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Reinforcement Learning Example FinRL Package.ipynb
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reinforcement-learning-example-finrl-package.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/firmai/feb736498ba0267b1d02fb7e7d17e571/reinforcement-learning-example-finrl-package.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gXaoZs2lh1hi"
},
"source": [
"# Automated stock trading using FinRL with financial data\n",
"\n",
"* Here we will train a deep reinforcement learning model using market data and fundamental data for the 30 publicly traded stocks on the Dow Jones.\n",
"* The goal is to maximize the value of the portfolio at the end of the training period, and we will investigate if we can outperform the benchmark. \n",
"* In this problem, the agent of the model is a robot trader, and the environment is what the agent observes in the market; stock prices, volumes, and financial ratios. \n",
"* The agent observes a current state the environment shows and chooses a trading action from the action space. \n",
"* During the training period, the agent updates its policy (like an instruction manual for trading) to achieve a better performance in the future.\n",
"* The agent's actions are defined by the combination of what stocks the agent will trade, types of trading action(buy, sell and hold), and how many shares the agent will trade. \n",
"* Then, the agent receives a reward from the environment in correspondence with the action it took. \n",
"* Here, the reward is defined by the portfolio values after taking action. \n",
"The agent acts to maximize the total rewards it will receive in the future. \n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lGunVt8oLCVS"
},
"source": [
"# Content"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HOzAKQ-SLGX6"
},
"source": [
"* [1. Problem Definition](#0)\n",
"* [2. Getting Started - Load Python packages](#1)\n",
" * [2.1. Install Packages](#1.1) \n",
" * [2.2. Check Additional Packages](#1.2)\n",
" * [2.3. Import Packages](#1.3)\n",
" * [2.4. Create Folders](#1.4)\n",
"* [3. Download Data](#2)\n",
"* [4. Preprocess fundamental Data](#3) \n",
" * [4-1 Import financial data](#3.1)\n",
" * [4-2 Specify items needed to calculate financial ratios](#3.2)\n",
" * [4-3 Calculate financial ratios](#3.3)\n",
" * [4-4 Deal with NAs and infinite values](#3.4)\n",
" * [4-5 Merge stock price data and ratios into one dataframe](#3.5)\n",
" * [4-6 Calculate market valuation ratios using daily stock price data](#3.6)\n",
"* [5.Build Environment](#4) \n",
" * [5.1. Training & Trade Data Split](#4.1)\n",
" * [5.2. User-defined Environment](#4.2) \n",
" * [5.3. Initialize Environment](#4.3) \n",
"* [6.Implement DRL Algorithms](#5) \n",
"* [7.Backtesting Performance](#6) \n",
" * [7.1. BackTestStats](#6.1)\n",
" * [7.2. BackTestPlot](#6.2) \n",
" * [7.3. Baseline Stats](#6.3) \n",
" * [7.3. Compare to Stock Market Index](#6.4) "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sApkDlD9LIZv"
},
"source": [
"<a id='0'></a>\n",
"# Part 1. Problem Definition"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HjLD2TZSLKZ-"
},
"source": [
"This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
"\n",
"The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
"\n",
"\n",
"* Action: The action space describes the allowed actions that the agent interacts with the\n",
"environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
"selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
"an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
"10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
"\n",
"* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s', i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
"values at state s′ and s, respectively\n",
"\n",
"* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
"our trading agent observes many different features to better learn in an interactive environment.\n",
"\n",
"* Environment: Dow 30 consituents\n",
"\n",
"\n",
"The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ffsre789LY08"
},
"source": [
"<a id='1'></a>\n",
"# Part 2. Load Python Packages"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Uy5_PTmOh1hj"
},
"source": [
"<a id='1.1'></a>\n",
"## 2.1. Install all the packages through FinRL library\n"
]
},
{
"cell_type": "code",
"source": [
"# !python --version\n",
"# !pip install -q condacolab\n",
"# import condacolab\n",
"# condacolab.install_from_url(\"https://repo.anaconda.com/miniconda/Miniconda3-py37_4.12.0-Linux-x86_64.sh\")\n",
"# !python --version\n",
"# ## install finrl library (they have to work on their lib, its very slow)\n",
"# %pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
],
"metadata": {
"id": "H_vBrE_lp56C",
"outputId": "5483498e-19d1-4a81-8bbb-edf10764a62b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Python 3.8.15\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "osBHhVysOEzi"
},
"source": [
"\n",
"<a id='1.2'></a>\n",
"## 2.2. Check if the additional packages needed are present, if not install them. \n",
"* Yahoo Finance API\n",
"* pandas\n",
"* numpy\n",
"* matplotlib\n",
"* stockstats\n",
"* OpenAI gym\n",
"* stable-baselines\n",
"* tensorflow\n",
"* pyfolio"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nGv01K8Sh1hn"
},
"source": [
"<a id='1.3'></a>\n",
"## 2.3. Import Packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lPqeTTwoh1hn"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"# matplotlib.use('Agg')\n",
"import datetime\n",
"\n",
"%matplotlib inline\n",
"from finrl import config\n",
"from finrl import config_tickers\n",
"from finrl.finrl_meta.preprocessor.yahoodownloader import YahooDownloader\n",
"from finrl.finrl_meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
"from finrl.finrl_meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
"from finrl.agents.stablebaselines3.models import DRLAgent\n",
"from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
"\n",
"from pprint import pprint\n",
"\n",
"import sys\n",
"sys.path.append(\"../FinRL-Library\")\n",
"\n",
"import itertools"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T2owTj985RW4"
},
"source": [
"<a id='1.4'></a>\n",
"## 2.4. Create Folders"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "w9A8CN5R5PuZ"
},
"outputs": [],
"source": [
"import os\n",
"if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
" os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
"if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
" os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
"if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
" os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
"if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
" os.makedirs(\"./\" + config.RESULTS_DIR)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "A289rQWMh1hq"
},
"source": [
"<a id='2'></a>\n",
"# Part 3. Download Stock Data from Yahoo Finance\n",
"Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
"* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
"* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NPeQ7iS-LoMm"
},
"source": [
"\n",
"\n",
"-----\n",
"class YahooDownloader:\n",
" Provides methods for retrieving daily stock data from\n",
" Yahoo Finance API\n",
"\n",
" Attributes\n",
" ----------\n",
" start_date : str\n",
" start date of the data (modified from config.py)\n",
" end_date : str\n",
" end date of the data (modified from config.py)\n",
" ticker_list : list\n",
" a list of stock tickers (modified from config.py)\n",
"\n",
" Methods\n",
" -------\n",
" fetch_data()\n",
" Fetches data from yahoo API\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JzqRRTOX6aFu",
"outputId": "08decef0-35c3-49bc-f0d3-fe1537629bdd"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['AXP', 'AMGN', 'AAPL', 'BA', 'CAT', 'CSCO', 'CVX', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'KO', 'JPM', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'CRM', 'VZ', 'V', 'WBA', 'WMT', 'DIS', 'DOW']\n"
]
}
],
"source": [
"print(config_tickers.DOW_30_TICKER)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UrsxceLbZ8Qn"
},
"source": [
"We are especially interested in the performance of these models over the Covid slump of March 2020."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yCKm4om-s9kE",
"outputId": "10c1f97c-8292-4266-bbd5-f39b264ccbce"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[*********************100%***********************] 1 of 1 completed\n",
"[*********************100%***********************] 1 of 1 completed\n",
"[*********************100%***********************] 1 of 1 completed\n",
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"[*********************100%***********************] 1 of 1 completed\n",
"[*********************100%***********************] 1 of 1 completed\n",
"[*********************100%***********************] 1 of 1 completed\n",
"Shape of DataFrame: (88061, 8)\n"
]
}
],
"source": [
"df = YahooDownloader(start_date = '2009-01-01',\n",
" end_date = '2021-01-01',\n",
" ticker_list = config_tickers.DOW_30_TICKER).fetch_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CV3HrZHLh1hy",
"outputId": "67ae2408-fba3-4e02-e0bb-ec6cf94d4f9c"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(88061, 8)"
]
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "aBKF7sfV-Pi4",
"outputId": "60b90b6c-e1f5-41e3-c52f-1a3da95c67cf"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" date open high low close volume tic \\\n",
"0 2009-01-02 3.067143 3.251429 3.041429 2.775245 746015200 AAPL \n",
"1 2009-01-02 58.590000 59.080002 57.750000 45.228088 6547900 AMGN \n",
"2 2009-01-02 18.570000 19.520000 18.400000 15.535341 10955700 AXP \n",
"3 2009-01-02 42.799999 45.560001 42.779999 33.941101 7010200 BA \n",
"4 2009-01-02 44.910000 46.980000 44.709999 32.164722 7117200 CAT \n",
"\n",
" day \n",
"0 4 \n",
"1 4 \n",
"2 4 \n",
"3 4 \n",
"4 4 "
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{
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"execution_count": null,
"metadata": {
"id": "QRWscKiPXXnj"
},
"outputs": [],
"source": [
"df['date'] = pd.to_datetime(df['date'],format='%Y-%m-%d')"
]
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" date open high low close volume tic day\n",
"0 2009-01-02 3.067143 3.251429 3.041429 2.775245 746015200 AAPL 4\n",
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"3 2009-01-02 42.799999 45.560001 42.779999 33.941101 7010200 BA 4\n",
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"source": [
"df.sort_values(['date','tic'],ignore_index=True).head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uqC6c40Zh1iH"
},
"source": [
"# Part 4: Preprocess fundamental data\n",
"- Import finanical data downloaded from Compustat via WRDS(Wharton Research Data Service)\n",
"- Preprocess the dataset and calculate financial ratios\n",
"- Add those ratios to the price data preprocessed in Part 3\n",
"- Calculate price-related ratios such as P/E and P/B"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VbXEllD2oROq"
},
"source": [
"## 4-1 Import the financial data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PmKP-1ii3RLS",
"outputId": "560179e3-1ce2-4a32-a0aa-7230e27f4bfd"
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"outputs": [
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"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2882: DtypeWarning: Columns (16,25) have mixed types.Specify dtype option on import or set low_memory=False.\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n"
]
}
],
"source": [
"# Import fundamental data from my GitHub repository\n",
"url = 'https://raw.githubusercontent.com/mariko-sawada/FinRL_with_fundamental_data/main/dow_30_fundamental_wrds.csv'\n",
"\n",
"fund = pd.read_csv(url)"
]
},
{
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"id": "Tslhs_O5pOTL",
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"outputs": [
{
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" gvkey datadate fyearq fqtr fyr indfmt consol popsrc datafmt tic ... \\\n",
"0 1447 19990630 1999 2 12 INDL C D STD AXP ... \n",
"1 1447 19990930 1999 3 12 INDL C D STD AXP ... \n",
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"3 1447 20000331 2000 1 12 INDL C D STD AXP ... \n",
"4 1447 20000630 2000 2 12 INDL C D STD AXP ... \n",
"\n",
" dvpsxq mkvaltq prccq prchq prclq adjex ggroup gind gsector \\\n",
"0 0.225 NaN 130.1250 142.6250 114.5000 3.0 4020 402020 40 \n",
"1 0.000 NaN 135.0000 150.6250 121.8750 3.0 4020 402020 40 \n",
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"4 0.080 NaN 52.1250 57.1875 43.9375 1.0 4020 402020 40 \n",
"\n",
" gsubind \n",
"0 40202010 \n",
"1 40202010 \n",
"2 40202010 \n",
"3 40202010 \n",
"4 40202010 \n",
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" + ' to learn more about interactive tables.';\n",
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},
"metadata": {},
"execution_count": 11
}
],
"source": [
"# Check the imported dataset\n",
"fund.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9yk1dHTYogEP"
},
"source": [
"## 4-2 Specify items needed to calculate financial ratios\n",
"- To learn more about the data description of the dataset, please check WRDS's website(https://wrds-www.wharton.upenn.edu/). Login will be required."
