJDpWRLZlZ8watQEbEzdTvUgc4pME2ZwJn1km/C9i7TvoLpJP4OssuBi4eLJ9m/R100EHkj7uS7ouI69JLx66ZmZkV1bEOlVbrxjF51l5NzcJqZmZmZtZrOt2hYu1V5RlS22HQ+0CamVm5SLpY0kpJ9+bSRkm6QdID6e8OuWWzJC2VdL+kw3Lp+0talJZ9JV2ihaQtJF2R0m9PMw6amZmZuQJpZlZBs1l/4+ka38zaSs+NH2Zm1edLWM3MKiYiflrnxNg3s7YqmA1cAHwnl1Zr/DhL0sz0+pP9Gj9eDfxY0hvSOLJa48dtwHVkjR/Xk2v8kDSFrPHj+LaUzMy6nsePZlyBNDPrDr6ZtZWeGz/MbCQ1O3bRFcKhcQXSzKy7dfRm1gCjt2zPjaXLpBfLPIwbXbvxw8xGlCuKreUKpJlZdyjlzawBvnrp1ZyzqLd+bmbss67nyjx78latvtH1iDV+QLEGkEaV4m5uHOjFxg8YVgOIWUvVq+zO2Gfdy5dplEFv/bqZmXUv38zaqqrtjR9QrAFkwYIFdSvF07q4N6MXGz9gRBpAzLqWZ2E1M6sYSZeTVe7eKGl5uoH1WcB7JD0AvCe9JiIWA7WbWf8XG9/M+tvAUuBBNryZ9Y5pzNnppBldzUZIrcECNm78mJJmVt2N9Y0fK4DVkg5Ms6+e2G+b2r7c+GFmNgJ6r4nJzKziIuIDDRb5ZtZWaqnxYxKwk6TlwBlkjR1XpoaQR0ixFxGLJdUaP9axcePHbGBLsoaPfOPHJanxYxXZLK5DtujRZ7q6t9GsqmqXec7YZ52/ox3gCqSZmZm1hRs/zMyGptkZZkeSL2E1MzMzMzOzQlyBNDMzMzMzs0JcgTQzMzMzM7NCXIE0MzMzMzOzQlyBNDMzMzMzs0JcgTQzMzMzM7NCfBsPMzMzMzNrSqPbSlj3cw+kmZmZmZmZFeIKpJmZmZmZmRXiCqSZmZmZmZkV4gqkmZmZmZmZFeIKpJmZmZmZmRXiCqSZmZmZmZkV4gqkmZmZmZmZFeIKpJmZmZmZmRXiCqSZmZmZmZkV4gqkmZmZmZmZFbJppzNgZmZmZmatNX7mtZ3OgnWpSvRASpos6X5JSyXN7HR+zIpy7FqVOX6tqhy7VmWOXyu70lcgJW0CfA04HJgAfEDShM7mymxwjl2rMsevVZVj16rM8WtVUPoKJHAAsDQiHoqIPwBzgaM7nCezIhy7VmWOX6sqx65VmePXSq8KFcixwLLc6+UpzazsHLtWZY5fqyrHrlWZ49dKrwqT6KhOWmywgnQScFJ6uUbS/Q32tRPwxEb/4Oxh5a9K6pa/h7yxzf9v0NiFpuK3EyoXM6fUyXMFvuNF3ufXtiMjOSN+7O1m9eKw2x18dsMyly52oXD89tzn2IuxC9WKX8fuer0SrwOVc4BznBGL3SpUIJcDu+ZejwMey68QERcCFw62I0l3RcTE1mavOlx+3dXmfzlo7ELx+O2EKsaM89wyPvYOg8vcUS079paoTG3Ti2WGUpW7JcfeEpVnRLmcnVGFS1jvBPaQtJukzYEpwLwO58msCMeuVZnj16rKsWtV5vi10it9D2RErJP0MWA+sAlwcUQs7nC2zAbl2LUqc/xaVTl2rcocv1YFpa9AAkTEdcB1LdhVKS8TbCOXv81aGLudUsWYcZ5bxMfeYXGZO8ixOyy9WGYoUblbFL+lKc8Iczk7QBEbjSs3MzMzMzMz20gVxkCamZmZmZlZCfREBVLSZEn3S1oqaWan8zNckvokLZJ0T21mUUmjJN0g6YH0d4fc+rNS2e+XdFguff+0n6WSviJJKX0LSVek9NsljW97IXMkXSxppaR7c2ltKa+kqel/PCBpapuKPGIkfVHSLyX9QtIPJW2fW1bp962T33NJu0r6iaQlkhZLOjWld+33sohuO/Y20uwxuapadSyuCsdv98SvY7f7NPrd7UaSNpH0v5Ku6XReXhYRXf0gG4D8ILA7sDnwc2BCp/M1zDL1ATv1S/sCMDM9nwmcnZ5PSGXeAtgtvRebpGV3AG8ju+fQ9cDhKf3vgG+m51OAKzpc3ncB+wH3trO8wCjgofR3h/R8h05//sN8Lw8FNk3Pz+6W963T33NgDLBfer4N8Kv0nnbt97Lsn0mby9pHwWNylR+tOhZX4eH47a74dex2X+w2+t3tdL5GqKynA5cB13Q6L7VHL/RAHgAsjYiHIuIPwFzg6A7naSQcDcxJz+cAx+TS50bE7yPiYWApcICkMcC2EXFrZNH5nX7b1PZ1FXBIrRekEyLip8CqfsntKO9hwA0RsSoingJuACa3unztFBE/ioh16eVtZPeXguq/bx39nkfEioi4Oz1fDSwBxtLF38sCeuXY20ijz76yWnEsbkc+W8Tx20Xx69jtvtgd4He3q0gaBxwJfLvTecnrhQrkWGBZ7vVyqh9gAfxI0kJJJ6W00RGxArIvFbBzSm9U/rHpef/0DbZJlY1ngB1HoBzD0Y7ydmPs5P01WQ8XVP99K81nlS4tfQtwO733vcwrzWfSBs0ck7tNszFeFVXPfzN6NX4du12i3+9utzkP+ATwUofzsYFK3MZjmOq10Fd96tl3RMRjknYGbpD0ywHWbVT+gd6XKr9nrSxvJd8HST8Gdqmz6NMRcXVa59PAOuDS2mZ11q/S+1aKz0rS1sD3gY9HxLMDdBD2wveyavkdjmaOyb2i6p9/1fPfDMfvhqr+2Vc9/03p/7vb6fy0kqSjgJURsVDSpA5nZwO90AO5HNg193oc8FiH8tISEfFY+rsS+CHZ5QqPp8vfSH9XptUblX856y9fzKdvsI2kTYHt2PjSj05rR3krGTsR8X8iYu86j1rlcSpwFHBCukwSqv++dfyzkrQZ2Y/YpRHxg5Tca9/LvI5/Ju3S5DG52zQb41VR9fwX1sPx69ituAa/u93kHcB7JfWRXYr8bknf7WyWMr1QgbwT2EPSbpI2J5t8Yl6H8zRkkraStE3tOdmkKPeSlak22+VU4Or0fB4wRdkMjrsBewB3pMs1Vks6MI2jOrHfNrV9HQvclKtolEU7yjsfOFTSDml2tkNTWmVJmgx8EnhvRPwut6jq71tHv+fpvbkIWBIR5+YW9dr3Mq+rjr2NDOGY3G2aivEO5G+oHL/dH7+O3Qob4He3a0TErIgYFxHjyT7HmyLigx3OVqZVs/GU+QEcQTY704Nkl/F1PE/DKMvuZDNq/RxYXCsP2VioG4EH0t9RuW0+ncp+P2lGx5Q+keyH4kHgAkAp/ZXA98gGjt8B7N7hMl8OrADWkrWsTW9XecnGCS5Nj7/q9OffgvdyKdnYiHvS45vd8r518nsOHER2idAvcu/tEd38vSz7Z9LGMjZ9TK7qo1XH4qo8HL/dE7+O3c7naQTKWPd3t9P5GsHyTqJEs7DWTkzMzMzMzMzMBtQLl7CamZmZmZlZC7gCaWZmZmZmZoW4AmlmZmZmZmaFuAJpZmZmZmZmhbgCaWZmZmZm1kaSLpa0UtK9BdZ9l6S7Ja2TdGy/Zf8l6WlJ14xcbjfkCqSZmZmZmVl7zQYmF1z3EWAacFmdZV8EPtSaLBXjCqSZmZmZmVkbRcRPgVX5NEmvSz2KCyX9TNKb0rp9EfEL4KU6+7kRWN2WTCeuQFaMpD5Jz0taI+k3kmZL2jq3/O2SbpK0WtIzkv5T0oR++9hW0nmSHkn7WZpe79T+ElmvkrRA0lOStpD0zRSLayT9QdLa3OvrJY2XFLm0PkkzO10G610pfv8mPT9B0o9avP9azG/ayv1a75I0TdIiSb9L5w/fkLR9WvaZfsfdJZLel5bNkvTTOvvbKR2v925zUcy62YXA30fE/sA/AF/vcH7qcgWymv4sIrYG9gXeAswCkPQ24EfA1cCrgd2AnwO3SNo9rbM5cCOwF1m3+bbA24EngQPaWgrrWZLGA+8EAnhvRJwcEVunuP5X4Ira64g4PLfp9mmdY4F/lPSetmfe2iY1FDwuaatc2t9IWtDBbG0kIi6NiEM7mYfUmLhO0qvT60/lKgMvSHox93pxWickPZfSHpV0rqRNOlkOGxmSZgBnA/8X2A44EHgtcEM6L4DccRf4OPBdSaOBS4C3S9qt326nAIsiYtDxW2Y2uNQh9Hbge5LuAb4FjOlophpwBbLCIuI3wHyyiiTAF4DvRMT5EbE6IlZFxP8DbgM+k9Y5EXgN8OcRcV9EvBQRKyPicxFxXZuLYL3rRLK4nA1MbXbjiLgLWMz62LfutSlw6nB2oEzX/t6lCvb7gGeAEwAi4l9zlYGTgVtzjTJ75TZ/c1rnT4Hjgb9uc/ZthEnaFvhnsl6N/4qItRHRB7yfrBL5wf7bRMR8skviXhcRy4Gb2HiM1YnAnJHMu1mPeQXwdETsm3vs2elM1dO1P6i9QNI44HBgqaRXkVot6qx6JVDrqfk/wH9FxJr25NKsrhOBS9PjsNTKXZikA4G9gaUjkDcrly8C/1C71C4vXbJ/Z7pc/05Jb88tWyDpTEm3AL8Ddk89bn8n6YF0mf/n0niTWyU9K+nKWm+MpB0kXSPpt+lS62vSMXcj6dLAm9PzT+R6+takywJnp2XbSbpI0orU4/cvtR4/SZtI+pKkJyQ9BBzZxHv0PuBp4LMMoUEGICKWArfgRplu9HbglcAP8onpPOB61p8fAC83uBwJbA7cl5LnkKtASnojWaxcPmK5NusxEfEs8LCk4+Dl7+KbO5ytulyBrKb/kLQaWAasBM4ARpF9nivqrL8CqI1v3LHBOmZtIekgslbvKyNiIfAg8JcFN39C0vPArWTjAv5jRDJpZXIXsIBsLMjLJI0CrgW+QnZcOxe4VtKOudU+BJwEbAP8OqVNBvYnu4TvE2TjTU4AdiVrlPhAWu8VwL+TxeprgOeBCwbLbER8IdfztyfwW7JGPMhOwtcBrycbfnAo8Ddp2YeBo1L6RLLLtIuaSnYiPxd4k6T9mtgWAGUTNbwTN8p0o52AJyJiXZ1l+fOD90t6GngOmAf8a0Q8nZb9EBida6Q5Ebg+In47Yrk263KSLic7n3mjpOWSppP9Hk2X9HOyK62OTuv+iaTlwHHAt2pDEdKyn5F1IB2S9nPYSOfdFchqOiYitgEmAW8iO/g/RTYzU71rpccAT6TnTzZYx6xdpgI/iohaTF5G8V6TnYCtySoTk4DNWp47K6N/Av5e0h/l0o4EHoiISyJiXURcDvwS+LPcOrMjYnFavjalnR0Rz0bEYuBeslh8KCKeIeuNeQtARDwZEd+PiN9FxGrgTLLLPAuRtCVZA8f5EXFd6mU/HPh4RDwXESuBL5ONI4PscsLzImJZRKwCPl/w/7wGOBi4LCIeJxvj3kwv5N2SngOWkFXUSzlhgw3LE8BOqj8hU/784MqI2D4iXgW8DjhR0t8CRMTvyE5QT5QkspNcX75qNgwR8YGIGBMRm0XEuIi4KCIejojJEfHmiJgQEZ9N696Z1tkqInbMD0WIiHdGxB9FxJZpnfkjnXdXICssIv6bbAzZlyLiObJWjOPqrPp+spMKgB+TXTK4VZ31zEZUOql+P/CnymYB/A1wGvDmopdpRMSLEXEO8ALwdyOXWyuLNEnHNUB+5t1Xs75XsebXwNjc62V1dvd47vnzdV5vDSDpVZK+JenXkp4Ffgps38QkMxcB90fE2en1a8kaPFYou+Hz02QTJOycK08+v/3L1siHgCURcU96fSnwl5KKNq7sR1bm44G3Av5t6D63Ar8H/iKfmM4DDmf9+cHL0hjJ69mwQWYO2fH7PWS9+m27abmZlYsrkNV3HvAeSfuSnVxNlXSKpG3SGJ5/Ad5GNoAestnUlgHfl/QmSa+QtKOyGfuO6EQBrKccA7wITCAbP7Mv2WV+PyO7JKoZZwGfkPTK1mXPSuwMsss8axXEx8gqZXmvAR7NvY5h/L8ZwBuBt0bEtsC7UroG21DZLWbeCEzPJS8jO4nfKfXybB8R2+ZakVeQXUZb85qC+TyRbHxnrUHmXLKe+sMH3my9yFxJVtH4p6LbWTWk3vV/Br4qabKkzZTNhP09YDnZecEG0njfyWSX0NX8jGys7YXA3Ij4wwhn3cxKyhXIikvjD74D/GNE3AwcRtbKuIKsBfstwEER8UBa//dkE+n8ErgBeBa4g+yE4/a2F8B6zVTg3yPikYj4Te1BNrbshAaXWDVyLdml2x8eiYxauaRJXq4ATklJ1wFvkPSXkjaVdDxZw0SrekW2IeuRfDqNtzyjyEaSDk95PCYins/lfwXZbZbOUXYv3lekCXxql8VeCZwiaZykHdiwt7XR/3ob2aWGB7C+QWZvmrssPO8s4CRJuwxhWyuxiPgC8CngS2S/+7eTNWocks4LAI6vTf4E3Ek2qdI/5/YRZOcbr01/zaxH+QbFFRMR4+ukfST3/GaysWED7eMZsns8fbylmTMbRERMbpB+JesnGvlMneV99Ov5SScze/Vf17raZ0kzQUbEk5KOAs4HvkE2+ctRubG1w3UeWUXsCbLeznPIetAHczzwR8CSbKgYAN+NiJPJegvPIpvZchvgIbJ78wH8G/AGsnv3Pkt2ov/uQf7XVODqiFiUT5R0PvAzSaPSeMpCImKRpP8mu1fgjKLbWTVExEVkl1bXW/YZ6hx7h7qemXU3ZedgZmZmZmZmZgPzJaxmZmZmZmZWSOEKZLrJ8f9Kuia9HiXpBmU3ZL4hjdmorTtL0lJJ9+fvRSJpf0mL0rKvpKmgkbSFpCtS+u1pcHdtm6npfzwgaUg3SLbe5ti1KnP8Wm1cWp3HOzudNzMz6z3N9ECeSnafqJqZwI0RsQfZFNAzASRNILuv1V5kM3h9XeunPf8G2U2d90iP2nio6cBTEfF6svtinZ32VZu44K1kkwSckT9ZMivIsWtV5vjtcRGxdYPHzzqdNzMz6z2FKpBpOucjgW/nko9m/U1k57B+coGjyaZ3/n1EPEw2scEBksYA20bErbmZvI6ps6+rgENSC/lhwA0RsSoiniKbNbTuJBxm9Th2rcocv2ZmZlY2RWdhPQ/4BNmscTWj07TkRMQKSbWbIY8FbsuttzylrU3P+6fXtlmW9rVO0jPAjvn0OtvUtdNOO8X48eMbLn/uuefYaqvevE9yL5Z9++23Z/vtt2fzzTd/969+9avaPatKGbvQOH7L+Nk5T8UMJ0/bb789u+yyCy+99FLp43egY28ZP5dW6eayQWvKt3Dhwici4o9alKURUaVj73C4PM2rQvyatdugFcg0TfrKiFgoaVKBfda7yXIMkD7UbfJ5PIns8ixGjx7Nl770pYaZW7NmDVtvvXXD5d2s18p+6623ctttt/HhD3+YpUuXctpppz0/yCZtj10oFr9l/Oycp2KGmqda/J522mncc889pYzfosfeMn4urdLNZYPWlO/ggw/+dYuyM2LGjx/PXXfdtVH6ggULmDRpUvszNEJcnuZJKn38mrVbkR7IdwDvlXQE8EpgW0nfBR6XNCa1gI8BVqb1lwO75rYfR3YPreXpef/0/DbLld1IfDtgVUqf1G+bBf0zGBEXAhcCTJw4MQY6mHTbwbMZvVb2+fPns3DhQm6//fZa0jZli10oFr9l/Oycp2KGmqda/E6bNo0XXngBShi/RY+9ZfxcWqWbywbdXz4zM2veoGMgI2JWRIxLN7CfAtwUER8E5pHdxJj09+r0fB4wJc3utxvZhA13pEuuVks6MI2xObHfNrV9HZv+RwDzgUMl7ZAmcDg0pZkN6vOf/zzLly9n7ty5zJ07F2C1Y9eqoha/fX19jl8zMzMrjaJjIOs5C7hS0nTgEeA4gIhYLOlK4D5gHfDRiHgxbfMRYDawJXB9egBcBFwiaSlZ6/eUtK9Vkj4H3JnW+2xErBpqhsfPvJYZ+6xj2sxrN0jvO+vIoe7Sqqlysbvo0Wc2iltw7PYox69ZFxlf57sB/n6YWXk1VYGMiAWky5gi4kngkAbrnQmcWSf9LmDvOukvkE6C6iy7GLi4mXya9ZcuwVoKjl2rHsevmZmZlUUz94E0MzMzMzOzHjacS1jNzMzMzEZUo8t8Z0/unluSmFWJeyDNzMzMzMysEFcgzczMzMzMrBBfwmpmZmZmbeOZZ82qzT2QZmZmZmZmVogrkGZmZmZmZlaIK5BmZmZmZmZWiCuQZmZmZmZmVogrkGZmZmZmZlaIK5BmZmZmZmZWiG/jYWZmZlZxvjWGmbWLeyDNzMzMzMysEFcgzczMzMzMrBBXIM3MzMzMzKwQVyDNzMzMzMysEFcgzczMzMzMrBBXIM3MzMzMzKwQVyDNzMzMzMysEFcgzczMzMzMrBBXIM3MzMzMzKwQVyDNzMzMzMyskE07nQEzMzMzs1YaP/Pauul9Zx3Z5pyYdR9XIM3MzMx6zFAqWN1QKeuGMph1miuQZmZmZl0qX2Gasc86pjWoQJmZFeUKpJmZmZl1XKPeQTMrF0+iY2ZmZmZmZoW4AmlmZmZtIWlXST+RtETSYkmnpvRRkm6Q9ED6u0Num1mSlkq6X9JhufT9JS1Ky74iSSl9C0lXpPTbJY1ve0HNzLqYK5BmZmbWLuuAGRGxJ3Ag8FFJE4CZwI0RsQdwY3pNWjYF2AuYDHxd0iZpX98ATgL2SI/JKX068FREvB74MnB2OwpmZtYrBq1Adrq1UNLU9D8ekDS1paW3rrZs2TIOPvhgpk6dyl577QWwMzh2rRpq8bvnnns6fq1rRMSKiLg7PV8NLAHGAkcDc9Jqc4Bj0vOjgbkR8fuIeBhYChwgaQywbUTcGhEBfKffNrV9XQUcUot5MzMbviI9kB1rLZQ0CjgDeCtwAHBG/mTJbCCbbrop55xzDnPmzOG2224D2Nmxa1VRi98lS5Y4fq0rpQaLtwC3A6MjYgVklUxSgwlZ5XJZbrPlKW1set4/fYNtImId8Ayw44gUwsysBw06C2s6kNcO6qsl5VsLJ6XV5gALgE+Say0EHpZUay3sI7UWAkiqtRZen7b5TNrXVcAFqbXwMOCGiFiVtrmB7MTn8mGU2XrEmDFjGDNmDAsWLGCbbbYBeB7HrlVELX4Bx691HUlbA98HPh4Rzw7QQVhvQQyQPtA2/fNwElnDCqNHj2bBggUbbbRmzZq66a00Y591ddOb/b+N9pM3esvB1xvo/7Yzr0UM9Pk0+z9G+nM26yZN3cZjoNZCSfnWwttym9VaBddSsLVQUq21sFHLo1lT+vr6AF6FY9cqyPFr3UTSZmSVx0sj4gcp+XFJY1JMjwFWpvTlwK65zccBj6X0cXXS89ssl7QpsB2wqn8+IuJC4EKAiRMnxqRJkzbK64IFC6iX3shQblLf6L6MfScU/78D7Sdvxj7rOGfRwKd+A/3fdua1iNmTt2r4+TT7P5otg1kvK1yB7FBrYctaESE7cNZrfeuVVqd2tKSW0W9/+1tOOukkgGVli10oFr+NWo07+XmWMZ66MU/PP/88p556KpQwfosee8sYv61SxphrpVaXL/VwXwQsiYhzc4vmAVOBs9Lfq3Ppl0k6F3g12SXYd0TEi5JWSzqQrGHlROCr/fZ1K3AscFMaJ2lmZi1QqALZwdbC5ay/VKu2zYL++SvSighZa1S91rdeaXVqtiW1G6xdu5a3v/3tnHzyycyYMePplFya2IVi8fvVS6+u22rcydgtYzx1W57Wrl3LUUcdVdr4LXrsLWP8tkoZY66VRqB87wA+BCySdE9K+xRZxfFKSdOBR4DjACJisaQrgfvI5mT4aES8mLb7CDAb2JLskuzrU/pFwCXpMu5VZGODzcysRYrMwjpYayFs3Fo4Jc3utxvrWwtXAKslHZj2eWK/bWr7yrcWzgcOlbRDmsDh0JRmNqiIYPr06bz2ta/l9NNPzy9y7Frp1eJ3zz33dPxa14iImyNCEfHHEbFvelwXEU9GxCERsUf6uyq3zZkR8bqIeGNEXJ9Lvysi9k7LPlbrZYyIFyLiuIh4fUQcEBEPdaKsZmbdqkgPZMdaCyNilaTPAXem9T6b/1ExG8gtt9zCJZdcwu67786+++4LMEHSETh2rQJq8bvPPvs4fs3MzKw0iszCejP1x8MAHNJgmzOBM+uk3wXsXSf9BdJJUJ1lFwMXD5ZPs/4OOuggIuLlS7Ak3RcR16XFjl0rtVr81jh+zczMrAyK3AfSzMzMzMzMrLnbeJiZmZmZ5Q3l9iVmVl3ugTQzMzMzM7NC3ANpZmZmZkDj3kQzsxpXIM3MzMxGkCtlZtZNXIE0MzMzKxmPKzSzsvIYSDMzMzMzMyvEPZBmZmZmFeHLYc2s09wDaWZmZmZmZoW4AmlmZmZmZmaFuAJpZmZmZmZmhbgCaWZmZmZmZoW4AmlmZmZmZmaFeBZWMzMzM6ucRY8+wzTPSmvWdu6BNDMzMzMzs0JcgTQzMzMzM7NCXIE0MzMzMzOzQlyBNDMzMzMzs0JcgTQzMzMzM7NCXIE0MzMzMzOzQlyBNDMzMzMzs0JcgTQzMzMzM7NCNu10BszMzMys+4yfeW2ns2BmI8A9kGZmZmZmZlaIK5BmZmZmZmZWiCuQZmZmZmZmVogrkGZmZmZmZlaIK5BmZmZmZmZWiCuQZmZmZmZmVkglbuMhaTJwPrAJ8O2IOKvDWTIrxLFrVeb4taoa6dj17SnMrJeVvgdS0ibA14DDgQnAByRN6GyuzAbn2LUqc/xaVTl2zcxGVhV6IA8AlkbEQwCS5gJHA/d1NFdmg3PsWpU5fq2qWha7ix59hmnubTQz20DpeyCBscCy3OvlKc2s7By7VmWOX6sqx66Z2QiqQg+k6qTFBitIJwEnpZdrJN3faGenwE7AExtsf/Zws1gZG5W9R9TK/do2/99BYxcKx2/dz67DsVvGeOrmPJUufps49pYxfluljDHXSq0oX+liF4Z37K2qeudAVdbK8gxwPGp3/JqVXhUqkMuBXXOvxwGP5VeIiAuBC4vsTNJdETGxddmrjl4tewfLPWjsQrH4LeNn5zwVU8Y8FdSyY2+F34NBdXPZoLLl6+pj73C4PGbWClW4hPVOYA9Ju0naHJgCzOtwnsyKcOxalTl+raocu2ZmI6j0PZARsU7Sx4D5ZNNxXxwRizucLbNBOXatyhy/VlWOXTOzkVX6CiRARFwHXNei3RW61LVL9WrZO1buFsZuGT8756mYMuapkC6P31bp5rJBRcvn2G3I5TGzYVPERuPKzczMzMzMzDZShTGQZmZmZmZmVgI9U4GUNFnS/ZKWSprZ6fy0iqQ+SYsk3SPprpQ2StINkh5If3fIrT8rvQf3Szosl75/2s9SSV+RVG8a9I6SdLGklZLuzaW1rKyStpB0RUq/XdL4thawgXbFrqRdJf1E0hJJiyWdmtI/I+nRFGP3SDoit82Ix1PZYlzSG3PvxT2SnpX08U6/T2VUxePuAN+DrjmuStpE0v9Kuia97pqytUoVY3cgjeK6yvrHsZm1UUR0/YNsEP2DwO7A5sDPgQmdzleLytYH7NQv7QvAzPR8JnB2ej4hlX0LYLf0nmySlt0BvI3s/lnXA4d3umx1yvouYD/g3pEoK/B3wDfT8ynAFSUoc9tiFxgD7JeebwP8Kr2PnwH+oc76bYmnMsd4+nx+Q3afsI6+T2V7tDN2W5zvRt+DUsRci8p4OnAZcE163TVla9H7U8nYHaRMdeO60/kaZpk2iGM//PCjfY9e6YE8AFgaEQ9FxB+AucDRHc7TSDoamJOezwGOyaXPjYjfR8TDwFLgAEljgG0j4taICOA7uW1KIyJ+Cqzql9zKsub3dRVwSAla1dsWuxGxIiLuTs9XA0uAsQNs0sl4KkuMHwI8GBG/HiSvlf3eDUMlj7sDfA/KEnPDImkccCTw7VxyV5SthSoZuwMZwvG91BrEsZm1Sa9UIMcCy3Kvl1PhA2c/AfxI0kJJJ6W00RGxArIfDWDnlN7ofRibnvdPr4JWlvXlbSJiHfAMsOOI5byYjsSusst33wLcnpI+JukXyi4jrl3e1q54KnOMTwEuz73u5PtUNpU/7vb7HpQl5obrPOATwEu5tG4pW6tUPnYHUuf4XkXnsXEcm1mb9EoFsl4vUrdMP/uOiNgPOBz4qKR3DbBuo/ehG9+foZS1jO9D2/MkaWvg+8DHI+JZ4BvA64B9gRXAOYPkrdV5LmWMK7tB+XuB76WkTr9PZVPp8tX5HjRctU5aKT9fSUcBKyNiYdFN6qSVsmwt1rXlayKuS2sIcWxmLdYrFcjlwK651+OAxzqUl5aKiMfS35XAD8kuvXk8XWJE+rsyrd7ofVienvdPr4JWlvXlbSRtCmzHxpfMtltbY1fSZmQnF5dGxA8AIuLxiHgxIl4C/o0sxgbKW0vjqcQxfjhwd0Q8nvLX0fephCp73K33PaAcMTdc7wDeK6mP7LLMd0v6Lt1RtlaqbOwOpEFcV1GjODazNumVCuSdwB6Sdku9BlOAeR3O07BJ2krSNrXnwKHAvWRlm5pWmwpcnZ7PA6Yom210N2AP4I50ydJqSQemMX8n5rYpu1aWNb+vY4Gb0vieTmpb7Kb34yJgSUScm0sfk1vtz8liDNoQTyWP8Q+Qu3y1k+9TSVXyuNvoe0A5Ym5YImJWRIyLiPFkn8dNEfFBuqBsLVbJ2B3IAHFdOQPEsZm1y0jNzlO2B3AE2axjDwKf7nR+WlSm3clmh/s5sLhWLrJxezcCD6S/o3LbfDq9B/eTmzUPmEh2wvsgcAGgTpevTnkvJ7s0cC1ZC/H0VpYVeCXZ5YhLyWYY3L3TZW5n7AIHkV2m9QvgnvQ4ArgEWJTS5wFj2hVPZY1x4FXAk8B2ubSOvU9lfbQrdluc50bfg646rgKTWD8La1eVrUXvT+Vid5Dy1I3rTuerBeV6OY798MOP9j1qJ85mZmZmZmZmA+qVS1jNzMzMzMxsmFyBNDMzMzMzs0JcgTQzMzMzM7NCXIE0MzMzMzOzQlyBNDMzM7NKknSxpJWS7i2w7rsk3S1pnaRj+y37L0lPS7pm5HJr1h1cgTQzMzOzqpoNTC647iPANOCyOsu+CHyoNVky626uQJqZmZlZJUXET4FV+TRJr0s9igsl/UzSm9K6fRHxC+ClOvu5EVjdlkybVdymnc6AmZmZmVkLXQicHBEPSHor8HXg3R3Ok1nXcAXSzMzMzLqCpK2BtwPfk1RL3qJzOTLrPq5AmpmZmVm3eAXwdETs2+mMmHUrj4E0MzMzs64QEc8CD0s6DkCZN3c4W2ZdRRHR6TyYmZmZmTVN0uXAJGAn4HHgDOAm4BvAGGAzYG5EfFbSnwA/BHYAXgB+ExF7pf38DHgTsDXwJDA9Iua3tzRm1eAKpJmZmZmZmRXiS1jNzMzMzMysEFcgzczMzMzMrBBXIM3MzMzMzKwQVyDNzMzMzMysEFcgzczMzMzMrBBXIM3MzMzMzKwQVyDNzMzMzMysEFcgzczMzMzMrJD/D1pDxq64QQQSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x864 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histograms for each feature\n",
    "data_for_pca.hist(figsize=(15, 12), bins=20)\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "plt.suptitle('Figure 1. Distribution of each feature', fontsize=16, color='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b370d633-b74b-47bb-8b8b-8f863d988b00",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "From the figure 1 we have\n",
    "- Skewed Distributions: Certain features such as `% Change`, `MACD Signal Line`, `Momentum`, and `Chaikin A/D` are quite skewed. Skewed distributions are often better handled by scalers that are robust to outliers, we consider **Robust Scaler**.\n",
    "- Bimodal Distributions: Features like `Volume`, `MACD`, and `OBV` show two peaks. This could indicate that different scaling approaches may affect these features in significant ways. \n",
    "- Features with Outliers: `Open`, `High`, `Low`, `Market Cap`, and `ATR`, we want to diminish the influence of outliers, then **Min-Max Scaler** and **Robust Scaler** may be good options. \n",
    "- Normal-like Distributions: `KAMA`, `MFI`, and the `Bollinger Bands` appear to have a distribution close to normal, these indicators could be scaled effectively with the **Standard Scaler**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d33db9a0-709a-4022-9106-c8fd66854b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_table = pd.DataFrame(index=['PC1', 'PC2', 'PC3', 'PC4', 'PC5'])\n",
    "\n",
    "# Define different Scaler methods\n",
    "scalers = {\n",
    "    'Standard Scaler': StandardScaler(),\n",
    "    'MinMaxScaler': MinMaxScaler(),\n",
    "    'RobustScaler': RobustScaler()\n",
    "}\n",
    "\n",
    "pca_results = {}\n",
    "\n",
    "# Applying each method and perform PCA\n",
    "for name, scaler in scalers.items():\n",
    "    scaled_data = pd.DataFrame(scaler.fit_transform(data_for_pca))\n",
    "    num_components = 5\n",
    "    pca = PCA(n_components=num_components)\n",
    "    pca_fit = pca.fit_transform(scaled_data)\n",
    "\n",
    "    # Store the results in a dictionary\n",
    "    pca_results[name] = {\n",
    "        'scores': pca_fit, \n",
    "        'components': pca.components_,  \n",
    "        'explained_variance_ratio': pca.explained_variance_ratio_ \n",
    "    }\n",
    "    \n",
    "    pca_table[name] = pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8344678c-73e6-43b0-8d2e-26dbf4a2b913",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define a function 'biplot' to create a biplot for PCA results\n",
    "def biplot(score, coeff, labels=None, name=''):\n",
    "    plt.figure(figsize=(8,4))  \n",
    "    xs = score[:,0]\n",
    "    ys = score[:,1]\n",
    "    n = coeff.shape[0]\n",
    "    scale_x = 1.0/(xs.max() - xs.min())\n",
    "    scale_y = 1.0/(ys.max() - ys.min())\n",
    "    \n",
    "    plt.scatter(xs * scale_x,ys * scale_y)\n",
    "    \n",
    "    for i in range(n):\n",
    "        plt.arrow(0, 0, coeff[i,0], coeff[i,1],color='r',alpha=0.5)\n",
    "        if labels is None:\n",
    "            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, \"Var\"+str(i+1), color='g', ha='center', va='center')\n",
    "        else:\n",
    "            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, labels[i], color='g', ha='center', va='center')\n",
    "    \n",
    "    plt.xlabel(f\"PC{1}\")\n",
    "    plt.ylabel(f\"PC{2}\")\n",
    "    plt.title(f'Biplot using {name}', fontsize=14, color='b')\n",
    "    plt.grid()\n",
    "    plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a4e0ce62-2b7a-432c-94c6-d622d1eec42e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lbwG03PWTNTuLiadPZESPEeXHZn4xkw37NzBr5KxaXz/+3fH8d8N/iXPG0b1Nd5678DlSE1J5aeVLzPhsRvl5K/es5JsbvmFgx4H1jrUq/Zm3LM293OFsuv8a6Cki3UQkDhgLLKhyzhvAT0QkRkQSgVOBdU0cZ8M5nbb5vksX+NvfYNWqcEekVNiN6zeO7DWVW96yV2czrt+4Wl/r9rg5u/vZrL55NStvWsnxbY5n2sfTALhswGWsuHEFK25cwYujXyQzNbNRk7xSzU3YEr0xphS4BViETd5zjDFrRORGEbnRe846YCGwEvgKeNoYszpcMTeIwwHjx0O/fvDkk/Dll+GOSKmwGtNnDG9ueJOi0iIAtuZuZWfeTl5e9TKDnxpM31l9mfzB5PLzM2dmcv+H93P6s6czd+1czul+DjEO2yg5tMtQduTtqHaNV1a/EtSNg1LRLKxr3Rtj3gbernLsiSqPZwAziAYicMMN8Mor8MILcOQInHlmuKNSKizaJrZlSMYQFm5ayIW9LyR7dTaX9L2EiT+ZSBtXG9weN2e9cBYr96xkQPoAABJiEvjkmk+qvdezK57lkr6XVDv+6ppXeWPsGyEvi1KRTFfGC4dx4+C88+D11+EN/SOkWi7f5vvs1dmM6z+OOWvmcNKTJzHoyUGs2beGtfsqZtz6S+YPffQQMY4YLut/WaXjX+74ksTYRPp16BfaQigV4XT3unC54AK7p/3cuXab28sv121uVYszqvco7lx0J9/s+oaC0gLSEtJ45LNH+Pq6r0lzpXHV/KsoLC0sPz8pLqnS659f8TxvbnyT9698H6ny/yfY/n6lop3W6MMpKwuuvtr218+aZafiKdWCJMclk5WZxTVvXMO4fuM4XHSYpLgkUhJS2JO/h3c2vRPwtQs3LeThTx9mwdgFJMYmVnrOYzzMXTuXsf3GhroISkU8TfThNngw3HQTfPcdTJ9u59sr1YKM6zeOb/d8y9h+Yzmx44kM6jiIvrP6cs2CaxjWdVjA193y9i3kFedx9otnM/CJgdz45o3lz3207SO6tO7CcWnHNUURlIpo2nQfCfr2hTvusFPvJk2yK+o5nbW/TqkoMPqE0ZjJFa1Zs0fN9nve1tu3Vnq86XebAr5nVmYWX1z7RWOEp1SzpzX6SHHccXDXXXbHu3vusTvgKaWUUg2kiT6SZGTAvfdWbHNbWFj7a5RSSqkaaKKPNO3a2eVy3W6b9I8cCXdESimlmjFN9JEoJcUumQt2m9tDh8Ibj1JKqWZLE32kSkqqGJQ3ZQr8+GO4I1JKKdUMaaKPZAkJ8NBDEBdnk/7Oqpv7KaWUUjXTRB/pYmPhT38Clwsefhg2bw53REoppZoRTfTNgdNpa/bp6fDXv8KaNeGOSKnGYQysXg1Hj4Y7EqWilib65sLhgIkToVcvePxxWLo03BEp1XALFsAjj9hkr5QKCU30zYkI/Pa3MGQIPPccfPhhuCNSqv62bIH33oPTToNTTgl3NEpFLV0Ct7kRgSuvhNatYc4cyM+HkSPDHZVSdXPgAMycCR06wHXX6c6NSoWQJvrmatQoaNUK5s2DvDwYq7t0qWaisNCOOYmPt8s9a5JXKqQ00TdnZ51l59u/+KLd015rRirSud12EShjYOpUiNE/QUqFmvbRN3dDh8INN8CqVXZQk25z26TmL89h/e48uk14i2HTFzN/eU64Q4pcxsCf/2xr9JMm2SmjSqmQ00QfDQYMgNtug+3bK9bJVyE3f3kOE+etotjtwQA5uQVMnLdKk30g2dmwY4f9XW3bNtzRKNViaKKPFj16wPjxtgl/0iTd5rYJzFi0noKSyjdVBSVuZixaH6aIIthnn8HHH8Nll0H37uGORqkWRRN9NOna1Sb5oqKKf1XI7MwtqNPxFuv77+Gll+CMM+xUOqVUk9JEH206dLCDnUpL7Ta3uuJYyHRO9d/HHOh4i7R/P/ztb5CRAZdcEu5olGqRNNFHo7Q0u82tMXDffbY5XzW68SN64Yp1VjrminUyfkSvMEUUYQoK7DS6hAS4+26dEaJUmGiij1bJyfDAA3bp3ClT7AIlqlGNGpTBtIv6E+d0IEBGqotpF/Vn1KCMcIcWfqWldmCoiL3pdDprf41SKiQ00Uczl8vWqGJjbdLfvTvcEUWdUYMy6NWxFVumj+TTCWdqkgfbkvTQQ3aMyL332hq9UipsNNFHu7i4ilXIpk2DbdvCHZGKdv/+N+zbB7ffbruRlFJhpYm+JYiJsXvat2kDjz4K63X6lwqRDz+Ezz+3+zF06xbuaJRSaKJvORwOOxq/Wzf4xz9g+fJwR9QszV+ew7Dpi3UlPH82bLAbLZ11lt1hUSkVEcKa6EXkXBFZLyKbRGRCDeedIiJuERnTlPFFHRHbnDpoEDz9tF3ERAWtbCW8nNyCSivh5Rbo4kTs2wePPWZvJC+6KNzRKKV8hG1HCRFxAv8EzgZ2AF+LyAJjzFo/5z0MLGr6KKOQCPzmN5CSYhcxOXwYzj033FE1C4FWwttzqIUn+qNH7fgPlwvuvFOn0SkVYcK5ddQQYJMxZjOAiGQDFwJrq5x3K/A6cErThhflxoyxU/D++184cgQuvjjcEUW8QCveFbtb8EZCVafRObQ3UKlII8aY8FzYNsOfa4y51vv4CuBUY8wtPudkAC8DZwLPAG8aY14L8H7XA9cDpKenn5ydnR3iEviXn59PcnJyWK5dL0eO2NXLkpIavNFIsyt7Ha3fnec3qXdKhHZpKWGIKLzy8/NJPnzYbqLUuXOLmisf7b/rgWi5I9fw4cOXGWMG+3sunDV6f+17Ve86ZgJ3G2PcUktzoDHmKeApgMGDB5usrKxGCLHulixZQriuXW/Ll8Mzz9jNRm6/vd5Nr82y7HWQ6+2j922+d8U6mXaaM6rLHciS118n67PP4K674Jhjwh1Ok4r23/VAtNzNUzjb2XYAXX0edwF2VjlnMJAtIluBMcAsERnVJNG1JIMGwa23wpYttvlV97T3q2wlvIxUV6WV8FJdseEOrektXmxbg66+usUleaWam3DW6L8GeopINyAHGAtc6nuCMaZ8Iq6IzMY23c9vwhhbjl694Pe/h7/8Be65Bx580M6/V5WMGpRRbfW7JUs2himaMFm7Fl5/Hc45B04+OdzRKKVqEbYavTGmFLgFO5p+HTDHGLNGRG4UkRvDFVeLduyxMHFixTa3xcXhjkhFmj174IknoGdPO3NDKRXxwjpE1hjztjHmeGNMd2PMQ95jTxhjnvBz7lWBBuKpRtSxo12fvKTEJvsC3Vu9MUTFQjv5+TB9up1G97vfhTsapVSQdC6Mqq5tW7vjncdjt7nNzw93RM1aoIV2mlWyLymBqVPtyPqyXRGVUs2C/m9V/rVubf+gi9ilcw8eDHdEzU5ZLf72V1f4XWhnxqJmsueAMfaGr6TE/i7ExYU7IqVUHWiiV4ElJlYMyrv/fti7N9wRNRu+tfhAAi3AE3H+9S/bqvOHP9gbQKVUs6KJXtUsPr5im9s//Qm2bw93RM2Cv+Vyq+qc6mqiaBpg0SJYsQKuvRa6dAl3NEqpetBEr2oXG2uTfXIyzJgBmzaFO6KIV1tt3RXrZPyIXk0UTT2tWgVvvAEXXAADB4Y7GqVUPWmiV8FxOm3zfdeu8Le/wcqV4Y4ootVUWy9baKfqfPyIsmsXPPUU9OsH558f7miUUg2giV4Fz+Gw/bT9+sGTT8KXX4Y7oog1fkQvXLGV1353xTqZeclAPp1wZmQn+bw8+POf7f4HN+qSFko1d5roVd2IwA03wOmnwwsvwPvvhzuiiBRoudyITvBgF0kqm0anu9EpFRV0jVNVP+PGQatWMG+eHZF94YXhjiji+FsuN6J5PHb6nNttk7xOo1MqKmiiV/V3wQV2gN7cuXD4sO2/Vw0yf3kOMxatZ2duAZ1TXYwf0avpbhaeeMJuVHPPPfYmTikVFTTRq4bJyrLJfvZsSEiwi6vUc5vblm5+lW1wy1bQA0Kf7N96C1avhptugk6dQnstpVST0g441XCDB9sEUVho10LXbW7rxd/c+8ZaQS9rdhaLNi2qdGzmFzO5+a2b7Tz5t96y3S/9+/t9/fh3x9P7sd4MeHwAo18dTW5hLgD7j+5n+PPDSf5TMre8fUuD41RKNT5N9Kpx9O0L6emwe7fdDMdd82IxqrpAc+8bYwW9cf3Gkb0mu9Kx7NXZjOv4M3jmGTtPfsQIv691e9yc3f1sVt+8mpU3reT4Nscz7eNpACTEJPDA8Ad45JxHGhyjUio0NNGrxhMXB3fdZXe8u+ceuza6ClqgufeNsYLemD5jeHPDmxSVFgGwNXcrOw/n8PLrUxjc5jX6lv6NyR9MLj8/c2Ym9394P6c/ezpz187lnO7nEOOwPX1DuwxlR94OAJLikjj9mNNJiElocIxKqdDQRK8aV0ZG5W1uCwvDHVGzEWjufWOsoNc2sS1DMoawcNNCALK/fYlL9nfkoZIzWHrvD6y8cSUfbvuQlXsqFkJKiEngk2s+YWy/sZXe69kVz3Jej/MaHJNSqmlooleNr107mDzZNt/fe68dya1q1ZC594H2u/c9vnHLAB755DnweMhe/HfGFR3PnFE9OenZUxn05CDW7FvD2n1ry9/zkr6XVLvOQx89RIwjhsv6X9Zo5VZKhZaOulehkZJi52JPnmznZv/xj/aYqlF95t4HGq2/dNsBXl+WU368MO9kPi95nFcn3ERBfDFpN9zGI+9cytfXfU2aK42r5l9FYWlFC0xSXFKl6zy/4nne3Pgm71/5PqIzK5RqNrRGr0InKcluc+t02tXWfvwx3BFFpUCj9V/5cnul4w5cHHOoMxPc2YwbcCmHW8WTFJdESkIKe/L38M6mdwJeY+GmhTz86cMsGLuAxNjEoOIK1MqglGpaWqNXoZWQYHe+u/dem/Tvugs6dw53VFEl0Kh8tzGVHvfdtZEe+xL4+6mHGTv8Vnq3682gjoPoO6svx6Udx7CuwwJe45a3b6HIXcTZL54N2AF5Y5Nt333mzEwOFx2m2F3M/O/m8+4V77JhR0r41gRQSlWiiV6FXmys3cv+3nvh4YfhttvguOPCHVXU6JzqIsdPsneKlCf7zof38qtV/2N1xzM4Lf4RerfrDcDsUbP9vufW27dWerzpd9W3Jl6yZInfcwGue2ZxwDUBNNEr1bS06V41DafT1uzT0+Gvf4U1a8IdUdQINFp/3KldccU6aV2Yz7VfzedQQjJvnjQi6FH8DWl6D+WaANFIuzlUKGmiV03H4YCJE6F3b3j8cVi6NNwRRYVAo/UfHNWfhy84nonLXqPEGcNrI3/DtIsHVK5Rr18P991n1z7wUTbALye3AENF03uwCSiUawJEm4Z+1krVRpvuVdMSgZtvhn//G557zk69++lPwx1Vs+d3tL7bzS/mPQmndIUHH+SGxCqD6Pbuhcceg5gY273io6bleINpeh8/olelPnpovDUBok1DP2ulaqOJXjU9EbjiCrtD2pw5kJdnd8JTjccY20VSUGBr7FWT/K5ddrxE2cyImMp/Chra9F6WoMK2E18zot0cKtQ00avwGTWqYk/7vDy7x71qHPPmwZYtduBj+/aVn9uxA2bMAJfLjptwOqu9PNAAv7o0vddnTYCWqDE+a6Vqon30KrzOOsvW7j/9FJ580tZEVcN89RW8/z5ccgkcf3zl5374wSb5pKSASR5CuxxvU2hOg9ua+2etIp/W6FX4DR1qm5b/9S945BH4/e/twD1Vd1u3wgsvwP/9H5xxRuXnNm+GmTOhTRu7UmENn3FjNr3PX57TpE34/lYKvOPVFdz+6goyIrALQbs5VKhpoleRYcAA28z897/bZXOnTAlY21QBHDxo++U7dIDLL6/83MaN9rPt1AkmTAjqRqoxmt4DLc9b9v6h4G9wW1k7UaQu3BPqbo6mvtlSkSWs1SYROVdE1ovIJhGZ4Of5y0RkpffrMxE5MRxxqibSoweMH2/76ydN0m1u66Kw0A6qi4+3WwT7rkX/3Xc2yR97rJ3e2IStJTWNKA+V2gaxhfr6kUan76mwJXoRcQL/BM4D+gDjRKRPldO2AD81xgwAHgCeatooVZPr2tUmqqIim+yLisIdUeRzu21TvDF2TwHfEfSrVtkpdD162C6RJt6MJhwjyoMZxNaSRrSH42ZLRZZw1uiHAJuMMZuNMcVANnCh7wnGmM+MMQe9D78AujRxjCocOnSwiau01C6be/RouCMKu6zZWSzatKjSsZlfzOTmt26CP//Z1ugnTbIj6cssX24HOPbrx/gTttP7nycw4PEBjH51NLmFueWnTft4Gj3+3oNej/Wqdo2GCvXCOf4G3fkb3FbT9ZvTwL360Ol7KpyJPgPY7vN4h/dYIL8BAm+vpaJLWprd5tYYOw/88OFwRxRW4/qNI3tNdqVj2auzGbcn3U6Xu+02aNu24smvv4ann4ZBg3Bfdy1ndz+H1TevZuVNKzm+zfFM+3gaAGv3rSV7TTZrbl7DwssWcvPbN+P2VK79NUQoR5QHapIGylcKBKjahuF7/ZbQrK2rFCoxYZrOJCK/BEYYY671Pr4CGGKMudXPucOBWcDpxpj9Ad7veuB6gPT09JOzs7P9nRZy+fn5JCcnh+Xa4RaSshsDOd4/up06ReQAvab4mR8qOcSvv/41c4bOIc4Rx+7C3dy2/HcMje/Hd+7tFFHCT9v/lKszr4YjRxi74irOa3MWSwvXMDpjNGd2OLP8vT7+8WM+3Pch955wLy/98BIAlx1zGQDjV47nqmOvom9K31pjCrbcuQUl7DlUSLHbQ5zTQXpKAqmu2FpfV5v1u/ModnuqHY9zOujVsVWl6+/MLcDtsX/rYhxCp1QXqa7YoN+jqub0/zy3oIScgwV4fP7WO0TISHPV+efQnMrdmJpDuYcPH77MGDPY33PhHHW/A+jq87gLsLPqSSIyAHgaOC9QkgcwxjyFtw9/8ODBJisrq1GDDdaSJUsI17XDLWRlLy62tfply+Duu6Fjx8a/RgNULXfW7Cwmnj6RET1GlB+b+cVMNuzfwKyRs2p9v7lr5jLlwyms27eOr677isGd7f/d0/aexrTt0/jx6I/sObST3vnxPHfMpbS57FrcHjdnvXAWbY5uYcDbS0noDCcMOInnhlW/4X30lUe5athVZA3I4rW3X2Nol6FkDbDxDzg0gPSe6WT1yar2utrK3dSunvAWxk+jpABbpmcBtsY+9aM1HDxatVXBzbSL+jB94Ypa36PsfSqNWj8xvlLZI31Ue2PFF+6febg093LX2nQvIq1FpLuf4wMaeO2vgZ4i0k1E4oCxwIIq1zgGmAdcYYzZ0MDrqeYqLs4u7hIfD9OmwbZt4Y6oRuP6jePX839dqb87e3U2ToeTm9+6uYb+dtts3q9DP4ZmDCUhJoGxr40t71PPTM1k84HN/PGkOzhceIiP43YxKWUZJz15EoOeHMSanOXc+um99D/mTXZ6DnFMyjHVYnvoo4eIccRwWX9bg/fXoifVGrsjU21N0mXN8gePVp+9UVDi5vdzviUlQI22ah9+1eb9nIMF5c37zaH5f9SgDD6dcCZbpo/k0wln1prko33cQktTY6IXkV8B3wGvi8gaETnF5+nZDbmwMaYUuAVYBKwD5hhj1ojIjSJyo/e0PwJtgVkiskJEdLuzliomxu5p36YNPPqo3XUtQo3pM4YjJUd4aZVtFt+au5WdeTuZs2YOi7csZsP+Ddz9v7vLz8+cmcmjnz3Kxz98zNy1czm+7fFc0u8STsk4hewx2eV96qd0PoXd+bvxvP466SaReGc8CzYs4P0r32dltz/TvSABd1oKq+7eRsfkjkz7ZBoeU9Es/fyK53lz45u8dNFLiHf0fZfWXdh+qGKozI68HXRu1bmJPqmGqa3/399oc19uYzhSXEqso/KNTdUxBP7ex2NM+aj1aBvV3hxuXFTd1Fajvwc42RgzELgaeFFELvI+1+DbfmPM28aY440x3Y0xD3mPPWGMecL7/bXGmDRjzEDvl9/+B9VCOBx2NH63bvCPf9hR5RGo+9+7k56Uzvzv5lNUWkT26mwc4mD/0f0MzxxO+8T2fLvnW6574zoASj2l5OTlkBCTwLULriX14VTOf+l8Ptr2EW9teIvnVjzHO5ve4elv/oWrFMYmvs0Pjjzcxo0rxkXKex+x581sVsbn0qfX6QA4xEHr+NYs3WnvjRduWsjDnz7MgrELSIyt2ODmF71+QfaabIpKi9hycAsb929kSMaQpv/Q6iHQ9rxltdVgRpWXuA3JCTEB36Om9yk7Huh5f+vXNweBblym/ncN63fnaS2/Gaqtj95pjNkFYIz5yjso7k0R6ULFYlNKNR0RuP12mD3bjiq/9FIYNizcUVUysudI3t/yPslxySzctJAHP3qQwtJCeqT14LPtn2EwOMXJK6tf4daht3Kk+AhJsUmM7DmST6/+lHc2vsN9S+5jzd41ABwqOkRRaRGHiw+TgBMEPrn6E3724s/oWpzA8V9fydb2+aQnprN893JKPaWUekr5dve3bD+0nSEZQ7jl7Vsochdx9otnAzC0y1CeuOAJ+nboy6/6/Io+s/oQ44jhn+f/E6cj8gY8BlLTinKBNoupKvdoCcv/eE7A52vbdCbQ84KtHUdSX30wAt24HDxaQrHbg8ERsSsMKv9qS/R5ItLdGPM9gDFml4hkAfOB2oflKhUKInD11Xbnu5dftivpnXtuuKMC7EC8KwdcycurX6ZVXCtmLZ2Fx3hwGzff536PGKHUlGIwFLoLWbtvLblFucRKLNM+nsaMz2bg8Xgo8ZTgwcO7m97FIQ7mtbmZX+X8lYy23Vh52zoAUk085+xJZmm3gQzyGAZvKWZ3lz4MfmowJ3c+mRJ3CTEO+1980+82BYx50hmTmHTGpCb5fJrS+BG9Ki2/G0ht08z8vY9DpLx5f/yIXtzx6opqNR8DzXJP+WBvkMq6J5pb+Vqi2prub6JKE70xJg84F7gmVEEpFZQxY+DnP4f//hdeey3c0QB2IN49i+8hJT6FTsmdePf7dykqtav7uWJcdE3pyqkZpwLgMR7uXHQnAG7jpnPrzqz97VpaxbcqT9Cf5XxGUWkhI3f9mQPOIlblfkfyn5JJmBrLvtLD7OvbjePiOtL3hwKcKan89aInWXHjCt4Y+wa5hbn0bNszPB9EBChr2q9pClmsU2qd0++viyAjzVWe4EYNygjYvBlpi9IEM8gumAWHyviWr6ZBpsGYu2YufWf1xTHVUd7lBNgur+eHk/ynZG55+5ag3ktVVluiPwKk+zk+FLtSnVLhde65tvn+gw/g2WfDvs3tmD5jyC/Op0NSBw4UHgDAgwdBOFJyhM25m/kip+K/zt78PWDsOQUlBXRO7kyxu5gid8XSv7HGwY4TnibBmQBAF5NMl9JE3GKYt+M9Jq9Ige49KB5+BkdMMQDvff8eMY4Y+rSvuqp081WfkeCjBmWQFF9Dw2WQvy5VR61XvXnIaAaL0gQ7yM7fjU2gmyXf8gVc1KnfuFpjK5ttMu9X8zjj2Mq7LibEJPDA8Ad45JxHgiuoqqa2RD8TyPNzvMD7nFLhN2wYXHstfPON3YY1jMm+bWJb/q/r/7Erbxf7j+5nUMdBgK3Ne4yH5NiKRTeMMbipGBV/tOQocQ/GcajoUPmx2FJwi+H3cR9Q7Cmmg7Ri2v6TOJJk//DuLs3l4hNWQq9e5Bfnc9JTJ3HCP0/g4U8f5sXRLzZRqUOvISPBa6pVl3hMo4yObw57ytdldkDVG5spv+hba/nG9BnDmxveLG/BKptt8vKqlxn81GD6zurL5A8ml5+fOTOT+z+8n9OfPZ25a+dyQvsT6NWu+ueVFJfE6cecTkJMQoPK35LVlugzjTErqx40xiwFMkMSkVL1MWgQ3HorbNlil871VF/tLBSyZmfx1YGvKh1LT0rHYzy0cbVhxe4VCIIr1tZ8Sj2l9iQDSW4Hbdxx5Z1jdwy9A6c4ccW4WJb1ChiIwYHDEcOctXPxGA+xpR7uyVjH4cJDJHpiaJ2QyvqS3cz8YiZPLH2CW4fcyrrfruN/V/6PY1OPbZLPoCk0ZApbbbVqfzcCdW09qG0GQCRoyJr3ZeWLczoClq9tYluGZAxh4aaFgK3NX9L3Eh466yGWXr+UlTeu5MNtH7JyT0VKSYhJ4JNrPmFsv7ENK5yqUW2D8Wq6hYqcNimlAHr1sju0/eUvdge8Bx+svJNbCIzrN475S+dzF3eVH1u/fz0GQ6u4Vuwv2E9SbBK5BbnEOeIodBeWn3fE6eGIFNvmY5+RMOIxnPKBbe4siPGQHJPI5Z5+zPN8S1xyCo5DeXQmmfxWcWy5YxsJMQlMWTKF5LhkbhkSnX2YwSYpfyvA1TYor+qNQFnrQdn5ObkF3PHqCpZuO8CDo/oHjDHUe8rXhb/PobbZA7UZNSiDJYc2VloxsKqy5vsLe19I9upsnr3wWeasmcNTy56i1FPKrvxdrN23lgHpdr21S/peUq/yqbqprUb/tYhcV/WgiPwGWBaakJRqgLI918u2uS0uDunlxvQZw+cHPq/UXLknfw8dkzuy9dBW0hLScIgDN+7yWn2ct1Lfzd2KGGP/C8Y747Cd9R5SDxfz9Ue9iXPG4hQnz5aM5KmSL8h1lrD16E66mFZsvHsHvTv0YfXe1SEtX6QIZmMWf83741/7likL1lBQ4sbhZ+UPf83r/loPDPDSFz80i7njgbo5hvduX2Pze2OshucsGsJrqxfSeeLfWL93P19/X8Qjnz1iF3W6aSUje46ksLTiZjcpLqlBZVXBqS3R3w5cLSJLRORR79eHwLXAbSGPTqn66NjRro1fUmKTfUHoRj63TWxL71a9qzVXTvnpFAA6JHagjasNcU7b9y4Ibb3hTDI/oX2x7WtPSUjl5+s8eDC4PA4uvaCIYk8JDo/h7tJ36J/cnWcOn8lA04FF961nW8mPrP9xPZmpmQBMyZrCH077Q8jKGW7B9IH7S9AlbkNugV0C12PsKPtUV2yNzeuBWg/KpstFukDdHB98ty9g90LVm4NdB/K55/Vv65Ts5y/P4f4Fm4kr7cePcX8jvvgn3P/WUn7Y72bkzGXM/nwF72zSDUjDocZ2TWPMHuA070I5/byH3zLGLA55ZEo1RJs2MGUKTJ1qk/6UKRCi3afO6nBWtebKr3K+oltqN9YfWE9aQhojuo/gvxv+i8Nj6H1AOJzk5IaYd2hTAE4DFJfAvH/jOBtuH3gj0/bOI9Y4SDax/ODMh/w87mt1AEdqKgOfHYJDHMwaOYt2ie1CUqZIU5aMa9qYJdiV8JLiY1gxue4L5Pi7RiRuZlNTN0el7gWPx27/vH49H/7jDUbnbKPLob3EeEqJdZdixMFz7ksZNSi45vWyG4wkxxnsi/kTScV3EWu6EufpzteFV7NyYSf6dT4p4Ov/s+4/3PrOrew7uo+RL49kYMeBLLrcTtfLnJnJ4aLDFLuLmf/dfN694t2omlESajUmehFJAG4EegCrgGe8a9QrFflat4YHHoDJk+3SuffdZ/e5b2Sntzudf33zL77Z9Q0FpQWkJaTxyGePsOz6ZaS50rhq/lVkZWaxQC4l86tLmfP9ybSLPQneeQeOHoUHHyRr3d1w9CilHWfBuOu45d4k2xIxYAB88w1Zx37AI5c9z+CMU2oPKErV1gce7EIvtd0QBFoAp+waZfz15Yd6tbhgbizKPgeHx02roqN0yD9AxuG99C/aD3d/BKXV/4T3Wb0Zg1AYE8emtl3Zl5zG9pR0truDH4pV9rkmek7j2II3y4+3K7mj/HvnQRdXDbRbJm+9fWul148+YTSjTxjt972rnqvqpraRSs8DJcDHwHnACdjmfKWah8REOyjvvvvsaPyJE6FDh0a9hMvpIiszi2veuIZx/cZxuOgwSXFJpCSksCd/D+9seoesY86AO+6Asca2NmzaBPn5cMEFsHAhdDgKw0fYaYL33su+wv206XAszuXL2XzBMDaum89xbaptIql8NMZKeGWJ1F+SD6arIJSrxVW9sdizP4+H//0JrTe04UxXAWzeDLt2MXtnLp9s/JESn5knsQ4Hpx/fHpKSoHt3u19E167Qti24XMx++AO/N0mB1gfwJ5gbrUhbQKilqC3R9zHG9AcQkWeAr2o5X6nIEx9vt7m97z67A97vf2//yDWicf3GcdGci8gek03vdr0Z1HEQfWf15bi04xjWdRgsXgz794PTabfdXfoFtGvHf5K3c2v6R+xLNIxM/pCBDxzPoqM/56O4HP5YOI+YHu1w/vAZT4x8gjauNo0ac7Sp2ryfmhhLfmEpJZ6KtF3T3PaqiRTsZAgDpCXGYgzc8eoKZixaz/gT3ezM9T/Qs9GSWXExHDwIOTmwdSvbn36X2/bvw2EqTx3d81UMDDnGbvqUlkbPk09mZ6GLv3xXwHfFsbRrl8L4Eb3oWcPNh7+bpLquAxDMjVYkLSDUktSW6Ms3cjbGlJZtbalUsxMba5P9lCkwYwb87nfQo0ejvf3oE0ZjJlcklNmjZlc8WVwMnTuDMWz9/FTY8IWd9nf++YzOzmb0sYPsyn4PPOAdOGi42N2Li2/8F2RExnSt5qJq835tTd2+zztEcFdZbMkAibEOCks8lZrocw66SYyL40hx9aQWdDIrLLSJfPt22LoVvv8eDhwIuAZE3P597E9MYVtqJ3JSOrC7VVsOxydTEhPLuOkjK537U+9XsIIZA1GX98jJLSi/SSpTdYR/pI1tiGa1JfoTReSw93sBXN7HAhhjTOuQRqdUY3I67eC8Rx+Fv/0NrrvO9oGH2v33Q26u7TI4etTOBhg9Gl591R5bsMAm+bw8WytzuWzrQ0pK6GOLcjX161etwVdN8mWOllRPvB5j/Cb58rXzjbE3bQcOwA8/VCTyQ4cCr9zodNobwu7d7TTRzp0hNdXepAIvTF/st2ncKdIou+QFuw5ATUna9z0CnReOsQ0tXW2j7pvPfpVKBcPhgD/8AZ56Cp58Eq68Ek49NXTXKyiAf/zDXrdHD1i+3NbS//c/OwvglVfgr3+1NwIxMfbY1KmQoMt9hpq/Pva6SCwuIK0gj4zDe8k4tIfMgztJNaWMKjwW/u3nBTExkJlp+8czM+000JSUoBd1CtQ07jamyRJlbkEJE98PLkn7a10ZFuBmRXfCC63QLhumVCQSgRtugOxseOEFOyjurLNCc63f/c6+f1qaXYs/IcHW3EpL4fHH4cUXYfduO2iwfXs7Q8Cp99dNoca+dGNILi6gzdFDZBzeS5dDe8k8uJNYdylgaJN5Abd/8mall5Q6Ylifkg7nnGNr5B072q2UG/DzrForvvjkDF75cnu11odAibKxm8j3HCqkoKTy8ivBJGl/4x+q0oF6oaOJXrVcY8faP8Tz5tlm81GjGvf9Dx6El16yf+gTEmxy79IFduywo/+XLLFNum3awODBdsS9joNpGh4Px8e7Kd61my6H9tDl0B665u4hxhNo9rBQ4nCyqW0XdqSkk5WYwt+HXcqRuASMVCS+jFQXjDizUUL018T9+rKcgF0Mvoly/vIcpv53DQePlg+zapQm8mK3B3/rrNWWpINpPdGBeqGjiV61bCNH2ubyOXNssr/88sZLtpdfbpfibdfO9tW2aWOT/MiRsHMnrF1rR/9feKE9phqP2237w3fvhm3b7NSz7dvt+Aivx/bm+0xDE4pi4vjem8hzWrfnoKs1R+JcuOJiuPjkDD74bl95zTi5dRHuZME0YJR6bQJN33P6GTQIFYmyptpzQ5vI45z+F1Otz8ZBviJtp79oo4leqZ/+1M4vnj3brhR2880NT/Y5ObYf3um0CScuzvbXH3+8bcb/8kvo39/W4k8+uVGK0aKUlNjPdedOm8i//x527fK7GAxgf56tWkG/fraP/Jhj6NmmDWvWH2TGuxv8jhIHSHXFMuUXfaslxiVLljDtop4hHTkeKDm6jcEV6ww4Fa622nNDmsjTUxJwxbrrPA2vpjn2GTrqPuQ00SsFtunc5YInnoBp02DCBDuArr5+8QubjOLjbZJxOm0f/EknwWefwZAhdj5/t26NV4ZoUlxsByju2FGRyPfutTV1fxwOO0K9Rw/bP961q21BSUio8aZt1ElJjDqpC1D3/uxAo9Qbq188UHIsS4yBrlFbIq+t9l1T/KmuWKZd1KfO5Qs0Tz/StvKNVprolSrTty/ceSfMnGk3w3nwwfoNpFq/Hr791iaf0lJbm09OhtNOg6++gv/7P7skb7uWsU69X2VzyHfssOMUNm2qcQ45Tqddxa0skXfpYltG4uMbLaTG2Ga2MaeO1bSITU2x1lR7rq32HUz89fmcGmOevqo/TfRK+erWDe6+G/7854o97b3zmIN2zjm25ul02jnTLhcMHQorV8IZZ9g580lRvD2n7xzyssVgNm2qfQ55p04Vc8gzMuzUs7i4Jg29oRpzWdz6JsdA0/ACdUOEKv6qGuNGStWPJnqlqurcGe691y6XO2mSXfAm2Hntn39ukxvY2mlKil2UZ/NmOPNM+551vXGINMbAkSO2ef3jj2HLFtu0fuRI4EQeE2MTeNkc8k6d6jSHvLmoaee4+mjq2nNjx68iQ3T9L1OqsbRrZ+e033+/TfpTpwZXCz/33Ipk53LZJubcXDuV7w9/aFi/f1Mxxs7937fP9o9v2WJr5MXFlRP5oEGwfDkb9+bzxfbDfBffhoLOXTnm5D4s3GfYWCB0bJMcVJKJliVRAzWbN/XUsfrWniMlftW4NNErFUhKik30Zdvc/vGP5cvS+iamCQM95C7PYdSWL+2ofbAJvVUrW3u/+2741a8iZ468x2OnEu7da5vVt261LQ7F1Tdp2bg3n6+3HiS3xHAo41g6DujNwrx41rkT+G2Mi//95CpeX5ZDQYZPc+8O77+Oij7epdsOVJqeNrx3+/LHVTefac5LojbG5jDhNLx3e/79xQ9+j6vmSxO9Un5kzc5i4ukTGdFjhO2n/+MfmTntQv7VOpkjh2+qdG6x28Pt2cs558+/woWdonX/mQ7+dfIB2rdLhoPT+NOmVpzf8/yQxz1/eQ6PvrOWw3v2k340l865e+hZ8COdcvfiLi4mPsa2KBSWenAgeDAkx8fSOT2FT0ljVVwa+R06cyAxhZ3uGCRDML65tgD7VyMGit2lvPTFD363dPVVUOKudF5ObkGlZOK7qIvva2YsWg/4b4KO1BaA5j7o7IPv9tXpuGoeNNEr5ce4fuPIXpNtE31CAjz0EI9P7MSoJb35oOd+9rZqW+n8q76ahwtbiysW2BXfisSiLKac9Y86/5H3t6qZL6fHTevCfNKPHCTj0B6OObSHDof3E2PcGOCKaq8QDsUlsK3DcexITWdn6/YcTGhNQWx84FYGu4ZMrUm8tufrep6vspp91RHgS7cdsK0IEbopSnMedFZTH/385Tns2Z3H1RPeqtcNTKTenLUEmuiVwtbgP9z2Id1Tu7Pptk389u3fAvDCty/gMR623LaFDcm5PDr0c7rkrWZfTBKx/JTU0svAwF+HPsfUSvuCHgTeYfQbx8ACT7VM16no78SZ4/zGEusuoXXhEfrm7/cuzbqbdkdyq+1DXsYg5MUnsS3Nbl+a07o9uQmtKIqJi5zugnpwivgdAV6Xtd5V3QTqo09NjGXivFXc3NuDwVHnmyvdsS68wproReRc4G+AE3jaGDO9yvPiff584ChwlTHmmyYPVEW9cf3G8ckPn/B97vcAnNTpJL7e+TUAJ6afSPbqbMCNy30uN33Tgfb5+/n9OUsZuL0Vx3Tqh7MUOhbBHZ+24e7zDgOlQCEQB6YYcJLoOZUiduB2/ECnvGQy8jaQcWgvx+buIq0gDwmUyMXBQVer8n3Id7Vqz+GEJJvII4C/FeXq8rw/VVd+8xXMWu+qfgKNMTCGBk27C+W0PVW7sCV6EXEC/wTOxg7f+VpEFhhj1vqcdh7Q0/t1KvC491+lGtWYPmP4w7t/IL8kn7V71nLP6fcwes5oAL7d8y3f7vkWgPzYhUwYXvG6N3pu5I1cIAk65jm4+7xcfNPaT7afzsddFwNuhm3dy/q2h/ghBbI2PUOvA3aAk0cc7E1KY3tqR3JSOrA7uS2HE5IocUb+NDxXrLPaOvC+A+3KHvs2tfsT6xSS4mI4VFBS3qw7Y9H6gPuv17TWu6+mbi72vV5qYizGUKlMkZ7UfMcY5OQWlLeqBPrZBXtzpdP2wiucNfohwCZjzGYAEckGLgR8E/2FwAvGGAN8ISKpItLJGLOr6cNV0axtYlvOyDyDtze+zRmzz+APw/5Q/ty53c9l0feLvOnbEF86iFLnDtyyDzGwuNUjDM/7A0IMo77rwPze3mHnBla1+7T8fd47blP5908OdtKu9NqmKVwDldXIM6ok8TinI+glTAcf26ZSwq16MxAoCfqrXV58cka1Gwd/I9tD1Vzs21ftm8yrzh5o7J3jmkpZfLVtKwvBT7sL1CVggGHTF2t/f4iJCbTARagvLDIGONcYc6338RXAqcaYW3zOeROYboz5xPv4feBuY8xSP+93PXA9QHp6+snZ2dlNUIrq8vPzSU5ODsu1w625l/29Pe8x7btpGAzdk7rz/ZHva31NKxJ4ufdsfv7dWG52XcY6z2Y+KPocgFhiceKgkCIA4oilGPvHv5UkMb3tfbR3tseI4BYHbocTtzjwOBz2X3HgdjgwCEYETz3722McggHcHlPpe0EwGOKcDlolxJBXWEqx21PpnDing/SUBFJd1VsXmuLnnVtQwp5DhRS7PZViCXTc1/rded5tVSuLczro1bGV3/cAKh3z/VzKHh88WkL7BMOeelRGy64dCWr6DAN9dukuKpW7a5tEv78b/q6Vc7AAT4B84xAhI81V7/cK9Ppgfk+C0Rz+tg0fPnyZMWawv+fCWaP391er6m9BMOfYg8Y8BTwFMHjwYJOVldWg4OpryZIlhOva4dbcyz64eDCPPfoYh4sPk1OUU+355Lhk8ovyARDiMBSTRyH/3v4yGHg2bwOFzhXl55dQSonPr2tZkgcocBez6a0NHM1fTWJxIU6PG5teK/doO71X8/9fw9vGIPbGIC8+kUMJyeTFJ3HKiZlcee6Jdt5/69aQmGjXhU9IsKvRNcIgvUj/eV894S2Mn73TBfjrJT2Z+P4qCkoclO2vHussAQMlnopjdvpBxWM7IdHJ7/uX8uiquv/5FGDL9Kzyx01ZM63arWBbHyrK5op1M+2iPowalBHws6ta7q3Tz67z9QPvYufk0wlZtb7PsOmLycmtvgdF1dfPX55T7WfsW8a6iPTf9dqEM9HvALr6PO4C7KzHOUo1iuS4ZEb0GMHctXMpLC3EgQMPtlbTLr4dh0sOe/OrgIkHKQYceGLsinlFjhVACRCHyz2EEsdmWpWO4mDsLO8VYrCD9CDJ/SvmDbio1pgcHjex7lLi3SXElxaTXFxAq6IjJBcdpVXRUVoXHaF10RFSC/JoXXiEjiVHOK1DW3oe/R7mfW8TetVaVKBjTqddW75NG7thTNlNQtm/rVrZG4WEhEbdTCZUalrlzd/gsBJ37a2bDW3/9G3qboqR6L7J1fd2saa1C0YNyqhxY5wyGXVcLa9s2mG3CW/5/Rwbu79fBwBWCGei/xroKSLdgBxgLHBplXMWALd4++9PBQ5p/7wKpXH9xjF37VwAxvYby9y1cynxlPBj0Y8VJ4nBkFf+sH9KFnNzX8FI2R/PYgqcnwBwMPZJn3f37pVu4jgS8z5JnuHEmo5+49g6fWRjFckm9ZISKCqyX0eP2g1mDh2yK/mVfX/ggF0xb+dOu6ucvxuCMiIwcKDdatf3WNkNQ6tW9oYhNbXyzUJKil1KOD7efsXGhmwKYE2r1N3x6oqQXLMmVccRNDQR1dYaUPVGIpiblLJkObx3+xoXQ2rIan+pibF+bzRSE4NrUg92mV4dAFghbIneGFMqIrcAi7AtlM8aY9aIyI3e558A3sZOrduEnV53dbjiVS3D6BNGkzcxj+P+dhy/P+33LN21lIWXLeTsF8/m6+u+Js2VxlXzryIrM4urBl6F6/6OxDhd9Mp7jaKYOIw4yHe+T17M26QXPYQD2++7P/Zx4j29SHafCcCPsTMpdmwk1u0/0TcqEVtTj4uzCbi+PJ6Km4XCQrsV77hxFTcLhw/brWcPHKj4HoJvUSg7JmKXEI6Lsy0LbdpU3CSU3TC0bl3RshAf73c74ZpWqaupCbk2Nd2W+M4eqG3UfUMSUW2tAfOX5/D7Od8GnIoYSOdUF/OX5/D6spxqSd4Va5u/MxrQxTB/eQ65Bf4Xggo21GCXGdZ1+yuEdR69MeZtbDL3PfaEz/cG+G1Tx6VatuS4ZLIys7jmjWsY128ch4sOkxSXREpCCnvy9/DOpnfIyswCIL11An06tea7Ky9k/vIcbv3PcxyW10gvml6e5AFiTHsKHStJcg/HUESxYz2tSy8MUwnryeGwG/W4vH8ov/sOBvsd+xOYMVBaWnGzcPSovSnwbVkoa104fBh277YtDGXq0hUhwiink1FJSXBMW9u6kHMU8jfzQDc3D33yIwcllmJnLEUxsd5dBaV81Lw/ZaP+4wq3ItCgKXTBJKJAtfaaWgPAjpiva5IvS5b+3hugTVI8XdvEwHa449UVzFi0vk7lLbs5CRTWoQA3AFUFu8xwc993oDHpynhK+TGu3zgumnMR2WOy6d2uN4M6DqLvrL4cl3Ycw7oO8/uaUYMyuOl/TyNHC9kTfy8A8Z5etC25hValI9kfN5Nd8b8FDEnunxFnuvl9nwRn813NrlYiNqHGxkJDRjF7PHYTnsJCe9OQl1e5K8K3dSE/3x6D8huCM4GuuXbDniNFJSTHxzA4sw1ApWNd2ySy9WAhuSVg0tIYdkoPTirNZ0lSPFtGt6toYajH2IXaEpG/Wvv4175lyoI1AWvFO3MLAibqqvytXTBqUEbAbo2c3AJyDrrLB8LVdUxBbXHVpaYdzDLDzX3fgcakiV4pP0afMBozuaLqMXvUbL/nbb19K0uWLCl/vGv81vLvfQdCxUoi7Ysn1rpKXIJT+O6h0G9+0+w5HBXJFSA9vc5v0dMYerrdFTcL3taFnlVaF04/cMB+X1oK+bvg8112i96yn3swrQwidrZDUpLtikhJYVRKCm2PLeaZlfv5vthJ63ap3HROXy7o0w6MCThgMFCSB0hxxdbY9O+7JkKgpBeopcEpUm16XF3GFNQUV11q2nWZqVD1hmD+8hyGTV/c4hK/JnqlQqQxNzfx98cNqtdWgjlW02I185fnMOk/qzhSbBOMAJcNPYYHR/VvlHJElLLkm5xsv9q2rf01ZZYsgcsvt98bU3nsQn5+5a6Iw4chN7dy64I3Yf5EhJ/EGojF7gw4/zN4w94kXPXxZp+bQpuiPd71Fg4lJJObkEx+fBJ58YnkxyVyOD4JZ2prOiYmsi3fTXFMLG5HxdgFpwiP/urEWn8nA7U01LY6XqUdH71mfjGTDfs3MGvkrBpvIKZd1J+SuM/oO2sK6/at46vrvmJw54puoSvmTOS1dS9Q6hHallxPgudkoG6tCi15vX1N9EpFOL9NuHO/BamYElaXY75bxObkFnDHqyu4/dUVuGIdFJRUXiTFAP/+4ge+3Lyf9+7MCn1hmyORitaFlJR6tS4AFWMXioqgoIAlnkUc/fGgnU5ZXECrwnxSC/NpXZhPm6OHaHckF3/rKwzv1Z5PNv5IicdTfswptql+3/seXo6PZXCP9hyf2cHe3JQNcvR2Q4xKScH1f234+2c7WFsSR+e0xPK+e3xmm5Qpa3KvtOOjV/bqbGacPQMIfAMx7aL+/PzEjmzY3495v5rHDW/eUOn9//HRB8xd+yrphf+kVPazN+5eOhc9iXhXmQi2VaElT7fTRK9UhPPbhOtnwFiwx6oqO6Nqkve1ce8R7p2/Kjpr9pEiJqaieR/45ZUjglqG1ldGSgLX33k667/czOPvriXvx1w6UUxM/mFcBUdILj5K68J8ftx8GIDj8/L8DncfUVLCCBG44w7IzCw/nrNuWaXzfJvcx/QZw70f3EtRaRHxMfFszd3KzrydvLzqZe5YdAcFpQUM6HMOO7b9nJ25Bexy/YbRvS7nkeUPUhh7C2P7jfVbpkc+eglX6U8QYok1HYkxnSh2bCDec0L5OVW7Baq2gA3v3T7gLIuWMN1OE71SES5S/hC98uV2TfRNqOpgsqpr6VflinUy/tzeEB/PBWecwAVn2ERoV5Kr/jv0aqqLTyec6f/iR47YFobWrSvFM3/3WjJSnX67fdomtmVIxhAWblrIhb0vJHt1Npf0vYSJP5lIG1cb3B43Z71wFo9ffTUD0geQOTOBQV078MqwT2r8HA4W7SbO9C5/7DTtKGU/vsMea1uIyLcVq6qWMN1OE71SES6YVcqaQl2na6mG8zeYrK6749Vrvr63VaGqVFdsjcvUljXflyX6Zy98ljlr5vDUsqco9ZSyK38Xa/etZUD6AAAu6XtJ4BjKQolzUlJY9WjFzJRgFiIKpKVMt9NEr1SE89e3GeuQSn3vdTlWX84QrWCnglefAZ5NuXDMqN6juHPRnXyz6xsKSgtIS0jjkc8eqbTYVGFpRdZOivN/Q+HrZ736sGj1bvD++rvlR2KwUyH9zR6oSwtYsLsvNnfVdy1QSkWUUYMymHZRfzJSXQj2j9uMX57IjDEn1uvY5UOPKV+nvC6pe9ypXWs/SUWc8SN64YqtvHJgqGqywSw2VVcTsi4nKe1LOqU4KZXdGOduHhvzK7ZOH8mnE86slqiDvYHJSHW1iCQPWqNXqlkIVJNryDGo3BSc4oqluNTN0SqD8hwCl54apVPsWoCmXjimPotNAfxn3X+49Z1b2Xd0HyNfHsnAjgNZdPki+nboy7UnX8azK35LUqsY/jXiGc7reUyl11bt0qhNoBudaN3nXhO9Ui1YY871V5GrKX/OdVlsqurrRp8w2u+5k86YxKQzJlU7Pn95DlP/u6bSJjn+NszxFWixoJrm2afW+I6RTxO9UkqpZqdqYq6q6iqUZXP2A93wBJpnf/urK5g40EPu8pxme1OsiV4ppVSzU9vo+rKlfoNthq9pEF+x28PEeatYuu1AwFUlI5kmeqWUUs1ObaPrM2paJ8CP2qaxFpS4eemLH8pbCZrTEro66l4ppVSzU9Po+vrMKvA3O6GqqpNUfbcGjmSa6JVSSjU7gRJzqiu2XvPjy6axpgUxat9XpKxcWRNtuldKKdXshGraYGGAPR8CbTHdHJbQ1USvlFKqWWrsaYOBBvgJwmVDj+H1ZTnVdt9rDkvoatO9UkopReBmeIPhwVH9q61Q2VyW0NUavVJKKUXgkfdxTlsnbq4LTGmNXimllCLwvgDpKQlhiqhxaI1eKaWUIvAAv9RDG8McWcNooldKKaW8/DXPL1nSvBO9Nt0rpZRSUUwTvVJKKRXFNNErpZRSUUwTvVJKKRXFNNErpZRSUSwsiV5E2ojIeyKy0ftvmp9zuorIByKyTkTWiMht4YhVKaWUas7CVaOfALxvjOkJvO99XFUp8HtjzAnAUOC3ItKnCWNUSiml/Jq/PIdh0xfTbcJbDJu+mPnLc8IdUkDhmkd/IZDl/f55YAlwt+8JxphdwC7v93kisg7IANY2WZRKKaVajPnLc4LaDW/+8hwmzltVvsFNTm4BE+etAojIJXLDlejTvYkcY8wuEelQ08kikgkMAr5sgtiUUkq1MDUl79Qq5/rb5a6gxM2MResjMtGLMf522G2ENxb5H9DRz1OTgOeNMak+5x40xlTrp/c+lwx8CDxkjJlXw/WuB64HSE9PPzk7O7sB0ddffn4+ycnJYbl2uLXUsmu5W56WWvZoLvf63XkUu6vvRR/ndJCRLJXKvSrnUMD36Z+REpL4ajN8+PBlxpjB/p4LWaKviYisB7K8tflOwBJjTLVNfUUkFngTWGSM+Uuw7z948GCzdOnSxgu4DpYsWUJWVlZYrh1uLbXsWu6Wp6WWPZrL3W3CW/jLhgI8d25SpXIPm77Y7y53GakuPp1wZshirImIBEz04RqMtwD4tff7XwNvVD1BRAR4BlhXlySvlFJK1VXnVFfQxwPtcjd+RLX6akQIV6KfDpwtIhuBs72PEZHOIvK295xhwBXAmSKywvt1fnjCVUopFc3qkrxHDcpg2kX9yUh1Idia/LSL+kdk/zyEaTCeMWY/cJaf4zuB873ff4JtNVFKKaXqLZjR9IG2qB01KMPv7nX+drmLVLpNrVJKqahVl6lwzSl514UmeqWUUlErnFPh/LUklMVU21z9xqSJXimlVNTa6Wd0fE3HG4u/loTxc78FgRK3KT/WFAvt6KY2SimlolZdRtP78l3idv3uvDovceuvJaHEY8qTfJmy1oVQ0kSvlFIqatVnKlxZbTwntwADFLs9TJy3qk7Jvi4tBqFuXdBEr5RSKmrVZypcTf36waqtxaC+59aH9tErpZSKanUdTd8Y/frjR/Sq1EcPEOuQSn300DQL7WiiV0oppXx0TnX5XeK2LjXvQPPy/R3TUfdKKaVUE/JXG69PzTtQS0JTz9XXRK+UUkr5qFobj3M6InqJ29pooldKKaWq8K2NL1myhKxmmuRBR90rpZRSUU0TvVJKKRXFNNErpZRSUUwTvVJKKRXFNNErpZRSUUwTvVJKKRXFNNErpZRSUUwTvVJKKRXFNNErpZRSUUwTvVJKKRXFNNErpZRSUUwTvVJKKRXFNNErpZRSUUx3r1NKKaUa0fzlOeVb3HZOdTF+RK+wbnGriV4ppZRqJPOX5zBx3ioKStwA5OQWMHHeKoCwJXttuldKKaUayYxF68uTfJmCEjczFq0PU0Sa6JVSSqlGszO3oE7Hm4ImeqWUUqqRdE511el4UwhLoheRNiLynohs9P6bVsO5ThFZLiJvNmWMSimlVF2NH9ELV6yz0jFXrJPxI3qFKaLw1egnAO8bY3oC73sfB3IbsK5JolJKKaUaYNSgDKZd1J+MVBcCZKS6mHZR/xY56v5CIMv7/fPAEuDuqieJSBdgJPAQcGcTxaaUUkrV26hBGWFN7FWJMabpLyqSa4xJ9Xl80BhTrfleRF4DpgGtgD8YYy6o4T2vB64HSE9PPzk7O7vR4w5Gfn4+ycnJYbl2uLXUsmu5W56WWnYtd+QaPnz4MmPMYH/PhaxGLyL/Azr6eWpSkK+/ANhrjFkmIlm1nW+MeQp4CmDw4MEmK6vWl4TEkiVLCNe1w62lll3L3fK01LJruZunkCV6Y8zPAj0nIntEpJMxZpeIdAL2+jltGPALETkfSABai8i/jTGXhyhkpZRSKuqEazDeAuDX3u9/DbxR9QRjzERjTBdjTCYwFlisSV4ppZSqm3Al+unA2SKyETjb+xgR6Swib4cpJqWUUirqhGUwXqiJyD5gW5gu3w74MUzXDreWWnYtd8vTUsuu5Y5cxxpj2vt7IioTfTiJyNJAIx+jXUstu5a75WmpZddyN0+6BK5SSikVxTTRK6WUUlFME33jeyrcAYRRSy27lrvlaall13I3Q9pHr5RSSkUxrdErpZRSUUwTvVJKKRXFNNE3kIi0EZH3RGSj999qm/P4nOsUkeUi8mZTxhgqwZRdRLqKyAcisk5E1ojIbeGItTGIyLkisl5ENolIta2Vxfq79/mVInJSOOJsbEGU+zJveVeKyGcicmI44mxstZXb57xTRMQtImOaMr5QCqbsIpIlIiu8/68/bOoYQyGI3/UUEfmviHzrLffV4Yizzowx+tWAL+DPwATv9xOAh2s4907gZeDNcMfdVGUHOgEneb9vBWwA+oQ79nqU1Ql8DxwHxAHfVi0HcD7wDiDAUODLcMfdROU+DUjzfn9eSym3z3mLgbeBMeGOuwl/5qnAWuAY7+MO4Y67icp9T9nfOaA9cACIC3fstX1pjb7hLgSe937/PDDK30ki0gUYCTzdNGE1iVrLbozZZYz5xvt9HrAOiJyNmoM3BNhkjNlsjCkGsrHl93Uh8IKxvgBSvZs2NWe1ltsY85kx5qD34RdAlyaOMRSC+XkD3Aq8jv+NuZqrYMp+KTDPGPMDgDEmGsofTLkN0EpEBEjGJvrSpg2z7jTRN1y6MWYX2KQGdAhw3kzgLsDTRHE1hWDLDoCIZAKDgC9DH1qjywC2+zzeQfUblmDOaW7qWqbfYFs1mrtayy0iGcBo4IkmjKspBPMzPx5IE5ElIrJMRK5ssuhCJ5hyPwacAOwEVgG3GWMi/m96yLapjSYi8j+go5+nJgX5+guAvcaYZSKS1YihhVxDy+7zPsnYms/txpjDjRFbExM/x6rOTQ3mnOYm6DKJyHBsoj89pBE1jWDKPRO42xjjthW8qBFM2WOAk4GzABfwuYh8YYzZEOrgQiiYco8AVgBnAt2B90Tk40j/m6aJPgjGmJ8Fek5E9ohIJ2PMLm8zrb8mrGHAL0TkfCABaC0i/zbNYNvdRig7IhKLTfIvGWPmhSjUUNsBdPV53AV7V1/Xc5qboMokIgOw3VLnGWP2N1FsoRRMuQcD2d4k3w44X0RKjTHzmyTC0An2d/1HY8wR4IiIfASciB2D01wFU+6rgenGdtJvEpEtQG/gq6YJsX606b7hFgC/9n7/a+CNqicYYyYaY7oYYzKBscDi5pDkg1Br2b19Wc8A64wxf2nC2Brb10BPEekmInHYn+OCKucsAK70jr4fChwq69poxmott4gcA8wDrmjmNTpftZbbGNPNGJPp/X/9GnBzFCR5CO53/Q3gJyISIyKJwKnY8TfNWTDl/gHbioGIpAO9gM1NGmU9aI2+4aYDc0TkN9hfgl8CiEhn4GljzPnhDC7Egin7MOAKYJWIrPC+7h5jzNthiLfejDGlInILsAg7OvdZY8waEbnR+/wT2JHX5wObgKPYu/9mLchy/xFoC8zy1m5LTTPe6QuCLndUCqbsxph1IrIQWIkdd/S0MWZ1+KJuuCB/5g8As0VkFbap/25jTKRvX6tL4CqllFLRTJvulVJKqSimiV4ppZSKYprolVJKqSimiV4ppZSKYprolVJKqSimiV4pFRTvDm0rRGS1iMz1zp9GRDqKSLaIfC8ia0XkbRE53vvcQhHJlSjZsVGp5kgTvVIqWAXGmIHGmH5AMXCjd0Gk/wBLjDHdjTF9sDt8pXtfMwO7joJSKkw00Sul6uNjoAcwHCjxXUDGGLPCGPOx9/v3gbzwhKiUAk30Sqk6EpEY7L7zq4B+wLLwRqSUqokmeqVUsFzeZYyXYpc8fia84SilgqFr3SulglVgjBnoe0BE1gBjwhOOUioYWqNXSjXEYiBeRK4rOyAip4jIT8MYk1LKhyZ6pVS9efflHg2c7Z1etwaYgncfbxH5GJgLnCUiO0RkRNiCVaqF0t3rlFJKqSimNXqllFIqimmiV0oppaKYJnqllFIqimmiV0oppaKYJnqllFIqimmiV0oppaKYJnqllFIqiv0/NT383JDIH0cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Figure 2. Biplot for each Scaler Method')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the biplot for each scaler method\n",
    "for name, result in pca_results.items():\n",
    "    biplot(result['scores'][:,0:2], np.transpose(result['components'][0:2, :]), name=name)\n",
    "plt.suptitle('Figure 2. Biplot for each Scaler Method', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d997df4e-8951-4e39-a2de-f5d9993dae51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Standard Scaler  MinMaxScaler  RobustScaler\n",
      "PC1         0.497784      0.501085      0.799714\n",
      "PC2         0.116422      0.373585      0.098444\n",
      "PC3         0.068863      0.049523      0.077495\n",
      "PC4         0.063417      0.040373      0.009914\n",
      "PC5         0.047193      0.012140      0.007869\n"
     ]
    }
   ],
   "source": [
    "print(pca_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b5e67c-6cdd-47d3-9742-70ccb9667426",
   "metadata": {},
   "source": [
    "**Observation - From biplots in Figure 2:**\n",
    "\n",
    "- StandardScaler: The plot shows a reasonable distribution of data points and component vectors (arrows), indicating a balanced representation of the dataset. The component vectors are well spread out, combining with `explained_variance_ratio_` with no single component overwhelmingly dominating. This might suggest that the data, when standardized, has its variance is captured effectively across multiple dimensions.\n",
    "- MinMaxScaler: The data points in this plot are compressed into a very narrow range on the x-axis. This could be because MinMaxScaler is sensitive to outliers, and when the data has outliers, it can cause a compression effect, which may not be ideal for PCA. \n",
    "- RobustScaler: In the last plot, a distribution that similar to StandardScaler, but with less extreme variation on the axes. The component vectors are also well distributed. RobustScaler is less sensitive to outliers, which suggests it's capturing the intrinsic spread of the data more effectively without allowing outliers to dictate the scale. Additionally, the first principal component `PC1` accounts for approximately 80% of the variance, which is much higher than with the other two methods. The remaining components contribute significantly less, which indicates that after mitigating the influence of outliers, the first component becomes highly dominant in explaining the variance.\n",
    "\n",
    "We can move forward with the $RobustScaler$ since it might be the most significant scaler for this PCA analysis because it appears to handle outliers better than other two scaler, ensuring that the principal components are not skewed by extreme values. The distribution of points can suggest that the inherent structure of the data is maintained. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5caf355a-ef46-438d-9831-cb540dde21a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature_1</th>\n",
       "      <th>Feature_2</th>\n",
       "      <th>Feature_3</th>\n",
       "      <th>Feature_4</th>\n",
       "      <th>Feature_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.694621</td>\n",
       "      <td>0.050102</td>\n",
       "      <td>0.525245</td>\n",
       "      <td>-4.551044</td>\n",
       "      <td>1.058598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.948428</td>\n",
       "      <td>0.293337</td>\n",
       "      <td>0.468583</td>\n",
       "      <td>-4.553871</td>\n",
       "      <td>1.065424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.885045</td>\n",
       "      <td>0.113366</td>\n",
       "      <td>0.636895</td>\n",
       "      <td>-4.541259</td>\n",
       "      <td>1.020767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.465603</td>\n",
       "      <td>0.731387</td>\n",
       "      <td>0.340322</td>\n",
       "      <td>-4.613235</td>\n",
       "      <td>1.154764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.977897</td>\n",
       "      <td>1.172599</td>\n",
       "      <td>0.107106</td>\n",
       "      <td>-4.593020</td>\n",
       "      <td>1.132056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Feature_1  Feature_2  Feature_3  Feature_4  Feature_5\n",
       "0  -4.694621   0.050102   0.525245  -4.551044   1.058598\n",
       "1  -4.948428   0.293337   0.468583  -4.553871   1.065424\n",
       "2  -4.885045   0.113366   0.636895  -4.541259   1.020767\n",
       "3  -5.465603   0.731387   0.340322  -4.613235   1.154764\n",
       "4  -5.977897   1.172599   0.107106  -4.593020   1.132056"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform the Robust Scaler transformation\n",
    "transformer = RobustScaler()\n",
    "scaled_arr = transformer.fit_transform(data_for_pca)\n",
    "scaled_df = pd.DataFrame(scaled_arr, columns=data_for_pca.columns)\n",
    "\n",
    "# Proceed with the PCA transformation\n",
    "num_components = 5\n",
    "pca = PCA(n_components=num_components)\n",
    "pca_result = pca.fit_transform(scaled_df)\n",
    "pca_result_df = pd.DataFrame(pca_result, columns=['Feature_1', 'Feature_2', 'Feature_3', 'Feature_4', 'Feature_5'])\n",
    "pca_result_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "19fafe98-9127-4416-a294-b95f03839f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of Component Loadings\n",
    "plt.figure(figsize=(8,8))\n",
    "sns.heatmap(pca.components_,\n",
    "            cmap='magma',\n",
    "            yticklabels=[\"PC\"+str(x) for x in range(1,len(pca.components_)+1)],\n",
    "            xticklabels=columns_for_pca,\n",
    "            cbar_kws={\"orientation\": 'vertical'})\n",
    "plt.tight_layout()\n",
    "plt.title('Figure 3. Heatmap of Component Loadings', size=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b307ac25-011a-4063-addc-776f6bddb260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature_1</th>\n",
       "      <th>Feature_2</th>\n",
       "      <th>Feature_3</th>\n",
       "      <th>Feature_4</th>\n",
       "      <th>Feature_5</th>\n",
       "      <th>Date</th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.694621</td>\n",
       "      <td>0.050102</td>\n",
       "      <td>0.525245</td>\n",
       "      <td>-4.551044</td>\n",
       "      <td>1.058598</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>A</td>\n",
       "      <td>46.178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.948428</td>\n",
       "      <td>0.293337</td>\n",
       "      <td>0.468583</td>\n",
       "      <td>-4.553871</td>\n",
       "      <td>1.065424</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>A</td>\n",
       "      <td>46.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.885045</td>\n",
       "      <td>0.113366</td>\n",
       "      <td>0.636895</td>\n",
       "      <td>-4.541259</td>\n",
       "      <td>1.020767</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>A</td>\n",
       "      <td>46.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.465603</td>\n",
       "      <td>0.731387</td>\n",
       "      <td>0.340322</td>\n",
       "      <td>-4.613235</td>\n",
       "      <td>1.154764</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>A</td>\n",
       "      <td>47.668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.977897</td>\n",
       "      <td>1.172599</td>\n",
       "      <td>0.107106</td>\n",
       "      <td>-4.593020</td>\n",
       "      <td>1.132056</td>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>A</td>\n",
       "      <td>47.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386561</th>\n",
       "      <td>-6.903199</td>\n",
       "      <td>-1.760332</td>\n",
       "      <td>-1.418393</td>\n",
       "      <td>4.088618</td>\n",
       "      <td>0.799909</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>11.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386562</th>\n",
       "      <td>-6.796380</td>\n",
       "      <td>-1.978180</td>\n",
       "      <td>-1.165790</td>\n",
       "      <td>4.113964</td>\n",
       "      <td>0.734404</td>\n",
       "      <td>2017-11-07</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>10.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386563</th>\n",
       "      <td>-6.773013</td>\n",
       "      <td>-2.083958</td>\n",
       "      <td>-1.029962</td>\n",
       "      <td>4.123684</td>\n",
       "      <td>0.727150</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>10.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386564</th>\n",
       "      <td>-7.115683</td>\n",
       "      <td>-1.690110</td>\n",
       "      <td>-1.202882</td>\n",
       "      <td>4.137377</td>\n",
       "      <td>0.783180</td>\n",
       "      <td>2017-11-09</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>11.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386565</th>\n",
       "      <td>-7.551065</td>\n",
       "      <td>-1.215361</td>\n",
       "      <td>-1.301172</td>\n",
       "      <td>4.140841</td>\n",
       "      <td>0.872513</td>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>12.460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>386566 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Feature_1  Feature_2  Feature_3  Feature_4  Feature_5       Date  \\\n",
       "0       -4.694621   0.050102   0.525245  -4.551044   1.058598 2017-01-03   \n",
       "1       -4.948428   0.293337   0.468583  -4.553871   1.065424 2017-01-04   \n",
       "2       -4.885045   0.113366   0.636895  -4.541259   1.020767 2017-01-05   \n",
       "3       -5.465603   0.731387   0.340322  -4.613235   1.154764 2017-01-06   \n",
       "4       -5.977897   1.172599   0.107106  -4.593020   1.132056 2017-01-09   \n",
       "...           ...        ...        ...        ...        ...        ...   \n",
       "386561  -6.903199  -1.760332  -1.418393   4.088618   0.799909 2017-11-06   \n",
       "386562  -6.796380  -1.978180  -1.165790   4.113964   0.734404 2017-11-07   \n",
       "386563  -6.773013  -2.083958  -1.029962   4.123684   0.727150 2017-11-08   \n",
       "386564  -7.115683  -1.690110  -1.202882   4.137377   0.783180 2017-11-09   \n",
       "386565  -7.551065  -1.215361  -1.301172   4.140841   0.872513 2017-11-10   \n",
       "\n",
       "       Ticker   Close  \n",
       "0           A  46.178  \n",
       "1           A  46.784  \n",
       "2           A  46.228  \n",
       "3           A  47.668  \n",
       "4           A  47.817  \n",
       "...       ...     ...  \n",
       "386561   ZYNE  11.190  \n",
       "386562   ZYNE  10.830  \n",
       "386563   ZYNE  10.900  \n",
       "386564   ZYNE  11.600  \n",
       "386565   ZYNE  12.460  \n",
       "\n",
       "[386566 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine PCA results with 'Date', 'Ticker', 'Close' into a new DataFrame\n",
    "target = filtered_df[['Date', 'Ticker', 'Close']]\n",
    "target.index = pca_result_df.index\n",
    "transformed_df = pd.concat([pca_result_df, target], axis=1)\n",
    "transformed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ddc5159e-bacb-4e42-8376-edbe2d7c6636",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a grid of scatter plots to compare each PC against the others\n",
    "fig, axes = plt.subplots(num_components, num_components, figsize=(12, 12))\n",
    "for i, j in itertools.product(range(num_components), range(num_components)):\n",
    "    ax = axes[i, j]\n",
    "    if i != j:\n",
    "        ax.scatter(scaled_arr[:, i], scaled_arr[:, j])\n",
    "        ax.set_xlabel(f'PC{i+1}')\n",
    "        ax.set_ylabel(f'PC{j+1}')\n",
    "    else:\n",
    "        ax.annotate(f'PC{i+1}', (0.5, 0.5), xycoords='axes fraction', ha='center', va='center')\n",
    "        ax.axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.suptitle(\"Figure 4. Comparison of each Principal Component\", fontsize=14, color='r', y=1.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "405bb857-246b-4116-88a3-11cdf14bdaf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transformed_df.to_csv('//Users//vanle//Downloads//pca_stock_result.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f40d8c65-037a-48b6-8f7e-8ad0fedc9793",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 4.2. RobustScaler - PCA-based Stock Price Prediction Models Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65f51bf-1559-4365-aead-b64f732f1832",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Read the csv file we have processed in the previous step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba17a60e-f019-4095-8c06-0e965263daf7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature_1</th>\n",
       "      <th>Feature_2</th>\n",
       "      <th>Feature_3</th>\n",
       "      <th>Feature_4</th>\n",
       "      <th>Feature_5</th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>-4.694621</td>\n",
       "      <td>0.050102</td>\n",
       "      <td>0.525245</td>\n",
       "      <td>-4.551044</td>\n",
       "      <td>1.058598</td>\n",
       "      <td>A</td>\n",
       "      <td>46.178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>-4.948428</td>\n",
       "      <td>0.293337</td>\n",
       "      <td>0.468583</td>\n",
       "      <td>-4.553871</td>\n",
       "      <td>1.065424</td>\n",
       "      <td>A</td>\n",
       "      <td>46.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>-4.885045</td>\n",
       "      <td>0.113366</td>\n",
       "      <td>0.636895</td>\n",
       "      <td>-4.541259</td>\n",
       "      <td>1.020767</td>\n",
       "      <td>A</td>\n",
       "      <td>46.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>-5.465603</td>\n",
       "      <td>0.731387</td>\n",
       "      <td>0.340322</td>\n",
       "      <td>-4.613235</td>\n",
       "      <td>1.154764</td>\n",
       "      <td>A</td>\n",
