notebooks/supporting/adapted-03-stock-price-prediction-machine-learning-20-minutes-intervals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stock Price Movement Prediction Using The Deutsche Börse Public Dataset & Machine Learning - Notebook 3 (Applying A Neural Network)\n",
    "\n",
    "Here we apply the neural network approach suggested in Notebook 1 to the data set product in Notebook 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important: 20 minute by 20 minute predictions\n",
    "\n",
    "We adapt the approaches of notebook `03-stock-price-prediction-machine-learning.ipynb` and\n",
    "notebook `supporting/simple-linear-model.ipynb` to work on 20 minutes by 20 minute basis rather than on a minute by minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob, os\n",
    "from datetime import datetime\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "mpl.rcParams['figure.figsize'] = (15, 10) # use bigger graphs\n",
    "\n",
    "from math import sqrt\n",
    "from numpy import concatenate\n",
    "from matplotlib import pyplot\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten, Reshape, Conv1D, MaxPooling1D, BatchNormalization, LeakyReLU\n",
    "from keras.layers import LSTM\n",
    "from keras import regularizers\n",
    "\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We read the file we processed in the second notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file = '/data/processed/cooked_v3.pkl'\n",
    "df = pd.read_pickle(input_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking what stocks are available in the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['SNH', 'DBK', 'EOAN', 'DTE', 'CBK', 'RWE', 'IFX', 'SVAB', 'LHA',\n",
       "        'DAI', 'O2D', 'TKA', 'DPW', 'SIE', 'HDD', 'BAYN', 'AIXA', 'SAP',\n",
       "        'BAS', 'EVT', 'AT1', 'PSM', 'BMW', 'VOW3', 'FRE', 'SDF', 'ALV',\n",
       "        'CEC', 'GAZ', 'VNA', 'B4B', 'SHA', 'AB1', 'DLG', 'NDX1', 'UN01',\n",
       "        'NOA3', 'IGY', 'ADS', 'VODI', '1COV', 'TUI1', 'BPE5', 'HEI', 'USE',\n",
       "        'KCO', 'SZU', 'EVK', 'ADV', 'HEN3', 'MUV2', 'WDI', 'DWNI', 'MRK',\n",
       "        'PAH3', 'DEZ', 'FME', 'FNTN', 'G1A', 'RKET', 'QIA', 'DB1', 'ZAL',\n",
       "        'RIB', 'QSC', 'CON', 'SGL', 'BVB', 'SHL', 'PNE3', 'PBB', 'OSR',\n",
       "        'TINA', 'BEI', 'TEG', 'AOX', 'UTDI', 'ARL', 'MDG1', 'SANT', 'LXS',\n",
       "        'KGX', 'TTI', 'LINU', 'GYC', 'ARO', 'SOW', 'ANO', 'SZG', 'BOSS',\n",
       "        'BNR', 'LLD', 'WAF', 'DRI', 'NDA', 'SY1', 'LIN', 'ZIL2', 'CAP',\n",
       "        '3W9K'], dtype=object), 100)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_mnemonics = df.Mnemonic.unique()\n",
    "df.Mnemonic.unique(), df.Mnemonic.unique().shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will select the most liquid stocks from this set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Liquidity</th>\n",
       "      <th>LiquidityNormalized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mnemonic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SIE</th>\n",
       "      <td>3.743603e+10</td>\n",
       "      <td>5.516107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALV</th>\n",
       "      <td>3.392598e+10</td>\n",
       "      <td>4.998910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAYN</th>\n",
       "      <td>3.320337e+10</td>\n",
       "      <td>4.892436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAI</th>\n",
       "      <td>3.313211e+10</td>\n",
       "      <td>4.881935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBK</th>\n",
       "      <td>3.312050e+10</td>\n",
       "      <td>4.880225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Liquidity  LiquidityNormalized\n",
       "Mnemonic                                   \n",
       "SIE       3.743603e+10             5.516107\n",
       "ALV       3.392598e+10             4.998910\n",
       "BAYN      3.320337e+10             4.892436\n",
       "DAI       3.313211e+10             4.881935\n",
       "DBK       3.312050e+10             4.880225"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Liquidity'] = df['TradedVolume']*df['EndPrice']\n",
    "tmp = df.groupby('Mnemonic')['Liquidity'].sum().to_frame()\n",
    "tmp['LiquidityNormalized'] = 100.0*tmp['Liquidity']/(tmp['Liquidity'].sum())\n",
    "df = df.drop(columns=['Liquidity'])\n",
    "\n",
    "tmp.sort_values('LiquidityNormalized', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 Most liquid stocks: SIE, ALV, BAYN, DAI, DBK, VOW3, SAP, BAS, DTE, ADS, BMW, CBK, EOAN, IFX, MUV2, RWE, LHA, DPW, CON, FRE, HEN3, TKA, 1COV, HEI, LINU, WDI, PSM, MRK, DB1, VNA, FME, LIN, PAH3, BEI, WAF, EVT, IGY, SDF, OSR, AIXA, DLG, SNH, KGX, LXS, DWNI, ZAL, BOSS, G1A, UN01, UTDI\n"
     ]
    }
   ],
   "source": [
    "most_liquid_stocks = list(tmp.sort_values('LiquidityNormalized', ascending=False).index[0:50])\n",
    "print(\"50 Most liquid stocks:\", \", \".join(most_liquid_stocks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check what dates are available in the file. We will split the original set\n",
    "into three parts, train, valid, test based on the dates.\n",
    "If the dates are ordered chronologically, we take the first dates for the test set,\n",
    "then we take the next dates for the validation set and finally we take what is\n",
    "left for the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(217, ['2017-07-03', '2017-07-04'], ['2018-05-11', '2018-05-14'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def date_part(dt):\n",
    "    return str(dt).split(' ')[0]\n",
    "unique_days = sorted(list(set(map(date_part , list(df.index.unique())))))\n",
    "len(unique_days), unique_days[0:2], unique_days[-3:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('train', 130, 'valid', 10, 'test', 77)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_train = 60.0\n",
    "percent_valid = 5.0\n",
    "percent_test = 100.0 - percent_train - percent_valid\n",
    "\n",
    "offset_train = int(len(unique_days)*percent_train/100.0)\n",
    "offset_test = offset_train + int(len(unique_days)*percent_valid/100.0)\n",
    "\n",
    "train_valid_days = list(set(unique_days[0:offset_test]))\n",
    "\n",
    "np.random.seed(484811945)\n",
    "np.random.shuffle(train_valid_days)\n",
    "\n",
    "train_days = train_valid_days[0:offset_train]\n",
    "valid_days = train_valid_days[offset_train:]\n",
    "test_days = set(unique_days[offset_test:])\n",
    "'train', len(train_days), 'valid', len(valid_days), 'test', len(test_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CalcDateTime'] = df.index\n",
    "df['Date'] = df['CalcDateTime'].dt.strftime(\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df[df.Date.isin(list(train_days))]\n",
    "df_valid = df[df.Date.isin(list(valid_days))]\n",
    "df_test = df[df.Date.isin(list(test_days))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've prepared the train, test and valid sets. Make sure the days do not overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>CalcDateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9373000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>93730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2017-08-09 08:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>2017-07-03 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>2018-01-23 20:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               CalcDateTime\n",
       "count               9373000\n",
       "unique                93730\n",
       "top     2017-08-09 08:10:00\n",
       "freq                    100\n",
       "first   2017-07-03 08:00:00\n",
       "last    2018-01-23 20:00:00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[['CalcDateTime']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CalcDateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>721000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2017-09-15 14:29:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>2017-08-14 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>2018-01-10 20:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               CalcDateTime\n",
       "count                721000\n",
       "unique                 7210\n",
       "top     2017-09-15 14:29:00\n",
       "freq                    100\n",
       "first   2017-08-14 08:00:00\n",
       "last    2018-01-10 20:00:00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valid[['CalcDateTime']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CalcDateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5551700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>55517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2018-03-16 10:22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>2018-01-24 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>2018-05-15 20:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               CalcDateTime\n",
       "count               5551700\n",
       "unique                55517\n",
       "top     2018-03-16 10:22:00\n",
       "freq                    100\n",
       "first   2018-01-24 08:00:00\n",
       "last    2018-05-15 20:00:00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[['CalcDateTime']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the class below we create features from the raw features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "enable_non_linear_features = True\n",
    "\n",
    "def closer_to_with_normalization(pnt, a, b, norm):\n",
    "    \"\"\"\n",
    "    Returns the \"directed\" and normalized distance to the closer.\n",
    "    @pnt: pnt which is compared to other two points, called a and b here\n",
    "    @a: point a\n",
    "    @b: point b\n",
    "    @norm: normalization constant\n",
    "    \"\"\"    \n",
    "    return (np.absolute(pnt - a) - np.absolute(pnt - b))/norm\n",
    "\n",
    "def resample_single_stock(single_stock, interval):\n",
    "    df = pd.DataFrame({\n",
    "        'MaxPrice': single_stock['MaxPrice'].resample(interval).max(),\n",
    "        'MinPrice': single_stock['MinPrice'].resample(interval).min(),\n",
    "        'LastEndPrice': single_stock['EndPrice'].resample(interval).last(),\n",
    "        'FirstStartPrice': single_stock['StartPrice'].resample(interval).first(),         \n",
    "        'MeanEndPrice': single_stock['EndPrice'].resample(interval).mean(),        \n",
    "        'HasTrade': single_stock['HasTrade'].resample(interval).max(),\n",
    "        'Mnemonic': single_stock['Mnemonic'].resample(interval).last(),\n",
    "        'Date': single_stock['Date'].resample(interval).last(),        \n",
    "    })\n",
    "    \n",
    "    # Warning: this works because we had forward filled the prices\n",
    "    df = df[df['HasTrade'] == 1.0]\n",
    "    return df\n",
    "\n",
    "def rev_pct_change(a, t):\n",
    "    one_step_in_past = a\n",
    "    t_steps_in_past = a.shift(t).ffill()\n",
    "    return ((one_step_in_past - t_steps_in_past)/one_step_in_past).fillna(0.0)\n",
    "\n",
    "def add_non_linear_features(main, resampled, interval):\n",
    "    main['tmp:SignDirection@' + interval] = np.sign(main['x:Direction@' + interval])\n",
    "\n",
    "    main['tmp:D1@' + interval] = np.where( \n",
    "        (main['tmp:SignDirection@' + interval] == 1.0) &\n",
    "        (main['tmp:SignDirection@' + interval].shift(1) == 1.0), 1.0, 0.0)\n",
    "\n",
    "    main['tmp:D2@' + interval] = np.where( \n",
    "        (main['tmp:SignDirection@' + interval] == -1.0) &\n",
    "        (main['tmp:SignDirection@' + interval].shift(1) == -1.0), -1.0, 0.0)        \n",
    "\n",
    "    main['x:D@' + interval] = main['tmp:D1@' + interval] + main['tmp:D2@' + interval]\n",
