Trading Bitcoin with Reinforcement Learning
This post describes how to apply reinforcement learning algorithm to trade Bitcoin. This repository provides an implementation aims to reproduce the result.
- **BnH**
A *buy-and-hold* strategy that always hold 2 Bitcoins starting from the beginning of the test period.
- **RL**
A trained RL agent making trading decisions to hold 0~4 Bitcoins given the current market condition.
- **MMT**
A *momentum* strategy that holds 4 Bitcoins when the 30-period SMA cross-over than the current closing price and 0 Bitcoin otherwise.
Dependencies
- Python3.6
- NumPy 1.17.1
- Pandas 0.25.1
- Matplotlib 3.1.1
- PyTorch 1.2.0 (CPU only)
## Data
The minute-by-minute data is downloaded from [Kaggle](https://www.kaggle.com/mczielinski/bitcoin-historical-data/data). I resample them into 15-minute interval and compute all the features we need. Then I save the two dataframes under `bitcoin-historical-data`.
Note that,
- I delete the row indexed `2017-04-15 23:00:00` after resampling since there is a clear error. This is done in the `remove_outlier()` method under the `Data` class.
- Due to request, I include the 15-minute data in `bitcoin-historical-data` (due to size constraint on GitHub, I cannot update the 1-minute data and the feature dataframe generated from the 15-minute data.)
## How to run
```bash
# E.g. clone to local (say to Downloads)
cd ~/Downloads/trading-bitcoin-with-reinforcement-learning/
# Usage: python main.py