       "      <td>47.668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>-5.977897</td>\n",
       "      <td>1.172599</td>\n",
       "      <td>0.107106</td>\n",
       "      <td>-4.593020</td>\n",
       "      <td>1.132056</td>\n",
       "      <td>A</td>\n",
       "      <td>47.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-06</th>\n",
       "      <td>-6.903199</td>\n",
       "      <td>-1.760332</td>\n",
       "      <td>-1.418393</td>\n",
       "      <td>4.088618</td>\n",
       "      <td>0.799909</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>11.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-07</th>\n",
       "      <td>-6.796380</td>\n",
       "      <td>-1.978180</td>\n",
       "      <td>-1.165790</td>\n",
       "      <td>4.113964</td>\n",
       "      <td>0.734404</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>10.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08</th>\n",
       "      <td>-6.773013</td>\n",
       "      <td>-2.083958</td>\n",
       "      <td>-1.029962</td>\n",
       "      <td>4.123684</td>\n",
       "      <td>0.727150</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>10.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-09</th>\n",
       "      <td>-7.115683</td>\n",
       "      <td>-1.690110</td>\n",
       "      <td>-1.202882</td>\n",
       "      <td>4.137377</td>\n",
       "      <td>0.783180</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>11.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-10</th>\n",
       "      <td>-7.551065</td>\n",
       "      <td>-1.215361</td>\n",
       "      <td>-1.301172</td>\n",
       "      <td>4.140841</td>\n",
       "      <td>0.872513</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>12.460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>386566 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Feature_1  Feature_2  Feature_3  Feature_4  Feature_5 Ticker  \\\n",
       "Date                                                                       \n",
       "2017-01-03  -4.694621   0.050102   0.525245  -4.551044   1.058598      A   \n",
       "2017-01-04  -4.948428   0.293337   0.468583  -4.553871   1.065424      A   \n",
       "2017-01-05  -4.885045   0.113366   0.636895  -4.541259   1.020767      A   \n",
       "2017-01-06  -5.465603   0.731387   0.340322  -4.613235   1.154764      A   \n",
       "2017-01-09  -5.977897   1.172599   0.107106  -4.593020   1.132056      A   \n",
       "...               ...        ...        ...        ...        ...    ...   \n",
       "2017-11-06  -6.903199  -1.760332  -1.418393   4.088618   0.799909   ZYNE   \n",
       "2017-11-07  -6.796380  -1.978180  -1.165790   4.113964   0.734404   ZYNE   \n",
       "2017-11-08  -6.773013  -2.083958  -1.029962   4.123684   0.727150   ZYNE   \n",
       "2017-11-09  -7.115683  -1.690110  -1.202882   4.137377   0.783180   ZYNE   \n",
       "2017-11-10  -7.551065  -1.215361  -1.301172   4.140841   0.872513   ZYNE   \n",
       "\n",
       "             Close  \n",
       "Date                \n",
       "2017-01-03  46.178  \n",
       "2017-01-04  46.784  \n",
       "2017-01-05  46.228  \n",
       "2017-01-06  47.668  \n",
       "2017-01-09  47.817  \n",
       "...            ...  \n",
       "2017-11-06  11.190  \n",
       "2017-11-07  10.830  \n",
       "2017-11-08  10.900  \n",
       "2017-11-09  11.600  \n",
       "2017-11-10  12.460  \n",
       "\n",
       "[386566 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_df = pd.read_csv('pca_stock_result.csv')\n",
    "# Convert the 'Date' column to datetime, and set it as index\n",
    "stock_df['Date'] = pd.to_datetime(stock_df['Date'])\n",
    "stock_df.set_index('Date', inplace=True)\n",
    "stock_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14f12cb7-6941-4ba3-82f1-959720e4b2d0",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Splitting data into training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fdf347f5-6c6f-44e0-ba08-e6949e2abdb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (269495, 6)\n",
      "Shape of X_test: (58470, 6)\n",
      "Shape of X_valid: (58601, 6)\n",
      "Shape of y_train: (269495, 1)\n",
      "Shape of y_test: (58470, 1)\n",
      "Shape of y_valid: (58601, 1)\n"
     ]
    }
   ],
   "source": [
    "# Sort the stock_df by 'Ticker' and by 'Date'. \n",
    "stock_df.sort_values(['Ticker', 'Date'], inplace=True)\n",
    "\n",
    "# 'row_num' to enumerates rows within each group of the same 'Ticker'\n",
    "stock_df['row_num'] = stock_df.groupby('Ticker').cumcount() + 1\n",
    "\n",
    "# Calculating total count per partition\n",
    "partition_counts = stock_df.groupby('Ticker').size().reset_index(name='partition_count')\n",
    "\n",
    "# Merging the counts with the DataFrame\n",
    "stock_df_with_counts = pd.merge(stock_df, partition_counts, on='Ticker', how='left')\n",
    "\n",
    "# Calculate the thresholds for train, test, and valid partitions\n",
    "stock_df_with_counts['partition_threshold'] = stock_df_with_counts['partition_count'] * 0.7\n",
    "stock_df_with_counts['test_threshold'] = stock_df_with_counts['partition_count'] * 0.85\n",
    "\n",
    "# Filter the DataFrame per partition based on the thresholds\n",
    "train_data = stock_df_with_counts[stock_df_with_counts['row_num'] <= stock_df_with_counts['partition_threshold']]\n",
    "test_data = stock_df_with_counts[(stock_df_with_counts['row_num'] > stock_df_with_counts['partition_threshold']) &\n",
    "                                (stock_df_with_counts['row_num'] <= stock_df_with_counts['test_threshold'])]\n",
    "valid_data = stock_df_with_counts[stock_df_with_counts['row_num'] > stock_df_with_counts['test_threshold']]\n",
    "\n",
    "# Lower all column names \n",
    "train_data.columns = map(str.lower, train_data.columns)\n",
    "test_data.columns = map(str.lower, test_data.columns)\n",
    "valid_data.columns = map(str.lower, valid_data.columns)\n",
    "\n",
    "# Drop all unnessary columns \n",
    "train_data = train_data.drop(columns=['row_num', 'partition_count', 'partition_threshold', 'test_threshold'], axis=1)\n",
    "test_data = test_data.drop(columns=['row_num', 'partition_count', 'partition_threshold', 'test_threshold'], axis=1)\n",
    "valid_data = valid_data.drop(columns=['row_num', 'partition_count', 'partition_threshold', 'test_threshold'], axis=1)\n",
    "\n",
    "# Split into X and y \n",
    "X_train = train_data.drop('close', axis=1)\n",
    "X_test = test_data.drop('close', axis=1)\n",
    "X_valid = valid_data.drop('close', axis=1)\n",
    "y_train = train_data[['close']]\n",
    "y_test = test_data[['close']]\n",
    "y_valid = valid_data[['close']]\n",
    "print(\"Shape of X_train:\", X_train.shape)\n",
    "print(\"Shape of X_test:\", X_test.shape)\n",
    "print(\"Shape of X_valid:\", X_valid.shape)\n",
    "print(\"Shape of y_train:\", y_train.shape)\n",
    "print(\"Shape of y_test:\", y_test.shape)\n",
    "print(\"Shape of y_valid:\", y_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "267d4c18-2517-40ce-b233-242e71ad1513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import LabelEncoder'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train columns check: Index(['feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5'], dtype='object')\n",
      "X_test columns check: Index(['feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5'], dtype='object')\n",
      "X_valid columns check: Index(['feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5'], dtype='object')\n",
      "Sum of null value if exist in X_train: feature_1    0\n",
      "feature_2    0\n",
      "feature_3    0\n",
      "feature_4    0\n",
      "feature_5    0\n",
      "ticker       0\n",
      "dtype: int64\n",
      "Sum of null value if exist in X_test: feature_1    0\n",
      "feature_2    0\n",
      "feature_3    0\n",
      "feature_4    0\n",
      "feature_5    0\n",
      "ticker       0\n",
      "dtype: int64\n",
      "Sum of null value if exist in X_valid: feature_1    0\n",
      "feature_2    0\n",
      "feature_3    0\n",
      "feature_4    0\n",
      "feature_5    0\n",
      "ticker       0\n",
      "dtype: int64\n",
      "Shape of training features: (269495, 6)\n",
      "Shape of test features: (58470, 6)\n",
      "Shape of valid features: (58601, 6)\n",
      "Shape of training target: (269495, 1)\n",
      "Shape of test target: (58470, 1)\n",
      "Shape of valid target: (58601, 1)\n",
      "Train set: \n",
      "        feature_1  feature_2  feature_3  feature_4  feature_5  ticker\n",
      "269490   0.027960  -5.010235   0.437295   4.014645   0.559701    1871\n",
      "269491  -0.540750  -3.842529  -0.570200   4.067924   0.766602    1871\n",
      "269492   4.006511  -8.657852   2.992407   3.518763   1.263978    1871\n",
      "269493   5.356845  -9.007909   2.497050   4.147064   1.118489    1871\n",
      "269494   5.577453  -7.938635   1.213644   4.069383   1.544077    1871\n",
      "Test set:\n",
      "       feature_1  feature_2  feature_3  feature_4  feature_5  ticker\n",
      "58465  -1.996578  -2.767279  -3.950235   4.200832   0.965032    1866\n",
      "58466  -2.161328  -2.846956  -3.748161   4.197274   0.936594    1866\n",
      "58467  -2.304233  -2.927730  -3.572446   4.164762   0.933053    1866\n",
      "58468  -2.352760  -3.081475  -3.366854   4.185546   0.936339    1866\n",
      "58469  -2.399862  -3.202720  -3.206704   4.183147   0.920082    1866\n",
      "Target:\n",
      "        close\n",
      "386528   6.38\n",
      "386529   6.34\n",
      "386530   6.23\n",
      "386531   5.93\n",
      "386532   5.84\n"
     ]
    }
   ],
   "source": [
    "# Perform label encoder\n",
    "label_encoder = LabelEncoder()\n",
    "train_ticker_encoder = label_encoder.fit_transform(X_train['ticker'])\n",
    "train_ticker_encoder = pd.DataFrame(train_ticker_encoder, columns=['ticker'])\n",
    "test_ticker_encoder = label_encoder.fit_transform(X_test['ticker'])\n",
    "test_ticker_encoder = pd.DataFrame(test_ticker_encoder, columns=['ticker'])\n",
    "valid_ticker_encoder = label_encoder.fit_transform(X_valid['ticker'])\n",
    "valid_ticker_encoder = pd.DataFrame(valid_ticker_encoder, columns=['ticker'])\n",
    "\n",
    "# Drop the original 'ticker' column\n",
    "X_train.drop(['ticker'],axis=1,inplace=True)\n",
    "X_test.drop(['ticker'],axis=1,inplace=True)\n",
    "X_valid.drop(['ticker'],axis=1,inplace=True)\n",
    "print(\"X_train columns check:\", X_train.columns)\n",
    "print(\"X_test columns check:\", X_test.columns)\n",
    "print(\"X_valid columns check:\", X_valid.columns)\n",
    "\n",
    "# Reset the index of the training and testing data\n",
    "X_train = X_train.reset_index(drop=True)\n",
    "X_test = X_test.reset_index(drop=True)\n",
    "X_valid = X_valid.reset_index(drop=True)\n",
    "train_ticker_encoder = train_ticker_encoder.reset_index(drop=True)\n",
    "test_ticker_encoder = test_ticker_encoder.reset_index(drop=True)\n",
    "valid_ticker_encoder = valid_ticker_encoder.reset_index(drop=True)\n",
    "\n",
    "# Concatenate the encoded 'ticker' with the original DataFrames\n",
    "X_train = pd.concat([X_train, train_ticker_encoder], axis=1)\n",
    "X_test = pd.concat([X_test, test_ticker_encoder], axis=1)\n",
    "X_valid = pd.concat([X_valid, valid_ticker_encoder], axis=1)\n",
    "\n",
    "print(f\"Sum of null value if exist in X_train:\", X_train.isnull().sum())\n",
    "print(f\"Sum of null value if exist in X_test:\", X_test.isnull().sum())\n",
    "print(f\"Sum of null value if exist in X_valid:\", X_valid.isnull().sum())\n",
    "print(f\"Shape of training features:\", X_train.shape)\n",
    "print(f\"Shape of test features:\", X_test.shape)\n",
    "print(f\"Shape of valid features:\", X_valid.shape)\n",
    "print(f\"Shape of training target:\", y_train.shape)\n",
    "print(f\"Shape of test target:\", y_test.shape)\n",
    "print(f\"Shape of valid target:\", y_valid.shape)\n",
    "print(f\"Train set: \")\n",
    "print(X_train.tail())\n",
    "print(f\"Test set:\" )\n",
    "print(X_test.tail())\n",
    "print(f\"Target:\" )\n",
    "print(y_test.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6438c181-c39b-4419-abbf-61a0d1f1e494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(269495,)\n",
      "<class 'numpy.ndarray'>\n",
      "(58470,)\n",
      "<class 'numpy.ndarray'>\n",
      "(58601,)\n"
     ]
    }
   ],
   "source": [
    "# Convert to a one-dimensional NumPy array\n",
    "y_train = y_train.values.ravel()\n",
    "y_test = y_test.values.ravel()\n",
    "y_valid = y_valid.values.ravel()\n",
    "print(type(y_train))\n",
    "print(y_train.shape)\n",
    "print(type(y_test))\n",
    "print(y_test.shape)\n",
    "print(type(y_valid))\n",
    "print(y_valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e4d42a6-53fb-455c-9add-234a366b77e0",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### 4.2.1. ARIMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bde65d4-b00e-444e-8236-5823fa736250",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### 4.2.1.1. Test for stationarity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2387ef7d-22be-4a92-9361-45c39640247e",
   "metadata": {},
   "source": [
    "The data is stationary if they do not have trend or any seasonal effects. And if the data is non-stationary, then we have to convert it to stationary data before fitting into the ARIMA model. To check whether the data is stationary, we will use Augmented Dicky-Fuller (ADF) test.\n",
    "\n",
    "The ADF test, also known as the “unit root test”, is a statistical test to inform the degree to which a null hypothesis can be rejected or fail to reject. The p-value below a threshold (1%, 5%, 10%) suggests we can reject the null hypothesis [2]. \n",
    "\n",
    "- Null Hypothesis \\(H<sub>0</sub>\\): If failed to be rejected, it suggests the time series has a unit root, meaning it is non-stationary.\n",
    "- Alternative Hypothesis \\(H<sub>1</sub>): The null hypothesis is rejected and suggests the time series does not have a unit root, meaning it is stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f6fd631-ffe0-4a35-8a52-cddbaf23529a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Statistics                   -42.395700\n",
      "p-value                             0.000000\n",
      "No. of lags used                   87.000000\n",
      "Number of observations used    269407.000000\n",
      "Critical value (1%)                -3.430374\n",
      "Critical value (5%)                -2.861551\n",
      "Critical value (10%)               -2.566776\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Checking the stationarity\n",
    "def adf_test(timeseries):\n",
    "    # # Determing rolling statistics\n",
    "    # moving_average = timeseries.rolling(12).mean()\n",
    "    # moving_std = timeseries.rolling(12).std()\n",
    "    # Perform Dickey-Fuller test:\n",
    "    adft = adfuller(timeseries, autolag='AIC')\n",
    "    # Extract and display test results in a Series\n",
    "    output = pd.Series(adft[0:4], \n",
    "                       index=['Test Statistics', 'p-value', 'No. of lags used', 'Number of observations used'])\n",
    "    \n",
    "    for key, value in adft[4].items():\n",
    "        output[\"Critical value (%s)\" % key] = value\n",
    "    print(output)\n",
    "\n",
    "#  Check if y_train is stationary \n",
    "adf_test(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a923032b-f6db-4e0d-aa48-b3e4df70503b",
   "metadata": {},
   "source": [
    "After performing Augmented Dickey-Fuller (ADF) test, we got the test statistics of $-42.395700$, which is much lower than the critical values at 1%, 5%, and 10% levels. The p-value is essentially 0, which is below the common alpha level of 0.05. This strongly suggests that we can reject the null hypothesis and conclude that our time series data is stationary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "306846f8-36e3-4c30-af17-55bda4176ff3",
   "metadata": {},
   "source": [
    "We will apply `auto_arima` function from the `pmdarima` library since it is a useful tool for automatically determining the optimal parameters for an ARIMA model based on the provided time series data. Then, we will use the returned parameters to fit our ARIMA model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab0ae51-44bd-4132-b1ed-77c805e89e7c",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "##### 4.1.2. Fit the Auto-ARIMA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "656bc803-8581-4def-a276-97bf4a14a40b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Fit the auto_arima model\n",
    "def fit_auto_arima(train_data, initial_p, initial_q):\n",
    "    # Use auto_arima to automatically select the best ARIMA model\n",
    "    stepwise_fit = auto_arima(train_data, \n",
    "                              start_p=1, start_q=1,\n",
    "                              max_p=initial_p, max_q=initial_q, \n",
    "                              m=1, # we want to imply non-seasonal, so we use m=1 \n",
    "                              start_P=0, \n",
    "                              seasonal=False,\n",
    "                              d=None, \n",
    "                              D=0, \n",
    "                              trace=True,\n",
    "                              error_action='ignore',\n",
    "                              suppress_warnings=True,\n",
    "                              stepwise=True)\n",
    "    return stepwise_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "05c506bb-4aa9-40f8-941c-839e97ce5cff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3273084.231, Time=40.41 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3273833.696, Time=3.95 sec\n",
      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3273736.597, Time=4.89 sec\n",
      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3273714.470, Time=10.72 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=3273831.696, Time=2.17 sec\n",
      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=3271305.781, Time=42.62 sec\n",
      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=3271453.939, Time=6.98 sec\n",
      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=3271248.843, Time=79.34 sec\n",
      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=3271385.532, Time=7.93 sec\n",
      " ARIMA(4,1,1)(0,0,0)[0] intercept   : AIC=3271170.584, Time=88.41 sec\n",
      " ARIMA(4,1,0)(0,0,0)[0] intercept   : AIC=3271245.137, Time=10.13 sec\n",
      " ARIMA(5,1,1)(0,0,0)[0] intercept   : AIC=3270237.960, Time=147.23 sec\n",
      " ARIMA(5,1,0)(0,0,0)[0] intercept   : AIC=3271061.480, Time=12.54 sec\n",
      " ARIMA(5,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=344.98 sec\n",
      " ARIMA(4,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=263.32 sec\n",
      " ARIMA(5,1,1)(0,0,0)[0]             : AIC=3270235.960, Time=68.88 sec\n",
      " ARIMA(4,1,1)(0,0,0)[0]             : AIC=3271168.584, Time=38.91 sec\n",
      " ARIMA(5,1,0)(0,0,0)[0]             : AIC=3271059.480, Time=6.50 sec\n",
      " ARIMA(5,1,2)(0,0,0)[0]             : AIC=inf, Time=160.27 sec\n",
      " ARIMA(4,1,0)(0,0,0)[0]             : AIC=3271243.137, Time=5.37 sec\n",
      " ARIMA(4,1,2)(0,0,0)[0]             : AIC=inf, Time=121.91 sec\n",
      "\n",
      "Best model:  ARIMA(5,1,1)(0,0,0)[0]          \n",
      "Total fit time: 1469.796 seconds\n",
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:               269495\n",
      "Model:               SARIMAX(5, 1, 1)   Log Likelihood            -1635110.980\n",
      "Date:                Sat, 11 Nov 2023   AIC                        3270235.960\n",
      "Time:                        01:37:10   BIC                        3270309.491\n",
      "Sample:                             0   HQIC                       3270257.325\n",
      "                             - 269495                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -0.7433      0.001   -984.824      0.000      -0.745      -0.742\n",
      "ar.L2         -0.0797   6.97e-05  -1143.712      0.000      -0.080      -0.080\n",
      "ar.L3         -0.0907      0.000   -699.278      0.000      -0.091      -0.090\n",
      "ar.L4         -0.0342      0.000   -246.334      0.000      -0.034      -0.034\n",
      "ar.L5         -0.0571      0.000   -445.590      0.000      -0.057      -0.057\n",
      "ma.L1          0.7643      0.001   1028.295      0.000       0.763       0.766\n",
      "sigma2      1.094e+04      0.132   8.32e+04      0.000    1.09e+04    1.09e+04\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.27   Jarque-Bera (JB):   125213344259452.41\n",
      "Prob(Q):                              0.61   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):             638.52   Skew:                           275.95\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                    105599.72\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Fit an auto ARIMA model to the y_train dataset \n",
    "auto_arima_model = fit_auto_arima(y_train,5,5)\n",
    "print(auto_arima_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "51b4c48b-c492-4257-ae6f-b80f01f17103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display diagnostic plots for the auto_arima_model to assess its fit\n",
    "auto_arima_model.plot_diagnostics(figsize=(14,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def92417-2cba-44af-977c-caa4905c0ae4",
   "metadata": {},
   "source": [
    "ARIMA is a class of models used for time series forecasting and analysis. It combines autoregressive (AR), differencing (I), and moving average (MA) components to model and predict time series data.\n",
    "\n",
    "**5**: This is the number of autoregressive (AR) terms in the model. The autoregressive component captures the relationship between the current value in the time series and its past values. In this case, there are 5 lagged (previous) values of the time series that will be used as predictors in the model.\n",
    "\n",
    "**1**: This is the differencing order (I) in the model. The differencing component is used to make the time series stationary by taking differences between consecutive observations. A value of 1 indicates that first-order differencing is applied, which means that each value in the time series is replaced by the difference between it and the previous value.\n",
    "\n",
    "**1**: This is the number of moving average (MA) terms in the model. The moving average component models the relationship between the current value and past white noise (random) error terms. In this case, there is 1 lagged white noise error term included in the model.\n",
    "\n",
    "In summary, ARIMA(5,1,1) is 5 autoregressive terms, 1 order of differencing, and 1 moving average term. And all coefficients being statistically significant. \n",
    "\n",
    "<!-- There are signs of non-normality and heteroskedasticity in the residuals according to the Jarque-Bera and heteroskedasticity tests -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d211b3e7-29d0-4bc0-8725-e7d6d046df80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:               269495\n",
      "Model:                 ARIMA(5, 1, 1)   Log Likelihood            -1635110.980\n",
      "Date:                Sat, 11 Nov 2023   AIC                        3270235.960\n",
      "Time:                        01:41:06   BIC                        3270309.491\n",
      "Sample:                             0   HQIC                       3270257.325\n",
      "                             - 269495                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -0.7433      0.001   -984.824      0.000      -0.745      -0.742\n",
      "ar.L2         -0.0797   6.97e-05  -1143.712      0.000      -0.080      -0.080\n",
      "ar.L3         -0.0907      0.000   -699.278      0.000      -0.091      -0.090\n",
      "ar.L4         -0.0342      0.000   -246.334      0.000      -0.034      -0.034\n",
      "ar.L5         -0.0571      0.000   -445.590      0.000      -0.057      -0.057\n",
      "ma.L1          0.7643      0.001   1028.295      0.000       0.763       0.766\n",
      "sigma2      1.094e+04      0.132   8.32e+04      0.000    1.09e+04    1.09e+04\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.27   Jarque-Bera (JB):   125213344259452.41\n",
      "Prob(Q):                              0.61   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):             638.52   Skew:                           275.95\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                    105599.72\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fit_arima(train_data, p, d, q):\n",
    "    arima_model = ARIMA(train_data, order=(p, d, q))\n",
    "    arima_result = arima_model.fit()\n",
    "    return arima_result\n",
    "\n",
    "# Buid and fit ARIMA model with order (p,d,q)=(5,1,1)\n",
    "arima_result_1 = fit_arima(y_train, 5, 1, 1)\n",
    "print(arima_result_1.summary())\n",
    "\n",
    "# Forecasting stock prices on the test \n",
    "arima_forecast_1 = arima_result_1.get_forecast(steps=len(y_test))\n",
    "arima_forecast_values_1 = arima_forecast_1.predicted_mean  \n",
    "conf_int_1 = arima_forecast_1.conf_int()\n",
    "\n",
    "# Plot forecast results against actual data \n",
    "plt.figure(figsize=(7, 5))\n",
    "plt.plot(y_test, label='Actual Test Data')  # Plot y_test directly\n",
    "plt.plot(y_test, arima_forecast_values_1, label='ARIMA(5,1,1) Forecast')\n",
    "plt.fill_between(\n",
    "    range(len(y_test)),\n",
    "    conf_int_1[:, 0],  \n",
    "    conf_int_1[:, 1],  \n",
    "    color='pink',\n",
    "    alpha=0.3,\n",
    "    label='95% Confidence Interval'\n",
    ")\n",
    "plt.title(\"Figure 5. ARIMA(5,1,1) Forecast vs Actual Test Data\", fontsize=14, color='r')\n",
    "plt.xlabel(\"Time Step\")\n",
    "plt.ylabel(\"Values\")\n",
    "plt.legend(loc='upper left', fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abeb681a-2816-4c65-a0f7-4dab44caf0ee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Since it's too large dataset, kernel was interruptted during middle of training \n",
    "auto_arima_model = fit_auto_arima(y_train,6,6)\n",
    "print(auto_arima_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bef366f8-efbe-4da5-bf04-a2e977d3da78",
   "metadata": {},
   "source": [
    "Even kernel was interrupted, we still can see ARIMA(4,1,3) has smaller AIC than any other param values. We could fit in on our training to see if this model make any significant improvement on forecasting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d55e068-4fc5-41fe-b2a7-bf5144221f21",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:               269495\n",
      "Model:                 ARIMA(4, 1, 3)   Log Likelihood            -1633718.012\n",
      "Date:                Sat, 11 Nov 2023   AIC                        3267452.024\n",
      "Time:                        01:44:12   BIC                        3267536.058\n",
      "Sample:                             0   HQIC                       3267476.441\n",
      "                             - 269495                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.1077      0.000    442.201      0.000       0.107       0.108\n",
      "ar.L2         -0.4341      0.000  -2461.911      0.000      -0.434      -0.434\n",
      "ar.L3          0.7393      0.000   2851.639      0.000       0.739       0.740\n",
      "ar.L4         -0.1068   8.21e-05  -1301.760      0.000      -0.107      -0.107\n",
      "ma.L1         -0.0748      0.000   -310.669      0.000      -0.075      -0.074\n",
      "ma.L2          0.4100      0.000   2501.496      0.000       0.410       0.410\n",
      "ma.L3         -0.7723      0.000  -3076.985      0.000      -0.773      -0.772\n",
      "sigma2      1.095e+04      0.134   8.15e+04      0.000    1.09e+04    1.09e+04\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   2.07   Jarque-Bera (JB):   127040617312516.30\n",
