From 73ae101ba77d7a988e23eaf37ad530dd078561fe Mon Sep 17 00:00:00 2001
From: Rishi <62225369+rishipython@users.noreply.github.com>
Date: Mon, 24 May 2021 20:45:49 -0400
Subject: [PATCH] Created using Colaboratory
---
Q_Learning_Stock_Trading.ipynb | 7467 ++++++++++++++++++++++++++++++++
1 file changed, 7467 insertions(+)
create mode 100644 Q_Learning_Stock_Trading.ipynb
diff --git a/Q_Learning_Stock_Trading.ipynb b/Q_Learning_Stock_Trading.ipynb
new file mode 100644
index 0000000..2003318
--- /dev/null
+++ b/Q_Learning_Stock_Trading.ipynb
@@ -0,0 +1,7467 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Q-Learning_Stock_Trading.ipynb",
+ "provenance": [],
+ "collapsed_sections": [
+ "T8ldRTOODvtH"
+ ],
+ "toc_visible": true,
+ "machine_shape": "hm",
+ "authorship_tag": "ABX9TyN4eGdPVW+hZP4NHtmcMbhQ",
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "M-wzC5aNKEps",
+ "outputId": "ceb15500-b3aa-4f78-e0fe-e1d692cd23e4"
+ },
+ "source": [
+ "!pip install yfinance"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Collecting yfinance\n",
+ " Downloading https://files.pythonhosted.org/packages/a7/ee/315752b9ef281ba83c62aa7ec2e2074f85223da6e7e74efb4d3e11c0f510/yfinance-0.1.59.tar.gz\n",
+ "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
+ "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
+ "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n",
+ "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.9)\n",
+ "Collecting lxml>=4.5.1\n",
+ "\u001b[?25l Downloading https://files.pythonhosted.org/packages/30/c0/d0526314971fc661b083ab135747dc68446a3022686da8c16d25fcf6ef07/lxml-4.6.3-cp37-cp37m-manylinux2014_x86_64.whl (6.3MB)\n",
+ "\u001b[K |████████████████████████████████| 6.3MB 11.7MB/s \n",
+ "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
+ "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2020.12.5)\n",
+ "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
+ "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
+ "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
+ "Building wheels for collected packages: yfinance\n",
+ " Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for yfinance: filename=yfinance-0.1.59-py2.py3-none-any.whl size=23442 sha256=947a99b06d416c2f8f70423096711ad01e68773f1c5873244c0223614567c4a4\n",
+ " Stored in directory: /root/.cache/pip/wheels/f8/2a/0f/4b5a86e1d52e451757eb6bc17fd899629f0925c777741b6d04\n",
+ "Successfully built yfinance\n",
+ "Installing collected packages: lxml, yfinance\n",
+ " Found existing installation: lxml 4.2.6\n",
+ " Uninstalling lxml-4.2.6:\n",
+ " Successfully uninstalled lxml-4.2.6\n",
+ "Successfully installed lxml-4.6.3 yfinance-0.1.59\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0QyyYygRD4vX"
+ },
+ "source": [
+ "import pandas as pd\n",
+ "import datetime\n",
+ "from datetime import date\n",
+ "import yfinance as yf\n",
+ "from matplotlib import pyplot as plt\n",
+ "import keras\n",
+ "from keras.initializers import HeUniform\n",
+ "from keras.layers import Input, Dense\n",
+ "from keras.losses import Huber\n",
+ "from keras.optimizers import Adam\n",
+ "from keras.models import Sequential, Model\n",
+ "from sklearn.preprocessing import Normalizer\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from pickle import dump, load\n",
+ "import numpy as np\n",
+ "import random\n",
+ "from collections import deque\n",
+ "from IPython.display import clear_output\n",
+ "import os"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Se_JDOp5HC2e",
+ "outputId": "f83f80a9-6290-40a5-e480-a12ba4d7d8e5"
+ },
+ "source": [
+ "# Access GPU\n",
+ "gpu_info = !nvidia-smi\n",
+ "gpu_info = '\\n'.join(gpu_info)\n",
+ "if gpu_info.find('failed') >= 0:\n",
+ " print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
+ " print('and then re-execute this cell.')\n",
+ "else:\n",
+ " print(gpu_info)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mon May 24 03:12:05 2021 \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| NVIDIA-SMI 465.19.01 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
+ "|-------------------------------+----------------------+----------------------+\n",
+ "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
+ "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
+ "| | | MIG M. |\n",
+ "|===============================+======================+======================|\n",
+ "| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n",
+ "| N/A 47C P0 27W / 250W | 0MiB / 16280MiB | 0% Default |\n",
+ "| | | N/A |\n",
+ "+-------------------------------+----------------------+----------------------+\n",
+ " \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| Processes: |\n",
+ "| GPU GI CI PID Type Process name GPU Memory |\n",
+ "| ID ID Usage |\n",
+ "|=============================================================================|\n",
+ "| No running processes found |\n",
+ "+-----------------------------------------------------------------------------+\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6xnGdDBGHNkp",
+ "outputId": "98c301c0-9225-45af-fdd2-081c01b1d068"
+ },
+ "source": [
+ "# Access more memory\n",
+ "from psutil import virtual_memory\n",
+ "ram_gb = virtual_memory().total / 1e9\n",
+ "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
+ "\n",
+ "if ram_gb < 20:\n",
+ " print('To enable a high-RAM runtime, select the Runtime > \"Change runtime type\"')\n",
+ " print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')\n",
+ " print('re-execute this cell.')\n",
+ "else:\n",
+ " print('You are using a high-RAM runtime!')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Your runtime has 27.3 gigabytes of available RAM\n",
+ "\n",
+ "You are using a high-RAM runtime!\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vKgc9PIqHSSp",
+ "outputId": "33f8aa8c-8404-400f-bee0-606852e62261"
+ },
+ "source": [
+ "# See if Tensorflow is using GPU\n",
+ "%tensorflow_version 2.x\n",
+ "import tensorflow as tf\n",
+ "device_name = tf.test.gpu_device_name()\n",
+ "if device_name != '/device:GPU:0':\n",
+ " raise SystemError('GPU device not found')\n",
+ "print('Found GPU at: {}'.format(device_name))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Found GPU at: /device:GPU:0\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2bhrdpyWHTXX",
+ "outputId": "08a9e681-bbbc-42a8-cba6-f30b150dcc93"
+ },
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mounted at /content/drive\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_SXDFzHSH37R"
+ },
+ "source": [
+ "drive_dir = f\"{os.sep}content{os.sep}drive{os.sep}My Drive{os.sep}S&P 500 Stock Trading{os.sep}\""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qD7TeVaTH9Ru",
+ "outputId": "db0f73b3-a2c6-4470-a091-b3b86995ff1b"
+ },
+ "source": [
+ "ls \"/content/drive/My Drive/S&P 500 Stock Trading/\""
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "\u001b[0m\u001b[01;34mmodel_1\u001b[0m/ \u001b[01;34mmodel_4\u001b[0m/ x.npy x_val.npy y_train.npy\n",
+ "\u001b[01;34mmodel_2\u001b[0m/ normalizer_x.pkl x_test.npy y.npy y_val.npy\n",
+ "\u001b[01;34mmodel_3\u001b[0m/ normalizer_y.pkl x_train.npy y_test.npy\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "T8ldRTOODvtH"
+ },
+ "source": [
+ "# Get S&P 500 Stock Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "LJ0O_u_yKjYL"
+ },
+ "source": [
+ "class Company:\n",
+ " def __init__(self, symbol, name, stock):\n",
+ " self.symbol = symbol\n",
+ " self.name = name\n",
+ " self.stock = stock"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "S_V_90epHsGO"
+ },
+ "source": [
+ "symbols = []\n",
+ "names = []"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "MSD_Eu0wFY2m",
+ "outputId": "030cf405-7e74-4b3e-b997-9248c5b14a6a"
+ },
+ "source": [
+ "today = datetime.datetime.now()\n",
+ "print(\"Today is \" + str(today.strftime(\"%b-%d-%Y\")))\n",
+ "fifteen_years_ago = today - datetime.timedelta(days=15*365)\n",
+ "print(\"The day fifteen years ago was \" + str(fifteen_years_ago.strftime(\"%b-%d-%Y\")))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Today is May-24-2021\n",
+ "The day fifteen years ago was May-28-2006\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VM5kOqJ6Dye4"
+ },
+ "source": [
+ "# Get symbols and names of all S&P 500 companies started 15 years or more from today\n",
+ "table=pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')\n",
+ "df = table[0]\n",
+ "for i in range(len(df)):\n",
+ " if df.loc[i, \"Symbol\"] == \"BF.B\": # finance can't find data for BF.B, symbol may be delisted\n",
+ " continue\n",
+ " date_first_added = df.loc[i, \"Date first added\"]\n",
+ " if not isinstance(date_first_added, str):\n",
+ " continue\n",
+ " date_first_added = date_first_added[:10]\n",
+ " date_first_added = datetime.datetime.strptime(date_first_added, \"%Y-%m-%d\")\n",
+ " if date_first_added > fifteen_years_ago:\n",
+ " continue\n",
+ " symbols.append(df.loc[i, \"Symbol\"])\n",
+ " names.append(df.loc[i, \"Security\"])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "uICkXScyJunz",
+ "outputId": "5cac0ac2-91cb-4f58-9f27-227ff948f9e6"
+ },
+ "source": [
+ "print(len(symbols))\n",
+ "print(len(names))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "205\n",
+ "205\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "yTw1Ir1yKtbg"
+ },
+ "source": [
+ "str_symbols = \" \".join(symbols)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gMFOjV7pLiUa",
+ "outputId": "a33d869c-77e8-4a80-df0b-e9cff0670b01"
+ },
+ "source": [
+ "data = yf.download(str_symbols, start=fifteen_years_ago, end=today)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[*********************100%***********************] 205 of 205 completed\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 400
+ },
+ "id": "B7zayGFNLyhm",
+ "outputId": "7d08ff30-69cd-45f9-ab8f-2ab461c76e48"
+ },
+ "source": [
+ "data.head()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
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+ "
\n",
+ " \n",
+ " | 2006-05-31 | \n",
+ " 21.545885 | \n",
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+ " ... | \n",
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+ "
\n",
+ " \n",
+ " | 2006-06-01 | \n",
+ " 21.632332 | \n",
+ " 1.909291 | \n",
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+ " ... | \n",
+ " 2661200 | \n",
+ " 597900 | \n",
+ " 5847900 | \n",
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+ " 1419000 | \n",
+ " 8289800 | \n",
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+ " 5121900 | \n",
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+ " 440100 | \n",
+ "
\n",
+ " \n",
+ " | 2006-06-02 | \n",
+ " 21.934923 | \n",
+ " 1.893629 | \n",
+ " 17.328453 | \n",
+ " 14.396259 | \n",
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+ " ... | \n",
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+ " 591900 | \n",
+ "
\n",
+ " \n",
+ " | 2006-06-05 | \n",
+ " 21.206238 | \n",
+ " 1.842649 | \n",
+ " 17.031263 | \n",
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+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 1230 columns
\n",
+ "