]
},
{
"cell_type": "markdown",
"source": [
"Since the imported data contains more than 300 data items, we specify the items to calculate financial ratios. Then we rename the column names for readability."
],
"metadata": {
"id": "cAt0QkRTzHA_"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CD0kFC7Ap02K"
},
"outputs": [],
"source": [
"# List items that are used to calculate financial ratios\n",
"\n",
"items = [\n",
" 'datadate', # Date\n",
" 'tic', # Ticker\n",
" 'oiadpq', # Quarterly operating income\n",
" 'revtq', # Quartely revenue\n",
" 'niq', # Quartely net income\n",
" 'atq', # Total asset\n",
" 'teqq', # Shareholder's equity\n",
" 'epspiy', # EPS(Basic) incl. Extraordinary items\n",
" 'ceqq', # Common Equity\n",
" 'cshoq', # Common Shares Outstanding\n",
" 'dvpspq', # Dividends per share\n",
" 'actq', # Current assets\n",
" 'lctq', # Current liabilities\n",
" 'cheq', # Cash & Equivalent\n",
" 'rectq', # Recievalbles\n",
" 'cogsq', # Cost of Goods Sold\n",
" 'invtq', # Inventories\n",
" 'apq',# Account payable\n",
" 'dlttq', # Long term debt\n",
" 'dlcq', # Debt in current liabilites\n",
" 'ltq' # Liabilities \n",
"]\n",
"\n",
"# Omit items that will not be used\n",
"fund_data = fund[items]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jE7UNYtIqFkv"
},
"outputs": [],
"source": [
"# Rename column names for the sake of readability\n",
"fund_data = fund_data.rename(columns={\n",
" 'datadate':'date', # Date\n",
" 'oiadpq':'op_inc_q', # Quarterly operating income\n",
" 'revtq':'rev_q', # Quartely revenue\n",
" 'niq':'net_inc_q', # Quartely net income\n",
" 'atq':'tot_assets', # Assets\n",
" 'teqq':'sh_equity', # Shareholder's equity\n",
" 'epspiy':'eps_incl_ex', # EPS(Basic) incl. Extraordinary items\n",
" 'ceqq':'com_eq', # Common Equity\n",
" 'cshoq':'sh_outstanding', # Common Shares Outstanding\n",
" 'dvpspq':'div_per_sh', # Dividends per share\n",
" 'actq':'cur_assets', # Current assets\n",
" 'lctq':'cur_liabilities', # Current liabilities\n",
" 'cheq':'cash_eq', # Cash & Equivalent\n",
" 'rectq':'receivables', # Receivalbles\n",
" 'cogsq':'cogs_q', # Cost of Goods Sold\n",
" 'invtq':'inventories', # Inventories\n",
" 'apq': 'payables',# Account payable\n",
" 'dlttq':'long_debt', # Long term debt\n",
" 'dlcq':'short_debt', # Debt in current liabilites\n",
" 'ltq':'tot_liabilities' # Liabilities \n",
"})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 299
},
"id": "A0sszApfqO6D",
"outputId": "a7a52000-fe9e-4143-fda9-fd6035dbe44c"
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{
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"text/plain": [
" date tic op_inc_q rev_q net_inc_q tot_assets sh_equity \\\n",
"0 19990630 AXP 896.0 5564.0 646.0 132452.0 9762.0 \n",
"1 19990930 AXP 906.0 5584.0 648.0 132616.0 9744.0 \n",
"2 19991231 AXP 845.0 6009.0 606.0 148517.0 10095.0 \n",
"3 20000331 AXP 920.0 6021.0 656.0 150662.0 10253.0 \n",
"4 20000630 AXP 1046.0 6370.0 740.0 148553.0 10509.0 \n",
"\n",
" eps_incl_ex com_eq sh_outstanding ... cur_assets cur_liabilities \\\n",
"0 2.73 9762.0 449.0 ... NaN NaN \n",
"1 4.18 9744.0 447.6 ... NaN NaN \n",
"2 5.54 10095.0 446.9 ... NaN NaN \n",
"3 1.48 10253.0 444.7 ... NaN NaN \n",
"4 1.05 10509.0 1333.0 ... NaN NaN \n",
"\n",
" cash_eq receivables cogs_q inventories payables long_debt short_debt \\\n",
"0 6096.0 46774.0 4668.0 448.0 22282.0 7005.0 24785.0 \n",
"1 5102.0 48827.0 4678.0 284.0 23587.0 6720.0 24683.0 \n",
"2 10391.0 54033.0 5164.0 277.0 25719.0 4685.0 32437.0 \n",
"3 7425.0 53663.0 5101.0 315.0 26379.0 5670.0 29342.0 \n",
"4 6841.0 54286.0 5324.0 261.0 29536.0 5336.0 26170.0 \n",
"\n",
" tot_liabilities \n",
"0 122690.0 \n",
"1 122872.0 \n",
"2 138422.0 \n",
"3 140409.0 \n",
"4 138044.0 \n",
"\n",
"[5 rows x 21 columns]"
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" <td>19990630</td>\n",
" <td>AXP</td>\n",
" <td>896.0</td>\n",
" <td>5564.0</td>\n",
" <td>646.0</td>\n",
" <td>132452.0</td>\n",
" <td>9762.0</td>\n",
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" <td>NaN</td>\n",
" <td>6096.0</td>\n",
" <td>46774.0</td>\n",
" <td>4668.0</td>\n",
" <td>448.0</td>\n",
" <td>22282.0</td>\n",
" <td>7005.0</td>\n",
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" <td>19990930</td>\n",
" <td>AXP</td>\n",
" <td>906.0</td>\n",
" <td>5584.0</td>\n",
" <td>648.0</td>\n",
" <td>132616.0</td>\n",
" <td>9744.0</td>\n",
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" <td>9744.0</td>\n",
" <td>447.6</td>\n",
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" <td>NaN</td>\n",
" <td>5102.0</td>\n",
" <td>48827.0</td>\n",
" <td>4678.0</td>\n",
" <td>284.0</td>\n",
" <td>23587.0</td>\n",
" <td>6720.0</td>\n",
" <td>24683.0</td>\n",
" <td>122872.0</td>\n",
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" <th>2</th>\n",
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" <td>AXP</td>\n",
" <td>845.0</td>\n",
" <td>6009.0</td>\n",
" <td>606.0</td>\n",
" <td>148517.0</td>\n",
" <td>10095.0</td>\n",
" <td>5.54</td>\n",
" <td>10095.0</td>\n",
" <td>446.9</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
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" <td>10391.0</td>\n",
" <td>54033.0</td>\n",
" <td>5164.0</td>\n",
" <td>277.0</td>\n",
" <td>25719.0</td>\n",
" <td>4685.0</td>\n",
" <td>32437.0</td>\n",
" <td>138422.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>20000331</td>\n",
" <td>AXP</td>\n",
" <td>920.0</td>\n",
" <td>6021.0</td>\n",
" <td>656.0</td>\n",
" <td>150662.0</td>\n",
" <td>10253.0</td>\n",
" <td>1.48</td>\n",
" <td>10253.0</td>\n",
" <td>444.7</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7425.0</td>\n",
" <td>53663.0</td>\n",
" <td>5101.0</td>\n",
" <td>315.0</td>\n",
" <td>26379.0</td>\n",
" <td>5670.0</td>\n",
" <td>29342.0</td>\n",
" <td>140409.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20000630</td>\n",
" <td>AXP</td>\n",
" <td>1046.0</td>\n",
" <td>6370.0</td>\n",
" <td>740.0</td>\n",
" <td>148553.0</td>\n",
" <td>10509.0</td>\n",
" <td>1.05</td>\n",
" <td>10509.0</td>\n",
" <td>1333.0</td>\n",
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" <td>26170.0</td>\n",
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},
"metadata": {},
"execution_count": 14
}
],
"source": [
"# Check the data\n",
"fund_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xPvwtMQUqZdP"
},
"source": [
"## 4-3 Calculate financial ratios\n",
"- For items from Profit/Loss statements, we calculate LTM (Last Twelve Months) and use them to derive profitability related ratios such as Operating Maring and ROE. For items from balance sheets, we use the numbers on the day.\n",
"- To check the definitions of the financial ratios calculated here, please refer to CFI's website: https://corporatefinanceinstitute.com/resources/knowledge/finance/financial-ratios/"
]
},
{
"cell_type": "markdown",
"source": [
"Using the items we specified in the previous subpart, we calculate 15 financial ratios to represent companies' financial conditions. The list of the ratios is as in the following:\n",
"- Profitability ratios: Operating Margin, Net Income Margin, Return on Equity, Return on Assets\n",
"- Liquidity ratios: Current ratio, Cash ratio, Quick ratio\n",
"- Efficiency ratios: Inventory turnover ratio, Payable turnover ratio, Receivable turnover ratio\n",
"- Leverage financial ratios: Debt ratio, Debt to Equity\n",
"- Market valuation ratios: P/E, P/B, Dividend yield\n",
"We need to calculate LTM(Last Twelve Months) for items from Profit/Loss statements since we are dealing with quarterly data. We use the values of items from balance sheets as they are since they are stock numbers. For example, we want to calculate ROE at the end of the third quarter in FY2018. For the numerator, we sum up four quarterly net income data."
