{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nwaAZRu1NTiI"
      },
      "source": [
        "# Q-learning \n",
        "\n",
        "#### This version implements q-learning using a custom enviroment 1 day, with synthetic data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DDf1gLC2NTiK"
      },
      "outputs": [],
      "source": [
        "# !pip install -r ./requirements.txt\n",
        "# !pip install stable_baselines3[extra]\n",
        "# !pip install yfinance\n",
        "# !pip install talib-binary\n",
        "# !pip install huggingface_sb3\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "LNXxxKojNTiL"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "import gym\n",
        "from gym import spaces\n",
        "from gym.utils import seeding\n",
        "\n",
        "import talib as ta\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_syntetic_data(tf, start_date, end_date, plot=True, add_noise=None):\n",
        "    df = pd.date_range(start=start_date, end=end_date, freq=tf)\n",
        "    df = df.to_frame()\n",
        "\n",
        "    df['v1'] = np.arange(len(df.index))\n",
        "    df[['Open','High','Low','Close','Volume']] = 0.0\n",
        "    df = df.drop([0], axis=1)\n",
        "\n",
        "    # df[\"Close\"]=df[\"v1\"].map(lambda x: np.sin(x)+10 )\n",
        "    df[\"Close\"]=df[\"v1\"].map(lambda x: np.sin(x)+10 + np.sin(x/2) )\n",
        "    if add_noise is not None: # could be 0.5\n",
        "        noise = np.random.normal(0, add_noise, len(df))\n",
        "        df[\"Close\"] += noise\n",
        "\n",
        "    if plot:\n",
        "        plt.figure(figsize=(15,6))\n",
        "        df['Close'].tail(30).plot()\n",
        "\n",
        "    df[\"Open\"]=df[\"Close\"].shift(1)\n",
        "    df = df.dropna()\n",
        "    x = 1.5\n",
        "    df[\"High\"] = np.where( df[\"Close\"] > df['Open'], df[\"Close\"]+x, df[\"Open\"]+x )\n",
        "    df[\"Low\"] = np.where( df[\"Close\"] < df['Open'], df[\"Close\"]-x, df[\"Open\"]-x )\n",
        "    df[\"Volume\"] = 10\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "dmAuEhZZNTiL"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3075\n",
            "1926\n"
          ]
        }
      ],
      "source": [
        "# Get data\n",
        "eth_usd = yf.Ticker(\"ETH-USD\")\n",
        "eth = eth_usd.history(period=\"max\")\n",
        "\n",
        "btc_usd = yf.Ticker(\"BTC-USD\")\n",
        "btc = btc_usd.history(period=\"max\")\n",
        "print(len(btc))\n",
        "print(len(eth))\n",
        "\n",
        "btc_train = eth[-3015:-200]\n",
        "# btc_test = eth[-200:]\n",
        "eth_train = eth[-1864:-200]\n",
        "eth_test = eth[-200:]\n",
        "# len(eth_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# use synthetic data\n",
        "synthetic_data = get_syntetic_data(tf=\"D\", start_date=\"2015-01-01\", end_date=\"2023-01-01\", add_noise=None)\n",
        "eth_train = synthetic_data[-1864:-200]\n",
        "eth_test = synthetic_data[-200:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "def initialize_q_table(state_space, action_space):\n",
        "  Qtable = np.zeros((state_space, action_space))\n",
        "  return Qtable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "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>v1</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-06-16</th>\n",
              "      <td>2723</td>\n",
              "      <td>10.345167</td>\n",
              "      <td>11.845167</td>\n",
              "      <td>8.261013</td>\n",
              "      <td>9.761013</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-17</th>\n",
              "      <td>2724</td>\n",
              "      <td>9.761013</td>\n",
              "      <td>11.261013</td>\n",
              "      <td>7.270246</td>\n",
              "      <td>8.770246</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-18</th>\n",
              "      <td>2725</td>\n",
              "      <td>8.770246</td>\n",
              "      <td>10.270246</td>\n",
              "      <td>6.740366</td>\n",
              "      <td>8.240366</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-19</th>\n",
