A production-grade systematic equity pipeline designed to harvest the Quality risk premium. This project moves beyond simple stock-picking to construct a mathematically rigorous, dollar-neutral, long-short portfolio, validated against established asset pricing models.
Institutional asset management relies on systematic factor exposure rather than discretionary selection. This engine systematically identifies high-quality equities (high profitability, low leverage) and constructs a portfolio that isolates this specific factor premium.
Key architectural features include strict mitigation of look-ahead bias via programmatic data lagging, cross-sectional standardization to neutralize macroeconomic regime shifts, and Alpha validation using OLS regression.
The Quality factor is constructed using a composite Z-score of Return on Assets (ROA) and the Debt-to-Equity (D/E) ratio. By standardizing the cross-section of equities daily, the model isolates relative firm quality independent of market conditions:
To ensure the strategy generates true excess returns (
A statistically significant, positive
- Core Engine:
pandas,numpy(Vectorized cross-sectional operations) - Statistical Validation:
statsmodels(OLS Regression for Fama-French) - Performance Analytics:
alphalens(Tear sheets, quantile analysis, forward return calculations) - Data Hygiene: Automated 90-day lagging of fundamental accounting data to strictly prevent look-ahead bias.
For educational and portfolio demonstration purposes only. The code and financial models provided in this repository do not constitute financial advice, investment recommendations, or an offer to buy/sell securities. Systematic trading involves substantial risk. Past performance of any factor model is not indicative of future results.