Systematic Trading
Democratized.
The library I use to research, backtest, and run systematic strategies.
Pure Python, explicit assumptions, no black boxes.
# Install from PyPI
pip install pyfundlib
# Or install with poetry
poetry add pyfundlibWhat pyfundlib ships with
Covers the path from a quick notebook idea to a production deployment.
Backtesting & Research
- ✓Vectorized engine (100× faster)
- ✓Walk-forward analysis
- ✓Deflated Sharpe & PSR
- ✓Monte Carlo stress testing
Portfolio Construction
- ✓Risk Parity (ERC, HRP)
- ✓Black-Litterman
- ✓Mean-CVaR optimization
- ✓Hierarchical Risk Parity
Execution & Live Trading
- ✓Alpaca & Interactive Brokers
- ✓Paper + live modes
- ✓Slippage modeling
- ✓Multi-asset support
Machine Learning
- ✓MLflow model registry
- ✓LSTM, XGBoost integration
- ✓Rolling feature pipeline
- ✓Reproducible workflows
Where it actually fits
A few realistic places pyfundlib tends to land in people's workflows.
Research notebook to production
Start in a Jupyter notebook, end with a deployable, versioned strategy that survives code review.
Signal research and factor testing
Quickly try new signals on clean data without rewriting backtest infrastructure for every idea.
Existing stack integration
Drop pyfundlib into an existing Python stack instead of replacing everything with a framework.
What it is actually good at
A few honest highlights from how PyFund is meant to be used day to day.
Our Philosophy
No Jupyter Lock-in
Works in scripts, Docker, cloud functions, and production servers. Not just notebooks.
Transparent
Every calculation is vectorized, readable, and testable. No compiled C++ black boxes you can't inspect.
Zero Lock-in
MIT licensed. Fork it. Deploy it. Own your IP. No subscriptions.