              "      <td>2726</td>\n",
              "      <td>8.240366</td>\n",
              "      <td>10.279113</td>\n",
              "      <td>6.740366</td>\n",
              "      <td>8.779113</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-20</th>\n",
              "      <td>2727</td>\n",
              "      <td>8.779113</td>\n",
              "      <td>11.646191</td>\n",
              "      <td>7.279113</td>\n",
              "      <td>10.146191</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-28</th>\n",
              "      <td>2918</td>\n",
              "      <td>11.717571</td>\n",
              "      <td>13.217571</td>\n",
              "      <td>9.977599</td>\n",
              "      <td>11.477599</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-29</th>\n",
              "      <td>2919</td>\n",
              "      <td>11.477599</td>\n",
              "      <td>12.977599</td>\n",
              "      <td>9.029289</td>\n",
              "      <td>10.529289</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-30</th>\n",
              "      <td>2920</td>\n",
              "      <td>10.529289</td>\n",
              "      <td>12.029289</td>\n",
              "      <td>8.251117</td>\n",
              "      <td>9.751117</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-31</th>\n",
              "      <td>2921</td>\n",
              "      <td>9.751117</td>\n",
              "      <td>11.251117</td>\n",
              "      <td>8.204337</td>\n",
              "      <td>9.704337</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-01</th>\n",
              "      <td>2922</td>\n",
              "      <td>9.704337</td>\n",
              "      <td>11.654716</td>\n",
              "      <td>8.204337</td>\n",
              "      <td>10.154716</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              v1       Open       High       Low      Close  Volume\n",
              "2022-06-16  2723  10.345167  11.845167  8.261013   9.761013      10\n",
              "2022-06-17  2724   9.761013  11.261013  7.270246   8.770246      10\n",
              "2022-06-18  2725   8.770246  10.270246  6.740366   8.240366      10\n",
              "2022-06-19  2726   8.240366  10.279113  6.740366   8.779113      10\n",
              "2022-06-20  2727   8.779113  11.646191  7.279113  10.146191      10\n",
              "...          ...        ...        ...       ...        ...     ...\n",
              "2022-12-28  2918  11.717571  13.217571  9.977599  11.477599      10\n",
              "2022-12-29  2919  11.477599  12.977599  9.029289  10.529289      10\n",
              "2022-12-30  2920  10.529289  12.029289  8.251117   9.751117      10\n",
              "2022-12-31  2921   9.751117  11.251117  8.204337   9.704337      10\n",
              "2023-01-01  2922   9.704337  11.654716  8.204337  10.154716      10\n",
              "\n",
              "[200 rows x 6 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "eth_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Policy\n",
        "\n",
        "def greedy_policy(Qtable, state):\n",
        "    # Exploitation: take the action with the highest state, action value\n",
        "    # if we dont have a state with values return DO_NOTHING \n",
        "    if abs(np.max(Qtable[state])) > 0:\n",
        "        action = np.argmax(Qtable[state])\n",
        "    else:\n",
        "        action = 2\n",
        "    # action = np.argmax(Qtable[state])\n",
        "    return action\n",
        "\n",
        "\n",
        "def epsilon_greedy_policy(Qtable, state, epsilon, env):\n",
        "  # Randomly generate a number between 0 and 1\n",
        "  random_num = np.random.uniform(size=1)\n",
        "  # if random_num > greater than epsilon --> exploitation\n",
        "  if random_num > epsilon:\n",
        "    # Take the action with the highest value given a state\n",
        "    # np.argmax can be useful here\n",
        "    action = greedy_policy(Qtable, state)\n",
        "  # else --> exploration\n",
        "  else:\n",
        "    # action = np.random.random_integers(4,size=1)[0]\n",
        "    action = env.action_space.sample()\n",
