Abstract
This study evaluates multiple machine learning models for predicting daily cryptocurrency market movements and optimizing trading strategies. The models are trained to forecast binary relative price changes of the top 100 cryptocurrencies by market capitalization. Key findings include:
- Prediction Accuracy: Models achieve an average accuracy of 52.9%–54.1% across all cryptocurrencies.
- High-Confidence Predictions: Accuracy improves to 57.5%–59.5% when focusing on the top 10% most confident predictions per class/day.
- Trading Performance: An LSTM-GRU ensemble strategy generates an annualized Sharpe ratio of 3.23 (after transaction costs), outperforming the buy-and-hold benchmark (Sharpe ratio: 1.33).
These results suggest potential inefficiencies in cryptocurrency market efficiency, though arbitrage constraints may partially influence outcomes.
Keywords
- Cryptocurrency Trading
- Financial Market Prediction
- Machine Learning Models (LSTM, GRU, Random Forest, Gradient Boosting)
- Statistical Arbitrage
- Market Efficiency
Core Analysis
1. Machine Learning Models in Crypto Trading
Machine learning leverages historical data to identify patterns in volatile crypto markets. This study tests:
- Neural Networks: LSTM, GRU, and Temporal CNNs for sequential data analysis.
- Ensemble Methods: Random Forest and Gradient Boosting for feature importance.
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2. Prediction Accuracy Insights
- Baseline Accuracy: Models slightly outperform random guessing (50%), indicating statistically significant predictive power.
- Confidence Thresholds: Filtering predictions by model confidence boosts accuracy by ~5%, highlighting the value of probabilistic thresholds.
3. Trading Strategy Performance
- Long-Short Portfolio: Combines top predicted gainers (long) and losers (short), yielding a Sharpe ratio >3.0.
- Benchmark Comparison: Outperforms passive strategies by 140% after costs.
FAQ
Q1: Can machine learning reliably predict crypto prices?
A1: While no model guarantees 100% accuracy, this study demonstrates statistically significant predictive edges (54%+ accuracy), especially with high-confidence filters.
Q2: What are the risks of ML-based crypto trading?
A2: Key risks include market volatility, overfitting to historical data, and liquidity constraints for arbitrage.
Q3: Which cryptocurrencies benefit most from ML predictions?
A3: High-liquidity coins (e.g., Bitcoin, Ethereum) show more stable patterns, but emerging altcoins may offer inefficiencies.
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Conclusion
Machine learning offers tangible advantages in cryptocurrency market prediction, with ensemble models (LSTM/GRU) delivering robust trading performance. Future research could explore real-time adaptation and cross-market arbitrage opportunities.
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