quant-factor-research/README.md

Quant Factor Research (QFR)

A lightweight, reproducible workspace for researching, backtesting, and evaluating quantitative equity factors.

Goals

• Factor definition library (cross-sectional / time-series)
• Data ingestion + caching
• Standardized preprocessing (winsorize, z-score, neutralization)
• IC / rank IC / turnover / decay analysis
• Simple backtests (long-short / top-k) with transaction cost hooks

Quickstart

1. Create env (pick one)

• Conda:

  • conda create -n qfr python=3.11 -y
  • conda activate qfr
  • pip install -r requirements.txt

• venv:

  • python3 -m venv .venv && source .venv/bin/activate
  • pip install -r requirements.txt
  • Note: some servers ship Python without ensurepip/venv support; you may need the OS package python3-venv (root required).

2. Run a smoke test

• python -c "import qfr; print('ok')"

Layout

• src/qfr/ core library
• notebooks/ research notebooks
• data/raw/ raw data (not committed)
• data/processed/ derived data (not committed)
• configs/ config templates
• scripts/ CLI utilities

Notes

• Keep secrets out of git. Use .env locally.