Superset Trading Dashboard

This is an experimental setup with simple goal to tryout Apache Superset as Trading dashboard + simple implementation of Reinforcement learning approach for algo-trading with Bitcoin. If you have an idea for how to add more hype to the pile, open an issue!

Setup consists of 3 main modules:

• trader: Trading scripts for fetching historical Bitcoin prices and backtesting and live (paper) trading on Bitstamp, with simple Q-table RL algo-trader implemented with tensorflow and pyalgotrade: $$\mathbf{b}$$
• airflow: Apache Airflow DAGs for scheduling initial and weekly tasks.
• superset: Apache Superset Dashboard

Building

First, make superset-config.env and trader-config.env files based on examples and populate keys (no spaces around = sign and no quotes).

Build docker images:

docker-compose build

Running

docker-compose up

or

docker-compose up --build

Make/Load Dashboard

In order to make or load Dashboard you'll need to prepare database, load it to Apache Superset and import Dashboard pickle file.

• Go to Airflow UI (localhost:8090) and turn-on weekly DAG.
• After DAG finishes, go to Superset UI (localhost:8088) and login with credentials from env file.
• Import database: internally it is mounted to /etc/superset/db and called trader.db.
• Import pickled Dashboard from superset/dashboard directory.
• Make some changes (add other traders, tweak RLtrader, etc..)
• Deploy somewhere, make guest user and invite people to show off your Dashboards!