        "  \n",
        "  return action"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "wlC-EdLENTiN"
      },
      "outputs": [],
      "source": [
        "def train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable, learning_rate, gamma):\n",
        "  state_history = []\n",
        "#   np.random.seed(42)\n",
        "  for episode in range(n_training_episodes):\n",
        "    # Reduce epsilon (because we need less and less exploration)\n",
        "    epsilon = min_epsilon + (max_epsilon - min_epsilon)*np.exp(-decay_rate*episode)\n",
        "    # Reset the environment\n",
        "    state = env.reset()\n",
        "    step = 0\n",
        "    done = False\n",
        "\n",
        "    # repeat\n",
        "    for step in range(max_steps):\n",
        "      # Choose the action At using epsilon greedy policy\n",
        "      action = epsilon_greedy_policy(Qtable, state, epsilon, env)\n",
        "\n",
        "      # Take action At and observe Rt+1 and St+1\n",
        "      # Take the action (a) and observe the outcome state(s') and reward (r)\n",
        "      new_state, reward, done, info = env.step(action)\n",
        "\n",
        "      # Update Q(s,a):= Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]\n",
        "      Qtable[state][action] = Qtable[state][action] + learning_rate * (reward + gamma * ( np.max(Qtable[new_state])  ) -  Qtable[state][action] )\n",
        "\n",
        "      # If done, finish the episode\n",
        "      if done:\n",
        "        break\n",
        "      \n",
        "      # Our next state is the new state\n",
        "      state = new_state\n",
        "\n",
        "      state_history.append(state)  \n",
        "\n",
        "  return Qtable, state_history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "from enum import Enum\n",
        "class Actions(Enum):\n",
        "    Sell = 0\n",
        "    Buy = 1\n",
        "    Do_nothing = 2\n",
        "\n",
        "class CustTradingEnv(gym.Env):\n",
        "\n",
        "    def __init__(self, df, max_steps=0, random_start=True):\n",
        "        self.seed()\n",
        "        self.df = df\n",
        "        self.prices, self.signal_features = self._process_data()\n",
        "\n",
        "        # spaces\n",
        "        self.action_space = spaces.Discrete(3)\n",
        "        self.observation_space = spaces.Box(low=0, high=1999, shape=(1,) , dtype=np.float64)\n",
        "\n",
        "        # episode\n",
        "        self._start_tick = 0\n",
        "        self._end_tick = 0\n",
        "        self._done = None\n",
        "        self._current_tick = None\n",
        "        self._last_trade_tick = None\n",
        "        self._position = None\n",
        "        self._position_history = None\n",
        "        self._total_reward = None\n",
        "        self._total_profit = None\n",
        "        self._first_rendering = None\n",
        "        self.history = None\n",
        "        self._max_steps = max_steps\n",
        "        self._start_episode_tick = None\n",
        "        self._trade_history = None\n",
        "        self._random_start = random_start\n",
        "\n",
        "    def reset(self):\n",
        "        self._done = False\n",
        "        if self._random_start:\n",
        "            self._start_episode_tick = np.random.randint(1,high=len(self.df)- self._max_steps )\n",
        "            self._end_tick = self._start_episode_tick + self._max_steps\n",
        "        else:\n",
        "            self._start_episode_tick = 1\n",
        "            self._end_tick = len(self.df)-1\n",
        "        # self._start_episode_tick = np.random.randint(1,len(self.df)- self._max_steps )\n",
        "        # self._end_tick = self._start_episode_tick + self._max_steps\n",
        "        self._current_tick = self._start_episode_tick\n",
        "        self._last_trade_tick = self._current_tick - 1\n",
        "        self._position = 0\n",
        "        self._position_history = []\n",
        "        # self._position_history = (self.window_size * [None]) + [self._position]\n",
        "        self._total_reward = 0.\n",
