{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "nwaAZRu1NTiI" }, "source": [ "# DQN\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 12:47:16.481995: 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": 66, "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 = None\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", " self.history = self.model.fit(X, y, batch_size = self.batch_size, shuffle = False, verbose=0)\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", " else:\n", " model_predict = self.model.predict(np.array([state]), verbose=0)\n", " action = np.argmax(model_predict)\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", " print(f\"epsilon:{self.epsilon} step:{current_step} episode:{current_episode} last_score {rewards[-1]} Profit {info['total_profit']} Loss {self.history.history['loss']}\")\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": 67, "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", " 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": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3024\n", "1875\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": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_12\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_48 (Dense) (None, 256) 1280 \n", " \n", " dense_49 (Dense) (None, 128) 32896 \n", " \n", " dense_50 (Dense) (None, 64) 8256 \n", " \n", " dense_51 (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 -137.1817626953125 Loss None\n", "epsilon:1.0 step:20 episode:1 last_score 0 Profit -134.0233154296875 Loss None\n", "epsilon:0.95 step:25 episode:2 last_score -966.53455078125 Profit -3.1496124267578125 Loss None\n", "epsilon:0.95 step:30 episode:2 last_score -966.53455078125 Profit 2.0914306640625 Loss None\n", "epsilon:0.95 step:35 episode:2 last_score -966.53455078125 Profit 5.436676025390625 Loss None\n", "epsilon:0.95 step:40 episode:2 last_score -966.53455078125 Profit 7.9377899169921875 Loss None\n", "epsilon:0.9025 step:45 episode:3 last_score 5.3660481262207025 Profit 395.0810546875 Loss None\n", "epsilon:0.9025 step:50 episode:3 last_score 5.3660481262207025 Profit 505.3583984375 Loss None\n", "epsilon:0.9025 step:55 episode:3 last_score 5.3660481262207025 Profit 590.62158203125 Loss None\n", "epsilon:0.9025 step:60 episode:3 last_score 5.3660481262207025 Profit 453.9375 Loss None\n", "epsilon:0.8573749999999999 step:65 episode:4 last_score 1016.5273071289062 Profit 0.0 Loss None\n", "epsilon:0.8573749999999999 step:70 episode:4 last_score 1016.5273071289062 Profit -9.22235107421875 Loss None\n", "epsilon:0.8573749999999999 step:75 episode:4 last_score 1016.5273071289062 Profit -5.6952667236328125 Loss None\n", "epsilon:0.8573749999999999 step:80 episode:4 last_score 1016.5273071289062 Profit -7.02288818359375 Loss None\n", "epsilon:0.8145062499999999 step:85 episode:5 last_score -23.508456420898437 Profit 0.0 Loss None\n", "epsilon:0.8145062499999999 step:90 episode:5 last_score -23.508456420898437 Profit 139.99359130859375 Loss None\n", "epsilon:0.8145062499999999 step:95 episode:5 last_score -23.508456420898437 Profit 139.99359130859375 Loss None\n", "epsilon:0.8145062499999999 step:100 episode:5 last_score -23.508456420898437 Profit 162.66473388671875 Loss None\n", "epsilon:0.7737809374999999 step:105 episode:6 last_score 243.0426364135742 Profit 2.303466796875 Loss None\n", "epsilon:0.7737809374999999 step:110 episode:6 last_score 243.0426364135742 Profit 12.927566528320312 Loss None\n", "epsilon:0.7737809374999999 step:115 episode:6 last_score 243.0426364135742 Profit 5.7935028076171875 Loss None\n", "epsilon:0.7737809374999999 step:120 episode:6 last_score 243.0426364135742 Profit 10.906723022460938 Loss None\n", "epsilon:0.7350918906249998 step:125 episode:7 last_score 21.333234558105467 Profit 27.886993408203125 Loss None\n", "epsilon:0.7350918906249998 step:130 episode:7 last_score 21.333234558105467 Profit 29.575958251953125 Loss None\n", "epsilon:0.7350918906249998 step:135 episode:7 last_score 21.333234558105467 Profit -22.57904052734375 Loss None\n", "epsilon:0.7350918906249998 step:140 episode:7 last_score 21.333234558105467 Profit -22.57904052734375 Loss None\n", "epsilon:0.6983372960937497 step:145 episode:8 last_score -153.12630615234374 Profit 0.0 Loss None\n", "epsilon:0.6983372960937497 step:150 episode:8 last_score -153.12630615234374 Profit 0.0 Loss None\n", "epsilon:0.6983372960937497 step:155 episode:8 last_score -153.12630615234374 Profit -72.052490234375 Loss None\n", "epsilon:0.6983372960937497 step:160 episode:8 last_score -153.12630615234374 Profit -72.052490234375 Loss None\n", "epsilon:0.6634204312890623 step:165 episode:9 last_score -1187.3944995117188 Profit 488.588623046875 Loss None\n", "epsilon:0.6634204312890623 step:170 episode:9 last_score -1187.3944995117188 Profit 1267.70751953125 Loss None\n", "epsilon:0.6634204312890623 step:175 episode:9 last_score -1187.3944995117188 Profit 1267.70751953125 Loss None\n", "epsilon:0.6634204312890623 step:180 episode:9 last_score -1187.3944995117188 Profit 1046.099365234375 Loss None\n", "epsilon:0.6302494097246091 step:185 episode:10 last_score 503.37905273437514 Profit 0.15612030029296875 Loss None\n", "epsilon:0.6302494097246091 step:190 episode:10 last_score 503.37905273437514 Profit 14.161880493164062 Loss None\n", "epsilon:0.6302494097246091 step:195 episode:10 last_score 503.37905273437514 Profit 14.161880493164062 Loss None\n", "epsilon:0.6302494097246091 step:200 episode:10 last_score 503.37905273437514 Profit 14.161880493164062 Loss None\n", "epsilon:0.5987369392383786 step:205 episode:11 last_score 30.34539779663086 Profit 0.0 Loss [4362.67333984375]\n", "epsilon:0.5987369392383786 step:210 episode:11 last_score 30.34539779663086 Profit 0.7960052490234375 Loss [4195.63623046875]\n", "epsilon:0.5987369392383786 step:215 episode:11 last_score 30.34539779663086 Profit 0.7960052490234375 Loss [3907.09130859375]\n", "epsilon:0.5987369392383786 step:220 episode:11 last_score 30.34539779663086 Profit 0.7960052490234375 Loss [3958.101318359375]\n", "epsilon:0.5688000922764596 step:225 episode:12 last_score -45.63256057739258 Profit 0.0 Loss [4289.54296875]\n", "epsilon:0.5688000922764596 step:230 episode:12 last_score -45.63256057739258 Profit 331.529052734375 Loss [4067.746826171875]\n", "epsilon:0.5688000922764596 step:235 episode:12 last_score -45.63256057739258 Profit 407.494140625 Loss [3949.726806640625]\n", "epsilon:0.5688000922764596 step:240 episode:12 last_score -45.63256057739258 Profit 427.132568359375 Loss [4019.71875]\n", "epsilon:0.5403600876626365 step:245 episode:13 last_score 348.78031250000004 Profit 0.0 Loss [4609.63623046875]\n", "epsilon:0.5403600876626365 step:250 episode:13 last_score 348.78031250000004 Profit 299.311279296875 Loss [4688.31201171875]\n", "epsilon:0.5403600876626365 step:255 episode:13 last_score 348.78031250000004 Profit 260.854248046875 Loss 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[1260.976318359375]\n", "epsilon:0.0694428401872336 step:1045 episode:53 last_score -787.0493847656252 Profit 11.1455078125 Loss [1558.4332275390625]\n", "epsilon:0.0694428401872336 step:1050 episode:53 last_score -787.0493847656252 Profit 192.385009765625 Loss [3267.190673828125]\n", "epsilon:0.0694428401872336 step:1055 episode:53 last_score -787.0493847656252 Profit 192.385009765625 Loss [3873.492919921875]\n", "epsilon:0.0694428401872336 step:1060 episode:53 last_score -787.0493847656252 Profit 255.745849609375 Loss [4078.318359375]\n", "epsilon:0.0659706981778719 step:1065 episode:54 last_score 2.120605468749986 Profit 0.0 Loss [1493.6756591796875]\n", "epsilon:0.0659706981778719 step:1070 episode:54 last_score 2.120605468749986 Profit 122.44998168945312 Loss [2609.72021484375]\n", "epsilon:0.0659706981778719 step:1075 episode:54 last_score 2.120605468749986 Profit 122.44998168945312 Loss [1359.8560791015625]\n", "epsilon:0.0659706981778719 step:1080 episode:54 last_score 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[2700.312744140625]\n", "epsilon:0.053733545982740265 step:1160 episode:58 last_score -150.64427734375005 Profit 1529.35302734375 Loss [1446.0665283203125]\n", "epsilon:0.05104686868360325 step:1165 episode:59 last_score 1810.7448095703126 Profit 0.0 Loss [1545.9669189453125]\n", "epsilon:0.05104686868360325 step:1170 episode:59 last_score 1810.7448095703126 Profit 42.574981689453125 Loss [2363.37060546875]\n", "epsilon:0.05104686868360325 step:1175 episode:59 last_score 1810.7448095703126 Profit 72.34597778320312 Loss [2315.095458984375]\n", "epsilon:0.05104686868360325 step:1180 episode:59 last_score 1810.7448095703126 Profit 61.913970947265625 Loss [1094.87646484375]\n", "epsilon:0.04849452524942309 step:1185 episode:60 last_score 23.27654571533203 Profit 0.0 Loss [3523.248779296875]\n", "epsilon:0.04849452524942309 step:1190 episode:60 last_score 23.27654571533203 Profit -9.745147705078125 Loss [847.3668823242188]\n", "epsilon:0.04849452524942309 step:1195 episode:60 last_score 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[1012.5181884765625]\n", "epsilon:0.0009833015279105794 step:2745 episode:138 last_score 34.18773620605468 Profit 0.0 Loss [1148.1363525390625]\n", "epsilon:0.0009833015279105794 step:2750 episode:138 last_score 34.18773620605468 Profit 0.0 Loss [1323.0927734375]\n", "epsilon:0.0009833015279105794 step:2755 episode:138 last_score 34.18773620605468 Profit -124.10452270507812 Loss [1572.1912841796875]\n", "epsilon:0.0009833015279105794 step:2760 episode:138 last_score 34.18773620605468 Profit -124.10452270507812 Loss [1797.91845703125]\n", "epsilon:0.0009833015279105794 step:2765 episode:139 last_score -184.0582537841797 Profit 0.0 Loss [1298.83349609375]\n", "epsilon:0.0009833015279105794 step:2770 episode:139 last_score -184.0582537841797 Profit 0.0 Loss [1129.09033203125]\n", "epsilon:0.0009833015279105794 step:2775 episode:139 last_score -184.0582537841797 Profit 0.0 Loss [1491.28369140625]\n", "epsilon:0.0009833015279105794 step:2780 episode:139 last_score -184.0582537841797 Profit -107.136962890625 Loss [2533.358642578125]\n", "epsilon:0.0009833015279105794 step:2785 episode:140 last_score -621.728125 Profit 0.0 Loss [937.3993530273438]\n", "epsilon:0.0009833015279105794 step:2790 episode:140 last_score -621.728125 Profit 206.9100341796875 Loss [1270.2254638671875]\n", "epsilon:0.0009833015279105794 step:2795 episode:140 last_score -621.728125 Profit 318.1400146484375 Loss [1641.99609375]\n", "epsilon:0.0009833015279105794 step:2800 episode:140 last_score -621.728125 Profit 318.1400146484375 Loss [1188.871826171875]\n", "epsilon:0.0009833015279105794 step:2805 episode:141 last_score 319.1931640625 Profit 0.0 Loss [3156.840087890625]\n", "epsilon:0.0009833015279105794 step:2810 episode:141 last_score 319.1931640625 Profit 0.0 Loss [1772.11962890625]\n", "epsilon:0.0009833015279105794 step:2815 episode:141 last_score 319.1931640625 Profit 6.75592041015625 Loss [651.678955078125]\n", "epsilon:0.0009833015279105794 step:2820 episode:141 last_score 319.1931640625 Profit 6.75592041015625 Loss [2829.39306640625]\n", "epsilon:0.0009833015279105794 step:2825 episode:142 last_score -37.36776473999023 Profit 131.78399658203125 Loss [1290.314453125]\n", "epsilon:0.0009833015279105794 step:2830 episode:142 last_score -37.36776473999023 Profit 131.78399658203125 Loss [2458.82275390625]\n", "epsilon:0.0009833015279105794 step:2835 episode:142 last_score -37.36776473999023 Profit 121.83697509765625 Loss [1580.5211181640625]\n", "epsilon:0.0009833015279105794 step:2840 episode:142 last_score -37.36776473999023 Profit 133.04193115234375 Loss [2257.62158203125]\n", "epsilon:0.0009833015279105794 step:2845 episode:143 last_score 78.44485595703127 Profit 0.0 Loss [980.6052856445312]\n", "epsilon:0.0009833015279105794 step:2850 episode:143 last_score 78.44485595703127 Profit 0.0 Loss [1038.64794921875]\n", "epsilon:0.0009833015279105794 step:2855 episode:143 last_score 78.44485595703127 Profit 0.0 Loss [1193.1812744140625]\n", "epsilon:0.0009833015279105794 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"epsilon:0.0009833015279105794 step:2935 episode:147 last_score -21.84056793212891 Profit 304.40704345703125 Loss [467.71368408203125]\n", "epsilon:0.0009833015279105794 step:2940 episode:147 last_score -21.84056793212891 Profit 304.40704345703125 Loss [2614.6376953125]\n", "epsilon:0.0009833015279105794 step:2945 episode:148 last_score 376.1661767578125 Profit 0.0 Loss [2548.1298828125]\n", "epsilon:0.0009833015279105794 step:2950 episode:148 last_score 376.1661767578125 Profit 0.0 Loss [1610.510986328125]\n", "epsilon:0.0009833015279105794 step:2955 episode:148 last_score 376.1661767578125 Profit 0.0 Loss [2213.456787109375]\n", "epsilon:0.0009833015279105794 step:2960 episode:148 last_score 376.1661767578125 Profit 0.0 Loss [788.6964111328125]\n", "epsilon:0.0009833015279105794 step:2965 episode:149 last_score -870.6196044921872 Profit 0.0 Loss [1546.4937744140625]\n", "epsilon:0.0009833015279105794 step:2970 episode:149 last_score -870.6196044921872 Profit 3.92010498046875 Loss [1564.7884521484375]\n", "epsilon:0.0009833015279105794 step:2975 episode:149 last_score -870.6196044921872 Profit 14.810630798339844 Loss [1110.8936767578125]\n", "epsilon:0.0009833015279105794 step:2980 episode:149 last_score -870.6196044921872 Profit 16.333351135253906 Loss [2391.906982421875]\n", "epsilon:0.0009833015279105794 step:2985 episode:150 last_score 7.388717575073241 Profit 0.0 Loss [2459.706298828125]\n", "epsilon:0.0009833015279105794 step:2990 episode:150 last_score 7.388717575073241 Profit 0.0 Loss [1554.0919189453125]\n", "epsilon:0.0009833015279105794 step:2995 episode:150 last_score 7.388717575073241 Profit 0.0 Loss [936.9368896484375]\n", "epsilon:0.0009833015279105794 step:3000 episode:150 last_score 7.388717575073241 Profit 0.0 Loss [1479.03662109375]\n", "epsilon:0.0009833015279105794 step:3005 episode:151 last_score -195.5280529785156 Profit 0.0 Loss [462.2049865722656]\n", "epsilon:0.0009833015279105794 step:3010 episode:151 last_score -195.5280529785156 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Loss [673.0296020507812]\n", "epsilon:0.0009833015279105794 step:4820 episode:241 last_score -9.732988357543949 Profit -7.572662353515625 Loss [1023.0925903320312]\n", "epsilon:0.0009833015279105794 step:4825 episode:242 last_score -178.77785766601562 Profit 370.45458984375 Loss [855.7435913085938]\n", "epsilon:0.0009833015279105794 step:4830 episode:242 last_score -178.77785766601562 Profit 370.45458984375 Loss [709.695068359375]\n", "epsilon:0.0009833015279105794 step:4835 episode:242 last_score -178.77785766601562 Profit 370.45458984375 Loss [857.8800048828125]\n", "epsilon:0.0009833015279105794 step:4840 episode:242 last_score -178.77785766601562 Profit 986.112548828125 Loss [962.1507568359375]\n", "epsilon:0.0009833015279105794 step:4845 episode:243 last_score 1252.2026293945312 Profit 0.0 Loss [640.199462890625]\n", "epsilon:0.0009833015279105794 step:4850 episode:243 last_score 1252.2026293945312 Profit 0.0 Loss [874.3414306640625]\n", "epsilon:0.0009833015279105794 step:4855 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-25.323979339599607 Profit 7.012939453125 Loss [2005.9412841796875]\n", "epsilon:0.0009833015279105794 step:5135 episode:257 last_score -25.323979339599607 Profit 7.012939453125 Loss [459.7632751464844]\n", "epsilon:0.0009833015279105794 step:5140 episode:257 last_score -25.323979339599607 Profit 7.012939453125 Loss [1275.4063720703125]\n", "epsilon:0.0009833015279105794 step:5145 episode:258 last_score -7.330002746582034 Profit 48.2349853515625 Loss [763.6040649414062]\n", "epsilon:0.0009833015279105794 step:5150 episode:258 last_score -7.330002746582034 Profit 108.31097412109375 Loss [888.428466796875]\n", "epsilon:0.0009833015279105794 step:5155 episode:258 last_score -7.330002746582034 Profit 112.70697021484375 Loss [1902.20556640625]\n", "epsilon:0.0009833015279105794 step:5160 episode:258 last_score -7.330002746582034 Profit 184.48797607421875 Loss [1090.7064208984375]\n", "epsilon:0.0009833015279105794 step:5165 episode:259 last_score 216.51551879882808 Profit 16.804161071777344 Loss [686.6370849609375]\n", "epsilon:0.0009833015279105794 step:5170 episode:259 last_score 216.51551879882808 Profit 16.804161071777344 Loss [507.9825134277344]\n", "epsilon:0.0009833015279105794 step:5175 episode:259 last_score 216.51551879882808 Profit 16.804161071777344 Loss [1970.657958984375]\n", "epsilon:0.0009833015279105794 step:5180 episode:259 last_score 216.51551879882808 Profit 16.804161071777344 Loss [1159.2757568359375]\n", "epsilon:0.0009833015279105794 step:5185 episode:260 last_score -19.025206146240233 Profit 0.0 Loss [872.9078979492188]\n", "epsilon:0.0009833015279105794 step:5190 episode:260 last_score -19.025206146240233 Profit -3.3604736328125 Loss [1086.6600341796875]\n", "epsilon:0.0009833015279105794 step:5195 episode:260 last_score -19.025206146240233 Profit -3.3604736328125 Loss [3572.677490234375]\n", "epsilon:0.0009833015279105794 step:5200 episode:260 last_score -19.025206146240233 Profit -3.3604736328125 Loss [778.3804931640625]\n", "epsilon:0.0009833015279105794 step:5205 episode:261 last_score -556.8948815917968 Profit 0.0 Loss [792.3167114257812]\n", "epsilon:0.0009833015279105794 step:5210 episode:261 last_score -556.8948815917968 Profit 0.0 Loss [564.5901489257812]\n", "epsilon:0.0009833015279105794 step:5215 episode:261 last_score -556.8948815917968 Profit -21.9439697265625 Loss [547.922119140625]\n", "epsilon:0.0009833015279105794 step:5220 episode:261 last_score -556.8948815917968 Profit -21.9439697265625 Loss [1886.6199951171875]\n", "epsilon:0.0009833015279105794 step:5225 episode:262 last_score -140.07094543457032 Profit 0.0 Loss [2233.560302734375]\n", "epsilon:0.0009833015279105794 step:5230 episode:262 last_score -140.07094543457032 Profit 0.0 Loss [594.6702270507812]\n", "epsilon:0.0009833015279105794 step:5235 episode:262 last_score -140.07094543457032 Profit 0.0 Loss [1550.9407958984375]\n", "epsilon:0.0009833015279105794 step:5240 episode:262 last_score -140.07094543457032 Profit 0.0 Loss [984.3479614257812]\n", "epsilon:0.0009833015279105794 step:5245 episode:263 last_score -80.91436645507812 Profit 0.0 Loss [2835.909912109375]\n", "epsilon:0.0009833015279105794 step:5250 episode:263 last_score -80.91436645507812 Profit 1.44940185546875 Loss [1356.2471923828125]\n", "epsilon:0.0009833015279105794 step:5255 episode:263 last_score -80.91436645507812 Profit 10.005813598632812 Loss [1294.7091064453125]\n", "epsilon:0.0009833015279105794 step:5260 episode:263 last_score -80.91436645507812 Profit 10.005813598632812 Loss [944.7119750976562]\n", "epsilon:0.0009833015279105794 step:5265 episode:264 last_score -6.462390747070313 Profit 0.0 Loss [1018.9400024414062]\n", "epsilon:0.0009833015279105794 step:5270 episode:264 last_score -6.462390747070313 Profit 0.0 Loss [519.6035766601562]\n", "epsilon:0.0009833015279105794 step:5275 episode:264 last_score -6.462390747070313 Profit 24.45581817626953 Loss [772.61767578125]\n", "epsilon:0.0009833015279105794 step:5280 episode:264 last_score -6.462390747070313 Profit 24.45581817626953 Loss [397.1816711425781]\n", "epsilon:0.0009833015279105794 step:5285 episode:265 last_score 1.6511288452148434 Profit 17.9764404296875 Loss [1569.4642333984375]\n", "epsilon:0.0009833015279105794 step:5290 episode:265 last_score 1.6511288452148434 Profit 1.5624847412109375 Loss [476.02227783203125]\n", "epsilon:0.0009833015279105794 step:5295 episode:265 last_score 1.6511288452148434 Profit 6.965248107910156 Loss [876.5223388671875]\n", "epsilon:0.0009833015279105794 step:5300 episode:265 last_score 1.6511288452148434 Profit 16.152015686035156 Loss [2265.810546875]\n", "epsilon:0.0009833015279105794 step:5305 episode:266 last_score 18.83384811401368 Profit 0.0 Loss [871.6963500976562]\n", "epsilon:0.0009833015279105794 step:5310 episode:266 last_score 18.83384811401368 Profit 0.0 Loss [2007.2403564453125]\n", "epsilon:0.0009833015279105794 step:5315 episode:266 last_score 18.83384811401368 Profit 0.0 Loss [847.6343994140625]\n", "epsilon:0.0009833015279105794 step:5320 episode:266 last_score 18.83384811401368 Profit 0.0 Loss [947.0127563476562]\n", "epsilon:0.0009833015279105794 step:5325 episode:267 last_score -594.9999609375001 Profit 33.45501708984375 Loss [557.9074096679688]\n", "epsilon:0.0009833015279105794 step:5330 episode:267 last_score -594.9999609375001 Profit 34.969970703125 Loss [825.1599731445312]\n", "epsilon:0.0009833015279105794 step:5335 episode:267 last_score -594.9999609375001 Profit 34.969970703125 Loss [997.1265869140625]\n", "epsilon:0.0009833015279105794 step:5340 episode:267 last_score -594.9999609375001 Profit -91.15399169921875 Loss [3268.75927734375]\n", "epsilon:0.0009833015279105794 step:5345 episode:268 last_score -249.07345947265628 Profit 0.394989013671875 Loss [2957.016357421875]\n", "epsilon:0.0009833015279105794 step:5350 episode:268 last_score -249.07345947265628 Profit 0.394989013671875 Loss [1512.5096435546875]\n", "epsilon:0.0009833015279105794 step:5355 episode:268 last_score -249.07345947265628 Profit -5.0250091552734375 Loss [219.24679565429688]\n", "epsilon:0.0009833015279105794 step:5360 episode:268 last_score -249.07345947265628 Profit 5.4210052490234375 Loss [1057.6466064453125]\n", "epsilon:0.0009833015279105794 step:5365 episode:269 last_score -8.041907806396482 Profit 0.0 Loss [887.988525390625]\n", "epsilon:0.0009833015279105794 step:5370 episode:269 last_score -8.041907806396482 Profit 213.558349609375 Loss [1030.024658203125]\n", "epsilon:0.0009833015279105794 step:5375 episode:269 last_score -8.041907806396482 Profit 309.5550537109375 Loss [2583.83056640625]\n", "epsilon:0.0009833015279105794 step:5380 episode:269 last_score -8.041907806396482 Profit 309.5550537109375 Loss [270.1651916503906]\n", "epsilon:0.0009833015279105794 step:5385 episode:270 last_score 96.00610229492185 Profit 0.0 Loss [584.6072998046875]\n", "epsilon:0.0009833015279105794 step:5390 episode:270 last_score 96.00610229492185 Profit 0.83447265625 Loss [1705.938720703125]\n", "epsilon:0.0009833015279105794 step:5395 episode:270 last_score 96.00610229492185 Profit 0.83447265625 Loss [1642.7796630859375]\n", "epsilon:0.0009833015279105794 step:5400 episode:270 last_score 96.00610229492185 Profit 226.908935546875 Loss [2188.154052734375]\n", "epsilon:0.0009833015279105794 step:5405 episode:271 last_score -75.10574951171868 Profit 0.0 Loss [865.5610961914062]\n", "epsilon:0.0009833015279105794 step:5410 episode:271 last_score -75.10574951171868 Profit 93.498046875 Loss [306.5556335449219]\n", "epsilon:0.0009833015279105794 step:5415 episode:271 last_score -75.10574951171868 Profit 282.66259765625 Loss [478.8494567871094]\n", "epsilon:0.0009833015279105794 step:5420 episode:271 last_score -75.10574951171868 Profit 282.66259765625 Loss [1234.5439453125]\n", "epsilon:0.0009833015279105794 step:5425 episode:272 last_score -316.954384765625 Profit 257.16796875 Loss [832.07421875]\n", "epsilon:0.0009833015279105794 step:5430 episode:272 last_score -316.954384765625 Profit 393.098388671875 Loss [1686.705810546875]\n", "epsilon:0.0009833015279105794 step:5435 episode:272 last_score -316.954384765625 Profit 393.098388671875 Loss [439.2247619628906]\n", "epsilon:0.0009833015279105794 step:5440 episode:272 last_score -316.954384765625 Profit 375.36767578125 Loss [1006.9620361328125]\n", "epsilon:0.0009833015279105794 step:5445 episode:273 last_score 343.7551660156249 Profit 9.984695434570312 Loss [1387.3397216796875]\n", "epsilon:0.0009833015279105794 step:5450 episode:273 last_score 343.7551660156249 Profit 14.02972412109375 Loss [1844.2607421875]\n", "epsilon:0.0009833015279105794 step:5455 episode:273 last_score 343.7551660156249 Profit 14.02972412109375 Loss [1362.79296875]\n", "epsilon:0.0009833015279105794 step:5460 episode:273 last_score 343.7551660156249 Profit 14.02972412109375 Loss [260.2430725097656]\n", "epsilon:0.0009833015279105794 step:5465 episode:274 last_score -18.98009963989258 Profit 0.0 Loss [368.0233459472656]\n", "epsilon:0.0009833015279105794 step:5470 episode:274 last_score -18.98009963989258 Profit 0.0 Loss [1084.6656494140625]\n", "epsilon:0.0009833015279105794 step:5475 episode:274 last_score -18.98009963989258 Profit -59.60504150390625 Loss [441.12628173828125]\n", "epsilon:0.0009833015279105794 step:5480 episode:274 last_score -18.98009963989258 Profit 18.990966796875 Loss [2549.681640625]\n", "epsilon:0.0009833015279105794 step:5485 episode:275 last_score -144.01936370849612 Profit 0.0 Loss [1213.5706787109375]\n", "epsilon:0.0009833015279105794 step:5490 episode:275 last_score -144.01936370849612 Profit 0.0 Loss [348.6705322265625]\n", "epsilon:0.0009833015279105794 step:5495 episode:275 last_score -144.01936370849612 Profit 0.0 Loss [657.8125610351562]\n", "epsilon:0.0009833015279105794 step:5500 episode:275 last_score -144.01936370849612 Profit 0.0 Loss [629.4419555664062]\n", "epsilon:0.0009833015279105794 step:5505 episode:276 last_score -554.0372973632813 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last_score -180.047060546875 Profit -2.354461669921875 Loss [195.29200744628906]\n", "epsilon:0.0009833015279105794 step:5550 episode:278 last_score -180.047060546875 Profit -2.354461669921875 Loss [833.7620849609375]\n", "epsilon:0.0009833015279105794 step:5555 episode:278 last_score -180.047060546875 Profit -2.354461669921875 Loss [426.955078125]\n", "epsilon:0.0009833015279105794 step:5560 episode:278 last_score -180.047060546875 Profit -2.354461669921875 Loss [601.5980224609375]\n", "epsilon:0.0009833015279105794 step:5565 episode:279 last_score -29.548711395263673 Profit 15.717010498046875 Loss [563.9603271484375]\n", "epsilon:0.0009833015279105794 step:5570 episode:279 last_score -29.548711395263673 Profit 15.717010498046875 Loss [2170.01953125]\n", "epsilon:0.0009833015279105794 step:5575 episode:279 last_score -29.548711395263673 Profit 15.717010498046875 Loss [291.9898376464844]\n", "epsilon:0.0009833015279105794 step:5580 episode:279 last_score -29.548711395263673 Profit 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"epsilon:0.0009833015279105794 step:5620 episode:281 last_score 362.90034667968746 Profit 1082.37939453125 Loss [992.43212890625]\n", "epsilon:0.0009833015279105794 step:5625 episode:282 last_score 1072.083525390625 Profit 0.0 Loss [1730.134521484375]\n", "epsilon:0.0009833015279105794 step:5630 episode:282 last_score 1072.083525390625 Profit 40.13800048828125 Loss [871.0663452148438]\n", "epsilon:0.0009833015279105794 step:5635 episode:282 last_score 1072.083525390625 Profit 61.28399658203125 Loss [1044.5889892578125]\n", "epsilon:0.0009833015279105794 step:5640 episode:282 last_score 1072.083525390625 Profit 61.28399658203125 Loss [3133.402099609375]\n", "epsilon:0.0009833015279105794 step:5645 episode:283 last_score -7.957510986328118 Profit 0.0 Loss [1524.0723876953125]\n", "epsilon:0.0009833015279105794 step:5650 episode:283 last_score -7.957510986328118 Profit 0.0 Loss [787.0302124023438]\n", "epsilon:0.0009833015279105794 step:5655 episode:283 last_score -7.957510986328118 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Loss [933.1570434570312]\n", "epsilon:0.0009833015279105794 step:5735 episode:287 last_score -916.753935546875 Profit 56.38703918457031 Loss [473.8637390136719]\n", "epsilon:0.0009833015279105794 step:5740 episode:287 last_score -916.753935546875 Profit 76.14532470703125 Loss [469.90118408203125]\n", "epsilon:0.0009833015279105794 step:5745 episode:288 last_score 58.80071517944336 Profit 125.8653564453125 Loss [1144.3734130859375]\n", "epsilon:0.0009833015279105794 step:5750 episode:288 last_score 58.80071517944336 Profit 125.8653564453125 Loss [968.767822265625]\n", "epsilon:0.0009833015279105794 step:5755 episode:288 last_score 58.80071517944336 Profit 125.8653564453125 Loss [1812.4940185546875]\n", "epsilon:0.0009833015279105794 step:5760 episode:288 last_score 58.80071517944336 Profit -123.7147216796875 Loss [1873.36767578125]\n", "epsilon:0.0009833015279105794 step:5765 episode:289 last_score -364.63886962890626 Profit 6.683807373046875 Loss [2518.5380859375]\n", 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last_score -447.53942626953125 Profit 3.7310943603515625 Loss [1820.7918701171875]\n", "epsilon:0.0009833015279105794 step:5960 episode:298 last_score -447.53942626953125 Profit 3.7310943603515625 Loss [655.8548583984375]\n", "epsilon:0.0009833015279105794 step:5965 episode:299 last_score -30.049903259277347 Profit 0.0 Loss [1538.5369873046875]\n", "epsilon:0.0009833015279105794 step:5970 episode:299 last_score -30.049903259277347 Profit -0.625030517578125 Loss [360.44451904296875]\n", "epsilon:0.0009833015279105794 step:5975 episode:299 last_score -30.049903259277347 Profit 1.4778900146484375 Loss [1337.63525390625]\n", "epsilon:0.0009833015279105794 step:5980 episode:299 last_score -30.049903259277347 Profit -5.6605072021484375 Loss [637.25439453125]\n", "epsilon:0.0009833015279105794 step:5985 episode:300 last_score -32.26794372558594 Profit 0.0 Loss [787.7514038085938]\n", "epsilon:0.0009833015279105794 step:5990 episode:300 last_score -32.26794372558594 Profit 0.0 Loss [2046.79150390625]\n", "epsilon:0.0009833015279105794 step:5995 episode:300 last_score -32.26794372558594 Profit 0.0 Loss [1339.264404296875]\n", "epsilon:0.0009833015279105794 step:6000 episode:300 last_score -32.26794372558594 Profit 0.0 Loss [1237.489501953125]\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": 70, "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": 70, "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": 16, "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": 87, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f0eff2ef3b0a4e12a23709db72722a25", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00here for more info. View Jupyter log for further details." ] } ], "source": [ "max_steps = 20 \n", "env_test = 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, max_steps, n_eval_episodes, model)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a7b0edb264fe43edbe5cea55fac21688", "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": [] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 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