    "\n",
    "    main['x:SignDirection-2@' + interval] = np.sign(\n",
    "        (resampled['LastEndPrice'] - resampled['FirstStartPrice'].shift(2).ffill()\n",
    "    )).fillna(0.0)\n",
    "\n",
    "    main['tmp:D1@' + interval] = np.where( \n",
    "        (main['tmp:SignDirection@' + interval] == 1.0) &\n",
    "        (main['tmp:SignDirection@' + interval].shift(1) == -1.0), main['x:SignDirection-2@' + interval], 0.0)\n",
    "\n",
    "    main['tmp:D2@' + interval] = np.where( \n",
    "        (main['tmp:SignDirection@' + interval] == -1.0) &\n",
    "        (main['tmp:SignDirection@' + interval].shift(1) == 1.0), main['x:SignDirection-2@' + interval], 0.0)        \n",
    "\n",
    "    main['x:Da@' + interval] = main['tmp:D1@' + interval] + main['tmp:D2@' + interval]\n",
    "    \n",
    "    main = main.drop(columns=[\n",
    "        'tmp:SignDirection@' + interval,\n",
    "        'tmp:D1@' + interval,\n",
    "        'tmp:D2@' + interval\n",
    "    ])\n",
    "        \n",
    "    return main\n",
    "\n",
    "def prepare_single_stock_multi_intervals(single_stock, predicted_price, main_interval, intervals):\n",
    "        \n",
    "    main = resample_single_stock(single_stock, main_interval)\n",
    "    # we use the same anchor\n",
    "    anchor = main['MeanEndPrice']\n",
    "    future_mean_price = main[predicted_price].shift(-1)\n",
    "    \n",
    "    main['y(Return)'] = (future_mean_price - anchor)/anchor\n",
    "\n",
    "    # do not normalize\n",
    "    main['pseudo_y(SignReturn)'] = np.sign(main['y(Return)'])\n",
    "\n",
    "    # actual return won't be normalized\n",
    "    main['pseudo_y(pctChange)'] = (future_mean_price - anchor)/anchor\n",
    "    \n",
    "    # baseline will be normalized\n",
    "    main['baseline'] = main['pseudo_y(pctChange)'].shift(1).fillna(0.0)    \n",
    "    \n",
    "    all_intervals = [main_interval] + intervals\n",
    "    \n",
    "    for interval in all_intervals:\n",
    "        sub = resample_single_stock(single_stock, interval)\n",
    "        resampled = sub.resample(main_interval).last() \n",
    "\n",
    "        main['x:Direction@' + interval] = \\\n",
    "            2.0*(resampled['LastEndPrice'] - resampled['FirstStartPrice'])/ \\\n",
    "            anchor\n",
    "\n",
    "        if enable_non_linear_features:\n",
    "            main = add_non_linear_features(main, resampled, interval)\n",
    "\n",
    "        main['x:H1@' + interval] = - closer_to_with_normalization(\n",
    "                                                 resampled['LastEndPrice'], \n",
    "                                                 resampled['MaxPrice'], \n",
    "                                                 resampled['MinPrice'],\n",
    "                                                 anchor)    \n",
    "        \n",
    "        main['x:EndToMean@' + interval] = (resampled['LastEndPrice'] - resampled['MeanEndPrice'])/anchor\n",
    "        \n",
    "        main['x:AdjustedPctChange@' + interval] = (resampled['LastEndPrice'] - resampled['MeanEndPrice'])/resampled['MeanEndPrice']\n",
    "        main['x:RevPctChange@' + interval] = rev_pct_change(resampled['LastEndPrice'], 1)\n",
    "    \n",
    "    main = main[main['HasTrade'] == 1.0]\n",
    "    meta = main[['MeanEndPrice', 'HasTrade', 'LastEndPrice']]\n",
    "    main = main.drop(columns = [\n",
    "        'MaxPrice',\n",
    "        'MinPrice',\n",
    "        'LastEndPrice',\n",
    "        'FirstStartPrice',         \n",
    "        'MeanEndPrice',     \n",
    "        'HasTrade'       \n",
    "    ])\n",
    "    return main, meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NARemover:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "    def transform(self, single_stock):\n",
    "        before = single_stock.shape[0]\n",
    "        single_stock = single_stock.dropna()\n",
    "        after = single_stock.shape[0]\n",
    "        print(\"{}: Dropped {:2.2f} % of records due to NA\".format(self.name, 100.0*(before - after)/(0.0001 + before)))\n",
    "        return single_stock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable we use for predictions start with `x(`, while the variables that should be predicted start with `y(`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Mnemonic</th>\n",
       "      <th>Date</th>\n",
       "      <th>y(Return)</th>\n",
       "      <th>pseudo_y(SignReturn)</th>\n",
       "      <th>pseudo_y(pctChange)</th>\n",
       "      <th>baseline</th>\n",
       "      <th>x:Direction@20Min</th>\n",
       "      <th>x:D@20Min</th>\n",
       "      <th>x:SignDirection-2@20Min</th>\n",
       "      <th>x:Da@20Min</th>\n",
       "      <th>...</th>\n",
       "      <th>x:AdjustedPctChange@5Min</th>\n",
       "      <th>x:RevPctChange@5Min</th>\n",
       "      <th>x:Direction@10Min</th>\n",
       "      <th>x:D@10Min</th>\n",
       "      <th>x:SignDirection-2@10Min</th>\n",
       "      <th>x:Da@10Min</th>\n",
       "      <th>x:H1@10Min</th>\n",
       "      <th>x:EndToMean@10Min</th>\n",
       "      <th>x:AdjustedPctChange@10Min</th>\n",
       "      <th>x:RevPctChange@10Min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CalcDateTime</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-03 08:00:00</th>\n",
       "      <td>BMW</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>0.002151</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000976</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03 08:20:00</th>\n",
       "      <td>BMW</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>0.001739</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001739</td>\n",
       "      <td>0.002151</td>\n",
       "      <td>0.005845</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000146</td>\n",
       "      <td>0.002919</td>\n",
       "      <td>0.002436</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001096</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.002919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03 08:40:00</th>\n",
       "      <td>BMW</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>-0.000097</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.000097</td>\n",
       "      <td>0.001739</td>\n",
       "      <td>0.002918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.001336</td>\n",
       "      <td>0.002675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.001336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03 09:00:00</th>\n",
       "      <td>BMW</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>-0.000109</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.000109</td>\n",
       "      <td>-0.000097</td>\n",
       "      <td>-0.002918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>-0.001582</td>\n",
       "      <td>-0.002918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000486</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>-0.001582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03 09:20:00</th>\n",
       "      <td>BMW</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>0.000863</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000863</td>\n",
       "      <td>-0.000109</td>\n",
       "      <td>0.001702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000073</td>\n",
       "      <td>0.000851</td>\n",
       "      <td>0.001702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000608</td>\n",
       "      <td>-0.000036</td>\n",
       "      <td>-0.000036</td>\n",
       "      <td>0.000851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Mnemonic        Date  y(Return)  pseudo_y(SignReturn)  \\\n",
       "CalcDateTime                                                                \n",
       "2017-07-03 08:00:00      BMW  2017-07-03   0.002151                   1.0   \n",
       "2017-07-03 08:20:00      BMW  2017-07-03   0.001739                   1.0   \n",
       "2017-07-03 08:40:00      BMW  2017-07-03  -0.000097                  -1.0   \n",
       "2017-07-03 09:00:00      BMW  2017-07-03  -0.000109                  -1.0   \n",
       "2017-07-03 09:20:00      BMW  2017-07-03   0.000863                   1.0   \n",
       "\n",
       "                     pseudo_y(pctChange)  baseline  x:Direction@20Min  \\\n",
       "CalcDateTime                                                            \n",
       "2017-07-03 08:00:00             0.002151  0.000000           0.000488   \n",
       "2017-07-03 08:20:00             0.001739  0.002151           0.005845   \n",
       "2017-07-03 08:40:00            -0.000097  0.001739           0.002918   \n",
       "2017-07-03 09:00:00            -0.000109 -0.000097          -0.002918   \n",
       "2017-07-03 09:20:00             0.000863 -0.000109           0.001702   \n",
       "\n",
       "                     x:D@20Min  x:SignDirection-2@20Min  x:Da@20Min  ...  \\\n",
       "CalcDateTime                                                         ...   \n",
       "2017-07-03 08:00:00        0.0                      0.0         0.0  ...   \n",
       "2017-07-03 08:20:00        1.0                      0.0         0.0  ...   \n",
       "2017-07-03 08:40:00        1.0                      1.0         0.0  ...   \n",
       "2017-07-03 09:00:00        0.0                      1.0         1.0  ...   \n",
       "2017-07-03 09:20:00        0.0                      1.0         1.0  ...   \n",
       "\n",
       "                     x:AdjustedPctChange@5Min  x:RevPctChange@5Min  \\\n",
       "CalcDateTime                                                         \n",
       "2017-07-03 08:00:00                  0.000000             0.000000   \n",
       "2017-07-03 08:20:00                 -0.000146             0.002919   \n",
       "2017-07-03 08:40:00                  0.000170             0.001336   \n",
       "2017-07-03 09:00:00                  0.000438            -0.001582   \n",
       "2017-07-03 09:20:00                 -0.000073             0.000851   \n",
       "\n",
       "                     x:Direction@10Min  x:D@10Min  x:SignDirection-2@10Min  \\\n",
       "CalcDateTime                                                                 \n",
       "2017-07-03 08:00:00           0.000488        0.0                      0.0   \n",
       "2017-07-03 08:20:00           0.002436        1.0                      0.0   \n",
       "2017-07-03 08:40:00           0.002675        1.0                      1.0   \n",
       "2017-07-03 09:00:00          -0.002918        0.0                      1.0   \n",
       "2017-07-03 09:20:00           0.001702        0.0                      1.0   \n",
       "\n",
       "                     x:Da@10Min  x:H1@10Min  x:EndToMean@10Min  \\\n",
       "CalcDateTime                                                     \n",
       "2017-07-03 08:00:00         0.0    0.000976           0.000439   \n",
       "2017-07-03 08:20:00         0.0    0.001096           0.000379   \n",
       "2017-07-03 08:40:00         0.0    0.001216           0.000413   \n",
       "2017-07-03 09:00:00         1.0   -0.000486          -0.000012   \n",
       "2017-07-03 09:20:00         1.0    0.000608          -0.000036   \n",
       "\n",
       "                     x:AdjustedPctChange@10Min  x:RevPctChange@10Min  \n",
       "CalcDateTime                                                          \n",
       "2017-07-03 08:00:00                   0.000439              0.000000  \n",
       "2017-07-03 08:20:00                   0.000379              0.002919  \n",
       "2017-07-03 08:40:00                   0.000413              0.001336  \n",
       "2017-07-03 09:00:00                  -0.000012             -0.001582  \n",
