{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "nwaAZRu1NTiI" }, "source": [ "# DQN v2\n", "\n", "#### This version implements DQN using a custom enviroment " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# !pip install talib-binary\n", "# !pip install yfinance" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "LNXxxKojNTiL" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-12-27 23:42:40.080325: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "\n" ] } ], "source": [ "import tensorflow as tf\n", "from tensorflow.keras import layers\n", "from tensorflow.keras.utils import to_categorical\n", "import gym\n", "from gym import spaces\n", "from gym.utils import seeding\n", "from gym import wrappers\n", "\n", "from tqdm.notebook import tqdm\n", "from collections import deque\n", "import numpy as np\n", "import random\n", "from matplotlib import pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler\n", "import joblib\n", "import talib as ta\n", "import yfinance as yf\n", "import pandas as pd\n", "\n", "import io\n", "import base64\n", "from IPython.display import HTML, Video\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "class DQN:\n", " def __init__(self, env=None, replay_buffer_size=1000):\n", " self.replay_buffer = deque(maxlen=replay_buffer_size)\n", "\n", " self.action_size = env.action_space.n\n", "\n", " # Hyperparameters\n", " self.gamma = 0.95 # Discount rate\n", " self.epsilon = 1.0 # Exploration rate\n", " self.epsilon_min = 0.001 # Minimal exploration rate (epsilon-greedy)\n", " self.epsilon_decay = 0.95 # Decay rate for epsilon\n", " self.update_rate = 5 # Number of steps until updating the target network\n", " self.batch_size = 200\n", " self.learning_rate = 1e-4\n", " \n", " # Construct DQN models\n", " self.model = self._build_model()\n", " self.target_model = self._build_model()\n", " self.target_model.set_weights(self.model.get_weights())\n", " self.model.summary()\n", " self.env = env\n", "\n", " self.history = []\n", " self.scaler = None\n", "\n", " def _build_model(self):\n", " model = tf.keras.Sequential()\n", " \n", " model.add(tf.keras.Input(shape=(4,)))\n", " model.add(layers.Dense(256, activation = 'relu'))\n", " model.add(layers.Dense(128, activation = 'relu'))\n", " model.add(layers.Dense(64, activation = 'relu'))\n", " model.add(layers.Dense(self.action_size, activation = 'linear'))\n", " \n", " optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)\n", " model.compile(loss='mse', optimizer=optimizer, metrics = ['mse'])\n", " return model\n", "\n", "\n", " #\n", " # Trains the model using randomly selected experiences in the replay memory\n", " #\n", " def _train(self):\n", " X, y = [], []\n", " # state, action, reward, next_state, done \n", " # create the targets \n", " if self.batch_size > len(self.replay_buffer):\n", " return\n", " minibatch = random.sample(self.replay_buffer, self.batch_size)\n", " mb_arr = np.array(minibatch, dtype=object)\n", "\n", " next_state_arr = np.stack(mb_arr[:,3])\n", " future_qvalues = self.target_model.predict(next_state_arr, verbose=0)\n", "\n", " state_arr = np.stack(mb_arr[:,0])\n", " qvalues = self.model.predict(state_arr, verbose=0)\n", "\n", " for index, (state, action, reward, next_state, done) in enumerate(minibatch):\n", " if done == True:\n", " q_target = reward\n", " else:\n", " q_target = reward + self.gamma * np.max(future_qvalues[index])\n", "\n", " q_curr = qvalues[index]\n", " q_curr[action] = q_target \n", " X.append(state)\n", " y.append(q_curr)\n", "\n", " # Perform gradient step\n", " X, y = np.array(X), np.array(y)\n", " history = self.model.fit(X, y, batch_size = self.batch_size, shuffle = False, verbose=0)\n", " self.history.append(history.history['loss'])\n", " # history = self.model.fit(X, y, epochs=1, verbose=0)\n", " # print(f\"Loss: {history.history['loss']} \")\n", "\n", "\n", " def learn(self, total_steps=None):\n", " current_episode = 