# ScottfreeLLC/AlphaPy

Python AutoML for Trading Systems and Sports Betting

Repository: https://github.com/ScottfreeLLC/AlphaPy
Canonical: https://ross.abutalabs.com/products/alphapy
Language: Python
License: Apache-2.0
License Family: permissive
Topics: machine-learning, predictive-analytics, classification, regression, scikit-learn, pandas, trading, stocks, sports, portfolio, cryptocurrency, trading-strategies, keras, data-science, python, iex, deep-learning, trading-platform, time-series-analysis, backtesting
Last push: 2025-08-24T13:55:25+00:00

## Health v2 (maintenance only)
Score: 40/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 38, release rhythm 8, longevity 100
- inputs: {"age_days": 3854, "days_push": 374, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1745, forks 274 (observed 2026-08-28T04:05:30.618990+00:00)

## What it is
AlphaPy is a Python machine learning framework built on scikit-learn, pandas, Keras, XGBoost, LightGBM, and CatBoost for building classification and regression pipelines. It includes MarketFlow for trading system development and portfolio analysis, and SportFlow for predicting sporting events.

## Use cases
- build machine learning pipelines for stock market prediction
- backtest trading strategies with machine learning models
- predict sports game outcomes with classification models
- create stacked or blended ensembles of gradient boosting models
- analyze portfolio performance with pyfolio tear sheets
- run automated feature engineering on time series market data

## When to avoid
- you need actively developed features - development has moved to AlphaPy Pro
- you need a modern Python 3.12 environment or meta-labeling support
- you want a general-purpose AutoML tool outside finance or sports domains

## Facets
- artifact type: framework
- maturity: maintenance
- function: machine-learning, data-science, benchmarking, data-visualization, etl
- domain: machine-learning, data-science, fintech, time-series, sports
- platform: python, cross-platform
- tags: automl, trading-systems, sports-betting, backtesting, scikit-learn, marketflow, sportflow, ensemble-learning, pyfolio, legacy

## Member repositories
- ScottfreeLLC/AlphaPy (main) score 40

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:30.618990+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 2026-08-30T03:29:13.123518+00:00, confidence not recorded.
  - readme: https://github.com/ScottfreeLLC/AlphaPy (fetched 2026-08-28T04:05:30.618990+00:00, sha 6e0e57f701c8)
  - registry_pypi: https://pypi.org/pypi/alphapy/json (fetched 2026-08-29T11:07:05.518249+00:00, sha 45d8d98c01d0)
- Data as of 2026-08-30T08:39:29.467469+00:00.