Multi-Factor Alpha Engine — Indian Equity Quantitative Research

Walk-forward validated momentum + mean-reversion factor model on Nifty 50 stocks.

What this project does

• Constructs momentum (12-1 month) and mean-reversion factors on 20 Nifty 50 stocks
• Validates factors using Information Coefficient (IC) and ICIR
• Applies cross-sectional z-scoring and winsorisation to clean raw signals
• Tests statistical significance using bootstrap p-value and Deflated Sharpe Ratio (Lopez de Prado, 2018)
• Models Indian market transaction costs: STT 0.1% + brokerage 0.03% + market impact 0.05%

Tech stack

Python · pandas · numpy · scipy · vectorbt · yfinance · matplotlib

Project structure

Key concepts implemented

• Walk-forward validation (no look-ahead bias)
• Information Coefficient (IC) and ICIR for factor evaluation
• Deflated Sharpe Ratio to correct for selection bias across strategy trials
• Bootstrap resampling for statistical significance testing
• Indian market transaction cost modelling

References

• Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.
• Grinold, R. & Kahn, R. (1999). Active Portfolio Management. McGraw-Hill.