Neural network models¶

BaseNeuralNet is the root of all PyTorch models in this package. It wraps torch.nn.Module with higher-level training, prediction and serialization helpers — set_optimizer, train_on, predict, set_data, save_model / load_model — so that subclasses only need to implement forward.

MultiLayerPerceptron is the feed-forward specialization: a configurable stack of Linear → Dropout → Activation blocks, best suited for tabular or sliding-window features (technical indicators, volatility signals). For time-ordered sequences, prefer the recurrent architectures described in Recurrent neural networks.

Objective-aligned training

ObjectiveModel trains a network directly on a differentiable financial objective — e.g. SharpeLoss — rather than MSE against a target. The net outputs positions and the loss is computed on positions * returns: fit(X, y) reads y as the realized returns, and predict(X) returns positions in [-1, 1]. It is a SignalModel, so it drops straight into a Strategy with an identity signal:

from fynance.models import ObjectiveModel, SharpeLoss
from fynance.strategy import Strategy

model = ObjectiveModel(layers=(16, 8), loss=SharpeLoss(), epochs=60, seed=0)
strat = Strategy(model=model, signal=lambda positions: positions)

Feed it through the research harness via the X path with y = returns; see Research workflow.

Inheritance

BaseNeuralNet → MultiLayerPerceptron

BaseNeuralNet → _RecurrentBase → RecurrentNeuralNetwork / GRUCell / LSTMCell (see Recurrent neural networks)

Classes

`_base.BaseNeuralNet`()	Base object for neural network model with PyTorch.
`mlp.MultiLayerPerceptron`(X, y[, layers, ...])	Neural network with MultiLayer Perceptron architecture.
`objective.ObjectiveModel`([net, layers, ...])	Train a net to maximize a differentiable financial objective.