43 lines
1.1 KiB
Markdown
43 lines
1.1 KiB
Markdown
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# Quant Factor Research (QFR)
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A lightweight, reproducible workspace for researching, backtesting, and evaluating quantitative equity factors.
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## Goals
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- Factor definition library (cross-sectional / time-series)
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- Data ingestion + caching
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- Standardized preprocessing (winsorize, z-score, neutralization)
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- IC / rank IC / turnover / decay analysis
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- Simple backtests (long-short / top-k) with transaction cost hooks
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## Quickstart
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1) Create env (pick one)
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- Conda:
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- `conda create -n qfr python=3.11 -y`
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- `conda activate qfr`
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- `pip install -r requirements.txt`
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- venv:
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- `python3 -m venv .venv && source .venv/bin/activate`
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- `pip install -r requirements.txt`
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- Note: some servers ship Python without ensurepip/venv support; you may need the OS package `python3-venv` (root required).
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2) Run a smoke test
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- `python -c "import qfr; print('ok')"`
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## Layout
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- `src/qfr/` core library
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- `notebooks/` research notebooks
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- `data/raw/` raw data (not committed)
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- `data/processed/` derived data (not committed)
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- `configs/` config templates
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- `scripts/` CLI utilities
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## Notes
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- Keep secrets out of git. Use `.env` locally.
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