Instructions for updating the code on 10 4 2025

Parametric Study of Matrix Size Effect

1, UpdatedNetwork.py: matrix 128-64-32 (larger) ------baseline 2. UpdatedNetworkLargermatrix.py: matrix 256-128-64 (huge) ------ fork 1 3. baselinebettertest.py: 1. checkpoint =100. 2. at each epoch, perform validation and test 3. matrix 128-64-32

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Rephrased Instructions

Here is a summary of your request, confirming my understanding before presenting the code. You want to systematically refactor the existing script to:

1. Normalize Input Data: Instead of feeding raw price differences to the model, we will normalize them. This will be done by calculating the mean and standard deviation of the price differences from the training set only and then using these values to scale the inputs for all data sets (training, validation, and test). This prevents data leakage.

2. Partition Data: The entire historical dataset will be split into three distinct, non-overlapping periods:

   • Training Set (70%): The model will be trained exclusively on this data. All learning and weight updates will happen here.

   • Validation Set (15%): After each training checkpoint, the model's performance will be evaluated on this unseen data to monitor for overfitting and see how well it generalizes. Results from this set will be reported during the training loop.

   • Test Set (15%): After all training is complete, the final, "champion" model will be evaluated just once on this holdout set. This provides the final, unbiased performance report.

3. Refactor Classes for Data Slicing: The Agent and StrategyAnalyzer classes will be modified to operate on specific data slices (defined by start and end indices) rather than the entire dataset, ensuring the strict separation of the data partitions.

4. add Volume diff, Volatility and RSI as additional training elements. normalizing all of them. enlarge the hidden layer node number

5. enlarge hidden layer to - [128, 64, 32]