# je-suis-tm/quant-trading

Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD

Repository: https://github.com/je-suis-tm/quant-trading
Canonical: https://ross.abutalabs.com/products/quant-trading
Homepage: https://je-suis-tm.github.io/quant-trading
Language: Python
License: Apache-2.0
License Family: permissive
Topics: trading-strategies, quantitative-trading, trading-bot, quantitative-finance, algorithmic-trading, macd, statistical-arbitrage, bollinger-bands, momentum-trading-strategy, options-trading, options-strategies, commodity-trading, quant, trading-algorithms, trading-systems, trading-strategy, momentum-strategy, quantitative-trading-strategies, quantimental-analysis, pair-trading
Last push: 2026-06-20T12:16:37+00:00

## Health v2 (maintenance only)
Score: 72/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 88, release rhythm 35, longevity 100
- inputs: {"age_days": 3074, "days_push": 74, "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 10625, forks 1877 (observed 2026-08-28T04:10:42.596031+00:00)

## What it is
A collection of Python scripts implementing quantitative and algorithmic trading strategies, including technical indicator strategies (MACD, Bollinger Bands, RSI, Parabolic SAR), options strategies, statistical arbitrage, and quantamental analysis projects like Monte Carlo simulation and pair trading. All scripts are designed for historical backtesting and forward testing with frictionless-trade assumptions and can be embedded into a larger trading system via their main functions.

## Use cases
- backtest technical indicator trading strategies in python
- learn quantitative trading strategy implementations
- implement pair trading and statistical arbitrage
- calculate VIX and price options straddle strategies
- run monte carlo simulations on trading ideas
- build a momentum or breakout trading bot
- study algorithmic trading strategy code

## When to choose
- you want readable Python implementations of classic quant strategies to learn from or embed in your own system
- you need backtesting scripts for momentum, breakout, reversal, and stat-arb strategies
- you are studying quantitative finance concepts like options pricing and portfolio optimization

## When to avoid
- you need production live trading with real broker connectivity, slippage, and transaction costs
- you require high-frequency or low-latency execution
- you want a polished framework with documentation and docstrings rather than standalone scripts

## Facets
- artifact type: library
- maturity: active
- function: trading, data-science, math, analytics
- domain: fintech, data-science
- platform: python, cli
- tags: quantitative-trading, backtesting, technical-indicators, algorithmic-trading, statistical-arbitrage, options-strategies, momentum-strategies, quantitative-finance, cryptocurrency, algorithms

## Member repositories
- je-suis-tm/quant-trading (main) score 72

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:42.596031+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 2026-08-29T17:18:27.503208+00:00, confidence not recorded.
  - readme: https://github.com/je-suis-tm/quant-trading (fetched 2026-08-28T04:10:42.596031+00:00, sha 4fc81198d98c)
  - homepage: https://je-suis-tm.github.io/quant-trading (fetched 2026-08-29T08:17:27.832649+00:00, sha d87309239233)
- Data as of 2026-08-30T08:39:29.467469+00:00.