From e60c4d79bc30f38388d34f8761ca7fcf0bfb06ae Mon Sep 17 00:00:00 2001 From: dptrup69 <131466311+dptrup69@users.noreply.github.com> Date: Sun, 19 May 2024 15:23:30 +0530 Subject: [PATCH] RL Model Reinforcement Learning trading model on custom dataset using gym anytrading environment --- RL Model/RL Trading.ipynb | 983 ++++++++++++++++++++++ RL Model/gmedata.csv | 252 ++++++ RL Model/requirents.txt | 0 Self Driving Car-RL/car.png | Bin 35597 -> 0 bytes Self Driving Car-RL/map.png | Bin 88564 -> 0 bytes Self Driving Car-RL/map2.png | Bin 74986 -> 0 bytes Self Driving Car-RL/map3.png | Bin 112252 -> 0 bytes Self Driving Car-RL/map4.png | Bin 35625 -> 0 bytes Self Driving Car-RL/map5.png | Bin 38300 -> 0 bytes Self Driving Car-RL/model.py | 264 ------ Self Driving Car-RL/tempCodeRunnerFile.py | 1 - 11 files changed, 1235 insertions(+), 265 deletions(-) create mode 100644 RL Model/RL Trading.ipynb create mode 100644 RL Model/gmedata.csv create mode 100644 RL Model/requirents.txt delete mode 100644 Self Driving Car-RL/car.png delete mode 100644 Self Driving Car-RL/map.png delete mode 100644 Self Driving Car-RL/map2.png delete mode 100644 Self Driving Car-RL/map3.png delete mode 100644 Self Driving Car-RL/map4.png delete mode 100644 Self Driving Car-RL/map5.png delete mode 100644 Self Driving Car-RL/model.py delete mode 100644 Self Driving Car-RL/tempCodeRunnerFile.py diff --git a/RL Model/RL Trading.ipynb b/RL Model/RL Trading.ipynb new file mode 100644 index 00000000..11dbc612 --- /dev/null +++ b/RL Model/RL Trading.ipynb @@ -0,0 +1,983 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 0. Install and Import dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: tensorflow-gpu==1.15.0 in c:\\users\\user\\appdata\\roaming\\python\\python37\\site-packages (1.15.0)\n", + "Requirement already satisfied: tensorflow==1.15.0 in c:\\users\\user\\appdata\\roaming\\python\\python37\\site-packages (1.15.0)\n", + "Requirement already satisfied: stable-baselines in c:\\programdata\\anaconda3\\lib\\site-packages (2.10.1)\n", + "Requirement already satisfied: gym-anytrading in c:\\users\\user\\appdata\\roaming\\python\\python37\\site-packages (1.2.0)\n", + "Requirement already satisfied: gym in c:\\programdata\\anaconda3\\lib\\site-packages (0.18.0)\n", + "Requirement already satisfied: wrapt>=1.11.1 in c:\\users\\user\\appdata\\roaming\\python\\python37\\site-packages (from tensorflow-gpu==1.15.0) (1.12.1)\n", + 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matplotlib->stable-baselines) (2.4.0)\n", + "Requirement already satisfied: future in c:\\users\\user\\appdata\\roaming\\python\\python37\\site-packages (from pyglet<=1.5.0,>=1.4.0->gym) (0.18.2)\n", + "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in c:\\programdata\\anaconda3\\lib\\site-packages (from markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15.0) (0.17)\n", + "Requirement already satisfied: zipp>=0.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15.0) (0.5.1)\n" + ] + } + ], + "source": [ + "!pip install tensorflow-gpu==1.15.0 tensorflow==1.15.0 stable-baselines gym-anytrading gym" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:\n", + "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + " * https://github.com/tensorflow/io (for I/O related ops)\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n" + ] + } + ], + "source": [ + "# Gym stuff\n", + "import gym\n", + "import gym_anytrading\n", + "\n", + "# Stable baselines - rl stuff\n", + "from stable_baselines.common.vec_env import DummyVecEnv\n", + "from stable_baselines import A2C\n", + "\n", + "# Processing libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Bring in Marketwatch GME Data \n", + "https://www.marketwatch.com/investing/stock/gme/download-data?startDate=11/1/2019&endDate=03/12/2021" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('data/gmedata.