benchmarks_dynamic

Introduction

Due to the non-stationary nature of the environment of the financial market, the data distribution may change in different periods, which makes the performance of models build on training data decays in the future test data. So adapting the forecasting models/strategies to market dynamics is very important to the model/strategies' performance.

The table below shows the performances of different solutions on different forecasting models.

Alpha158 dataset

Model Name	Dataset	IC	ICIR	Rank IC	Rank ICIR	Annualized Return	Information Ratio	Max Drawdown
RR[Linear]	Alpha158	0.088	0.570	0.102	0.622	0.077	1.175	-0.086
DDG-DA[Linear]	Alpha158	0.093	0.622	0.106	0.670	0.085	1.213	-0.093
RR[LightGBM]	Alpha158	0.079	0.566	0.088	0.592	0.075	1.226	-0.096
DDG-DA[LightGBM]	Alpha158	0.084	0.639	0.093	0.664	0.099	1.442	-0.071

• The label horizon of the Alpha158 dataset is set to 20.
• The rolling time intervals are set to 20 trading days.
• The test rolling periods are from January 2017 to August 2020.