RL - Stock Trading Algo4.ipynb

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   "source": [
    "<h1>Reinforcement Learning Final Project - Stock Trading Algorithm</h1>\n",
    "    \n",
    "<br>\n",
    "Implementation using the \n",
    "\"anytrading\" environment in OpenAIGym\n",
    "<br>\n",
    "https://github.com/AminHP/gym-anytrading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3780bdab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gym-anytrading in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (1.2.0)\n",
      "Requirement already satisfied: pandas>=0.24.2 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from gym-anytrading) (1.0.1)\n",
      "Requirement already satisfied: matplotlib>=3.1.1 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from gym-anytrading) (3.1.3)\n",
      "Requirement already satisfied: gym>=0.12.5 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from gym-anytrading) (0.21.0)\n",
      "Requirement already satisfied: numpy>=1.16.4 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from gym-anytrading) (1.21.5)\n",
      "Requirement already satisfied: importlib-metadata>=4.8.1 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from gym>=0.12.5->gym-anytrading) (4.11.3)\n",
      "Requirement already satisfied: cloudpickle>=1.2.0 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from gym>=0.12.5->gym-anytrading) (2.0.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.1.1->gym-anytrading) (1.3.2)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.1.1->gym-anytrading) (2.8.2)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.1.1->gym-anytrading) (3.0.4)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.1.1->gym-anytrading) (0.11.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.24.2->gym-anytrading) (2021.3)\n",
      "Requirement already satisfied: typing-extensions>=3.6.4 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from importlib-metadata>=4.8.1->gym>=0.12.5->gym-anytrading) (4.1.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from importlib-metadata>=4.8.1->gym>=0.12.5->gym-anytrading) (3.7.0)\n",
      "Requirement already satisfied: six>=1.5 in /Users/forestshi/opt/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.1->matplotlib>=3.1.1->gym-anytrading) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install gym-anytrading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1eeff598",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the OpenAIGym Environment for stock trading\n",
    "import gym\n",
    "import gym_anytrading\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from gym_anytrading.datasets import STOCKS_GOOGL\n",
    "from gym.envs.registration import register\n",
    "from gym_anytrading.envs import Positions, Actions, StocksEnv\n",
    "from gym import spaces\n",
    "import random\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "943245b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2009-05-22</th>\n",
       "      <td>198.528534</td>\n",
       "      <td>199.524521</td>\n",
       "      <td>196.196198</td>\n",
       "      <td>196.946945</td>\n",
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       "      <th>2009-05-26</th>\n",
       "      <td>196.171173</td>\n",
       "      <td>202.702698</td>\n",
       "      <td>195.195190</td>\n",
       "      <td>202.382385</td>\n",
       "      <td>202.382385</td>\n",
       "      <td>6202700</td>\n",
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       "    <tr>\n",
       "      <th>2009-05-27</th>\n",
       "      <td>203.023026</td>\n",
       "      <td>206.136139</td>\n",
       "      <td>202.607605</td>\n",
       "      <td>202.982986</td>\n",
       "      <td>202.982986</td>\n",
       "      <td>6062500</td>\n",
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       "    <tr>\n",
       "      <th>2009-05-28</th>\n",
       "      <td>204.544540</td>\n",
       "      <td>206.016022</td>\n",
       "      <td>202.507507</td>\n",
       "      <td>205.405411</td>\n",
       "      <td>205.405411</td>\n",
       "      <td>5332200</td>\n",
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       "      <th>2009-05-29</th>\n",
       "      <td>206.261261</td>\n",
       "      <td>208.823822</td>\n",
       "      <td>205.555557</td>\n",
       "      <td>208.823822</td>\n",
       "      <td>208.823822</td>\n",
       "      <td>5291100</td>\n",
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       "      <th>2018-08-23</th>\n",
       "      <td>1219.880005</td>\n",
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       "      <td>1221.160034</td>\n",