],
"metadata": {
"id": "tu_t73zIzPV-"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cfWtEophqS33",
"outputId": "a449c5b6-89c3-4f76-d90f-682d489f8cf9"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in double_scalars\n",
" from ipykernel import kernelapp as app\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in double_scalars\n",
" from ipykernel import kernelapp as app\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: RuntimeWarning: divide by zero encountered in double_scalars\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: RuntimeWarning: invalid value encountered in double_scalars\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:77: RuntimeWarning: divide by zero encountered in double_scalars\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:77: RuntimeWarning: invalid value encountered in double_scalars\n"
]
}
],
"source": [
"# Calculate financial ratios\n",
"date = pd.to_datetime(fund_data['date'],format='%Y%m%d')\n",
"\n",
"tic = fund_data['tic'].to_frame('tic')\n",
"\n",
"# Profitability ratios\n",
"# Operating Margin\n",
"OPM = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='OPM')\n",
"for i in range(0, fund_data.shape[0]):\n",
" if i-3 < 0:\n",
" OPM[i] = np.nan\n",
" elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
" OPM.iloc[i] = np.nan\n",
" else:\n",
" OPM.iloc[i] = np.sum(fund_data['op_inc_q'].iloc[i-3:i])/np.sum(fund_data['rev_q'].iloc[i-3:i])\n",
"\n",
"# Net Profit Margin \n",
"NPM = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='NPM')\n",
"for i in range(0, fund_data.shape[0]):\n",
" if i-3 < 0:\n",
" NPM[i] = np.nan\n",
" elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
" NPM.iloc[i] = np.nan\n",
" else:\n",
" NPM.iloc[i] = np.sum(fund_data['net_inc_q'].iloc[i-3:i])/np.sum(fund_data['rev_q'].iloc[i-3:i])\n",
"\n",
"# Return On Assets\n",
"ROA = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='ROA')\n",
"for i in range(0, fund_data.shape[0]):\n",
" if i-3 < 0:\n",
" ROA[i] = np.nan\n",
" elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
" ROA.iloc[i] = np.nan\n",
" else:\n",
" ROA.iloc[i] = np.sum(fund_data['net_inc_q'].iloc[i-3:i])/fund_data['tot_assets'].iloc[i]\n",
"\n",
"# Return on Equity\n",
"ROE = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='ROE')\n",
"for i in range(0, fund_data.shape[0]):\n",
" if i-3 < 0:\n",
" ROE[i] = np.nan\n",
" elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
" ROE.iloc[i] = np.nan\n",
" else:\n",
" ROE.iloc[i] = np.sum(fund_data['net_inc_q'].iloc[i-3:i])/fund_data['sh_equity'].iloc[i] \n",
"\n",
"# For calculating valuation ratios in the next subpart, calculate per share items in advance\n",
"# Earnings Per Share \n",
"EPS = fund_data['eps_incl_ex'].to_frame('EPS')\n",
"\n",
"# Book Per Share\n",
"BPS = (fund_data['com_eq']/fund_data['sh_outstanding']).to_frame('BPS') # Need to check units\n",
"\n",
"#Dividend Per Share\n",
"DPS = fund_data['div_per_sh'].to_frame('DPS')\n",
"\n",
"# Liquidity ratios\n",
"# Current ratio\n",
"cur_ratio = (fund_data['cur_assets']/fund_data['cur_liabilities']).to_frame('cur_ratio')\n",
"\n",
"# Quick ratio\n",
"quick_ratio = ((fund_data['cash_eq'] + fund_data['receivables'] )/fund_data['cur_liabilities']).to_frame('quick_ratio')\n",
"\n",
"# Cash ratio\n",
"cash_ratio = (fund_data['cash_eq']/fund_data['cur_liabilities']).to_frame('cash_ratio')\n",
"\n",
"\n",
"# Efficiency ratios\n",
"# Inventory turnover ratio\n",
"inv_turnover = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='inv_turnover')\n",
"for i in range(0, fund_data.shape[0]):\n",
" if i-3 < 0:\n",
" inv_turnover[i] = np.nan\n",
" elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
" inv_turnover.iloc[i] = np.nan\n",
" else:\n",
" inv_turnover.iloc[i] = np.sum(fund_data['cogs_q'].iloc[i-3:i])/fund_data['inventories'].iloc[i]\n",
"\n",
"# Receivables turnover ratio \n",
"acc_rec_turnover = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='acc_rec_turnover')\n",
"for i in range(0, fund_data.shape[0]):\n",
" if i-3 < 0:\n",
" acc_rec_turnover[i] = np.nan\n",
" elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
" acc_rec_turnover.iloc[i] = np.nan\n",
" else:\n",
" acc_rec_turnover.iloc[i] = np.sum(fund_data['rev_q'].iloc[i-3:i])/fund_data['receivables'].iloc[i]\n",
"\n",
"# Payable turnover ratio\n",
"acc_pay_turnover = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='acc_pay_turnover')\n",
"for i in range(0, fund_data.shape[0]):\n",
" if i-3 < 0:\n",
" acc_pay_turnover[i] = np.nan\n",
" elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
" acc_pay_turnover.iloc[i] = np.nan\n",
" else:\n",
" acc_pay_turnover.iloc[i] = np.sum(fund_data['cogs_q'].iloc[i-3:i])/fund_data['payables'].iloc[i]\n",
" \n",
"## Leverage financial ratios\n",
"# Debt ratio\n",
"debt_ratio = (fund_data['tot_liabilities']/fund_data['tot_assets']).to_frame('debt_ratio')\n",
"\n",
"# Debt to Equity ratio\n",
"debt_to_equity = (fund_data['tot_liabilities']/fund_data['sh_equity']).to_frame('debt_to_equity')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wwFVopRDqcby"
},
"outputs": [],
"source": [
"# Create a dataframe that merges all the ratios\n",
"ratios = pd.concat([date,tic,OPM,NPM,ROA,ROE,EPS,BPS,DPS,\n",
" cur_ratio,quick_ratio,cash_ratio,inv_turnover,acc_rec_turnover,acc_pay_turnover,\n",
" debt_ratio,debt_to_equity], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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" date tic OPM NPM ROA ROE EPS BPS \\\n",
"0 1999-06-30 AXP NaN NaN NaN NaN 2.73 21.741648 \n",
"1 1999-09-30 AXP NaN NaN NaN NaN 4.18 21.769437 \n",
"2 1999-12-31 AXP NaN NaN NaN NaN 5.54 22.588946 \n",
"3 2000-03-31 AXP 0.154281 0.110742 0.012611 0.185312 1.48 23.055993 \n",
"4 2000-06-30 AXP 0.151641 0.108436 0.012857 0.181749 1.05 7.883721 \n",
"\n",
" DPS cur_ratio quick_ratio cash_ratio inv_turnover acc_rec_turnover \\\n",
"0 0.225 NaN NaN NaN NaN NaN \n",
"1 0.225 NaN NaN NaN NaN NaN \n",
"2 0.225 NaN NaN NaN NaN NaN \n",
"3 0.225 NaN NaN NaN 46.063492 0.319717 \n",
"4 0.080 NaN NaN NaN 57.252874 0.324467 \n",
"\n",
" acc_pay_turnover debt_ratio debt_to_equity \n",
"0 NaN 0.926298 12.568121 \n",
"1 NaN 0.926525 12.610016 \n",
"2 NaN 0.932028 13.711937 \n",
"3 0.550059 0.931947 13.694431 \n",
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]
},
"metadata": {},
"execution_count": 17
}
],
"source": [
"# Check the ratio data\n",
"ratios.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 270
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"id": "AvG67ouguUKF",
"outputId": "184b556e-2b01-4080-9aad-7786c2675347"
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{
"output_type": "execute_result",
"data": {
"text/plain": [
" date tic OPM NPM ROA ROE EPS BPS \\\n",
"2451 2020-03-31 V 0.667517 0.521213 0.129058 0.271736 2.85 13.647142 \n",
"2452 2020-06-30 V 0.668385 0.519867 0.120448 0.264075 3.92 14.203947 \n",
"2453 2020-09-30 V 0.654464 0.52129 0.107873 0.241066 4.90 14.653484 \n",
"2454 2020-12-31 V 0.638994 0.480876 0.094422 0.201545 1.42 15.908283 \n",
"2455 2021-03-31 V 0.640128 0.488704 0.095218 0.202568 2.80 16.088525 \n",
"\n",
" DPS cur_ratio quick_ratio cash_ratio inv_turnover acc_rec_turnover \\\n",
"2451 0.30 1.248714 1.140070 0.955150 inf 6.11635 \n",
"2452 0.30 1.553478 1.443292 1.221925 inf 5.063131 \n",
"2453 0.30 1.905238 1.784838 1.579807 inf 5.628571 \n",
"2454 0.32 2.121065 1.969814 1.700081 inf 4.725314 \n",
"2455 0.32 2.116356 1.954292 1.700574 inf 4.844961 \n",
"\n",
" acc_pay_turnover debt_ratio debt_to_equity \n",
"2451 2.697537 0.525062 1.105537 \n",
"2452 1.889507 0.543886 1.192433 \n",
"2453 2.730366 0.552515 1.234714 \n",
"2454 2.347866 0.531507 1.134505 \n",
"2455 2.367357 0.529946 1.127414 "
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]
},
"metadata": {},
"execution_count": 18
}
],
"source": [
"ratios.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JULhnNv8uaOB"
},
"source": [
"## 4-4 Deal with NAs and infinite values\n",
"- We replace N/A and infinite values with zero so that they can be recognized as a state"
]
},
{
"cell_type": "markdown",
"source": [
"Since there are NA, 0 , and very small values in our original dataset, we have NA and infinite values after calculating ratios. Here, we replace them with zeros so that we can regard them as states."
