The concept of neurosymbolic systems combines classic trading patterns with neural networks to enhance algorithmic trading. Traditional patterns like "head and shoulders" are well-known but can fail as markets evolve. Neural networks such as LSTM provide powerful predictions but lack transparency in decision-making. By integrating these two approaches, a neurosymbolic system can adapt to market changes while maintaining a framework of rules.

Pattern analysis in trading involves encoding price movements as binary sequences. Patterns can be evaluated by their frequency, win rate, and a reliability metric to avoid statistical anomalies. Proper analysis requires balancing pattern length and forecast horizon for effective predictions.

In neural network architecture, LSTMs are suitable for time-series market data. A hybrid setup with LSTM and dense ...

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