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Build Environment" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Discrete(2)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.action_space" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "info {'total_reward': 64.09000000000002, 'total_profit': 0.7841440186360266, 'position': 1}\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "state = env.reset()\n", + "while True: \n", + " action = env.action_space.sample()\n", + " n_state, reward, done, info = env.step(action)\n", + " if done: \n", + " print(\"info\", info)\n", + " break\n", + " \n", + "plt.figure(figsize=(15,6))\n", + "plt.cla()\n", + "env.render_all()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Build Environment and Train" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "env_maker = lambda: gym.make('stocks-v0', df=df, frame_bound=(5,100), window_size=5)\n", + "env = DummyVecEnv([env_maker])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrapping the env in a DummyVecEnv.\n", + "---------------------------------\n", + "| explained_variance | 0.112 |\n", + "| fps | 27 |\n", + "| nupdates | 1 |\n", + "| policy_entropy | 0.692 |\n", + "| total_timesteps | 5 |\n", + "| value_loss | 2.28 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.0877 |\n", + "| fps | 454 |\n", + "| nupdates | 100 |\n", + "| policy_entropy | 0.692 |\n", + "| total_timesteps | 500 |\n", + "| value_loss | 0.00127 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.056 |\n", + "| fps | 490 |\n", + "| nupdates | 200 |\n", + "| policy_entropy | 0.693 |\n", + "| total_timesteps | 1000 |\n", + "| value_loss | 0.162 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -26.8 |\n", + "| fps | 502 |\n", + "| nupdates | 300 |\n", + "| policy_entropy | 0.693 |\n", + "| total_timesteps | 1500 |\n", + "| value_loss | 0.000155 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -1.07 |\n", + "| fps | 509 |\n", + "| nupdates | 400 |\n", + "| policy_entropy | 0.693 |\n", + "| total_timesteps | 2000 |\n", + "| value_loss | 9.54 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -4.53 |\n", + "| fps | 514 |\n", + "| nupdates | 500 |\n", + "| policy_entropy | 0.693 |\n", + "| total_timesteps | 2500 |\n", + "| value_loss | 0.000328 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -0.0447 |\n", + "| fps | 518 |\n", + "| nupdates | 600 |\n", + "| policy_entropy | 0.688 |\n", + "| total_timesteps | 3000 |\n", + "| value_loss | 0.0261 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -0.537 |\n", + "| fps | 520 |\n", + "| nupdates | 700 |\n", + "| policy_entropy | 0.693 |\n", + "| total_timesteps | 3500 |\n", + "| value_loss | 0.0308 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.152 |\n", + "| fps | 523 |\n", + "| nupdates | 800 |\n", + "| policy_entropy | 0.69 |\n", + "| total_timesteps | 4000 |\n", + "| value_loss | 0.0947 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -0.168 |\n", + "| fps | 524 |\n", + "| nupdates | 900 |\n", + "| policy_entropy | 0.617 |\n", + "| total_timesteps | 4500 |\n", + "| value_loss | 0.0563 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -0.801 |\n", + "| fps | 525 |\n", + "| nupdates | 1000 |\n", + "| policy_entropy | 0.636 |\n", + "| total_timesteps | 5000 |\n", + "| value_loss | 0.115 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -0.172 |\n", + "| fps | 527 |\n", + "| nupdates | 1100 |\n", + "| policy_entropy | 0.627 |\n", + "| total_timesteps | 5500 |\n", + "| value_loss | 0.504 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.217 |\n", + "| fps | 527 |\n", + "| nupdates | 1200 |\n", + "| policy_entropy | 0.691 |\n", + "| total_timesteps | 6000 |\n", + "| value_loss | 0.629 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -0.011 |\n", + "| fps | 528 |\n", + "| nupdates | 1300 |\n", + "| policy_entropy | 0.556 |\n", + "| total_timesteps | 6500 |\n", + "| value_loss | 3.48 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.204 |\n", + "| fps | 529 |\n", + "| nupdates | 1400 |\n", + "| policy_entropy | 0.679 |\n", + "| total_timesteps | 7000 |\n", + "| value_loss | 0.892 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -1.2e+03 |\n", + "| fps | 529 |\n", + "| nupdates | 1500 |\n", + "| policy_entropy | 0.556 |\n", + "| total_timesteps | 7500 |\n", + "| value_loss | 0.0178 