Cryptocurrency price correlations

Aim of the project

This project explains how to compute price correlations between different cryptocurrencies in Python based on price history files of cryptocurrencies of interest.

Dependencies

First set-up

1. Download virtualenv if you do not alread have it

pip install virtualenv

2. Create virtual environment in your folder of interest

virtualenv crypto-correlations

3. Activate the virtual environment

source crypto-correlations/bin/activate

4. Install the libraries of interest in the virtual environment based on the requirements.txt file

pip install -r requirements.txt

Re-activate virtual environment every time you need to extract data

source crypto-correlations/bin/activate

Instructions

Input files

Each cryptocurrency history data file should be stored in the folder crypto_history. The price correlation script price_correlations.py will extract the history data based on the following format:

price   volume  time    buy/sell        market/limit    miscellaneous   Since
1.114431        18.07332223     1606777316.4328 b       l               1606777200.0
1.121256        17.96331051     1606777381.7706 b       l               1606777200.0
1.125000        998.40255591    1606777840.5439 b       m               1606777200.0
...

The input file crypto_history/LSKEUR_close_price.txt was provided as an example. In case you want to use a different format, make sure to modify the function extract_mean_price_ph in the script price_correlation.py. My cryptocurrency price history extraction workflow can be found in my repository crypto-history-extraction.

Date and cryptocurrency list configuration

The start date and end date timestamps should be defined in config.txt (please refer to the file for more details regarding the format). The graph first start and second start dates, which are simply two different time frames for the graphs should be defined in this same configuration file. A large list of cryptocurrency symbols should be defined in this same configuration file to compute large correlations. A short list of cryptocurrency symbols should be defined in this same configuration file.

Compute the correlations

Once the input price history files and the configuration file are ready, the following command will compute the correlations:

python3 price_correlation.py

Results

Output example 1 - Clustermap

Output example 2 - Large correlation matrix

Output example 3 - Small correlation matrix

Warning

There seems to be an issue with the significance symbol association on the plots.

Project Timeline

• Start Date: November 27, 2020
• Completion Date: November 28, 2020
• Maintenance status: Inactive