       "2017-07-03 09:20:00                  -0.000036              0.000851  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = df_train[df_train.Mnemonic == 'BMW'].copy()\n",
    "dummy = dummy[dummy.HasTrade == 1.0]\n",
    "#main_interval_dummy, intervals_dummy = '20Min', ['5Min', '10Min', '15Min']\n",
    "main_interval_dummy, intervals_dummy = '20Min', ['5Min', '10Min']\n",
    "\n",
    "dummy, meta = prepare_single_stock_multi_intervals(dummy, 'MeanEndPrice', main_interval_dummy, intervals_dummy)\n",
    "dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TrainingSet:\n",
    "    def __init__(self, X, y, orig_df):\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "        self.baseline = orig_df['baseline'].values\n",
    "        self.original_df = orig_df\n",
    "        \n",
    "class TrainingSetBuilder:\n",
    "    def transform(self, single_stock):\n",
    "        x_features = filter(lambda name: name.startswith('x(') or name.startswith('x:'), list(single_stock.dtypes.index))\n",
    "        X = single_stock[x_features].values\n",
    "        y = single_stock[['pseudo_y(SignReturn)']].values \n",
    "        return TrainingSet(X, y, single_stock)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Predictions:\n",
    "    def __init__(self, predictions, training_set):\n",
    "\n",
    "        self.predictions = predictions\n",
    "        self.training_set = training_set\n",
    "        \n",
    "    def evaluate(self):\n",
    "        single_feature = 'baseline'\n",
    "        stats_df = pd.DataFrame({\n",
    "                      'predictions': self.predictions[:,0],\n",
    "                      'single_feature_pred': self.training_set.original_df[single_feature].values,\n",
    "                      'pseudo_y(SignReturn)': self.training_set.y[:,0],\n",
    "                      'pseudo_y(pctChange)': self.training_set.original_df['pseudo_y(pctChange)'].values,\n",
    "                      'y(Return)': self.training_set.original_df['y(Return)'].values})\n",
    "        \n",
    "        corr = stats_df. \\\n",
    "            corr()[['predictions', 'single_feature_pred']]. \\\n",
    "            iloc[1:]\n",
    "            \n",
    "        pred_signs = np.sign(stats_df['predictions'])\n",
    "        y_signs = np.sign(stats_df['y(Return)'])\n",
    "        has_answer = np.absolute(pred_signs * y_signs).sum()\n",
    "        correct = np.where(pred_signs * y_signs == 1.0, 1.0, 0.0).sum()\n",
    "        \n",
    "        thresholds = []\n",
    "        accuracy = []\n",
    "        correct_lst = []\n",
    "        errors = []\n",
    "        percent_has_answer = []\n",
    "        abs_has_answer = []\n",
    "        achieved_returns = []\n",
    "\n",
    "        preds = stats_df['predictions']\n",
    "        \n",
    "        for d in range(5, 46, 5):\n",
    "            low = np.percentile(preds, d) \n",
    "            high = np.percentile(preds, 100 - d)\n",
    "            thresholded = np.where(preds > high, 1.0, np.where(preds < low, -1.0, 0.0))\n",
    "            c = np.where(np.sign(thresholded)*np.sign(y_signs) == 1.0, 1.0, 0.0).sum()\n",
    "            e = np.where(np.sign(thresholded)*np.sign(y_signs) == -1.0, 1.0, 0.0).sum()\n",
    "            achieved_ret = (stats_df['pseudo_y(pctChange)']*thresholded).sum()\n",
    "            correct_lst.append(c)\n",
    "            errors.append(e)\n",
    "            accuracy.append(c/(c + e))\n",
    "            percent_has_answer.append(100.0*(c + e)/pred_signs.shape[0])\n",
    "            abs_has_answer.append((c + e))\n",
    "            achieved_returns.append(achieved_ret)\n",
    "            thresholds.append(d)\n",
    "            \n",
    "        at_cutoff = DataFrame({\n",
    "                    'thresholds': thresholds,\n",
    "                    'accuracy': accuracy,\n",
    "                    'percent_with_answer': percent_has_answer,\n",
    "                    'absolute_has_answer': abs_has_answer,\n",
    "                    'achieved_returns': achieved_returns,\n",
    "                    'correct': correct_lst,\n",
    "                    'errors': errors\n",
    "        })\n",
    "        at_cutoff['achieved_norm_returns'] = at_cutoff['achieved_returns']/at_cutoff['absolute_has_answer']\n",
    "        \n",
    "        ret = stats_df['pseudo_y(pctChange)']\n",
    "        rand_feature = np.where(np.random.rand(ret.shape[0]) > 0.5, 1.0, -1.0)    \n",
    "        random_returns = (ret * rand_feature).sum()\n",
    "        always_up_returns = (ret*1.0).sum()\n",
    "        always_down_returns = (ret*-1.0).sum()\n",
    "        omnicient_returns = (np.absolute(ret)).sum()\n",
    "        achieved = (ret * pred_signs).sum()\n",
    "        return {\n",
    "            'corr': corr,\n",
    "            'accuracy_at_cutoff': at_cutoff,\n",
    "            'matches': {\n",
    "                'percent_correct': 100*correct/has_answer,\n",
    "                'percent_has_answer': has_answer/pred_signs.shape[0],\n",
    "                'absolute_with_answer': has_answer,\n",
    "                'size': pred_signs.shape[0]\n",
    "            },\n",
    "            'strategies': {\n",
    "                'omniscient': omnicient_returns,\n",
    "                'random': random_returns,\n",
    "                'always_up': always_up_returns,\n",
    "                'always_down': always_down_returns,\n",
    "                'achieved': achieved,\n",
    "                'num_trials': np.absolute(pred_signs).sum()\n",
    "            }\n",
    "        }\n",
    "    \n",
    "class MLModel:\n",
    "    def __init__(self):\n",
    "        self.model = None\n",
    "        \n",
    "    def fit(self, training_set, valid_set = None):\n",
    "        train_X, train_y = training_set.X, training_set.y\n",
    "        \n",
    "        if valid_set is None:\n",
    "            valid_X, valid_y = train_X, train_y\n",
    "        else:\n",
    "            valid_X, valid_y = valid_set.X, valid_set.y\n",
    "\n",
    "        model = Sequential()\n",
    "\n",
    "        model.add(Dense(10, activation='relu', input_shape =(train_X.shape[1],),\n",
    "                        kernel_regularizer=regularizers.l2(0.1))) \n",
    "        model.add(Dense(5, activation='relu', kernel_regularizer=regularizers.l2(0.1)))        \n",
    "\n",
    "        model.add(Dense(1))\n",
    "        \n",
    "        model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "        self.model = model            \n",
    "\n",
    "        # fit network\n",
    "        history = model.fit(train_X, train_y, epochs=150, batch_size=2500, validation_data=(valid_X, valid_y), verbose=2, shuffle=True)\n",
    "        # plot history\n",
    "        pyplot.plot(history.history['loss'], label='train')\n",
    "        pyplot.plot(history.history['val_loss'], label='valid')\n",
    "        pyplot.legend()\n",
    "        pyplot.show()\n",
    "        \n",
    "    def transform(self, input_set):\n",
    "        predictions = self.model.predict(input_set.X)\n",
    "        return Predictions(predictions, input_set)\n",
    "    \n",
    "    def fit_transform(self, training_set, valid_set):\n",
    "        self.fit(training_set, valid_set)\n",
    "        return self.transform(training_set), self.transform(valid_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_inverter(m, s, th):\n",
    "    return lambda y: y*s + m\n",
    "\n",
    "def make_transformer(m, s, th):\n",
    "    def transform(fn):\n",
    "        norm = (fn-m)/s\n",
    "        return np.where(norm > th, th, np.where(norm < -th, -th, norm))\n",
    "    return transform\n",
    "\n",
    "def fit_normalize_features(prepared_single_stock):\n",
    "    th = 2.5  \n",
    "    inp = prepared_single_stock.copy()\n",
    "    inverters = {}\n",
    "    transformers = {}\n",
    "    \n",
    "    for f in list(inp.dtypes.index):\n",
    "        if f.startswith('x(') or f.startswith('x:') or f == 'baseline':\n",
    "            fn = inp[f]\n",
    "            s = 0.0000001 + np.std(fn.values)\n",
    "            m = np.mean(fn.values)\n",
    "\n",
    "            inverters[f] = make_inverter(m, s, th)\n",
    "            transformers[f] = make_transformer(m, s, th)\n",
    "            inp[f] = transformers[f](fn)\n",
    "        \n",
    "    return inp, transformers, inverters\n",
    "\n",
    "def normalize_features(prepared_single_stock, transformers):\n",
    "    inp = prepared_single_stock.copy()    \n",
    "    for f in list(inp.dtypes.index):\n",
    "        if f.startswith('x(') or f.startswith('x:') or f == 'baseline':\n",
    "            fn = inp[f]\n",
    "            inp[f] = transformers[f](fn)\n",
    "    return inp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SIE: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "SIE: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "SIE: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "ALV: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "ALV: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "ALV: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "BAYN: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "BAYN: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "BAYN: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "DAI: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "DAI: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "DAI: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "DBK: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "DBK: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "DBK: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "VOW3: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "VOW3: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "VOW3: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "SAP: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "SAP: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "SAP: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "BAS: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "BAS: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "BAS: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "DTE: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "DTE: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "DTE: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "ADS: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "ADS: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "ADS: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "BMW: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "BMW: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "BMW: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "CBK: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "CBK: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "CBK: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "EOAN: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "EOAN: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "EOAN: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "IFX: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "IFX: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "IFX: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "MUV2: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "MUV2: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "MUV2: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "RWE: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "RWE: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "RWE: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "LHA: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "LHA: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "LHA: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "DPW: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "DPW: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "DPW: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "CON: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "CON: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "CON: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "FRE: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "FRE: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "FRE: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "HEN3: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "HEN3: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "HEN3: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "TKA: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "TKA: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "TKA: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "1COV: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "1COV: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "1COV: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "HEI: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "HEI: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "HEI: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "LINU: Dropped 0.06 % of records due to NA\n",
