# letianzj/QuantResearch

Quantitative analysis, strategies and backtests

Repository: https://github.com/letianzj/QuantResearch
Canonical: https://ross.abutalabs.com/products/quantresearch
Homepage: https://letianzj.github.io/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: quantitative-finance, quantitative-trading, portfolio-management, risk-management, derivatives-pricing, machine-learning, deep-learning, backtesting-trading-strategies, trading-strategies, statistical-arbitrage, asset-management, asset-allocation, algotrading, trading-algorithms, financial-analysis, pairs-trading, algorithmic-trading, data-science, reinforcement-learning, backtests
Last push: 2023-08-26T12:16:05+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2259, "days_push": 1103, "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 3007, forks 573 (observed 2026-08-28T04:07:37.691184+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, machine-learning, deep-learning, reinforcement-learning, math, analytics
- domain: fintech, data-science, machine-learning, tutorials
- platform: python, cross-platform
- tags: quantitative-finance, backtesting, trading-strategies, portfolio-optimization, pairs-trading, jupyter-notebooks, risk-management, statistical-arbitrage

## Member repositories
- letianzj/QuantResearch (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:37.691184+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 2026-08-30T07:30:21.084247+00:00, confidence not recorded.
  - readme: https://github.com/letianzj/QuantResearch (fetched 2026-08-28T04:07:37.691184+00:00, sha 6e7d218a0ebb)
  - homepage: https://letianzj.github.io/ (fetched 2026-08-29T09:45:37.924167+00:00, sha e087d12fbc27)
- Data as of 2026-08-30T08:39:29.467469+00:00.