0\n", " total_reward = 0\n", " rewards = [0]\n", " current_step = 0\n", " while current_step < total_steps:\n", " current_episode += 1\n", " state = self.env.reset()\n", " total_reward = 0\n", " done = False\n", " while done != True:\n", " current_step +=1\n", " # e-greedy\n", " if np.random.random() > (1 - self.epsilon):\n", " action = np.random.randint(self.action_size)\n", " # print(\"Rand action\",action)\n", " else:\n", " model_predict = self.model.predict(np.array([state]), verbose=0)\n", " action = np.argmax(model_predict)\n", " # print(\"model action\",action)\n", "\n", " # step\n", " next_state, reward, done, info = self.env.step(action)\n", " total_reward += reward\n", "\n", " # add to buffer\n", " self.replay_buffer.append((state, action, reward, next_state, done))\n", "\n", " if current_step>10 and current_step % self.update_rate == 0:\n", " hist=None\n", " if len(self.history) > 0:\n", " hist = self.history[-1]\n", " print(f\"epsilon:{self.epsilon} step:{current_step} episode:{current_episode} last_score {rewards[-1]} Profit {info['total_profit']} Loss {hist}\")\n", " self._train()\n", " # update target\n", " self.target_model.set_weights(self.model.get_weights())\n", " \n", " state = next_state\n", "\n", " # update epsilon \n", " if current_step % 20 == 0:\n", " if self.epsilon > self.epsilon_min:\n", " self.epsilon *= self.epsilon_decay\n", "\n", " rewards.append(total_reward)\n", "\n", " #\n", " # Loads a saved model\n", " #\n", " def load(self, name):\n", " self.model = tf.keras.models.load_model(name)\n", " # self.scaler = joblib.load(name+\".scaler\") \n", "\n", " #\n", " # Saves parameters of a trained model\n", " #\n", " def save(self, name):\n", " self.model.save(name)\n", " # joblib.dump(self.scaler, name+\".scaler\") \n", "\n", " def play(self, state):\n", " # state = self._get_scaled_state(state)\n", " return np.argmax(self.model.predict(np.array([state]), verbose=0)[0])" ] }, { "cell_type": "code", "execution_count": 47, "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, seed=8, random_start=True, scaler=None):\n", " self.seed(seed=seed)\n", " self.df = df\n", " if scaler is None:\n", " self.scaler = MinMaxScaler()\n", " else:\n", " self.scaler = scaler\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=1, 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", "\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", "\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", " step_reward = step_reward/10\n", " return step_reward\n", "\n", " def get_scaler(self):\n", " return self.scaler\n", "\n", " def set_scaler(self, scaler):\n", " self.scaler = scaler\n", " \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['stoch_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['di_s']=self.df['di']\n", " # self.df['mfi_s']=self.df['mfi_r']\n", " # self.df['stoch_d_s']=self.df['stoch_d_r']\n", " # self.df['adx_s']=self.df['adx_r']\n", "\n", " self.df[['di_s','mfi_s','stoch_d_s','adx_s']] = self.scaler.fit_transform(self.df[['di','mfi_r','stoch_d_r','adx_r']])\n", "\n", " def f1(row):\n", " row['state'] = [row['di_s'], row['mfi_s'], row['stoch_d_s'], row['adx_s']]\n", " return row\n", "\n", " self.df = self.df.apply(f1, axis=1 )\n", "\n", " prices = self.df.loc[:, 'Close'].to_numpy()\n", " # print(self.df.head(30))\n", "\n", " signal_features = np.stack(self.df.loc[:, 'state'].to_numpy())\n", "\n", " return prices, signal_features" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3025\n", "1876\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": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_26\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_104 (Dense) (None, 256) 1280 \n", " \n", " dense_105 (Dense) (None, 128) 32896 \n", " \n", " dense_106 (Dense) (None, 64) 8256 \n", " \n", " dense_107 (Dense) (None, 3) 195 \n", " \n", "=================================================================\n", "Total params: 