zrowgz / bitcoin-predictor

Project Proposal

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.

Data Source

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

Installation

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

Requirements

• python >3.7
• pandas>=0.23.0
• hvplot
• pathlib
• numpy
• matplotlib
• sklearn
• tensorflow
• json
• requests

bitcoin-predictor's Issues

Any help needed for the project?

I have some spare time and I could help contribute. Any areas of the project that need help?