      "train (1653, 30)\n",
      "LINU: Dropped 0.54 % of records due to NA\n",
      "valid (183, 30)\n",
      "LINU: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "WDI: Dropped 0.03 % of records due to NA\n",
      "train (3086, 30)\n",
      "WDI: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "WDI: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "PSM: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "PSM: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "PSM: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "MRK: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "MRK: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "MRK: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "DB1: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "DB1: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "DB1: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "VNA: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "VNA: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "VNA: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "FME: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "FME: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "FME: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "LIN: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "LIN: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "LIN: Dropped 0.05 % of records due to NA\n",
      "test (1880, 30)\n",
      "PAH3: Dropped 0.03 % of records due to NA\n",
      "train (3087, 30)\n",
      "PAH3: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "PAH3: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "BEI: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "BEI: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "BEI: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "WAF: Dropped 0.03 % of records due to NA\n",
      "train (3086, 30)\n",
      "WAF: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "WAF: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "EVT: Dropped 0.03 % of records due to NA\n",
      "train (3086, 30)\n",
      "EVT: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "EVT: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "IGY: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "IGY: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "IGY: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "SDF: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "SDF: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "SDF: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "OSR: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "OSR: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "OSR: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "AIXA: Dropped 0.03 % of records due to NA\n",
      "train (3086, 30)\n",
      "AIXA: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "AIXA: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "DLG: Dropped 0.03 % of records due to NA\n",
      "train (3086, 30)\n",
      "DLG: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "DLG: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "SNH: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "SNH: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "SNH: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "KGX: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "KGX: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "KGX: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "LXS: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "LXS: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "LXS: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "DWNI: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "DWNI: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "DWNI: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "ZAL: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "ZAL: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "ZAL: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "BOSS: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "BOSS: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "BOSS: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "G1A: Dropped 0.03 % of records due to NA\n",
      "train (3088, 30)\n",
      "G1A: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G1A: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "UN01: Dropped 0.03 % of records due to NA\n",
      "train (3089, 30)\n",
      "UN01: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "UN01: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "UTDI: Dropped 0.03 % of records due to NA\n",
      "train (3086, 30)\n",
      "UTDI: Dropped 0.40 % of records due to NA\n",
      "valid (250, 30)\n",
      "UTDI: Dropped 0.05 % of records due to NA\n",
      "test (1888, 30)\n",
      "(152967, 30) (12433, 30) (94392, 30)\n"
     ]
    }
   ],
   "source": [
    "combined_training_set = []\n",
    "combined_valid_set = []\n",
    "combined_test_set = []\n",
    "\n",
    "#main_interval, intervals = '20Min', ['5Min', '10Min', '15Min']\n",
    "main_interval, intervals = '20Min', ['5Min', '10Min']\n",
    "#main_interval, intervals = '4H', ['30Min', '1H', '2H']\n",
    "\n",
    "normalizers = {}\n",
    "\n",
    "for mnemonic in most_liquid_stocks:\n",
    "    single_stock = df_train[df_train.Mnemonic == mnemonic].copy()\n",
    "    single_stock = single_stock[single_stock.HasTrade == 1.0]\n",
    "    single_stock, meta = prepare_single_stock_multi_intervals(single_stock, 'MeanEndPrice', main_interval, intervals)\n",
    "    single_stock = NARemover(mnemonic).transform(single_stock)\n",
    "    single_stock, single_stock_transformer, _ = fit_normalize_features(single_stock)\n",
    "    normalizers[mnemonic] = single_stock_transformer\n",
    "    \n",
    "    combined_training_set.append(single_stock)\n",
    "    print(\"train\", single_stock.shape)\n",
    "    \n",
    "    single_stock = df_valid[df_valid.Mnemonic == mnemonic].copy()\n",
    "    single_stock = single_stock[single_stock.HasTrade == 1.0] \n",
    "    single_stock, meta = prepare_single_stock_multi_intervals(single_stock, 'MeanEndPrice', main_interval, intervals)\n",
    "    single_stock = NARemover(mnemonic).transform(single_stock)\n",
    "    single_stock = normalize_features(single_stock, normalizers[mnemonic])\n",
    "    \n",
    "    combined_valid_set.append(single_stock)\n",
    "    print(\"valid\", single_stock.shape)    \n",
    "    \n",
    "    single_stock = df_test[df_test.Mnemonic == mnemonic].copy()\n",
    "    single_stock = single_stock[single_stock.HasTrade == 1.0] \n",
    "    single_stock, meta = prepare_single_stock_multi_intervals(single_stock, 'MeanEndPrice', main_interval, intervals)\n",
    "    single_stock = NARemover(mnemonic).transform(single_stock)\n",
    "    single_stock = normalize_features(single_stock, normalizers[mnemonic])\n",
    "    combined_test_set.append(single_stock)\n",
    "    print(\"test\", single_stock.shape) \n",
    "    \n",
    "combined_training_set_df = pd.concat(combined_training_set, axis=0)\n",
    "training_set = TrainingSetBuilder().transform(combined_training_set_df)\n",
    "    \n",
    "combined_valid_set_df = pd.concat(combined_valid_set, axis=0)\n",
    "valid_set = TrainingSetBuilder().transform(combined_valid_set_df) \n",
    "\n",
    "combined_test_set_df = pd.concat(combined_test_set, axis=0)\n",
    "test_set = TrainingSetBuilder().transform(combined_test_set_df) \n",
    "    \n",
    "print(training_set.original_df.shape, valid_set.original_df.shape,  test_set.original_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitted model\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "class LinearModel:\n",
    "    def __init__(self):\n",
    "        self.model = None\n",
    "        \n",
    "    def fit(self, training_set, valid_set = None):\n",
    "        train_X, train_y = training_set.X[:,:], training_set.y\n",
    "        \n",
    "        if valid_set is None:\n",
    "            valid_X, valid_y = train_X, train_y\n",
    "        else:\n",
    "            valid_X, valid_y = valid_set.X, valid_set.y\n",
    "            \n",
    "        self.model = Ridge(alpha=1.5)\n",
    "        # train_y should be -1/+1\n",
    "        self.model.fit(train_X, train_y)\n",
    "       \n",
    "\n",
    "    def transform(self, input_set):\n",
    "        return Predictions(self.model.predict(input_set.X), input_set)\n",
    "    \n",
    "    def fit_transform(self, training_set, valid_set):\n",
    "        self.fit(training_set, valid_set)\n",
    "        return self.transform(training_set), self.transform(valid_set)    \n",
    "\n",
    "model = LinearModel()\n",
    "train_predictions, valid_predictions = model.fit_transform(training_set, valid_set)\n",
    "print(\"Fitted model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'percent_correct': 71.44165478262008,\n",
       " 'percent_has_answer': 0.999339726869194,\n",
       " 'absolute_with_answer': 152866.0,\n",
       " 'size': 152967}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_predictions.evaluate()['matches']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'percent_correct': 71.85513078470825,\n",
       " 'percent_has_answer': 0.9993565511139709,\n",
       " 'absolute_with_answer': 12425.0,\n",
       " 'size': 12433}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_predictions.evaluate()['matches']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'percent_correct': 71.42857142857143,\n",
       " 'percent_has_answer': 0.9992901940842445,\n",
       " 'absolute_with_answer': 94325.0,\n",
       " 'size': 94392}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions = model.transform(test_set)\n",
    "test_predictions.evaluate()['matches']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>thresholds</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>percent_with_answer</th>\n",
       "      <th>absolute_has_answer</th>\n",
       "      <th>achieved_returns</th>\n",
       "      <th>correct</th>\n",
       "      <th>errors</th>\n",
       "      <th>achieved_norm_returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0.919456</td>\n",
       "      <td>9.999542</td>\n",
       "      <td>15296.0</td>\n",
       "      <td>43.929000</td>\n",
       "      <td>14064.0</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>0.002872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.892049</td>\n",