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       "      <th>2018-08-24</th>\n",
       "      <td>1226.000000</td>\n",
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       "      <td>1221.420044</td>\n",
       "      <td>1236.750000</td>\n",
       "      <td>1236.750000</td>\n",
       "      <td>1222700</td>\n",
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       "    <tr>\n",
       "      <th>2018-08-27</th>\n",
       "      <td>1244.140015</td>\n",
       "      <td>1257.869995</td>\n",
       "      <td>1240.680054</td>\n",
       "      <td>1256.270020</td>\n",
       "      <td>1256.270020</td>\n",
       "      <td>1429000</td>\n",
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       "      <th>2018-08-28</th>\n",
       "      <td>1255.900024</td>\n",
       "      <td>1256.560059</td>\n",
       "      <td>1242.969971</td>\n",
       "      <td>1245.859985</td>\n",
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       "      <th>2018-08-29</th>\n",
       "      <td>1255.000000</td>\n",
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       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2009-05-22   198.528534   199.524521   196.196198   196.946945   196.946945   \n",
       "2009-05-26   196.171173   202.702698   195.195190   202.382385   202.382385   \n",
       "2009-05-27   203.023026   206.136139   202.607605   202.982986   202.982986   \n",
       "2009-05-28   204.544540   206.016022   202.507507   205.405411   205.405411   \n",
       "2009-05-29   206.261261   208.823822   205.555557   208.823822   208.823822   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2018-08-23  1219.880005  1235.170044  1219.349976  1221.160034  1221.160034   \n",
       "2018-08-24  1226.000000  1237.400024  1221.420044  1236.750000  1236.750000   \n",
       "2018-08-27  1244.140015  1257.869995  1240.680054  1256.270020  1256.270020   \n",
       "2018-08-28  1255.900024  1256.560059  1242.969971  1245.859985  1245.859985   \n",
       "2018-08-29  1255.000000  1267.170044  1252.800049  1264.650024  1264.650024   \n",
       "\n",
       "             Volume  \n",
       "Date                 \n",
       "2009-05-22  3433700  \n",
       "2009-05-26  6202700  \n",
       "2009-05-27  6062500  \n",
       "2009-05-28  5332200  \n",
       "2009-05-29  5291100  \n",
       "...             ...  \n",
       "2018-08-23  1233300  \n",
       "2018-08-24  1222700  \n",
       "2018-08-27  1429000  \n",
       "2018-08-28  1366500  \n",
       "2018-08-29  1846300  \n",
       "\n",
       "[2335 rows x 6 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Google Stock dataset\n",
    "STOCKS_GOOGL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "13fe497f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# our custom environment for stock trading environment\n",
    "class AdvancedEnv(StocksEnv):\n",
    "    def __init__(self, df, window_size, frame_bound):\n",
    "        super().__init__(df, window_size, frame_bound)\n",
    "\n",
    "        self.trade_fee_bid_percent = 0.01  # unit\n",
    "        self.trade_fee_ask_percent = 0.005  # unit\n",
    "        \n",
    "        # observation is only the price\n",
    "        #self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape=(window_size,), dtype=np.float32)\n",
    "\n",
    "\n",
    "    def _update_mdd():\n",
    "        pass\n",
    "\n",
    "    def update_sharpe():\n",
    "        pass\n",
    "\n",
    "#     def _get_observation(self):\n",
    "#         obs = self.signal_features[(self._current_tick-self.window_size):self._current_tick]\n",
    "#         obs = np.array(obs)\n",
    "#         prices = obs[:, 0]\n",
    "#         return prices\n",
    "    \n",
    "    def _update_profit(self, action):\n",
    "        trade = False\n",
    "        if ((action == Actions.Buy.value and self._position == Positions.Short) or\n",
    "            (action == Actions.Sell.value and self._position == Positions.Long)):\n",
    "            trade = True\n",
    "\n",
    "        if trade or self._done:\n",
    "            current_price = self.prices[self._current_tick]\n",
    "            last_trade_price = self.prices[self._last_trade_tick]\n",
    "\n",
    "            if self._position == Positions.Long:\n",
    "                shares = (self._total_profit * (1 - self.trade_fee_ask_percent)) / last_trade_price\n",
    "                self._total_profit = (shares * (1 - self.trade_fee_bid_percent)) * current_price\n",
    "\n",
    "                \n",
    "    def _calculate_reward(self, action):\n",
