Use case: Investigating applications of Explainable AI to algorithmic trading
Context:
Study the influence of technical analysis and macroeconomic indicators on the future performance of an underlying asset for Geneva-based quantitative trader.
Objective:
Quantify the predictive power of individual indicators using an interpretable machine learning (ML) algorithm.
Technologies:
R, Python
Solution:
A generic R+Python ML framework for forecasting future price projections, using robust cross-validation and back testing strategies.
Precise quantification of individual feature importance via Shapley value ranking.