diff --git "a/fin_rl_dqn_v2.ipynb" "b/fin_rl_dqn_v2.ipynb" new file mode 100644--- /dev/null +++ "b/fin_rl_dqn_v2.ipynb" @@ -0,0 +1,2669 @@ +{ + "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 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Profit 24.14691162109375 Loss [10.101492881774902]\n", + "epsilon:0.0009833015279105794 step:2965 episode:149 last_score -6.489413269042968 Profit 0.0 Loss [11.229811668395996]\n", + "epsilon:0.0009833015279105794 step:2970 episode:149 last_score -6.489413269042968 Profit 0.0 Loss [9.282546997070312]\n", + "epsilon:0.0009833015279105794 step:2975 episode:149 last_score -6.489413269042968 Profit 0.0 Loss [14.473913192749023]\n", + "epsilon:0.0009833015279105794 step:2980 episode:149 last_score -6.489413269042968 Profit 0.0 Loss [7.75460958480835]\n", + "epsilon:0.0009833015279105794 step:2985 episode:150 last_score -2.9716099853515625 Profit 0.0 Loss [18.832921981811523]\n", + "epsilon:0.0009833015279105794 step:2990 episode:150 last_score -2.9716099853515625 Profit -149.3590087890625 Loss [6.609706878662109]\n", + "epsilon:0.0009833015279105794 step:2995 episode:150 last_score -2.9716099853515625 Profit -43.95098876953125 Loss [7.055361270904541]\n", + "epsilon:0.0009833015279105794 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Profit 8.010986328125 Loss [15.918774604797363]\n", + "epsilon:0.0009833015279105794 step:3115 episode:156 last_score 0.011040130615234067 Profit 8.010986328125 Loss [7.787980556488037]\n", + "epsilon:0.0009833015279105794 step:3120 episode:156 last_score 0.011040130615234067 Profit 8.010986328125 Loss [12.945913314819336]\n", + "epsilon:0.0009833015279105794 step:3125 episode:157 last_score -14.813270690917967 Profit 0.0 Loss [5.257415294647217]\n", + "epsilon:0.0009833015279105794 step:3130 episode:157 last_score -14.813270690917967 Profit 0.0 Loss [8.23066234588623]\n", + "epsilon:0.0009833015279105794 step:3135 episode:157 last_score -14.813270690917967 Profit 0.0 Loss [7.2421464920043945]\n", + "epsilon:0.0009833015279105794 step:3140 episode:157 last_score -14.813270690917967 Profit 0.4029998779296875 Loss [14.778495788574219]\n", + "epsilon:0.0009833015279105794 step:3145 episode:158 last_score -4.706281997680664 Profit 0.0 Loss [44.9527473449707]\n", + 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episode:165 last_score -6.977665069580079 Profit 2.7790069580078125 Loss [5.209012985229492]\n", + "epsilon:0.0009833015279105794 step:3300 episode:165 last_score -6.977665069580079 Profit 2.7790069580078125 Loss [7.885156154632568]\n", + "epsilon:0.0009833015279105794 step:3305 episode:166 last_score -2.4724400405883795 Profit 0.0 Loss [6.1995038986206055]\n", + "epsilon:0.0009833015279105794 step:3310 episode:166 last_score -2.4724400405883795 Profit 0.0 Loss [8.494258880615234]\n", + "epsilon:0.0009833015279105794 step:3315 episode:166 last_score -2.4724400405883795 Profit 0.0 Loss [5.8469557762146]\n", + "epsilon:0.0009833015279105794 step:3320 episode:166 last_score -2.4724400405883795 Profit 8.899642944335938 Loss [12.879145622253418]\n", + "epsilon:0.0009833015279105794 step:3325 episode:167 last_score -3.897376296997071 Profit 190.8399658203125 Loss [43.012413024902344]\n", + "epsilon:0.0009833015279105794 step:3330 episode:167 last_score -3.897376296997071 Profit 254.889892578125 Loss [24.882925033569336]\n", + "epsilon:0.0009833015279105794 step:3335 episode:167 last_score -3.897376296997071 Profit 254.889892578125 Loss [7.835921764373779]\n", + "epsilon:0.0009833015279105794 step:3340 episode:167 last_score -3.897376296997071 Profit 254.889892578125 Loss [8.439606666564941]\n", + "epsilon:0.0009833015279105794 step:3345 episode:168 last_score 16.546371154785152 Profit 0.0 Loss [14.56887435913086]\n", + "epsilon:0.0009833015279105794 step:3350 episode:168 last_score 