Can I use intraday data to trigger stop losses?

Yes, by enabling the 'touched_exit' parameter, the system checks high/low prices to see if stops or take-profit targets were hit during the trading day.

What performance metrics are included in the backtest report?

The report provides total return, CAGR, max drawdown, Sharpe and Sortino ratios, win ratio, and monthly return tables, among other metrics.

Does it support transaction costs like fees and taxes?

Yes, you can configure fee_ratio for all transactions and tax_ratio specifically for sell orders to simulate realistic trading environments.

What data format is required for polars-backtest?

The library requires a 'long format' DataFrame where each row represents a unique (date, symbol) pair containing price data and position weights or signals.

How does it handle rebalancing frequency?

It supports multiple resampling frequencies including daily, weekly, monthly, and yearly, and allows for custom offsets to simulate execution delays.

Polars Portfolio Backtester

Name: Polars Portfolio Backtester
Author: Yvictor

byYvictor

•

Ciencia de Datos y ML

Performs ultra-fast portfolio backtesting and trading strategy analysis using the Polars library.

Acerca de

Polars Portfolio Backtester is a specialized Claude Code skill designed for quantitative analysts and traders who require high-performance strategy simulation. It leverages the polars-backtest library to handle large datasets with blazingly fast execution, supporting complex rebalancing logic, realistic transaction costs, and advanced risk management features like trailing stops and touched exits. By integrating this skill, Claude can help users build, refine, and analyze the performance of multi-asset portfolios with detailed trade logs and comprehensive financial metrics.

Características Principales

2 GitHub stars
Advanced trade logging with MAE/MFE metrics and pending signal tracking.
Flexible rebalancing schedules and realistic transaction cost simulations.
Blazingly fast backtesting on large datasets using Polars expressions.
Detailed performance reporting with CAGR, Sharpe ratio, and drawdown analysis.
Comprehensive risk management including stop-loss, take-profit, and trailing stops.

Casos de Uso

Simulating trading costs and tax implications on high-frequency signals.
Evaluating the historical performance of multi-stock portfolio strategies.
Optimizing risk parameters like trailing stops based on historical intraday volatility.

Acerca de

Características Principales

2 GitHub stars
Advanced trade logging with MAE/MFE metrics and pending signal tracking.
Flexible rebalancing schedules and realistic transaction cost simulations.
Blazingly fast backtesting on large datasets using Polars expressions.
Detailed performance reporting with CAGR, Sharpe ratio, and drawdown analysis.
Comprehensive risk management including stop-loss, take-profit, and trailing stops.

Casos de Uso

Simulating trading costs and tax implications on high-frequency signals.
Evaluating the historical performance of multi-stock portfolio strategies.
Optimizing risk parameters like trailing stops based on historical intraday volatility.