Release codes of incremental learning and DoubleAdapt

[MogicianXD]

Add a new baseline for benchmarks_dynamic.

Description

Here is a new online retraining algorithm based on incremental learning, named DoubleAdapt.
The paper has been accepted by KDD 2023. [arXiv]

Motivation and Context

The existing baselines of benchmarks_dynamic, RR and DDG-DA, require expensive time and space consumption. The training
time increases with the size of the enlarged training data, which further causes an unbearable duration of hyperparameter tuning and retraining algorithm selection.

An alternative way known as Incremental Learning is to fine-tune the model only with the latest incremental data.
However, with the stock market dynamically evolving, the distribution of future data can differ from incremental data, hindering the effectiveness of incremental updates. To address this challenge, we propose DoubleAdapt to mitigate the effects of distribution shifts.

In this PR, we also provide the implementation of naive incremental learning with argument --naive True.

How Has This Been Tested?

• Pass the test by running: pytest qlib/tests/test_all_pipeline.py under upper directory of qlib.
• If you are adding a new feature, test on your own test scripts.

Screenshots of Test Results (if appropriate):

1. Pipeline test:
2. Your own tests:

Types of changes

• Fix bugs
• Add new feature
• Update documentation

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