daily update
This commit is contained in:
David Brazda
287
to_explore/pyquantnews/84_ForecastEarningsWithStraddles.ipynb
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287
to_explore/pyquantnews/84_ForecastEarningsWithStraddles.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": "cc262707",
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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": "e7482284",
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"metadata": {},
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"source": [
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"This code retrieves financial data and calculates the cost and implications of an options straddle strategy for a given stock. It fetches the earnings calendar, stock price, and options chain for a specific symbol. It then identifies the at-the-money (ATM) call and put options expiring shortly after the earnings report. The cost of the straddle and the implied daily price movement are calculated and printed. This is useful for traders and investors to understand potential price movements around earnings announcements."
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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": "5c23a683",
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"metadata": {},
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"outputs": [],
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"source": [
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"from datetime import datetime, timedelta\n",
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"import pandas as pd\n",
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"from openbb import obb"
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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": "b5b896a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"obb.user.preferences.output_type = \"dataframe\"\n",
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"obb.user.credentials.nasdaq_api_key = \"PLACE_HOLDER\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "5f4b030b",
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"metadata": {},
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"source": [
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"Retrieve earnings calendar data from OpenBB for the next 14 days from today"
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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": "a37bb2a5",
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"metadata": {},
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"outputs": [],
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"source": [
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"earnings_calendar = obb.equity.calendar.earnings(\n",
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" start_date=(datetime.now() + timedelta(days=1)).date(),\n",
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" end_date=(datetime.now() + timedelta(days=14)).date(),\n",
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" provider=\"nasdaq\"\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": "1b97c34f",
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"metadata": {},
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"source": [
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"Define the symbol for Costco (COST) and fetch the latest stock price"
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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": "759c836d",
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"metadata": {},
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"outputs": [],
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"source": [
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"symbol = \"COST\"\n",
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"last_price = (\n",
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" obb\n",
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" .equity\n",
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" .price\n",
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" .quote(symbol, provider=\"yfinance\")\n",
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" .T\n",
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" .loc[\"last_price\", 0]\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": "128df5e6",
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"metadata": {},
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"source": [
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"Retrieve options chains for the specified symbol from OpenBB"
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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": "28c03abc",
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"metadata": {},
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"outputs": [],
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"source": [
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"options = obb.derivatives.options.chains(symbol, provider=\"cboe\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "db27ab8f",
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"metadata": {},
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"source": [
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"Filter options chain for contracts expiring on the specified date (shortly after earnings)"
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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": "74cf187a",
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"metadata": {},
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"outputs": [],
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"source": [
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"expiration = datetime(2024, 3, 8).date()\n",
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"chain = options.query(\"`expiration` == @expiration\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "41460a1e",
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"metadata": {},
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"source": [
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"Extract strike prices and identify the ATM strike for calls and puts"
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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": "5ed6ce2b",
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"metadata": {},
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"outputs": [],
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"source": [
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"strikes = chain.strike.to_frame()"
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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": "d20e86f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"call_strike = (\n",
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" strikes\n",
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" .loc[strikes.query(\"`strike` > @last_price\").idxmin()][\"strike\"]\n",
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" .iloc[0]\n",
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")"
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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": "d2f6265e",
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"metadata": {},
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"outputs": [],
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"source": [
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"atm_call = chain.query(\"`strike` == @call_strike and `option_type` == 'call'\")\n",
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"atm_put = chain.query(\"`strike` == @call_strike and `option_type` == 'put'\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "56a532be",
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"metadata": {},
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"source": [
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"Concatenate ATM call and put options and calculate the total ask price for the straddle"
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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": "b5732e3c",
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"metadata": {},
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"outputs": [],
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"source": [
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"atm = pd.concat([atm_call, atm_put])\n",
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"straddle_price = round(atm.ask.sum(), 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": "986e2637",
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"metadata": {},
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"source": [
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"Calculate the implied daily price movement based on the straddle price and time to expiration"
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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": "ff7156bc",
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"metadata": {},
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"outputs": [],
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"source": [
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"days = (atm.expiration.iloc[0] - datetime.now().date()).days + 1\n",
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"implied_move = ((1 + straddle_price / last_price) ** (1 / days) - 1) * 100"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3fc92d6c",
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"metadata": {},
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"source": [
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"Calculate the upper and lower breakeven prices for the straddle"
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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": "3d711b56",
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"metadata": {},
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"outputs": [],
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"source": [
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"upper_price = round((last_price * (1 + (straddle_price / last_price))), 2)\n",
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"lower_price = round((last_price * (1 - (straddle_price / last_price))), 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": "0e54c3cc",
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"metadata": {},
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"source": [
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"Print the calculated straddle cost, its percentage of the share price, breakeven prices, and implied daily move"
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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": "59a33449",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\n",
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" f\"Cost of Straddle: ${straddle_price}\"\n",
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" f\"\\nCost as a % of Share Price: {round((straddle_price / last_price) * 100, 4)}%\"\n",
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" f\"\\nUpper Breakeven Price: ${upper_price}\"\n",
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" f\"\\nLower Breakeven Price: ${lower_price}\\n\"\n",
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" f\"\\nImplied Daily Move: {round(implied_move, 4)}%\\n\"\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": "78bbd8f9",
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"metadata": {},
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"source": [
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"Output the number of days until the specified options expiration 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": "fe3d43ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"days"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b250a148",
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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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