        "        self._total_profit = 0.\n",
        "        self._trade_history = []\n",
        "        self.history = {}\n",
        "        return self._get_observation()\n",
        "\n",
        "\n",
        "    def step(self, action):\n",
        "        self._done = False\n",
        "        self._current_tick += 1\n",
        "\n",
        "        if self._current_tick == self._end_tick:\n",
        "            self._done = True\n",
        "\n",
        "        step_reward = self._calculate_reward(action)\n",
        "        self._total_reward += step_reward\n",
        "\n",
        "        observation = self._get_observation()\n",
        "        info = dict(\n",
        "            total_reward = self._total_reward,\n",
        "            total_profit = self._total_profit,\n",
        "            position = self._position,\n",
        "            action = action\n",
        "        )\n",
        "        self._update_history(info)\n",
        "\n",
        "        return observation, step_reward, self._done, info\n",
        "\n",
        "    def seed(self, seed=None):\n",
        "        self.np_random, seed = seeding.np_random(seed)\n",
        "        return [seed]\n",
        "        \n",
        "    def _get_observation(self):\n",
        "        return self.signal_features[self._current_tick]\n",
        "\n",
        "    def _update_history(self, info):\n",
        "        if not self.history:\n",
        "            self.history = {key: [] for key in info.keys()}\n",
        "\n",
        "        for key, value in info.items():\n",
        "            self.history[key].append(value)\n",
        "\n",
        "\n",
        "    def render(self, mode='human'):\n",
        "        window_ticks = np.arange(len(self._position_history))\n",
        "        prices = self.prices[self._start_episode_tick:self._end_tick+1]\n",
        "        plt.plot(prices)\n",
        "\n",
        "        open_buy = []\n",
        "        close_buy = []\n",
        "        open_sell = []\n",
        "        close_sell = []\n",
        "        do_nothing = []\n",
        "\n",
        "        for i, tick in enumerate(window_ticks):\n",
        "            if self._position_history[i] == 1:\n",
        "                open_buy.append(tick)\n",
        "            elif self._position_history[i] == 2 :\n",
        "                close_buy.append(tick)\n",
        "            elif self._position_history[i] == 3 :\n",
        "                open_sell.append(tick)\n",
        "            elif self._position_history[i] == 4 :\n",
        "                close_sell.append(tick)\n",
        "            elif self._position_history[i] == 0 :\n",
        "                do_nothing.append(tick)\n",
        "\n",
        "        plt.plot(open_buy, prices[open_buy], 'go', marker=\"^\")\n",
        "        plt.plot(close_buy, prices[close_buy], 'go', marker=\"v\")\n",
        "        plt.plot(open_sell, prices[open_sell], 'ro', marker=\"v\")\n",
        "        plt.plot(close_sell, prices[close_sell], 'ro', marker=\"^\")\n",
        "    \n",
        "        plt.plot(do_nothing, prices[do_nothing], 'yo')\n",
        "\n",
        "        plt.suptitle(\n",
        "            \"Total Reward: %.6f\" % self._total_reward + ' ~ ' +\n",
        "            \"Total Profit: %.6f\" % self._total_profit\n",
        "        )\n",
        "\n",
        "    def _calculate_reward(self, action):\n",
        "        step_reward = 0\n",
        "\n",
        "        current_price = self.prices[self._current_tick]\n",
        "        last_price = self.prices[self._current_tick - 1]\n",
        "        price_diff = current_price - last_price\n",
        "\n",
        "        penalty = -1 * last_price * 0.01\n",
        "        # OPEN BUY - 1\n",
        "        if action == Actions.Buy.value and self._position == 0:\n",
        "            self._position = 1\n",
        "            step_reward += price_diff\n",
        "            self._last_trade_tick = self._current_tick - 1\n",
        "            self._position_history.append(1)\n",
        "\n",
        "        elif action == Actions.Buy.value and self._position > 0:\n",
