In the world of financial markets, few spaces are as exhilarating and unforgiving as the cryptocurrency market. The extreme volatility and complex, non-linear patterns of assets like Ethereum have rendered traditional forecasting methods largely obsolete, opening the door for advanced machine learning to seek a predictive edge. This article explores a sophisticated algorithmic trading model, developed in a Jupyter Notebook, that combines a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) architecture. By training on years of historical data, the model attempts to classify future price action as "Buy," "Sell," or "Hold," and its actual efficacy is measured not only by its predictive accuracy but also by its performance in a rigorously backtested trading simulation.

Data Foundation and Feature Engineering

The model's predictive power is rooted in a robust and extensive dataset. It was trained using approximately ten years of hourly OHLCV (Open, High, Low, Close, Volume) data for ETH/USD from the Kraken exchange, spanning from August 7, 2015, to March 31, 2025. This historical data, comprising over 81,000 candles, offers a comprehensive view of various market conditions, ranging from periods of stable growth to intense volatility.

Beyond the raw price and volume data, the model's feature set is enriched with several key technical indicators, which are calculated directly from the historical data:

• Relative Strength Index (RSI): A momentum oscillator that measures the speed and change of price movements.

• Moving Average Convergence Divergence (MACD): A trend-following indicator showing the relationship between two moving averages.

• Bollinger Bands (BBANDS): A volatility indicator that defines a range of price movement.

• On-Balance Volume (OBV): A cumulative volume-based indicator that links volume to price changes.

• Average True Range (ATR): A measure of market volatility, which is also used for dynamic risk management in the backtest.

Model Architecture and Training Methodology

The predictive core is a hybrid deep learning model combining the strengths of CNNs and LSTMs. The architecture is sequential and meticulously designed to handle time-series data:

1. CNN Layers: The initial layers consist of Conv1D and MaxPooling1D, which are highly effective at identifying local patterns and extracting meaningful features from the price and volume data.

2. LSTM Layer: The output of the CNN layers is then fed into an LSTM layer. LSTM LSTM layer. LSTMs are a type of recurrent neural network specialized in retaining long-term dependencies in sequential data, enabling the model to understand how past events influence current and future price action.

3. Dense Layers: The model concludes with standard dense layers, culminating in an Softmax output layer that provides a probability distribution for the "Buy," "Sell," and "Hold" signals.

A critical challenge addressed during training was the significant class imbalance, with "Hold" signals vastly outnumbering "Buy" and "Sell" opportunities. To mitigate this, a hybrid resampling pipeline was applied to the training data RandomUnderSampler to reduce the majority class and SMOTE to create synthetic samples for the minority classes. This ensured the model learned effectively from all three signal types. The model was trained over 150 epochs, achieving a test accuracy of 72.50%.

Performance Analysis and Backtesting Results

The model's performance was evaluated using several metrics, providing a comprehensive view of its predictive capabilities.

Model Performance Metrics:

• Test Loss: 0.6311

• Test Accuracy: 72.50%

• Classification Report: The report reveals a strong performance on "Hold" signals (precision: 0.81, recall: 0.74), but a lower, though still effective, performance on "Buy" (precision: 0.65, recall: 0.70) and "Sell" (precision: 0.67, recall: 0.73) signals.

• Confusion Matrix: A visual representation of the model's predictions. The matrix shows the model correctly identified 4,967 "Hold" signals, but also frequently misclassified "Buy" and "Sell" signals as "Hold" (1,641 and 1,014 instances, respectively). This highlights a common challenge: models often default to the most conservative prediction ("Hold") when faced with uncertainty.

Backtesting Simulation:

To validate the model's real-world viability, a high-conviction backtesting simulation was performed on the test data. The strategy was disciplined, with a high confidence threshold of 0.85 and adaptive take-profit and stop-loss levels based on the Average True Range (ATR). The backtest results were as follows:

• Initial Capital: $10,000.00

• Final Portfolio Value: $10,248.87

• Total Return: 2.49%

• Trades Executed: 14

• Winning Trades: 6

• Losing Trades: 8

• Win Rate: 42.86%

The modest but positive return of 2.49%, despite a sub-50% win rate, is a testament to the effectiveness of the backtesting strategy's risk management. The winning trades were structured to be more profitable than the losses, demonstrating a sound approach to algorithmic trading that prioritizes risk-adjusted returns over a simple high frequency of winning trades.

Conclusion

The project demonstrates that a hybrid CNN-LSTM model can effectively navigate the complexities of cryptocurrency trading, but its success hinges on more than just raw predictive power. While the model's test accuracy and classification metrics are strong, the backtesting results offer the most compelling narrative. A modest 2.49% return was achieved with a win rate below 50%, a seemingly counterintuitive outcome that underscores a fundamental truth of modern finance: a successful strategy is one that systematically manages risk and maximizes the gains from its correct predictions. This work serves as a powerful case study, illustrating that in the world of deep learning and algorithmic trading, a well-defined and rigorously tested risk management framework is just as critical to success as the model's predictive ability itself.

By FRANK MORALES

Keywords: Cryptocurrency, Predictive Analytics, Open Source

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