42,627\n", "Trainable params: 42,627\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "epsilon:1.0 step:15 episode:1 last_score 0 Profit 26.009002685546875 Loss None\n", "epsilon:1.0 step:20 episode:1 last_score 0 Profit 26.009002685546875 Loss None\n", "epsilon:0.95 step:25 episode:2 last_score -1.7153277282714827 Profit -290.9765625 Loss None\n", "epsilon:0.95 step:30 episode:2 last_score -1.7153277282714827 Profit -301.153564453125 Loss None\n", "epsilon:0.95 step:35 episode:2 last_score -1.7153277282714827 Profit -301.153564453125 Loss None\n", "epsilon:0.95 step:40 episode:2 last_score -1.7153277282714827 Profit -1073.752685546875 Loss None\n", "epsilon:0.9025 step:45 episode:3 last_score -188.36765087890623 Profit 0.4293365478515625 Loss None\n", "epsilon:0.9025 step:50 episode:3 last_score -188.36765087890623 Profit 0.4293365478515625 Loss None\n", "epsilon:0.9025 step:55 episode:3 last_score -188.36765087890623 Profit -34.68583679199219 Loss None\n", "epsilon:0.9025 step:60 episode:3 last_score -188.36765087890623 Profit -34.68583679199219 Loss None\n", "epsilon:0.8573749999999999 step:65 episode:4 last_score -8.211917434692381 Profit 5.1357421875 Loss None\n", "epsilon:0.8573749999999999 step:70 episode:4 last_score -8.211917434692381 Profit 10.0550537109375 Loss None\n", "epsilon:0.8573749999999999 step:75 episode:4 last_score -8.211917434692381 Profit -53.334716796875 Loss None\n", "epsilon:0.8573749999999999 step:80 episode:4 last_score -8.211917434692381 Profit -55.37939453125 Loss None\n", "epsilon:0.8145062499999999 step:85 episode:5 last_score -19.090488922119143 Profit -7.532646179199219 Loss None\n", "epsilon:0.8145062499999999 step:90 episode:5 last_score -19.090488922119143 Profit -17.243186950683594 Loss None\n", "epsilon:0.8145062499999999 step:95 episode:5 last_score -19.090488922119143 Profit -35.77172088623047 Loss None\n", "epsilon:0.8145062499999999 step:100 episode:5 last_score -19.090488922119143 Profit -34.12372589111328 Loss None\n", "epsilon:0.7737809374999999 step:105 episode:6 last_score -5.959070495605472 Profit 0.150665283203125 Loss None\n", "epsilon:0.7737809374999999 step:110 episode:6 last_score -5.959070495605472 Profit -1.469390869140625 Loss None\n", "epsilon:0.7737809374999999 step:115 episode:6 last_score -5.959070495605472 Profit -1.469390869140625 Loss None\n", "epsilon:0.7737809374999999 step:120 episode:6 last_score -5.959070495605472 Profit 16.850311279296875 Loss None\n", "epsilon:0.7350918906249998 step:125 episode:7 last_score 2.230551803588867 Profit 28.648681640625 Loss None\n", "epsilon:0.7350918906249998 step:130 episode:7 last_score 2.230551803588867 Profit -397.08935546875 Loss None\n", "epsilon:0.7350918906249998 step:135 episode:7 last_score 2.230551803588867 Profit -306.55224609375 Loss None\n", "epsilon:0.7350918906249998 step:140 episode:7 last_score 2.230551803588867 Profit -306.55224609375 Loss None\n", "epsilon:0.6983372960937497 step:145 episode:8 last_score -31.88900976562499 Profit 0.0 Loss None\n", "epsilon:0.6983372960937497 step:150 episode:8 last_score -31.88900976562499 Profit -358.39013671875 Loss None\n", "epsilon:0.6983372960937497 step:155 episode:8 last_score -31.88900976562499 Profit -223.93017578125 Loss None\n", "epsilon:0.6983372960937497 step:160 episode:8 last_score -31.88900976562499 Profit -271.1102294921875 Loss None\n", "epsilon:0.6634204312890623 step:165 episode:9 last_score -86.17328179931641 Profit 5.2030029296875 Loss None\n", "epsilon:0.6634204312890623 step:170 episode:9 last_score -86.17328179931641 Profit 147.71002197265625 Loss None\n", "epsilon:0.6634204312890623 step:175 episode:9 last_score -86.17328179931641 Profit 147.71002197265625 Loss None\n", "epsilon:0.6634204312890623 step:180 episode:9 last_score -86.17328179931641 Profit 147.71002197265625 Loss None\n", "epsilon:0.6302494097246091 step:185 episode:10 last_score 1.60178088378906 Profit 