Awesome
CryptoEnvironment
CryptoEnvironment is a gym environment for cryptocurrency trading. It provides a simulation environment for training and evaluating reinforcement learning agents.
Features
- Supports discrete buying, selling, and holding (optional) actions.
- Customizable:
- Initial balance
- Random initial asset split
- Maximum steps
- Trade fees
- Slippage
- Order execution fraction
- Reward function
- Low entropy action penalty
- Uses any trading data including custom indicators from a provided DataFrame.
- Provides observation and action spaces compatible with Gym.
- Easy integration with stable-baselines3.
- Supports one-line evaluation of trained models with visualization and reporting options.
Installation
Clone the repository:
git clone https://github.com/your-username/crypto-trading-gym.git
To use the CryptoEnvironment, you need to have the following dependencies installed:
- gym==0.21.0
- stable-baselines3==1.8.0
- matplotlib==3.7.1
- numpy
- pandas
You can install these dependencies using pip:
pip install -r requirements.txt
Usage
See more detailed examples in /examples/
.
Here's an example of how to use the CryptoEnvironment:
# Imports
import requests
import pandas as pd
import matplotlib.pyplot as plt
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3 import PPO
from tradinggym import CryptoEnvironment
# Load the data
data = pd.read_pickle('./data/measurement.pickle')
# Create the CryptoEnvironment
environment = CryptoEnvironment(observations=data, initial_value = 10000,
window_size=5, order_fraction=0.66, trade_fee=0.003375)
# Wrap the training environment in a vectorized form
environment = DummyVecEnv([lambda: environment])
# Create a model
model = PPO('MlpPolicy',env=environment)
# Train the model
model.learn(1000)
# Create an evaluation environment
testenv = CryptoEnvironment(observations=data, initial_value = 10000,
window_size=5, order_fraction=0.66, trade_fee=0.003375)
# Evaluate the model
testenv.evaluate(frame_length=50, render=True, model=model, marker_size=25, verbose=1)
Evaluation Metrics:
Initial value: 9999.00
Initial balance: 8728.51
Initial shares: 0.04
Initial split: 0.87
Final value: 10109.60
Profit: 110.60
Return Rate: 1.11%
Avg reward: -0.00
Max reward: 0.00
Min reward: -0.00
Cumulative reward: -0.13
License
This project is licensed under the GNU General Public License v3.0. See the LICENSE file for more details.