Popular Libraries

Import the torch and joblib libraries.

from AlgorithmImports import *
import torch
from torch import nn
import joblib

You need the joblib library to store models.

In the Initializeinitialize method, subscribe to some data so you can train the torch model and make predictions.

# Subscribe to security data and store symbol for referencing in the algorithm.
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol

In this example, build a neural-network regression model that uses the following features and labels:

The following image shows the time difference between the features and labels:

Follow these steps to create a method to build the model:

1. Define a subclass of nn.Module to be the model.

In this example, use the ReLU activation function for each layer.

# Define a feed-forward neural network with two hidden layers for learning complex features, ReLU activations to introduce non-linearity, and a single regression output for predicting continuous values.
class NeuralNetwork(nn.Module):
    # Model Structure
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(5, 5),   # input size, output size of the layer
            nn.ReLU(),         # Relu non-linear transformation
            nn.Linear(5, 5),
            nn.ReLU(),  
            nn.Linear(5, 1),   # Output size = 1 for regression
        )
    
    # Feed-forward training/prediction
    def forward(self, x):
        x = torch.from_numpy(x).float()   # Convert to tensor in type float
        result = self.linear_relu_stack(x)
        return result

2. Create an instance of the model and set its configuration to train on the GPU if it's available.

# Use GPU if available for faster computation, otherwise fallback to CPU, and move the model to the selected device.
device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model = NeuralNetwork().to(device)

You can train the model at the beginning of your algorithm and you can periodically re-train it as the algorithm executes.

Warm Up Training Data

You need historical data to initially train the model at the start of your algorithm. To get the initial training data, in the Initializeinitialize method, make a history request.

# Initialize training data with a rolling window of size 252*2 days.
training_length = 252*2
self.training_data = RollingWindow[float](training_length)
history = self.history[TradeBar](self._symbol, training_length, Resolution.DAILY)
for trade_bar in history:
    self.training_data.add(trade_bar.close)

Define a Training Method

To train the model, define a method that fits the model with the training data.

# Prepare feature and label data for training by processing rolling window data to create time-series sequences for model training.
def get_features_and_labels(self, n_steps=5):
    close_prices = list(self.training_data)[::-1]

    features = []
    labels = []
    for i in range(len(close_prices)-n_steps):
        features.append(close_prices[i:i+n_steps])
        labels.append(close_prices[i+n_steps])
    features = np.array(features)
    labels = np.array(labels)

    return features, labels

def my_training_method(self):
    features, labels = self.get_features_and_labels()

    # Set the loss and optimization functions
    # In this example, use the mean squared error as the loss function and stochastic gradient descent as the optimizer
    loss_fn = nn.MSELoss()
    learning_rate = 0.001
    optimizer = torch.optim.SGD(self.model.parameters(), lr=learning_rate)
    
    # Create a for-loop to train for preset number of epoch
    epochs = 5
    for t in range(epochs):
        # Create a for-loop to fit the model per batch
        for batch, (feature, label) in enumerate(zip(features, labels)):
            # Compute prediction and loss
            pred = self.model(feature)
            real = torch.from_numpy(np.array(label).flatten()).float()
            loss = loss_fn(pred, real)
        
            # Perform backpropagation
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

Set Training Schedule

To train the model at the beginning of your algorithm, in the Initializeinitialize method, call the Traintrain method.

# Train the model initially to provide a baseline for prediction and decision-making.
self.train(self.my_training_method)

To periodically re-train the model as your algorithm executes, in the Initializeinitialize method, call the Traintrain method as a Scheduled Event.

# Train the model every Sunday at 8:00 AM
self.train(self.date_rules.every(DayOfWeek.SUNDAY), self.time_rules.at(8, 0), self.my_training_method)

Update Training Data

To update the training data as the algorithm executes, in the OnDataon_data method, add the current TradeBar to the RollingWindow that holds the training data.

# Add the latest bar to training data to ensure the model is trained with the most recent market data.
def on_data(self, slice: Slice) -> None:
    if self._symbol in slice.Bars:
        self.training_data.Add(slice.Bars[self._symbol].Close)

To predict the labels of new data, in the OnDataon_data method, get the most recent set of features and pass it to the model.

# Generate feature set and predict with the latest data for current market decisions.
features, __ = self.get_features_and_labels()
prediction = self.model(features[-1].reshape(1, -1))
prediction = float(prediction.detach().numpy()[-1])

You can use the label prediction to place orders.

# Use label prediction to place orders based on forecasted market direction.
if prediction > slice[self._symbol].Price:
    self.SetHoldings(self._symbol, 1)
elif prediction < slice[self._symbol].Price:            
    self.SetHoldings(self._symbol, -1)

Follow these steps to save PyTorch models into the Object Store:

1. Set the key name of the model to be stored in the Object Store.

# Set the key to store the model in Object Store for reuse across sessions.
model_key = "model"

2. Call the GetFilePathget_file_path method with the key.

# Get the file path to correctly save and access the model in Object Store.
file_name = self.object_store.get_file_path(model_key)

This method returns the file path where the model will be stored.

3. Call the dump method the file path.

# Serialize Python objects into a file to save the model's state for other runs.
joblib.dump(self.model, file_name)

If you dump the model using the joblib module before you save the model, you don't need to retrain the model.

You can load and trade with pre-trained PyTorch models that you saved in the Object Store. To load a PyTorch model from the Object Store, in the Initializeinitialize method, get the file path to the saved model and then call the load method.

# Load the PyTorch model from Object Store to use its saved state and update with new data if needed.
def initialize(self) -> None:
    if self.object_store.contains_key(model_key):
        file_name = self.object_store.get_file_path(model_key)
        self.model = joblib.load(file_name)

The ContainsKeycontains_key method returns a boolean that represents if the model_key is in the Object Store. If the Object Store does not contain the model_key, save the model using the model_key before you proceed.