daily update
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David Brazda
251
to_explore/pyquantnews/73_OptionsBacktesting.ipynb
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251
to_explore/pyquantnews/73_OptionsBacktesting.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "2a7b4695",
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"metadata": {},
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"source": [
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"<div style=\"background-color:#000;\"><img src=\"pqn.png\"></img></div>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4dde6243",
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"metadata": {},
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"source": [
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"This code processes historical stock data for financial analysis and option strategy backtesting. It reads multiple CSV files containing end-of-day data, concatenates them, and saves the result in a single file. The script then loads this combined data and uses the Optopsy library to analyze option chains. It evaluates various option strategies such as short calls, long straddles, and short strangles, providing a practical tool for financial professionals to assess historical performance and strategy efficacy."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5d5d8246",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import glob\n",
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"import pandas as pd\n",
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"import optopsy as op"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9337e673",
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"metadata": {},
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"source": [
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"Collect all CSV files in the \"rut-eod\" directory"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "25707d5a",
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"metadata": {},
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"outputs": [],
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"source": [
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"files = glob.glob(os.path.join(\"rut-eod\", \"*.csv\"))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "da83ac55",
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"metadata": {},
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"source": [
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"Initialize an empty list for storing dataframes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "20abd76b",
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"metadata": {},
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"outputs": [],
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"source": [
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"dfs = []\n",
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"for fl in files:\n",
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" # Read each CSV file and append its dataframe to the list\n",
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" df = pd.read_csv(fl)\n",
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" dfs.append(df)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "106998bc",
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"metadata": {},
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"source": [
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"Concatenate all dataframes and save the combined data to a new CSV file"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "47998816",
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"metadata": {},
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"outputs": [],
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"source": [
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"pd.concat(dfs).to_csv(\"rut_historic.csv\", index=False)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3d36b3a7",
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"metadata": {},
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"source": [
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"Load the combined historical data from the new CSV file"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e504c82f",
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"metadata": {},
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"outputs": [],
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"source": [
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"rut = pd.read_csv(\"rut_historic.csv\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0d6640b5",
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"metadata": {},
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"source": [
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"Print the most recent date in the dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "67b0527c",
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"metadata": {},
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"outputs": [],
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"source": [
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"rut.date.max()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4c9823ce",
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"metadata": {},
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"source": [
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"Load the option chains from the combined historical data using specific column indices"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8240913f",
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"metadata": {},
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"outputs": [],
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"source": [
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"rut_chains = op.csv_data(\n",
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" \"rut_historic.csv\",\n",
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" underlying_symbol=1,\n",
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" underlying_price=4,\n",
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" option_type=8,\n",
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" expiration=6,\n",
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" quote_date=0,\n",
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" strike=7,\n",
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" bid=14,\n",
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" ask=15\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e21cd76d",
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"metadata": {},
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"source": [
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"Display the first few rows of the option chains dataframe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5850a16f",
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"metadata": {},
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"outputs": [],
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"source": [
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"rut_chains.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "219a8e53",
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"metadata": {},
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"source": [
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"Evaluate and round the results of the short calls strategy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6f7e6cd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"op.short_calls(rut_chains).round(2)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ad327b31",
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"metadata": {},
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"source": [
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"Evaluate and round the results of the long straddles strategy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "57f48082",
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"metadata": {},
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"outputs": [],
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"source": [
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"op.long_straddles(rut_chains).round(2)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1d4573d2",
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"metadata": {},
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"source": [
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"Evaluate and round the results of the short strangles strategy with specified parameters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bb9d8953",
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"metadata": {},
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"outputs": [],
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"source": [
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"op.short_strangles(\n",
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" rut_chains, \n",
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" dte_interval=60, \n",
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" max_entry_dte=70, \n",
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" exit_dte=10,\n",
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" otm_pct_interval=0.01,\n",
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" max_otm_pct=0.10\n",
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").round(2)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5baa0691",
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"metadata": {},
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"source": [
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"<a href=\"https://pyquantnews.com/\">PyQuant News</a> is where finance practitioners level up with Python for quant finance, algorithmic trading, and market data analysis. Looking to get started? Check out the fastest growing, top-selling course to <a href=\"https://gettingstartedwithpythonforquantfinance.com/\">get started with Python for quant finance</a>. For educational purposes. Not investment advise. Use at your own risk."
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]
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}
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],
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"metadata": {
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"jupytext": {
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"cell_metadata_filter": "-all",
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"main_language": "python",
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"notebook_metadata_filter": "-all"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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