strategy-lab/to_explore/pyquantnews/105_CalmarRatio.ipynb

This code downloads historical stock data for a specified list of symbols using the yfinance library. It computes daily returns for each stock and then aggregates these returns to form a portfolio. The Calmar Ratio, a performance metric used to evaluate the risk-adjusted returns of the portfolio, is calculated using the empyrical library. Finally, it prints the Calmar Ratio and plots the portfolio's daily returns over the specified period.

In [1]:

import empyrical as ep
import yfinance as yf
import warnings

In [2]:

warnings.filterwarnings("ignore")

In [3]:

# Define a list of stock symbols to download historical data for
symbols = [
    "AAPL",
    "AMZN",
    "GOOG",
    "META",
    "MSFT",
    "NVDA",
    "TSLA",
]

In [4]:

# Download adjusted closing prices for the specified symbols from Yahoo Finance
data = yf.download(symbols, start="2020-01-01")["Adj Close"]

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In [5]:

# Calculate daily percentage returns for each stock and remove any missing values
returns = data.pct_change().dropna()

In [6]:

# Aggregate the daily returns of all stocks to form a portfolio return series
portfolio_returns = returns.sum(axis=1)

In [7]:

# Compute the Calmar Ratio for the portfolio using the empyrical library
calmar_ratio = ep.calmar_ratio(portfolio_returns)
print(f"Calmar Ratio: {calmar_ratio}")

Calmar Ratio: -0.3244445113808806

In [8]:

# Plot the portfolio's daily returns over the specified period
portfolio_returns.plot()

Out[8]:

<Axes: xlabel='Date'>

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