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 observed · 2026-08-28
Health v2 · maintenance only
72/100
- Activity 88
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3074
- days_rel: n/a
- days_push: 74
- n_releases_24m: 0
Adoption not part of the score
10625 stars · 1877 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
library · maturity active
trading data-science math analytics fintech data-science python cli quantitative-trading backtesting technical-indicators algorithmic-trading statistical-arbitrage options-strategies momentum-strategies quantitative-finance cryptocurrency algorithms
2 sources
- readme: https://github.com/je-suis-tm/quant-trading · fetched 2026-08-28 · 4fc81198d98c
- homepage: https://je-suis-tm.github.io/quant-trading · fetched 2026-08-29 · d87309239233
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| je-suis-tm/quant-trading | main | 72 |
For agents
markdown · JSON · MCP: product_card(name="je-suis-tm/quant-trading")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem