[WITH CODE] Data transformations: Data shape and predictive features
Alternative data shape transformation
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From raw price paths to structurally aligned features
Data-shape transformation represents the first structural choice in model construction rather than a routine cleanup step. Every transformation operator selects specific variations in a market path while suppressing others, determining whether a model observes genuine economic signals or sample-specific noise. Aligning these operators with the strategy’s target holding period prevents false statistical stability and preserves the underlying alpha.
What’s inside:
Reframing data transformations: Treating transformations as active structural choices ensures the mathematical shape of the input feature matches the economic mechanism behind the trade.
Recognizing transformation risks: Indiscriminate differencing or scaling can amputate trend signals, introduce causality leakage through full-sample parameters, or distort high-dimensional distance geometry.
Extracting state-space surprises: Applying local linear trend models yields one-step-ahead forecast errors, isolating orthogonal price surprises from predictable local drift.
Neutralizing dynamic benchmarks: Rolling regression residuals project asset returns into the null space of a benchmark, stripping out market beta to expose pure relative-value dislocations.
Eliminating stochastic drift via cointegration: Engle-Granger equilibrium spreads remove shared unit-root trends between paired assets, producing stationary inputs designed for mean-reversion models.
Isolating multiscale wavelet details: Discrete Haar wavelet filter banks decouple overlapping frequency bands, targeting scale-specific shocks while filtering out macro drift and microstructural noise.
Encoding bounded state ranges: Rolling min-max positions compress nominal prices into a [0, 1] interval, removing cross-sectional scale disparities and allowing tree algorithms to process boundary rejections.
