Overview

Warning

This documentation is in the making…

Background

There are many algo-trading frameworks written in Python (for details, please refer to pytrade.org), yet none of them has fully addressed the demands of modern algo-trading:

  • TradFi, CeFi and DeFi support

  • Machine learning integration

  • Ease of use, transition seamlessly from backtesting to live trading

  • Modern frontend for monitoring and backend for tasks such as storing trade history

That is why pfund is created.

The goal for it is to become the go-to algo-trading framework for Python traders, no more reinventing the wheel!

What is pfund

pfund (/piː fʌnd/), which stands for “Personal Fund”, is an algo-trading framework designed for using machine learning models to trade across TradFi (Traditional Finance, e.g. Interactive Brokers), CeFi (Centralized Finance, e.g. Binance) and DeFi (Decentralized Finance, e.g. dYdX), or in simple terms, Stocks and Cryptos.

pfund allows traders to:

  • perform vectorized or event-driven backtesting with

    • different resolutions of data, e.g. orderbook data, tick data, bar data etc.

    • different data tools, e.g. pandas, polars etc.

  • train machine learning models using their favorite frameworks, i.e. pfund is ML-framework agnostic

  • tune strategy (hyper)parameters by splitting data into training sets, development sets and test sets

  • go from backtesting to live trading by just changing ONE line of code!!

  • execute trades manually/semi-manually via a trading app (frontend+backend)

It is created to enable trading for PFund.ai - a trading platform that bridges algo-trading and manual trading using AI (LLM).

Since PFund’s sole purpose is for trading only, for all the data work, there is a separate library to handle that:
PFeed - Data pipeline for algo-trading, helping traders in getting real-time and historical data, and storing them in a local data lake for quantitative research.

Why use pfund

You should use pfund if you want to:

  • Join PFund.ai’s ecosystem, which includes:

    • AI (LLM) capable of analyzing your trading strategies

    • PFund Hub for downloading trading strategies and machine learning models

    • Cloud deployment

  • Use a single framework to trade across markets - stocks, futures and cryptos etc.

  • Apply machine learning in algo-trading

  • Focus primarily on strategy development and let the framework handle the rest

See also

Comparisons with Other Frameworks