daily update
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David Brazda
214
to_explore/pyquantnews/56_UpsideCapture.ipynb
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214
to_explore/pyquantnews/56_UpsideCapture.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": "7b39d6ef",
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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": "09b6a2a7",
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"metadata": {},
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"source": [
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"This code fetches historical stock prices for AAPL, WMT, and SPY, calculates daily returns, and constructs a portfolio. It defines functions to compute the annual return and upside capture ratio of the portfolio compared to a benchmark. The upside capture ratio measures the portfolio's performance relative to the benchmark when the benchmark is up. This is useful in practice for portfolio performance analysis and risk management."
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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": "7589769e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from openbb_terminal.sdk import openbb"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d7af040b",
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"metadata": {},
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"source": [
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"Fetch historical stock prices for AAPL, WMT, and SPY starting from 2020-01-01"
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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": "01ac1e2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"prices = openbb.economy.index(\n",
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" [\"AAPL\", \"WMT\", \"SPY\"], \n",
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" start_date=\"2020-01-01\"\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": "6cf5e1ea",
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"metadata": {},
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"source": [
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"Calculate daily returns by taking the percentage change and dropping any NaN values"
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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": "fdd82c4a",
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"metadata": {},
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"outputs": [],
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"source": [
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"returns = prices.pct_change().dropna()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "859f62ce",
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"metadata": {},
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"source": [
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"Construct a portfolio's returns by summing the returns of AAPL and WMT, and selecting SPY and portfolio columns"
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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": "1138bf9a",
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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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"data = returns.assign(\n",
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" port=returns[[\"AAPL\", \"WMT\"]].sum(axis=1)\n",
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")[[\"SPY\", \"port\"]]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a8b57c5b",
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"metadata": {},
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"source": [
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"Compute annualized return for a series of returns"
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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": "a4341da4",
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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 annual_return(returns):\n",
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" \"\"\"Compute annualized return.\n",
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" \n",
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" This function calculates the annualized return of a series of returns.\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" returns : pd.Series\n",
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" Series of daily returns.\n",
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" \n",
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" Returns\n",
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" -------\n",
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" float\n",
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" Annualized return.\n",
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" \"\"\"\n",
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" \n",
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" # Calculate the number of years by dividing total days by 252 (trading days in a year)\n",
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" num_years = len(returns) / 252\n",
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" \n",
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" # Calculate the annualized return using the geometric average\n",
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" return (returns + 1).prod() ** (1 / num_years) - 1"
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]
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},
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{
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"cell_type": "markdown",
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"id": "567d5ef4",
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"metadata": {},
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"source": [
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"Compute upside capture ratio comparing portfolio returns to benchmark returns"
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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": "1dea9509",
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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 upside_capture(port_returns, bench_returns):\n",
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" \"\"\"Compute upside capture ratio.\n",
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" \n",
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" This function calculates the upside capture ratio of portfolio returns \n",
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" compared to benchmark returns.\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" port_returns : pd.Series\n",
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" Series of portfolio returns.\n",
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" bench_returns : pd.Series\n",
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" Series of benchmark returns.\n",
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" \n",
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" Returns\n",
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" -------\n",
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" float\n",
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" Upside capture ratio.\n",
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" \"\"\"\n",
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" \n",
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" # Filter portfolio and benchmark returns where benchmark returns are positive\n",
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" mask = bench_returns > 0\n",
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" port_returns = port_returns[mask]\n",
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" bench_returns = bench_returns[mask]\n",
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" \n",
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" # Calculate the upside capture ratio by dividing annualized portfolio return by annualized benchmark return\n",
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" return (\n",
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" annual_return(port_returns) \n",
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" / annual_return(bench_returns)\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": "600cd3ac",
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"metadata": {},
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"source": [
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"Calculate the upside capture ratio for the constructed portfolio compared to SPY"
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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": "47239c60",
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"metadata": {},
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"outputs": [],
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"source": [
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"upside_capture(data.port, data.SPY)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4d6184fc",
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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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