    "        step_reward = 0\n",
    "\n",
    "        trade = False\n",
    "        if ((action == Actions.Buy.value and self._position == Positions.Short) or\n",
    "            (action == Actions.Sell.value and self._position == Positions.Long)):\n",
    "            trade = True\n",
    "\n",
    "        if trade:\n",
    "            current_price = self.prices[self._current_tick]\n",
    "            last_trade_price = self.prices[self._last_trade_tick]\n",
    "            price_diff = current_price - last_trade_price\n",
    "\n",
    "            if self._position == Positions.Long:\n",
    "                step_reward += price_diff\n",
    "\n",
    "        return step_reward\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5ee8498a",
   "metadata": {},
   "outputs": [],
   "source": [
    "register(\n",
    "    id='advanced-stocks-v0',\n",
    "    entry_point=AdvancedEnv\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6cf2e657",
   "metadata": {},
   "outputs": [],
   "source": [
    "#env = gym.make('stocks-v0')\n",
    "# env_1 = gym.make('stocks-v0',\n",
    "#                df = STOCKS_GOOGL,\n",
    "#                window_size = 1,\n",
    "#                frame_bound = (1000, 1500))\n",
    "env_1 = gym.make('advanced-stocks-v0',\n",
    "               df = STOCKS_GOOGL,\n",
    "               window_size = 1,\n",
    "               frame_bound = (1500, 2000))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90715533",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "env_1.reset()\n",
    "env_1.render()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1197aa9f",
   "metadata": {},
   "source": [
    "Max_possible_profit signifies that if the market didn't have trade fees, you could have earned 12.468122338293616 units of currency by starting with 1.0. In other words, your money increased over 1200%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b75c3469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.429036853895969"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env_1.max_possible_profit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "71340fbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete(2)\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "print(env_1.action_space)\n",
    "print(env_1.observation_space.low.size)\n",
    "# 0 for sell and 1 for buying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c8ed3866",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([[548.950012,   6.910034]]), 0, False, {'total_reward': 0.0, 'total_profit': 1.0, 'position': 1})\n"
     ]
    }
   ],
   "source": [
    "action = 1\n",
    "print(env_1.step(action))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0de3e68f",
   "metadata": {},
   "outputs": [],
   "source": [
    "observation, reward, done, info = env_1.step(action)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1826502",
   "metadata": {},
   "source": [
    "<h2>Random and Heuristic Methods:</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c43cb901",
   "metadata": {},
   "source": [
    "<h3>Method 1: Random Buy/Sell</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6acba4f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "info:  {'total_reward': 102.18975599999999, 'total_profit': 0.17084659148899164, 'position': 0}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "state = env_1.reset()\n",
    "while True:\n",
    "    action = env_1.action_space.sample()\n",
    "    state, reward, done, info = env_1.step(action)\n",
    "\n",
    "    if done:\n",
    "        print(\"info: \", info)\n",
    "        break\n",
    "\n",
    "env_1.render_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "309d61fd",
   "metadata": {},
   "source": [
    "<h3>Method 2: Buy low sell high (last ten days moving average)</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5b15f637",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "info:  {'total_reward': 387.4603910000001, 'total_profit': 0.8215406231514627, 'position': 0}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "observation = env_1.reset()\n",
    "last_ten_days = [] # moving average\n",
    "moving_avg = 0\n",
    "while True:\n",
    "    if len(last_ten_days) < 10:\n",
    "        last_ten_days.append(observation[0][0])\n",
    "    else:\n",
    "        last_ten_days.pop(0)\n",
    "        last_ten_days.append(observation[0][0])\n",
    "    moving_avg = sum(last_ten_days) / len(last_ten_days)\n",
    "    \n",
    "    \n",
    "    if observation[0][0] < moving_avg:\n",
    "        action = 1 # buy)\n",
    "    elif observation[0][0] > moving_avg:\n",