],
"metadata": {
"id": "BHKah-YtzV3q"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nuKPlGe4sNzQ"
},
"outputs": [],
"source": [
"# Replace NAs infinite values with zero\n",
"final_ratios = ratios.copy()\n",
"final_ratios = final_ratios.fillna(0)\n",
"final_ratios = final_ratios.replace(np.inf,0)"
]
},
{
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"id": "wc_rvvm1sRDd",
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{
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" date tic OPM NPM ROA ROE EPS BPS \\\n",
"0 1999-06-30 AXP 0.000000 0.000000 0.000000 0.000000 2.73 21.741648 \n",
"1 1999-09-30 AXP 0.000000 0.000000 0.000000 0.000000 4.18 21.769437 \n",
"2 1999-12-31 AXP 0.000000 0.000000 0.000000 0.000000 5.54 22.588946 \n",
"3 2000-03-31 AXP 0.154281 0.110742 0.012611 0.185312 1.48 23.055993 \n",
"4 2000-06-30 AXP 0.151641 0.108436 0.012857 0.181749 1.05 7.883721 \n",
"\n",
" DPS cur_ratio quick_ratio cash_ratio inv_turnover acc_rec_turnover \\\n",
"0 0.225 0.0 0.0 0.0 0.000000 0.000000 \n",
"1 0.225 0.0 0.0 0.0 0.000000 0.000000 \n",
"2 0.225 0.0 0.0 0.0 0.000000 0.000000 \n",
"3 0.225 0.0 0.0 0.0 46.063492 0.319717 \n",
"4 0.080 0.0 0.0 0.0 57.252874 0.324467 \n",
"\n",
" acc_pay_turnover debt_ratio debt_to_equity \n",
"0 0.000000 0.926298 12.568121 \n",
"1 0.000000 0.926525 12.610016 \n",
"2 0.000000 0.932028 13.711937 \n",
"3 0.550059 0.931947 13.694431 \n",
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]
},
"metadata": {},
"execution_count": 20
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],
"source": [
"final_ratios.head()"
]
},
{
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{
"output_type": "execute_result",
"data": {
"text/plain": [
" date tic OPM NPM ROA ROE EPS BPS \\\n",
"2451 2020-03-31 V 0.667517 0.521213 0.129058 0.271736 2.85 13.647142 \n",
"2452 2020-06-30 V 0.668385 0.519867 0.120448 0.264075 3.92 14.203947 \n",
"2453 2020-09-30 V 0.654464 0.521290 0.107873 0.241066 4.90 14.653484 \n",
"2454 2020-12-31 V 0.638994 0.480876 0.094422 0.201545 1.42 15.908283 \n",
"2455 2021-03-31 V 0.640128 0.488704 0.095218 0.202568 2.80 16.088525 \n",
"\n",
" DPS cur_ratio quick_ratio cash_ratio inv_turnover \\\n",
"2451 0.30 1.248714 1.140070 0.955150 0.0 \n",
"2452 0.30 1.553478 1.443292 1.221925 0.0 \n",
"2453 0.30 1.905238 1.784838 1.579807 0.0 \n",
"2454 0.32 2.121065 1.969814 1.700081 0.0 \n",
"2455 0.32 2.116356 1.954292 1.700574 0.0 \n",
"\n",
" acc_rec_turnover acc_pay_turnover debt_ratio debt_to_equity \n",
"2451 6.116350 2.697537 0.525062 1.105537 \n",
"2452 5.063131 1.889507 0.543886 1.192433 \n",
"2453 5.628571 2.730366 0.552515 1.234714 \n",
"2454 4.725314 2.347866 0.531507 1.134505 \n",
"2455 4.844961 2.367357 0.529946 1.127414 "
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" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 21
}
],
"source": [
"final_ratios.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "66kjM0lhu91F"
},
"source": [
"## 4-5 Merge stock price data and ratios into one dataframe\n",
"- Merge the price dataframe preprocessed in Part 3 and the ratio dataframe created in this part\n",
"- Since the prices are daily and ratios are quartely, we have NAs in the ratio columns after merging the two dataframes. We deal with this by backfilling the ratios."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Kixon2tR3RLT"
},
"outputs": [],
"source": [
"list_ticker = df[\"tic\"].unique().tolist()\n",
"list_date = list(pd.date_range(df['date'].min(),df['date'].max()))\n",
"combination = list(itertools.product(list_date,list_ticker))\n",
"\n",
"# Merge stock price data and ratios into one dataframe\n",
"processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(df,on=[\"date\",\"tic\"],how=\"left\")\n",
"processed_full = processed_full.merge(final_ratios,how='left',on=['date','tic'])\n",
"processed_full = processed_full.sort_values(['tic','date'])\n",
"\n",
"# Backfill the ratio data to make them daily\n",
"processed_full = processed_full.bfill(axis='rows')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CGU69Ccfw_bR"
},
"source": [
"## 4-6 Calculate market valuation ratios using daily stock price data "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EhiYLZPBVZNW"
},
"outputs": [],
"source": [
"# Calculate P/E, P/B and dividend yield using daily closing price\n",
"processed_full['PE'] = processed_full['close']/processed_full['EPS']\n",
"processed_full['PB'] = processed_full['close']/processed_full['BPS']\n",
"processed_full['Div_yield'] = processed_full['DPS']/processed_full['close']\n",
"\n",
"# Drop per share items used for the above calculation\n",
"processed_full = processed_full.drop(columns=['day','EPS','BPS','DPS'])\n",
"# Replace NAs infinite values with zero\n",
"processed_full = processed_full.copy()\n",
"processed_full = processed_full.fillna(0)\n",
"processed_full = processed_full.replace(np.inf,0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 456
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"id": "grvhGJJII3Xn",
"outputId": "ff62d234-0540-4fbd-b4f8-e18e171233ae"
},
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{
"output_type": "execute_result",
"data": {
"text/plain": [
" date tic open high low close volume \\\n",
"0 2009-01-02 AAPL 3.067143 3.251429 3.041429 2.775245 746015200.0 \n",
"1 2009-01-02 AMGN 58.590000 59.080002 57.750000 45.228088 6547900.0 \n",
"2 2009-01-02 AXP 18.570000 19.520000 18.400000 15.535341 10955700.0 \n",
"3 2009-01-02 BA 42.799999 45.560001 42.779999 33.941101 7010200.0 \n",
"4 2009-01-02 CAT 44.910000 46.980000 44.709999 32.164722 7117200.0 \n",
"5 2009-01-02 CRM 8.025000 8.550000 7.912500 8.505000 4069200.0 \n",
"6 2009-01-02 CSCO 16.410000 17.000000 16.250000 12.265079 40980600.0 \n",
"7 2009-01-02 CVX 74.230003 77.300003 73.580002 45.176235 13695900.0 \n",
"8 2009-01-02 DIS 22.760000 24.030001 22.500000 20.597496 9796600.0 \n",
"9 2009-01-02 DOW 52.750000 53.500000 49.500000 42.358803 2350800.0 \n",
"\n",
" OPM NPM ROA ... quick_ratio cash_ratio inv_turnover \\\n",
"0 0.217886 0.163846 0.103222 ... 2.039779 1.818995 54.403846 \n",
"1 0.093973 0.072040 0.014094 ... 0.000000 0.000000 0.000000 \n",
"2 0.093973 0.072040 0.014094 ... 0.000000 0.000000 0.000000 \n",
"3 0.047307 0.032525 0.026400 ... 0.368463 0.148507 2.329670 \n",
"4 0.124545 0.066662 0.040891 ... 0.890488 0.163158 3.540791 \n",
"5 0.234698 0.196418 0.097593 ... 2.498162 2.170759 9.054201 \n",
"6 0.234698 0.196418 0.097593 ... 2.498162 2.170759 9.054201 \n",
"7 0.141417 0.097223 0.117691 ... 0.952878 0.373760 23.920348 \n",
"8 0.167221 0.102157 0.045834 ... 0.815629 0.330748 11.310223 \n",
"9 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 \n",
"\n",
" acc_rec_turnover acc_pay_turnover debt_ratio debt_to_equity PE \\\n",
"0 8.972003 4.269115 0.437727 0.778495 0.637987 \n",
"1 0.351354 0.653355 0.869784 6.679531 145.897059 \n",
"2 0.351354 0.653355 0.869784 6.679531 50.114004 \n",
"3 6.815203 2.076967 1.009198 -109.722986 39.012760 \n",
"4 2.460351 8.472455 0.893715 9.089489 -169.288013 \n",
"5 6.844634 16.036800 0.400215 0.667591 13.500000 \n",
"6 6.844634 16.036800 0.400215 0.667591 19.468380 \n",
"7 13.387209 11.276861 0.449174 0.815455 49.104603 \n",
"8 5.725855 4.287167 0.455848 0.837721 26.072780 \n",
"9 0.000000 0.000000 0.000000 0.000000 184.168708 \n",
"\n",
" PB Div_yield \n",
"0 0.101817 0.000000 \n",
"1 4.188583 0.003980 \n",
"2 1.438731 0.011586 \n",
"3 -35.751054 0.012374 \n",
"4 3.104561 0.013058 \n",
"5 1.351255 0.000000 \n",
"6 1.948648 0.000000 \n",
"7 1.037229 0.014388 \n",
"8 1.126511 0.016992 \n",
"9 0.000000 0.000000 \n",
"\n",
"[10 rows x 22 columns]"
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" <th></th>\n",
" <th>date</th>\n",
" <th>tic</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
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" <th>0</th>\n",
" <td>2009-01-02</td>\n",
" <td>AAPL</td>\n",
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" <td>AMGN</td>\n",
" <td>58.590000</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>2009-01-02</td>\n",
" <td>AXP</td>\n",
" <td>18.570000</td>\n",
" <td>19.520000</td>\n",
" <td>18.400000</td>\n",
" <td>15.535341</td>\n",
" <td>10955700.0</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.351354</td>\n",
" <td>0.653355</td>\n",
" <td>0.869784</td>\n",
" <td>6.679531</td>\n",
" <td>50.114004</td>\n",
" <td>1.438731</td>\n",
" <td>0.011586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2009-01-02</td>\n",
" <td>BA</td>\n",
" <td>42.799999</td>\n",
" <td>45.560001</td>\n",
" <td>42.779999</td>\n",
" <td>33.941101</td>\n",
" <td>7010200.0</td>\n",
" <td>0.047307</td>\n",
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" <td>0.148507</td>\n",
" <td>2.329670</td>\n",
" <td>6.815203</td>\n",
" <td>2.076967</td>\n",
" <td>1.009198</td>\n",
" <td>-109.722986</td>\n",
" <td>39.012760</td>\n",
" <td>-35.751054</td>\n",
" <td>0.012374</td>\n",
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" <td>2009-01-02</td>\n",
" <td>CAT</td>\n",
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" <td>46.980000</td>\n",
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" <td>32.164722</td>\n",
" <td>7117200.0</td>\n",
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" <td>8.472455</td>\n",
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" <td>9.089489</td>\n",
" <td>-169.288013</td>\n",
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" <td>2009-01-02</td>\n",
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" <td>2009-01-02</td>\n",
" <td>DIS</td>\n",
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"metadata": {},
"execution_count": 24
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],
"source": [
"# Check the final data\n",
"processed_full.sort_values(['date','tic'],ignore_index=True).head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-QsYaY0Dh1iw"
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"source": [
"<a id='4'></a>\n",
"# Part 5. Design Environment\n",
"Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
"\n",
"Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
"\n",
"The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
]
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"Our environment consists of a real-time stock market, quarterly financial data, and the value of our portfolio. The agent is able to observe current stock prices, trading volume for each stock, and companies' financial condition that is represented as financial ratios such as ROE. Since those numbers are all continuous, our state space consists of continuous states. The environment returns a reward to the agent as a result of the action it took. Here, the reward is the changes in the market value of our portfolio. The agent receives a positive reward when our portfolio increases and a negative reward when the portfolio decreases.\n",
"\n",
"In this problem, we assume we have one million dollar cash in our portfolio at the beginning. We also assume we need to pay $0.1 \\%$ of trading as a trading cost(buy and sell)."