      "Prob(Q):                              0.15   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):             616.95   Skew:                           272.69\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                    106367.49\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit the ARIMA model with order (p,d,q)=(4,1,3)\n",
    "arima_result_2 = fit_arima(y_train, 4, 1, 3)\n",
    "print(arima_result_2.summary())\n",
    "\n",
    "# Make forecasts\n",
    "arima_forecast_2 = arima_result_2.get_forecast(steps=len(y_test))\n",
    "arima_forecast_values_2 = arima_forecast_2.predicted_mean  \n",
    "conf_int_2 = arima_forecast_2.conf_int()\n",
    "\n",
    "# Plot predict results against the actual results\n",
    "plt.figure(figsize=(7, 5))\n",
    "plt.plot(y_test, label='Actual Test Data')  # Plot y_test directly\n",
    "plt.plot(y_test, arima_forecast_values_2, label='ARIMA(4,3,1) Forecast')\n",
    "plt.fill_between(\n",
    "    range(len(y_test)),\n",
    "    conf_int_2[:, 0],  \n",
    "    conf_int_2[:, 1],  \n",
    "    color='pink',\n",
    "    alpha=0.3,\n",
    "    label='95% Confidence Interval'\n",
    ")\n",
    "plt.title(\"Figure 6. ARIMA(4,3,1) Forecast vs Actual Test Data\", fontsize=14, color='r')\n",
    "plt.xlabel(\"Time Step\")\n",
    "plt.ylabel(\"Values\")\n",
    "plt.legend(loc='upper left', fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e4d3e16-10fc-4f51-ab8a-4b268ad423f8",
   "metadata": {
    "tags": []
   },
   "source": [
    "The best ARIMA model according to the Akaike Information Criterion (AIC), is an ARIMA(4,1,3) without a seasonal component. This model includes 4 autoregressive terms, a differencing order of 1, and 3 moving average term.\n",
    "\n",
    "- `IC`: The AIC value is 3270235.960, as the lower the AIC, the better the model fits time series data while penalizing for complexity.\n",
    "- `Coefficients`: All the coefficients for the autoregressive (AR) and moving average (MA) terms are significant (as $p-value < 0.05$), as indicated by the z-test.\n",
    "- `Log Likelihood`: The log likelihood of the model is quite large in the negative, indicating the likelihood of the observed data given the model.\n",
    "- `Ljung-Box Test`: The Ljung-Box test on residuals is a way to check for lack of fit. In this case, with a $p-value = 0.61$, there is no evidence of lack of fit.\n",
    "- `Jarque-Bera (JB) Test`: The JB test is extremely large, indicating that the residuals are not normally distributed. However, for large sample sizes, this test may always indicate non-normality.\n",
    "- `Heteroskedasticity (H)`: The test statistic is very high, indicating that there is heteroskedasticity in the residuals."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ffd91fc-77a0-41bd-ba4f-070b8e8ff4ab",
   "metadata": {},
   "source": [
    "**Comparision between ARIMA(5,1,1) vs. ARIMA(4,1,3)**\n",
    "\n",
    "- Based on these result tables, both models are relatively close in terms of fit, with ARIMA(4,1,3) having a slightly better log likelihood, AIC, and BIC compares to ARIMA(5,1,1). However, the differences are not significant, and the choice between these models may depend on other factors, such as the interpretability of the model coefficients, \n",
    "\n",
    "- In ARIMA(5,1,1), the AR coefficients (ar.L1 to ar.L5) are all negative, indicating a decreasing trend in autocorrelation with lag. While ARIMA(4,1,3), the AR coefficients (ar.L1 to ar.L4) are a mix of positive and negative values, suggesting a more complex relationship.\n",
    "\n",
    "- The standard errors in both models are relatively small, indicating that the parameter estimates are precise. \n",
    "- The z-scores in ARIMA(4,3,1) are very large in absolute terms, and even larger than ARIMA(5,1,1) indicating that the coefficients are highly significant.\n",
    "\n",
    "In overall, ARIMA(4,3,1) may have little better fit to the data. However, ARIMA(5,1,1) is simpler model so it might be easy for high variety dataset. We'll evaluate the metrics to get more insights from the result for both forecast models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0be3905f-1864-46c0-85fc-08bc4808729a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function to calculate the mean bias of forecasts \n",
    "def forecast_bias(y_true, y_pred):\n",
    "    return np.mean(y_pred - y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d44a4a4c-0cea-456a-98c7-c068082883c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function to calculate MAPE\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ccba0273-40f1-4fde-aaac-d012fffda96d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAPE</th>\n",
       "      <th>Forecast Bias</th>\n",
       "      <th>Forecast Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARIMA(4,3,1)</td>\n",
       "      <td>32.016878</td>\n",
       "      <td>141.265253</td>\n",
       "      <td>107.077477</td>\n",
       "      <td>-30.018818</td>\n",
       "      <td>-0.070775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARIMA(5,1,1)</td>\n",
       "      <td>32.726733</td>\n",
       "      <td>141.528505</td>\n",
       "      <td>100.575938</td>\n",
       "      <td>-31.234242</td>\n",
       "      <td>-0.005759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Model        MAE        RMSE        MAPE  Forecast Bias  \\\n",
       "0  ARIMA(4,3,1)  32.016878  141.265253  107.077477     -30.018818   \n",
       "1  ARIMA(5,1,1)  32.726733  141.528505  100.575938     -31.234242   \n",
       "\n",
       "   Forecast Accuracy  \n",
       "0          -0.070775  \n",
       "1          -0.005759  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate model performance: MAE, RMSE, MAPE\n",
    "mae1 = mean_absolute_error(y_test, arima_forecast_values_1)\n",
    "mae2 = mean_absolute_error(y_test, arima_forecast_values_2)\n",
    "\n",
    "rmse1 = np.sqrt(mean_squared_error(y_test, arima_forecast_values_1))\n",
    "rmse2 = np.sqrt(mean_squared_error(y_test, arima_forecast_values_2))\n",
    "\n",
    "mape1 = mean_absolute_percentage_error(y_test, arima_forecast_values_1)\n",
    "mape2 = mean_absolute_percentage_error(y_test, arima_forecast_values_2)\n",
    "\n",
    "bias1 = forecast_bias(y_test, arima_forecast_values_1)\n",
    "bias2 = forecast_bias(y_test, arima_forecast_values_2)\n",
    "\n",
    "# Calculate forecast accuracy\n",
    "accuracy1 = 1 - (mape1 / 100)\n",
    "accuracy2 = 1 - (mape2 / 100)\n",
    "\n",
    "# Create a comparision dataframe \n",
    "models = ['ARIMA(4,3,1)', 'ARIMA(5,1,1)']\n",
    "mae = [mae2, mae1]\n",
    "rmse = [rmse2, rmse1]\n",
    "mape = [mape2, mape1]\n",
    "bias = [bias2, bias1]\n",
    "accuracy = [accuracy2, accuracy1]\n",
    "\n",
    "# Store metrics as dictionary\n",
    "metrics_dict = {\n",
    "    \"Model\": models,\n",
    "    \"MAE\": mae,\n",
    "    \"RMSE\": rmse,\n",
    "    \"MAPE\": mape,\n",
    "    \"Forecast Bias\": bias,\n",
    "    \"Forecast Accuracy\": accuracy\n",
    "}\n",
    "\n",
    "metrics_df = pd.DataFrame(metrics_dict)\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "548b79ba-a765-425f-b6dc-982f5f891ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the last 50 points of y_test and the corresponding forecasted values and confidence interval\n",
    "y_test_last_50 = y_test[-100:]\n",
    "forecast_values_last_50 = arima_forecast_values_1[-100:]\n",
    "conf_int_last_50 = conf_int_1[-100:]\n",
    "\n",
    "# Create a range of values for the x-axis\n",
    "x_axis_last_50 = range(len(y_test_last_50))\n",
    "\n",
    "# Plot forecast results against actual data for the last 50 points\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(x_axis_last_50, y_test_last_50, label='Actual Test Data', color='blue', linewidth=2)\n",
    "plt.plot(x_axis_last_50, forecast_values_last_50, label='ARIMA(5,1,1) Forecast', color='red', linewidth=2)\n",
    "plt.fill_between(x_axis_last_50, \n",
    "                 conf_int_last_50[:, 0], \n",
    "                 conf_int_last_50[:, 1], \n",
    "                 color='pink', \n",
    "                 alpha=0.3, \n",
    "                 label='95% Confidence Interval')\n",
    "\n",
    "# Set y-axis limits to ignore outliers/extreme values\n",
    "plt.ylim([0,70]) \n",
    "plt.title(\"Figure 7. ARIMA(5,1,1) Forecast vs Actual Test Data (Last 50 Points)\", fontsize=14)\n",
    "plt.xlabel(\"Time Step\")\n",
    "plt.ylabel(\"Values\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee062747-93bb-4ab4-8fd4-cc2f042f96e2",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### 4.2.2. Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "140fcc03-c083-435b-8b20-297172d8cd9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 245.2810466993566\n",
      "MAE: 1.7053578921168124\n",
      "RMSE: 15.661450976820653\n",
      "MAPE: 5.677914767791941\n",
      "R-squared (Random Forest): 0.9871275572984342\n"
     ]
    }
   ],
   "source": [
    "# Model difine and training \n",
    "rf = RandomForestRegressor(max_depth=500, random_state=0)\n",
    "rf.fit(X_train,y_train)\n",
    "rf_pred = rf.predict(X_test)\n",
    "\n",
    "# evaluation\n",
    "rf_mse = mean_squared_error(y_test, rf_pred)\n",
    "rf_mae = mean_absolute_error(y_test, rf_pred)\n",
    "rf_rmse = np.sqrt(mean_squared_error(y_test, rf_pred))\n",
    "rf_mape = mean_absolute_percentage_error(y_test, rf_pred)\n",
    "rf_r2 = r2_score(y_test, rf_pred)\n",
    "\n",
    "# Print metrics\n",
    "print(\"MSE:\", rf_mse) \n",
    "print(\"MAE:\", rf_mae) \n",
    "print(\"RMSE:\", rf_rmse) \n",
    "print(\"MAPE:\", rf_mape) \n",
    "print(\"R-squared (Random Forest):\", rf_r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7b35a40-c332-41b6-8ac3-86970baa5123",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the last 50 points of y_test and the corresponding forecasted values and confidence interval\n",
    "y_test_last_50 = y_test[-100:]\n",
    "forecast_values_last_50 = rf_pred[-100:]\n",
    "#conf_int_last_50 = conf_int_1[-100:]\n",
    "\n",
    "# Create a range of values for the x-axis\n",
    "x_axis_last_50 = range(len(y_test_last_50))\n",
    "\n",
    "# Plot forecast results against actual data for the last 50 points\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(x_axis_last_50, y_test_last_50, label='Actual Test Data', color='blue', linewidth=2)\n",
    "plt.plot(x_axis_last_50, forecast_values_last_50, label='Random Forest Regressor', color='red', linewidth=2)\n",
    "\n",
    "# Set y-axis limits to ignore outliers/extreme values\n",
    "plt.ylim([0,70])\n",
    "plt.title(\"Figure 8. Random Forest Regressor vs Actual Test Data (Last 50 Points)\", fontsize=14)\n",
    "plt.xlabel(\"Time Step\")\n",
    "plt.ylabel(\"Values\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3539fb1-c832-4b70-9903-bcf36622ff2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rf = RandomForestRegressor(random_state=0)\n",
    "\n",
    "# # Define parameter grid \n",
    "# param_grid = {\n",
    "#     'n_estimators': [100, 200, 300],\n",
    "#     'max_depth': [10, 20, 30, None],\n",
    "#     'min_samples_split': [2, 5, 10],\n",
    "#     'min_samples_leaf': [1, 2, 4],\n",
    "#     'max_features': ['auto', 'sqrt', 'log2']\n",
    "# }\n",
    "\n",
    "# # Create GridSearchCV object with cross-validation\n",
    "# grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, \n",
    "#                            scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n",
    "\n",
    "# grid_search.fit(X_valid, y_train)\n",
    "# cv_results = grid_search.cv_results_\n",
    "# best_params = grid_search.best_params_\n",
    "\n",
    "# # # Plot result from cv_results\n",
    "# # mean_test_scores = cv_results['mean_test_score']\n",
    "# # max_depths = [param['max_depth'] for param in cv_results['params']]\n",
    "# # n_estimators = [param['n_estimators'] for param in cv_results['params']]\n",
    "\n",
    "# # mean_test_scores_grid = np.array(mean_test_scores).reshape(len(max_depths), len(n_estimators))\n",
    "\n",
    "# # Create a heatmap to visualize MSE\n",
    "# plt.figure(figsize=(10, 8))\n",
    "# plt.imshow(mean_test_scores_grid, cmap='viridis', origin='lower')\n",
    "# plt.colorbar(label='Mean Squared Error (MSE)')\n",
    "# plt.xticks(np.arange(len(n_estimators)), n_estimators, rotation=45)\n",
    "# plt.yticks(np.arange(len(max_depths)), max_depths)\n",
    "# plt.xlabel('Number of Estimators (n_estimators)')\n",
    "# plt.ylabel('Maximum Depth (max_depth)')\n",
    "# plt.title('Grid Search Results for Random Forest Hyperparameters')\n",
    "# plt.show()\n",
    "\n",
    "# print(\"Best Hyperparameters:\", best_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b40fa7-5ee9-452e-be6b-c6c7b4dce379",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### 4.2.3. SVR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "749059ad-6862-499d-ab16-887818e08742",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define basic SVR model and training on X_test\n",
    "svr_model = SVR()\n",
    "svr_model.fit(X_train, y_train)\n",
    "svr_pred = svr_model.predict(X_test)\n",
    "\n",
    "# Evaluation\n",
    "svr_mse = mean_squared_error(y_test, svr_pred)\n",
    "svr_mae = mean_absolute_error(y_test, svr_pred)\n",
    "svr_rmse = np.sqrt(mean_squared_error(y_test, svr_pred))\n",
    "svr_mape = mean_absolute_percentage_error(y_test, svr_pred)\n",
    "svr_r2 = r2_score(y_test, svr_pred)\n",
    "\n",
    "# Print the metrics result\n",
    "print(\"MSE:\", svr_mse) \n",
    "print(\"MAE:\", svr_mae) \n",
    "print(\"RMSE:\", svr_rmse) \n",
    "print(\"MAPE:\", svr_mape) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb20b2d8-fcf9-420c-b513-4e44b41333d8",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 4.2.4. Linear SVR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e76b0aa8-f756-47f2-9bd2-de9c36d1ddd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 53.25887128277389\n",
      "MAE: 1.1404649475441762\n",
      "RMSE: 7.297867584628669\n",
      "MAPE: 0.1311646933757877\n",
      "R-squared: 0.997204954161102\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Define basic linear SVR and training \n",
    "linear_svr = LinearSVR(loss='epsilon_insensitive', random_state=0)\n",
    "linear_svr.fit(X_train, y_train)\n",
    "linear_svr_pred = linear_svr.predict(X_test)\n",
    "\n",
    "# Evaluation \n",
    "linear_svr_mse = mean_squared_error(y_test, linear_svr_pred)\n",
    "linear_svr_mae = mean_absolute_error(y_test, linear_svr_pred)\n",
    "linear_svr_rmse = np.sqrt(mean_squared_error(y_test, linear_svr_pred))\n",
    "linear_svr_mape = mean_absolute_percentage_error(y_test, linear_svr_pred)\n",
    "linear_svr_r2 = r2_score(y_test, linear_svr_pred)\n",
    "\n",
    "# Print metrics\n",
    "print(\"MSE:\", linear_svr_mse) \n",
    "print(\"MAE:\", linear_svr_mae) \n",
    "print(\"RMSE:\", linear_svr_rmse) \n",
    "print(\"MAPE:\", linear_svr_mape) \n",
    "print(\"R-squared:\", linear_svr_r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "302a2ee4-5cff-4176-a44c-61a576e7afe9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'C': 0.01, 'epsilon': 0.01, 'loss': 'squared_epsilon_insensitive', 'max_iter': 100000, 'tol': 0.01}\n"
     ]
    }
   ],
   "source": [
    "svr = LinearSVR() \n",
    "# Define paramters for gridsearch \n",
    "params = {\n",
    "    'C': [0.01, 0.1, 1],\n",
    "    'epsilon': [0.01, 0.1, 0.5],\n",
    "    'loss': ['epsilon_insensitive', 'squared_epsilon_insensitive'],\n",
    "    'max_iter': [10000, 50000, 100000],\n",
    "    'tol': [0.01, 0.05, 0.1]\n",
    "}\n",
    "\n",
    "# Grid Search cross-validation\n",
    "grid_search = GridSearchCV(svr, params, cv=5, scoring='neg_mean_squared_error')\n",
    "grid_search.fit(X_valid, y_valid)\n",
    "best_svr = grid_search.best_estimator_\n",
    "\n",
    "# Print the best params\n",
    "print(\"Best Parameters:\", grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70a199ad-1530-4773-b868-3179541920a2",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'LinearSVR' object has no attribute 'coef_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[0;32mIn [18]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Make predictions with the best estimator\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m best_svr_pred \u001b[38;5;241m=\u001b[39m \u001b[43mbest_svr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py:362\u001b[0m, in \u001b[0;36mLinearModel.predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[1;32m    349\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    350\u001b[0m \u001b[38;5;124;03m    Predict using the linear model.\u001b[39;00m\n\u001b[1;32m    351\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    360\u001b[0m \u001b[38;5;124;03m        Returns predicted values.\u001b[39;00m\n\u001b[1;32m    361\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 362\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_decision_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py:346\u001b[0m, in \u001b[0;36mLinearModel._decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    343\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m    345\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(X, accept_sparse\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoo\u001b[39m\u001b[38;5;124m\"\u001b[39m], reset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 346\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m safe_sparse_dot(X, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoef_\u001b[49m\u001b[38;5;241m.\u001b[39mT, dense_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintercept_\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'LinearSVR' object has no attribute 'coef_'"
     ]
    }
   ],
   "source": [
    "# Make predictions with the best estimator\n",
    "best_svr_pred = best_svr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8a8de2b-5cac-46c2-a1f9-15705bbff13e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 39.26025937690754\n",
      "MAE: 0.8437431693448024\n",
      "RMSE: 6.265800776988328\n",
      "MAPE: 0.13438983595415918\n",
      "R-squared: 0.9979396066427534\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best estimator\n",
    "best_svr_mse = mean_squared_error(y_test, best_svr_pred)\n",
    "best_svr_mae = mean_absolute_error(y_test, best_svr_pred)\n",
    "best_svr_rmse = np.sqrt(mean_squared_error(y_test, best_svr_pred))\n",
    "best_svr_mape = mean_absolute_percentage_error(y_test, best_svr_pred)\n",
    "best_svr_r2 = r2_score(y_test, best_svr_pred)\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(\"MSE:\", best_svr_mse) \n",
    "print(\"MAE:\", best_svr_mae) \n",
    "print(\"RMSE:\", best_svr_rmse) \n",
    "print(\"MAPE:\", best_svr_mape) \n",
    "print(\"R-squared:\", best_svr_r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb7280c-c9e4-4856-963b-2a501877ca32",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "\n",
    "The tuned LinearSVR model demonstrates outperforms compared to the default model in stock price prediction, as evidenced by its improved metrics across the board. Specifically, the tuned model shows a significant reduction in Mean Squared Error (MSE) from 53.259 to 39.157, indicating a more accurate prediction with fewer errors. The Mean Absolute Error (MAE) is almost halved, decreasing from 1.140 to 0.618, which reflects a more precise average prediction error. Furthermore, the Root Mean Squared Error (RMSE) is reduced from 7.298 to 6.258, demonstrating the tuned model's enhanced capability in handling large errors. The Mean Absolute Percentage Error (MAPE) also shows notable improvement, dropping from 13.12% to 9.15%, which implies better accuracy in terms of relative prediction error. \n",
    "\n",
    "Based on these metrics, the tuned LinearSVR model is the better choice as it shows significant improvements in prediction accuracy and error reduction. This makes it more reliable for stock price prediction, especially in a field where precision is crucial. However, it is important to consider the model's performance on unseen data to ensure its generalizability."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e620a6a-73a6-435e-9a8f-a26c28933b39",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### 4.2.5. XGBoost Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "837e3497-0810-4a90-a61b-596add122dd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 240.83413885424852\n",
      "MAE: 1.680959056491725\n",
      "RMSE: 15.518831749015405\n",
      "MAPE: 0.0908025775250961\n"
     ]
    }
   ],
   "source": [
    "# Baseline XGB Regressor\n",
    "xgb_reg = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=100)\n",
    "xgb_reg.fit(X_train, y_train)\n",
    "xgb_pred = xgb_reg.predict(X_test)\n",
    "\n",
    "# Evaluations \n",
    "xgb_mse = mean_squared_error(y_test, xgb_pred)\n",
    "xgb_mae = mean_absolute_error(y_test, xgb_pred)\n",
    "xgb_rmse = np.sqrt(mean_squared_error(y_test, xgb_pred))\n",
    "xgb_mape = mean_absolute_percentage_error(y_test, xgb_pred)\n",
    "\n",
    "# Print metrics \n",
    "print(\"MSE:\", xgb_mse) \n",
    "print(\"MAE:\", xgb_mae) \n",
    "print(\"RMSE:\", xgb_rmse) \n",
    "print(\"MAPE:\", xgb_mape) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c92dafc6-6329-4f7a-824d-ca99a180a676",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Regularization \n",
    "\n",
    "Regularization is a technique used to prevent overfitting by penalizing models with extreme coefficient values. XGBoost offers two parameters for regularization [6]:\n",
    "\n",
    "`lambda`: L2 regularization term on weights, also known as reg_lambda. It's used to help prevent overfitting by adding a penalty for larger weights in the model.\n",
    "\n",
    "`alpha`: L1 regularization term on weights, also known as reg_alpha. It encourages sparsity, meaning it can set some weight coefficients in the model to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "172c14de-892e-4b96-819d-e37ddcbac463",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 239.5020945901269\n",
      "MAE: 1.7075537845152722\n",
      "RMSE: 15.475855213529458\n",
      "MAPE: 0.08958831238328308\n"
     ]
    }
   ],
   "source": [
    "# Initialize an XGBRegressor with specified parameter\n",
    "xgb_reg = xgb.XGBRegressor(\n",
    "    objective='reg:squarederror',\n",
    "    n_estimators=100,\n",
    "    seed=0,\n",
    "    reg_lambda=1,  \n",
    "    reg_alpha=1    \n",
    ")\n",
    "# Fit model \n",
    "xgb_reg.fit(X_train, y_train)\n",
    "# Make prediction on X_test \n",
    "xgb_pred = xgb_reg.predict(X_test)\n",
    "# Evaluation\n",
    "xgb_mse = mean_squared_error(y_test, xgb_pred)\n",
    "xgb_mae = mean_absolute_error(y_test, xgb_pred)\n",
    "xgb_rmse = np.sqrt(mean_squared_error(y_test, xgb_pred))\n",
    "xgb_mape = mean_absolute_percentage_error(y_test, xgb_pred)\n",
    "# Print metrics \n",
    "print(\"MSE:\", xgb_mse) \n",
    "print(\"MAE:\", xgb_mae) \n",
    "print(\"RMSE:\", xgb_rmse) \n",
    "print(\"MAPE:\", xgb_mape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5f380ba3-d03d-49d6-9299-eca7bad6e11d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot feature importance\n",
    "xgb.plot_importance(xgb_reg)\n",
    "plt.title(\"Figure 11. Feature Importance in XGBoost Regression after Regularization\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d77f5b97-beb2-4290-aae3-3f143be978f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Initialize model\n",
    "# xgb_reg = xgb.XGBRegressor()\n",
    "\n",
    "# # Define the parameter grid\n",
    "# param_grid = {\n",
    "#     'n_estimators': [100, 200, 300, 400],\n",
    "#     'learning_rate': [0.01, 0.1, 0.2, 0.4],\n",
    "#     'reg_alpha': [1e-5, 1e-2, 0.1, 1, 100],\n",
    "#     'reg_lambda': [1e-5, 1e-2, 0.1, 1, 100]\n",
    "# }\n",
    "\n",
    "# # Initialize grid search\n",
    "# grid_search = GridSearchCV(estimator=xgb_reg, \n",
    "#                            param_grid=param_grid, \n",
    "#                            cv=5, \n",
    "#                            scoring='neg_mean_squared_error'\n",
    "#                           )\n",
    "\n",
    "# grid_search.fit(X_valid, y_valid)\n",
    "# print(\"Best parameters found: \", grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59d67f72-6ecf-4310-abbe-9560fbfc2bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Train the final model with the best parameters\n",
    "# best_xgb = xgb.XGBRegressor(**grid_search.best_params_)\n",
    "# best_xgb.fit(X_train, y_train)\n",
    "# xgb_pred = final_model.predict(X_test)\n",
    "\n",
    "# # Evaluate\n",
    "# xgb_mse = mean_squared_error(y_test, xgb_pred)\n",
    "# xgb_mae = mean_absolute_error(y_test, xgb_pred)\n",
    "# xgb_rmse = np.sqrt(mean_squared_error(y_test, xgb_pred))\n",
    "# xgb_mape = mean_absolute_percentage_error(y_test, xgb_pred)\n",
    "# print(f\"Tuned XGBoost for Regression MSE:\", xgb_mse) \n",
    "# print(f\"Tuned XGBoost for Regression MAE:\", xgb_mae) \n",
    "# print(f\"Tuned XGBoost for Regression RMSE:\", xgb_rmse) \n",
    "# print(f\"Tuned XGBoost for Regression MAPE:\", xgb_mape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e27d7975-7e39-4169-a42e-1c28985cc7b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Initialize random search with a fixed number of iterations and cross-validation folds\n",
    "# random_search = RandomizedSearchCV(estimator=xgb_reg,\n",
    "#                                    param_distributions=param_grid,\n",
    "#                                    n_iter=25,\n",
    "#                                    scoring='neg_mean_squared_error',\n",
    "#                                    cv=5,\n",
    "#                                    verbose=1,\n",
    "#                                    random_state=0\n",
    "#                                   )\n",
    " \n",
    "# random_search.fit(X_valid, y_valid)\n",
    "\n",
    "# print(\"Best parameters found: \", random_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2957682-a5d3-4ae9-a808-d9b424d1985f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Train model using the best parameters found\n",
    "# best_reg_alpha = random_search.best_params_['reg_alpha']\n",
    "# best_reg_lambda = random_search.best_params_['reg_lambda']\n",
    "\n",
    "# best_xgb = xgb.XGBRegressor(\n",
    "#     objective='reg:squarederror',\n",
    "#     n_estimators=100,\n",
    "#     seed=0,\n",
    "#     reg_alpha=best_reg_alpha,\n",
    "#     reg_lambda=best_reg_lambda\n",
    "# )\n",
    "\n",
    "# best_xgb.fit(X_train, y_train)\n",
    "# xgb_pred = best_xgb.predict(X_test)\n",
    "\n",
    "# xgb_mse = mean_squared_error(y_test, xgb_pred)\n",
    "# xgb_mse = mean_squared_error(y_test, xgb_pred)\n",
    "# xgb_mae = mean_absolute_error(y_test, xgb_pred)\n",
    "# xgb_rmse = np.sqrt(mean_squared_error(y_test, xgb_pred))\n",
    "# xgb_mape = mean_absolute_percentage_error(y_test, xgb_pred)\n",
    "\n",
    "# print(\"MSE:\", xgb_mse) \n",
    "# print(\"MAE:\", xgb_mae) \n",
    "# print(\"RMSE:\", xgb_rmse) \n",
    "# print(\"MAPE:\", xgb_mape) \n",
    "# print(f\"Best Mean Squared Error: {xgb_mse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ad593c-14c5-4bc8-84e6-0bc619bbed39",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Final Stock Price Prediction after Hyperparameters Tuning "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f25da7-89c6-4b5c-94f8-b04273da0263",
   "metadata": {},
   "source": [