    "        action = 0 # sell\n",
    "    \n",
    "    observation, reward, done, info = env_1.step(action)\n",
    "\n",
    "    if done:\n",
    "        print(\"info: \", info)\n",
    "        break\n",
    "\n",
    "env_1.render_all()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f885fa41",
   "metadata": {},
   "source": [
    "<h3>Method 3: Buy and Hold Strategy</h3>\n",
    "<br>\n",
    "Buy the stock at the beginning and hold throughout the period and sell stock at the very end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e3b0a9d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "info:  {'total_reward': 345.6599729999999, 'total_profit': 1.6089137148880541, 'position': 0}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "state = env_1.reset()\n",
    "count = 0\n",
    "while True:\n",
    "    count += 1\n",
    "    action = 1\n",
    "    if count > 495:\n",
    "        action = 0\n",
    "    state, reward, done, info = env_1.step(action)\n",
    "    \n",
    "    if done:\n",
    "        print(\"info: \", info)\n",
    "        break\n",
    "\n",
    "env_1.render_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7df050ff",
   "metadata": {},
   "source": [
    "<h2>Reinforcement Learning Methods:</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c5becd4",
   "metadata": {},
   "source": [
    "<h3>Method 4: Deep Q-Learning (DQN)</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "id": "15f194d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# environment for training\n",
    "env_training = gym.make('advanced-stocks-v0', df = STOCKS_GOOGL, window_size = 1, frame_bound = (1, 1500))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "id": "d5dfa0ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "gamma = 0.99\n",
    "actsize = env_training.action_space.n\n",
    "obssize = int(env_training.observation_space.low.size/2) # divide by two to get rid of the price diff as obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 999,
   "id": "3994d96b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_creator():\n",
    "    model = keras.Sequential()\n",
    "    model.add(layers.Dense(32,activation=\"relu\"))\n",
    "    model.add(layers.Dense(128,activation=\"relu\"))\n",
    "    model.add(layers.Dense(128,activation=\"relu\"))\n",
    "    model.add(layers.Dense(actsize,activation=\"linear\"))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6cdc499",
   "metadata": {},
   "source": [
    "**Training the Model:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1000,
   "id": "d1820fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.backend.set_floatx('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1001,
   "id": "f7b983e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorize_state(state):\n",
    "    vector = []\n",
    "    for i in range(len(state)):\n",
    "        vector.append(state[i][0])\n",
    "        #vector.append(state[i][1])\n",
    "    \n",
    "    return vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1002,
   "id": "7dc9132e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# training multiple epochs with the same dataset with epsilon decaying over epochs\n",
    "def epoch_train(model, model_target, env_training, max_epochs, epsilon, adpative):\n",
    "    optimizer = keras.optimizers.Adam(learning_rate=0.0005)\n",
    "\n",
    "    # Our Experience Replay memory \n",
    "    action_history = []\n",
    "    state_history = []\n",
    "    state_next_history = []\n",
    "    rewards_history = []\n",
    "    done_history = []\n",
    "    running_rewards_history = []\n",
    "\n",
    "    # Replay memory size\n",
    "    max_memory = 100 # You can experiment with different sizes.\n",
    "\n",
    "    running_reward = 0\n",
    "    frame_count = 0\n",
    "    \n",
    "    update_after_actions = 10\n",
    "\n",
    "    # How often to update the target network\n",
    "    target_update_every = 50\n",
    "    loss_function = keras.losses.MeanSquaredError()\n",
    "\n",
    "    for epoch in range(max_epochs): \n",
    "        state = env_training.reset()\n",
    "        timestep_count = 0\n",
    "        \n",
    "        # adpative epsilon decay\n",
    "        if adpative:\n",
    "            epsilon = np.exp(-1e-2 * epoch)\n",
    "\n",
    "        while True:\n",
    "            timestep_count += 1\n",
    "            frame_count += 1\n",
    "\n",
    "            # exploration\n",
    "            rand = np.random.uniform(0, 1)\n",
    "            if rand < epsilon:\n",
    "                # Take random action\n",
