AlphaGen

Automatic formulaic alpha generation with reinforcement learning.

Paper Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning accepted by KDD 2023, Applied Data Science (ADS) track, more info TBD.

How to reproduce?

Data preparation

We need some of the metadata (but not the actual stock price/volume data) given by Qlib, so follow the data preparing process in Qlib first.
The actual stock data we use are retrieved from baostock, due to concerns on the timeliness and truthfulness of the data source used by Qlib.
The data can be downloaded by running the script data_collection/fetch_baostock_data.py. The newly downloaded data is saved into ~/.qlib/qlib_data/cn_data_baostock_fwdadj by default. This path can be customized to fit your specific needs, but make sure to use the correct path when loading the data (In alphagen_qlib/stock_data.py, function StockData._init_qlib, the path should be passed to qlib with qlib.init(provider_uri=path)).

Before running

All principle components of our expriment are located in train_maskable_ppo.py. You should focus on the following parameters:

instruments (Set of instruments)
pool_capacity (Size of combination model)
steps (Limit of RL steps)
batch_size (PPO batch size)
features_extractor_kwargs (Arguments for LSTM shared net)
seed (Random seed)
device (PyTorch device)
start_time & end_time (Data range for each dataset)
save_path (Path for checkpoints)
tensorboard_log (Path for TensorBoard)

Run!

Simply run train_maskable_ppo.py, or DIY if you understand our code well.

After running

Model checkpoints and alpha pools are located in save_path;
- The model is compatiable with stable-baselines3
- Alpha pools are formatted in human-readable JSON.
Tensorboard logs are located in tensorboard_log.

Baselines

GP-based methods

gplearn implements Genetic Programming, a commonly used method for symbolic regression. We maintained a modified version of gplearn to make it compatiable with our task. The corresponding experiment scipt is gp.py

Deep Symbolic Regression

DSO is a mature deep learning framework for symbolic optimization tasks. We maintained a minimal version of DSO to make it compatiable with our task. The corresponding experiment scipt is dso.py

Repository Structure

/alphagen contains the basic data structures and the essential modules for starting an alpha mining pipeline;
/alphagen_qlib contains the qlib-specific APIs for data preparation;
/alphagen_generic contains data structures and utils designed for our baselines, which basically follow gplearn APIs, but with modifications for quant pipeline;
/gplearn and /dso contains modified versions of our baselines.

Citing our work

TBD

Contributing

Feel free to submit Issues or Pull requests.

Contributors

This work is maintained by the MLDM research group, IIP, ICT, CAS.

Contributors include:

bluebell136 / alphagen