# chrisconlan/algorithmic-trading-with-python

Source code for Algorithmic Trading with Python (2020) by Chris Conlan

Repository: https://github.com/chrisconlan/algorithmic-trading-with-python
Canonical: https://ross.abutalabs.com/products/algorithmic-trading-with-python
Language: Python
License: NOASSERTION
License Family: other
Last push: 2021-06-01T12:56:44+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2344, "days_push": 1919, "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 3479, forks 612 (observed 2026-08-28T04:08:06.830755+00:00)

## What it is
Source code companion to the book 'Algorithmic Trading with Python' (2020) by Chris Conlan, including a small library (pypm) with performance metrics, technical indicators, signal generation, portfolio simulation, and ML cross-validation utilities. Also ships free simulated end-of-day stock data.

## Use cases
- backtest trading strategies in python
- compute trading strategy performance metrics
- calculate technical indicators with pandas
- simulate a stock portfolio in python
- learn algorithmic trading from a book
- cross-validate ml models for stock prediction

## When to choose
- you are reading the book and want its code
- you need simple pandas-based indicators and metrics for research
- you want free simulated EOD data for experiments

## When to avoid
- you need a production live-trading or broker-execution system
- you want actively maintained trading software with support
- you need real-time or intraday market data

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: trading, data-science, machine-learning, benchmarking
- domain: fintech, data-science, machine-learning, tutorials
- platform: python, cross-platform
- tags: algorithmic-trading, quantitative-finance, backtesting, technical-indicators, portfolio-simulation, book-companion

## Member repositories
- chrisconlan/algorithmic-trading-with-python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:06.830755+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 2026-08-29T18:36:28.427878+00:00, confidence not recorded.
  - readme: https://github.com/chrisconlan/algorithmic-trading-with-python (fetched 2026-08-28T04:08:06.830755+00:00, sha 1de425b3b046)
- Data as of 2026-08-30T08:39:29.467469+00:00.