Factor Backtest
A library of individually-decent factors is not a strategy. This skill combines them into one composite signal and backtests the portfolio that signal implies — the level at which transaction costs and capacity actually bite.
Workflow
1. Combine and backtest
bashfactorminer combine output/run1/factor_library.json \ --data path/to/market_data.csv \ --method all --fit-period train --eval-period test
--method—equal-weight,ic-weighted,orthogonal, orallto compare every method.--fit-period— split used to fit weights / run selection (usetrain).--eval-period— split used to score the composite (usetest).--selection— optional pre-filter:lasso,stepwise,xgboost, ornone.--top-k— keep only the top-K factors before combining.
The report gives composite IC Mean, ICIR, Long-Short return, Monotonicity, and Avg Turnover.
2. Generate tearsheets
For the visual portfolio view — quintile returns, IC time series, correlation heatmap:
bashfactorminer -o output/run1 visualize output/run1/factor_library.json \ --data market_data.csv --period test --tearsheet --quintile --correlation
What to look for
- Monotonicity — quintile returns should step up Q1→Q5. A non-monotone composite is fragile regardless of headline IC.
- Long-short return net of turnover — high
Avg Turnovermeans the gross return is optimistic; FactorMiner's transaction-cost model is what makes the net number honest. - Method spread — if
orthogonalandequal-weightdisagree sharply, the library has redundant or unstable factors; revisitfactor-evaluation.
Guardrails
- Fit weights on
train, score ontest— never fit and score on the same split. - The backtest estimates historical behavior; it is not a forward return promise. Present it as a research artifact for review.
- Report net-of-cost numbers as the headline; gross numbers only as context.