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -5.12 |\n", + "| fps | 530 |\n", + "| nupdates | 1600 |\n", + "| policy_entropy | 0.384 |\n", + "| total_timesteps | 8000 |\n", + "| value_loss | 0.0238 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.0393 |\n", + "| fps | 530 |\n", + "| nupdates | 1700 |\n", + "| policy_entropy | 0.638 |\n", + "| total_timesteps | 8500 |\n", + "| value_loss | 0.725 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.169 |\n", + "| fps | 530 |\n", + "| nupdates | 1800 |\n", + "| policy_entropy | 0.494 |\n", + "| total_timesteps | 9000 |\n", + "| value_loss | 1.59 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -0.0176 |\n", + "| fps | 531 |\n", + "| nupdates | 1900 |\n", + "| policy_entropy | 0.609 |\n", + "| total_timesteps | 9500 |\n", + "| value_loss | 0.881 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.205 |\n", + "| fps | 531 |\n", + "| nupdates | 2000 |\n", + "| policy_entropy | 0.599 |\n", + "| total_timesteps | 10000 |\n", + "| value_loss | 0.194 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -84 |\n", + "| fps | 531 |\n", + "| nupdates | 2100 |\n", + "| policy_entropy | 0.447 |\n", + "| total_timesteps | 10500 |\n", + "| value_loss | 0.000616 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.0155 |\n", + "| fps | 531 |\n", + "| nupdates | 2200 |\n", + "| policy_entropy | 0.593 |\n", + "| total_timesteps | 11000 |\n", + "| value_loss | 0.812 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.208 |\n", + "| fps | 532 |\n", + "| nupdates | 2300 |\n", + "| policy_entropy | 0.489 |\n", + "| total_timesteps | 11500 |\n", + "| value_loss | 0.298 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.0635 |\n", + "| fps | 532 |\n", + "| nupdates | 2400 |\n", + "| policy_entropy | 0.41 |\n", + "| total_timesteps | 12000 |\n", + "| value_loss | 0.68 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -96.8 |\n", + "| fps | 532 |\n", + "| nupdates | 2500 |\n", + "| policy_entropy | 0.314 |\n", + "| total_timesteps | 12500 |\n", + "| value_loss | 0.00619 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -0.121 |\n", + "| fps | 532 |\n", + "| nupdates | 2600 |\n", + "| policy_entropy | 0.444 |\n", + "| total_timesteps | 13000 |\n", + "| value_loss | 1.23 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -2.41 |\n", + "| fps | 532 |\n", + "| nupdates | 2700 |\n", + "| policy_entropy | 0.0403 |\n", + "| total_timesteps | 13500 |\n", + "| value_loss | 0.00241 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.215 |\n", + "| fps | 532 |\n", + "| nupdates | 2800 |\n", + "| policy_entropy | 0.499 |\n", + "| total_timesteps | 14000 |\n", + "| value_loss | 0.287 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.00763 |\n", + "| fps | 533 |\n", + "| nupdates | 2900 |\n", + "| policy_entropy | 0.31 |\n", + "| total_timesteps | 14500 |\n", + "| value_loss | 6.79 |\n", + "---------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------\n", + "| explained_variance | 0.16 |\n", + "| fps | 533 |\n", + "| nupdates | 3000 |\n", + "| policy_entropy | 0.219 |\n", + "| total_timesteps | 15000 |\n", + "| value_loss | 0.534 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | -41.6 |\n", + "| fps | 533 |\n", + "| nupdates | 3100 |\n", + "| policy_entropy | 0.169 |\n", + "| total_timesteps | 15500 |\n", + "| value_loss | 0.0158 |\n", + "---------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.903 |\n", + "| fps | 533 |\n", + "| nupdates | 3200 |\n", + "| policy_entropy | 0.179 |\n", + "| total_timesteps | 16000 |\n", + "| value_loss | 0.0276 |\n", + "---------------------------------\n", + "----------------------------------\n", + "| explained_variance | -3.44e+03 |\n", + "| fps | 533 |\n", + "| nupdates | 3300 |\n", + "| policy_entropy | 0.063 |\n", + "| total_timesteps | 16500 |\n", + "| value_loss | 0.0168 |\n", + "----------------------------------\n", + "---------------------------------\n", + "| explained_variance | 0.129 |\n", + "| fps | 533 |\n", + "| nupdates | 3400 |\n", + "| policy_entropy | 0.204 |\n", + "| total_timesteps | 17000 |\n", + "| value_loss | 0.0669 |\n", + "---------------------------------\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mA2C\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'MlpLstmPolicy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1000000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\stable_baselines\\a2c\\a2c.py\u001b[0m in \u001b[0;36mlearn\u001b[1;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps)\u001b[0m\n\u001b[0;32m 261\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_rollout_start\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 262\u001b[0m \u001b[1;31m# true_reward is the reward without discount\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 263\u001b[1;33m \u001b[0mrollout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrunner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 264\u001b[0m \u001b[1;31m# unpack\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 265\u001b[0m \u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrewards\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmasks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mep_infos\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrue_reward\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrollout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\stable_baselines\\common\\runners.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, callback)\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallback\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m 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self.neglogp],\n\u001b[1;32m--> 508\u001b[1;33m {self.obs_ph: obs, self.states_ph: state, self.dones_ph: mask})\n\u001b[0m\u001b[0;32m 509\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 510\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mproba_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\tensorflow_core\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 954\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 955\u001b[0m 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\u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\tensorflow_core\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m 1441\u001b[0m return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\n\u001b[0;32m 1442\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1443\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1444\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1445\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "model = A2C('MlpLstmPolicy', env, verbose=1) \n", + "model.learn(total_timesteps=1000000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "info {'total_reward': 1.8100000000000023, 'total_profit': 1.0550400909748883, 'position': 1}\n" + ] + } + ], + "source": [ + "env = gym.make('stocks-v0', df=df, frame_bound=(90,110), window_size=5)\n", + "obs = env.reset()\n", + "while True: \n", + " obs = obs[np.newaxis, ...]\n", + " action, _states = model.predict(obs)\n", + " obs, rewards, done, info = env.step(action)\n", + " if done:\n", + " print(\"info\", info)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,6))\n", + "plt.cla()\n", + "env.render_all()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/RL Model/gmedata.csv b/RL Model/gmedata.csv new file mode 100644 index 00000000..698c4faf --- /dev/null +++ b/RL Model/gmedata.csv @@ -0,0 +1,252 @@ +Date,Open,High,Low,Close,Volume +03/12/2021,"275.00","295.50","262.27","264.50","25,845,900" +03/11/2021,"241.64","281.50","232.60","260.00","28,312,490" +03/10/2021,"269.43","348.50","172.00","265.00","71,570,570" +03/09/2021,"217.71","249.85","208.51","246.90","39,099,328" +03/08/2021,"154.89","210.87","146.10","194.50","63,565,621" +03/05/2021,"128.17","151.53","127.50","137.74","30,733,670" +03/04/2021,"125.00","147.87","115.30","132.35","32,606,891" +03/03/2021,"122.51","127.75","113.12","124.18","19,273,881" +03/02/2021,"116.93","133.20","112.20","118.18","33,783,039" +03/01/2021,"104.54","133.99","99.97","120.40","49,793,969" +02/26/2021,"117.46","142.90","86.00","101.74","92,194,148" +02/25/2021,"169.56","184.68","101.00","108.73","150,308,797" +02/24/2021,"44.70","91.71","44.70","91.71","83,111,742" +02/23/2021,"44.97","46.23","40.00","44.97","7,565,215" +02/22/2021,"46.69","48.51","42.40","46.00","19,476,020" +02/19/2021,"41.28","43.89","38.50","40.59","14,828,240" +02/18/2021,"48.49","48.87","40.65","40.69","23,990,561" 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+04/07/2020,"3.39","3.44","3.10","3.27","2,836,859" +04/06/2020,"2.86","3.20","2.83","3.09","3,331,070" +04/03/2020,"2.85","2.94","2.57","2.80","3,830,761" +04/02/2020,"3.26","3.35","2.85","2.85","4,064,339" +04/01/2020,"3.45","3.49","3.12","3.25","4,568,695" +03/31/2020,"3.63","3.80","3.50","3.50","2,304,132" +03/30/2020,"4.22","4.27","3.65","3.65","3,350,571" +03/27/2020,"4.94","5.09","4.15","4.22","7,024,763" +03/26/2020,"4.24","4.71","4.24","4.41","6,210,979" +03/25/2020,"4.15","4.49","4.04","4.17","3,592,059" +03/24/2020,"3.95","4.21","3.89","4.16","6,805,659" +03/23/2020,"3.58","3.86","3.50","3.81","6,009,784" +03/20/2020,"4.08","4.08","3.65","3.76","7,722,194" +03/19/2020,"3.71","4.20","3.55","4.19","5,039,539" +03/18/2020,"4.10","4.25","3.50","3.77","3,651,709" +03/17/2020,"4.40","4.65","4.11","4.23","3,562,210" +03/16/2020,"3.93","4.57","3.90","4.37","4,866,696" diff --git a/RL Model/requirents.txt b/RL Model/requirents.txt new file mode 100644 index 00000000..e69de29b diff 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zxPu5!pC$JHd8dTW{PV*t;2?p;?*LY;%msa&1+74cE?8gX5stg^kT5U_r(m6A2t)Oo zlW=D9)psD6kjVoD*6>T(%10NM76Dr+l1r4i}?(qB9Exx4XLauFke`p+M2rV zjqWlNgkqc6FPA$Cs9_A6a{jBc)SYOjMxPN!IO@>}$GMjr|K Crash - # Assumes Rectangle - if game_map.get_at((int(point[0]), int(point[1]))) == BORDER_COLOR: - self.alive = False - break - - def check_radar(self, degree, game_map): - length = 0 - x = int(self.center[0] + math.cos(math.radians(360 - (self.angle + degree))) * length) - y = int(self.center[1] + math.sin(math.radians(360 - (self.angle + degree))) * length) - - # While We Don't Hit BORDER_COLOR AND length < 300 (just a max) -> go further and further - while not game_map.get_at((x, y)) == BORDER_COLOR and length < 300: - length = length + 1 - x = int(self.center[0] + math.cos(math.radians(360 - (self.angle + degree))) * length) - y = int(self.center[1] + math.sin(math.radians(360 - (self.angle + degree))) * length) - - # Calculate Distance To Border And Append To Radars List - dist = int(math.sqrt(math.pow(x - self.center[0], 2) + math.pow(y - self.center[1], 2))) - self.radars.append([(x, y), dist]) - - def update(self, game_map): - # Set The Speed To 20 For The First Time - # Only When Having 4 Output Nodes With Speed Up and Down - if not self.speed_set: - self.speed = 20 - self.speed_set = True - - # Get Rotated Sprite And Move Into The Right X-Direction - # Don't Let The Car Go Closer Than 20px To The Edge - self.rotated_sprite = self.rotate_center(self.sprite, self.angle) - self.position[0] += math.cos(math.radians(360 - self.angle)) * self.speed - self.position[0] = max(self.position[0], 20) - self.position[0] = min(self.position[0], WIDTH - 120) - - # Increase Distance and Time - self.distance += self.speed - self.time += 1 - - # Same For Y-Position - self.position[1] += math.sin(math.radians(360 - self.angle)) * self.speed - self.position[1] = max(self.position[1], 20) - self.position[1] = min(self.position[1], WIDTH - 120) - - # Calculate New Center - self.center = [int(self.position[0]) + CAR_SIZE_X / 2, int(self.position[1]) + CAR_SIZE_Y / 2] - - # Calculate Four Corners - # Length Is Half The Side - length = 0.5 * CAR_SIZE_X - left_top = [self.center[0] + math.cos(math.radians(360 - (self.angle + 30))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 30))) * length] - right_top = [self.center[0] + math.cos(math.radians(360 - (self.angle + 150))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 150))) * length] - left_bottom = [self.center[0] + math.cos(math.radians(360 - (self.angle + 210))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 210))) * length] - right_bottom = [self.center[0] + math.cos(math.radians(360 - (self.angle + 330))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 330))) * length] - self.corners = [left_top, right_top, left_bottom, right_bottom] - - # Check Collisions And Clear Radars - self.check_collision(game_map) - self.radars.clear() - - # From -90 To 120 With Step-Size 45 Check Radar - for d in range(-90, 120, 45): - self.check_radar(d, game_map) - - def get_data(self): - # Get Distances To Border - radars = self.radars - return_values = [0, 0, 0, 0, 0] - for i, radar in enumerate(radars): - return_values[i] = int(radar[1] / 30) - - return return_values - - def is_alive(self): - # Basic Alive Function - return self.alive - - def get_reward(self): - # Calculate Reward (Maybe Change?) - # return self.distance / 50.0 - return self.distance / (CAR_SIZE_X / 2) - - def rotate_center(self, image, angle): - # Rotate The Rectangle - rectangle = image.get_rect() - rotated_image = pygame.transform.rotate(image, angle) - rotated_rectangle = rectangle.copy() - rotated_rectangle.center = rotated_image.get_rect().center - rotated_image = rotated_image.subsurface(rotated_rectangle).copy() - return rotated_image - - -def run_simulation(genomes, config): - - # Empty Collections For Nets and Cars - nets = [] - cars = [] - - # Initialize PyGame And The Display - pygame.init() - screen = pygame.display.set_mode((WIDTH, HEIGHT), pygame.FULLSCREEN) - - # For All Genomes Passed Create A New Neural Network - for i, g in genomes: - net = neat.nn.FeedForwardNetwork.create(g, config) - nets.append(net) - g.fitness = 0 - - cars.append(Car()) - - # Clock Settings - # Font Settings & Loading Map - clock = pygame.time.Clock() - generation_font = pygame.font.SysFont("Arial", 30) - alive_font = pygame.font.SysFont("Arial", 20) - game_map = pygame.image.load('map3.png').convert() # Convert Speeds Up A Lot - - global current_generation - current_generation += 1 - - # Simple Counter To Roughly Limit Time (Not Good Practice) - counter = 0 - - while True: - # Exit On Quit Event - for event in pygame.event.get(): - if event.type == pygame.QUIT: - sys.exit(0) - - # For Each Car Get The Acton It Takes - for i, car in enumerate(cars): - output = nets[i].activate(car.get_data()) - choice = output.index(max(output)) - if choice == 0: - car.angle += 10 # Left - elif choice == 1: - car.angle -= 10 # Right - elif choice == 2: - if(car.speed - 2 >= 12): - car.speed -= 2 # Slow Down - else: - car.speed += 2 # Speed Up - - # Check If Car Is Still Alive - # Increase Fitness If Yes And Break Loop If Not - still_alive = 0 - for i, car in enumerate(cars): - if car.is_alive(): - still_alive += 1 - car.update(game_map) - genomes[i][1].fitness += car.get_reward() - - if still_alive == 0: - break - - counter += 1 - if counter == 30 * 40: # Stop After About 20 Seconds - break - - # Draw Map And All Cars That Are Alive - screen.blit(game_map, (0, 0)) - for car in cars: - if car.is_alive(): - car.draw(screen) - - # Display Info - text = generation_font.render("Generation: " + str(current_generation), True, (0,0,0)) - text_rect = text.get_rect() - text_rect.center = (900, 450) - screen.blit(text, text_rect) - - text = alive_font.render("Still Alive: " + str(still_alive), True, (0, 0, 0)) - text_rect = text.get_rect() - text_rect.center = (900, 490) - screen.blit(text, text_rect) - - pygame.display.flip() - clock.tick(60) # 60 FPS - -if __name__ == "__main__": - - # Load Config - config_path = "./config.txt" - config = neat.config.Config(neat.DefaultGenome, - neat.DefaultReproduction, - neat.DefaultSpeciesSet, - neat.DefaultStagnation, - config_path) - - # Create Population And Add Reporters - population = neat.Population(config) - population.add_reporter(neat.StdOutReporter(True)) - stats = neat.StatisticsReporter() - population.add_reporter(stats) - - # Run Simulation For A Maximum of 1000 Generations - population.run(run_simulation, 1000) - - - - diff --git a/Self Driving Car-RL/tempCodeRunnerFile.py b/Self Driving Car-RL/tempCodeRunnerFile.py deleted file mode 100644 index 19b11ce5..00000000 --- a/Self Driving Car-RL/tempCodeRunnerFile.py +++ /dev/null @@ -1 +0,0 @@ -png \ No newline at end of file