visualize_GOOG.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from train import main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_stock = \"data/GOOG_Train.csv\"\n",
    "# val_stock = \"data/GOOG_Test.csv\"\n",
    "# strategy = \"dqn\"\n",
    "# window_size = 10\n",
    "# batch_size = 32\n",
    "# ep_count = 50\n",
    "# model_name = \"model_dqn_GOOG_epidodes_50\"\n",
    "# pretrained = False\n",
    "# debug = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# main(train_stock, val_stock, window_size, batch_size,\n",
    "#         ep_count, strategy=strategy, model_name=model_name, \n",
    "#         pretrained=pretrained, debug=debug)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization: Trading Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import altair as alt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Define parameters and Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from trading_bot.agent import Agent\n",
    "\n",
    "model_name = 'model_dqn_GOOG_epidodes_50_40'\n",
    "# 50 ep = +$7\n",
    "# 40 ep = +$109.63\n",
    "# 20 ep = +$68.34\n",
    "test_stock = 'data/GOOG_Test.csv'\n",
    "window_size = 10\n",
    "debug = True\n",
    "\n",
    "agent = Agent(window_size, pretrained=True, model_name=model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>date</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-10</td>\n",
       "      <td>138.574005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-11</td>\n",
       "      <td>140.017502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-12</td>\n",
       "      <td>141.647995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>139.130997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-14</td>\n",
       "      <td>139.786499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date      actual\n",
       "0 2022-01-10  138.574005\n",
       "1 2022-01-11  140.017502\n",
       "2 2022-01-12  141.647995\n",
       "3 2022-01-13  139.130997\n",
       "4 2022-01-14  139.786499"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read csv into dataframe\n",
    "df = pd.read_csv(test_stock)\n",
    "# filter out the desired features\n",
    "df = df[['Date', 'Adj Close']]\n",
    "# rename feature column names\n",
    "df = df.rename(columns={'Adj Close': 'actual', 'Date': 'date'})\n",
    "# convert dates from object to DateTime type\n",
    "dates = df['date']\n",
    "dates = pd.to_datetime(dates, infer_datetime_format=True)\n",
    "df['date'] = dates\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Running Eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:37\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mswitching to TensorFlow for CPU\u001b[0m\n",
      "\u001b[32m2023-01-16 16:31:37\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $138.57\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 82ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:37\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mSell at: $140.02 | Position: +$1.44\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:37\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $139.13\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 20ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mSell at: $139.79 | Position: +$0.66\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 20ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $136.29\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 25ms/step\n"
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    {
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     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $135.65\u001b[0m\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 27ms/step\n"
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     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $133.51\u001b[0m\n"
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    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 18ms/step\n"
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     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $130.09\u001b[0m\n"
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n"
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    {
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     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $126.74\u001b[0m\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 16ms/step\n"
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     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $129.24\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 25ms/step\n"
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    {
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     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $129.12\u001b[0m\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 25ms/step\n"
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     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $133.29\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 25ms/step\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mSell at: $135.70 | Position: -$0.59\u001b[0m\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 22ms/step\n"
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     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mSell at: $137.88 | Position: +$2.23\u001b[0m\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 19ms/step\n"
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     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mSell at: $148.04 | Position: +$14.53\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 18ms/step\n"
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     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mSell at: $142.65 | Position: +$12.56\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 17ms/step\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:38\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mSell at: $143.02 | Position: +$16.28\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 18ms/step\n"
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     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:39\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $138.94\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 18ms/step\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:39\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mSell at: $139.21 | Position: +$9.97\u001b[0m\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 10ms/step\n"
