portfolio-backtesting

Performance analytics, portfolio backtesting, risk analysis, and factsheet reporting in Python

Python backtesting engine built on NautilusTrader for end-to-end quant research: market data ingestion/validation, microstructure calibration, single-asset and stat-arb strategy creation, walk-forward optimization, analytics, portfolio-of-strategies backtesting, and portfolio weight optimization.

Streamlit-based portfolio construction dashboard implementing a multifactor model with factor exposure targeting, portfolio optimization, and interactive visualization of portfolio weights and risk exposures.

Local-first portfolio backtesting, strategy comparison, risk analytics, paper simulation, reports, and Streamlit dashboard.

Python-based A-share multi-factor quantitative research framework with factor analysis, portfolio backtesting, transaction cost simulation and CSI300 benchmark comparison.

Long-horizon daily asset-class datasets with reproducible Python pipelines, source provenance, and validation for portfolio backtesting.

Python backtesting framework for testing crypto trading strategies with configurable parameters, historical data processing, performance metrics, and risk analysis.

A Qlib-based PIT research pipeline for CSI800 equity selection, centered on GRU and CCC ranking with financial slow-fast fusion and attention, FinBERT text factors, and cost-aware portfolio backtesting.

Backtest stock portfolio performance with historical price data.