],
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{
"cell_type": "markdown",
"metadata": {
"id": "5TOhcryx44bb"
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"source": [
"## 5-1 Split data into training and trade dataset\n",
"- Training data split: 2009-01-01 to 2018-12-31\n",
"- Trade data split: 2019-01-01 to 2020-09-30"
]
},
{
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{
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"name": "stdout",
"text": [
"109530\n",
"21930\n"
]
}
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"source": [
"train = data_split(processed_full, '2009-01-01','2019-01-01')\n",
"trade = data_split(processed_full, '2019-01-01','2021-01-01')\n",
"# Check the length of the two datasets\n",
"print(len(train))\n",
"print(len(trade))"
]
},
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" date tic open high low close volume \\\n",
"0 2009-01-02 AAPL 3.067143 3.251429 3.041429 2.775245 746015200.0 \n",
"0 2009-01-02 AMGN 58.590000 59.080002 57.750000 45.228088 6547900.0 \n",
"0 2009-01-02 AXP 18.570000 19.520000 18.400000 15.535341 10955700.0 \n",
"0 2009-01-02 BA 42.799999 45.560001 42.779999 33.941101 7010200.0 \n",
"0 2009-01-02 CAT 44.910000 46.980000 44.709999 32.164722 7117200.0 \n",
"\n",
" OPM NPM ROA ... quick_ratio cash_ratio inv_turnover \\\n",
"0 0.217886 0.163846 0.103222 ... 2.039779 1.818995 54.403846 \n",
"0 0.093973 0.072040 0.014094 ... 0.000000 0.000000 0.000000 \n",
"0 0.093973 0.072040 0.014094 ... 0.000000 0.000000 0.000000 \n",
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"0 0.124545 0.066662 0.040891 ... 0.890488 0.163158 3.540791 \n",
"\n",
" acc_rec_turnover acc_pay_turnover debt_ratio debt_to_equity PE \\\n",
"0 8.972003 4.269115 0.437727 0.778495 0.637987 \n",
"0 0.351354 0.653355 0.869784 6.679531 145.897059 \n",
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"0 6.815203 2.076967 1.009198 -109.722986 39.012760 \n",
"0 2.460351 8.472455 0.893715 9.089489 -169.288013 \n",
"\n",
" PB Div_yield \n",
"0 0.101817 0.000000 \n",
"0 4.188583 0.003980 \n",
"0 1.438731 0.011586 \n",
"0 -35.751054 0.012374 \n",
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"source": [
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" date tic open high low close \\\n",
"0 2019-01-01 AAPL 38.722500 39.712502 38.557499 38.277519 \n",
"0 2019-01-01 AMGN 192.520004 193.199997 188.949997 174.294479 \n",
"0 2019-01-01 AXP 93.910004 96.269997 93.769997 91.087921 \n",
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"0 2019-01-01 CAT 124.029999 127.879997 123.000000 115.741959 \n",
"\n",
" volume OPM NPM ROA ... quick_ratio cash_ratio \\\n",
"0 148158800.0 0.258891 0.227773 0.133360 ... 1.134347 0.854114 \n",
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" <td>...</td>\n",
" <td>0.919490</td>\n",
" <td>0.266175</td>\n",
" <td>2.135008</td>\n",
" <td>2.339630</td>\n",
" <td>3.660183</td>\n",
" <td>0.803394</td>\n",
" <td>4.086316</td>\n",
" <td>35.179927</td>\n",
" <td>4.286449</td>\n",
" <td>0.007430</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 22 columns</p>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-daa99e87-5a8e-4ca2-970d-3f0fff3ada89')\"\n",
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" \n",
" <style>\n",
" .colab-df-container {\n",
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"\n",
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"\n",
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"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-daa99e87-5a8e-4ca2-970d-3f0fff3ada89');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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" element.appendChild(docLink);\n",
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" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 27
}
],
"source": [
"trade.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qqGG78pLyCX7"
},
"source": [
"## 5-2 Set up the training environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LPD0wZLO-Pse"
},
"outputs": [],
"source": [
"import gym\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"from gym import spaces\n",
"from gym.utils import seeding\n",
"from stable_baselines3.common.vec_env import DummyVecEnv\n",
"\n",
"matplotlib.use(\"Agg\")\n",
"\n",
"# from stable_baselines3.common import logger\n",
"\n",
"\n",
"class StockTradingEnv(gym.Env):\n",
" \"\"\"A stock trading environment for OpenAI gym\"\"\"\n",
"\n",
" metadata = {\"render.modes\": [\"human\"]}\n",
"\n",
" def __init__(\n",
" self,\n",
" df,\n",
" stock_dim,\n",
" hmax,\n",
" initial_amount,\n",
" buy_cost_pct,\n",
" sell_cost_pct,\n",
" reward_scaling,\n",
" state_space,\n",
" action_space,\n",
" tech_indicator_list,\n",
" turbulence_threshold=None,\n",
" risk_indicator_col=\"turbulence\",\n",
" make_plots=False,\n",
" print_verbosity=10,\n",
" day=0,\n",
" initial=True,\n",
" previous_state=[],\n",
" model_name=\"\",\n",
" mode=\"\",\n",
" iteration=\"\",\n",
" ):\n",
" self.day = day\n",
" self.df = df\n",
" self.stock_dim = stock_dim\n",
" self.hmax = hmax\n",
" self.initial_amount = initial_amount\n",
" self.buy_cost_pct = buy_cost_pct\n",
" self.sell_cost_pct = sell_cost_pct\n",
" self.reward_scaling = reward_scaling\n",
" self.state_space = state_space\n",
" self.action_space = action_space\n",
" self.tech_indicator_list = tech_indicator_list\n",
" self.action_space = spaces.Box(low=-1, high=1, shape=(self.action_space,))\n",
" self.observation_space = spaces.Box(\n",
" low=-np.inf, high=np.inf, shape=(self.state_space,)\n",
" )\n",
" self.data = self.df.loc[self.day, :]\n",
" self.terminal = False\n",
" self.make_plots = make_plots\n",
" self.print_verbosity = print_verbosity\n",
" self.turbulence_threshold = turbulence_threshold\n",
" self.risk_indicator_col = risk_indicator_col\n",
" self.initial = initial\n",
" self.previous_state = previous_state\n",
" self.model_name = model_name\n",
" self.mode = mode\n",
" self.iteration = iteration\n",
" # initalize state\n",
" self.state = self._initiate_state()\n",
"\n",
" # initialize reward\n",
" self.reward = 0\n",
" self.turbulence = 0\n",
" self.cost = 0\n",
" self.trades = 0\n",
" self.episode = 0\n",
" # memorize all the total balance change\n",
" self.asset_memory = [self.initial_amount]\n",
" self.rewards_memory = []\n",
" self.actions_memory = []\n",
" self.date_memory = [self._get_date()]\n",
" # self.reset()\n",
" self._seed()\n",
"\n",
" def _sell_stock(self, index, action):\n",
" def _do_sell_normal():\n",
" if self.state[index + 1] > 0:\n",
" # Sell only if the price is > 0 (no missing data in this particular date)\n",
" # perform sell action based on the sign of the action\n",
" if self.state[index + self.stock_dim + 1] > 0:\n",
" # Sell only if current asset is > 0\n",
" sell_num_shares = min(\n",
" abs(action), self.state[index + self.stock_dim + 1]\n",
" )\n",
" sell_amount = (\n",
" self.state[index + 1]\n",
" * sell_num_shares\n",
" * (1 - self.sell_cost_pct)\n",
" )\n",
" # update balance\n",
" self.state[0] += sell_amount\n",
"\n",
" self.state[index + self.stock_dim + 1] -= sell_num_shares\n",
" self.cost += (\n",
" self.state[index + 1] * sell_num_shares * self.sell_cost_pct\n",
" )\n",
" self.trades += 1\n",
" else:\n",
" sell_num_shares = 0\n",
" else:\n",
" sell_num_shares = 0\n",
"\n",
" return sell_num_shares\n",
"\n",
" # perform sell action based on the sign of the action\n",
" if self.turbulence_threshold is not None:\n",
" if self.turbulence >= self.turbulence_threshold:\n",
" if self.state[index + 1] > 0:\n",
" # Sell only if the price is > 0 (no missing data in this particular date)\n",
" # if turbulence goes over threshold, just clear out all positions\n",
" if self.state[index + self.stock_dim + 1] > 0:\n",
" # Sell only if current asset is > 0\n",
" sell_num_shares = self.state[index + self.stock_dim + 1]\n",
" sell_amount = (\n",
" self.state[index + 1]\n",
" * sell_num_shares\n",
" * (1 - self.sell_cost_pct)\n",
" )\n",
" # update balance\n",
" self.state[0] += sell_amount\n",
" self.state[index + self.stock_dim + 1] = 0\n",
" self.cost += (\n",
" self.state[index + 1] * sell_num_shares * self.sell_cost_pct\n",
" )\n",
" self.trades += 1\n",
" else:\n",
" sell_num_shares = 0\n",
" else:\n",
" sell_num_shares = 0\n",
" else:\n",
" sell_num_shares = _do_sell_normal()\n",
" else:\n",
" sell_num_shares = _do_sell_normal()\n",
"\n",
" return sell_num_shares\n",
"\n",
" def _buy_stock(self, index, action):\n",
" def _do_buy():\n",
" if self.state[index + 1] > 0:\n",
" # Buy only if the price is > 0 (no missing data in this particular date)\n",
" available_amount = self.state[0] // self.state[index + 1]\n",
" # print('available_amount:{}'.format(available_amount))\n",
