# edtechre/pybroker

Algorithmic Trading in Python with Machine Learning

Repository: https://github.com/edtechre/pybroker
Canonical: https://ross.abutalabs.com/products/pybroker
Homepage: https://www.pybroker.com
Language: Python
License: NOASSERTION
License Family: other
Topics: algotrading, backtesting, machine-learning, python, trading, quantitative-finance, stocks, framework, investment, ai, artificial-intelligence, algorithmic-trading, data-science, finance, trading-strategies, crypto, cryptocurrency
Last push: 2026-08-25T18:22:25+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 86, longevity 94
- inputs: {"age_days": 1326, "days_push": 8, "days_rel": 16, "gap_med": 53.0, "n_releases_24m": 13}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3515, forks 453 (observed 2026-08-28T04:08:07.825305+00:00)

## What it is
PyBroker is a Python framework for developing algorithmic trading strategies with a focus on machine learning. It provides a fast NumPy/Numba-based backtesting engine, model training with walkforward analysis, and data integration from sources like Alpaca and Yahoo Finance.

## Use cases
- backtest algorithmic trading strategies in python
- train machine learning models for stock trading
- run walkforward analysis on a trading strategy
- optimize trading strategy parameters with optuna
- download historical stock data for backtesting
- test trading strategies across multiple time intervals
- backtest crypto trading strategies

## When to choose
- you want to combine machine learning with systematic trading strategy development
- you need fast backtesting across multiple instruments in Python
- you want realistic simulation of strategy performance via walkforward analysis

## When to avoid
- you need live order execution against a broker out of the box
- you want a no-code or GUI trading platform
- you need options or fixed-income asset classes rather than equities and crypto

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, benchmarking, caching, data-science
- domain: fintech, machine-learning, data-science
- platform: python
- tags: algorithmic-trading, backtesting, quantitative-finance, trading-strategies, walkforward-analysis, stocks, numba, cryptocurrency

## Member repositories
- edtechre/pybroker (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:07.825305+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:35:35.737006+00:00, confidence not recorded.
  - readme: https://github.com/edtechre/pybroker (fetched 2026-08-28T04:08:07.825305+00:00, sha 41159b1011da)
- Data as of 2026-08-30T08:39:29.467469+00:00.