Power. Precision.

Tools I kept rewriting for every quant project, wrapped into one library so you can stay in Python instead of rebuilding infrastructure.

Vectorized Backtesting

Simulate 10 years of tick data in milliseconds. Our engine is built on NumPy and optimized for speed.

Risk Parity Engine

Allocators for ERC, HRP, and IVP built-in. Just pass your covariance matrix.

Walk-Forward Analysis

Avoid overfitting with robust out-of-sample testing pipelines.

Clean Data API

Unified interface for Yahoo Finance, Alpaca, and custom CSVs.

Designed for Developers

Full mypy compliance with strict type hints. No more guessing what existing DataFrame columns are required.

Examples for GitHub Actions included. Run your backtests on every pull request automatically.

# Type-safe portfolio construction

def

optimize_weights

(
  returns: pd.DataFrame,
  method: OptimizationMethod
) -> pd.Series:
  """Calculate optimal asset allocation."""
  ...