GARCH Modeling for Cryptocurrency Trading

This code demonstrates how to implement GARCH (Generalized Autoregressive Conditional Heteroskedasticity) modeling for cryptocurrency trading using Python. GARCH models are powerful tools for forecasting volatility, aiding traders and investors in making informed decisions. Here's a breakdown of the steps covered in this tutorial:

Fetching Historical Data: Utilize the ccxt library to fetch historical OHLCV (Open-High-Low-Close-Volume) data from the Binance cryptocurrency exchange for the ETH/USDT trading pair at 4-hour intervals.

Calculating Returns: Calculate the percentage returns based on the closing prices of the fetched data.

Fitting GARCH Model: Fit a GARCH(1,1) model to the returns data using the arch library, allowing for the estimation of volatility dynamics.

Generating Trading Signals: Simulate buy and sell signals based on predicted volatility, indicating potential entry and exit points for trading.

Visualizing Results: Plot the closing prices alongside the buy and sell signals, providing a graphical representation of the trading strategy.

This code provides insight into implementing GARCH models for cryptocurrency trading, enhancing their ability to navigate market volatility and make informed trading decisions.