factor-research

Factor combination merges multiple signals using equal-weight, IC-weighted, or orthogonalized methods. This creates a robust composite alpha signal that diversifies individual factor weaknesses and enhances predictive power for quantitative trading.

To backtest a momentum factor without look-ahead bias, align factor exposures with subsequent return series using pandas and numpy. Ensure timestamps match correctly so current period factors only predict future returns, preventing data leakage in quantile backtesting.

Factor combination merges multiple signals using equal-weight, IC-weighted, or orthogonalized methods. This creates a robust composite alpha signal that diversifies individual factor weaknesses and enhances predictive power for quantitative trading.

Quantile backtesting evaluates factor predictive power by sorting stocks into quantiles based on factor exposures and comparing their subsequent returns. This method validates whether the factor effectively differentiates high and low performing assets.

Yes, you need pandas, numpy, and scipy to process factor exposures and return series. These libraries handle data alignment, statistical testing, and numerical computations required for IC/IR analysis and quantile backtesting.