# 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.