CNN Crypto Chart Analysis Model Training
Convolutional neural networks traditionally used for image processing. In crypto trading two approaches: analyze charts as images (literally screenshots) and apply 1D CNN to numeric OHLCV time series data. Both give interesting results.
1D CNN for time series: applies convolutional filters along temporal axis. Each filter learns to recognize local patterns of specific length - like automated manual pattern search.
Multi-scale convolutions: different kernel sizes for different timeframes. Captures patterns at multiple scales simultaneously.
TCN (Temporal Convolutional Network): more modern alternative with dilated causal convolutions. Exponentially increases receptive field without parameter growth.
CNN rendered charts: render candlestick chart as image, feed into ResNet/EfficientNet for classification.
Pretraining on synthetic data: important technique - pretrain CNN on synthetic candlestick data with known patterns (programmatically generate "head and shoulders", triangles with labels). Gives model initial pattern understanding before fine-tuning on real data.
CNN+LSTM hybrid: CNN extracts local patterns, LSTM captures long-term dependencies.
Develop CNN-based system: 1D TCN on numeric data, 2D CNN on rendered charts, CNN+LSTM hybrid, ensemble predictions and interpretability via activation maps.