16.546371154785152 Profit 0.0 Loss [45.85297393798828]\n", + "epsilon:0.0009833015279105794 step:3355 episode:168 last_score 16.546371154785152 Profit 0.0 Loss [11.032805442810059]\n", + "epsilon:0.0009833015279105794 step:3360 episode:168 last_score 16.546371154785152 Profit 0.0 Loss [10.168937683105469]\n", + "epsilon:0.0009833015279105794 step:3365 episode:169 last_score -54.14766589355468 Profit 9.135101318359375 Loss [41.06747055053711]\n", + "epsilon:0.0009833015279105794 step:3370 episode:169 last_score -54.14766589355468 Profit 9.135101318359375 Loss [5.891995906829834]\n", + "epsilon:0.0009833015279105794 step:3375 episode:169 last_score -54.14766589355468 Profit 19.632431030273438 Loss [14.31420612335205]\n", + "epsilon:0.0009833015279105794 step:3380 episode:169 last_score -54.14766589355468 Profit 19.632431030273438 Loss [14.659886360168457]\n", + "epsilon:0.0009833015279105794 step:3385 episode:170 last_score 0.5064123992919922 Profit 0.0 Loss [4.581434726715088]\n", + "epsilon:0.0009833015279105794 step:3390 episode:170 last_score 0.5064123992919922 Profit 0.0 Loss [10.4402437210083]\n", + "epsilon:0.0009833015279105794 step:3395 episode:170 last_score 0.5064123992919922 Profit 0.0 Loss [11.40463638305664]\n", + "epsilon:0.0009833015279105794 step:3400 episode:170 last_score 0.5064123992919922 Profit 0.0 Loss [8.8650541305542]\n", + "epsilon:0.0009833015279105794 step:3405 episode:171 last_score -1.1483961181640598 Profit 0.0 Loss 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episode:173 last_score -2.744105987548828 Profit 0.0 Loss [7.5683722496032715]\n", + "epsilon:0.0009833015279105794 step:3450 episode:173 last_score -2.744105987548828 Profit 0.0 Loss [19.290149688720703]\n", + "epsilon:0.0009833015279105794 step:3455 episode:173 last_score -2.744105987548828 Profit -4.714691162109375 Loss [9.879924774169922]\n", + "epsilon:0.0009833015279105794 step:3460 episode:173 last_score -2.744105987548828 Profit 12.045013427734375 Loss [15.247215270996094]\n", + "epsilon:0.0009833015279105794 step:3465 episode:174 last_score -3.480512298583985 Profit 0.0 Loss [7.3034186363220215]\n", + "epsilon:0.0009833015279105794 step:3470 episode:174 last_score -3.480512298583985 Profit 0.0 Loss [12.125536918640137]\n", + "epsilon:0.0009833015279105794 step:3475 episode:174 last_score -3.480512298583985 Profit 0.0 Loss [9.54719066619873]\n", + "epsilon:0.0009833015279105794 step:3480 episode:174 last_score -3.480512298583985 Profit 0.0 Loss [10.410090446472168]\n", + 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Profit 354.64697265625 Loss [10.399243354797363]\n", + "epsilon:0.0009833015279105794 step:4720 episode:236 last_score -47.61711206054689 Profit 354.64697265625 Loss [5.324916362762451]\n", + "epsilon:0.0009833015279105794 step:4725 episode:237 last_score -5.929374755859374 Profit 0.0 Loss [10.820331573486328]\n", + "epsilon:0.0009833015279105794 step:4730 episode:237 last_score -5.929374755859374 Profit 0.0 Loss [6.47983455657959]\n", + "epsilon:0.0009833015279105794 step:4735 episode:237 last_score -5.929374755859374 Profit 0.0 Loss [5.951162338256836]\n", + "epsilon:0.0009833015279105794 step:4740 episode:237 last_score -5.929374755859374 Profit 21.655181884765625 Loss [4.286913871765137]\n", + "epsilon:0.0009833015279105794 step:4745 episode:238 last_score -0.600257263183594 Profit 0.0 Loss [5.643301010131836]\n", + "epsilon:0.0009833015279105794 step:4750 episode:238 last_score -0.600257263183594 Profit 0.0 Loss [4.377361297607422]\n", + "epsilon:0.0009833015279105794 step:4755 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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 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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": [] + }, + { + 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