qlib/examples/benchmarks_dynamic/DDG-DA/README.md

Introduction

This is the implementation of DDG-DA based on Meta Controller component provided by Qlib.

Background

In many real-world scenarios, we often deal with streaming data that is sequentially collected over time. Due to the non-stationary nature of the environment, the streaming data distribution may change in unpredictable ways, which is known as concept drift. To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data. However, there are still many cases that some underlying factors of environment evolution are predictable, making it possible to model the future concept drift trend of the streaming data, while such cases are not fully explored in previous work.

Therefore, we propose a novel method DDG-DA, that can effectively forecast the evolution of data distribution and improve the performance of models. Specifically, we first train a predictor to estimate the future data distribution, then leverage it to generate training samples, and finally train models on the generated data.

Dataset

The data in the paper are private. So we conduct experiments on Qlib's public dataset. Though the dataset is different, the conclusion remains the same. By applying DDG-DA, users can see rising trends at the test phase both in the proxy models' ICs and the performances of the forecasting models.

Run the Code

Users can try DDG-DA by running the following command:

    python workflow.py run_all

The default forecasting models are Linear. Users can choose other forecasting models by changing the forecast_model parameter when DDG-DA initializes. For example, users can try LightGBM forecasting models by running the following command:

    python workflow.py --forecast_model="gbdt" run_all

Results

The results of other methods in Qlib's public dataset can be found here