        "            step_reward += penalty\n",
        "            self._position_history.append(-1)\n",
        "        # CLOSE SELL - 4\n",
        "        elif action == Actions.Buy.value and self._position < 0:\n",
        "            self._position = 0\n",
        "            step_reward += -1 * (self.prices[self._current_tick -1] - self.prices[self._last_trade_tick]) \n",
        "            self._total_profit += step_reward\n",
        "            self._position_history.append(4)\n",
        "            self._trade_history.append(step_reward)\n",
        "\n",
        "        # OPEN SELL - 3\n",
        "        elif action == Actions.Sell.value and self._position == 0:\n",
        "            self._position = -1\n",
        "            step_reward += -1 * price_diff\n",
        "            self._last_trade_tick = self._current_tick - 1\n",
        "            self._position_history.append(3)\n",
        "        # CLOSE BUY - 2\n",
        "        elif action == Actions.Sell.value and self._position > 0:\n",
        "            self._position = 0\n",
        "            step_reward += self.prices[self._current_tick -1] - self.prices[self._last_trade_tick] \n",
        "            self._total_profit += step_reward\n",
        "            self._position_history.append(2)\n",
        "            self._trade_history.append(step_reward)\n",
        "        elif action == Actions.Sell.value and self._position < 0:\n",
        "            step_reward += penalty\n",
        "            self._position_history.append(-1)\n",
        "\n",
        "        # DO NOTHING - 0\n",
        "        elif action == Actions.Do_nothing.value and self._position > 0:\n",
        "            step_reward += price_diff\n",
        "            self._position_history.append(0)\n",
        "        elif action == Actions.Do_nothing.value and self._position < 0:\n",
        "            step_reward += -1 * price_diff\n",
        "            self._position_history.append(0)\n",
        "        elif action == Actions.Do_nothing.value and self._position == 0:\n",
        "            step_reward += -1 * abs(price_diff)\n",
        "            self._position_history.append(0)\n",
        "\n",
        "        return step_reward\n",
        "\n",
        "    def _do_bin(self,df):\n",
        "        df = pd.cut(df,bins=[0,10,20,30,40,50,60,70,80,90,100],labels=False, include_lowest=True)\n",
        "        return df\n",
        "    # Our state will be encode with 4 features MFI and Stochastic(only D line), ADX and DI+DI-\n",
        "    # the values of each feature will be binned in 10 bins, ex:\n",
        "    # MFI goes from 0-100, if we get 25 will put on the second bin \n",
        "    # DI+DI-  if DI+ is over DI- set (1 otherwise 0) \n",
        "    # \n",
        "    # that will give a state space of 10(MFI) * 10(STOCH) * 10(ADX) * 2(DI) = 2000 states\n",
        "    # encoded as bins of  DI MFI STOCH ADX = 1 45.2  25.4  90.1 , binned = 1 4 2 9 state = 1429   \n",
        "    def _process_data(self):\n",
        "        timeperiod = 14\n",
        "        self.df = self.df.copy()\n",
        "        \n",
        "        self.df['mfi_r'] = ta.MFI(self.df['High'], self.df['Low'], self.df['Close'],self.df['Volume'], timeperiod=timeperiod)\n",
        "        _, self.df['stock_d_r'] = ta.STOCH(self.df['High'], self.df['Low'], self.df['Close'], fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)\n",
        "        self.df['adx_r'] = ta.ADX(self.df['High'], self.df['Low'], self.df['Close'], timeperiod=timeperiod)\n",
        "        self.df['p_di'] = ta.PLUS_DI(self.df['High'], self.df['Low'], self.df['Close'], timeperiod=timeperiod)\n",
        "        self.df['m_di'] = ta.MINUS_DI(self.df['High'], self.df['Low'], self.df['Close'], timeperiod=timeperiod)\n",
        "        self.df['di'] = np.where( self.df['p_di'] > self.df['m_di'], 1, 0)\n",
        "\n",
        "        self.df = self.df.dropna()\n",
        "        self.df['mfi'] = self._do_bin(self.df['mfi_r'])\n",
        "        self.df['stock_d'] = self._do_bin(self.df['stock_d_r'])\n",
        "        self.df['adx'] = self._do_bin(self.df['adx_r'])\n",
