In the field of finance, measuring risk-adjusted returns allows investors to assess the performance of an investment while considering the amount of risk taken to achieve those returns. One of the powerful libraries available for Python users to analyze these metrics is quantstats. This tool offers comprehensive capabilities for evaluating portfolios through various risk metrics and performance measures.
Overview of quantstats
QuantStats is a powerful Python library designed to facilitate the calculation and analysis of financial time series data, helping traders evaluate performance, pinpoint risks, and enhance strategic decisions. It's useful for generating beautiful reports and charts, ensuring investors have rich data analytics presented in an understandable format. To get started, you can install quantstats using pip:
pip install quantstatsLoading Your Data
First, we need to load financial time series data, typically consisting of your portfolio or asset's price or returns. Quantstats works smoothly with Pandas DataFrames or Series, facilitating integration with various data sources. Suppose we have daily returns of a particular stock or portfolio throughout a year:
import pandas as pd
# Example returns data
returns = pd.Series(
[0.0003, -0.0001, 0.0028, ..., 0.0016], # a list of daily returns
index=pd.date_range('2022-01-01', periods=365, freq='D'),
name="Portfolio"
)Calculating Sharpe and Sortino Ratios
Two commonly used risk-adjusted return metrics are the Sharpe ratio and the Sortino ratio. Quantstats makes it easy to compute these. The Sharpe ratio measures the average return earned in excess of a risk-free rate per unit of volatility or total risk. The Sortino ratio, on the other hand, focuses only on the downside deviation to reward better stabilization during rough markets.
import quantstats as qs
# Assuming 'returns' is our returns series from above
sharpe_ratio = qs.stats.sharpe(returns)
sortino_ratio = qs.stats.sortino(returns)
print(f"Sharpe Ratio: {sharpe_ratio}")
print(f"Sortino Ratio: {sortino_ratio}")Max Drawdown
Max drawdown is a popular risk metric in finance, reflecting the largest peak-to-valley decline experienced by an investment before a new peak is attained.
max_drawdown = qs.stats.max_drawdown(returns)
print(f"Max Drawdown: {max_drawdown*100:.2f}%")CAGR (Compound Annual Growth Rate)
CAGR is a useful measure to determine the mean annual growth rate of an investment over a specified time period longer than one year.
cagr = qs.stats.cagr(returns)
print(f"CAGR: {cagr*100:.2f}%")Visualizing Performance
The visual output helps in getting intuitive insights. Quantstats provides several out-of-the-box functions for visualization:
# Create a tear sheet report
qs.reports.full(returns)This function generates a comprehensive performance report including excess returns, volatilities, drawdowns, and risk-return charts.
Conclusion
Utilizing quantstats allows finance professionals to conduct detailed evaluations of returns, considering aspects of risk that may significantly impact potential future returns. Its integration capabilities and well-documented interface make it an essential tool for both novice and experienced market participants aiming to refine their investment critique. Combining powerful programming capabilities with financial theory, quantstats is helping reshape how investors approach performance analytics.