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     "text": [
      "\u001b[32m2023-01-16 16:31:39\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $141.45\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:39\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $136.43\u001b[0m\n"
     ]
    },
    {
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     "text": [
      "1/1 [==============================] - 0s 25ms/step\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:39\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $137.49\u001b[0m\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:39\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $129.40\u001b[0m\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 25ms/step\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:39\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $127.58\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 17ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:39\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $132.67\u001b[0m\n"
     ]
    },
    {
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     "text": [
      "1/1 [==============================] - 0s 17ms/step\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:39\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $134.52\u001b[0m\n"
     ]
    },
    {
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     "text": [
      "1/1 [==============================] - 0s 17ms/step\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:39\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $134.89\u001b[0m\n"
     ]
    },
    {
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     "text": [
      "1/1 [==============================] - 0s 17ms/step\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:40\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $134.17\u001b[0m\n"
     ]
    },
    {
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      "\u001b[32m2023-01-16 16:31:48\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $114.70\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:49\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mBuy at: $98.09\u001b[0m\n"
     ]
    },
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-01-16 16:31:54\u001b[0m \u001b[35mDESKTOP-6JVVUQ4\u001b[0m \u001b[34mroot[2348]\u001b[0m \u001b[1;30mINFO\u001b[0m model_dqn_GOOG_epidodes_50_40: +$109.63\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import coloredlogs\n",
    "\n",
    "from trading_bot.utils import show_eval_result, switch_k_backend_device, get_stock_data\n",
    "from trading_bot.methods import evaluate_model\n",
    "\n",
    "coloredlogs.install(level='DEBUG')\n",
    "switch_k_backend_device()\n",
    "\n",
    "test_data = get_stock_data(test_stock)\n",
    "initial_offset = test_data[1] - test_data[0]\n",
    "\n",
    "test_result, history = evaluate_model(agent, test_data, window_size, debug)\n",
    "show_eval_result(model_name, test_result, initial_offset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize(df, history, title=\"trading session\"):\n",
    "    # add history to dataframe\n",
    "    position = [history[0][0]] + [x[0] for x in history]\n",
    "    actions = ['HOLD'] + [x[1] for x in history]\n",
    "    df['position'] = position\n",
    "    df['action'] = actions\n",
    "    \n",
    "    # specify y-axis scale for stock prices\n",
    "    scale = alt.Scale(domain=(min(min(df['actual']), min(df['position'])) - 50, max(max(df['actual']), max(df['position'])) + 50), clamp=True)\n",
    "    \n",
    "    # plot a line chart for stock positions\n",
    "    actual = alt.Chart(df).mark_line(\n",
    "        color='green',\n",
    "        opacity=0.5\n",
    "    ).encode(\n",
    "        x='date:T',\n",
    "        y=alt.Y('position', axis=alt.Axis(format='$.2f', title='Price'), scale=scale)\n",
    "    ).interactive(\n",
    "        bind_y=False\n",
    "    )\n",
    "    \n",
    "    points = alt.Chart(df).mark_point(\n",
    "        filled=True\n",
    "    ).encode(\n",
    "        x=alt.X('date:T', axis=alt.Axis(title='Date')),\n",
    "        y=alt.Y('position', axis=alt.Axis(format='$.2f', title='Price'), scale=scale),\n",
    "        color='action'\n",
    "    ).interactive(bind_y=False)\n",
    "\n",
    "    # merge the two charts\n",
    "    chart = alt.layer(actual, points, title=title).properties(height=300, width=1000)\n",
    "    \n",
    "    return chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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\"HOLD\"}, {\"date\": \"2022-12-19T00:00:00\", \"actual\": 89.150002, \"position\": 90.860001, \"action\": \"HOLD\"}, {\"date\": \"2022-12-20T00:00:00\", \"actual\": 89.629997, \"position\": 89.150002, \"action\": \"HOLD\"}, {\"date\": \"2022-12-21T00:00:00\", \"actual\": 90.25, \"position\": 89.629997, \"action\": \"HOLD\"}, {\"date\": \"2022-12-22T00:00:00\", \"actual\": 88.260002, \"position\": 90.25, \"action\": \"HOLD\"}, {\"date\": \"2022-12-23T00:00:00\", \"actual\": 89.809998, \"position\": 88.260002, \"action\": \"HOLD\"}, {\"date\": \"2022-12-27T00:00:00\", \"actual\": 87.93, \"position\": 89.809998, \"action\": \"HOLD\"}, {\"date\": \"2022-12-28T00:00:00\", \"actual\": 86.459999, \"position\": 87.93, \"action\": \"HOLD\"}, {\"date\": \"2022-12-29T00:00:00\", \"actual\": 88.949997, \"position\": 86.459999, \"action\": \"HOLD\"}, {\"date\": \"2022-12-30T00:00:00\", \"actual\": 88.730003, \"position\": 88.949997, \"action\": \"HOLD\"}, {\"date\": \"2023-01-03T00:00:00\", \"actual\": 89.699997, \"position\": 88.730003, \"action\": \"HOLD\"}, {\"date\": \"2023-01-04T00:00:00\", \"actual\": 88.709999, \"position\": 89.699997, \"action\": \"HOLD\"}, {\"date\": \"2023-01-05T00:00:00\", \"actual\": 86.769997, \"position\": 88.709999, \"action\": \"HOLD\"}, {\"date\": \"2023-01-06T00:00:00\", \"actual\": 88.160004, \"position\": 86.769997, \"action\": \"HOLD\"}, {\"date\": \"2023-01-09T00:00:00\", \"actual\": 88.800003, \"position\": 88.160004, \"action\": \"HOLD\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart = visualize(df, history, title='GOOGLE Stock +$109.63 (DQN 40 ep)')\n",
    "chart"
   ]
  }
 ],
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