Paper Alpha

15 academic trading strategies. Fully implemented. No paywalls.

Each chapter takes a landmark academic paper and turns it into clean, runnable Python — from volatility targeting to machine learning alphas. Free data, no Bloomberg terminal required.

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Quickstart

git clone https://github.com/hakvinv/paper-alpha.git
cd paper-alpha
pip install -r requirements.txt
python ch01_volatility_targeting.py

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Strategies

#	File	Strategy	Paper
01	ch01_volatility_targeting.py	EWMA Vol Targeting	Moreira & Muir (2017)
02	ch02_momentum.py	12-1 Sector Momentum	Jegadeesh & Titman (1993)
03	ch03_value.py	Value vs Growth via ETFs	Fama & French (1992)
04	ch04_carry.py	FX Carry Trade AUD/JPY	Lustig et al. (2011)
05	ch05_low_volatility.py	Low-Vol Anomaly SPLV/SPHB	Baker et al. (2011)
06	ch06_trend_following.py	3-Asset Trend Following	Moskowitz et al. (2012)
07	ch07_quality.py	Quality Minus Junk	Asness et al. (2019)
08	ch08_betting_against_beta.py	Betting Against Beta	Frazzini & Pedersen (2014)
09	ch09_reversal.py	Weekly Sector Reversal	Lehmann (1990)
10	ch10_pairs_trading.py	Z-Score Pairs: KO/PEP, XOM/CVX	Gatev et al. (2006)
11	ch11_risk_parity.py	Inverse-Vol Risk Parity	Qian (2005)
12	ch12_factor_timing.py	Value Spread Analysis	Asness (2016)
13	ch13_ml_alpha.py	XGBoost on ETF Features	Gu, Kelly & Xiu (2020)
14	ch14_volatility_risk_premium.py	VRP + Conservative Short-Vol	Ilmanen (2011)
15	ch15_combined.py	Trend + Risk Parity Combined	Hamill et al. (2016)

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Dependencies

Package	Purpose
yfinance	Free market data
pandas	Data manipulation
numpy	Numerical computing
matplotlib	Plotting
scipy	Statistics
xgboost	Ch. 13 (falls back to sklearn)

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Notes

All scripts pull free data from Yahoo Finance — no paid subscriptions, no API keys. If your numbers differ slightly from the book, Yahoo Finance may have revised historical data since publication (dividend adjustments, splits).

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