A repository for training reinforcement learning agents to provide liquidity in high-frequency order-driven markets. It is set up to use data provided by LOBSTER.

For an example of using this repository to test a variety of hand-crafted agents using Beta policies to distribute orders, see the associated paper on arXiv.

python main.py -g 0 --min_date "2018-02-21" --max_date "2018-02-28" --min_date_eval "2018-03-01" --max_date_eval "2018-03-15" -psr AD --num_workers 10 --ticker SPY -epsr PnL

To use the rl4mm package from within a docker container, first change directory into the docker subdirectory using cd docker and then follow the instructions below.

To build the container (including PSQL setup):

sh build_image.sh

Run the start container script (mounting ../, therefore mounting rl4mm), and specify a port for jupyter notebook and the path to your LOBSTER data:

sh start_container.sh 8877 /PATH/TO/LOBSTER/DATA/

(Note: if you wish to add gpus to container, just add --gpus device=0 to start_container.sh to use one gpu or --gpus all to add all gpus available.)

To work in the container via shell:

sh exec_rl4mm.sh

At this point you will be attached to the container. PSQL will be running, but the database will be empty.

You can populate the database by running (from rl4mm/rl4mm/database/) something like

python3 populate_database.py -mintd "2018-02-20" -maxtd "2018-04-15" --path_to_lobster_data /home/data/SPY --book_snapshot_freq "S" --max_rows 100000000 --ticker SPY

Or to avoid ever uncompressing all the .7z files:

python3 run_populate_database_from_zipped.py --path_to_lobster_data /home/data/7z --book_snapshot_freq "S"

To look up the jupyter notebook link with token:

jupyter notebook list

To use Weights and Biases to log experiments:

1. install weights and biases using pip install wandb and create an account at wandb.ai.
2. Log in on your machine (or from inside the docker container) using the command wandb login
3. Add rl4mm to git's safe directories using git config --global --add safe.directory /PATH/TO/rl4mm (inside the docker container, /PATH/TO/rl4mm = /home/rl4mm).
4. Run, for example, main.py with the flag -wandb True.

If not running the rl4mm from within a docker container outlined above it is necessary to set up a postgres database locally using the following steps:

1. Download and install docker

2. Create a docker container running postgres (with username {USER} and password {PASSWORD}) by running:

   sudo docker run --name lobster -e POSTGRES_PASSWORD={PASSWORD} -e POSTGRES_USER={USER} -e POSTGRES_DB=lobster -p {LOCAL_PORT}:5432 --restart unless-stopped -d postgres -c shared_buffers=1GB

3. Check it is running with command: docker ps -a

4. Create a text file named .env in the root of rl4mm directory with the following contents:

   POSTGRES_HOST=localhost
   POSTGRES_PORT=<port>
   POSTGRES_DB=rl4mm
   POSTGRES_USER=<username>
   POSTGRES_PASSWORD=<password>
   

We use unittest for unit testing, mypy as a static type checker, Flake8 to enforce PEP8 and Black to enforce consistent styling.

• Code will be automatically reformatted with: invoke black-reformat
• Styling and type checking tests can be run locally with: invoke check-python
• Unit tests can be run by calling nose2 from the root directory.