    "Now, we use LinearSVR with the `best parameters: {'C': 0.01, 'epsilon': 0.01, 'loss': 'squared_epsilon_insensitive', 'max_iter': 100000, 'tol': 0.01}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "11145054-3684-4362-bdab-a3c0a98ce6bf",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'LinearSVR' object has no attribute 'coef_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[0;32mIn [17]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Predict the price for the whole dataset `stock_df`\u001b[39;00m\n\u001b[1;32m      2\u001b[0m X_all \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([X_train, X_test, X_valid])\n\u001b[0;32m----> 3\u001b[0m predicted_prices \u001b[38;5;241m=\u001b[39m \u001b[43mbest_svr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_all\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# Add pred_price into new column \u001b[39;00m\n\u001b[1;32m      6\u001b[0m stock_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpredicted_price\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m predicted_prices\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py:362\u001b[0m, in \u001b[0;36mLinearModel.predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[1;32m    349\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    350\u001b[0m \u001b[38;5;124;03m    Predict using the linear model.\u001b[39;00m\n\u001b[1;32m    351\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    360\u001b[0m \u001b[38;5;124;03m        Returns predicted values.\u001b[39;00m\n\u001b[1;32m    361\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 362\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_decision_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py:346\u001b[0m, in \u001b[0;36mLinearModel._decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    343\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m    345\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(X, accept_sparse\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoo\u001b[39m\u001b[38;5;124m\"\u001b[39m], reset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 346\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m safe_sparse_dot(X, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoef_\u001b[49m\u001b[38;5;241m.\u001b[39mT, dense_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintercept_\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'LinearSVR' object has no attribute 'coef_'"
     ]
    }
   ],
   "source": [
    "# Predict the price for the whole dataset `stock_df`\n",
    "X_all = pd.concat([X_train, X_test, X_valid])\n",
    "predicted_prices = best_svr.predict(X_all)\n",
    "\n",
    "# Add pred_price into new column \n",
    "stock_df['predicted_price'] = predicted_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e77db6ea-7053-459b-a120-b537dee33afa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "best_linear_svr = LinearSVR(C=0.01,\n",
    "                            epsilon=0.01,\n",
    "                            loss='squared_epsilon_insensitive',\n",
    "                            max_iter=100000,\n",
    "                            tol=0.01)\n",
    "\n",
    "# Fit the model to the entire dataset\n",
    "X_all = pd.concat([X_train, X_test, X_valid])\n",
    "best_linear_svr.fit(X_train, y_train)  \n",
    "\n",
    "# Predict the prices for the entire dataset\n",
    "predicted_prices = best_linear_svr.predict(X_all)\n",
    "\n",
    "# Add the predicted prices into a new column in 'stock_df'\n",
    "stock_df['predicted_price'] = predicted_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4511c37e-4aee-4d19-8e39-4f3d09115ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate `%error` to compare the true value and predicted value\n",
    "stock_df['%error'] = (abs(stock_df['Close'] - stock_df['predicted_price']) / stock_df['Close']) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b4d2f0e3-b4af-407c-b8f1-6730e9ffb9f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature_1</th>\n",
       "      <th>Feature_2</th>\n",
       "      <th>Feature_3</th>\n",
       "      <th>Feature_4</th>\n",
       "      <th>Feature_5</th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Close</th>\n",
       "      <th>row_num</th>\n",
       "      <th>predicted_price</th>\n",
       "      <th>%error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>-4.694621</td>\n",
       "      <td>0.050102</td>\n",
       "      <td>0.525245</td>\n",
       "      <td>-4.551044</td>\n",
       "      <td>1.058598</td>\n",
       "      <td>A</td>\n",
       "      <td>46.178</td>\n",
       "      <td>1</td>\n",
       "      <td>46.159938</td>\n",
       "      <td>0.039114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>-4.948428</td>\n",
       "      <td>0.293337</td>\n",
       "      <td>0.468583</td>\n",
       "      <td>-4.553871</td>\n",
       "      <td>1.065424</td>\n",
       "      <td>A</td>\n",
       "      <td>46.784</td>\n",
       "      <td>2</td>\n",
       "      <td>46.544546</td>\n",
       "      <td>0.511830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>-4.885045</td>\n",
       "      <td>0.113366</td>\n",
       "      <td>0.636895</td>\n",
       "      <td>-4.541259</td>\n",
       "      <td>1.020767</td>\n",
       "      <td>A</td>\n",
       "      <td>46.228</td>\n",
       "      <td>3</td>\n",
       "      <td>46.638431</td>\n",
       "      <td>0.887840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>-5.465603</td>\n",
       "      <td>0.731387</td>\n",
       "      <td>0.340322</td>\n",
       "      <td>-4.613235</td>\n",
       "      <td>1.154764</td>\n",
       "      <td>A</td>\n",
       "      <td>47.668</td>\n",
       "      <td>4</td>\n",
       "      <td>46.799169</td>\n",
       "      <td>1.822671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>-5.977897</td>\n",
       "      <td>1.172599</td>\n",
       "      <td>0.107106</td>\n",
       "      <td>-4.593020</td>\n",
       "      <td>1.132056</td>\n",
       "      <td>A</td>\n",
       "      <td>47.817</td>\n",
       "      <td>5</td>\n",
       "      <td>47.257096</td>\n",
       "      <td>1.170931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-06</th>\n",
       "      <td>-6.903199</td>\n",
       "      <td>-1.760332</td>\n",
       "      <td>-1.418393</td>\n",
       "      <td>4.088618</td>\n",
       "      <td>0.799909</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>11.190</td>\n",
       "      <td>214</td>\n",
       "      <td>11.790536</td>\n",
       "      <td>5.366720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-07</th>\n",
       "      <td>-6.796380</td>\n",
       "      <td>-1.978180</td>\n",
       "      <td>-1.165790</td>\n",
       "      <td>4.113964</td>\n",
       "      <td>0.734404</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>10.830</td>\n",
       "      <td>215</td>\n",
       "      <td>12.097984</td>\n",
       "      <td>11.708067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08</th>\n",
       "      <td>-6.773013</td>\n",
       "      <td>-2.083958</td>\n",
       "      <td>-1.029962</td>\n",
       "      <td>4.123684</td>\n",
       "      <td>0.727150</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>10.900</td>\n",
       "      <td>216</td>\n",
       "      <td>12.111996</td>\n",
       "      <td>11.119226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-09</th>\n",
       "      <td>-7.115683</td>\n",
       "      <td>-1.690110</td>\n",
       "      <td>-1.202882</td>\n",
       "      <td>4.137377</td>\n",
       "      <td>0.783180</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>11.600</td>\n",
       "      <td>217</td>\n",
       "      <td>12.299534</td>\n",
       "      <td>6.030469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-10</th>\n",
       "      <td>-7.551065</td>\n",
       "      <td>-1.215361</td>\n",
       "      <td>-1.301172</td>\n",
       "      <td>4.140841</td>\n",
       "      <td>0.872513</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>12.460</td>\n",
       "      <td>218</td>\n",
       "      <td>12.679370</td>\n",
       "      <td>1.760596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>386566 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Feature_1  Feature_2  Feature_3  Feature_4  Feature_5 Ticker  \\\n",
       "Date                                                                       \n",
       "2017-01-03  -4.694621   0.050102   0.525245  -4.551044   1.058598      A   \n",
       "2017-01-04  -4.948428   0.293337   0.468583  -4.553871   1.065424      A   \n",
       "2017-01-05  -4.885045   0.113366   0.636895  -4.541259   1.020767      A   \n",
       "2017-01-06  -5.465603   0.731387   0.340322  -4.613235   1.154764      A   \n",
       "2017-01-09  -5.977897   1.172599   0.107106  -4.593020   1.132056      A   \n",
       "...               ...        ...        ...        ...        ...    ...   \n",
       "2017-11-06  -6.903199  -1.760332  -1.418393   4.088618   0.799909   ZYNE   \n",
       "2017-11-07  -6.796380  -1.978180  -1.165790   4.113964   0.734404   ZYNE   \n",
       "2017-11-08  -6.773013  -2.083958  -1.029962   4.123684   0.727150   ZYNE   \n",
       "2017-11-09  -7.115683  -1.690110  -1.202882   4.137377   0.783180   ZYNE   \n",
       "2017-11-10  -7.551065  -1.215361  -1.301172   4.140841   0.872513   ZYNE   \n",
       "\n",
       "             Close  row_num  predicted_price     %error  \n",
       "Date                                                     \n",
       "2017-01-03  46.178        1        46.159938   0.039114  \n",
       "2017-01-04  46.784        2        46.544546   0.511830  \n",
       "2017-01-05  46.228        3        46.638431   0.887840  \n",
       "2017-01-06  47.668        4        46.799169   1.822671  \n",
       "2017-01-09  47.817        5        47.257096   1.170931  \n",
       "...            ...      ...              ...        ...  \n",
       "2017-11-06  11.190      214        11.790536   5.366720  \n",
       "2017-11-07  10.830      215        12.097984  11.708067  \n",
       "2017-11-08  10.900      216        12.111996  11.119226  \n",
       "2017-11-09  11.600      217        12.299534   6.030469  \n",
       "2017-11-10  12.460      218        12.679370   1.760596  \n",
       "\n",
       "[386566 rows x 10 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b36b64af-464c-432d-9e65-3cadf515e40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save our data to csv file \n",
    "# stock_df.to_csv('predicted_price_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d8b83efc-b107-4ea4-b00d-2be6618e9250",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ticker_plot(ticker):\n",
    "    ticker_df = stock_df[stock_df['Ticker']==ticker]\n",
    "\n",
    "    A = pd.DataFrame()\n",
    "    A['Date'] = ticker_df['Date']\n",
    "    A['Close'] = ticker_df['Close']\n",
    "\n",
    "    B = pd.DataFrame()\n",
    "    B['Date'] = ticker_df['Date']\n",
    "    B['Close'] = ticker_df['predicted_price']\n",
    "    #fig = px.line(df, x='date', y='close', color='new', markers=True)\n",
    "    \n",
    "    column = pd.DataFrame() \n",
    "    column['NewColumn'] = np.zeros(A.shape[0])\n",
    "    column['NewColumn'] = 'actual_values'\n",
    "\n",
    "    column1 = pd.DataFrame()\n",
    "    column1['NewColumn'] = np.zeros(A.shape[0])\n",
    "    column1['NewColumn'] = 'predicted_price'\n",
    "\n",
    "    new = pd.concat([column, column1], ignore_index=True)\n",
    "    \n",
    "    result = pd.concat([A, B], ignore_index=True)\n",
    "    result['Actual Vs Predicted'] = new\n",
    "    result\n",
    "    #result.drop(['NewColumn'],axis=1,inplace=True)\n",
    "    result\n",
    "    print(result)\n",
    "\n",
    "    #fig=px.line(For_prediction_ticker,x='date',y='close')\n",
    "    #fig.add_scatter(x=For_prediction_ticker['date'],y=For_prediction_ticker['Predicted_values'],name='Predicted_values')\n",
    "    #fig.update_traces(name='Actual')\n",
    "    #fig.update_traces(name='Actual', selector=dict(name='line'))\n",
    "    #line_trace_index = 0\n",
    "    #fig.data[line_trace_index].name = 'Actual'\n",
    "    fig = px.line(result, x='date', y='close', color='Actual Vs Predicted', markers=False)\n",
    "\n",
    "    fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3381af21-1364-4e55-a7ec-6630d8d60e97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ticker_plot('YY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbc94959-7d24-4263-8216-9260f7223c3a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b48173f2-f4ce-4b35-8284-940f14fbb7a3",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 5. Financial News Sentiment Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b70d829-5c66-482f-96bf-5c622c251e52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to Drive \n",
    "from google.colab import drive\n",
    "drive.mount('/content/Mydrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f27c5d5-b06c-47c6-9942-395060894a49",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read data \n",
    "df1 = pd.read_csv('/content/Mydrive/MyDrive/raw_partner_headlines.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c47c0d9-252a-4475-bb13-a08684c2f4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copy of `df1`\n",
    "df1_copy = df1.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23bb917f-6c6b-402c-94d9-ab25b7af21f6",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 5.1. Pre-trained Hugging Face Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "299b0172-8a36-4140-8dee-82215930cc0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a pipeline as a high-level helper\n",
    "from transformers import pipeline\n",
    "\n",
    "pipe_sentiment = pipeline(\"text-classification\", model=\"mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efa91219-b2a2-44e0-a387-1eb50c63e7a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter df1 for dates between '2017-01-01' and '2017-12-31'\n",
    "df1 = df1[(df1['date'] > '2017-01-01') & (df1['date'] < '2017-12-31')]\n",
    "\n",
    "# Remove rows with missing values\n",
    "df1 = df1.dropna(axis=0)\n",
    "\n",
    "# Drop duplicate headlines, keeping the first occurrence\n",
    "df1.drop_duplicates(subset='headline', keep='first', inplace=True)\n",
    "\n",
    "# Reset the index of df1 to new column\n",
    "df1 = df1.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb8307ad-8b44-4d25-8273-e5f0a3c12925",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to extract content of news from url\n",
    "def extract_paragraphs_from_url(url):\n",
    "    import requests\n",
    "    from bs4 import BeautifulSoup\n",
    "    # Send an HTTP GET request to the URL\n",
    "    response = requests.get(url)\n",
    "\n",
    "    if response.status_code == 200:\n",
    "        # Parse the HTML content using BeautifulSoup\n",
    "        soup = BeautifulSoup(response.text, 'html.parser')\n",
    "\n",
    "        # Find all the <p> elements and extract their text\n",
    "        paragraphs = [p.get_text() for p in soup.find_all('p')]\n",
    "\n",
    "        # Join the paragraphs into a single text\n",
    "        extracted_text = '\\n'.join(paragraphs)\n",
    "\n",
    "        return extracted_text\n",
    "    else:\n",
    "        flag=1\n",
    "        return flag\n",
    "\n",
    "# Example usage\n",
    "url = \"https://seekingalpha.com/article/4133429-agilent-technologies-good-business-total-return-pricey?source=partner_benzinga\"  # Replace with the URL you want to extract data from\n",
    "extracted_data = extract_paragraphs_from_url(url)\n",
    "\n",
    "if extracted_data:\n",
    "    # Print or store the extracted data as a paragraph\n",
    "    print(extracted_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ff65423-4622-458f-83cd-7cbbcdf20cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing text function\n",
    "def clean_data(text, lang):\n",
    "    # Remove HTML tags\n",
    "    soup = BeautifulSoup(text, 'lxml')\n",
    "    text = soup.get_text()\n",
    "    \n",
    "    # Replace Emoticons/Emojis with Text\n",
    "    text = emoji.demojize(text, language=lang)\n",
    "    text = emoticon_fix.emoticon_fix(text)\n",
    "    \n",
    "    # Decoding of abbreviations (commented out)\n",
    "    # text = abbr_conversion(text)\n",
    "    \n",
    "    # Remove mentions (usernames)\n",
    "    text = re.sub(\"@[A-Za-z0-9]+\", \"\", text)\n",
    "    \n",
    "    # Remove hashtags\n",
    "    text = re.sub(\"#[A-Za-z0-9_]+\", \"\", text)\n",
    "    \n",
    "    # Remove links (URLs)\n",
    "    text = re.sub('https:\\/\\/\\S+', '', text)\n",
    "    \n",
    "    # Remove punctuation\n",
    "    text = re.sub('[%s]' % re.escape(string.punctuation), ' ', text)\n",
    "    \n",
    "    # Remove newline characters\n",
    "    text = re.sub(r'[^ \\w\\.]', '', text)\n",
    "    \n",
    "    # Remove words containing numbers\n",
    "    text = re.sub('\\w*\\d\\w*', '', text)\n",
    "    \n",
    "    return text\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5d832d3-b8cf-4ea9-9cfe-aa531aba9e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean the extracted data using a function called 'clean_data' with the language parameter set to 'eng'\n",
    "cleaned_data = clean_data(extracted_data, 'eng')\n",
    "\n",
    "# Print cleaned data\n",
    "cleaned_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3c3c676-6d44-42c2-b9d6-25b98250aca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set \"Labels\" and \"Scores\" columns to NaN in DataFrame df1\n",
    "df1[\"Labels\"] = np.nan\n",
    "df1[\"Scores\"] = np.nan\n",
    "\n",
    "# Drop unnecessary columns 'index' and 'Unnamed: 0' from df1\n",
    "df1 = df1.drop(['index', 'Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff40ca52-c2a5-4572-822b-2f90a308781d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # start_time = time.time()\n",
    "# for i in range(0,len(df1)):\n",
    "#     try:\n",
    "#         extracted_data = extract_paragraphs_from_url(df1['url'][i])\n",
    "#         if extracted_data == 1:\n",
    "#             extracted_data = clean_data(df1['headline'][i],'eng')\n",
    "#             sentiment1 = pipe_sentiment(extracted_data)\n",
    "#             df1['Labels'][i] = sentiment1[0]['label']\n",
    "#             df1['Scores'][i] = sentiment1[0]['score']\n",
    "#         else:\n",
    "#             cleaned_data = clean_data(extracted_data,'eng')\n",
    "#             sentiment2 = pipe_sentiment(cleaned_data,truncation=True)\n",
    "#             df1['Labels'][i] = sentiment2[0]['label']\n",
    "#             df1['Scores'][i] = sentiment2[0]['score']\n",
    "#         if i%100==0:\n",
    "#             df1.to_csv('Set.csv')\n",
    "\n",
    "#     except:\n",
    "#         pass\n",
    "# # print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e008af7f-2f96-4613-b1d7-19aa6d8abbf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1.to_csv('Final_ouput.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d959e9a6-28fd-4d26-aa88-87212c278eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_df = pd.read_csv('/content/Mydrive/MyDrive/Final_ouput.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0e7cae2-6f17-41fb-992d-85b13a9c1d7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_df = new_df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfeb3cef-9b3c-4940-b70a-8f45248bdb11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_df.to_csv(\"SentimentAnalysis.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a29f875a-938b-4186-b330-1791dc456355",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 5.2. Naive Bayes Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f566f336-6fc8-40d6-a5cb-bc826a41430c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the CSV file into a DataFrame\n",
    "data = pd.read_csv('/content/Mydrive/MyDrive/SentimentAnalysis.csv')\n",
    "# Make copy of data\n",
    "data_copy = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56116a2d-fd75-4fe9-971b-6bc76f5bf337",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the 'en_core_web_sm' model\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "# Check the names of pipeline components\n",
    "print(nlp.pipe_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8baa1d21-3b2a-4139-b57c-799be30585ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create spaCy NLP object with a blank English model\n",
    "nlp = spacy.blank('en')\n",
    "\n",
    "# There should be no pipeline components.\n",
    "nlp.pipe_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "130f1b15-7443-4deb-8bd5-81446abd240e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a custom tokenizer function called 'spacy_tokenizer'\n",
    "def spacy_tokenizer(doc):\n",
    "    \n",
    "    # Use spaCy to process the input text document ('doc') and iterate over its tokens\n",
    "    # while applying the following conditions to filter the tokens.\n",
    "    return [t.text for t in nlp(doc) if \\  # Return a list of tokenized words\n",
    "    \n",
    "            # Check if the token is not a punctuation mark\n",
    "            not t.is_punct and \\\n",
    "            \n",
    "            # Check if the token is not a space (whitespace character)\n",
    "            not t.is_space and \\\n",
    "            \n",
    "            # Check if the token consists of alphabetic characters (letters)\n",
    "            t.is_alpha]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df1564d7-5652-4df6-b65d-22469700b1d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a LabelEncoder object\n",
    "encoder = LabelEncoder()\n",
    "# Extract the 'Labels' column from the 'data' DataFrame and store it in the 'labels' variable\n",
    "labels = data['Labels'].values\n",
    "# Use the LabelEncoder to fit and transform the 'labels' array, encoding the labels as integers\n",
    "encoded_labels = encoder.fit_transform(labels)\n",
    "\n",
    "# Split the 'headline' column as features (X) and the encoded labels as targets (y)\n",
    "X_train, X_test, y_train, y_test = train_test_split(data['headline'], encoded_labels, stratify=encoded_labels, train_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7567d0fb-0eb0-4b41-9118-e4cac8942205",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Measure the execution time of this code block\n",
    "%%time\n",
    "# Create a TfidfVectorizer object and specify the custom tokenizer function\n",
    "vectorizer = TfidfVectorizer(tokenizer=spacy_tokenizer)\n",
    "\n",
    "# Fit the vectorizer to the training data (X_train) and transform it into TF-IDF vectors\n",
    "train_feature_vects = vectorizer.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68268ef4-6221-4a0c-8ba7-6f85f1cafd4f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Train Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ce671a1-013a-4abd-b764-e123734bc914",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Multinomial Naive Bayes classifier\n",
    "nb_classifier = MultinomialNB()\n",
    "# Fit the classifier to the training feature vectors and corresponding labels\n",
    "nb_classifier.fit(train_feature_vects, y_train)\n",
    "# Make predictions on the training feature vectors\n",
    "train_preds = nb_classifier.predict(train_feature_vects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0afa814-1eda-4f2c-bdc2-a9982d133aa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the parameters of the classifier\n",
    "nb_classifier.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ee89272-d79c-43f7-a51a-4319a605c09d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get class probabilities for the training feature vectors\n",
    "train_preds_score = nb_classifier.predict_proba(train_feature_vects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4224926a-5758-42c6-aa59-4982a7edb583",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate and print accuracy score on the training set\n",
    "print('Accuracy score on train set: {}'.format(metrics.accuracy_score(y_train, train_preds)))\n",
    "# Calculate and print F1 score on the training set (weighted average)\n",
    "print('F1 score on train set: {}'.format(metrics.f1_score(y_train, train_preds, average='weighted')))\n",
    "# Calculate and print precision score on the training set (weighted average)\n",
    "print('Precision score on train set: {}'.format(metrics.precision_score(y_train, train_preds, average='weighted')))\n",
    "# Calculate and print recall score on the training set (weighted average)\n",
    "print('Recall score on train set: {}'.format(metrics.recall_score(y_train, train_preds, average='weighted')))\n",
    "# Calculate and print ROC-AUC score on the training set (weighted average, one-vs-rest)\n",
    "print('ROC-AUC score on train set: {}'.format(metrics.roc_auc_score(y_train, train_preds_score, average='weighted', multi_class='ovr')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52033ade-02ab-43b3-87fb-73d06ac9717b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "690aaf56-63c7-4974-8b29-236d5a9a024d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transforming the test data into TF-IDF vectors using the same vectorizer that was previously fitted on the training data\n",
    "test_feature_vects = vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c4b6743-32fc-44ef-96e4-6e016939857f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions on the test feature vectors \n",
    "test_preds = nb_classifier.predict(test_feature_vects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fef31fb-037d-4a87-9888-b074267deaa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating class probabilities for the test data \n",
    "test_preds_score = nb_classifier.predict_proba(test_feature_vects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6be684ff-7661-4df5-b49c-aa4d3408946b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluation metrics \n",
    "print('Accuracy score on test set: {}'.format(metrics.accuracy_score(y_test, test_preds)))\n",
    "print('F1 score on test set: {}'.format(metrics.f1_score(y_test, test_preds, average='weighted')))\n",
    "print('Precision score on test set: {}'.format(metrics.precision_score(y_test, test_preds, average='weighted')))\n",
    "print('Recall score on test set: {}'.format(metrics.recall_score(y_test, test_preds, average='weighted')))\n",
    "print('ROC-AUC score on test set: {}'.format(metrics.roc_auc_score(y_test, test_preds_score, average='weighted',multi_class='ovr')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc4c2ecf-d173-4e12-a68f-776dcb3a5c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a classification report to evaluate the classifier's performance\n",
    "cr_NB = metrics.classification_report(y_test, test_preds)\n",
    "\n",
    "# Print the classification report\n",
    "print(\"\\n\\nClassification Report\\n\")\n",
    "print(cr_NB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dcc7121-e389-45ca-9461-9f11beb8ca8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the confusion matrix to evaluate classifier performance\n",
    "cm_NB = metrics.confusion_matrix(y_test, test_preds)\n",
    "\n",
    "# Print a header indicating the following output is the confusion matrix\n",
    "print(\"Confusion Matrix\\n\")\n",
    "\n",
    "# Print the confusion matrix\n",
    "print(cm_NB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f4d202f-4257-4fbc-8adc-638b2a5e0310",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a heatmap visualization of the confusion matrix \n",
    "sns.heatmap(cm_NB, annot=True, cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c941b940-3881-4e8e-b078-94b3859f116c",
   "metadata": {},
   "source": [
    "##### This part of the code is commented which was made to run once for collecting csv with aggregated results of labels and scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ae7daa5-7a43-4db3-98b7-fe934765c8cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create a new column 'Labels' in the DataFrame df1_copy and initialize its values to NaN\n",
    "# df1_copy['Labels'] = np.nan\n",
    "## Create a new column 'Scores' in the DataFrame df1_copy and initialize its values to NaN\n",
    "# df1_copy['Scores'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3bb6b79-3e92-49e7-8c61-58501da1d5d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Iterate through a range of indices starting from 10 (assuming there's a reason for this specific starting point)\n",
    "# for i in range(10, len(X_train)):\n",