    "                action = np.random.choice(actsize)\n",
    "            else:\n",
    "                # Predict action Q-values\n",
    "                # From environment state\n",
    "                state_t = tf.convert_to_tensor(state)\n",
    "                state_t = tf.expand_dims(state_t, 0)\n",
    "                action_vals = model(state_t, training=False)\n",
    "\n",
    "                # Choose the best action\n",
    "                action = int(tf.keras.backend.argmax(action_vals))\n",
    "\n",
    "            state_next, reward, done, info = env_training.step(action)\n",
    "\n",
    "            # Save action/states and other information in replay buffer\n",
    "            action_history.append(action)\n",
    "            state_history.append(state)\n",
    "            state_next_history.append(state_next)\n",
    "            rewards_history.append(reward)\n",
    "            done_history.append(done)\n",
    "            running_rewards_history.append(info['total_reward'])\n",
    "\n",
    "            running_reward = info['total_reward']\n",
    "\n",
    "            state = state_next\n",
    "\n",
    "            # Update every Xth frame to speed up (optional)\n",
    "            # and if you have sufficient history\n",
    "            if timestep_count % update_after_actions == 0 and len(action_history) > batch_size:\n",
    "                # Sample a set of batch_size memories from the history\n",
    "                rand_samples = np.random.randint(0, len(action_history), batch_size)\n",
    "\n",
    "                action_sample = np.array(action_history)[rand_samples]\n",
    "                state_sample = np.array(state_history)[rand_samples]\n",
    "                state_next_sample = np.array(state_next_history)[rand_samples]\n",
    "                rewards_sample = np.array(rewards_history)[rand_samples]\n",
    "                done_sample = np.array(done_history)[rand_samples]\n",
    "\n",
    "                # Create for the sample states the targets (r+gamma * max Q(...)\n",
    "                state_next_sample_t = tf.convert_to_tensor(state_next_sample)\n",
    "                Q_next_state = np.max(model_target.predict(state_next_sample_t), axis=1)   \n",
    "                Q_targets = rewards_sample + gamma * Q_next_state\n",
    "\n",
    "                # What actions are relevant and need updating\n",
    "                relevant_actions = tf.cast(tf.one_hot(action_sample, actsize), tf.double)\n",
    "\n",
    "                # we will use Gradient tape to do a custom gradient \n",
    "                # in the `with` environment we will record a set of operations\n",
    "                # and then we will take gradients with respect to the trainable parameters\n",
    "                # in the neural network\n",
    "                with tf.GradientTape() as tape:\n",
    "                    # Train the model on your action selecting network\n",
    "                    q_values = model(state_sample) \n",
    "                    # We consider only the relevant actions\n",
    "                    Q_of_actions = tf.reduce_sum(tf.multiply(q_values, relevant_actions), axis=1)\n",
    "                    # Calculate loss between principal network and target network\n",
    "                    loss = loss_function(Q_targets, Q_of_actions)\n",
    "\n",
    "                    # Nudge the weights of the trainable variables towards \n",
    "                    grads = tape.gradient(loss, model.trainable_variables)\n",
    "                    optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
    "\n",
    "            if timestep_count % target_update_every == 0:\n",
    "                # update the the target network with new weights\n",
    "                model_target.set_weights(model.get_weights())\n",
    "                # Log details\n",
    "                #template = \"running reward: {:.2f} at episode {}, total profit: {} epsilon {}\"\n",
    "                #print(template.format(running_reward, timestep_count, info[\"total_profit\"], epsilon))\n",
    "\n",
    "            # Don't let the memory grow beyond the limit\n",
    "            if len(rewards_history) > max_memory:\n",
    "                del action_history[:1]\n",
    "                del state_history[:1]\n",
    "                del state_next_history[:1]\n",
    "                del rewards_history[:1]\n",
    "                del done_history[:1]\n",
    "\n",
    "            if done:\n",
    "                #print(\"info: \", info)\n",
    "                # Log details\n",
