# AlgoTraders/stock-analysis-engine

Backtest 1000s of minute-by-minute trading algorithms for training AI with automated pricing data from: IEX, Tradier and FinViz. Datasets and trading performance automatically published to S3 for building AI training datasets for teaching DNNs how to trade. Runs on Kubernetes and docker-compose. >150 million trading history rows generated from +5000 algorithms. Heads up: Yahoo's Finance API was disabled on 2019-01-03 https://developer.yahoo.com/yql/

Repository: https://github.com/AlgoTraders/stock-analysis-engine
Canonical: https://ross.abutalabs.com/products/stock-analysis-engine
Homepage: https://stock-analysis-engine.readthedocs.io/en/latest/README.html
Language: Jupyter Notebook
License Family: other
Topics: algorithmic-trading, stocks, options, tensorflow, keras, kubernetes, iex, tradier, backtesting, docker, redis, minio, s3, deep-neural-networks, deep-learning, jupyter, deep-learning-tutorial, iexcloud, helm, helm-charts
Last push: 2020-09-05T13:01:38+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2908, "days_push": 2188, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1238, forks 272 (observed 2026-08-28T04:04:05.456765+00:00)

## What it is
A distributed stock analysis and backtesting framework that ingests automated pricing data from IEX Cloud, Tradier, and FinViz and runs thousands of minute-by-minute trading algorithm backtests. It publishes datasets and trading performance to S3 for building AI training datasets used to teach deep neural networks how to trade, and deploys via Kubernetes/Helm or docker-compose.

## Use cases
- backtest thousands of minute-by-minute trading algorithms
- fetch stock pricing, options, and news data from IEX Cloud and Tradier
- build AI training datasets of trading performance for deep neural networks
- run distributed backtesting jobs on Kubernetes with Helm
- train DNNs to predict stock closing prices
- publish compressed trading datasets to S3 automatically

## When to choose
- you need to backtest large numbers of intraday trading algorithms at scale
- you want automated market data ingestion from IEX Cloud, Tradier, or FinViz
- you want to generate training datasets for deep learning models that trade stocks
- you deploy on Kubernetes or docker-compose and want Helm-based orchestration

## When to avoid
- you need a maintained, actively updated project - the latest release is from 2020 and Yahoo Finance integration is broken
- you want live trading execution rather than backtesting and dataset generation
- you need a lightweight single-machine tool without Docker or Kubernetes
- you require a permissively licensed dependency - the repo has no license, restricting reuse

## Facets
- artifact type: framework
- maturity: maintenance
- function: machine-learning, deep-learning, etl, data-science, cli, trading
- domain: fintech, machine-learning, big-data
- platform: python, cloud, self-hosted
- tags: algorithmic-trading, backtesting, iex-cloud, tradier, finviz, stock-market-data, s3, redis, minio, tensorflow, keras, jupyter-notebooks, helm-charts, trading-algorithms, market-data-ingestion, data-engineering, cryptocurrency, docker, kubernetes

## Member repositories
- AlgoTraders/stock-analysis-engine (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:05.456765+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 2026-08-30T05:10:00.649596+00:00, confidence not recorded.
  - readme: https://github.com/AlgoTraders/stock-analysis-engine (fetched 2026-08-28T04:04:05.456765+00:00, sha 3a4694d57425)
  - registry_pypi: https://pypi.org/pypi/stock-analysis-engine/json (fetched 2026-08-29T12:21:31.076552+00:00, sha f07fe5457b2e)
- Data as of 2026-08-30T08:39:29.467469+00:00.