{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "nwaAZRu1NTiI" }, "source": [ "# Q-learning \n", "\n", "#### This version implements q-learning using a custom enviroment \n" ] }, { "cell_type": "code", "execution_count": null, "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": 130, "metadata": { "id": "LNXxxKojNTiL" }, "outputs": [], "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": 151, "metadata": { "id": "dmAuEhZZNTiL" }, "outputs": [], "source": [ "# Get data\n", "eth_usd = yf.Ticker(\"ETH-USD\")\n", "eth = eth_usd.history(period=\"max\")\n", "eth_train = eth[-900:-200]\n", "eth_test = eth[-200:]" ] }, { "cell_type": "code", "execution_count": 153, "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": 10, "metadata": {}, "outputs": [], "source": [ "# Policy\n", "\n", "def greedy_policy(Qtable, state):\n", " # Exploitation: take the action with the highest state, action value\n", " action = np.argmax(Qtable[state])\n", " \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": 11, "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", " 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", " return Qtable" ] }, { "cell_type": "code", "execution_count": 327, "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", " metadata = {'render.modes': ['human']}\n", "\n", " def __init__(self, df, max_steps=0):\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", "\n", " def reset(self):\n", " self._done = False\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", " # 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 += 0\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 += 0\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": 330, "metadata": {}, "outputs": [], "source": [ "# Training parameters\n", "n_training_episodes = 10000 # Total training episodes\n", "learning_rate = 0.5 # 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", "min_epsilon = 0.05 # Minimum exploration probability \n", "decay_rate = 0.0005 # Exponential decay rate for exploration prob" ] }, { "cell_type": "code", "execution_count": 331, "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": 332, "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": 333, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "690" ] }, "execution_count": 333, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Qtable_trading = 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": 334, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 417 }, "id": "FIQ0OqtsO3jo", "outputId": "f98374ad-c7de-4dc4-80b1-25f018ad96eb" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,6))\n", "plt.cla()\n", "env.render()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 335, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[152.30224609375,\n", " 209.1220703125,\n", " 305.837158203125,\n", " 11.605224609375,\n", " 92.665771484375]" ] }, "execution_count": 335, "metadata": {}, "output_type": "execute_result" } ], "source": [ "env._trade_history" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [], "source": [ "def evaluate_agent(env, max_steps, n_eval_episodes, Q):\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_rewards = []\n", " episode_profits = []\n", " for episode in tqdm(range(n_eval_episodes)):\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", " action = greedy_policy(Q, state)\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", " 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", "\n", " return mean_reward, std_reward, mean_profit, std_profit" ] }, { "cell_type": "code", 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