       "      <td>19.996470</td>\n",
       "      <td>30588.0</td>\n",
       "      <td>67.264799</td>\n",
       "      <td>27286.0</td>\n",
       "      <td>3302.0</td>\n",
       "      <td>0.002199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>0.868518</td>\n",
       "      <td>29.991436</td>\n",
       "      <td>45877.0</td>\n",
       "      <td>84.475350</td>\n",
       "      <td>39845.0</td>\n",
       "      <td>6032.0</td>\n",
       "      <td>0.001841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.844188</td>\n",
       "      <td>39.989017</td>\n",
       "      <td>61170.0</td>\n",
       "      <td>97.846112</td>\n",
       "      <td>51639.0</td>\n",
       "      <td>9531.0</td>\n",
       "      <td>0.001600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>0.821615</td>\n",
       "      <td>49.987252</td>\n",
       "      <td>76464.0</td>\n",
       "      <td>108.669062</td>\n",
       "      <td>62824.0</td>\n",
       "      <td>13640.0</td>\n",
       "      <td>0.001421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>0.799185</td>\n",
       "      <td>59.977642</td>\n",
       "      <td>91746.0</td>\n",
       "      <td>116.867187</td>\n",
       "      <td>73322.0</td>\n",
       "      <td>18424.0</td>\n",
       "      <td>0.001274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35</td>\n",
       "      <td>0.776832</td>\n",
       "      <td>69.967379</td>\n",
       "      <td>107027.0</td>\n",
       "      <td>122.666545</td>\n",
       "      <td>83142.0</td>\n",
       "      <td>23885.0</td>\n",
       "      <td>0.001146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>40</td>\n",
       "      <td>0.755782</td>\n",
       "      <td>79.962999</td>\n",
       "      <td>122317.0</td>\n",
       "      <td>126.745088</td>\n",
       "      <td>92445.0</td>\n",
       "      <td>29872.0</td>\n",
       "      <td>0.001036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45</td>\n",
       "      <td>0.735089</td>\n",
       "      <td>89.954696</td>\n",
       "      <td>137601.0</td>\n",
       "      <td>128.973332</td>\n",
       "      <td>101149.0</td>\n",
       "      <td>36452.0</td>\n",
       "      <td>0.000937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   thresholds  accuracy  percent_with_answer  absolute_has_answer  \\\n",
       "0           5  0.919456             9.999542              15296.0   \n",
       "1          10  0.892049            19.996470              30588.0   \n",
       "2          15  0.868518            29.991436              45877.0   \n",
       "3          20  0.844188            39.989017              61170.0   \n",
       "4          25  0.821615            49.987252              76464.0   \n",
       "5          30  0.799185            59.977642              91746.0   \n",
       "6          35  0.776832            69.967379             107027.0   \n",
       "7          40  0.755782            79.962999             122317.0   \n",
       "8          45  0.735089            89.954696             137601.0   \n",
       "\n",
       "   achieved_returns   correct   errors  achieved_norm_returns  \n",
       "0         43.929000   14064.0   1232.0               0.002872  \n",
       "1         67.264799   27286.0   3302.0               0.002199  \n",
       "2         84.475350   39845.0   6032.0               0.001841  \n",
       "3         97.846112   51639.0   9531.0               0.001600  \n",
       "4        108.669062   62824.0  13640.0               0.001421  \n",
       "5        116.867187   73322.0  18424.0               0.001274  \n",
       "6        122.666545   83142.0  23885.0               0.001146  \n",
       "7        126.745088   92445.0  29872.0               0.001036  \n",
       "8        128.973332  101149.0  36452.0               0.000937  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_predictions.evaluate()['accuracy_at_cutoff']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conclusion:\n",
      "\n",
      "If you play 152967 times on the Training Set with 1 EUR and you always guess the movement,\n",
      "ignoring all transactions cost (which would likely be significant), you will make 243.48361373226663. \n",
      "\n",
      "Instead you make 130.64905697726385 or 53.658254440450726 percent of the ideally achievable.\n",
      "\n",
      "If you use the baseline you will make 47.11323501206654 or 19.349653264088655 percent of ideal.\n",
      "\n",
      "Disclaimer:\n",
      "\n",
      "It is important to note that the purpose of this evaluation is to make\n",
      "an interpretation of prediction accuracy in the context of financial gains.\n",
      "However, the current evaluation does not mean that such gains are practically\n",
      "achievable. For example, slippage is not taken into account.\n",
      "\n",
      "\n",
      "For proper evaluation one should run backtests. \n",
      "It's important to know that a preditive model in itself is insufficient\n",
      "as there are other components.\n",
      "\n",
      "\n",
      "Those experiments are not a financial advise.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def pred_baseline(d):\n",
    "    single_feature = 'baseline'\n",
    "    preds = d.training_set.original_df[single_feature].values\n",
    "    preds = preds.reshape((preds.shape[0], 1))\n",
    "    return Predictions(preds, d.training_set).evaluate()\n",
    "\n",
    "def readable_summary(which_set, p):\n",
    "    achieved = p.evaluate()['strategies']['achieved']\n",
    "    achieved_baseline = pred_baseline(p)['strategies']['achieved']\n",
    "    per_change = np.mean(np.absolute(p.training_set.original_df['pseudo_y(pctChange)']))\n",
    "    n = p.training_set.original_df.shape[0]\n",
    "    print (\"\"\"Conclusion:\n",
    "\n",
    "If you play {} times on the {} with 1 EUR and you always guess the movement,\n",
    "ignoring all transactions cost (which would likely be significant), you will make {}. \n",
    "\n",
    "Instead you make {} or {} percent of the ideally achievable.\n",
    "\n",
    "If you use the baseline you will make {} or {} percent of ideal.\n",
    "\n",
    "Disclaimer:\n",
    "\n",
    "It is important to note that the purpose of this evaluation is to make\n",
    "an interpretation of prediction accuracy in the context of financial gains.\n",
    "However, the current evaluation does not mean that such gains are practically\n",
    "achievable. For example, slippage is not taken into account.\n",
    "\n",
    "\n",
    "For proper evaluation one should run backtests. \n",
    "It's important to know that a preditive model in itself is insufficient\n",
    "as there are other components.\n",
    "\n",
    "\n",
    "Those experiments are not a financial advise.\n",
    "\n",
    "\"\"\".format(\n",
    "        n, which_set, n * per_change, achieved, 100.0*achieved/(n*per_change),\n",
    "          achieved_baseline, 100.0*achieved_baseline/(n*per_change)))\n",
    "readable_summary('Training Set', train_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'omniscient': 243.48361373226663,\n",
       " 'random': 1.4376786194139808,\n",
       " 'always_up': 6.152936104145533,\n",
       " 'always_down': -6.152936104145533,\n",
       " 'achieved': 130.64905697726385,\n",
       " 'num_trials': 152967.0}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_predictions.evaluate()['strategies']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'omniscient': 35.15962063916294,\n",
       " 'random': 1.272734284669614,\n",
       " 'always_up': 5.065717134003669,\n",
       " 'always_down': -5.065717134003669,\n",
       " 'achieved': 2.5580695863993164,\n",
       " 'num_trials': 12433.0}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_baseline(valid_predictions)['strategies'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>thresholds</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>percent_with_answer</th>\n",
       "      <th>absolute_has_answer</th>\n",
       "      <th>achieved_returns</th>\n",
       "      <th>correct</th>\n",
       "      <th>errors</th>\n",
       "      <th>achieved_norm_returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0.925241</td>\n",
       "      <td>10.005630</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>3.284452</td>\n",
       "      <td>1151.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.002640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.893044</td>\n",
       "      <td>20.003217</td>\n",
       "      <td>2487.0</td>\n",
       "      <td>4.498675</td>\n",
       "      <td>2221.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>0.001809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>0.864270</td>\n",
       "      <td>29.984718</td>\n",
       "      <td>3728.0</td>\n",
       "      <td>5.780089</td>\n",
       "      <td>3222.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>0.001550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.846138</td>\n",
       "      <td>39.990348</td>\n",
       "      <td>4972.0</td>\n",
       "      <td>7.019511</td>\n",
       "      <td>4207.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>0.001412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>0.826493</td>\n",
       "      <td>49.971849</td>\n",
       "      <td>6213.0</td>\n",
       "      <td>7.832817</td>\n",
       "      <td>5135.0</td>\n",
       "      <td>1078.0</td>\n",
       "      <td>0.001261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>0.799920</td>\n",
       "      <td>59.977479</td>\n",
       "      <td>7457.0</td>\n",
       "      <td>8.379726</td>\n",
       "      <td>5965.0</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>0.001124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35</td>\n",
       "      <td>0.779795</td>\n",
       "      <td>69.983109</td>\n",
       "      <td>8701.0</td>\n",
       "      <td>9.687775</td>\n",
       "      <td>6785.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>0.001113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>40</td>\n",
       "      <td>0.759807</td>\n",
       "      <td>79.964610</td>\n",
       "      <td>9942.0</td>\n",
       "      <td>11.554939</td>\n",
       "      <td>7554.0</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>0.001162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45</td>\n",
       "      <td>0.738174</td>\n",
       "      <td>89.946111</td>\n",
       "      <td>11183.0</td>\n",
       "      <td>9.812031</td>\n",
       "      <td>8255.0</td>\n",
       "      <td>2928.0</td>\n",
       "      <td>0.000877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   thresholds  accuracy  percent_with_answer  absolute_has_answer  \\\n",
       "0           5  0.925241            10.005630               1244.0   \n",
       "1          10  0.893044            20.003217               2487.0   \n",
       "2          15  0.864270            29.984718               3728.0   \n",
       "3          20  0.846138            39.990348               4972.0   \n",
       "4          25  0.826493            49.971849               6213.0   \n",
       "5          30  0.799920            59.977479               7457.0   \n",
       "6          35  0.779795            69.983109               8701.0   \n",
       "7          40  0.759807            79.964610               9942.0   \n",
       "8          45  0.738174            89.946111              11183.0   \n",
       "\n",
       "   achieved_returns  correct  errors  achieved_norm_returns  \n",
       "0          3.284452   1151.0    93.0               0.002640  \n",
       "1          4.498675   2221.0   266.0               0.001809  \n",
