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Backtest Engine

Simulate. Measure. Validate. 

Description

Portfolio backtesting engine that simulates how your strategies would have performed historically. It loads market data and portfolio definitions, executes rebalances with realistic trading costs (commissions and slippage), computes 30+ performance and risk metrics against a benchmark, and delivers results in an interactive dashboard and a multi-sheet Excel report. You can configure it entirely from an Excel workbook with no code required, run it programmatically in Python, or deploy it with Docker.
 

Features

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  • Configurable from an Excel workbook, with no code required to run a backtest, or directly from a Python script. A Docker image is also available, and a CLI handles project initialization and execution.

  • Realistic execution: rebalances are simulated with pluggable commission and slippage models, a configurable execution price (VWAP or Adjusted Close), commission per share, and a cash reserve percentage.

  • 30+ performance and risk metrics computed automatically, including CAGR, Sharpe, Sortino, Maximum Drawdown, VaR (historical and Gaussian), CVaR, Alpha, Beta and Information Ratio.

  • Built-in benchmark comparison: track your portfolio against any benchmark (e.g., SPY) with alpha, beta and relative returns across annual and cumulative periods.

  • Backtest Suite for multiple scenarios: run parameter sweeps or compare different portfolios on a single shared data pipeline, with side-by-side comparative reporting.

  • Interactive dashboards showing portfolio value, cumulative returns vs. benchmark, composition, weight evolution, drawdowns and annual returns.

  • Professional Excel reports with 8+ sheets: Summary, Daily Performance, Drawdown Analysis, Commission Tracking, Holdings, Benchmark Comparison, Annual Returns and Portfolio Definition.

  • High-performance PyArrow engine with a component-oriented architecture, in which portfolio state, trade execution and reporting are separate, extensible modules.

  • Robust data handling: automatic date format detection, flexible column name mapping and data integrity validation.
     

Supported Input Formats
 

  • Market Data: CSV, Parquet (one file per ticker with OHLCV data)

  • Portfolios: CSV, Excel (ticker weights by rebalancing date)

  • Configuration: Excel workbook or programmatic Python configuration
     

Everything you need to get started!
 

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