# enzoampil/fastquant

fastquant — Backtest and optimize your ML trading strategies with only 3 lines of code!

Repository: https://github.com/enzoampil/fastquant
Canonical: https://ross.abutalabs.com/products/fastquant
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: trading-strategies, backtesting, stocks, financial-data-science, cryptocurrency, algotrading, quantitative-finance, data-science, machine-learning
Last push: 2023-09-15T10:23:58+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": 2625, "days_push": 1083, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1754, forks 262 (observed 2026-08-28T04:05:31.927234+00:00)

## What it is
fastquant is a Python library that makes backtesting and optimizing trading strategies accessible with as few as 3 lines of code. It also provides easy access to historical stock data from Yahoo Finance and the Philippine Stock Exchange, as well as crypto data from Binance.

## Use cases
- backtest a trading strategy with minimal code
- optimize parameters of a moving average crossover strategy
- fetch historical stock data for analysis
- pull crypto price data from Binance
- apply machine learning to trading strategies
- learn quantitative finance with Python

## When to choose
- you want quick, simple backtesting of trading strategies in Python
- you need easy access to Yahoo Finance, PSE, or Binance historical data
- you are a beginner in quantitative finance or algo trading

## When to avoid
- you need high-performance, production-grade backtesting with fine-grained control
- you require real-time trading execution or broker integration
- you need data sources beyond Yahoo Finance, PSE, or Binance

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, data-science, trading
- domain: fintech, data-science, machine-learning
- platform: python
- tags: backtesting, trading-strategies, quantitative-finance, stocks, cryptocurrency, algotrading

## Member repositories
- enzoampil/fastquant (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:31.927234+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 2026-08-30T03:28:07.154068+00:00, confidence not recorded.
  - readme: https://github.com/enzoampil/fastquant (fetched 2026-08-28T04:05:31.927234+00:00, sha 2536c8ed1232)
  - registry_pypi: https://pypi.org/pypi/fastquant/json (fetched 2026-08-29T11:06:08.834053+00:00, sha 0cc28ec709ca)
- Data as of 2026-08-30T08:39:29.467469+00:00.