btutils 0.1.2

A lightweight Python library for backtesting analysis and visualization of trading strategies

BTUtils

Overview

BTUtils (Backtest Utilities) is a lightweight Python library for backtesting analysis and visualization of trading strategies. Inspired by the excellent QuantStats package, BTUtils provides a simplified and streamlined approach to analyze and visualize trading performance.

Features

• Calculate key performance metrics:

  • Returns (cumulative, annual)
  • Risk metrics (volatility, drawdowns, VaR/CVaR)
  • Ratios (Sharpe, Sortino)
  • Alpha/Beta analysis
  • Win rate and payoff statistics

• Visualize strategy performance:

  • Cumulative returns with drawdowns
  • Return distribution analysis
  • Monthly/yearly heatmaps
  • Rolling metrics (volatility, Sharpe, Sortino, etc.)
  • Comparative analysis against benchmarks

Installation

pip install btutils

Quick Start

import pandas as pd
import btutils
from btutils import Backtest

# Create a Backtest instance from a pandas Series of returns
returns = pd.Series(...)  # Your daily returns data
bt = Backtest(returns, name="My Strategy")

# Display key metrics
print(bt.metrics())

# Compare with a benchmark
benchmark = pd.Series(...)  # Benchmark returns
print(bt.metrics(index_list=[benchmark]))

# Visualize performance
bt.plots.line(benchmark=benchmark, show_drawdown=True)
bt.plots.hist()
bt.plots.heatmap(freq="ME")  # Monthly heatmap
bt.plots.rolling_sharpe(window=60)

Documentation

The library consists of three main classes:

• Backtest: The main class for handling return series
• Stats: For calculating performance metrics
• Plots: For visualizing performance

Requirements

• Python 3.7+
• pandas
• numpy
• matplotlib
• seaborn
• scipy

License

MIT License

Acknowledgements

BTUtils was inspired by QuantStats but with the goal of providing a more streamlined API focused on the most essential backtesting analytics.