{
  "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, this version fix render method\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",
        "    # adjust here the function to generate the data\n",
        "    # df[\"Close\"]=df[\"v1\"].map(lambda x: np.sin(x)+10 ) # Simple sine wave all positive values \n",
        "    df[\"Close\"]=df[\"v1\"].map(lambda x: np.sin(x)+10 + np.sin(x/2) )  # a composition of 2 sine waves \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": [
            "3080\n",
            "1931\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 = btc[-3015:-200]\n",
        "btc_test = btc[-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=\"2015-02-05\", add_noise=None)\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:]\n",
        "\n",
        "# eth_train = btc_train\n",
        "# eth_test = btc_test\n"
      ]
    },
    {
      "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": [],
      "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": 8,
      "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": 9,
      "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._trade_tick_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 = [-1] * (len(self.prices)) \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._trade_tick_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.prices))\n",
        "        prices = self.prices\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",
        "        penalty = []\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",
        "            elif self._position_history[i] == 5 :\n",
        "                penalty.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], 'oc')\n",
        "        plt.plot(penalty, prices[penalty], 'yo')\n",
        "\n",
        "        plt.suptitle(\n",
        "            \"Total Reward: %.6f\" % self._total_reward + ' ~ ' +\n",
        "            \"Total Profit: %.6f\" % self._total_profit\n",
        "        )\n",
        "\n",
        "    # the action is taken when the market is off after the session ends and we receive the close price of the day\n",
        "    # this will be the current price \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[self._current_tick-1]=1\n",
        "\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[self._current_tick-1]=2\n",
        "            self._trade_history.append(step_reward)\n",
        "            self._trade_tick_history.append((self._last_trade_tick, self._current_tick-1, self.prices[self._last_trade_tick], self.prices[self._current_tick-1], step_reward))\n",
        "\n",
        "        elif action == Actions.Buy.value and self._position > 0:\n",
        "            step_reward += penalty\n",
        "            self._position_history[self._current_tick-1]=5\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[self._current_tick-1]=4\n",
        "            self._trade_history.append(step_reward)\n",
        "            self._trade_tick_history.append((self._last_trade_tick, self._current_tick-1, self.prices[self._last_trade_tick], self.prices[self._current_tick-1], 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[self._current_tick-1]=3\n",
        "\n",
        "        elif action == Actions.Sell.value and self._position < 0:\n",
        "            step_reward += penalty\n",
        "            self._position_history[self._current_tick-1]=5\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[self._current_tick-1]=0\n",
        "        elif action == Actions.Do_nothing.value and self._position < 0:\n",
        "            step_reward += -1 * price_diff\n",
        "            self._position_history[self._current_tick-1]=0\n",
        "        elif action == Actions.Do_nothing.value and self._position == 0:\n",
        "            step_reward += -1 * abs(price_diff)\n",
        "            self._position_history[self._current_tick-1]=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['adx_r'] = ta.ADX(self.df['High'], self.df['Low'], self.df['Close'], timeperiod=timeperiod)\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['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",
        "        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",
        "\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",
        "        signal_features = self.df.loc[:, 'state'].to_numpy()\n",
        "        # signal_features = self.df.loc[:, ['di', 'mfi', 'stock_d','adx']].to_numpy()\n",
        "\n",
        "        return prices, signal_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "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": 11,
      "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, random_start=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "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": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "97"
            ]
          },
          "execution_count": 13,
          "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": 14,
      "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",
        "  \"\"\"\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": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b6c2093f5c824b4faeff7bad9a401fb8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(24.435464104143733,\n",
              " 5.499791149875003,\n",
              " 18.5904204053222,\n",
              " 2.3777410865463082,\n",
              " 0.9845059065934068)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "max_steps = 60 \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": 16,
      "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": 20,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "602403a96bba4a0c91db842c27e5e1e1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(75.04391344152532, 0.0, 56.51131547865238, 0.0, 0.9761904761904762)"
            ]
          },
          "execution_count": 20,
          "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": 21,
      "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_tick_history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.8.13 ('rl2')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
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