Deep Learning Statistical Arbitrage

This repo contains the official code for our paper Deep Learning Statistical Arbitrage, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3862004 and https://arxiv.org/abs/2106.04028.

Quickstart

To test a trading policy model on a residual time series, use run_train_test.py. This file exports a function, run(), which can be imported and used in e.g. a grid search, or run from the command line. Command line usage will suit most users. To run from the command line, use

python3 run_train_test.py -c configs/config_name_here.yaml

where config_name_here.yaml is a configuration file from the configs folder. You can write your own configuration file to edit hyperparameters and other settings for the trading test. See run_train_test.py for other command line options.

Structure

This repo is organized as follows:

• train_test.py contains the code for training a trading policy model and simulating trading.
• run_train_test.py is a user interface to train_test.py which deals with configuration, logging, saving results, etc.
• preprocess.py contains functions for preprocessing residual time series data into a form usable by a trading policy model
• data.py contains miscellaneous functions for altering residual time series data
• config contains configuration files which define various tests of trading policy models on residual time series
• data should contain raw input data used to create residuals
• factor_models contains code for creating residuals from raw input data
• residuals stores residual time series data sets created by the code in factor_models
• models contains code for trading policy models
• results will contain the results of and plots for trading policy model tests conducted by run_train_test.py
• logs will contain logs for runs of models and factor models
• tools should contain miscellaneous code for interpreting and exploring results and saved models
• utils.py contains helpful functions used throughout

Generating residuals

To create residuals, first ensure that input data is present in the data directory, then run run_factor_model.py, providing the name of a factor model in the factor_models directory, e.g.

python3 run_factor_model.py -m factor_model_name_here

Generated residuals for the factor model will be saved in the residuals folder.

Contributing

Code is released as is, but we welcome pull requests for any issues.

Notes

Note that use_residual_weights must be set to True in configuration files to reproduce results of the paper. Unfortunately, we can't release original asset return and characteristic data due to licensing agreements with our data providers. The corresponding author for this work is Greg Zanotti.