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.

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

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

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.

Trading Strategy Backtesting Suite helps traders and quants validate ideas with event-driven backtesting, portfolio simulation, and Python backtesting workflows. Run free backtesting trading experiments on forex, options, and crypto data before risking capital in live markets.

Backtest stock portfolio performance with historical price data.

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.