       "2          5.780089   3222.0   506.0               0.001550  \n",
       "3          7.019511   4207.0   765.0               0.001412  \n",
       "4          7.832817   5135.0  1078.0               0.001261  \n",
       "5          8.379726   5965.0  1492.0               0.001124  \n",
       "6          9.687775   6785.0  1916.0               0.001113  \n",
       "7         11.554939   7554.0  2388.0               0.001162  \n",
       "8          9.812031   8255.0  2928.0               0.000877  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_predictions.evaluate()['accuracy_at_cutoff']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline on Testset: Accuracy at different levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>thresholds</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>percent_with_answer</th>\n",
       "      <th>absolute_has_answer</th>\n",
       "      <th>achieved_returns</th>\n",
       "      <th>correct</th>\n",
       "      <th>errors</th>\n",
       "      <th>achieved_norm_returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0.625358</td>\n",
       "      <td>9.993432</td>\n",
       "      <td>9433.0</td>\n",
       "      <td>8.836721</td>\n",
       "      <td>5899.0</td>\n",
       "      <td>3534.0</td>\n",
       "      <td>0.000937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.627590</td>\n",
       "      <td>19.990042</td>\n",
       "      <td>18869.0</td>\n",
       "      <td>16.219807</td>\n",
       "      <td>11842.0</td>\n",
       "      <td>7027.0</td>\n",
       "      <td>0.000860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>0.619695</td>\n",
       "      <td>29.982414</td>\n",
       "      <td>28301.0</td>\n",
       "      <td>22.040602</td>\n",
       "      <td>17538.0</td>\n",
       "      <td>10763.0</td>\n",
       "      <td>0.000779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.610144</td>\n",
       "      <td>39.979024</td>\n",
       "      <td>37737.0</td>\n",
       "      <td>26.470858</td>\n",
       "      <td>23025.0</td>\n",
       "      <td>14712.0</td>\n",
       "      <td>0.000701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>0.602752</td>\n",
       "      <td>49.969277</td>\n",
       "      <td>47167.0</td>\n",
       "      <td>29.995626</td>\n",
       "      <td>28430.0</td>\n",
       "      <td>18737.0</td>\n",
       "      <td>0.000636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>0.595166</td>\n",
       "      <td>59.963768</td>\n",
       "      <td>56601.0</td>\n",
       "      <td>33.018019</td>\n",
       "      <td>33687.0</td>\n",
       "      <td>22914.0</td>\n",
       "      <td>0.000583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35</td>\n",
       "      <td>0.587767</td>\n",
       "      <td>69.956140</td>\n",
       "      <td>66033.0</td>\n",
       "      <td>34.956086</td>\n",
       "      <td>38812.0</td>\n",
       "      <td>27221.0</td>\n",
       "      <td>0.000529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>40</td>\n",
       "      <td>0.579888</td>\n",
       "      <td>79.951691</td>\n",
       "      <td>75468.0</td>\n",
       "      <td>36.904705</td>\n",
       "      <td>43763.0</td>\n",
       "      <td>31705.0</td>\n",
       "      <td>0.000489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45</td>\n",
       "      <td>0.573401</td>\n",
       "      <td>89.940885</td>\n",
       "      <td>84897.0</td>\n",
       "      <td>38.010867</td>\n",
       "      <td>48680.0</td>\n",
       "      <td>36217.0</td>\n",
       "      <td>0.000448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   thresholds  accuracy  percent_with_answer  absolute_has_answer  \\\n",
       "0           5  0.625358             9.993432               9433.0   \n",
       "1          10  0.627590            19.990042              18869.0   \n",
       "2          15  0.619695            29.982414              28301.0   \n",
       "3          20  0.610144            39.979024              37737.0   \n",
       "4          25  0.602752            49.969277              47167.0   \n",
       "5          30  0.595166            59.963768              56601.0   \n",
       "6          35  0.587767            69.956140              66033.0   \n",
       "7          40  0.579888            79.951691              75468.0   \n",
       "8          45  0.573401            89.940885              84897.0   \n",
       "\n",
       "   achieved_returns  correct   errors  achieved_norm_returns  \n",
       "0          8.836721   5899.0   3534.0               0.000937  \n",
       "1         16.219807  11842.0   7027.0               0.000860  \n",
       "2         22.040602  17538.0  10763.0               0.000779  \n",
       "3         26.470858  23025.0  14712.0               0.000701  \n",
       "4         29.995626  28430.0  18737.0               0.000636  \n",
       "5         33.018019  33687.0  22914.0               0.000583  \n",
       "6         34.956086  38812.0  27221.0               0.000529  \n",
       "7         36.904705  43763.0  31705.0               0.000489  \n",
       "8         38.010867  48680.0  36217.0               0.000448  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for the baseline\n",
    "pred_baseline(test_predictions)['accuracy_at_cutoff'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline on Testset: Comparison of Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'omniscient': 185.98927815669765,\n",
       " 'random': 1.135187076096661,\n",
       " 'always_up': -3.7637348158124975,\n",
       " 'always_down': 3.7637348158124975,\n",
       " 'achieved': 38.16610494514903,\n",
       " 'num_trials': 94392.0}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for the baseline\n",
    "pred_baseline(test_predictions)['strategies']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ML Model on Testset: Accuracy at different levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>thresholds</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>percent_with_answer</th>\n",
       "      <th>absolute_has_answer</th>\n",
       "      <th>achieved_returns</th>\n",
       "      <th>correct</th>\n",
       "      <th>errors</th>\n",
       "      <th>achieved_norm_returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0.918512</td>\n",
       "      <td>9.997669</td>\n",
       "      <td>9437.0</td>\n",
       "      <td>31.231169</td>\n",
       "      <td>8668.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>0.003309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.888477</td>\n",
       "      <td>19.996398</td>\n",
       "      <td>18875.0</td>\n",
       "      <td>49.435511</td>\n",
       "      <td>16770.0</td>\n",
       "      <td>2105.0</td>\n",
       "      <td>0.002619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>0.865838</td>\n",
       "      <td>29.990889</td>\n",
       "      <td>28309.0</td>\n",
       "      <td>63.853908</td>\n",
       "      <td>24511.0</td>\n",
       "      <td>3798.0</td>\n",
       "      <td>0.002256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.844784</td>\n",
       "      <td>39.983261</td>\n",
       "      <td>37741.0</td>\n",
       "      <td>75.597596</td>\n",
       "      <td>31883.0</td>\n",
       "      <td>5858.0</td>\n",
       "      <td>0.002003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>0.822258</td>\n",
       "      <td>49.977752</td>\n",
       "      <td>47175.0</td>\n",
       "      <td>84.607390</td>\n",
       "      <td>38790.0</td>\n",
       "      <td>8385.0</td>\n",
       "      <td>0.001793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>0.801152</td>\n",
       "      <td>59.969065</td>\n",
       "      <td>56606.0</td>\n",
       "      <td>91.535937</td>\n",
       "      <td>45350.0</td>\n",
       "      <td>11256.0</td>\n",
       "      <td>0.001617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35</td>\n",
       "      <td>0.779386</td>\n",
       "      <td>69.957200</td>\n",
       "      <td>66034.0</td>\n",
       "      <td>96.754410</td>\n",
       "      <td>51466.0</td>\n",
       "      <td>14568.0</td>\n",
       "      <td>0.001465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>40</td>\n",
       "      <td>0.757497</td>\n",
       "      <td>79.950631</td>\n",
       "      <td>75467.0</td>\n",
       "      <td>99.725626</td>\n",
       "      <td>57166.0</td>\n",
       "      <td>18301.0</td>\n",
       "      <td>0.001321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45</td>\n",
       "      <td>0.736080</td>\n",
       "      <td>89.940885</td>\n",
       "      <td>84897.0</td>\n",
       "      <td>102.186883</td>\n",
       "      <td>62491.0</td>\n",
       "      <td>22406.0</td>\n",
       "      <td>0.001204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   thresholds  accuracy  percent_with_answer  absolute_has_answer  \\\n",
       "0           5  0.918512             9.997669               9437.0   \n",
       "1          10  0.888477            19.996398              18875.0   \n",
       "2          15  0.865838            29.990889              28309.0   \n",
       "3          20  0.844784            39.983261              37741.0   \n",
       "4          25  0.822258            49.977752              47175.0   \n",
       "5          30  0.801152            59.969065              56606.0   \n",
       "6          35  0.779386            69.957200              66034.0   \n",
       "7          40  0.757497            79.950631              75467.0   \n",
       "8          45  0.736080            89.940885              84897.0   \n",
       "\n",
       "   achieved_returns  correct   errors  achieved_norm_returns  \n",
       "0         31.231169   8668.0    769.0               0.003309  \n",
       "1         49.435511  16770.0   2105.0               0.002619  \n",
       "2         63.853908  24511.0   3798.0               0.002256  \n",
       "3         75.597596  31883.0   5858.0               0.002003  \n",
       "4         84.607390  38790.0   8385.0               0.001793  \n",
       "5         91.535937  45350.0  11256.0               0.001617  \n",
       "6         96.754410  51466.0  14568.0               0.001465  \n",
       "7         99.725626  57166.0  18301.0               0.001321  \n",
       "8        102.186883  62491.0  22406.0               0.001204  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for ML\n",
    "test_predictions.evaluate()['accuracy_at_cutoff']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ML Model on Testset: Comparison of Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'omniscient': 185.98927815669765,\n",
       " 'random': -0.28116685056831764,\n",
       " 'always_up': -3.7637348158124975,\n",
       " 'always_down': 3.7637348158124975,\n",
       " 'achieved': 102.59568176937755,\n",
       " 'num_trials': 94392.0}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for ML\n",
    "Predictions(test_predictions.predictions, test_predictions.training_set).evaluate()['strategies']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conclusion:\n",
      "\n",
      "If you play 94392 times on the Test set with 1 EUR and you always guess the movement,\n",
      "ignoring all transactions cost (which would likely be significant), you will make 185.98927815669768. \n",
      "\n",
      "Instead you make 102.59568176937755 or 55.162148477687914 percent of the ideally achievable.\n",
      "\n",
      "If you use the baseline you will make 38.16610494514903 or 20.520594156504945 percent of ideal.\n",
      "\n",
      "Disclaimer:\n",
      "\n",
      "It is important to note that the purpose of this evaluation is to make\n",