11.498489379882812 Loss None\n", "epsilon:0.6302494097246091 step:190 episode:10 last_score 1.60178088378906 Profit 11.530960083007812 Loss None\n", "epsilon:0.6302494097246091 step:195 episode:10 last_score 1.60178088378906 Profit 2.2613677978515625 Loss None\n", "epsilon:0.6302494097246091 step:200 episode:10 last_score 1.60178088378906 Profit 1.99407958984375 Loss None\n", "epsilon:0.5987369392383786 step:205 episode:11 last_score 0.3134893035888678 Profit 0.0 Loss [23.698774337768555]\n", "epsilon:0.5987369392383786 step:210 episode:11 last_score 0.3134893035888678 Profit 103.34698486328125 Loss [23.838058471679688]\n", "epsilon:0.5987369392383786 step:215 episode:11 last_score 0.3134893035888678 Profit -56.1080322265625 Loss [22.928876876831055]\n", "epsilon:0.5987369392383786 step:220 episode:11 last_score 0.3134893035888678 Profit -56.1080322265625 Loss [23.9970703125]\n", "epsilon:0.5688000922764596 step:225 episode:12 last_score -37.52977984619142 Profit 0.0 Loss [24.2506160736084]\n", "epsilon:0.5688000922764596 step:230 episode:12 last_score -37.52977984619142 Profit 52.135986328125 Loss [23.867956161499023]\n", "epsilon:0.5688000922764596 step:235 episode:12 last_score -37.52977984619142 Profit 151.529052734375 Loss [22.896703720092773]\n", "epsilon:0.5688000922764596 step:240 episode:12 last_score -37.52977984619142 Profit 137.31103515625 Loss [13.36294174194336]\n", "epsilon:0.5403600876626365 step:245 episode:13 last_score 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[12.258398056030273]\n", "epsilon:0.0009833015279105794 step:3245 episode:163 last_score 0.8959925994873043 Profit 3.530303955078125 Loss [10.27142333984375]\n", "epsilon:0.0009833015279105794 step:3250 episode:163 last_score 0.8959925994873043 Profit 3.530303955078125 Loss [13.854328155517578]\n", "epsilon:0.0009833015279105794 step:3255 episode:163 last_score 0.8959925994873043 Profit 3.530303955078125 Loss [5.415991306304932]\n", "epsilon:0.0009833015279105794 step:3260 episode:163 last_score 0.8959925994873043 Profit 3.530303955078125 Loss [19.98992156982422]\n", "epsilon:0.0009833015279105794 step:3265 episode:164 last_score -3.623697647094727 Profit 5.322418212890625 Loss [26.575300216674805]\n", "epsilon:0.0009833015279105794 step:3270 episode:164 last_score -3.623697647094727 Profit 5.322418212890625 Loss [11.538870811462402]\n", "epsilon:0.0009833015279105794 step:3275 episode:164 last_score -3.623697647094727 Profit 5.322418212890625 Loss [11.882733345031738]\n", 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[16.799528121948242]\n", "epsilon:0.0009833015279105794 step:4080 episode:204 last_score -6.906621017456055 Profit 146.42044067382812 Loss [11.155864715576172]\n", "epsilon:0.0009833015279105794 step:4085 episode:205 last_score 14.188127044677735 Profit 23.873046875 Loss [11.704388618469238]\n", "epsilon:0.0009833015279105794 step:4090 episode:205 last_score 14.188127044677735 Profit 91.50103759765625 Loss [15.722434997558594]\n", "epsilon:0.0009833015279105794 step:4095 episode:205 last_score 14.188127044677735 Profit 91.50103759765625 Loss [41.121280670166016]\n", "epsilon:0.0009833015279105794 step:4100 episode:205 last_score 14.188127044677735 Profit 91.50103759765625 Loss [23.137924194335938]\n", "epsilon:0.0009833015279105794 step:4105 episode:206 last_score 8.024438842773439 Profit 0.0 Loss [25.988866806030273]\n", "epsilon:0.0009833015279105794 step:4110 episode:206 last_score 8.024438842773439 Profit 0.0 Loss [3.7431650161743164]\n", "epsilon:0.0009833015279105794 step:4115 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[41.44712448120117]\n", "epsilon:0.0009833015279105794 step:4345 episode:218 last_score -36.618736938476566 Profit 66.22705078125 Loss [9.114002227783203]\n", "epsilon:0.0009833015279105794 step:4350 episode:218 last_score -36.618736938476566 Profit 127.0660400390625 Loss [11.8075590133667]\n", "epsilon:0.0009833015279105794 