Hands-On Machine Learning for Algorithmic Trading

Data integration

The value of data for an investment strategy often depends on combining complementary sources of market, fundamental and alternative data. We saw that the predictive power of ML algorithms like tree-based ensembles or neural networks is in part due to their ability to detect non-linear relationships, in particular, interaction effects among variables.

The ability to modulate the impact of a variable as a function of other model features thrives on data inputs that capture different aspects of a target outcome. The combination of asset prices with macro fundamentals, social sentiment, credit card payment, and satellite data will likely yield significantly more reliable predictions throughout different economic and market regimes ...