Analyze Bitcoin data from Glassnode API to create Buy or Sell signal. Utilize machine learning models to find which one is the most accurate. * SVM: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html * Logistic Regression: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression * Neural Networks: https://scikit-learn.org/stable/modules/neural_networks_supervised.html
- Backtest the algorithm through the previous 4 years, reduce if data load is too great.
- Analyze the profitability of the trading algorithm through visualizations.
For our data source we used data from Glassnode. Running the code in the GLASSNODEPULL.ipynb file will export the data into a csv file. The 16 metrics we used for our models were:
| price | a-sopr | puell_multiple | exchange_netflow |
| difficulty_compression_band | mvrv_z_score | nonzero_balance_addresses | %_utxo_in_profit |
| nvt | nupl | stablecoin_supply | rhodl |
| cvdd | rpv | balanced_price | investor_capitalization |
Information about these metrics can be found at the following site: Glassnode Academy
To clone the repository use the following line of code:
git clone https://github.com/ZrowGz/trading-bot.git
To use the code the required libraries will need to be installed (unless using Google Colab). This can be installed by running the following line of code:
pip install -r ./requirements.txt
An API Key from Glassnode is required for pulling data. How to get an API Key
If wanting to run the code on MacBook an M1 chip, Google Colab will need to be used
- python >3.7
- pandas>=0.23.0
- hvplot
- pathlib
- numpy
- matplotlib
- sklearn
- tensorflow
- json
- requests