    "                template = \"running reward: {:.2f} at episode {}, frame {}, total profit: {} epsilon {}\"\n",
    "                print(template.format(running_reward, epoch, frame_count, info[\"total_profit\"], epsilon))\n",
    "                break\n",
    "\n",
    "    env_training.render_all()\n",
    "    \n",
    "    return running_rewards_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1003,
   "id": "c42ef7aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train over a single epoch and not seeing the same states again, epsilon decaying across the single epoch\n",
    "def train(model, model_target, env_training, epsilon, adpative):\n",
    "    optimizer = keras.optimizers.Adam(learning_rate=0.0005)\n",
    "\n",
    "    # Our Experience Replay memory \n",
    "    action_history = []\n",
    "    state_history = []\n",
    "    state_next_history = []\n",
    "    rewards_history = []\n",
    "    done_history = []\n",
    "    running_rewards_history = []\n",
    "\n",
    "    # Replay memory size\n",
    "    max_memory = 100 # You can experiment with different sizes.\n",
    "\n",
    "    running_reward = 0\n",
    "    timestep_count = 0\n",
    "    \n",
    "    update_after_actions = 4\n",
    "\n",
    "    # How often to update the target network\n",
    "    target_update_every = 50\n",
    "    loss_function = keras.losses.MeanSquaredError()\n",
    "\n",
    "\n",
    "    state = env_training.reset()\n",
    "    state = vectorize_state(state)\n",
    "\n",
    "    while True:\n",
    "        timestep_count += 1\n",
    "        \n",
    "        # adpative epsilon decay\n",
    "        if adpative:\n",
    "            epsilon = np.exp(-1e-2 * timestep_count)\n",
    "\n",
    "        # exploration\n",
    "        rand = np.random.uniform(0, 1)\n",
    "        if rand < epsilon:\n",
    "            # Take random action\n",
    "            action = np.random.choice(actsize)\n",
    "        else:\n",
    "            # Predict action Q-values\n",
    "            # From environment state\n",
    "            state_t = tf.convert_to_tensor(state)\n",
    "            state_t = tf.expand_dims(state_t, 0)\n",
    "            action_vals = model(state_t, training=False)\n",
    "\n",
    "            # Choose the best action\n",
    "            action = int(tf.keras.backend.argmax(action_vals))\n",
    "\n",
    "        state_next, reward, done, info = env_training.step(action)\n",
    "        state_next = vectorize_state(state_next)\n",
    "\n",
    "        # Save action/states and other information in replay buffer\n",
    "        action_history.append(action)\n",
    "        state_history.append(state)\n",
    "        state_next_history.append(state_next)\n",
    "        rewards_history.append(reward)\n",
    "        done_history.append(done)\n",
    "        running_rewards_history.append(info['total_reward'])\n",
    "\n",
    "        running_reward = info['total_reward']\n",
    "\n",
    "        state = state_next\n",
    "\n",
    "        # Update every Xth frame to speed up (optional)\n",
    "        # and if you have sufficient history\n",
    "        if timestep_count % update_after_actions == 0 and len(action_history) > batch_size:\n",
    "            # Sample a set of batch_size memories from the history\n",
    "            rand_samples = np.random.randint(0, len(action_history), batch_size)\n",
    "\n",
    "            action_sample = np.array(action_history)[rand_samples]\n",
    "            state_sample = np.array(state_history)[rand_samples]\n",
    "            state_next_sample = np.array(state_next_history)[rand_samples]\n",
    "            rewards_sample = np.array(rewards_history)[rand_samples]\n",
    "            done_sample = np.array(done_history)[rand_samples]\n",
    "\n",
    "            # Create for the sample states the targets (r+gamma * max Q(...)\n",
    "            state_next_sample_t = tf.convert_to_tensor(state_next_sample)\n",
    "            Q_next_state = np.max(model_target.predict(state_next_sample_t), axis=1)   \n",
    "            Q_targets = rewards_sample + gamma * Q_next_state\n",
    "\n",
    "            # What actions are relevant and need updating\n",
    "            relevant_actions = tf.cast(tf.one_hot(action_sample, actsize), tf.double)\n",
    "\n",
    "            # we will use Gradient tape to do a custom gradient \n",
    "            # in the `with` environment we will record a set of operations\n",
    "            # and then we will take gradients with respect to the trainable parameters\n",
    "            # in the neural network\n",
    "            with tf.GradientTape() as tape:\n",
