In financial markets, true predictive signals are rare and buried in noise. Success depends not on complex algorithms, but on the craft of engineering informative features.
A quantitative strategy is built upon a diverse foundation of data. Each category provides a unique perspective on market dynamics.
This chart shows a conceptual breakdown of data types used in modern quantitative finance, from ubiquitous market data to specialized alternative sources.
Raw price series are non-stationary, which violates the assumptions of many ML models. The goal is to make the data stationary while preserving its predictive 'memory'.
This technique finds the minimum differencing needed, balancing stationarity with memory preservation.
Confidence in stationarity while maximizing correlation with the original series.
Not all features are created equal. Determining which are truly predictive is critical to avoid overfitting. This requires robust, out-of-sample evaluation methods.
Comparison based on robustness against overfitting and reliability with correlated features. Higher scores indicate greater reliability for live trading.
This labeling technique aligns the ML problem with the reality of trading by using dynamic, volatility-adjusted profit-take and stop-loss levels.
A two-stage model that separates signal generation from bet sizing. An ML model learns to predict which signals from a simple primary model will be profitable.
(High Recall / Many Signals)
(High Precision / Filters Signals)
(Trade Sizing & Execution)