      "an interpretation of prediction accuracy in the context of financial gains.\n",
      "However, the current evaluation does not mean that such gains are practically\n",
      "achievable. For example, slippage is not taken into account.\n",
      "\n",
      "\n",
      "For proper evaluation one should run backtests. \n",
      "It's important to know that a preditive model in itself is insufficient\n",
      "as there are other components.\n",
      "\n",
      "\n",
      "Those experiments are not a financial advise.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "readable_summary('Test set', test_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis by Mnemonic and by Date\n",
    "\n",
    "It is known that when it comes to stock predictions, different stocks and different days will exibit different performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_accuracy_by(grouping_feature, predictions):\n",
    "    df = predictions.training_set.original_df\n",
    "    s = df[['Date', 'Mnemonic', 'pseudo_y(SignReturn)']].copy()\n",
    "    s['Predictions'] = predictions.predictions\n",
    "    s['Baseline'] = df['baseline']\n",
    "\n",
    "    def agg(group):\n",
    "        pred = group['Predictions']\n",
    "        baseline = group['Baseline']\n",
    "        rets = group['pseudo_y(SignReturn)']\n",
    "        c = pred.corr(rets)\n",
    "        c = np.where(np.sign(pred)*np.sign(rets) == 1.0, 1.0, 0.0).sum()\n",
    "        e = np.where(np.sign(pred)*np.sign(rets) == -1.0, 1.0, 0.0).sum()\n",
    "        acc = c/(c + e)\n",
    "\n",
    "        c_baseline = np.where(np.sign(baseline)*np.sign(rets) == 1.0, 1.0, 0.0).sum()\n",
    "        e_baseline = np.where(np.sign(baseline)*np.sign(rets) == -1.0, 1.0, 0.0).sum()\n",
    "        acc_baseline = c_baseline/(c_baseline + e_baseline)\n",
    "\n",
    "        l = group.shape[0]\n",
    "        return {\"corr\": c, 'size': l, 'accuracy': acc, 'acc_baseline': acc_baseline}\n",
    "    f = s.groupby(grouping_feature).apply(agg).to_frame(\"agg\")\n",
    "\n",
    "    f['AccuracyPred'] = f['agg'].map(lambda i: i['accuracy'])\n",
    "    f['AccuracyBaseline'] = f['agg'].map(lambda i: i['acc_baseline'])\n",
    "    f['AccPred - AccBaseline'] = f['AccuracyPred'] - f['AccuracyBaseline']\n",
    "    f = f.drop(columns=['agg'])\n",
    "\n",
    "    f = f[f.index != '2017-10-14'] # remove this date which has one data point\n",
    "    f[['AccuracyPred', 'AccuracyBaseline']].plot()\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "perf = plot_accuracy_by('Mnemonic', test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>AccuracyPred</th>\n",
       "      <th>AccuracyBaseline</th>\n",
       "      <th>AccPred - AccBaseline</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mnemonic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CON</th>\n",
       "      <td>0.739269</td>\n",
       "      <td>0.554849</td>\n",
       "      <td>0.184420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CBK</th>\n",
       "      <td>0.737010</td>\n",
       "      <td>0.563627</td>\n",
       "      <td>0.173383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1COV</th>\n",
       "      <td>0.735419</td>\n",
       "      <td>0.581124</td>\n",
       "      <td>0.154295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RWE</th>\n",
       "      <td>0.735169</td>\n",
       "      <td>0.585805</td>\n",
       "      <td>0.149364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAI</th>\n",
       "      <td>0.732379</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.163752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAH3</th>\n",
       "      <td>0.730403</td>\n",
       "      <td>0.559322</td>\n",
       "      <td>0.171081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTE</th>\n",
       "      <td>0.729830</td>\n",
       "      <td>0.569002</td>\n",
       "      <td>0.160828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEI</th>\n",
       "      <td>0.728814</td>\n",
       "      <td>0.567797</td>\n",
       "      <td>0.161017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADS</th>\n",
       "      <td>0.726020</td>\n",
       "      <td>0.584526</td>\n",
       "      <td>0.141494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRK</th>\n",
       "      <td>0.726020</td>\n",
       "      <td>0.587175</td>\n",
       "      <td>0.138845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "      <td>0.724960</td>\n",
       "      <td>0.552199</td>\n",
       "      <td>0.172761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DWNI</th>\n",
       "      <td>0.724960</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.156333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LXS</th>\n",
       "      <td>0.724284</td>\n",
       "      <td>0.564157</td>\n",
       "      <td>0.160127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAYN</th>\n",
       "      <td>0.724047</td>\n",
       "      <td>0.556674</td>\n",
       "      <td>0.167373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBK</th>\n",
       "      <td>0.724047</td>\n",
       "      <td>0.565148</td>\n",
       "      <td>0.158898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TKA</th>\n",
       "      <td>0.722694</td>\n",
       "      <td>0.568399</td>\n",
       "      <td>0.154295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDF</th>\n",
       "      <td>0.722694</td>\n",
       "      <td>0.576882</td>\n",
       "      <td>0.145811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.561970</td>\n",
       "      <td>0.159958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DLG</th>\n",
       "      <td>0.721781</td>\n",
       "      <td>0.552199</td>\n",
       "      <td>0.169581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRE</th>\n",
       "      <td>0.721251</td>\n",
       "      <td>0.570217</td>\n",
       "      <td>0.151033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AccuracyPred  AccuracyBaseline  AccPred - AccBaseline\n",
       "Mnemonic                                                       \n",
       "CON           0.739269          0.554849               0.184420\n",
       "CBK           0.737010          0.563627               0.173383\n",
       "1COV          0.735419          0.581124               0.154295\n",
       "RWE           0.735169          0.585805               0.149364\n",
       "DAI           0.732379          0.568627               0.163752\n",
       "PAH3          0.730403          0.559322               0.171081\n",
       "DTE           0.729830          0.569002               0.160828\n",
       "HEI           0.728814          0.567797               0.161017\n",
       "ADS           0.726020          0.584526               0.141494\n",
       "MRK           0.726020          0.587175               0.138845\n",
       "BAS           0.724960          0.552199               0.172761\n",
       "DWNI          0.724960          0.568627               0.156333\n",
       "LXS           0.724284          0.564157               0.160127\n",
       "BAYN          0.724047          0.556674               0.167373\n",
       "DBK           0.724047          0.565148               0.158898\n",
       "TKA           0.722694          0.568399               0.154295\n",
       "SDF           0.722694          0.576882               0.145811\n",
       "BMW           0.721928          0.561970               0.159958\n",
       "DLG           0.721781          0.552199               0.169581\n",
       "FRE           0.721251          0.570217               0.151033"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf.sort_values(by='AccuracyPred', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>AccuracyPred</th>\n",
       "      <th>AccuracyBaseline</th>\n",
       "      <th>AccPred - AccBaseline</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mnemonic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EOAN</th>\n",
       "      <td>0.714361</td>\n",
       "      <td>0.587705</td>\n",
       "      <td>0.126656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WDI</th>\n",
       "      <td>0.711712</td>\n",
       "      <td>0.580286</td>\n",
       "      <td>0.131426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEN3</th>\n",
       "      <td>0.711406</td>\n",
       "      <td>0.568700</td>\n",
       "      <td>0.142706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1A</th>\n",
       "      <td>0.710345</td>\n",
       "      <td>0.560743</td>\n",
       "      <td>0.149602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEI</th>\n",
       "      <td>0.710122</td>\n",
       "      <td>0.572867</td>\n",
       "      <td>0.137255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSM</th>\n",
       "      <td>0.709746</td>\n",
       "      <td>0.574153</td>\n",
       "      <td>0.135593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IFX</th>\n",
       "      <td>0.709130</td>\n",
       "      <td>0.572718</td>\n",
       "      <td>0.136412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPW</th>\n",
       "      <td>0.705570</td>\n",
       "      <td>0.562334</td>\n",
       "      <td>0.143236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOSS</th>\n",
       "      <td>0.703075</td>\n",
       "      <td>0.566278</td>\n",
       "      <td>0.136797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNH</th>\n",
       "      <td>0.702173</td>\n",
       "      <td>0.569687</td>\n",
       "      <td>0.132485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIXA</th>\n",
       "      <td>0.701801</td>\n",
       "      <td>0.569915</td>\n",
       "      <td>0.131886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KGX</th>\n",
       "      <td>0.701643</td>\n",
       "      <td>0.552199</td>\n",
       "      <td>0.149444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTDI</th>\n",
       "      <td>0.700053</td>\n",
       "      <td>0.562798</td>\n",
       "      <td>0.137255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALV</th>\n",
       "      <td>0.698623</td>\n",
       "      <td>0.560381</td>\n",
       "      <td>0.138242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZAL</th>\n",
       "      <td>0.693856</td>\n",
       "      <td>0.565678</td>\n",
       "      <td>0.128178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LHA</th>\n",
       "      <td>0.689820</td>\n",
       "      <td>0.565748</td>\n",
       "      <td>0.124072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UN01</th>\n",
       "      <td>0.687732</td>\n",
       "      <td>0.572491</td>\n",
       "      <td>0.115242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EVT</th>\n",
       "      <td>0.678855</td>\n",
       "      <td>0.563858</td>\n",
       "      <td>0.114997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IGY</th>\n",
       "      <td>0.670382</td>\n",
       "      <td>0.553609</td>\n",
       "      <td>0.116773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIN</th>\n",
       "      <td>0.668620</td>\n",
       "      <td>0.543953</td>\n",
       "      <td>0.124667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AccuracyPred  AccuracyBaseline  AccPred - AccBaseline\n",
       "Mnemonic                                                       \n",
       "EOAN          0.714361          0.587705               0.126656\n",
       "WDI           0.711712          0.580286               0.131426\n",
       "HEN3          0.711406          0.568700               0.142706\n",
       "G1A           0.710345          0.560743               0.149602\n",
       "BEI           0.710122          0.572867               0.137255\n",