"\n",
" # update balance\n",
" buy_num_shares = min(available_amount, action)\n",
" buy_amount = (\n",
" self.state[index + 1] * buy_num_shares * (1 + self.buy_cost_pct)\n",
" )\n",
" self.state[0] -= buy_amount\n",
"\n",
" self.state[index + self.stock_dim + 1] += buy_num_shares\n",
"\n",
" self.cost += self.state[index + 1] * buy_num_shares * self.buy_cost_pct\n",
" self.trades += 1\n",
" else:\n",
" buy_num_shares = 0\n",
"\n",
" return buy_num_shares\n",
"\n",
" # perform buy action based on the sign of the action\n",
" if self.turbulence_threshold is None:\n",
" buy_num_shares = _do_buy()\n",
" else:\n",
" if self.turbulence < self.turbulence_threshold:\n",
" buy_num_shares = _do_buy()\n",
" else:\n",
" buy_num_shares = 0\n",
" pass\n",
"\n",
" return buy_num_shares\n",
"\n",
" def _make_plot(self):\n",
" plt.plot(self.asset_memory, \"r\")\n",
" plt.savefig(\"results/account_value_trade_{}.png\".format(self.episode))\n",
" plt.close()\n",
"\n",
" def step(self, actions):\n",
" self.terminal = self.day >= len(self.df.index.unique()) - 1\n",
" if self.terminal:\n",
" # print(f\"Episode: {self.episode}\")\n",
" if self.make_plots:\n",
" self._make_plot()\n",
" end_total_asset = self.state[0] + sum(\n",
" np.array(self.state[1 : (self.stock_dim + 1)])\n",
" * np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])\n",
" )\n",
" df_total_value = pd.DataFrame(self.asset_memory)\n",
" tot_reward = (\n",
" self.state[0]\n",
" + sum(\n",
" np.array(self.state[1 : (self.stock_dim + 1)])\n",
" * np.array(\n",
" self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)]\n",
" )\n",
" )\n",
" - self.initial_amount\n",
" )\n",
" df_total_value.columns = [\"account_value\"]\n",
" df_total_value[\"date\"] = self.date_memory\n",
" df_total_value[\"daily_return\"] = df_total_value[\"account_value\"].pct_change(\n",
" 1\n",
" )\n",
" if df_total_value[\"daily_return\"].std() != 0:\n",
" sharpe = (\n",
" (252 ** 0.5)\n",
" * df_total_value[\"daily_return\"].mean()\n",
" / df_total_value[\"daily_return\"].std()\n",
" )\n",
" df_rewards = pd.DataFrame(self.rewards_memory)\n",
" df_rewards.columns = [\"account_rewards\"]\n",
" df_rewards[\"date\"] = self.date_memory[:-1]\n",
" if self.episode % self.print_verbosity == 0:\n",
" print(f\"day: {self.day}, episode: {self.episode}\")\n",
" print(f\"begin_total_asset: {self.asset_memory[0]:0.2f}\")\n",
" print(f\"end_total_asset: {end_total_asset:0.2f}\")\n",
" print(f\"total_reward: {tot_reward:0.2f}\")\n",
" print(f\"total_cost: {self.cost:0.2f}\")\n",
" print(f\"total_trades: {self.trades}\")\n",
" if df_total_value[\"daily_return\"].std() != 0:\n",
" print(f\"Sharpe: {sharpe:0.3f}\")\n",
" print(\"=================================\")\n",
"\n",
" if (self.model_name != \"\") and (self.mode != \"\"):\n",
" df_actions = self.save_action_memory()\n",
" df_actions.to_csv(\n",
" \"results/actions_{}_{}_{}.csv\".format(\n",
" self.mode, self.model_name, self.iteration\n",
" )\n",
" )\n",
" df_total_value.to_csv(\n",
" \"results/account_value_{}_{}_{}.csv\".format(\n",
" self.mode, self.model_name, self.iteration\n",
" ),\n",
" index=False,\n",
" )\n",
" df_rewards.to_csv(\n",
" \"results/account_rewards_{}_{}_{}.csv\".format(\n",
" self.mode, self.model_name, self.iteration\n",
" ),\n",
" index=False,\n",
" )\n",
" plt.plot(self.asset_memory, \"r\")\n",
" plt.savefig(\n",
" \"results/account_value_{}_{}_{}.png\".format(\n",
" self.mode, self.model_name, self.iteration\n",
" ),\n",
" index=False,\n",
" )\n",
" plt.close()\n",
"\n",
" # Add outputs to logger interface\n",
" # logger.record(\"environment/portfolio_value\", end_total_asset)\n",
" # logger.record(\"environment/total_reward\", tot_reward)\n",
" # logger.record(\"environment/total_reward_pct\", (tot_reward / (end_total_asset - tot_reward)) * 100)\n",
" # logger.record(\"environment/total_cost\", self.cost)\n",
" # logger.record(\"environment/total_trades\", self.trades)\n",
"\n",
" return self.state, self.reward, self.terminal, {}\n",
"\n",
" else:\n",
"\n",
" actions = actions * self.hmax # actions initially is scaled between 0 to 1\n",
" actions = actions.astype(\n",
" int\n",
" ) # convert into integer because we can't by fraction of shares\n",
" if self.turbulence_threshold is not None:\n",
" if self.turbulence >= self.turbulence_threshold:\n",
" actions = np.array([-self.hmax] * self.stock_dim)\n",
" begin_total_asset = self.state[0] + sum(\n",
" np.array(self.state[1 : (self.stock_dim + 1)])\n",
" * np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])\n",
" )\n",
" # print(\"begin_total_asset:{}\".format(begin_total_asset))\n",
"\n",
" argsort_actions = np.argsort(actions)\n",
"\n",
" sell_index = argsort_actions[: np.where(actions < 0)[0].shape[0]]\n",
" buy_index = argsort_actions[::-1][: np.where(actions > 0)[0].shape[0]]\n",
"\n",
" for index in sell_index:\n",
" # print(f\"Num shares before: {self.state[index+self.stock_dim+1]}\")\n",
" # print(f'take sell action before : {actions[index]}')\n",
" actions[index] = self._sell_stock(index, actions[index]) * (-1)\n",
" # print(f'take sell action after : {actions[index]}')\n",
" # print(f\"Num shares after: {self.state[index+self.stock_dim+1]}\")\n",
"\n",
" for index in buy_index:\n",
" # print('take buy action: {}'.format(actions[index]))\n",
" actions[index] = self._buy_stock(index, actions[index])\n",
"\n",
" self.actions_memory.append(actions)\n",
"\n",
" # state: s -> s+1\n",
" self.day += 1\n",
" self.data = self.df.loc[self.day, :]\n",
" if self.turbulence_threshold is not None:\n",
" if len(self.df.tic.unique()) == 1:\n",
" self.turbulence = self.data[self.risk_indicator_col]\n",
" elif len(self.df.tic.unique()) > 1:\n",
" self.turbulence = self.data[self.risk_indicator_col].values[0]\n",
" self.state = self._update_state()\n",
"\n",
" end_total_asset = self.state[0] + sum(\n",
" np.array(self.state[1 : (self.stock_dim + 1)])\n",
" * np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])\n",
" )\n",
" self.asset_memory.append(end_total_asset)\n",
" self.date_memory.append(self._get_date())\n",
" self.reward = end_total_asset - begin_total_asset\n",
" self.rewards_memory.append(self.reward)\n",
" self.reward = self.reward * self.reward_scaling\n",
"\n",
" return self.state, self.reward, self.terminal, {}\n",
"\n",
" def reset(self):\n",
" # initiate state\n",
" self.state = self._initiate_state()\n",
"\n",
" if self.initial:\n",
" self.asset_memory = [self.initial_amount]\n",
" else:\n",
" previous_total_asset = self.previous_state[0] + sum(\n",
" np.array(self.state[1 : (self.stock_dim + 1)])\n",
" * np.array(\n",
" self.previous_state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)]\n",
" )\n",
" )\n",
" self.asset_memory = [previous_total_asset]\n",
"\n",
" self.day = 0\n",
" self.data = self.df.loc[self.day, :]\n",
" self.turbulence = 0\n",
" self.cost = 0\n",
" self.trades = 0\n",
" self.terminal = False\n",
" # self.iteration=self.iteration\n",
" self.rewards_memory = []\n",
" self.actions_memory = []\n",
" self.date_memory = [self._get_date()]\n",
"\n",
" self.episode += 1\n",
"\n",
" return self.state\n",
"\n",
" def render(self, mode=\"human\", close=False):\n",
" return self.state\n",
"\n",
" def _initiate_state(self):\n",
" if self.initial:\n",
" # For Initial State\n",
" if len(self.df.tic.unique()) > 1:\n",
" # for multiple stock\n",
" state = (\n",
" [self.initial_amount]\n",
" + self.data.close.values.tolist()\n",
" + [0] * self.stock_dim\n",
" + sum(\n",
" [\n",
" self.data[tech].values.tolist()\n",
" for tech in self.tech_indicator_list\n",
" ],\n",
" [],\n",
" )\n",
" )\n",
" else:\n",
" # for single stock\n",
" state = (\n",
" [self.initial_amount]\n",
" + [self.data.close]\n",
" + [0] * self.stock_dim\n",
" + sum([[self.data[tech]] for tech in self.tech_indicator_list], [])\n",
" )\n",
" else:\n",
" # Using Previous State\n",
" if len(self.df.tic.unique()) > 1:\n",
" # for multiple stock\n",
" state = (\n",
" [self.previous_state[0]]\n",
" + self.data.close.values.tolist()\n",
" + self.previous_state[\n",
" (self.stock_dim + 1) : (self.stock_dim * 2 + 1)\n",
" ]\n",
" + sum(\n",
" [\n",
" self.data[tech].values.tolist()\n",
" for tech in self.tech_indicator_list\n",
" ],\n",
" [],\n",
" )\n",
" )\n",
" else:\n",
" # for single stock\n",
" state = (\n",
" [self.previous_state[0]]\n",
" + [self.data.close]\n",
" + self.previous_state[\n",
" (self.stock_dim + 1) : (self.stock_dim * 2 + 1)\n",
" ]\n",
" + sum([[self.data[tech]] for tech in self.tech_indicator_list], [])\n",
" )\n",
" return state\n",
"\n",
" def _update_state(self):\n",
" if len(self.df.tic.unique()) > 1:\n",
" # for multiple stock\n",
" state = (\n",
" [self.state[0]]\n",