    "                # Train the model on your action selecting network\n",
    "                q_values = model(state_sample) \n",
    "                # We consider only the relevant actions\n",
    "                Q_of_actions = tf.reduce_sum(tf.multiply(q_values, relevant_actions), axis=1)\n",
    "                # Calculate loss between principal network and target network\n",
    "                loss = loss_function(Q_targets, Q_of_actions)\n",
    "\n",
    "                # Nudge the weights of the trainable variables towards \n",
    "                grads = tape.gradient(loss, model.trainable_variables)\n",
    "                optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
    "\n",
    "        if timestep_count % target_update_every == 0:\n",
    "            # update the the target network with new weights\n",
    "            model_target.set_weights(model.get_weights())\n",
    "            # Log details\n",
    "            template = \"running reward: {:.2f} at episode {}, total profit: {} epsilon {}\"\n",
    "            print(template.format(running_reward, timestep_count, info[\"total_profit\"], epsilon))\n",
    "\n",
    "        # Don't let the memory grow beyond the limit\n",
    "        if len(rewards_history) > max_memory:\n",
    "            del action_history[:1]\n",
    "            del state_history[:1]\n",
    "            del state_next_history[:1]\n",
    "            del rewards_history[:1]\n",
    "            del done_history[:1]\n",
    "\n",
    "        if done:\n",
    "            print(\"info: \", info)\n",
    "            break\n",
    "\n",
    "    env_training.render_all()\n",
    "    \n",
    "    return running_rewards_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1028,
   "id": "10afb994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_117\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_442 (Dense)            (10, 32)                  64        \n",
      "_________________________________________________________________\n",
      "dense_443 (Dense)            (10, 128)                 4224      \n",
      "_________________________________________________________________\n",
      "dense_444 (Dense)            (10, 128)                 16512     \n",
      "_________________________________________________________________\n",
      "dense_445 (Dense)            (10, 2)                   258       \n",
      "=================================================================\n",
      "Total params: 21,058\n",
      "Trainable params: 21,058\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "batch_size = 10\n",
    "# Model used for selecting actions (principal)\n",
    "model = model_creator()\n",
    "# Then create the target model. This will periodically be copied from the principal network \n",
    "model_target = model_creator()\n",
    "\n",
    "model.build((batch_size,obssize))\n",
    "model_target.build((batch_size,obssize))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1030,
   "id": "321ea504",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running reward: 45.49 at episode 50, total profit: 1.0822637355442422 epsilon 0.6065306597126334\n",
      "running reward: 53.98 at episode 100, total profit: 0.9940430859701644 epsilon 0.36787944117144233\n",
      "running reward: 72.37 at episode 150, total profit: 0.9971753471420964 epsilon 0.22313016014842982\n",
      "running reward: 58.35 at episode 200, total profit: 0.8803346145321961 epsilon 0.1353352832366127\n",
      "running reward: 52.78 at episode 250, total profit: 0.8137058969136891 epsilon 0.0820849986238988\n",
      "running reward: 52.78 at episode 300, total profit: 0.8137058969136891 epsilon 0.049787068367863944\n",
      "running reward: 53.25 at episode 350, total profit: 0.8030159235131347 epsilon 0.0301973834223185\n",
      "running reward: 72.00 at episode 400, total profit: 0.846478555529826 epsilon 0.01831563888873418\n",
      "running reward: 72.00 at episode 450, total profit: 0.846478555529826 epsilon 0.011108996538242306\n",
      "running reward: 67.03 at episode 500, total profit: 0.8198293952939049 epsilon 0.006737946999085467\n",
      "running reward: 38.27 at episode 550, total profit: 0.7212704655279448 epsilon 0.004086771438464067\n",
      "running reward: 38.27 at episode 600, total profit: 0.7212704655279448 epsilon 0.0024787521766663585\n",
      "running reward: 38.27 at episode 650, total profit: 0.7212704655279448 epsilon 0.0015034391929775724\n",
      "running reward: 84.71 at episode 700, total profit: 0.828797913266384 epsilon 0.0009118819655545162\n",