step:4355 episode:218 last_score -36.618736938476566 Profit 196.8570556640625 Loss [6.677075386047363]\n", "epsilon:0.0009833015279105794 step:4360 episode:218 last_score -36.618736938476566 Profit 196.8570556640625 Loss [6.604525089263916]\n", "epsilon:0.0009833015279105794 step:4365 episode:219 last_score 19.011182434082034 Profit 0.0 Loss [9.081844329833984]\n", "epsilon:0.0009833015279105794 step:4370 episode:219 last_score 19.011182434082034 Profit 0.0 Loss [21.401966094970703]\n", "epsilon:0.0009833015279105794 step:4375 episode:219 last_score 19.011182434082034 Profit 0.0 Loss [11.105128288269043]\n", "epsilon:0.0009833015279105794 step:4380 episode:219 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[6.626563549041748]\n", "epsilon:0.0009833015279105794 step:5305 episode:266 last_score 0.30726652526855475 Profit 0.0 Loss [8.164430618286133]\n", "epsilon:0.0009833015279105794 step:5310 episode:266 last_score 0.30726652526855475 Profit 99.4801025390625 Loss [13.955129623413086]\n", "epsilon:0.0009833015279105794 step:5315 episode:266 last_score 0.30726652526855475 Profit 99.4801025390625 Loss [13.924943923950195]\n", "epsilon:0.0009833015279105794 step:5320 episode:266 last_score 0.30726652526855475 Profit 99.4801025390625 Loss [23.872882843017578]\n", "epsilon:0.0009833015279105794 step:5325 episode:267 last_score -7.011894531249999 Profit 0.0 Loss [10.662208557128906]\n", "epsilon:0.0009833015279105794 step:5330 episode:267 last_score -7.011894531249999 Profit 0.0 Loss [24.520160675048828]\n", "epsilon:0.0009833015279105794 step:5335 episode:267 last_score -7.011894531249999 Profit 0.0 Loss [3.191028118133545]\n", "epsilon:0.0009833015279105794 step:5340 episode:267 last_score 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Profit 0.0 Loss [15.649762153625488]\n", "epsilon:0.0009833015279105794 step:5765 episode:289 last_score -27.82074328613281 Profit 0.0 Loss [17.634899139404297]\n", "epsilon:0.0009833015279105794 step:5770 episode:289 last_score -27.82074328613281 Profit 0.0 Loss [18.1439208984375]\n", "epsilon:0.0009833015279105794 step:5775 episode:289 last_score -27.82074328613281 Profit -129.249755859375 Loss [14.573076248168945]\n", "epsilon:0.0009833015279105794 step:5780 episode:289 last_score -27.82074328613281 Profit 34.7724609375 Loss [19.992996215820312]\n", "epsilon:0.0009833015279105794 step:5785 episode:290 last_score -18.74013562011719 Profit 0.0 Loss [9.361413955688477]\n", "epsilon:0.0009833015279105794 step:5790 episode:290 last_score -18.74013562011719 Profit 0.0 Loss [6.239295482635498]\n", "epsilon:0.0009833015279105794 step:5795 episode:290 last_score -18.74013562011719 Profit 17.572067260742188 Loss [48.84450149536133]\n", "epsilon:0.0009833015279105794 step:5800 episode:290 last_score -18.74013562011719 Profit 20.267379760742188 Loss [11.32089614868164]\n", "epsilon:0.0009833015279105794 step:5805 episode:291 last_score -0.7181309814453125 Profit 0.0 Loss [19.042160034179688]\n", "epsilon:0.0009833015279105794 step:5810 episode:291 last_score -0.7181309814453125 Profit 0.0 Loss [5.770021915435791]\n", "epsilon:0.0009833015279105794 step:5815 episode:291 last_score -0.7181309814453125 Profit 0.0 Loss [4.061857223510742]\n", "epsilon:0.0009833015279105794 step:5820 episode:291 last_score -0.7181309814453125 Profit 0.0 Loss [5.530233860015869]\n", "epsilon:0.0009833015279105794 step:5825 episode:292 last_score -19.09116912841797 Profit 0.0 Loss [59.16610336303711]\n", "epsilon:0.0009833015279105794 step:5830 episode:292 last_score -19.09116912841797 Profit 0.0 Loss [25.3385009765625]\n", "epsilon:0.0009833015279105794 step:5835 episode:292 last_score -19.09116912841797 Profit 0.0 Loss [5.5523905754089355]\n", "epsilon:0.0009833015279105794 step:5840 episode:292 last_score -19.09116912841797 Profit 0.0 Loss [8.211041450500488]\n", "epsilon:0.0009833015279105794 step:5845 episode:293 last_score -5.110333374023439 