        "        self.df['state'] = self.df['di']*1000+ self.df['mfi']*100 + self.df['stock_d']*10 + self.df['adx']\n",
        "\n",
        "        prices = self.df.loc[:, 'Close'].to_numpy()\n",
        "        # print(self.df.head(30))\n",
        "\n",
        "        signal_features = self.df.loc[:, 'state'].to_numpy()\n",
        "\n",
        "        return prices, signal_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Training parameters\n",
        "n_training_episodes = 20000  # Total training episodes\n",
        "learning_rate = 0.2          # Learning rate\n",
        "\n",
        "# Environment parameters\n",
        "max_steps = 20   # Max steps per episode\n",
        "gamma = 0.95                 # Discounting rate\n",
        "\n",
        "# Exploration parameters\n",
        "max_epsilon = 1.0             # Exploration probability at start\n",
        "# max_epsilon = 1.0             # Exploration probability at start\n",
        "min_epsilon = 0.05            # Minimum exploration probability \n",
        "# min_epsilon = 0.05            # Minimum exploration probability \n",
        "decay_rate = 0.0005            # Exponential decay rate for exploration prob"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REhmfLkYNTiN",
        "outputId": "cf676f6d-83df-43f5-89fe-3258e0041d9d"
      },
      "outputs": [],
      "source": [
        "# create env\n",
        "env = CustTradingEnv(df=eth_train, max_steps=max_steps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "# create q-table\n",
        "\n",
        "action_space = env.action_space.n # buy sell do_nothing\n",
        "state_space = 2000\n",
        "\n",
        "Qtable_trading = initialize_q_table(state_space, action_space)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "99"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# train with ETH\n",
        "Qtable_trading, state_history = train(n_training_episodes, min_epsilon, max_epsilon, \n",
        "                        decay_rate, env, max_steps, Qtable_trading, learning_rate, gamma )\n",
        "len(np.where( Qtable_trading > 0 )[0])\n",
        "\n",
        "# #train with BTC\n",
        "# env = CustTradingEnv(df=btc_train, max_steps=max_steps)\n",
        "# Qtable_trading, state_history = train(n_training_episodes, min_epsilon, max_epsilon, \n",
        "#                         decay_rate, env, max_steps, Qtable_trading, learning_rate, gamma )\n",
        "# len(np.where( Qtable_trading > 0 )[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "def evaluate_agent(env, max_steps, n_eval_episodes, Q, random=False):\n",
        "  \"\"\"\n",
        "  Evaluate the agent for ``n_eval_episodes`` episodes and returns average reward and std of reward.\n",
        "  :param env: The evaluation environment\n",
        "  :param n_eval_episodes: Number of episode to evaluate the agent\n",
        "  :param Q: The Q-table\n",
        "  :param seed: The evaluation seed array (for taxi-v3)\n",
        "  \"\"\"\n",
        "  episode_positive_perc_trades = []\n",
        "  episode_rewards = []\n",
        "  episode_profits = []\n",
        "  for episode in tqdm(range(n_eval_episodes), disable=random):\n",
        "    state = env.reset()\n",
        "    step = 0\n",
        "    done = False\n",
        "    total_rewards_ep = 0\n",
        "    total_profit_ep = 0\n",
        "    \n",
        "    for step in range(max_steps):\n",
        "      # Take the action (index) that have the maximum expected future reward given that state\n",
        "      if random:\n",
        "        action = env.action_space.sample()\n",
        "      else:\n",
        "        action = greedy_policy(Q, state)\n",
        "\n",
        "      new_state, reward, done, info = env.step(action)\n",
        "      total_rewards_ep += reward\n",
        "        \n",
        "      if done:\n",
        "        break\n",
        "      state = new_state\n",
        "\n",
        "    if len(env._trade_history) > 0:\n",
        "        episode_positive_perc_trades.append(np.count_nonzero(np.array(env._trade_history) > 0)/len(env._trade_history))\n",
        "    episode_rewards.append(total_rewards_ep)\n",
        "    episode_profits.append(env.history['total_profit'][-1])\n",
