letianzj/QuantResearch resource
Quantitative analysis, strategies and backtests observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- 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 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2259
- days_rel: n/a
- days_push: 1103
- n_releases_24m: 0
Adoption not part of the score
3007 stars · 573 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A collection of Jupyter notebooks and accompanying blog posts covering quantitative trading research, including portfolio optimization, pairs trading, risk measures, and machine learning models for markets. It serves as educational reference material rather than a production trading library.
Use cases
- learn quantitative trading strategies with python notebooks
- backtest pairs trading and cointegration strategies
- portfolio optimization and asset allocation examples
- value at risk and risk management calculations
- apply machine learning and reinforcement learning to stock prediction
- kalman filter and hidden markov model examples for finance
- study arima garch and fama-french factor models
When to choose
- learning systematic investing concepts with worked code examples
- prototyping quant research ideas in jupyter notebooks
- studying statistical methods like cointegration, MCMC, or regime switching applied to markets
When to avoid
- needing a production-ready backtesting or live trading engine (use the companion quanttrader package instead)
- requiring maintained, tested software with an API
- enterprise-grade portfolio management systems
Facets
learning-resource · maturity maintenance
data-science machine-learning deep-learning reinforcement-learning math analytics fintech data-science machine-learning tutorials python cross-platform quantitative-finance backtesting trading-strategies portfolio-optimization pairs-trading jupyter-notebooks risk-management statistical-arbitrage
2 sources
- readme: https://github.com/letianzj/QuantResearch · fetched 2026-08-28 · 6e7d218a0ebb
- homepage: https://letianzj.github.io/ · fetched 2026-08-29 · e087d12fbc27
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| letianzj/QuantResearch | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="letianzj/QuantResearch")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem