409 lines
11 KiB
Plaintext
409 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e2c3f24b",
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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": "35d6d902",
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"metadata": {},
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"source": [
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"This code processes and analyzes equity options data, extracting volatility curves for specific options chains. It reads options data from CSV files, stores them in an Arctic database, and then queries the database to retrieve options chains for analysis. The code includes functions to read the options data, filter it based on specific criteria, and query it for expiration dates. Finally, it plots the implied volatility curves for the options, visualizing how volatility changes with different strikes and expiration dates."
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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": "c11513ab",
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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 matplotlib.pyplot as plt\n",
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"import datetime as dt"
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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": "92e29c6f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import arcticdb as adb\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ae881285",
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"metadata": {},
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"source": [
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"Initialize an Arctic database connection and get the \"options\" library"
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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": "8669ee9a",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"arctic = adb.Arctic(\"lmdb://equity_options\")\n",
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"lib = arctic.get_library(\"options\", create_if_missing=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "23e2dfbe",
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"metadata": {},
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"source": [
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"Define a function to read options chains from CSV files"
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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": "c99d8bf0",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"def read_chains(fl):\n",
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" \"\"\"Reads options chains from a CSV file\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" fl : str\n",
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" Path to the CSV file\n",
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" \n",
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" Returns\n",
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" -------\n",
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" df : pd.DataFrame\n",
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" Dataframe containing the options chains\n",
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" \"\"\"\n",
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" \n",
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" # Read the CSV file, set the \"date\" column as the index, and convert to datetime\n",
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" df = (\n",
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" pd\n",
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" .read_csv(fl)\n",
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" .set_index(\"date\")\n",
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" )\n",
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" df.index = pd.to_datetime(df.index)\n",
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" return 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": "691e962c",
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"metadata": {},
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"source": [
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"Read all CSV files from the \"rut-eod\" directory and store their data in the Arctic database"
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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": "12d9662e",
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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\"))\n",
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"for fl in files:\n",
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" chains = read_chains(fl)\n",
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" chains.option_expiration = pd.to_datetime(chains.option_expiration)\n",
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" underlyings = chains.symbol.unique()\n",
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" for underlying in underlyings:\n",
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" df = chains[chains.symbol == underlying]\n",
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" adb_sym = f\"options/{underlying}\"\n",
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" adb_fcn = lib.update if lib.has_symbol(adb_sym) else lib.write\n",
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" adb_fcn(adb_sym, 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": "7921ad3e",
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"metadata": {},
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"source": [
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"Read data for the \"RUT\" underlying from the Arctic database"
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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": "d66b0041",
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"metadata": {},
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"outputs": [],
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"source": [
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"rut = lib.read(\"options/RUT\").data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e836326c",
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"metadata": {},
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"source": [
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"Print the minimum and maximum dates from the \"RUT\" data"
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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": "9868b99f",
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"metadata": {},
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"outputs": [],
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"source": [
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"rut.index.min(), rut.index.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": "c6bbb827",
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"metadata": {},
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"source": [
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"Print information about the \"RUT\" 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": "83ec5802",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"rut.info()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1f7d0264",
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"metadata": {},
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"source": [
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"Define a function to read the volatility curve for a specific options chain"
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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": "18f49a7c",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"def read_vol_curve(as_of_date, underlying, expiry, delta_low, delta_high):\n",
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" \"\"\"Reads the volatility curve for specific options\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" as_of_date : pd.Timestamp\n",
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" The date for which to read the data\n",
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" underlying : str\n",
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" The underlying asset symbol\n",
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" expiry : pd.Timestamp\n",
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" The expiration date of the options\n",
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" delta_low : float\n",
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" The lower bound of the delta range\n",
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" delta_high : float\n",
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" The upper bound of the delta range\n",
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" \n",
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" Returns\n",
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" -------\n",
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" df : pd.DataFrame\n",
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" Dataframe containing the volatility curve\n",
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" \"\"\"\n",
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" \n",
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" # Build a query to filter options data based on expiration date and delta range\n",
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" q = adb.QueryBuilder()\n",
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" filter = (\n",
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" (q[\"option_expiration\"] == expiry) & \n",
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" (\n",
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" \t(\n",
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" \t\t(q[\"delta\"] >= delta_low) & (q[\"delta\"] <= delta_high)\n",
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" \t) | (\n",
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" \t\t(q[\"delta\"] >= -delta_high) & (q[\"delta\"] <= -delta_low)\n",
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" \t)\n",
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" )\n",
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" )\n",
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" q = (\n",
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" q[filter]\n",
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" .groupby(\"strike\")\n",
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" .agg({\"iv\": \"mean\"})\n",
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" )\n",
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" \n",
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" # Read the filtered data from the Arctic database\n",
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" return lib.read(\n",
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" f\"options/{underlying}\", \n",
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" date_range=(as_of_date, as_of_date),\n",
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" query_builder=q\n",
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" ).data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "86dbe228",
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"metadata": {},
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"source": [
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"Define a function to query expiration dates for options"
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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": "9d68eb55",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"def query_expirations(as_of_date, underlying, dte=30):\n",
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" \"\"\"Queries expiration dates for options\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" as_of_date : pd.Timestamp\n",
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" The date for which to query the data\n",
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" underlying : str\n",
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" The underlying asset symbol\n",
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" dte : int, optional\n",
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" Days to expiration threshold, by default 30\n",
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" \n",
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" Returns\n",
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" -------\n",
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" expirations : pd.Index\n",
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" Index containing the expiration dates\n",
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" \"\"\"\n",
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" \n",
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" # Build a query to filter options data based on expiration date threshold\n",
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" q = adb.QueryBuilder()\n",
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" filter = (q.option_expiration > as_of_date + dt.timedelta(days=dte))\n",
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" q = q[filter].groupby(\"option_expiration\").agg({\"volume\": \"sum\"})\n",
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" \n",
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" # Read the filtered data from the Arctic database and sort by expiration date\n",
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" return (\n",
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" lib\n",
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" .read(\n",
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" f\"options/{underlying}\", \n",
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" date_range=(as_of_date, as_of_date), \n",
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" query_builder=q\n",
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" )\n",
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" .data\n",
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" .sort_index()\n",
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" .index\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": "a344b771",
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"metadata": {},
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"source": [
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"Define input parameters for querying and plotting the volatility curves"
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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": "d8c667fe",
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"metadata": {},
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"outputs": [],
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"source": [
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"as_of_date = pd.Timestamp(\"2013-06-03\")\n",
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"expiry = pd.Timestamp(\"2013-06-22\")\n",
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"underlying = \"RUT\"\n",
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"dte = 30\n",
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"delta_low = 0.05\n",
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"delta_high = 0.50"
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]
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},
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{
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"cell_type": "markdown",
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"id": "421cce89",
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"metadata": {},
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"source": [
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"Query expiration dates for the given underlying asset and date"
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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": "f138d4b9",
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"metadata": {},
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"outputs": [],
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"source": [
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"expiries = query_expirations(as_of_date, underlying, dte)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "37f2012c",
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"metadata": {},
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"source": [
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"Plot the implied volatility curves for the retrieved expiration dates"
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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": "8152b931",
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"metadata": {},
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"outputs": [],
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"source": [
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"_, ax = plt.subplots(1, 1)\n",
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"cmap = plt.get_cmap(\"rainbow\", len(expiries))\n",
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"format_kw = {\"linewidth\": 0.5, \"alpha\": 0.85}\n",
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"for i, expiry in enumerate(expiries):\n",
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" curve = read_vol_curve(\n",
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" as_of_date, \n",
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" underlying, \n",
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" expiry, \n",
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" delta_low, \n",
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" delta_high\n",
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" )\n",
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" (\n",
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" curve\n",
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" .sort_index()\n",
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" .plot(\n",
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" ax=ax, \n",
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" y=\"iv\", \n",
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" label=expiry.strftime(\"%Y-%m-%d\"),\n",
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" grid=True,\n",
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" color=cmap(i),\n",
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" **format_kw\n",
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" )\n",
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" )\n",
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"ax.set_ylabel(\"implied volatility\")\n",
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"ax.legend(loc=\"upper right\", framealpha=0.7)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f120b062",
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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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