signalflow-labs 0.8.2

Neural network and RL extension for signalflow (torch/lightning/SB3).

signalflow-labs

Neural-network & RL extension for SignalFlow - 14 encoders, 7 heads, 4 losses, RL strategy

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Part of the SignalFlow ecosystem.

A plugin: a PyTorch/Lightning library for financial time-series classification (modular encoders, classification heads, loss functions) plus a reinforcement-learning strategy. Installing it auto-registers its components with the core signalflow registry via entry points.

Installation

pip install signalflow-labs           # core: torch + lightning
pip install "signalflow-labs[rl]"     # + stable-baselines3, gymnasium (RL strategy)
# or, from the core:
pip install "signalflow-trading[labs]"

Requires: Python ≥ 3.12, signalflow-trading ≥ 0.8.0, PyTorch ≥ 2.2, Lightning ≥ 2.5.

Quick Start

from signalflow.labs.encoder import TransformerEncoder
from signalflow.labs.head import MLPClassifierHead
from signalflow.labs.model import TemporalClassificator
from signalflow.labs.data import SignalDataModule
import lightning as pl

# Create model
model = TemporalClassificator(
    encoder_type="encoder/transformer",
    encoder_params={"d_model": 64, "nhead": 4, "num_layers": 2},
    head_type="head/cls/mlp",
    head_params={"hidden_sizes": [32]},
    num_classes=3,  # fall, neutral, rise
)

# Create data module
dm = SignalDataModule(
    data=df,
    window_size=60,
    batch_size=32,
    split_strategy="temporal",
)

# Train
trainer = pl.Trainer(max_epochs=50, accelerator="auto")
trainer.fit(model, dm)

Encoders (14)

Encoder	Architecture	Best For
LSTMEncoder	Bidirectional LSTM	Sequential patterns
GRUEncoder	Gated Recurrent Unit	Faster training
TCNEncoder	Temporal Convolutional Network	Long-range dependencies
TransformerEncoder	Self-attention + positional encoding	Complex relationships
PatchTSTEncoder	Patch-based Transformer	Multivariate time series
TSMixerEncoder	All-MLP (Google 2023)	Efficient mixing
InceptionTimeEncoder	Multi-scale convolutions	Multi-resolution features
ResNet1dEncoder	1D ResNet	Deep representations
XceptionTimeEncoder	Depthwise separable conv	Efficient computation
Conv1dEncoder	1D CNN	Local patterns
XCMEncoder	Cross-Channel Mixing	Channel interactions
gMLPEncoder	Gating MLP	Spatial/channel gating
OmniScaleCNNEncoder	Multi-scale CNN	Scale-invariant features
ConvTranEncoder	Conv + Transformer hybrid	Combined strengths

Classification Heads (7)

Head	Use Case
LinearClassifierHead	Simple baseline
MLPClassifierHead	Non-linear classification
ResidualClassifierHead	Deep with skip connections
AttentionClassifierHead	Attention-weighted pooling
OrdinalRegressionHead	Ordered classes (fall < neutral < rise)
DistributionHead	Probability distributions
ClassificationWithConfidenceHead	Class + confidence score

Loss Functions (4)

Loss	Purpose
FocalLoss	Class imbalance - down-weights easy examples
DiceLoss	Imbalanced multi-class
LDAMLoss	Large margin for rare classes
SymmetricCrossEntropyLoss	Noisy labels

SignalFlow Integration

Importing signalflow.labs registers its components. The RL strategy plugs into a Flow as the strategy slot (needs the [rl] extra):

import signalflow as sf
import signalflow.labs as labs               # registers neural + RL components
from stable_baselines3 import PPO

base = sf.Flow(name="rl", detectors=[sf.SmaCrossDetector()])
env = labs.make_env(base, ds)                # gymnasium env over an Engine replay
policy = PPO("MlpPolicy", env).learn(10_000)

flow = base.replace(strategy=labs.RLStrategy(model=policy, size_pct=0.1))
run = flow.backtest(ds, capital=50_000)
print(run.scorecard())

The neural TemporalClassificator can also back a ForecastModel for signal validation; see signalflow.labs.validator.

Package Structure

Module	Description
signalflow.labs.data	Data loading, windowing, temporal splitting
signalflow.labs.encoder	14 feature encoding architectures
signalflow.labs.head	7 output head architectures
signalflow.labs.layer	Custom neural network layers
signalflow.labs.loss	4 specialized loss functions
signalflow.labs.model	TemporalClassificator - complete model
signalflow.labs.validator	SignalFlow validator integration
signalflow.labs.strategy	RLStrategy + make_env (RL)
signalflow.labs.backend	TorchMLPBackend for ForecastModel

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License: MIT  ·  Part of SignalFlow