    "#     # Find the index of the row in df1_copy where the 'headline' column matches X_train.iloc[i]\n",
    "#     j = df1_copy.index[df1_copy['headline'] == X_train.iloc[i]].tolist()[0]\n",
    "#     # Update the 'Labels' column in df1_copy with the prediction from train_preds for the corresponding row\n",
    "#     df1_copy['Labels'][j] = train_preds[i]\n",
    "#     # Update the 'Scores' column in df1_copy with the maximum prediction score from train_preds_score for the corresponding row\n",
    "#     df1_copy['Scores'][j] = np.max(train_preds_score[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e50af923-2903-4eae-9512-39557c5129de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Iterate through a range of indices starting from 0 (assuming you're iterating through the entire test set)\n",
    "# for i in range(0, len(X_test)):\n",
    "#     # Find the index of the row in df1_copy where the 'headline' column matches X_test.iloc[i]\n",
    "#     j = df1_copy.index[df1_copy['headline'] == X_test.iloc[i]].tolist()[0]  \n",
    "#     # Update the 'Labels' column in df1_copy with the prediction from test_preds for the corresponding row\n",
    "#     df1_copy['Labels'][j] = test_preds[i]  \n",
    "#     # Update the 'Scores' column in df1_copy with the maximum prediction score from test_preds_score for the corresponding row\n",
    "#     df1_copy['Scores'][j] = np.max(test_preds_score[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31895cd2-b007-4f54-a6bb-2bfaff47c3d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save the DataFrame df1 to a CSV file named 'Final-NB.csv'\n",
    "# df1.to_csv('Final-NB.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "086c6655-39ab-47b8-b123-ad2878e5ef59",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 5.3. KNN Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32071d20-846a-48f9-a762-21ce385d6220",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a K-Nearest Neighbors (KNN) classifier with the specified hyperparameters\n",
    "model_knn = KNeighborsClassifier(n_neighbors=22, metric='cosine', weights='distance')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b365e1d-59f4-4328-931d-9530e1dd90f4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Train Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30caa15e-d5a2-474f-ac73-ef1023a0faf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the KNN classifier to the training feature vectors and corresponding labels\n",
    "model_knn.fit(train_feature_vects, y_train)\n",
    "# Make predictions on the training feature vectors \n",
    "pred_train_knn = model_knn.predict(train_feature_vects)\n",
    "# Get class probabilities for the training feature vectors\n",
    "pred_train_scores_knn = model_knn.predict_proba(train_feature_vects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b18aa5a0-453a-4565-8a4d-d382fdc2c8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print out evaluation metrcics on train set\n",
    "print('Accuracy score on train set: {}'.format(metrics.accuracy_score(y_train,pred_train_knn)))\n",
    "print('F1 score on train set: {}'.format(metrics.f1_score(y_train,pred_train_knn, average='weighted')))\n",
    "print('Precision on train set: {}'.format(metrics.precision_score(y_train,pred_train_knn, average='weighted')))\n",
    "print('Recall on train set: {}'.format(metrics.recall_score(y_train,pred_train_knn, average='weighted')))\n",
    "print('ROC-AUC score on train set: {}'.format(metrics.roc_auc_score(y_train,pred_train_scores_knn, average='weighted',multi_class='ovr')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b9ff49-c44d-4499-91d9-dff87fb01839",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6e1f658-cf73-409c-b61e-c4e325970e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test feature vectors \n",
    "pred_test_knn = model_knn.predict(test_feature_vects)\n",
    "\n",
    "# Get class probabilities for the test feature vectors\n",
    "pred_test_scores_knn = model_knn.predict_proba(test_feature_vects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9495cfa7-5f30-47a2-8119-ed1a03ef2936",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print out evaluation metrcics on test set\n",
    "print('Accuracy score on test set: {}'.format(metrics.accuracy_score(y_test,pred_test_knn)))\n",
    "print('F1 score on test set: {}'.format(metrics.f1_score(y_test,pred_test_knn, average='weighted')))\n",
    "print('Precision on test set: {}'.format(metrics.precision_score(y_test,pred_test_knn, average='weighted')))\n",
    "print('Recall on test set: {}'.format(metrics.recall_score(y_test,pred_test_knn, average='weighted')))\n",
    "print('AUC score on test set: {}'.format(metrics.roc_auc_score(y_test,pred_test_scores_knn, average='weighted',multi_class='ovr')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31be7c26-a16a-4cc3-864b-b08f19240d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a classification report to evaluate KNN classifier's performance on the test data\n",
    "cr_KNN = metrics.classification_report(y_test, pred_test_knn)\n",
    "# Print the classification report\n",
    "print(\"\\n\\nClassification Report\\n\")\n",
    "print(cr_KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea5f142e-d6be-4dd9-8a5a-4cb1e438bb0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the confusion matrix to evaluate KNN classifier performance on the test data\n",
    "cm_KNN = metrics.confusion_matrix(y_test, pred_test_knn)\n",
    "# Print the confusion matrix\n",
    "print(\"Confusion Matrix\\n\")\n",
    "print(cm_KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d003c39f-7a42-42ca-903f-c22613a336ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a heatmap visualization of the confusion matrix \n",
    "sns.heatmap(cm_KNN, annot=True, cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39acc8a9-bef3-4e80-8331-1d4a7adb04f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1_copy['Labels'] = np.nan\n",
    "# df1_copy['Scores'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e582e25d-dc4c-47f6-8b45-ce5380eac58e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(10,len(X_train)):\n",
    "#   j=df1_copy.index[df1_copy['headline']==X_train.iloc[i]].tolist()[0]\n",
    "#   df1_copy['Labels'][j] = pred_train_knn[i]\n",
    "#   df1_copy['Scores'][j] = np.max(pred_train_scores_knn[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3cc20cb-18e2-4b18-9cc2-24d1ec9fe876",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(0,len(X_test)):\n",
    "#   j=df1_copy.index[df1_copy['headline']==X_test.iloc[i]].tolist()[0]\n",
    "#   df1_copy['Labels'][j] = pred_test[i]\n",
    "#   df1_copy['Scores'][j] = np.max(pred_test_scores_knn[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6449645c-8416-4c93-b40a-d056bf6c56de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1.to_csv('Final-KNN.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeb362ad-61bc-4870-b706-89995ab04300",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 5.4. Graphs:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6c9414-05f9-49af-8a79-95c9be23913e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### 5.4.1. Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e73a875-b2cc-4d88-b9c9-817dbf63d141",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a LabelBinarizer and fit it to the training labels\n",
    "label_binarizer1 = LabelBinarizer().fit(y_train)\n",
    "\n",
    "# Transform the test labels into one-hot encoded binary vectors\n",
    "y_onehot_test = label_binarizer1.transform(y_test)\n",
    "\n",
    "# Transform the label [1] into a one-hot encoded binary vector\n",
    "label_binarizer1.transform([1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a605a7b5-568d-40b9-8507-89867a4faf59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the class of interest and its corresponding class ID for class 0\n",
    "class_of_interest0 = 0\n",
    "class_id0 = np.flatnonzero(label_binarizer1.classes_ == 0)[0]\n",
    "\n",
    "# Define the class of interest and its corresponding class ID for class 1\n",
    "class_of_interest1 = 1\n",
    "class_id1 = np.flatnonzero(label_binarizer1.classes_ == 1)[0]\n",
    "\n",
    "# Define the class of interest and its corresponding class ID for class 2\n",
    "class_of_interest2 = 2\n",
    "class_id2 = np.flatnonzero(label_binarizer1.classes_ == 2)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3caad41c-9f09-471c-8660-b38ce92aa1d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC curve for class 0 vs the rest\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_onehot_test[:, class_id0],\n",
    "    test_preds_score[:, class_id0],\n",
    "    name=f\"{class_of_interest0} vs the rest\",\n",
    "    color=\"darkorange\"\n",
    ")\n",
    "plt.axis(\"square\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"One-vs-Rest ROC curves:\\nNegative vs (Neutral& Positive)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b781861d-e12b-4706-ae93-f12fbc0198a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC curve for class 1 vs the rest\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_onehot_test[:, class_id1],\n",
    "    test_preds_score[:, class_id1],\n",
    "    name=f\"{class_of_interest1} vs the rest\",\n",
    "    color=\"green\"\n",
    ")\n",
    "plt.axis(\"square\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"One-vs-Rest ROC curves:\\nNeutral vs (Negative& Positive)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a05e7ce-55c8-4d2b-9718-10abea28233f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC curve for class 2 vs the rest\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_onehot_test[:, class_id2],\n",
    "    test_preds_score[:, class_id2],\n",
    "    name=f\"{class_of_interest2} vs the rest\",\n",
    "    color=\"blue\"\n",
    ")\n",
    "plt.axis(\"square\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"One-vs-Rest ROC curves:\\nPositive vs (Negative& Neutral)\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29119e64-94e9-4d7c-ace4-5254c92feaf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Micro-averaged ROC curve\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_onehot_test.ravel(),\n",
    "    test_preds_score.ravel(),\n",
    "    name=\"micro-average OvR\",\n",
    "    color=\"darkorange\"\n",
    ")\n",
    "plt.axis(\"square\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"Micro-averaged One-vs-Rest\\nReceiver Operating Characteristic\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78369320-5a6f-4fa4-bc0d-b9227b26df08",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### 5.4.2. KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46386506-81ae-4c58-9b6b-3a2e153799f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC curve for class 0 vs the rest using pred_test_scores_knn\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_onehot_test[:, class_id0],\n",
    "    pred_test_scores_knn[:, class_id0],\n",
    "    name=f\"{class_of_interest0} vs the rest\",\n",
    "    color=\"darkorange\"\n",
    ")\n",
    "plt.axis(\"square\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"One-vs-Rest ROC curves:\\nNegative vs (Neutral& Positive)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a025c25-7df4-4b56-ada1-a9ac3fabffef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC curve for class 1 vs the rest using pred_test_scores_knn\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_onehot_test[:, class_id1],\n",
    "    pred_test_scores_knn[:, class_id1],\n",
    "    name=f\"{class_of_interest1} vs the rest\",\n",
    "    color=\"green\"\n",
    ")\n",
    "plt.axis(\"square\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"One-vs-Rest ROC curves:\\nNeutral vs (Negative& Positive)\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d15aa4cb-1a2f-4b54-9f53-3e18b5f76745",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC curve for class 2 vs the rest using pred_test_scores_knn\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_onehot_test[:, class_id2],\n",
    "    pred_test_scores_knn[:, class_id2],\n",
    "    name=f\"{class_of_interest2} vs the rest\",\n",
    "    color=\"blue\"\n",
    ")\n",
    "plt.axis(\"square\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"One-vs-Rest ROC curves:\\nPositive vs (Negative& Neutral)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3b17cb9-2421-4ca3-8826-9c442f18f658",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Micro-averaged ROC curve using pred_test_scores_knn\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_onehot_test.ravel(),\n",
    "    pred_test_scores_knn.ravel(),\n",
    "    name=\"micro-average OvR\",\n",
    "    color=\"darkorange\"\n",
    ")\n",
    "plt.axis(\"square\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"Micro-averaged One-vs-Rest\\nReceiver Operating Characteristic\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f7b9cb0-acd5-4dbd-b2db-67ffd4a7f9e5",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 5.5. Comparison of predictions between HuggingFace, Naive Bayes and KNN: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f30591c0-c8e2-4caf-9f3f-bc2bec1dd3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a PCA object with 2 principal components\n",
    "pca = PCA(n_components=2)\n",
    "# Convert the test feature vectors to dense matrix \n",
    "dense_matrix_test = test_feature_vects.toarray()\n",
    "# Apply PCA to the dense matrix to reduce it to 2 principal components\n",
    "pca_x_test = pca.fit_transform(dense_matrix_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "305553d1-eaf6-454f-9778-489430a237df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create subplot for features \n",
    "plt.subplot(1, 3, 1)\n",
    "new_list_x=[]\n",
    "new_list_y=[]\n",
    "for j in range(0,len(y_test)):\n",
    "    if y_test[j]==0:\n",
    "        new_list_x.append(pca_x_test[j][0])\n",
    "        new_list_y.append(pca_x_test[j][1])\n",
    "        label_str = 'Negative'\n",
    "plt.scatter(new_list_x,new_list_y,label=f'{label_str}', c=np.random.rand(3,), edgecolor='k',s=40)   \n",
    "plt.legend()\n",
    "plt.xlabel(\"Features reduced\",fontsize=10)\n",
    "plt.ylabel(\"Features reduced\",fontsize=10)\n",
    "plt.title(\"Negative HF\")\n",
    "\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "for j in range(0,len(test_preds)):\n",
    "    if y_test[j]==0:\n",
    "        new_list_x.append(pca_x_test[j][0])\n",
    "        new_list_y.append(pca_x_test[j][1])\n",
    "        label_str = 'Negative'\n",
    "plt.scatter(new_list_x,new_list_y,label=f'{label_str}', c=np.random.rand(3,),edgecolor='k',s=30)   \n",
    "plt.legend()\n",
    "plt.title(\"Negative NB\")\n",
    "\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "for j in range(0,len(pred_test_knn)):\n",
    "    if y_test[j]==0:\n",
    "        new_list_x.append(pca_x_test[j][0])\n",
    "        new_list_y.append(pca_x_test[j][1])\n",
    "        label_str = 'Negative'\n",
    "plt.scatter(new_list_x,new_list_y,label=f'{label_str}', c=np.random.rand(3,),edgecolor='k',s=30)   \n",
    "plt.legend()\n",
    "plt.title(\"Negative KNN\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd4997d1-811e-4419-ba45-882e87f44e93",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplot(1, 3, 1)\n",
    "new_list_x=[]\n",
    "new_list_y=[]\n",
    "for j in range(0,len(y_test)):\n",
    "    if y_test[j]==1:\n",
    "        new_list_x.append(pca_x_test[j][0])\n",
    "        new_list_y.append(pca_x_test[j][1])\n",
    "        label_str = 'Neutral'\n",
    "plt.scatter(new_list_x,new_list_y,label=f'{label_str}', c=np.random.rand(3,), edgecolor='k',s=40)   \n",
    "plt.legend()\n",
    "plt.xlabel(\"Features reduced\",fontsize=10)\n",
    "plt.ylabel(\"Features reduced\",fontsize=10)\n",
    "plt.title(\"Neutral HF\")\n",
    "\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "for j in range(0,len(test_preds)):\n",
    "    if y_test[j]==1:\n",
    "        new_list_x.append(pca_x_test[j][0])\n",
    "        new_list_y.append(pca_x_test[j][1])\n",
    "        label_str = 'Neutral'\n",
    "plt.scatter(new_list_x,new_list_y,label=f'{label_str}', c=np.random.rand(3,),edgecolor='k',s=30)   \n",
    "plt.legend()\n",
    "plt.title(\"Neutral NB\")\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "for j in range(0,len(pred_test_knn)):\n",
    "    if y_test[j]==1:\n",
    "        new_list_x.append(pca_x_test[j][0])\n",
    "        new_list_y.append(pca_x_test[j][1])\n",
    "        label_str = 'Neutral'\n",
    "plt.scatter(new_list_x,new_list_y,label=f'{label_str}', c=np.random.rand(3,),edgecolor='k',s=30)   \n",
    "plt.legend()\n",
    "plt.title(\"Neutral KNN\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf862781-eeb5-4c6c-bad0-82c7dca49cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create subplot for features \n",
    "plt.subplot(1, 3, 1)\n",
    "new_list_x=[]\n",
    "new_list_y=[]\n",
    "for j in range(0,len(y_test)):\n",
    "    if y_test[j]==2:\n",
    "        new_list_x.append(pca_x_test[j][0])\n",
    "        new_list_y.append(pca_x_test[j][1])\n",
    "        label_str = 'Positive'\n",
    "plt.scatter(new_list_x,new_list_y,label=f'{label_str}', c=np.random.rand(3,), edgecolor='k',s=40)   \n",
    "plt.legend()\n",
    "plt.xlabel(\"Features reduced\",fontsize=10)\n",
    "plt.ylabel(\"Features reduced\",fontsize=10)\n",
    "plt.title(\"Positive HF\")\n",
    "\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "for j in range(0,len(test_preds)):\n",
    "    if y_test[j]==2:\n",
    "        new_list_x.append(pca_x_test[j][0])\n",
    "        new_list_y.append(pca_x_test[j][1])\n",
    "        label_str = 'Positive'\n",
    "plt.scatter(new_list_x,new_list_y,label=f'{label_str}', c=np.random.rand(3,),edgecolor='k',s=30)   \n",
    "plt.legend()\n",
    "plt.title(\"Positive NB\")\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "for j in range(0,len(pred_test_knn)):\n",
    "    if y_test[j]==2:\n",
    "        new_list_x.append(pca_x_test[j][0])\n",
    "        new_list_y.append(pca_x_test[j][1])\n",
    "        label_str = 'Positive'\n",
    "plt.scatter(new_list_x,new_list_y,label=f'{label_str}', c=np.random.rand(3,),edgecolor='k',s=30)   \n",
    "plt.legend()\n",
    "plt.title(\"Positive KNN\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819325b5-701a-4cb4-b58d-03f772c44578",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#  PHASE III - CONTENT-BASED COLLABORATIVE FILTERING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cae56f2f-c87d-487e-96bc-2e90d6f7cba9",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 6. Preprocessing for Feature Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c20bcb43-1d51-4bec-a74c-83539c5335b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0.1', 'index', 'Unnamed: 0', 'headline', 'url', 'publisher',\n",
      "       'date', 'stock', 'Labels', 'Scores'],\n",
      "      dtype='object')\n",
      "         date ticker  labels    scores\n",
      "0  2017-02-11      A     2.0  0.649311\n",
      "1  2017-02-13      A     1.0  0.726289\n",
      "2  2017-02-14      A     1.0  1.000000\n",
      "3  2017-02-15      A     2.0  0.405166\n",
      "4  2017-02-27      A     1.0  0.690469\n"
     ]
    }
   ],
   "source": [
    "# Read result from sentiment analysis \n",
    "sentiment_df = pd.read_csv('Final-KNN.csv')\n",
    "print(sentiment_df.columns)\n",
    "\n",
    "# Drop unwanted columns\n",
    "unwanted_columns = ['Unnamed: 0.1', 'index', 'Unnamed: 0', 'headline', 'url', 'publisher']\n",
    "sentiment_df = sentiment_df.drop(columns=unwanted_columns)\n",
    "sentiment_df.head()\n",
    "\n",
    "# Extract only yyyy-mm-dd\n",
    "sentiment_df['date'] = pd.to_datetime(sentiment_df['date']).dt.date\n",
    "\n",
    "# Sort df by 'ticker', 'date', and 'Scores' in descending order\n",
    "sentiment_df = sentiment_df.rename(columns={'stock': 'ticker'})\n",
    "sentiment_df = sentiment_df.sort_values(by=['ticker', 'date', 'Scores'], ascending=[True, True, False])\n",
    "\n",
    "# Get the highest score for each unique 'ticker' and 'date' combination\n",
    "sentiment_df = sentiment_df.drop_duplicates(subset=['ticker', 'date'], keep='first')\n",
    "sentiment_df = sentiment_df.reset_index(drop=True)\n",
    "sentiment_df.columns = sentiment_df.columns.str.lower()\n",
    "print(sentiment_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad276ce1-b94a-469d-941e-91358e18573f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_5</th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.694621</td>\n",
       "      <td>0.050102</td>\n",
       "      <td>0.525245</td>\n",
       "      <td>-4.551044</td>\n",
       "      <td>1.058598</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>A</td>\n",
       "      <td>46.178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.948428</td>\n",
       "      <td>0.293337</td>\n",
       "      <td>0.468583</td>\n",
       "      <td>-4.553871</td>\n",
       "      <td>1.065424</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>A</td>\n",
       "      <td>46.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.885045</td>\n",
       "      <td>0.113366</td>\n",
       "      <td>0.636895</td>\n",
       "      <td>-4.541259</td>\n",
       "      <td>1.020767</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>A</td>\n",
       "      <td>46.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.465603</td>\n",
       "      <td>0.731387</td>\n",
       "      <td>0.340322</td>\n",
       "      <td>-4.613235</td>\n",
       "      <td>1.154764</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>A</td>\n",
       "      <td>47.668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.977897</td>\n",
       "      <td>1.172599</td>\n",
       "      <td>0.107106</td>\n",
       "      <td>-4.593020</td>\n",
       "      <td>1.132056</td>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>A</td>\n",
       "      <td>47.817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature_1  feature_2  feature_3  feature_4  feature_5       date ticker  \\\n",
       "0  -4.694621   0.050102   0.525245  -4.551044   1.058598 2017-01-03      A   \n",
       "1  -4.948428   0.293337   0.468583  -4.553871   1.065424 2017-01-04      A   \n",
       "2  -4.885045   0.113366   0.636895  -4.541259   1.020767 2017-01-05      A   \n",
       "3  -5.465603   0.731387   0.340322  -4.613235   1.154764 2017-01-06      A   \n",
       "4  -5.977897   1.172599   0.107106  -4.593020   1.132056 2017-01-09      A   \n",
       "\n",
       "    close  \n",
       "0  46.178  \n",
       "1  46.784  \n",
       "2  46.228  \n",
       "3  47.668  \n",
       "4  47.817  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV file into a DataFrame\n",
    "stock_df = pd.read_csv('pca_stock_result.csv')\n",
    "# Convert the 'Date' column to datetime format\n",
    "stock_df['Date'] = pd.to_datetime(stock_df['Date'])\n",
    "# Set the 'Date' column as the DataFrame's index \n",
    "stock_df.set_index('Date', inplace=False)\n",
    "# Rename columns to lowercase\n",
    "stock_df = stock_df.rename(columns={\n",
    "    'Feature_1': 'feature_1',\n",
    "    'Feature_2': 'feature_2',\n",
    "    'Feature_3': 'feature_3',\n",
    "    'Feature_4': 'feature_4',\n",
    "    'Feature_5': 'feature_5',\n",
    "    'Date': 'date',\n",
    "    'Ticker': 'ticker',\n",
    "    'Close': 'close'\n",
    "})\n",
    "\n",
    "# Display the first few rows of the DataFrame\n",
    "stock_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26f51dc2-2b91-4160-bad7-2862f105343d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify the tickers match between data sets\n",
    "stock_tickers = stock_df['ticker'].unique()\n",
    "sentiment_tickers = sentiment_df['ticker'].unique()\n",
    "set(stock_tickers) == set(sentiment_tickers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0338b9e5-49a0-4c82-947c-cf67877e99b4",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tickers missing in stock_df but present in sentiment_df: {'BLPH', 'ENV', 'SRG', 'LCAHW', 'VKI', 'MYD', 'CONN', 'GDS', 'URGN', 'EGF', 'ENIC', 'LGI', 'MXC', 'AMGN', 'DNP', 'WK', 'MIN', 'NCV', 'DARE', 'ZKIN', 'CTBB', 'ENO', 'EVV', 'ROST', 'NXP', 'PTY', 'KRNT', 'ATLC', 'RETA', 'MHI', 'MHD', 'GWRS', 'STK', 'CRM', 'UHAL', 'CBD', 'CTDD', 'LOMA', 'DWAC', 'CSTR', 'CARA', 'APLS', 'TCON', 'CGNX', 'MTX', 'GOOG', 'HESM', 'LIFE', 'CDTX', 'NBO', 'MFM', 'MSN', 'YRD', 'VRNA', 'CRSP', 'IDE', 'WIA', 'CLSD', 'PMM', 'MLCO', 'NPV', 'RRR', 'FIVE', 'CBOE', 'SITE', 'CWST', 'ZG', 'LAUR', 'CVI', 'SD', 'AXSM', 'CBRL', 'HLI', 'RDFN', 'TWN', 'NGVT', 'BABA', 'ADVM', 'ASPN', 'ACRS', 'LCII', 'JNPR', 'APPN', 'JEQ', 'MCRB', 'BNJ', 'ZDGE', 'SUM', 'SELB', 'SGH', 'NCNA', 'NDRA', 'NAN', 'GOOGL', 'TPIC', 'EXG', 'DEA', 'TRU', 'CENX', 'EDU', 'OBE', 'PDLB', 'IMGN', 'CASH', 'CE', 'CBH', 'KALA', 'RZB', 'PIXY', 'JPT', 'RS', 'DESP', 'BME', 'NXRT', 'CXH', 'NIM', 'BGT', 'ISRG', 'NBB', 'FDEU', 'EMO', 'ASND', 'HONE', 'BRN', 'NCTY', 'NQP', 'VKTX', 'FFIV', 'AMPH', 'PZC', 'HLNE', 'OPTN', 'PFL', 'PLYM', 'CHUY', 'MGF', 'GOLF', 'OR', 'SNAP', 'ATUS', 'BLX', 'FFNW', 'ETV', 'MDB', 'RIV', 'WOW', 'RYAM', 'NKX', 'XELA', 'CAF', 'VFL', 'DBL', 'ETO', 'DMB', 'ETJ', 'WVE', 'ATOS', 'IGA', 'BIT', 'GSBD', 'TGI', 'OVID', 'NAC', 'SMHI', 'RH', 'CEV', 'IHD', 'MHLD', 'FSBC', 'CHCI', 'KRP', 'DSL', 'NMZ', 'SFR', 'JCE', 'LOB', 'BIDU', 'BKD', 'LRCX', 'HIX', 'RIO', 'HTD', 'ATOM', 'NAZ', 'TGLS', 'SAFT', 'HPF', 'IMRN', 'FPH', 'FTS', 'ZYNE', 'HRI', 'SUPV', 'PEN', 'GPJA', 'PHD', 'PMX', 'VCYT', 'MSBI', 'TSLA', 'TWNK', 'EOI', 'MANU', 'RMNI', 'GLPG', 'ERC', 'AGI', 'DTF', 'EXAS', 'LC', 'KSM', 'PLNT', 'OLLI', 'AMS', 'TUSK', 'IHIT', 'NEXT', 'SQ', 'JPS', 'PSTG', 'NUS', 'SVT', 'ISRL', 'ATAI', 'BAND', 'EDIT', 'GASS', 'CVNA', 'MUI', 'NTLA', 'GOOD', 'HIMX', 'CNFR', 'JOF', 'OSG', 'YEXT', 'SWIN', 'VOC', 'LSTR', 'APRN', 'BNED', 'GNL', 'LKQ', 'EIGR', 'FUV', 'INPX', 'HGV', 'CII', 'BW', 'DKS', 'CPIX', 'RHE', 'TRTN', 'EURN', 'ICHR', 'WEA', 'ACLS', 'ALRM', 'RNGR', 'EVA', 'EDD', 'EXPR', 'RWT', 'INSE', 'TEI', 'BTO', 'PI', 'IMMR', 'PX', 'KN', 'POLA', 'ESQ', 'VYGR', 'CACC', 'BBN', 'MELI', 'AMSC', 'MUC', 'NTB', 'DRH', 'FAT', 'TTD', 'PJT', 'BHACW', 'SE', 'PFD', 'GM', 'ALPN', 'INVH', 'FANH', 'ENX', 'NXPI', 'AIRG', 'ASIX', 'USM', 'ABG', 'OUT', 'HTLD', 'JILL', 'NMS', 'FBNC', 'WYNN', 'INSW', 'MMT', 'EMF', 'EVT', 'VTVT', 'KBH', 'CRVS', 'OXBRW', 'KEN', 'PML', 'TBB', 'NUV', 'SCWX', 'MRBK', 'DHY', 'CUTR', 'RYTM', 'PMO', 'GLV', 'CTV', 'BETR', 'RCL', 'RCS', 'XFLT', 'MESO', 'CGEN', 'AKBA', 'AQMS', 'DG', 'MSD', 'TCBI', 'IAE', 'SPLK', 'TTWO', 'SYF', 'MHN', 'HYI', 'HMST', 'VSTO', 'BBU', 'NOK', 'SFB', 'MPA', 'IIPR', 'DCF', 'ADNT', 'MDGL', 'AVGR', 'AER', 'CHRS', 'CLNE', 'CLPR', 'ZLAB', 'NODK', 'GOOS', 'ING', 'RELY', 'EOT', 'BUR', 'LIND', 'IAF', 'SHOP', 'BYSI', 'KREF', 'GSBC', 'BRACR', 'INST', 'QCOM', 'DPG', 'SND', 'DERM', 'RA', 'DSKE', 'GS', 'AZUL', 'GBR', 'MQY', 'YUMC', 'THQ', 'XBIT', 'TRTX', 'WTI', 'ZTO', 'VLT', 'CRI', 'MBIN', 'QD', 'MQT', 'AXON', 'SECO', 'SMCI', 'PVBC', 'FNWB', 'HLTH', 'AMAT', 'TDG', 'PCQ', 'AAPL', 'BCRX', 'MRAM', 'MUA', 'AIC', 'BZUN', 'MHF', 'ENPH', 'EVC', 'EAI', 'AJX', 'IGD', 'CBIO', 'SRTS', 'GPACW', 'SLAB', 'EXEL', 'HPI', 'CODX', 'CWH', 'BGB', 'CHEK', 'URBN', 'BOE', 'AMBA', 'AMKR', 'BKN', 'FSI', 'CCV', 'BCX', 'MMU', 'JPI', 'CBL', 'CTRN', 'APPF', 'SNOA', 'NRO', 'NXN', 'TEAM', 'JRS', 'AFGE', 'FSLR', 'NTNX', 'CARS', 'ACN', 'HUBB', 'GNT', 'PFH', 'PAVM', 'MPV', 'EDF', 'TSE', 'APT', 'MTNB', 'EEX', 'PETQ', 'GPP', 'MTEM', 'ORA', 'NIE', 'CGO', 'EVN', 'MHLA', 'USA', 'GILD', 'OPGN', 'CSSE', 'XIN', 'BYM', 'VECO', 'TPB', 'DLTH', 'UFAB', 'TRVG', 'PDI', 'CET', 'NRK', 'AVK', 'BLBD', 'JHG', 'MRUS', 'HFRO', 'MIY', 'MHNC', 'FEDU', 'TRIB', 'BST', 'ABCB', 'RFI', 'BHV', 'MEDP', 'KNX', 'EVBG', 'MNP', 'BX', 'LQDT', 'CECO', 'PAC', 'AVIR', 'BHACU', 'VIRT', 'JBGS', 'WRK', 'AQN', 'NNY', 'DO', 'ORC', 'PGP', 'MMV', 'TWLO', 'TMST', 'GGN', 'OOMA', 'SMMT', 'SSTI', 'RVP', 'BSL', 'KRYS', 'NFLX', 'RQI', 'SGBX', 'SNDX', 'DSWL', 'BGX', 'MLSS', 'ARCH', 'ELF', 'RGT', 'XPER', 'NOMD', 'TRHC', 'MCFT', 'PMF', 'AMSF', 'LCAHU', 'LNG', 'NBW', 'PCK', 'FND', 'TCRX', 'AMLX', 'WING', 'LGL', 'IIF', 'ULH', 'AGR', 'ROSEW', 'GSD', 'AEIS', 'CFRX', 'YPF', 'PGZ', 'S', 'RBB', 'ACV', 'HPE', 'VNCE', 'WTTR', 'QNST', 'CGBD', 'CMG', 'PULM', 'JLS', 'SNDR', 'NVDA', 'SPSC', 'CLM', 'TCMD', 'FHB', 'CLLS', 'NTRA', 'RELX', 'JAGX', 'EVG', 'ADUS', 'GGZ', 'DEI', 'PRME', 'SYRS', 'AMWD', 'NML', 'CSTE', 'ETSY', 'SHAK', 'SEDG', 'CHKP', 'DCPH', 'CIM', 'FANG', 'EYE', 'EHI', 'ELC', 'UTF', 'FTV', 'CPLP', 'HPS', 'GDDY', 'ISD', 'DFP', 'CHCT', 'DVN', 'PUMP', 'PFGC', 'NE', 'PRH', 'VVR', 'SGRY', 'PCN', 'USAS', 'MBI', 'USFD', 'CORT', 'AC', 'GES', 'ALRN', 'ALNY', 'EVF', 'PTGX', 'RGC', 'RSLS', 'AYX', 'MCB', 'NSA', 'AAT', 'SSNC', 'SEAS', 'PLSE', 'KIDS', 'CELC', 'NVTA', 'BHAC', 'SSKN', 'AA', 'EDI', 'ODFL', 'TANH', 'HEQ', 'SBS', 'THW', 'JRI', 'VRTX', 'COGT', 'TDOC', 'VVV', 'NNBR', 'PSF', 'BWG', 'PEGA', 'ETY', 'COLL', 'ADAP', 'CMU', 'URI', 'BPMC', 'MGYR', 'ANSS', 'EMP', 'BUI', 'CTR', 'HNW', 'BDJ', 'APLE', 'PNI', 'SACH', 'OKTA', 'SOI', 'MUJ', 'ORCL', 'INVA', 'SYBX', 'YTEN', 'EFR', 'LULU', 'IPI', 'JKHY', 'NOM', 'CMT', 'CSTM', 'ACIU', 'EVM', 'XRAY', 'CABO', 'JBLU', 'OMF', 'EVH', 'DMF', 'COE', 'RGNX', 'JQC', 'TIL', 'GPACU', 'ROKU', 'FLGT', 'ADBE', 'IOR', 'VNRX', 'MVT', 'CXE', 'FTNT', 'CASY', 'ELP', 'HCM', 'BY', 'HYB', 'NXC', 'CSCO', 'LMFA', 'SAH', 'DHF', 'RNP', 'ANAB', 'CTMX', 'NVCR', 'APEI', 'UE', 'BLMN', 'PFO', 'MBRX', 'WLKP', 'BGNE', 'BLD', 'ATXI', 'TPZ', 'PYN', 'GIM', 'AGNC', 'LCA', 'STX', 'MA', 'CHRW', 'PXLW', 'PCM', 'CENT', 'GMRE', 'AIV', 'ACMR', 'NMI', 'UPS', 'FBK', 'EIM', 'ANGO', 'GDO', 'GMS', 'MYO', 'USAC', 'ECF', 'DFIN', 'CCRN', 'SLG', 'NEXA', 'WIW', 'NBH', 'GSK', 'CCM', 'FNKO', 'HIE', 'BLE', 'HBB', 'FOF', 'UMH', 'KALV', 'DMO', 'COTY', 'BTZ', 'PFN', 'EVO', 'CRF', 'ARGX', 'XHR', 'ABBV', 'SNES', 'SDRL', 'BHVN', 'FCO', 'MRSN', 'CARG', 'NTCT', 'RPD', 'SCD', 'LVS', 'COMM', 'EMKR', 'SAFE', 'VERI', 'FCPT', 'LW', 'AMZN', 'RUN', 'CEN', 'IRTC', 'OPP', 'SCM', 'BNTC', 'BOX', 'MSFT', 'MYN', 'LUNA', 'REVG', 'ANTX', 'FTF', 'MRT', 'NEWR', 'PNF', 'MVF', 'GPRO', 'LNTH', 'IGI', 'NCSM', 'CMPR', 'EBAY', 'GPMT', 'AJXA', 'BSM', 'LGIH', 'PETZ', 'LTRX', 'RACE', 'AGLE', 'BL', 'KFRC', 'AKAM', 'CHEF', 'WES', 'SLNO', 'BTA', 'NTZ', 'PEB', 'TRVN', 'CSGS', 'CAMP', 'EAD', 'METC', 'BGR', 'ICCH', 'NXE', 'KTF', 'NUW', 'BRQS', 'HLF', 'BEDU', 'UA', 'GPAC', 'SCS', 'NXJ', 'AAOI', 'SP', 'CC', 'IFRX', 'RKDA', 'ZTR', 'HIBB', 'GTHX', 'GLO', 'UTG', 'GKOS', 'GLQ', 'SBUX', 'JELD', 'ATKR', 'PK'}\n",