        "    # print(env.history)\n",
        "    # env.render()\n",
        "    # assert 0\n",
        "\n",
        "  mean_reward = np.mean(episode_rewards)\n",
        "  std_reward = np.std(episode_rewards)\n",
        "  mean_profit = np.mean(episode_profits)\n",
        "  std_profit = np.std(episode_profits)\n",
        "  positive_perc_trades = np.mean(episode_positive_perc_trades)\n",
        "\n",
        "  return mean_reward, std_reward, mean_profit, std_profit, positive_perc_trades"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "831f1fb725f640c39c55dc9895d015bd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(7.366191151612702,\n",
              " 3.8748133853943463,\n",
              " 5.053396330156885,\n",
              " 1.8437773613293116,\n",
              " 0.9042809523809524)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "max_steps = 20 \n",
        "env_test = CustTradingEnv(df=eth_test, max_steps=max_steps, random_start=True)\n",
        "n_eval_episodes = 1000\n",
        "\n",
        "evaluate_agent(env_test, max_steps, n_eval_episodes, Qtable_trading)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "plt.cla()\n",
        "env_test.render()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5ed3da62f4a84f59b4785bb5a59e9bd7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(68.78781733724034, 0.0, 47.24655221993224, 0.0, 0.9019607843137255)"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# trade sequential\n",
        "max_steps = len(eth_test)\n",
        "env_test = CustTradingEnv(df=eth_test, max_steps=max_steps, random_start=False)\n",
        "n_eval_episodes = 1\n",
        "\n",
        "evaluate_agent(env_test, max_steps, n_eval_episodes, Qtable_trading)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "plt.cla()\n",
        "env_test.render()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "env_test._trade_history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(-2.0168048208113647,\n",
              " 4.899233122278165,\n",
              " 0.06689442639249506,\n",
              " 2.7236034572875427,\n",
              " 0.5067877401210734)"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Test for random n_eval_episodes\n",
        "max_steps = 20 \n",
        "env_test_rand = CustTradingEnv(df=eth_test, max_steps=max_steps, random_start=True)\n",
        "n_eval_episodes = 1000\n",
        "\n",
        "evaluate_agent(env_test_rand, max_steps, n_eval_episodes, Qtable_trading, random=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean profit 0.06453745988505065\n"
          ]
        }
      ],
      "source": [
        "# trade sequentially with random actions \n",
        "max_steps = len(eth_test)\n",
        "env_test = CustTradingEnv(df=eth_test, max_steps=max_steps, random_start=False)\n",
        "n_eval_episodes = 1\n",
        "\n",
        "all_profit=[]\n",
        "for i in range(1000):\n",
        "    _,_,profit,_,_=evaluate_agent(env_test, max_steps, n_eval_episodes, Qtable_trading, random=True)\n",
        "    all_profit.append(profit)\n",
        "print(f\"Mean profit {np.mean(all_profit)}\")"
      ]
    },
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        "## This is the result\n",
        "\n",
        "| Model      | 1000 trades 20 steps | Sequential trading | 1000 trades 20 steps random actions | Sequential random|\n",
        "|------------|----------------------|--------------------|-------------------------------------|------------------|\n",
        "|Q-learning  | 113.14               | 563.67             | -18.10                              | 39.30            |\n",
        "\n"
      ]
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        "    count=0\n",
        "    for i in env.signal_features:\n",
        "        if abs(np.max(Qtable[i])) > 0:\n",
        "            count+=1\n",
        "        # else:\n",
        "        #     print(i)\n",
        "        #     assert 0\n",
        "    \n",
        "    print(len(env.signal_features), count, count / len(env.signal_features))\n",
        "\n",
        "count_equal(env_test, Qtable_trading)"
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