Identifying the S-score as a trading signal is half the work. The other half is the research infrastructure that validates whether a signal edge is real, how to combine it with other signals, and how quickly it decays. Without this infrastructure, even a genuinely profitable signal will be over-fit, under-sized, or abandoned too soon.
This topic covers the full pipeline from raw data to a deployable composite alpha: cross-sectional signal construction (ranking and neutralisation), signal combination via the information coefficient, feature engineering tools (data bars, fractional differentiation), and signal decay analysis. Together, these determine whether a strategy's edge is real or spurious — the essential complement to the backtesting framework in Topic 5.
By the end of this topic you should be able to:
- construct a cross-sectional alpha signal: compute raw scores, neutralise sector and cap exposures, z-score to a long-short rank;
- define the information coefficient (IC) and use it to weight signals in a linear combination;
- explain why dollar bars and volume bars produce better-behaved return series than time bars;
- apply fractional differentiation to achieve stationarity without sacrificing predictive memory;
- compute signal half-life from IC autocorrelation and use it to calibrate position holding periods.
In this lesson
- Contents
- The big picture
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