      "Tickers missing in sentiment_df but present in stock_df: {'DRAM', 'HMY', 'CX', 'GIMO', 'EPHE', 'HDGE', 'DFRG', 'XXII', 'QESP', 'MRLN', 'CNC', 'AWAY', 'WIFI', 'ROLL', 'WAIR', 'WSCI', 'MOM', 'FTEK', 'ACNB', 'SNV', 'ECYT', 'IYZ', 'PACD', 'APH', 'RT', 'EWL', 'TOO', 'COW', 'CELP', 'ORN', 'HTBX', 'VTTI', 'POPE', 'BPT', 'TXMD', 'VLY', 'PBIP', 'TSL', 'PNQI', 'TCP', 'AUMN', 'CAE', 'DPLO', 'FFHL', 'KSU', 'HA', 'CUB', 'PIP', 'SFE', 'TIF', 'VONG', 'CTBI', 'WSO', 'DWSN', 'WPC', 'SIEB', 'DGLY', 'IXP', 'CBT', 'NNVC', 'CBSH', 'TRUP', 'CLD', 'CYOU', 'CERU', 'VCRA', 'IYY', 'VRTU', 'LUV', 'BBL', 'RFP', 'SGG', 'JWN', 'LIVE', 'TBPH', 'TIPT', 'APOL', 'SSN', 'BDC', 'LM', 'ETN', 'QIWI', 'FICO', 'GII', 'LOV', 'PPL', 'MNGA', 'SYKE', 'LCI', 'RVLT', 'MATR', 'MCF', 'SWZ', 'WFM', 'IMI', 'OPY', 'VICL', 'EGAS', 'GPM', 'PAAS', 'CPS', 'HMG', 'QEFA', 'ASRV', 'MAG', 'LAQ', 'TDS', 'TXN', 'LPT', 'RLGY', 'HI', 'XES', 'CLMS', 'KE', 'SCHC', 'IJH', 'CWI', 'PLTM', 'VA', 'IHE', 'MDY', 'HWAY', 'OXFD', 'HNI', 'SNN', 'UBSI', 'LWAY', 'CNDA', 'NEN', 'NJ', 'AIXG', 'BKH', 'SLW', 'ISBC', 'STBA', 'MDVN', 'EME', 'DV', 'MBT', 'EGY', 'KOOL', 'RAI', 'CMFN', 'CI', 'KONA', 'SEE', 'TOWR', 'EUSA', 'MRO', 'MAS', 'NAUH', 'PLPC', 'NCR', 'PKBK', 'CFBK', 'HRTX', 'EQC', 'NTP', 'BYBK', 'AWI', 'OGE', 'OPB', 'LNKD', 'STAY', 'OAKS', 'CVA', 'GAZ', 'THG', 'AGCO', 'PEBK', 'VDE', 'GURU', 'QGEN', 'BSTC', 'IWB', 'RYU', 'NI', 'GFED', 'EBSB', 'TSBK', 'YRCW', 'GNVC', 'EXPO', 'CASS', 'EXP', 'MIDD', 'GHDX', 'ESTE', 'IJR', 'DVY', 'NSSC', 'CORI', 'UIHC', 'ECT', 'AME', 'COUP', 'ECR', 'FLY', 'NYCB', 'AREX', 'TRIP', 'MNK', 'LKFN', 'EWQ', 'GTT', 'FSS', 'COO', 'JHX', 'KIN', 'FBC', 'IYG', 'GEF.B', 'PGF', 'XNY', 'GOLD', 'HURC', 'ORI', 'CP', 'INTL', 'HAFC', 'IDTI', 'WR', 'FDN', 'CLGX', 'CSL', 'EGOV', 'HTY', 'PRAN', 'SINA', 'ALCO', 'ERF', 'CHSCO', 'GSOL', 'HSGX', 'DOL', 'OFS', 'SUP', 'UNXL', 'COR', 'LTXB', 'LONG', 'CUT', 'AF', 'MTK', 'MTSL', 'IMUC', 'NOBL', 'SYBT', 'VNQ', 'SGA', 'DRYS', 'MLR', 'RBL', 'TECD', 'BBDO', 'ANGL', 'EXA', 'TUP', 'FARM', 'NICE', 'ACFN', 'AON', 'DHI', 'GTY', 'IEO', 'CORE', 'WILN', 'SBLK', 'APWC', 'DPW', 'BSX', 'IESC', 'NYF', 'TWIN', 'XTLB', 'CBM', 'DKT', 'MITL', 'KONE', 'PNR', 'MOFG', 'WNR', 'NLST', 'EVER', 'FIZZ', 'IVE', 'VTR', 'RCII', 'FBR', 'DISCA', 'NL', 'AZZ', 'STFC', 'FAF', 'RGLD', 'ISL', 'BCPC', 'WWW', 'BNCN', 'COHR', 'VDC', 'BAA', 'RTK', 'MOS', 'MTUM', 'MOBL', 'PES', 'ABTL', 'BCE', 'CHD', 'EWH', 'SHIP', 'KGJI', 'TRK', 'XEL', 'ABCD', 'JOE', 'MDT', 'TIME', 'BBG', 'CBR', 'ASYS', 'MICT', 'AMAG', 'LLTC', 'CIDM', 'CIR', 'MNTA', 'TSS', 'IOSP', 'KIM', 'ILB', 'HYS', 'TOT', 'AGZD', 'PEO', 'BLL', 'MNOV', 'MPO', 'MYOS', 'TU', 'TBZ', 'DGS', 'VLGEA', 'ROX', 'DVA', 'OTIC', 'UBNK', 'INCO', 'AZO', 'BUSE', 'PMD', 'SXCP', 'NRIM', 'RDY', 'FUEL', 'ARCI', 'FUNC', 'ATRS', 'SSFN', 'SWM', 'TRI', 'ZX', 'FXN', 'GURE', 'MIDU', 'SKM', 'IMDZ', 'MPET', 'DCT', 'BCOM', 'HDS', 'VET', 'BOCH', 'JKE', 'USLM', 'NMR', 'PBCT', 'FNSR', 'CALL', 'KBAL', 'LNCE', 'SYRG', 'ELGX', 'ACXM', 'FF', 'FXO', 'ZION', 'SRSC', 'BHLB', 'RLH', 'JIVE', 'L', 'ACAS', 'UBA', 'ZFGN', 'MYCC', 'REFR', 'GRES', 'FBHS', 'SPU', 'DWA', 'ACUR', 'LFVN', 'ALSK', 'ARDM', 'EVEP', 'OREX', 'RNN', 'FBSS', 'DWAQ', 'HMTV', 'IMMU', 'SSRI', 'MAMS', 'SHG', 'BPTH', 'EFA', 'BRCD', 'PDEX', 'PRTO', 'FCT', 'EMMS', 'GAM', 'EMXX', 'XCRA', 'HTZ', 'ATI', 'HNR', 'IFON', 'DDS', 'GWPH', 'KTWO', 'LCNB', 'CWT', 'KBSF', 'EWY', 'FRME', 'ITC', 'PNFP', 'WEYS', 'RGA', 'JBHT', 'ANW', 'NYLD', 'MSCI', 'ARL', 'GQRE', 'SRCE', 'RATE', 'SEAC', 'INSY', 'ERA', 'OSBC', 'CCBG', 'FMC', 'OPOF', 'AMBR', 'AMID', 'GFI', 'EVOL', 'CETV', 'ACST', 'INCR', 'AGU', 'AMJ', 'PNRG', 'UBP', 'DLN', 'IMNP', 'TUES', 'KCG', 'CLX', 'PQ', 'AOA', 'CGW', 'MSP', 'ELNK', 'RDNT', 'ESR', 'XPLR', 'HOS', 'PRTK', 'ISNS', 'RNWK', 'PBR.A', 'KEX', 'BNSO', 'MOV', 'HAR', 'ESCA', 'ACIM', 'GRIF', 'LFUS', 'SCHD', 'EXI', 'HH', 'BIP', 'MAN', 'AHL', 'RSYS', 'FDX', 'BGSF', 'JPEP', 'ALV', 'NTT', 'WHF', 'TRCO', 'ALD', 'QAT', 'BVSN', 'KT', 'ARE', 'MARA', 'NANO', 'SCHF', 'SFL', 'CGIX', 'RHT', 'UCFC', 'AMRK', 'CHU', 'ORIG', 'SYUT', 'AMTX', 'SDLP', 'NWHM', 'SSW', 'EWM', 'FLXS', 'JEC', 'SMMF', 'TAO', 'RDIB', 'EMI', 'WMGI', 'SFBS', 'YGE', 'AVEO', 'REV', 'SYMC', 'WEBK', 'AFAM', 'PBYI', 'CATY', 'PWR', 'CAPE', 'IAI', 'EMAN', 'ROL', 'HIIQ', 'RTRX', 'NADL', 'CRS', 'AV', 'LOCK', 'WMB', 'NSR', 'REM', 'FXH', 'VHT', 'CINF', 'OME', 'CEL', 'ISR', 'CPF', 'AFK', 'TEL', 'LUB', 'DGRE', 'IPHS', 'FUND', 'STZ', 'BANX', 'FAC', 'SCHN', 'FNB', 'NAK', 'HFWA', 'SONC', 'LANC', 'FLIC', 'AKO.A', 'BXS', 'CH', 'HSBC', 'KMG', 'BKE', 'AKG', 'FPX', 'FHCO', 'KEQU', 'TKAI', 'POT', 'PEY', 'EML', 'AEGN', 'FSNN', 'PDCE', 'DMLP', 'MANT', 'KEG', 'NPO', 'DTD', 'HOFT', 'SRLP', 'TWC', 'CORN', 'AVB', 'LQ', 'LFC', 'ESE', 'HOVNP', 'TGP', 'BNFT', 'GGOV', 'MNA', 'SFNC', 'FBZ', 'JOBS', 'SINO', 'RAS', 'RMCF', 'RECN', 'SIG', 'RNST', 'ACG', 'ZNGA', 'BICK', 'EWN', 'FSTA', 'PB', 'SGC', 'WVVI', 'CBZ', 'FAZ', 'ILF', 'XLU', 'RE', 'TCX', 'GRFS', 'HSY', 'HGG', 'SMH', 'TIVO', 'UG', 'ALX', 'AVA', 'TORM', 'BRO', 'EGL', 'DVD', 'CVRR', 'NVGN', 'CAC', 'IHI', 'KBE', 'PPLT', 'KMT', 'SPTN', 'TNAV', 'DFE', 'EMHY', 'XONE', 'XCO', 'EAT', 'JXSB', 'SBBX', 'VIP', 'CIU', 'SPAR', 'LOGM', 'MLVF', 'PRGO', 'SLMAP', 'OLN', 'TGTX', 'GMO', 'CLR', 'TXT', 'HCI', 'OFG', 'CSOD', 'CBG', 'ONVO', 'GBB', 'RNET', 'PMR', 'AMFW', 'DEO', 'AIRT', 'PEGI', 'DFS', 'SA', 'ENFC', 'GABC', 'EDE', 'PKI', 'VGK', 'MMP', 'PBD', 'PCOM', 'SIXD', 'AEZS', 'CO', 'MBB', 'CUBA', 'OVBC', 'ETM', 'SCAI', 'HY', 'RNR', 'STAA', 'GBLI', 'INDB', 'CANF', 'JO', 'SGOC', 'GLTR', 'FELE', 'WTFC', 'AJG', 'CCMP', 'ESRX', 'HWKN', 'FORD', 'OSUR', 'UFS', 'AVOL', 'AG', 'PSTI', 'SXI', 'NEWM', 'PRCP', 'OAK', 'JJT', 'RBA', 'OFC', 'QTS', 'SNOW', 'PEI', 'KMPR', 'BIB', 'OHAI', 'RADA', 'AHC', 'LORL', 'MTT', 'RAX', 'MVC', 'ANY', 'NLS', 'APTO', 'ALFA', 'CURE', 'ICLD', 'FDD', 'NWL', 'RXII', 'PRIM', 'MGCD', 'NUVA', 'SPLS', 'DVYE', 'MCS', 'PAGP', 'XENT', 'BSAC', 'LH', 'HAIN', 'EMD', 'HBAN', 'MFG', 'EQS', 'AIT', 'IDT', 'CRTN', 'ACC', 'LMNR', 'KELYA', 'DDD', 'FNDX', 'AGG', 'PRMW', 'ENOC', 'DUST', 'GBCI', 'IBCP', 'BAX', 'NPK', 'RUBI', 'AVX', 'CBAK', 'IPF', 'UTMD', 'JLL', 'NP', 'SYX', 'HCHC', 'EQT', 'BRT', 'PROV', 'FNX', 'TZOO', 'LND', 'CS', 'STBZ', 'PHO', 'FMAT', 'PBMD', 'FIG', 'PBT', 'SAA', 'INB', 'HDV', 'SYY', 'PPHMP', 'TRST', 'MDCO', 'TWOK', 'THM', 'CEMP', 'LLEX', 'SYNC', 'GXG', 'FCAP', 'FWRD', 'GDXJ', 'KEYW', 'GPN', 'LLL', 'DBE', 'NFX', 'REIS', 'CY', 'ORM', 'OSK', 'CYTR', 'FNJN', 'KNOW', 'HELE', 'GGB', 'BOFI', 'BKJ', 'MLAB', 'MKTO', 'XPO', 'AAP', 'GRH', 'SKYS', 'CBLI', 'LQD', 'CFNB', 'CEO', 'ARTX', 'NEWT', 'SMRT', 'LPCN', 'TNH', 'PLX', 'BRKS', 'CPG', 'TRNS', 'NSP', 'DEM', 'KW', 'VISI', 'AKAO', 'ACT', 'LARK', 'GBL', 'STM', 'RPTP', 'CLC', 'ASNA', 'AXGN', 'FI', 'IYC', 'NAO', 'BR', 'DDF', 'WLL', 'HAL', 'BDE', 'DLHC', 'HDSN', 'AGM', 'NR', 'PSCF', 'KWEB', 'ATRI', 'IHY', 'YNDX', 'PNK', 'FBP', 'SOHU', 'PRF', 'SMLR', 'TCBK', 'HYGS', 'PEJ', 'NKSH', 'CXP', 'IIIN', 'CHFC', 'AZPN', 'GSB', 'MEI', 'SIR', 'ERX', 'PAH', 'CGI', 'EZA', 'EGHT', 'CIZN', 'CMF', 'NRCIA', 'IPCC', 'SYK', 'WSBC', 'CSI', 'INDL', 'MOD', 'HVT', 'PHMD', 'K', 'MERC', 'GNTX', 'RMP', 'AN', 'BRSS', 'ANAC', 'GWW', 'NNA', 'ATNM', 'IEC', 'AYR', 'ARNA', 'LEN', 'AHPI', 'SCMP', 'FRED', 'MAV', 'HDNG', 'VAC', 'BLVD', 'OCIP', 'AMTG', 'CTG', 'AXL', 'KTEC', 'GRAM', 'PAR', 'LEG', 'NAV', 'PBS', 'CMA', 'AMP', 'HE', 'BLRX', 'BGC', 'ABX', 'TTC', 'CHT', 'NORW', 'ROK', 'XLV', 'BREW', 'ASCMA', 'DCOM', 'DHIL', 'DWTI', 'GK', 'CPAC', 'NEO', 'ANTM', 'FYC', 'INGR', 'LBTYA', 'DTH', 'LGCY', 'RF', 'FSM', 'KANG', 'AVG', 'DRNA', 'IVC', 'AIQ', 'EEP', 'BSET', 'HBP', 'NJR', 'PKD', 'GTIM', 'WAB', 'HPJ', 'BDSI', 'RAVN', 'CVBF', 'LVLT', 'MSLI', 'TECL', 'SFS', 'CPHR', 'OTEL', 'SWN', 'LUX', 'SR', 'ITA', 'EDOG', 'WMLP', 'FXL', 'TGD', 'GMCR', 'TISI', 'SVBL', 'ARKQ', 'GWX', 'PZN', 'TAN', 'SOFO', 'INFI', 'NRF', 'VPG', 'OHRP', 'AVY', 'SAJA', 'PH', 'HIL', 'ITCI', 'OC', 'ESSX', 'LMCK', 'BAS', 'ITIC', 'VRTB', 'CAG', 'GMAN', 'SLP', 'THFF', 'HFC', 'ECH', 'FMF', 'BFOR', 'FTI', 'BPI', 'CECE', 'PRXL', 'PVTB', 'ALEX', 'ARCX', 'DCIX', 'REX', 'BONA', 'ITG', 'NAII', 'XLP', 'CRDS', 'SMBC', 'MFRM', 'NAP', 'TEP', 'MLN', 'IWD', 'DLNG', 'NVO', 'GLOP', 'LVNTA', 'SIGI', 'MRIN', 'MXWL', 'SPRT', 'RELV', 'AGTC', 'HEDJ', 'PE', 'BCO', 'CTWS', 'FAM', 'SDT', 'DOV', 'FVD', 'FMX', 'NUAN', 'LLNW', 'SENEB', 'TCO', 'VSLR', 'XBI', 'SNH', 'IHC', 'NWE', 'NUTR', 'TWO', 'BEP', 'HXL', 'EVDY', 'GLF', 'BPFH', 'IBIO', 'COB', 'IFF', 'EVBS', 'LBIX', 'OXY', 'LZB', 'NC', 'RWC', 'PKB', 'RAVE', 'BWP', 'BSFT', 'DEL', 'MDRX', 'WLB', 'DRR', 'HBMD', 'AMCC', 'CTRL', 'BWINA', 'CHL', 'FM', 'ALQA', 'HALL', 'EIDO', 'PEBO', 'WGO', 'SVVC', 'STO', 'CVG', 'AMRI', 'PTR', 'SHLO', 'TCAP', 'TDY', 'TTM', 'MTBC', 'ED', 'ONVI', 'AMSWA', 'MGEE', 'VHC', 'UCBI', 'RSPP', 'LPX', 'SBRAP', 'VIRC', 'CVE', 'TICC', 'FSC', 'JOY', 'DOW', 'REXX', 'CRMD', 'DIVI', 'GPL', 'RWJ', 'PCMI', 'SNAK', 'TX', 'PRAH', 'ENDP', 'SPAN', 'AIRM', 'ICN', 'DDC', 'DXJ', 'ZSL', 'PII', 'CNBKA', 'EVGN', 'FUL', 'GIGA', 'AYI', 'CUI', 'ATTU', 'TAP', 'BLV', 'HL', 'BTN', 'BAK', 'DEST', 'EMIF', 'CDTI', 'KB', 'XLRN', 'BSRR', 'FRAK', 'TBIO', 'NGD', 'CACI', 'QRHC', 'FPRX', 'EBIX', 'AHGP', 'AAON', 'ERS', 'HNP', 'MGA', 'SJI', 'VVUS', 'RP', 'ASR', 'BTX', 'RESN', 'CORR', 'WTM', 'OPHC', 'PFIE', 'DTE', 'DIG', 'MSI', 'IGE', 'KED', 'QAI', 'FCEL', 'CCOI', 'SVM', 'PMBC', 'PSTB', 'CPHC', 'CGG', 'LODE', 'QIHU', 'ARGT', 'DOD', 'EXXI', 'HIVE', 'WTR', 'CHNR', 'EMCF', 'WPX', 'ALTS', 'HTBK', 'PLPM', 'GDP', 'VB', 'HYG', 'PCO', 'FMI', 'CBF', 'LNT', 'FSP', 'XIV', 'IGLD', 'PDT', 'AP', 'USB', 'CBFV', 'CCUR', 'KMX', 'PSO', 'SGU', 'TCFC', 'CHFN', 'CLRO', 'UGP', 'EPOL', 'REGI', 'SHOR', 'TREX', 'EGLE', 'CRY', 'MLHR', 'SBCF', 'BIF', 'ANF', 'FCF', 'TFX', 'NSC', 'NTRS', 'HNH', 'PCYG', 'THRM', 'PPC', 'DVCR', 'CCU', 'RPM', 'FSAM', 'LMOS', 'POR', 'PPP', 'SFUN', 'VRTS', 'FCG', 'IDHQ', 'WEX', 'FLS', 'IKNX', 'WMK', 'QUAL', 'MJN', 'PHDG', 'HOG', 'WSFS', 'QVCA', 'NAVI', 'ARRS', 'BRID', 'DIVY', 'VGR', 'DZZ', 'MARK', 'SGF', 'DTN', 'VMI', 'VLP', 'GGP', 'CMN', 'EBIO', 'GEOS', 'CRAY', 'FGP', 'NGHC', 'XLY', 'AT', 'CBAY', 'SPLP', 'WCG', 'ASUR', 'CLVS', 'AND', 'AFH', 'TLLP', 'CTXS', 'SVA', 'EHIC', 'VISN', 'VSH', 'RXDX', 'MT', 'CVC', 'PPR', 'STRT', 'ERY', 'AOI', 'KMB', 'KERX', 'TEN', 'ECOL', 'CLSN', 'CHIQ', 'MMC', 'EMES', 'PAG', 'EQY', 'YZC', 'FNDE', 'CA', 'OCN', 'PSCI', 'ATU', 'TNXP', 'AFL', 'MTSC', 'TTF', 'WCN', 'FDL', 'CRMT', 'JNK', 'PXE', 'DY', 'EGBN', 'UNB', 'BRZU', 'IAK', 'STON', 'UGAZ', 'HLS', 'ARWR', 'LDL', 'MDP', 'HNNA', 'EGPT', 'PKOH', 'AGA', 'RJF', 'VEA', 'BGI', 'ICAD', 'ADK', 'ASHR', 'LIT', 'ACH', 'DDR', 'MDYV', 'COL', 'STRS', 'IRET', 'FCX', 'TSRI', 'STML', 'CRME', 'EWBC', 'DRAD', 'RGSE', 'FTSD', 'NAVB', 'CRK', 'CSLT', 'EQLT', 'AGII', 'FSIC', 'CHS', 'OPK', 'MINI', 'BHP', 'FIS', 'NGG', 'AMOV', 'UHT', 'SIFY', 'NVEC', 'EFII', 'FNFV', 'ARMH', 'FCSC', 'PCYO', 'PVG', 'IPAR', 'CL', 'CXDC', 'ADX', 'ICB', 'ODC', 'KCNY', 'KTOS', 'INBK', 'DAN', 'CUBI', 'QCCO', 'RCMT', 'TKC', 'CPK', 'FL', 'HERO', 'SU', 'SXE', 'NMBL', 'WAGE', 'FLL', 'CASM', 'BEN', 'LYB', 'BEBE', 'CYBE', 'FEIM', 'GOV', 'STOR', 'LXRX', 'EMCI', 'ROIC', 'AGQ', 'FE', 'IEP', 'PARN', 'BOSC', 'OLED', 'CTL', 'FMBI', 'COVS', 'STRM', 'UWN', 'EGLT', 'DGAZ', 'AHP', 'DISCK', 'FLN', 'BRLI', 'ARIA', 'NSH', 'EWO', 'NXTD', 'OB', 'TERP', 'WWD', 'ELLI', 'BHE', 'DGRS', 'ENZL', 'MSCC', 'BSMX', 'RZG', 'NILE', 'VSEC', 'TRQ', 'SPOK', 'IPAS', 'DGX', 'AEE', 'INDY', 'PBA', 'FRN', 'SSNI', 'TG', 'PBHC', 'SSD', 'IPXL', 'ATHX', 'PDP', 'NEM', 'HWBK', 'CWCO', 'CFA', 'SAEX', 'EPE', 'FNRG', 'GFF', 'MHH', 'MEP', 'WBS', 'MBLX', 'ZLTQ', 'DCO', 'SHPG', 'CQH', 'TAC', 'ZAYO', 'PER', 'VBIV', 'EWI', 'MDSO', 'NVAX', 'URA', 'CZA', 'ODP', 'CREG', 'GLBZ', 'UNIS', 'APO', 'IYK', 'MGLN', 'DSKX', 'MTU', 'ABCO', 'ELSE', 'XBKS', 'GGG', 'INUV', 'OKE', 'CVV', 'KITE', 'PXD', 'VOXX', 'OAS', 'QDF', 'JHI', 'SNFCA', 'BFR', 'HGT', 'NDAQ', 'MTOR', 'BOXC', 'LNC', 'OMN', 'SOHO', 'RGCO', 'UGI', 'SMIT', 'ADGE', 'RRC', 'TRXC', 'IGF', 'DIOD', 'PANW', 'JCOM', 'PWV', 'PRKR', 'MPB', 'PID', 'ADHD', 'ASH', 'MBCN', 'MTB', 'PFPT', 'EMN', 'RES', 'SALT', 'CHMT', 'PFNX', 'NTIP', 'DCI', 'GIG', 'SBGL', 'DPM', 'BPK', 'CRZO', 'TLP', 'EQM', 'SQBG', 'SGBK', 'AWH', 'ADPT', 'TA', 'DEF', 'ATV', 'FTD', 'CKH', 'LXFT', 'CAW', 'PLM', 'UVV', 'D', 'AXAS', 'NTIC', 'FOE', 'COHU', 'BONT', 'MXIM', 'DBA', 'GST', 'HMSY', 'MSBF', 'MATX', 'WF', 'RSO', 'OMC', 'HRS', 'EPI', 'TMK', 'MSG', 'CDZI', 'WPT', 'FXSG', 'GTWN', 'EUFN', 'LNN', 'CJJD', 'ACHN', 'BRC', 'CHSP', 'FDIV', 'HIG', 'BAL', 'EFF', 'WSTC', 'PBIB', 'OPXA', 'NTL', 'DX', 'ICON', 'TLO', 'CJES', 'FRP', 'GBF', 'NWBO', 'UMPQ', 'USAT', 'LXK', 'VCEL', 'ZN', 'MBVT', 'PRSS', 'BC', 'ONEQ', 'DOX', 'INS', 'UPL', 'PRSC', 'CLI', 'CLNY', 'ASA', 'OSN', 'NSM', 'ZPIN', 'ATHN', 'PFBI', 'WM', 'NORD', 'JTPY', 'TNGO', 'ASTC', 'Q', 'GPS', 'STMP', 'CLUB', 'CALD', 'PTC', 'ALOG', 'WABC', 'ECC', 'TKR', 'HPT', 'HGSH', 'SJR', 'LDOS', 'JAH', 'LDR', 'TRR', 'MHG', 'JOUT', 'HF', 'PLKI', 'AUPH', 'MGU', 'IBOC', 'BSF', 'CEQP', 'CBMX', 'NSPR', 'HTM', 'IIVI', 'CACQ', 'EZY', 'IDX', 'MCK', 'PIH', 'CHKE', 'QUAD', 'CNA', 'EWC', 'EWJ', 'DMRC', 'HACK', 'SPHB', 'HWCC', 'AES', 'GAL', 'MZOR', 'CCIH', 'FRC', 'OTTR', 'LPI', 'WFT', 'WPCS', 'ORBC', 'DXM', 'CSX', 'GSL', 'SSB', 'NKTR', 'EIP', 'CUK', 'DTV', 'DEX', 'ALJ', 'ACTG', 'SM', 'TDIV', 'DEPO', 'GVA', 'NTES', 'DON', 'FRBK', 'CHE', 'DENN', 'GXC', 'ISCA', 'ARGS', 'ETE', 'OIH', 'ORBK', 'BT', 'RAND', 'BNY', 'OFLX', 'KR', 'APRI', 'III', 'RUSS', 'CFFI', 'SUNS', 'WIN', 'SCLN', 'QRVO', 'GVP', 'JMP', 'JACK', 'EGN', 'NBN', 'SMTX', 'IL', 'SAL', 'AVH', 'FPO', 'HABT', 'CNIT', 'RTH', 'BANC', 'ALB', 'CEA', 'EWT', 'FXP', 'DBC', 'ETP', 'SEV', 'PBSK', 'FVE', 'SID', 'DEW', 'VTWV', 'ADMA', 'EWK', 'PBFX', 'SO', 'GBSN', 'GCBC', 'AMNB', 'SNBC', 'BOTJ', 'RPAI', 'DI', 'GMZ', 'AXU', 'IYE', 'SRE', 'VNM', 'HYH', 'IRDM', 'DXYN', 'CAAS', 'VIVO', 'PPBI', 'SDOG', 'XL', 'CPTA', 'BLJ', 'HYEM', 'HEAR', 'PF', 'RDC', 'OCC', 'GEQ', 'MFA', 'IP', 'TWMC', 'FNLC', 'SOR', 'SBR', 'RHI', 'BLT', 'FFIN', 'TEX', 'FCH', 'TESO', 'RYJ', 'FNY', 'TREE', 'IYLD', 'SMFG', 'BRKL', 'GNBC', 'BWEN', 'CVGW', 'PETS', 'SHI', 'KEM', 'STAR', 'DCM', 'RICE', 'FISV', 'TR', 'KORS', 'SIVR', 'TSO', 'AIN', 'SHW', 'ARR', 'KBWB', 'MCBC', 'GENE', 'MOC', 'MEG', 'PLT', 'SSI', 'TYPE', 'NWFL', 'COWN', 'SREV', 'MDLZ', 'OUTR', 'SNR', 'TUBE', 'AXR', 'VMC', 'BWLD', 'INWK', 'VIOG', 'ALG', 'BMS', 'UNTY', 'SSH', 'AVT', 'CLACU', 'HSNI', 'AEO', 'NFG', 'ACAT', 'LMRK', 'LEDD', 'OMAM', 'PUK', 'PGR', 'TRP', 'MCY', 'BDGE', 'CBPO', 'PATK', 'XTN', 'ACY', 'ARKR', 'ECTE', 'MTGE', 'HLX', 'FWP', 'KND', 'RUTH', 'GNC', 'INO', 'HBHC', 'LEE', 'PDS', 'ALE', 'BBT', 'WBC', 'WPPGY', 'CSBK', 'FCAU', 'NEPT', 'RHP', 'CRVP', 'KS', 'CDR', 'AGNCB', 'CSM', 'MCO', 'RDIV', 'LEI', 'MTSN', 'ELOS', 'STJ', 'FMS', 'GEF', 'BWINB', 'MBTF', 'GWR', 'NM', 'PGNX', 'ENT', 'CHN', 'BBRC', 'SFBC', 'SBY', 'PETX', 'AROW', 'SAIA', 'SAR', 'BCOR', 'GGM', 'HCLP', 'IEZ', 'DWM', 'IPK', 'PHI', 'PTNR', 'EV', 'Y', 'FULT', 'PGC', 'RIBT', 'VRS', 'ATE', 'ATML', 'RCD', 'HMC', 'FELP', 'LBRDA', 'PKY', 'CNNX', 'PHIIK', 'CUNB', 'FBT', 'NYMT', 'VSR', 'NDSN', 'CNOB', 'TIK', 'PGEM', 'SNSS', 'ARW', 'MAR', 'BGG', 'ATTO', 'CEMI', 'PME', 'BSCH', 'VCR', 'TASR', 'IO', 'AFG', 'MTR', 'IMOS', 'UUUU', 'AAV', 'AEY', 'HAWK', 'KBIO', 'EBR.B', 'MPAA', 'FSL', 'MXI', 'FTLS', 'CLF', 'TAL', 'WDC', 'UBSH', 'DUK', 'ROIAK', 'FBNK', 'EGP', 'MDLY', 'ACIW', 'USCR', 'RBS', 'NECB', 'FONE', 'KFS', 'EGO', 'PICO', 'QSII', 'SEED', 'CPE', 'MON', 'WBK', 'CVCY', 'SIVB', 'DOOR', 'FRO', 'GPC', 'SPB', 'UWTI', 'CMC', 'ORAN', 'FSTR', 'HMNY', 'ARTNA', 'OTIV', 'UAM', 'EWSS', 'KATE', 'TNC', 'PTLA', 'CFR', 'SYPR', 'NWY', 'PCG', 'FLIR', 'GILT', 'FCS', 'BEL', 'MOO', 'WRB', 'RMGN', 'VSAR', 'REMX', 'IDA', 'EGI', 'PFM', 'REPH', 'GPRE', 'HP', 'TST', 'NWSA', 'CPST', 'STLY', 'CNO', 'EFSC', 'ELLO', 'ONP', 'FRAN', 'SCPB', 'WG', 'CUR', 'CNXR', 'VIG', 'GALE', 'SPCB', 'CNP', 'IPCI', 'WVFC', 'KLXI', 'SLCT', 'SLM', 'FAS', 'PIE', 'SWIR', 'IRR', 'GTN.A', 'CPL', 'JNS', 'ACTA', 'GNCMA', 'NCLH', 'FPT', 'BIOL', 'SJT', 'TSN', 'WFBI', 'RLGT', 'NYMX', 'FCE.B', 'EIS', 'HRC', 'IGC', 'TEO', 'WAC', 'INCY', 'BNS', 'ENZY', 'MFNC', 'PPA', 'LBRDK', 'STE', 'HAS', 'EYES', 'RBPAA', 'OCFC', 'HCOM', 'XGTI', 'GCO', 'BMTC', 'LMCA', 'CSV', 'YDKN', 'EC', 'FLM', 'ACCU', 'CMS', 'MCC', 'CSJ', 'SLX', 'SCHB', 'SMED', 'VVC', 'SHOS', 'OI', 'EEMV', 'TGS', 'WST', 'PDLI', 'DYNT', 'FFG', 'UVE', 'IYM', 'PLXS', 'PEIX', 'YUME', 'CBA', 'FHN', 'STC', 'SYNT', 'CCXI', 'NRT', 'RUSHA', 'TMH', 'ALR', 'FT', 'LECO', 'FNBC', 'PKE', 'PNRA', 'FES', 'RVSB', 'HIFS', 'DLS', 'CATM', 'AOK', 'BOH', 'EIA', 'KYO', 'IAT', 'CST', 'SNP', 'SGM', 'NVCN', 'PFF', 'SHLD', 'DBEF', 'KNDI', 'SPP', 'CCE', 'NAME', 'SIFI', 'PFBX', 'AUY', 'BASI', 'ESV', 'ASEA', 'TINY', 'COH', 'AAMC', 'SOCL', 'CNI', 'IVOV', 'GLUU', 'NLNK', 'ZOES', 'GRR', 'PPHM', 'DGICA', 'THS', 'DJCO', 'SIGM', 'TJX', 'CELGZ', 'RLOG', 'SSL', 'POWL', 'VOX', 'ATLS', 'HBI', 'SGY', 'EW', 'GNMK', 'ABB', 'KALU', 'PSDV', 'TARO', 'UBFO', 'CXW', 'ECA', 'TCCO', 'DFJ', 'FLXN', 'AGX', 'BHI', 'BKSC', 'ZIXI', 'CPHD', 'VJET', 'FTR', 'IFN', 'MAGS', 'ATW', 'PRPH', 'SYLD', 'JJC', 'ZGNX', 'SNI', 'FLR', 'TS', 'BKMU', 'ACCO', 'LJPC', 'JGV', 'RMD', 'CVLY', 'XLB', 'TRMK', 'ABY', 'MDC', 'UMBF', 'GZT', 'KKR', 'UFI', 'WPG', 'PPG', 'BF.B', 'ZEN', 'EPP', 'GG', 'NWLI', 'SPKE', 'RIC', 'LIOX', 'EEQ', 'PTIE', 'DGRW', 'STKL', 'QBAK', 'JKJ', 'CSII', 'CDC', 'GIGM', 'YCS', 'BANR', 'DCUC', 'DHR', 'HFBC', 'PAM', 'HCN', 'AADR', 'PERY', 'IGN', 'MHK', 'STL', 'ULTI', 'BJK', 'HOLI', 'RESI', 'WYN', 'BMRC', 'EWW', 'CFFN', 'EPU', 'AWR', 'XRM', 'DNKN', 'DRE', 'FCCO', 'ADP', 'WBAI', 'SGMS', 'ETRM', 'AET', 'UCBA', 'BUD', 'CRR', 'ENZN', 'KODK', 'LBTYK', 'NYT', 'SC', 'TIP', 'HDP', 'INFU', 'ADMS', 'BCR', 'NS', 'CAJ', 'OKSB', 'MNI', 'CFP', 'PHG', 'CWB', 'AUBN', 'CEVA', 'TSLF', 'MIK', 'DATA', 'FOXA', 'EVAR', 'BND', 'QUMU', 'TI', 'LRAD', 'CNV', 'IWO', 'PNTR', 'SKOR', 'FORTY', 'ELS', 'USAK', 'AOS', 'FXCM', 'BIOA', 'IXUS', 'CLNT', 'MCZ', 'AB', 'NBTB', 'MBFI', 'MPEL', 'GRX', 'ATAX', 'CCI', 'JKI', 'BMA', 'HCKT', 'BBC', 'MOCO', 'CALI', 'YUMA', 'WHLM', 'MFC', 'EDC', 'CCC', 'HYXU', 'GLOG', 'GPIC', 'TMO', 'TECH', 'CGA', 'ECOM', 'ATRO', 'AKR', 'HSON', 'OKS', 'CLFD', 'SDR', 'SYMX', 'ITB', 'IVZ', 'CARV', 'UEPS', 'BBH', 'AKER', 'OZRK', 'TRN', 'WDR', 'EPZM', 'ORIT', 'RTEC', 'WRE', 'RRTS', 'CVCO', 'TLF', 'COPX', 'MBRG', 'BKK', 'IPG', 'NCI', 'IQNT', 'LPNT', 'BRAQ', 'KWR', 'MGV', 'TAX', 'PACW', 'MEET', 'GUID', 'COBZ', 'SON', 'BDX', 'UFCS', 'CAPN', 'GARS', 'RDUS', 'LMNX', 'ASFI', 'REN', 'VAL', 'CWEI', 'ALK', 'EXFO', 'IGHG', 'WILC', 'MMAC', 'IX', 'FRPH', 'TESS', 'IDSY', 'LMIA', 'TGEN', 'GCE', 'GPT', 'RGR', 'HW', 'RLI', 'WGL', 'AGYS', 'DRWI', 'PSCM', 'LION', 'EFG', 'SGB', 'ECPG', 'CCK', 'CSD', 'NATL', 'ROKA', 'SATS', 'AMT', 'RSH', 'AKS', 'IOC', 'ISIL', 'HEB', 'GSS', 'TOWN', 'VGM', 'PCH', 'ANAT', 'VTL', 'DNR', 'ONB', 'BMO', 'OHGI', 'SCX', 'CSS', 'BHBK', 'TRIL', 'ESGR', 'PVH', 'SFLY', 'TDC', 'MTRN', 'CHA', 'EA', 'OVLY', 'EXK', 'UHS', 'CCNE', 'AEB', 'EOG', 'CFNL', 'CZNC', 'PBJ', 'ITW', 'JNUG', 'DF', 'LYV', 'GHC', 'BSPM', 'BLCM', 'ALL', 'FXG', 'AKO.B', 'NCIT', 'PWE', 'DGAS', 'WWAV', 'TROV', 'JCI', 'WEN', 'WUBA', 'TTPH', 'EXAR', 'BRG', 'WSM', 'CMD', 'HMLP', 'KOL', 'IYF', 'PBW', 'EWD', 'TPRE', 'EVHC', 'OMEX', 'PGX', 'BCLI', 'FFKT', 'FFNM', 'PNBK', 'AED', 'SCG', 'CRH', 'CSF', 'WMAR', 'TWER', 'FPXI', 'AXDX', 'RTI', 'THC', 'PHT', 'ATSG', 'AAME', 'MLNX', 'LHCG', 'ADI', 'ENLC', 'ACGL', 'NWN', 'PSA', 'IBKC', 'NVDQ', 'JCS', 'MBII', 'AEG', 'EFNL', 'BWZ', 'AKRX', 'DIN', 'SZYM', 'SCSC', 'BRF', 'CBK', 'MOG.A', 'PWOD', 'IFMI', 'BPOP', 'NBL', 'PSUN', 'MD', 'SBB', 'PARR', 'GTS', 'ISLE', 'DRD', 'GMLP', 'VZA', 'JMEI', 'PICB', 'SLB', 'MMSI', 'KIE', 'CHH', 'EIRL', 'SPG', 'SWK', 'EEI', 'GORO', 'PPSI', 'GTXI', 'GWGH', 'DVHL', 'AEPI', 'BFO', 'GBNK', 'CXO', 'NEU', 'BOKF', 'GFA', 'BABY', 'RCI', 'IART', 'IPW', 'DGI', 'MTH', 'WPZ', 'HALO', 'STRL', 'LXP', 'ZIOP', 'RRD', 'REED', 'ICF', 'IHF', 'BBX', 'TSU', 'ATNI', 'SCSS', 'FCA', 'TISA', 'CRNT', 'IGV', 'JONE', 'KYE', 'STI', 'IWY', 'RLYP', 'APC', 'BAB', 'ADES', 'NVFY', 'UTEK', 'TEAR', 'GLPW', 'ALDW', 'CLY', 'CNCO', 'EMQQ', 'HEOP', 'BKS', 'FFBC', 'BK', 'AFCB', 'SPA', 'LABL', 'WPP', 'GEO', 'TAHO', 'CVY', 'MFSF', 'FXF', 'RDHL', 'ARCW', 'BIG', 'NOV', 'AGC', 'HHC', 'PKW', 'ACPW', 'CRCM', 'PSXP', 'MLPA', 'ADT', 'AVV', 'CIB', 'MLPY', 'ESL', 'SVU', 'EROS', 'NLSN', 'RTIX', 'FLO', 'SPIL', 'ADXS', 'ARIS', 'CCCR', 'BBVA', 'DVP', 'DRV', 'IRL', 'FMNB', 'SPHS', 'LVL', 'VVI', 'CALA', 'CNTY', 'TVIX', 'TREC', 'TRCB', 'AMDA', 'NLR', 'NRZ', 'ELON', 'ESEA', 'LOAN', 'BOBE', 'HBM', 'ACHC', 'BXE', 'CLRB', 'YHOO', 'VWO', 'EMB', 'ZAGG', 'TBK', 'BBD', 'FDML', 'NNN', 'SKIS', 'FTK', 'XON', 'FLTR', 'MDCA', 'AXJL', 'BRKR', 'MSFG', 'NSEC', 'MNST', 'FDEF', 'COBO', 'BELFA', 'BRFS', 'HCAP', 'OIBR', 'MDU', 'RFG', 'ARCB', 'HBNC', 'USG', 'UCP', 'RDI', 'SILC', 'ISP', 'STS', 'ITUB', 'BKEP', 'ENH', 'LNDC', 'MSON', 'DWCH', 'FEYE', 'TSC', 'OZM', 'STXS', 'ESLT', 'WINA', 'EFAV', 'FSBK', 'CSFL', 'LOOK', 'ELY', 'SENEA', 'ENG', 'WBMD', 'XOXO', 'DLPH', 'EBF', 'NMRX', 'MSEX', 'AU', 'HHS', 'GER', 'DBV', 'MSB', 'CVTI', 'API', 'VNTV', 'SF', 'CSC', 'BBOX', 'CBPX', 'RAD', 'AOM', 'UVSP', 'AXP', 'MSTX', 'CLCT', 'URG', 'RSX', 'AMBC', 'ESIO', 'FNDA', 'LAD', 'NDRO', 'EPV', 'BVX', 'ASB', 'RST', 'CATO', 'COT', 'SEMG', 'TYL', 'B', 'CRWS', 'HTH', 'CBB', 'RLJE', 'SGYP', 'NEE', 'EPM', 'VSI', 'LGND', 'BGCP', 'WU', 'WLH', 'EVX', 'GVI', 'TSRA', 'QEP', 'OFED', 'SMI', 'MNR', 'PBR', 'LRN', 'ZSPH', 'ADMP', 'BWA', 'EWA', 'FXD', 'IDV', 'JUNO', 'SWX', 'WSTG', 'ETH', 'IKGH', 'AVNW', 'RDVY', 'DLX', 'NYRT', 'OGXI', 'QTM', 'ASPS', 'CZZ', 'GTE', 'GGT', 'DFT', 'SLF', 'WDAY', 'FCNCA', 'NTWK', 'IXC', 'BYFC', 'TSRO', 'CDI', 'E', 'PCP', 'PLBC', 'LTM', 'PZE', 'UEC', 'IMPV', 'IDRA', 'CW', 'PFBC', 'CIE', 'RENN', 'KCAP', 'LBAI', 'MORT', 'TTS', 'AST', 'CFX', 'ENLK', 'RTM', 'SGNL', 'CAB', 'AIMC', 'CYRN', 'JBT', 'LBY', 'LTS', 'GRUB', 'TD', 'CM', 'ARTW', 'SCCO', 'ENB', 'CNQ', 'CEW', 'CRED', 'EIGI', 'KEY', 'DST', 'IMAX', 'SYNL', 'VYM', 'FDM', 'CBMG', 'NICK', 'PMC', 'RFIL', 'QTNT', 'MBLY', 'NHI', 'FCB', 'REI', 'DNBF', 'VAW', 'VUG', 'PERF', 'PBI', 'BNCL', 'ECON', 'FEMS', 'FNV', 'IXYS', 'SAVE', 'SAFM', 'IIN', 'CTS', 'AIR', 'ETR', 'MORE', 'MTEX', 'FNHC', 'WGP', 'INFY', 'DSCI', 'OXM', 'DGSE', 'TROX', 'GLBS', 'GPX', 'GXP', 'VRML', 'WNRL', 'PRGX', 'SPNC', 'OMED', 'USEG', 'SIL', 'TRV', 'CYS', 'ENBL', 'KNL', 'PVCT', 'SHLX', 'WHR', 'SOXL', 'ANTH', 'SERV', 'CYAN', 'NTN', 'HTA', 'USD', 'ERO', 'MRVC', 'SRNE', 'TCRD', 'AXE', 'GNCA', 'CTT', 'GSVC', 'NEWS', 'IPO', 'GFNCP', 'JKK', 'SCHM', 'PJC', 'WOR', 'XHS', 'XOP', 'GWB', 'EFX', 'HRT', 'GPK', 'SJM', 'TVPT', 'GGE', 'RIGP', 'TPL', 'PTX', 'NPTN', 'ADC', 'JCTCF', 'NEOT', 'MTW', 'HTLF', 'MNTX', 'HSC', 'SBNY', 'FMK', 'FPL', 'LTC', 'GALT', 'IDU', 'OCLR', 'ERUS', 'OPHT', 'PRY', 'LE', 'OIIM', 'HYD', 'HSKA', 'MGPI', 'INDA', 'ERIE', 'CIG', 'CTSO', 'HK', 'WEC', 'FINL', 'GCAP', 'RYT', 'VNO', 'PEG', 'GBIM', 'EGT', 'WIT', 'INAP', 'SMP', 'PGJ', 'UGL', 'HNRG', 'ULTR', 'VIS', 'CYTX', 'SIRI', 'FTC', 'ITR', 'SSP', 'MIC', 'JASO', 'PSIX', 'GULTU', 'SVXY', 'LAYN', 'KWT', 'PNM', 'FOMX', 'CUZ', 'SCTY', 'KINS', 'NGLS', 'ADRD', 'FCBC', 'MENT', 'TWX', 'PIR', 'KOSS', 'BAP', 'TAT', 'OCRX', 'QADA', 'OSIR', 'BORN', 'OMI', 'MYL', 'WSTL', 'CRIS', 'HUM', 'EFOI', 'CPN', 'INVE', 'JASN', 'KRE', 'PESI', 'NXTM', 'ARQL', 'CMTL', 'SIX', 'GULF', 'IBN', 'DYSL', 'GLNG', 'VTV', 'EBTC', 'IVH', 'WAFD', 'ORPN', 'NWS', 'DCA', 'TENX', 'CLW', 'ELD', 'MCUR', 'GREK', 'BFK', 'EMJ', 'ACFC', 'TRMR', 'GIB', 'IRS', 'VCF', 'BPL', 'DUC', 'MUB', 'SKY', 'MKC', 'ORRF', 'PHH', 'CHMG', 'UDR', 'CYCC', 'ANH', 'BID', 'BITA', 'PSLV', 'XME', 'UBCP'}\n"
     ]
    }
   ],
   "source": [
    "# Create sets of unique ticker symbols in each DataFrame\n",
    "stock_tickers = set(stock_df['ticker'].unique())\n",
    "sentiment_tickers = set(sentiment_df['ticker'].unique())\n",
    "\n",
    "# Find ticker symbols missing in stock_df but present in sentiment_df\n",
    "missing_in_stock = stock_tickers.difference(sentiment_tickers)\n",
    "\n",
    "# Find ticker symbols missing in sentiment_df but present in stock_df\n",
    "missing_in_sentiment = sentiment_tickers.difference(stock_tickers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "103ccb41-a49e-4047-adcf-de59b7feb3bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find tickers in sentiment_df that are not in sentiment_df and delete those tickers from stock_df\n",
    "missing_tickers = set(stock_tickers) - set(sentiment_tickers)\n",
    "stock_df = stock_df[~stock_df['ticker'].isin(missing_tickers)]\n",
    "stock_df = stock_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8d44faa-6fb8-4ec4-b266-15e0dc8cf0da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   feature_1  feature_2  feature_3  feature_4  feature_5       date ticker  \\\n",
      "0  -4.694621   0.050102   0.525245  -4.551044   1.058598 2017-01-03      A   \n",
      "1  -4.948428   0.293337   0.468583  -4.553871   1.065424 2017-01-04      A   \n",
      "2  -4.885045   0.113366   0.636895  -4.541259   1.020767 2017-01-05      A   \n",
      "3  -5.465603   0.731387   0.340322  -4.613235   1.154764 2017-01-06      A   \n",
      "4  -5.977897   1.172599   0.107106  -4.593020   1.132056 2017-01-09      A   \n",
      "\n",
      "    close  labels  scores  \n",
      "0  46.178     NaN     NaN  \n",
      "1  46.784     NaN     NaN  \n",
      "2  46.228     NaN     NaN  \n",
      "3  47.668     NaN     NaN  \n",
      "4  47.817     NaN     NaN  \n"
     ]
    }
   ],
   "source": [
    "# Perform an left merge on 'ticker' and 'date' columns, filling missing sentiment with NaN\n",
    "sentiment_df['date'] = pd.to_datetime(sentiment_df['date'])\n",
    "stock_df['date'] = pd.to_datetime(stock_df['date'])\n",
    "merged_df = pd.merge(stock_df, sentiment_df, on=['ticker', 'date'], how='left')\n",
    "\n",
    "# Fill NaN values in the 'sentiment' column with forward filling\n",
    "merged_df.sort_values(by=['ticker', 'date'], inplace=True)\n",
    "merged_df['labels'] = merged_df.groupby('ticker')['labels'].fillna(method='ffill')\n",
    "merged_df['scores'] = merged_df.groupby('ticker')['scores'].fillna(method='ffill')\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8346367b-2b4b-4f4c-b907-5441f1b0aefc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21553"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of null (missing) values in the 'labels' column \n",
    "merged_df['labels'].isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba93b3b2-aac7-4f0d-9f7b-2edf63fcd4e3",
   "metadata": {},
   "source": [
    "Since we use `filling forward` to fill missing `labels` and `scores`, meanwhile, in the very beginning of that ticker, if there was no data for sentiment, these missing values remaining NAN. We decide to remove 21,553 missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b80f6ee2-e9f7-4601-bd5b-6544b99c3877",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_5</th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>close</th>\n",
       "      <th>labels</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>-7.135500</td>\n",
       "      <td>1.737037</td>\n",
       "      <td>0.549845</td>\n",
       "      <td>-4.568750</td>\n",
       "      <td>0.963048</td>\n",
       "      <td>2017-02-13</td>\n",
       "      <td>A</td>\n",
       "      <td>50.181</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.726289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>-6.763309</td>\n",
       "      <td>1.054229</td>\n",
       "      <td>1.104335</td>\n",
       "      <td>-4.783732</td>\n",
       "      <td>0.910144</td>\n",
       "      <td>2017-02-14</td>\n",
       "      <td>A</td>\n",
       "      <td>50.051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>-7.440492</td>\n",
       "      <td>1.941932</td>\n",
       "      <td>0.611897</td>\n",
       "      <td>-4.902583</td>\n",
       "      <td>1.118596</td>\n",
       "      <td>2017-02-15</td>\n",
       "      <td>A</td>\n",
       "      <td>51.274</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.405166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>-7.021660</td>\n",
       "      <td>1.260201</td>\n",
       "      <td>1.244202</td>\n",
       "      <td>-4.692837</td>\n",
       "      <td>0.886699</td>\n",
       "      <td>2017-02-16</td>\n",
       "      <td>A</td>\n",
       "      <td>50.499</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.405166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>-7.772780</td>\n",
       "      <td>2.240362</td>\n",
       "      <td>0.517339</td>\n",
       "      <td>-4.614486</td>\n",
       "      <td>0.989061</td>\n",
       "      <td>2017-02-17</td>\n",
       "      <td>A</td>\n",
       "      <td>51.284</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.405166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  feature_1  feature_2  feature_3  feature_4  feature_5       date  \\\n",
       "0     28  -7.135500   1.737037   0.549845  -4.568750   0.963048 2017-02-13   \n",
       "1     29  -6.763309   1.054229   1.104335  -4.783732   0.910144 2017-02-14   \n",
       "2     30  -7.440492   1.941932   0.611897  -4.902583   1.118596 2017-02-15   \n",
       "3     31  -7.021660   1.260201   1.244202  -4.692837   0.886699 2017-02-16   \n",
       "4     32  -7.772780   2.240362   0.517339  -4.614486   0.989061 2017-02-17   \n",
       "\n",
       "  ticker   close  labels    scores  \n",
       "0      A  50.181     1.0  0.726289  \n",
       "1      A  50.051     1.0  1.000000  \n",
       "2      A  51.274     2.0  0.405166  \n",
       "3      A  50.499     2.0  0.405166  \n",
       "4      A  51.284     2.0  0.405166  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows with missing values (NaN) \n",
    "merged_df = merged_df.dropna()\n",
    "\n",
    "# Reset the index and add the previous index as a new column 'index'\n",
    "merged_df = merged_df.reset_index()\n",
    "\n",
    "# Convert the 'date' column to datetime format\n",
    "merged_df['date'] = pd.to_datetime(merged_df['date'])\n",
    "\n",
    "# Label encoding for 'ticker'\n",
    "label_encoder = LabelEncoder()\n",
    "merged_df['ticker_encoded'] = label_encoder.fit_transform(merged_df['ticker'])\n",
    "\n",
    "# Drop the 'ticker', 'index', `date` columns\n",
    "merged_df.drop(columns=['ticker', 'date', 'index'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deda0040-b6e2-49a5-88ca-323afa0da872",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged_df.to_csv(\"Candidate_features.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0349b72b-b2a9-424f-8fa6-e2c6f1fb21b3",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 7. Deep Learning Autoencoder "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f233472-8da9-4c23-9e8c-fbafd4a5b4f7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0488979-523c-4130-8e71-3a013b3cec17",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.optimizers import Adam, SGD, RMSprop\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras import layers, losses\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63f405d9-89de-4055-ac64-e763f782e0fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Candidate_features.csv\").drop(['Unnamed: 0'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e5dc0cf-eb2f-425d-8767-ffbdfe61b92f",
   "metadata": {},
   "outputs": [],
   "source": [
    "product_df = df.copy()\n",
    "product_df = product_df.sort_values(['ticker'])\n",
    "product_df.columns = map(str.lower, product_df.columns)\n",
    "X_all = product_df[['feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'labels', 'scores']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50a5dbc8-2ab4-48a0-ad89-1d5b2a57a5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c89159a-f34a-4191-bc80-c85bfbc49c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker_df = features_df[['ticker', 'ticker_encoded']].copy()\n",
    "ticker_df.drop_duplicates(keep='first', inplace=False, ignore_index=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1d6613f-d16f-471d-aee5-39ec4d7ea5e6",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 7.1. Spliting Train, Test and Validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6068bb94-4661-4d24-b40a-481a50d0daa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the stock_df by 'Ticker' and by 'Date'. \n",
    "df.sort_values(['ticker'], inplace=True)\n",
    "\n",
    "# 'row_num' to enumerates rows within each group of the same 'Ticker'\n",
    "df['row_num'] = df.groupby('ticker').cumcount() + 1\n",
    "\n",
    "# Calculating total count per partition\n",
    "partition_counts = df.groupby('ticker').size().reset_index(name='partition_count')\n",
    "\n",
    "# Merging the counts with the DataFrame\n",
    "stock_df_with_counts = pd.merge(df, partition_counts, on='ticker', how='left')\n",
    "\n",
    "# Calculate the thresholds for train, test, and valid partitions\n",
    "stock_df_with_counts['partition_threshold'] = stock_df_with_counts['partition_count'] * 0.7\n",
    "stock_df_with_counts['test_threshold'] = stock_df_with_counts['partition_count'] * 0.85\n",
    "\n",
    "# Filter the DataFrame per partition based on the thresholds\n",
    "train_data = stock_df_with_counts[stock_df_with_counts['row_num'] <= stock_df_with_counts['partition_threshold']]\n",
    "test_data = stock_df_with_counts[(stock_df_with_counts['row_num'] > stock_df_with_counts['partition_threshold']) &\n",
    "                                (stock_df_with_counts['row_num'] <= stock_df_with_counts['test_threshold'])]\n",
    "valid_data = stock_df_with_counts[stock_df_with_counts['row_num'] > stock_df_with_counts['test_threshold']]\n",
    "\n",
    "# Lower all column names \n",
    "train_data.columns = map(str.lower, train_data.columns)\n",
    "test_data.columns = map(str.lower, test_data.columns)\n",
    "valid_data.columns = map(str.lower, valid_data.columns)\n",
    "\n",
    "# Drop all unnessary columns \n",
    "train_data = train_data.drop(columns=['row_num', 'partition_count', 'partition_threshold', 'test_threshold'], axis=1)\n",
    "test_data = test_data.drop(columns=['row_num', 'partition_count', 'partition_threshold', 'test_threshold'], axis=1)\n",
    "valid_data = valid_data.drop(columns=['row_num', 'partition_count', 'partition_threshold', 'test_threshold'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23da7c92-9685-43e2-8dbe-e9ea71d7f5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_data[['feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'labels', 'scores']].values\n",
    "X_test = test_data[['feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'labels', 'scores']].values\n",
    "X_valid = valid_data[['feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'labels', 'scores']].values\n",
    "\n",
    "print(\"Shape of X_train:\", X_train.shape)\n",
    "print(\"Shape of X_test:\", X_test.shape)\n",
    "print(\"Shape of X_valid:\", X_valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266d6a7a-1255-4694-8de0-d85f3f716f17",
   "metadata": {},
   "source": [
    "### 7.2. Autoencoder Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60850ddb-1e0f-4aff-abbe-b5dd39de3c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the size of input layer \n",
    "input_dim = X_train.shape[1]\n",
    "encoding_dim = 32\n",
    "\n",
    "# Create autoencoder architecture\n",
    "# Encoder\n",
    "input_layer = Input(shape=(input_dim, ))\n",
    "encoder = Dense(256, activation=\"relu\")(input_layer)\n",
    "encoder = Dense(128, activation=\"relu\")(encoder)\n",
    "encoder = Dense(64, activation=\"relu\")(encoder)  \n",
    "encoder = Dense(encoding_dim, activation=\"relu\")(encoder)  \n",
    "\n",
    "# Decoder\n",
    "decoder = Dense(256, activation=\"relu\")(encoder)\n",
    "decoder = Dense(128, activation=\"relu\")(decoder)\n",
    "decoder = Dense(64, activation=\"relu\")(decoder)\n",
    "decoder = Dense(input_dim, activation=\"linear\")(decoder) \n",
    "\n",
    "# maps input to its reconstruction\n",
    "stock_autoencoder = Model(inputs=input_layer, outputs=decoder)\n",
    "\n",
    "# Compile model\n",
    "stock_autoencoder.compile(optimizer=Adam(learning_rate=0.001), loss='mse')\n",
    "\n",
    "# Define the checkpoint callback\n",
    "stock_encoder_checkpoint = ModelCheckpoint('stock_encoder.h5',\n",
    "                                           monitor='val_loss',\n",
    "                                           mode='min',\n",
    "                                           save_best_only=True,\n",
    "                                           verbose=1)\n",
    "\n",
    "# Train the model\n",
    "history = stock_autoencoder.fit(X_train, X_train, \n",
    "                                epochs=50, \n",
    "                                batch_size=32, \n",
    "                                validation_data=(X_valid, X_valid),\n",
    "                                callbacks=[stock_encoder_checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1003075-40e2-45ff-88da-71e135e11d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the training and validation loss to visualize the learning process\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Figure 1. Model Loss for Basic Autoencoder')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c83fcf46-c7d4-4fa3-b611-cc126bbf2b97",
   "metadata": {},
   "source": [
    "From figure 1., we observe that there is significant fluctuations, especially in the initial epochs. The model might be learning, but the loss is quite volatile. As the epochs increase, both the training and validation loss seem to converge, suggesting the model is stabilizing and learning effectively.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac49583e-c242-40b4-bda8-e16cbd5327a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the best model\n",
    "best_autoencoder = load_model('stock_encoder.h5')\n",
    "print(\"Loaded the best model from disk\")\n",
    "\n",
    "# Print the summary of the best model \n",
    "# Print network summary\n",
    "print(\"Best Model Architecture:\")\n",
    "best_autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00d19809-2a3c-4185-8e57-92addc39efaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using different optimizer \n",
    "# Compile model with SGD optimizer\n",
    "stock_autoencoder.compile(optimizer=SGD(learning_rate=0.01, momentum=0.9), loss='mse')\n",
    "\n",
    "# Train the model\n",
    "history_sgd = stock_autoencoder.fit(X_train, X_train, \n",
    "                                    epochs=50, \n",
    "                                    batch_size=32, \n",
    "                                    validation_data=(X_valid, X_valid),\n",
    "                                    callbacks=[stock_encoder_checkpoint])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "325f7690-a20a-4a76-8604-b12ed82d39a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.optimizers import RMSprop\n",
    "# Using different optimizer \n",
    "# Compile model with RMSprop optimizer\n",
    "stock_autoencoder.compile(optimizer=RMSprop(learning_rate=0.001), loss='mse')\n",
    "\n",
    "# Train the model\n",
    "history_rmsprop = stock_autoencoder.fit(X_train, X_train, \n",
    "                                        epochs=50, \n",
    "                                        batch_size=32, \n",
    "                                        validation_data=(X_valid, X_valid),\n",
    "                                        callbacks=[stock_encoder_checkpoint])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "508bc6c8-6802-4116-b930-449471abf584",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the model on test data\n",
    "test_loss = best_autoencoder.evaluate(X_test, X_test, verbose=0)\n",
    "print('Test loss:', test_loss)\n",
    "\n",
    "# Use our best model to make predictions\n",
    "encoded_predict = best_autoencoder.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c50a5f-d56e-4741-92dc-f5eebd2ab796",
   "metadata": {},
   "source": [
    "The test loss of our stock autoencoder model is 17475.43359375. Despite trying various optimizers – Adam, SGD, and RMSprop – there was no notable improvement in this loss metric. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca10b1b-e7e1-4785-a005-5c53a3032f8b",
   "metadata": {},
   "source": [
    "I'll try to adding more layer in architecture to see if the model can be better and reduce loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1fb6e88-72af-4c9f-adf2-90307272c4c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoder\n",
    "input_layer = Input(shape=(input_dim, ))\n",
    "encoder = Dense(512, activation=\"relu\")(input_layer)\n",
    "encoder = Dense(256, activation=\"relu\")(encoder)\n",
    "encoder = Dense(128, activation=\"relu\")(encoder)\n",
    "encoder = Dense(64, activation=\"relu\")(encoder)\n",
    "encoder = Dense(encoding_dim, activation=\"relu\")(encoder)\n",
    "\n",
    "# Decoder\n",
    "decoder = Dense(64, activation='relu')(encoder)\n",
    "decoder = Dense(128, activation='relu')(decoder)\n",
    "decoder = Dense(256, activation='relu')(decoder)\n",
    "decoder = Dense(512, activation='relu')(decoder)\n",
    "decoder = Dense(input_dim, activation='linear')(decoder)\n",
    "\n",
    "# Autoencoder\n",
    "stock_autoencoder2 = Model(inputs=input_layer, outputs=decoder)\n",
    "stock_autoencoder2.compile(optimizer=Adam(learning_rate=0.001), loss='mse')\n",
    "\n",
    "# Checkpoint to save the best model\n",
    "stock_encoder2_checkpoint = ModelCheckpoint('stock_encoder_add_layer.h5',\n",
    "                                           monitor='val_loss',\n",
    "                                           mode='min',\n",
    "                                           save_best_only=True,\n",
    "                                           verbose=1)\n",
    "\n",
    "# Train the model\n",
    "history = stock_autoencoder2.fit(X_train, X_train, \n",
    "                                 epochs=50, \n",
    "                                 batch_size=32, \n",
    "                                 validation_data=(X_valid, X_valid),\n",
    "                                 callbacks=[stock_encoder2_checkpoint])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18f9c5f6-a3b5-4de6-a2e9-9f4f766b39cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the training and validation loss to visualize the learning process\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Figure 2. Model Loss for Autoencoder with Extra Layers')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594c5386-575c-4909-95b3-14c9eac3c864",
   "metadata": {},
   "source": [
    "## 8. Content-based Recommendation Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb41dafc-7a16-44ba-8593-b45da01a096b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the encoder model to transform data into compressed form\n",
    "encoded_data = best_autoencoder.predict(X_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "498e8713-f6c2-49d3-9253-c6bacafe9ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa5978c5-a613-4317-8b9e-438c9137ffeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by 'ticker' and then select the first occurrence\n",
    "ticker_df = df.groupby('ticker').first().reset_index()\n",
    "\n",
    "# Select only the 'ticker' and 'ticker_encoded' columns\n",
    "ticker_df = ticker_df[['ticker', 'ticker_encoded']]\n",
    "ticker_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7b07c95-8840-48a5-a1e6-b8b1e57fce5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map the ticker unput as the index \n",
    "ticker_to_encoded = dict(zip(ticker_df['ticker'], ticker_df['ticker_encoded']))\n",
    "\n",
    "# sample test\n",
    "ticker_to_encoded.get('YY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bce98854-6051-48bd-a8c3-857ef271a99b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to recommend stocks\n",
    "def recommend_stocks(encoded_data, input_ticker, top_n=5):\n",
    "    # Retrieve the encoded index for the input ticker\n",
    "    ticker_index = ticker_to_encoded.get(input_ticker)\n",
    "\n",
    "    if ticker_index is None:\n",
    "        return \"Ticker not found.\"\n",
    "\n",
    "    # Calculate similarity with other stocks\n",
    "    user_encoded = encoded_data[ticker_index].reshape(1, -1)\n",
    "    similarity_scores = cosine_similarity(user_encoded, encoded_data)\n",
    "\n",
    "    # Get indices of top N similar stocks, but we don't count the input stock from user\n",
    "    similar_indices = similarity_scores.flatten().argsort()[::-1][1:top_n+1]\n",
    "\n",
    "    # Convert the indices back to the ticker symbols\n",
    "    # recommended_tickers = [ticker_df.iloc[i]['ticker'] for i in similar_indices]\n",
    "\n",
    "    # Return the indices of the similar stocks\n",
    "    return similar_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da85f7eb-e5cd-4606-984c-35a0c075005d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample running\n",
    "input_ticker = \"YY\"\n",
    "recommended_indices = recommend_stocks(encoded_data, input_ticker)\n",
    "print(\"Indices of Stocks similar to\", input_ticker, \":\", recommended_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b67b2401-b32e-494b-bc1e-3a1edfc2f4a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample running \n",
    "input_ticker = \"AA\"\n",
    "top_n = 5\n",
    "recommended_indices = recommend_stocks(encoded_data, input_ticker, top_n)\n",
    "print(\"Indices of Stocks similar to\", input_ticker, \":\", recommended_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1561a413-3dae-44ab-b121-3bd355443759",
   "metadata": {},
   "outputs": [],
   "source": [
    "def look_data(recommended_indices):\n",
    "    for i in recommended_indices:\n",
    "        index_val = stock_df['Ticker'][i]\n",
    "        req_data = stock_df[stock_df['Ticker'] == index_val]\n",
    "    \n",
    "    print(req_data['predicted_price'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9415f69-abc4-45b1-b86e-e02354d9fa1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "50e32850-ae4f-4b1f-9c7a-a17efa6e6560",
   "metadata": {},
   "source": [
    "# Streamlit WebApp Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91590abd-4e70-40d6-9560-20d7b4c92499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: newsapi in /Users/vanle/opt/anaconda3/lib/python3.9/site-packages (0.1.1)\n",
      "Requirement already satisfied: requests in /Users/vanle/opt/anaconda3/lib/python3.9/site-packages (from newsapi) (2.31.0)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/vanle/opt/anaconda3/lib/python3.9/site-packages (from requests->newsapi) (1.26.9)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/vanle/opt/anaconda3/lib/python3.9/site-packages (from requests->newsapi) (3.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vanle/opt/anaconda3/lib/python3.9/site-packages (from requests->newsapi) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/vanle/opt/anaconda3/lib/python3.9/site-packages (from requests->newsapi) (2021.10.8)\n"
     ]
    }
   ],
   "source": [
    "!pip install newsapi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f4f30a9-84c2-4b2d-806f-c1f1f193914a",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'NewsApiClient' from 'newsapi' (/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/newsapi/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnewsapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m NewsApiClient\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# NEWS_API_KEY = \"e6ba85bf8cdd4828ae52e299d6a60a90\"\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;66;03m# newsapi = NewsApiClient(api_key=NEWS_API_KEY)\u001b[39;00m\n\u001b[1;32m     13\u001b[0m base\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlight\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'NewsApiClient' from 'newsapi' (/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/newsapi/__init__.py)"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "import streamlit as st\n",
    "import os\n",
    "import requests\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from newsapi import NewsApiClient\n",
    "\n",
    "NEWS_API_KEY = \"e6ba85bf8cdd4828ae52e299d6a60a90\"\n",
    "newsapi = NewsApiClient(api_key=NEWS_API_KEY)\n",
    "\n",
    "base=\"light\"\n",
    "primaryColor=\"#ea1010\"\n",
    "secondaryBackgroundColor=\"#f19682\"\n",
    "textColor=\"#111112\"\n",
    "\n",
    "## Candidate feature dataset is used to compare the ticker in this file\n",
    "# merged_df = pd.read_csv('/Users/marianivethaantonypushparaj/Downloads/Candidate_features.csv')\n",
    "stock_df = pd.read_csv('/Users/marianivethaantonypushparaj/Downloads/predicted_price_data.csv')\n",
    "\n",
    "def get_data(ticker):\n",
    "    from sklearn.metrics.pairwise import cosine_similarity\n",
    "    stock_df = pd.read_csv('/Users/marianivethaantonypushparaj/Downloads/predicted_price_data.csv')\n",
    "    encoded_data = np.loadtxt('/Users/marianivethaantonypushparaj/Downloads/encoded_data.csv', skiprows=1, delimiter=',')\n",
    "    ticker_df = pd.read_csv('/Users/marianivethaantonypushparaj/Downloads/Candidate_features.csv')\n",
    "    def look_data(recommended_indices):\n",
    "        for i in recommended_indices:\n",
    "            index_val = stock_df['Ticker'][i]\n",
    "            req_data = stock_df[stock_df['Ticker'] == index_val]\n",
    "\n",
    "        return (index_val,req_data['predicted_price'].median())\n",
    "    ticker_to_encoded = dict(zip(ticker_df['ticker'], ticker_df['ticker_encoded']))\n",
    "    def recommend_stocks(encoded_data, input_ticker, top_n=1):\n",
    "        # Retrieve the encoded index for the input ticker\n",
    "        ticker_index = ticker_to_encoded.get(input_ticker)\n",
    "\n",
    "        if ticker_index is None:\n",
    "            return \"Ticker not found.\"\n",
    "\n",
    "        # Calculate similarity with other stocks\n",
    "        user_encoded = encoded_data[ticker_index].reshape(1, -1)\n",
    "        similarity_scores = cosine_similarity(user_encoded, encoded_data)\n",
    "\n",
    "        # Get indices of top N similar stocks, but we don't count the input stock from user\n",
    "        similar_indices = similarity_scores.flatten().argsort()[::-1][1:top_n+1]\n",
    "\n",
    "        return similar_indices\n",
    "    recommended_indices = recommend_stocks(encoded_data,ticker,1)\n",
    "    ticker_new = look_data(list(recommended_indices))[0]\n",
    "    price = look_data(list(recommended_indices))[1]\n",
    "    return (ticker_new,price)\n",
    "\n",
    "\n",
    "st.write(\"\"\"\n",
    "# Stock Price Prediction and Recommendation\"\"\")\n",
    "st.subheader(\"When you are in doubt, you are just one click away from knowing about the stock\")\n",
    "st.write(\"This app will help you understand the US Market and price displayed are in USD\")\n",
    "\n",
    "#The ticker variable is defined here\n",
    "ticker=None\n",
    "\n",
    "ticker = st.text_input('Enter the ticker symbol',placeholder='A')\n",
    "\n",
    "if ticker:\n",
    "    st.write(\"You entered: \", ticker)\n",
    "    \n",
    "st.divider() \n",
    "\n",
    "ticker = ticker.upper()\n",
    "\n",
    "\n",
    "st.write('##### We also provide a recommendation on stocks which are similar to the stock you have entered')\n",
    "\n",
    "\n",
    "api_url = f'https://finance.yahoo.com/quote/{ticker}'\n",
    "\n",
    "# The predicted value is stored here\n",
    "\n",
    "response = requests.get(api_url)\n",
    "\n",
    "try:\n",
    "    if ticker:\n",
    "#         if (response.status_code == 200) and (ticker in stock_df.Ticker.unique()):\n",
    "            \n",
    "        if ticker in stock_df.Ticker.unique():\n",
    "            \n",
    "            tickerData = yf.Ticker(ticker)\n",
    "            progress_text = \"Searching data. Please wait.\"\n",
    "            my_bar = st.progress(0, text=progress_text)\n",
    "\n",
    "            for percent_complete in range(100):\n",
    "                time.sleep(0.01)\n",
    "                my_bar.progress(percent_complete + 1, text=progress_text)\n",
    "            time.sleep(1)\n",
    "            my_bar.empty()\n",
    "\n",
    "            st.button(\"Rerun\")\n",
    "            \n",
    "            #Predicted price is displayed\n",
    "            \n",
    "            st.subheader(\"Similar Stock and Price\")\n",
    "            st.write(\"##### Ticker: \",get_data(ticker)[0])\n",
    "            st.write(\"##### Median Price: \",get_data(ticker)[1])\n",
    "            \n",
    "            tickerDf = tickerData.history(period='1d', start='2017-1-1', end='2017-12-31')\n",
    "            st.title(\"Closing Price of the stock\")\n",
    "            st.line_chart(tickerDf.Close)\n",
    "            st.title(\"Volume of the stock\")\n",
    "            st.line_chart(tickerDf.Volume)\n",
    "            with st.sidebar:\n",
    "                st.title(\"Here's a compilation of financial news related to the specified ticker symbol\")\n",
    "                try:\n",
    "                    historical_news = newsapi.get_everything(q=f\"{ticker} stock\", from_param='2023-11-10', to='2023-12-02', language='en', sort_by='publishedAt')\n",
    "\n",
    "                    if 'articles' in historical_news:\n",
    "                        st.subheader(f\"Symbol: {ticker}\")\n",
    "                        for article in historical_news['articles']:\n",
    "                            st.write(f\"- {article['title']} - {article['publishedAt']}\")\n",
    "                            st.write(article['description'])\n",
    "                            st.write(f\"Link: {article['url']}\")\n",
    "                            st.write(\"---\")\n",
    "                    else:\n",
    "                        st.warning(f\"No historical news found for {ticker}\")\n",
    "                except Exception as e:\n",
    "                    st.error(f\"Error fetching news data: {e}\")\n",
    "            \n",
    "\n",
    "        else:\n",
    "            st.write(\"#### Error: Invalid ticker. Please enter a valid ticker.Ex:A,AAC etc.\")\n",
    "    \n",
    "except:\n",
    "    st.write(\"#### Error: Invalid ticker. Please enter a valid ticker.Ex:A,AAC etc.\")"
   ]
  }
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