crossasset

Documentation

Overview

Experimental — this package is not yet wired into the main framework.

Package crossasset provides a cross-attention model for multi-source feature processing. Each source attends to features of all other sources via scaled dot-product multi-head attention, enabling the model to learn inter-source dependencies. This is useful for scenarios where multiple correlated data sources (e.g., different financial instruments or sensor streams) must be jointly analyzed.

The model architecture applies cross-attention layers where each source computes queries from its own features and keys/values from all sources' features. Layer normalization and residual connections stabilize training.

Index

• type Config
• type Direction
• type Model
  • func NewModel(config Config) *Model
  • func (m *Model) AttentionWeights(features [][]float64) ([][]float64, error)
  • func (m *Model) Forward(features [][]float64) ([][]float64, error)
  • func (m *Model) Predict(features [][]float64) ([]int, []float64, error)
  • func (m *Model) Train(data [][][]float64, labels [][]int, tc TrainConfig) error
  • func (m *Model) TrainGPU(data [][][]float64, labels [][]int, tc TrainConfig, ...) (*TrainResult, error)
• type TrainConfig
• type TrainResult

Types

type Config

type Config struct {
	NSources          int
	FeaturesPerSource int
	DModel            int
	NHeads            int
	NLayers           int
	DropoutRate       float64
	LearningRate      float64
}

Config holds the configuration for a cross-asset attention model.

type Direction

type Direction int

Direction represents a trading signal direction.

const (
	// Long indicates a buy signal.
	Long Direction = iota
	// Short indicates a sell signal.
	Short
	// Flat indicates no signal.
	Flat
)

type Model

type Model struct {
	// contains filtered or unexported fields
}

Model implements a cross-attention model for multi-source features.

func NewModel

func NewModel(config Config) *Model

NewModel creates a new cross-asset attention model with the given configuration.

func (*Model) AttentionWeights

func (m *Model) AttentionWeights(features [][]float64) ([][]float64, error)

AttentionWeights computes the attention weight matrix showing how much each source attends to each other source. Returns [n_sources][n_sources] where result[i][j] is how much source i attends to source j. Weights sum to 1 across the attended (j) dimension.

func (*Model) Forward

func (m *Model) Forward(features [][]float64) ([][]float64, error)

Forward processes features through the cross-attention model. features shape: [n_sources][features_per_source]. Returns: [n_sources][d_model].

func (*Model) Predict

func (m *Model) Predict(features [][]float64) ([]int, []float64, error)

Predict returns per-source direction and confidence. features shape: [n_sources][features_per_source]. Returns: directions [n_sources], confidences [n_sources].

func (*Model) Train

func (m *Model) Train(data [][][]float64, labels [][]int, tc TrainConfig) error

Train trains the model on the given data. data shape: [n_samples][n_sources][features_per_source]. labels shape: [n_samples][n_sources] with values in {0=Long, 1=Short, 2=Flat}.

func (*Model) TrainGPU

func (m *Model) TrainGPU(data [][][]float64, labels [][]int, tc TrainConfig,
	engine compute.Engine[float32]) (*TrainResult, error)

TrainGPU trains the model using GPU-accelerated float32 operations.

type TrainConfig

type TrainConfig struct {
	Epochs       int
	BatchSize    int
	LearningRate float64
}

TrainConfig holds training hyperparameters.

type TrainResult

type TrainResult struct {
	Losses        []float64 // per-epoch average loss
	FinalAccuracy float64   // fraction of correct predictions on last epoch
}

TrainResult holds outcomes from GPU training.