Profit 0.0 Loss [5.987751483917236]\n", "epsilon:0.0009833015279105794 step:5850 episode:293 last_score -5.110333374023439 Profit 22.89239501953125 Loss [6.730612754821777]\n", "epsilon:0.0009833015279105794 step:5855 episode:293 last_score -5.110333374023439 Profit 22.89239501953125 Loss [18.408754348754883]\n", "epsilon:0.0009833015279105794 step:5860 episode:293 last_score -5.110333374023439 Profit 22.89239501953125 Loss [58.27291488647461]\n", "epsilon:0.0009833015279105794 step:5865 episode:294 last_score -3.9640438842773436 Profit 0.0 Loss [20.470008850097656]\n", "epsilon:0.0009833015279105794 step:5870 episode:294 last_score -3.9640438842773436 Profit 0.0 Loss [11.9842529296875]\n", "epsilon:0.0009833015279105794 step:5875 episode:294 last_score -3.9640438842773436 Profit 13.877090454101562 Loss [13.180678367614746]\n", "epsilon:0.0009833015279105794 step:5880 episode:294 last_score -3.9640438842773436 Profit 30.4078369140625 Loss [13.060815811157227]\n", "epsilon:0.0009833015279105794 step:5885 episode:295 last_score 1.9652150268554682 Profit 0.0 Loss [39.315765380859375]\n", "epsilon:0.0009833015279105794 step:5890 episode:295 last_score 1.9652150268554682 Profit -140.11572265625 Loss [43.81814956665039]\n", "epsilon:0.0009833015279105794 step:5895 episode:295 last_score 1.9652150268554682 Profit 201.02294921875 Loss [44.110679626464844]\n", "epsilon:0.0009833015279105794 step:5900 episode:295 last_score 1.9652150268554682 Profit 201.02294921875 Loss [7.890228271484375]\n", "epsilon:0.0009833015279105794 step:5905 episode:296 last_score -45.034164794921885 Profit 0.0 Loss [11.061156272888184]\n", "epsilon:0.0009833015279105794 step:5910 episode:296 last_score -45.034164794921885 Profit 0.0 Loss [6.266054630279541]\n", "epsilon:0.0009833015279105794 step:5915 episode:296 last_score -45.034164794921885 Profit 0.0 Loss [40.25519561767578]\n", "epsilon:0.0009833015279105794 step:5920 episode:296 last_score -45.034164794921885 Profit -15.861862182617188 Loss [9.749181747436523]\n", "epsilon:0.0009833015279105794 step:5925 episode:297 last_score -6.23864532470703 Profit 0.0 Loss [13.9420747756958]\n", "epsilon:0.0009833015279105794 step:5930 episode:297 last_score -6.23864532470703 Profit 0.0 Loss [41.780921936035156]\n", "epsilon:0.0009833015279105794 step:5935 episode:297 last_score -6.23864532470703 Profit 0.0 Loss [18.511001586914062]\n", "epsilon:0.0009833015279105794 step:5940 episode:297 last_score -6.23864532470703 Profit 0.0 Loss [7.832507133483887]\n", "epsilon:0.0009833015279105794 step:5945 episode:298 last_score -28.695864440917973 Profit 0.0 Loss [13.247542381286621]\n", "epsilon:0.0009833015279105794 step:5950 episode:298 last_score -28.695864440917973 Profit 0.0 Loss [11.55147933959961]\n", "epsilon:0.0009833015279105794 step:5955 episode:298 last_score -28.695864440917973 Profit 0.0 Loss [7.927614212036133]\n", "epsilon:0.0009833015279105794 step:5960 episode:298 last_score -28.695864440917973 Profit 0.0 Loss [7.368149280548096]\n", "epsilon:0.0009833015279105794 step:5965 episode:299 last_score -35.97231848144531 Profit 0.0 Loss [9.854528427124023]\n", "epsilon:0.0009833015279105794 step:5970 episode:299 last_score -35.97231848144531 Profit 0.0 Loss [8.438786506652832]\n", "epsilon:0.0009833015279105794 step:5975 episode:299 last_score -35.97231848144531 Profit 0.0 Loss [8.892999649047852]\n", "epsilon:0.0009833015279105794 step:5980 episode:299 last_score -35.97231848144531 Profit 41.72064208984375 Loss [6.6703081130981445]\n", "epsilon:0.0009833015279105794 step:5985 episode:300 last_score -2.386716522216797 Profit 0.0 Loss [17.761951446533203]\n", "epsilon:0.0009833015279105794 step:5990 episode:300 last_score -2.386716522216797 Profit 17.516036987304688 Loss [11.30389404296875]\n", "epsilon:0.0009833015279105794 step:5995 episode:300 last_score -2.386716522216797 Profit 34.00274658203125 Loss [7.3961334228515625]\n", "epsilon:0.0009833015279105794 