Predicting Crypto Returns with Convolutional Neural Networks
The cryptocurrency market, a whirlwind of volatility and speculation, presents both thrilling opportunities and daunting challenges for investors. Navigating this turbulent landscape requires more than just gut feeling; it demands robust analytical tools and a deep understanding of market dynamics. Traditional prediction methods often fall short in capturing the complex, non-linear relationships that drive crypto prices. I’ve found that technical indicators and correlated assets offer crucial insights and Convolutional Neural Networks (CNNs) provide the computational muscle to unlock their predictive power. This tutorial provides a practical guide to building a CNN model for predicting crypto returns, incorporating these essential elements. I’ll share my own experiences and coding examples to help you understand this complex yet interesting field.
Table of Contents
- Data Acquisition and Preprocessing: Fetching cryptocurrency price data, calculating technical indicators (e.g., RSI, MACD, Bollinger Bands) and handling missing data and normalization.
- Feature Engineering and Selection: Constructing features from raw data, using techniques like moving averages and lagged features and selecting the most relevant ones.
- CNN Architecture Design: Designing a CNN for time…
