Task 1: Cryptocurrency Trading with Ensemble Methods
This task aims to develop robust and effective trading agents for cryptocurrencies using ensemble methods. Participants are expected to explore innovative ensemble methods for single cryptocurrency trading. They are also encouraged to take advantage of the power of massively parallel simulations by utilizing the provided vectorized environments.
Starter Kit
Dataset
A dataset containing second-level Limit Order Book (LOB) data for Bitcoin is provided. Contestants are free to apply various techniques to the data, design component models, and use innovative methods to increase the diversity of component models in the ensemble.
The dataset contains the following files:
BTC_1sec.csv : 1 second level LOB BTC data
BTC_1sec_predict.npy : strong RNN factors generated on the csv dataset to train the DRL agent
The BTC_1sec.csv contains all data used to train the RNN model and FinRL agent.
Notice that the timestamps in this dataset have been processed and are not the true timestamps.
Environments
The initial cash should be $1 million.
The contestants are required to:
Specify the state space, action space, and reward functions in the environment.
Ensure that the final ensemble model should be able to interact with the provided trading environment.
Model Evaluation
The performance of the model will be assessed by the following metrics:
Cumulative return. It is the total return generated by the trading strategy over a trading period.
Sharpe ratio. It takes into account both the returns of the portfolio and the level of risk.
Max drawdown. It is the portfolio’s largest percentage drop from a peak to a trough in a certain time period, which provides a measure of downside risk.