kochlisGit/TraderNet-CRv2

TraderNet-CRv2 - Combining Deep Reinforcement Learning with Technical Analysis and Trend Monitoring on Cryptocurrency Markets

TraderNet-CRv2

TraderNet-CRv2 - Combining Deep Reinforcement Learning with Technical Analysis and Trend Monitoring on Cryptocurrency Markets

Description

This system architecture is an extended version of the original TraderNet-CR architecture, which is described by this paper: https://link.springer.com/chapter/10.1007/978-3-031-08333-4_25. In this work, we combine Proximal Policy Optimization algorithm (PPO), which is a DRL learning algorithm, with 2 rule-based safety mechanisms: N-Consecutive & Smurfing. Our experiments on 5 popular cryptocurrencies show very promising results.

Technical Indicators

Technical analysis has been applied on market data in order to train TraderNet. The following popular technical indicators have been used:

• EMA (Exponential Moving Average)
• DEMA (Double-Exponential Moving Average)
• MACD (Moving Average Convergence/Divergenc)
• AROON
• CCI (Commodity Channel Inde)
• ADX (Average Directional Inde)
• STOCH (Stochastic Oscillator)
• RSI (Relative Strength Index)
• OBV (On-Balance Volume)
• BBANDS (Bolliger Bands)
• VWAP (Volume-Weighted Average Pric)
• ADL (Accumulation/Distribution Line)

Requirements

To run and evaluate our agent, You need to download the following libraries/packages:

• Python $\geq 3.6$ (https://www.python.org/)
• Numpy (https://numpy.org/)
• Pandas (https://pandas.pydata.org/)
• Matplotlib (https://matplotlib.org/)
• Tensorflow (https://www.tensorflow.org/)
• TF-Agents (https://www.tensorflow.org/agents)
• TA (https://technical-analysis-library-in-python.readthedocs.io/en/latest/)
• PyTrends (https://pypi.org/project/pytrends/)
• Scikit-Learn (https://scikit-learn.org/stable/)

Instructions

Download Python 3.6 or higher and the libraries that are described on requirements using pip installer (e.g. pip install numpy). Then:

1. Run download_datasets.py to download the datasets from CoinAPI platform (https://www.coinapi.io/).
2. Use train_tradernet.ipynb to train TraderNet module.
3. Use train_smurf.ipynb to train Smurf module.
4. Use integrated.ipynb to evaluate the Integrated agent.

Supported Cryptocurrencies

1. Bitcoin (BTC)
2. Ethereum (ETH)
3. Cardano (ADA)
4. Litecoin (LTC)
5. XRP

Paper

Cite us

Important Note

This AI is not a commercial product and is intended for research purposes only.