" + self.data.close.values.tolist()\n",
" + list(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])\n",
" + sum(\n",
" [\n",
" self.data[tech].values.tolist()\n",
" for tech in self.tech_indicator_list\n",
" ],\n",
" [],\n",
" )\n",
" )\n",
"\n",
" else:\n",
" # for single stock\n",
" state = (\n",
" [self.state[0]]\n",
" + [self.data.close]\n",
" + list(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])\n",
" + sum([[self.data[tech]] for tech in self.tech_indicator_list], [])\n",
" )\n",
" return state\n",
"\n",
" def _get_date(self):\n",
" if len(self.df.tic.unique()) > 1:\n",
" date = self.data.date.unique()[0]\n",
" else:\n",
" date = self.data.date\n",
" return date\n",
"\n",
" def save_asset_memory(self):\n",
" date_list = self.date_memory\n",
" asset_list = self.asset_memory\n",
" # print(len(date_list))\n",
" # print(len(asset_list))\n",
" df_account_value = pd.DataFrame(\n",
" {\"date\": date_list, \"account_value\": asset_list}\n",
" )\n",
" return df_account_value\n",
"\n",
" def save_action_memory(self):\n",
" if len(self.df.tic.unique()) > 1:\n",
" # date and close price length must match actions length\n",
" date_list = self.date_memory[:-1]\n",
" df_date = pd.DataFrame(date_list)\n",
" df_date.columns = [\"date\"]\n",
"\n",
" action_list = self.actions_memory\n",
" df_actions = pd.DataFrame(action_list)\n",
" df_actions.columns = self.data.tic.values\n",
" df_actions.index = df_date.date\n",
" # df_actions = pd.DataFrame({'date':date_list,'actions':action_list})\n",
" else:\n",
" date_list = self.date_memory[:-1]\n",
" action_list = self.actions_memory\n",
" df_actions = pd.DataFrame({\"date\": date_list, \"actions\": action_list})\n",
" return df_actions\n",
"\n",
" def _seed(self, seed=None):\n",
" self.np_random, seed = seeding.np_random(seed)\n",
" return [seed]\n",
"\n",
" def get_sb_env(self):\n",
" e = DummyVecEnv([lambda: self])\n",
" obs = e.reset()\n",
" return e, obs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Q2zqII8rMIqn",
"outputId": "f7eb4fd7-0eb7-42ce-d433-23df04d15621"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Stock Dimension: 30, State Space: 511\n"
]
}
],
"source": [
"ratio_list = ['OPM', 'NPM','ROA', 'ROE', 'cur_ratio', 'quick_ratio', 'cash_ratio', 'inv_turnover','acc_rec_turnover', 'acc_pay_turnover', 'debt_ratio', 'debt_to_equity',\n",
" 'PE', 'PB', 'Div_yield']\n",
"\n",
"stock_dimension = len(train.tic.unique())\n",
"state_space = 1 + 2*stock_dimension + len(ratio_list)*stock_dimension\n",
"print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "AWyp84Ltto19"
},
"outputs": [],
"source": [
"# Parameters for the environment\n",
"env_kwargs = {\n",
" \"hmax\": 100, \n",
" \"initial_amount\": 1000000, \n",
" \"buy_cost_pct\": 0.001,\n",
" \"sell_cost_pct\": 0.001,\n",
" \"state_space\": state_space, \n",
" \"stock_dim\": stock_dimension, \n",
" \"tech_indicator_list\": ratio_list, \n",
" \"action_space\": stock_dimension, \n",
" \"reward_scaling\": 1e-4\n",
" \n",
"}\n",
"\n",
"#Establish the training environment using StockTradingEnv() class\n",
"e_train_gym = StockTradingEnv(df = train, **env_kwargs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "64EoqOrQjiVf"
},
"source": [
"## Environment for Training\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xwSvvPjutpqS",
"outputId": "8cb5e464-5bed-4285-e60e-8232eb3be8bb"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
]
}
],
"source": [
"env_train, _ = e_train_gym.get_sb_env()\n",
"print(type(env_train))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HMNR5nHjh1iz"
},
"source": [
"<a id='5'></a>\n",
"# Part 6: Implement DRL Algorithms\n",
"* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
"* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
"Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
"design their own DRL algorithms by adapting these DRL algorithms."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "364PsqckttcQ"
},
"outputs": [],
"source": [
"# Set up the agent using DRLAgent() class using the environment created in the previous part\n",
"agent = DRLAgent(env = env_train)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YDmqOyF9h1iz"
},
"source": [
"### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uijiWgkuh1jB"
},
"source": [
"### Model 1: A2C\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GUCnkn-HIbmj",
"outputId": "8c7208f7-ffb7-4632-963f-eb40120d3d37"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0007}\n",
"Using cuda device\n"
]
}
],
"source": [
"agent = DRLAgent(env = env_train)\n",
"model_a2c = agent.get_model(\"a2c\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0GVpkWGqH4-D",
"outputId": "3066c92d-ffcc-490c-c0a9-a239d8862f0b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"----------------------------------------\n",
"| time/ | |\n",
"| fps | 84 |\n",
"| iterations | 100 |\n",
"| time_elapsed | 5 |\n",
"| total_timesteps | 500 |\n",
"| train/ | |\n",
"| entropy_loss | -42.9 |\n",
"| explained_variance | 0.00581 |\n",
"| learning_rate | 0.0007 |\n",
"| n_updates | 99 |\n",
"| policy_loss | 99.1 |\n",
"| reward | -0.003940227 |\n",
"| std | 1.01 |\n",
"| value_loss | 9.34 |\n",
"----------------------------------------\n"
]
}
],
"source": [
"trained_a2c = agent.train_model(model=model_a2c, \n",
" tb_log_name='a2c',\n",
" total_timesteps=500)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MRiOtrywfAo1"
},
"source": [
"### Model 2: DDPG"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "M2YadjfnLwgt",
"outputId": "b229fd78-eff0-406d-cf54-f139268b4d4b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
"Using cuda device\n"
]
}
],
"source": [
"agent = DRLAgent(env = env_train)\n",
"model_ddpg = agent.get_model(\"ddpg\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"id": "tCDa78rqfO_a"
},
"outputs": [],
"source": [
"trained_ddpg = agent.train_model(model=model_ddpg, \n",
" tb_log_name='ddpg',\n",
" total_timesteps=400)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_gDkU-j-fCmZ"
},
"source": [
"### Model 3: PPO"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "y5D5PFUhMzSV"
},
"outputs": [],
"source": [
"agent = DRLAgent(env = env_train)\n",
"PPO_PARAMS = {\n",
" \"n_steps\": 2048,\n",
" \"ent_coef\": 0.01,\n",
" \"learning_rate\": 0.00025,\n",
" \"batch_size\": 128,\n",
"}\n",
"model_ppo = agent.get_model(\"ppo\",model_kwargs = PPO_PARAMS)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"id": "Gt8eIQKYM4G3"
},
"outputs": [],
"source": [
"trained_ppo = agent.train_model(model=model_ppo, \n",
" tb_log_name='ppo',\n",
" total_timesteps=200)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Zpv4S0-fDBv"
},
"source": [
"### Model 4: TD3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JSAHhV4Xc-bh",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "47a18e1d-3057-4d11-9ec2-55f0349a6ec9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'batch_size': 100, 'buffer_size': 1000000, 'learning_rate': 0.001}\n",
"Using cuda device\n"
]
}
],
"source": [
"agent = DRLAgent(env = env_train)\n",
"TD3_PARAMS = {\"batch_size\": 100, \n",
" \"buffer_size\": 1000000, \n",
" \"learning_rate\": 0.001}\n",
"\n",
"model_td3 = agent.get_model(\"td3\",model_kwargs = TD3_PARAMS)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OSRxNYAxdKpU"
},
"outputs": [],
"source": [
"trained_td3 = agent.train_model(model=model_td3, \n",
" tb_log_name='td3',\n",
" total_timesteps=200)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Dr49PotrfG01"
},
"source": [
"### Model 5: SAC"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xwOhVjqRkCdM",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e5bd02d2-4f40-498e-f04c-0a883abe782e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'batch_size': 128, 'buffer_size': 1000000, 'learning_rate': 0.0001, 'learning_starts': 100, 'ent_coef': 'auto_0.1'}\n",
"Using cuda device\n"
]
}
],
"source": [
"agent = DRLAgent(env = env_train)\n",
"SAC_PARAMS = {\n",
" \"batch_size\": 128,\n",
" \"buffer_size\": 1000000,\n",
" \"learning_rate\": 0.0001,\n",
" \"learning_starts\": 100,\n",
" \"ent_coef\": \"auto_0.1\",\n",
"}\n",
"\n",
"model_sac = agent.get_model(\"sac\",model_kwargs = SAC_PARAMS)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "K8RSdKCckJyH"
},
"outputs": [],
"source": [
"trained_sac = agent.train_model(model=model_sac, \n",
" tb_log_name='sac',\n",
" total_timesteps=1000)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f2wZgkQXh1jE"
},
"source": [
"## Trading\n",
"Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "U5mmgQF_h1jQ"
},
"source": [
"### Trade\n",
"\n",
"DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2018-12 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
"\n",
"Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cIqoV0GSI52v"
