Crypto Token Correlation Heatmap

This project analyzes the correlation and volatility of selected crypto tokens using historical price data from the CoinGecko Pro API. It visualizes token correlations as a heatmap and computes volatility for each token.

Features

• Fetch historical price data for multiple tokens via CoinGecko Pro API.
• Save and load price data using joblib for efficient reuse.
• Calculate log returns and volatility for each token.
• Visualize token correlations with a heatmap using Seaborn and Matplotlib.

Setup

1. Clone the repository and navigate to the project folder.

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up your CoinGecko Pro API key:

   • Create a .env file in the project root:

     GECKO_API=your_actual_api_key_here
     

Usage

1. Fetch and save token price data:

   • Run the notebook or script that calls the CoinGecko API and saves the DataFrame with joblib.

2. Analyze correlations and volatility:

   • Open tokens_correl.ipynb.
   • Load the saved DataFrame.
   • Compute log returns and volatility.
   • Plot the correlation heatmap.

Example

import joblib
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Load saved price data
df_prices_loaded = joblib.load("Database/df_prices.joblib")

# Calculate log returns
returns = df_prices_loaded.pct_change().apply(lambda x: np.log1p(x))

# Correlation matrix
corr = returns.corr()

# Plot heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(corr, annot=True, cmap='coolwarm', vmin=-1, vmax=1)
plt.title("Token Correlation Heatmap")
plt.show()

Output

• Correlation heatmap showing relationships between token returns.
• Volatility values for each token.

Notes

• Ensure your API key is valid and correctly set in .env.
• Data is saved in the Database folder for reuse.
• You can customize the list of tokens and time period in the data fetching script.