factor-research

You can backtest a quantitative trading factor for look-ahead bias by aligning cross-sectional instrument return data with factor exposures before running quantile backtests. This process ensures temporal alignment, preventing future data from inflating the predictive validity of your signal.

IC/IR analysis in quantitative factor evaluation measures the predictive power and stability of a trading signal using Information Coefficient and Information Ratio statistics. It determines whether a factor consistently predicts future asset returns across a historical time series.

You test cross-sectional factor monotonicity using pandas and numpy by sorting instruments into quantile portfolios based on factor exposures. This quantile backtesting approach visualizes the return spread between top and bottom groups to confirm a linear predictive relationship.

Yes, you can use scipy to optimize multi-factor combination strategies for alpha by applying statistical testing to evaluate individual factor decay and predictive validity. This allows you to determine optimal weights for combining new signals with existing factors.

The best way to evaluate a momentum factor CSV against historical returns is to run IC/IR statistical testing alongside quantile backtesting. This validates the signal's predictive power, checks for decay, and outputs the long-short spread performance.