},
"outputs": [],
"source": [
"trade = data_split(processed_full, '2019-01-01','2021-01-01')\n",
"e_trade_gym = StockTradingEnv(df = trade, **env_kwargs)\n",
"# env_trade, obs_trade = e_trade_gym.get_sb_env()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "W_XNgGsBMeVw",
"outputId": "4cd1c89f-f665-4282-ca3e-aa0c4ff1ea2d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 299
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" date tic open high low close \\\n",
"0 2019-01-01 AAPL 38.722500 39.712502 38.557499 38.277527 \n",
"0 2019-01-01 AMGN 192.520004 193.199997 188.949997 174.294495 \n",
"0 2019-01-01 AXP 93.910004 96.269997 93.769997 91.087929 \n",
"0 2019-01-01 BA 316.190002 323.950012 313.709991 314.645142 \n",
"0 2019-01-01 CAT 124.029999 127.879997 123.000000 115.741959 \n",
"\n",
" volume OPM NPM ROA ... quick_ratio cash_ratio \\\n",
"0 148158800.0 0.258891 0.227773 0.133360 ... 1.134347 0.854114 \n",
"0 3009100.0 0.093973 0.072040 0.014094 ... 0.000000 0.000000 \n",
"0 4175400.0 0.203479 0.160494 0.026811 ... 0.000000 0.000000 \n",
"0 3292200.0 0.116496 0.102682 0.066409 ... 0.262465 0.092436 \n",
"0 4783200.0 0.186871 0.107064 0.056932 ... 0.919490 0.266175 \n",
"\n",
" inv_turnover acc_rec_turnover acc_pay_turnover debt_ratio \\\n",
"0 23.571867 7.620024 3.781658 0.690466 \n",
"0 0.000000 0.351354 0.653355 0.869784 \n",
"0 0.000000 0.231669 0.279424 0.887329 \n",
"0 0.933164 5.468453 4.151637 0.998070 \n",
"0 2.135008 2.339630 3.660183 0.803394 \n",
"\n",
" debt_to_equity PE PB Div_yield \n",
"0 2.230663 5.713064 1.665931 0.019071 \n",
"0 6.679531 562.240305 16.141450 0.001033 \n",
"0 7.875371 50.324823 3.431479 0.004282 \n",
"0 517.142241 83.019826 1418.196271 0.006531 \n",
"0 4.086316 35.179927 4.286449 0.007430 \n",
"\n",
"[5 rows x 22 columns]"
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>tic</th>\n",
" <th>open</th>\n",
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" <th>low</th>\n",
" <th>close</th>\n",
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" <td>2019-01-01</td>\n",
" <td>AAPL</td>\n",
" <td>38.722500</td>\n",
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" <td>AXP</td>\n",
" <td>93.910004</td>\n",
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" <td>2019-01-01</td>\n",
" <td>BA</td>\n",
" <td>316.190002</td>\n",
" <td>323.950012</td>\n",
" <td>313.709991</td>\n",
" <td>314.645142</td>\n",
" <td>3292200.0</td>\n",
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" environment = e_trade_gym)"
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"text/plain": [
" date account_value\n",
"726 2020-12-27 1.373068e+06\n",
"727 2020-12-28 1.373068e+06\n",
"728 2020-12-29 1.371886e+06\n",
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},
"metadata": {},
"execution_count": 48
}
],
"source": [
"df_actions.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "W6vvNSC6h1jZ"
},
"source": [
"<a id='6'></a>\n",
"# Part 7: Backtest Our Strategy\n",
"Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Lr2zX7ZxNyFQ"
},
"source": [
"<a id='6.1'></a>\n",
"## 7.1 BackTestStats\n",
"pass in df_account_value, this information is stored in env class\n"
]
},
{
"cell_type": "code",
"source": [
"print(\"==============Get Backtest Results===========\")\n",
"now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
"\n",
"perf_stats_all = backtest_stats(account_value=df_account_value)\n",
"perf_stats_all = pd.DataFrame(perf_stats_all)\n",
"perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bYtVlu1GfC1s",
"outputId": "96a9ea1a-0357-4c13-cf52-c258da384adb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"==============Get Backtest Results===========\n",
"Annual return 0.118590\n",
"Cumulative returns 0.384155\n",
"Annual volatility 0.226764\n",
"Sharpe ratio 0.609271\n",
"Calmar ratio 0.335041\n",
"Stability 0.253867\n",
"Max drawdown -0.353958\n",
"Omega ratio 1.176163\n",
"Sortino ratio 0.840216\n",
"Skew NaN\n",
"Kurtosis NaN\n",
"Tail ratio 0.945126\n",
"Daily value at risk -0.028021\n",
"dtype: float64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#baseline stats\n",
"print(\"==============Get Baseline Stats===========\")\n",
"baseline_df = get_baseline(\n",
" ticker=\"^DJI\", \n",
" start = '2019-01-01',\n",
" end = '2021-01-01')\n",
"\n",
"stats = backtest_stats(baseline_df, value_col_name = 'close')\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eKTx6rHYfJXm",
"outputId": "b74030be-a127-48fd-fe1f-b163e1dd9472"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"==============Get Baseline Stats===========\n",
"\r[*********************100%***********************] 1 of 1 completed\n",
"Shape of DataFrame: (505, 8)\n",
"Annual return 0.144674\n",
"Cumulative returns 0.310981\n",
"Annual volatility 0.274619\n",
"Sharpe ratio 0.631418\n",
"Calmar ratio 0.390102\n",
"Stability 0.116677\n",
"Max drawdown -0.370862\n",
"Omega ratio 1.149365\n",
"Sortino ratio 0.870084\n",
"Skew NaN\n",
"Kurtosis NaN\n",
"Tail ratio 0.860710\n",
"Daily value at risk -0.033911\n",
"dtype: float64\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9U6Suru3h1jc"
},
"source": [
"<a id='6.2'></a>\n",
"## 7.2 BackTestPlot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lKRGftSS7pNM",
"outputId": "86b70db6-4b62-4baa-f4b1-011270015d2c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"==============Compare to DJIA===========\n",
"\r[*********************100%***********************] 1 of 1 completed\n",
"Shape of DataFrame: (505, 8)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-01</td></tr>\n",
" <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-12-31</td></tr>\n",
" <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>34</td></tr>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Backtest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Annual return</th>\n",
" <td>11.859%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cumulative returns</th>\n",
" <td>38.415%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Annual volatility</th>\n",
" <td>22.676%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sharpe ratio</th>\n",
" <td>0.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Calmar ratio</th>\n",
" <td>0.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Stability</th>\n",
" <td>0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max drawdown</th>\n",
" <td>-35.396%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Omega ratio</th>\n",
" <td>1.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sortino ratio</th>\n",
" <td>0.84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Skew</th>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tail ratio</th>\n",
" <td>0.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Daily value at risk</th>\n",
" <td>-2.802%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alpha</th>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beta</th>\n",
" <td>0.68</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Worst drawdown periods</th>\n",
" <th>Net drawdown in %</th>\n",
" <th>Peak date</th>\n",
" <th>Valley date</th>\n",
" <th>Recovery date</th>\n",
" <th>Duration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>35.40</td>\n",
" <td>2020-02-19</td>\n",
" <td>2020-03-21</td>\n",
" <td>2020-11-10</td>\n",
" <td>190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.73</td>\n",
" <td>2019-07-12</td>\n",
" <td>2019-08-03</td>\n",
" <td>2019-09-06</td>\n",
" <td>41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.79</td>\n",
" <td>2019-04-30</td>\n",
" <td>2019-05-31</td>\n",
" <td>2019-06-14</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.56</td>\n",
" <td>2019-09-13</td>\n",
" <td>2019-10-02</td>\n",
" <td>2019-10-29</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.21</td>\n",
" <td>2020-01-16</td>\n",
" <td>2020-01-31</td>\n",
" <td>2020-02-12</td>\n",
" <td>20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Stress Events</th>\n",
" <th>mean</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>New Normal</th>\n",
" <td>0.05%</td>\n",
" <td>-12.99%</td>\n",
" <td>10.69%</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1008x5184 with 13 Axes>"
],
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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1008x432 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"print(\"==============Compare to DJIA===========\")\n",
"%matplotlib inline\n",
"# S&P 500: ^GSPC\n",
"# Dow Jones Index: ^DJI\n",
"# NASDAQ 100: ^NDX\n",
"backtest_plot(df_account_value, \n",
" baseline_ticker = '^DJI', \n",
" baseline_start = '2019-01-01',\n",
" baseline_end = '2021-01-01')"
]
}
],
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