few-shot-learning-finance

Adapt trading models to new market regimes using few-shot learning with episodic training. This approach applies context-based meta-learning to enable rapid generalization when data availability is severely constrained.

Episodic training mirrors test-time usage by constructing context sets that precede each target. This process improves generalization by training the model on context-target pairs sampled directly from time-series data.

You can optimize forecasting accuracy and trading performance simultaneously using a single joint loss function. This architecture applies cross-attention to transfer patterns from context to target for rapid adaptation.

Context set construction methods include random, time-equivalent, and CPD-segmented sampling. These methods maintain causality guarantees by ensuring context data chronologically precedes target data in time-series inputs.

Cross-attention meta-learning transfers patterns from context sets to target unseen assets. This architecture enables rapid adaptation during regime shifts and cross-asset transfer scenarios in finance.

You need asset time-series data formatted for context-target pair sampling. Construct a context set preceding each target sequence to run an episodic training loop and evaluate cross-asset transfer predictions.