step:6000 episode:300 last_score -2.386716522216797 Profit 34.00274658203125 Loss [11.636617660522461]\n" ] } ], "source": [ "# create env\n", "max_steps = 20 \n", "env = CustTradingEnv(df=eth_train, max_steps=max_steps)\n", "\n", "model = DQN(env=env, replay_buffer_size=10_000)\n", "model.learn(total_steps=6_000)\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "1161" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.plot(model.history)\n", "plt.show()\n", "len(model.history)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: ./alt/fin_rl_dqn_v1/assets\n" ] }, { "data": { "text/plain": [ "['./alt/fin_rl_dqn_v1.h5_scaler']" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.save(\"./alt/fin_rl_dqn_v1\")\n", "joblib.dump(env.get_scaler(),\"./alt/fin_rl_dqn_v1.h5_scaler\")\n" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "def evaluate_agent(env, max_steps, n_eval_episodes, model, 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 model: The DQN model\n", " \"\"\"\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 = model.play(state)\n", " # print(action)\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", " 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", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a76e107773bc48b0a7af5fdff9fbef6f", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "env_test.render()" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_18\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_72 (Dense) (None, 256) 1280 \n", " \n", " dense_73 (Dense) (None, 128) 32896 \n", " \n", " dense_74 (Dense) (None, 64) 8256 \n", " \n", " dense_75 (Dense) (None, 3) 195 \n", " \n", "=================================================================\n", "Total params: 42,627\n", "Trainable params: 42,627\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "# load model and scaler from file\n", "max_steps = 20 \n", "scaler_l = joblib.load(\"./alt/fin_rl_dqn_v1.h5_scaler\")\n", "env_l = CustTradingEnv(df=eth_test, max_steps=max_steps, scaler=scaler_l, random_start=False)\n", "\n", "model_l = DQN(env=env_l, replay_buffer_size=10_000)\n", "model_l.load(\"./alt/fin_rl_dqn_v1\")" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5af7f535b81047198bff5776f994ed8c", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,6))\n", "plt.cla()\n", "env_l.render()\n" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-156.66986416870117,\n", " 394.94783990529805,\n", " 4.957175903320312,\n", " 211.59187866264426)" ] }, "execution_count": 84, "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, scaler=env.get_scaler())\n", "n_eval_episodes = 1000\n", "\n", "evaluate_agent(env_test_rand, max_steps, n_eval_episodes, model, random=True)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean profit 3.7792178955078124\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, scaler=env.get_scaler())\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, model, random=True)\n", " all_profit.append(profit)\n", "print(f\"Mean profit {np.mean(all_profit)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results\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", "|DQN | 87.62 | 381.17 | 4.95 | 3.77 |\n", "\n", "\n", "#### Actions are: Buy/Sell/Hold 1 ETH \n", "1000 trades 20 steps - Made 1000 episodes, 20 trades each episode, result is the mean return of each episode \n", "\n", "Sequential trading (175 days)- Trade the test set sequentially from start to end day \n", "\n", "1000 trades 20 steps random actions - Made 1000 episodes, 20 trades each episode taking random actions \n", "\n", "Sequential random (175 days)- Trade the test set sequentially from start to end day with random actions " ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "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", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": 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