budget-data-split

Multi-asset data splitting for budget strategies is automated by dividing datasets into training, validation, and walk-forward sets based on trading dates. This ensures consistent data preparation across all assets for model training and validation.

Walk-forward data splitting is a validation technique that segments time-series trading data into chronological folds. You need it for budget strategies to prevent look-ahead bias and ensure model training respects temporal order across multi-asset panels.

Run the data splitting script via the command line, passing the budget run state JSON file as an argument. The tool automatically partitions the multi-asset panel into training and validation sets using trading dates as chronological boundaries.

Yes, you need a pre-generated multi-asset panel created using a data generation tool. The data splitting skill processes this existing panel to produce training, validation, and walk-forward sets required for subsequent model training.

The tool is specifically designed for multi-asset budget strategies, ensuring consistent data splits across all assets. While it may functionally process single-asset data, its core logic is optimized for multi-asset datasets.

Data splitting for budget strategies requires trading dates to maintain chronological order and prevent look-ahead bias. Random splits would disrupt the temporal sequence of financial data, invalidating walk-forward validation results.