Neural network training often involves optimizing model parameters. The Adam optimizer is popular for its adaptive learning rates for each parameter, but it demands high memory. This poses challenges with large-scale models, often leading to increased training time due to CPU offloading. The recent Adam-mini optimizer offers a solution by reducing memory usage while maintaining performance. It simplifies learning rate assignments by grouping parameters and using a single rate per block. This change cuts memory consumption significantly and boosts efficiency, enabling larger batch sizes on GPUs. For MQL5 implementation, modifications across classes are necessary to integrate Adam-mini, ensuring memory optimization and efficient computation.
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The study of neural networks in trading emphasizes pattern recognition and data selection. For new traders, optimizing inputs like MA indicators significantly influences outcomes. Testing geometric shapes through neural networks provides a unique approach but has limitations. Using filtered data such as Moving Averages helps in model training, while geometric shape models can influence trading results.

Key findings include counter-trend strategies and considerations for indicators like TEMA. Optimization challenges involve computing resources and require efficient strategies for substantial passes. Focus on finding patterns that offer a high profit factor ratio. Building an optimized database with weight ratios stored in CSV files can streamline the development of sophisticated trading systems. Continued research and experimentation are essential in develo...

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