      "running reward: 75.82 at episode 750, total profit: 0.793183720488631 epsilon 0.0005530843701478336\n",
      "running reward: 75.82 at episode 800, total profit: 0.793183720488631 epsilon 0.00033546262790251185\n",
      "running reward: 134.56 at episode 850, total profit: 0.9438323814402924 epsilon 0.00020346836901064417\n",
      "running reward: 134.56 at episode 900, total profit: 0.9438323814402924 epsilon 0.00012340980408667956\n",
      "running reward: 173.43 at episode 950, total profit: 1.041287007600605 epsilon 7.48518298877006e-05\n",
      "running reward: 145.61 at episode 1000, total profit: 0.956310460018403 epsilon 4.5399929762484854e-05\n",
      "running reward: 148.80 at episode 1050, total profit: 0.9487648408740235 epsilon 2.7536449349747158e-05\n",
      "running reward: 148.80 at episode 1100, total profit: 0.9487648408740235 epsilon 1.670170079024566e-05\n",
      "running reward: 151.37 at episode 1150, total profit: 0.9402198975751174 epsilon 1.013009359863071e-05\n",
      "running reward: 184.93 at episode 1200, total profit: 0.9862554130248049 epsilon 6.14421235332821e-06\n",
      "running reward: 190.77 at episode 1250, total profit: 0.9809596906229949 epsilon 3.726653172078671e-06\n",
      "running reward: 202.88 at episode 1300, total profit: 0.987831151546533 epsilon 2.2603294069810542e-06\n",
      "running reward: 182.83 at episode 1350, total profit: 0.9261713489738086 epsilon 1.3709590863840845e-06\n",
      "running reward: 161.97 at episode 1400, total profit: 0.8776536672737539 epsilon 8.315287191035679e-07\n",
      "running reward: 199.76 at episode 1450, total profit: 0.9293542222275301 epsilon 5.04347662567888e-07\n",
      "info:  {'total_reward': 184.89068500000016, 'total_profit': 0.8913086292832824, 'position': 0}\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train on Google stock data from timestep 1 to 1500\n",
    "running_rewards_history = train(model, model_target, env_training, 1, True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1031,
   "id": "5575fa1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: trained_model/assets\n"
     ]
    }
   ],
   "source": [
    "model.save(\"trained_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1032,
   "id": "f618ca6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Running Reward')"
      ]
     },
     "execution_count": 1032,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the running rewards\n",
    "plt.plot(running_rewards_history)\n",
    "plt.title(\"Running rewards during training\")\n",
    "plt.xlabel(\"Episodes\")\n",
    "plt.ylabel(\"Running Reward\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1034,
   "id": "3ff35d9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running reward: -2.78 at episode 50, total profit: 0.9800324701877283 epsilon 0\n",
      "running reward: 149.63 at episode 100, total profit: 1.2369956913531568 epsilon 0\n",
      "running reward: 259.90 at episode 150, total profit: 1.42839751686934 epsilon 0\n",
      "running reward: 259.90 at episode 200, total profit: 1.42839751686934 epsilon 0\n",
      "running reward: 343.42 at episode 250, total profit: 1.5739315530575189 epsilon 0\n",
      "running reward: 343.42 at episode 300, total profit: 1.5739315530575189 epsilon 0\n",
      "running reward: 438.48 at episode 350, total profit: 1.7591179993705173 epsilon 0\n",
      "running reward: 438.48 at episode 400, total profit: 1.7591179993705173 epsilon 0\n",
      "running reward: 397.91 at episode 450, total profit: 1.6487015322805292 epsilon 0\n",
      "info:  {'total_reward': 397.90991299999973, 'total_profit': 1.8140128803859459, 'position': 1}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test model results on the next leg of the stock data from time 1500 to 2000\n",
    "#model = create_model()\n",
    "#model_target = create_model()\n",
    "#model = tf.keras.models.load_model(\"trained_model\")\n",
    "_ = train(model, model_target, env_1, 0, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79b1a1ad",
   "metadata": {},
   "source": [
    "*The reward of the Deep Q Learning function on the test set after training for 1500 timesteps is: 397.90991299999973 and the total profit is 1.8140128803859459. The initial money increased by 181.4%. This beats the naive algorithm of buy and hold, random buy sell, and ten day moving average by a big margin.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72ab0674",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03f9c8a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e54332c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}