       "PSM           0.709746          0.574153               0.135593\n",
       "IFX           0.709130          0.572718               0.136412\n",
       "DPW           0.705570          0.562334               0.143236\n",
       "BOSS          0.703075          0.566278               0.136797\n",
       "SNH           0.702173          0.569687               0.132485\n",
       "AIXA          0.701801          0.569915               0.131886\n",
       "KGX           0.701643          0.552199               0.149444\n",
       "UTDI          0.700053          0.562798               0.137255\n",
       "ALV           0.698623          0.560381               0.138242\n",
       "ZAL           0.693856          0.565678               0.128178\n",
       "LHA           0.689820          0.565748               0.124072\n",
       "UN01          0.687732          0.572491               0.115242\n",
       "EVT           0.678855          0.563858               0.114997\n",
       "IGY           0.670382          0.553609               0.116773\n",
       "LIN           0.668620          0.543953               0.124667"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf.sort_values(by='AccuracyPred', ascending=False).tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "perf = plot_accuracy_by('Date', test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>AccuracyPred</th>\n",
       "      <th>AccuracyBaseline</th>\n",
       "      <th>AccPred - AccBaseline</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-28</th>\n",
       "      <td>0.762195</td>\n",
       "      <td>0.520906</td>\n",
       "      <td>0.241289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-03</th>\n",
       "      <td>0.761532</td>\n",
       "      <td>0.583116</td>\n",
       "      <td>0.178416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-25</th>\n",
       "      <td>0.751088</td>\n",
       "      <td>0.633594</td>\n",
       "      <td>0.117493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-07</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.569231</td>\n",
       "      <td>0.180769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-05</th>\n",
       "      <td>0.747498</td>\n",
       "      <td>0.604311</td>\n",
       "      <td>0.143187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-09</th>\n",
       "      <td>0.746736</td>\n",
       "      <td>0.586597</td>\n",
       "      <td>0.160139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-08</th>\n",
       "      <td>0.741538</td>\n",
       "      <td>0.604615</td>\n",
       "      <td>0.136923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29</th>\n",
       "      <td>0.741514</td>\n",
       "      <td>0.590078</td>\n",
       "      <td>0.151436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>0.741140</td>\n",
       "      <td>0.570878</td>\n",
       "      <td>0.170262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-04</th>\n",
       "      <td>0.740870</td>\n",
       "      <td>0.566087</td>\n",
       "      <td>0.174783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08</th>\n",
       "      <td>0.740769</td>\n",
       "      <td>0.581538</td>\n",
       "      <td>0.159231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-23</th>\n",
       "      <td>0.737692</td>\n",
       "      <td>0.603077</td>\n",
       "      <td>0.134615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-15</th>\n",
       "      <td>0.735181</td>\n",
       "      <td>0.607390</td>\n",
       "      <td>0.127791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <td>0.734411</td>\n",
       "      <td>0.564280</td>\n",
       "      <td>0.170131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-12</th>\n",
       "      <td>0.732102</td>\n",
       "      <td>0.539646</td>\n",
       "      <td>0.192456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-25</th>\n",
       "      <td>0.731538</td>\n",
       "      <td>0.546923</td>\n",
       "      <td>0.184615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-27</th>\n",
       "      <td>0.730354</td>\n",
       "      <td>0.567026</td>\n",
       "      <td>0.163328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-07</th>\n",
       "      <td>0.729792</td>\n",
       "      <td>0.507313</td>\n",
       "      <td>0.222479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-14</th>\n",
       "      <td>0.728462</td>\n",
       "      <td>0.530769</td>\n",
       "      <td>0.197692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-02</th>\n",
       "      <td>0.726957</td>\n",
       "      <td>0.593913</td>\n",
       "      <td>0.133043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AccuracyPred  AccuracyBaseline  AccPred - AccBaseline\n",
       "Date                                                             \n",
       "2018-03-28      0.762195          0.520906               0.241289\n",
       "2018-04-03      0.761532          0.583116               0.178416\n",
       "2018-04-25      0.751088          0.633594               0.117493\n",
       "2018-02-07      0.750000          0.569231               0.180769\n",
       "2018-03-05      0.747498          0.604311               0.143187\n",
       "2018-04-09      0.746736          0.586597               0.160139\n",
       "2018-02-08      0.741538          0.604615               0.136923\n",
       "2018-03-29      0.741514          0.590078               0.151436\n",
       "2018-03-01      0.741140          0.570878               0.170262\n",
       "2018-05-04      0.740870          0.566087               0.174783\n",
       "2018-03-08      0.740769          0.581538               0.159231\n",
       "2018-02-23      0.737692          0.603077               0.134615\n",
       "2018-02-15      0.735181          0.607390               0.127791\n",
       "2018-01-24      0.734411          0.564280               0.170131\n",
       "2018-02-12      0.732102          0.539646               0.192456\n",
       "2018-01-25      0.731538          0.546923               0.184615\n",
       "2018-02-27      0.730354          0.567026               0.163328\n",
       "2018-03-07      0.729792          0.507313               0.222479\n",
       "2018-03-14      0.728462          0.530769               0.197692\n",
       "2018-05-02      0.726957          0.593913               0.133043"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf.sort_values(by='AccuracyPred', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>AccuracyPred</th>\n",
       "      <th>AccuracyBaseline</th>\n",
       "      <th>AccPred - AccBaseline</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>0.704348</td>\n",
       "      <td>0.546087</td>\n",
       "      <td>0.158261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>0.703846</td>\n",
       "      <td>0.541538</td>\n",
       "      <td>0.162308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-16</th>\n",
       "      <td>0.703575</td>\n",
       "      <td>0.546643</td>\n",
       "      <td>0.156931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-12</th>\n",
       "      <td>0.702350</td>\n",
       "      <td>0.581375</td>\n",
       "      <td>0.120975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-11</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.550435</td>\n",
       "      <td>0.149565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <td>0.698999</td>\n",
       "      <td>0.573518</td>\n",
       "      <td>0.125481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-09</th>\n",
       "      <td>0.694615</td>\n",
       "      <td>0.602308</td>\n",
       "      <td>0.092308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-16</th>\n",
       "      <td>0.694167</td>\n",
       "      <td>0.595000</td>\n",
       "      <td>0.099167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-03</th>\n",
       "      <td>0.693043</td>\n",
       "      <td>0.580870</td>\n",
       "      <td>0.112174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>0.687452</td>\n",
       "      <td>0.542725</td>\n",
       "      <td>0.144727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-26</th>\n",
       "      <td>0.687452</td>\n",
       "      <td>0.597383</td>\n",
       "      <td>0.090069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-14</th>\n",
       "      <td>0.686684</td>\n",
       "      <td>0.513490</td>\n",
       "      <td>0.173194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>0.684373</td>\n",
       "      <td>0.559661</td>\n",
       "      <td>0.124711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <td>0.682589</td>\n",
       "      <td>0.547766</td>\n",
       "      <td>0.134823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-21</th>\n",
       "      <td>0.678737</td>\n",
       "      <td>0.537750</td>\n",
       "      <td>0.140986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>0.678261</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.118261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <td>0.677692</td>\n",
       "      <td>0.556923</td>\n",
       "      <td>0.120769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-05</th>\n",
       "      <td>0.676522</td>\n",
       "      <td>0.526957</td>\n",
       "      <td>0.149565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-20</th>\n",
       "      <td>0.671889</td>\n",
       "      <td>0.535248</td>\n",
       "      <td>0.136641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <td>0.641263</td>\n",
       "      <td>0.536567</td>\n",
       "      <td>0.104696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AccuracyPred  AccuracyBaseline  AccPred - AccBaseline\n",
       "Date                                                             \n",
       "2018-04-30      0.704348          0.546087               0.158261\n",
       "2018-02-20      0.703846          0.541538               0.162308\n",
       "2018-04-16      0.703575          0.546643               0.156931\n",
       "2018-04-12      0.702350          0.581375               0.120975\n",
       "2018-05-11      0.700000          0.550435               0.149565\n",
       "2018-01-29      0.698999          0.573518               0.125481\n",
       "2018-02-09      0.694615          0.602308               0.092308\n",
       "2018-03-16      0.694167          0.595000               0.099167\n",
       "2018-05-03      0.693043          0.580870               0.112174\n",
       "2018-01-30      0.687452          0.542725               0.144727\n",
       "2018-02-26      0.687452          0.597383               0.090069\n",
       "2018-05-14      0.686684          0.513490               0.173194\n",
       "2018-01-31      0.684373          0.559661               0.124711\n",
       "2018-03-22      0.682589          0.547766               0.134823\n",
       "2018-02-21      0.678737          0.537750               0.140986\n",
       "2018-03-27      0.678261          0.560000               0.118261\n",
       "2018-03-23      0.677692          0.556923               0.120769\n",
       "2018-04-05      0.676522          0.526957               0.149565\n",
       "2018-04-20      0.671889          0.535248               0.136641\n",
       "2018-02-13      0.641263          0.536567               0.104696"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf.sort_values(by='AccuracyPred', ascending=False).tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last run on Wed Jan 22 18:38:49 UTC 2020\r\n"
     ]
    }
   ],
   "source": [
    "!echo \"Last run on `date`\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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