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.

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.

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

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.

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