Popular Libraries
PyTorch
Create Subscriptions
In the Initialize
initialize
method, subscribe to some data so you can train the torch
model and make predictions.
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
Build Models
In this example, build a neural-network regression model that uses the following features and labels:
Data Category | Description |
---|---|
Features | The last 5 closing prices. |
Labels | The following day's closing price |
The following image shows the time difference between the features and labels:
Follow these steps to create a method to build the model:
- Define a subclass of
nn.Module
to be the model. - Create an instance of the model and set its configuration to train on the GPU if it's available.
In this example, use the ReLU activation function for each layer.
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
device = 'cuda' if torch.cuda.is_available() else 'cpu' self.model = NeuralNetwork().to(device)
Train Models
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 Initialize
initialize
method, make a history request.
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.
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 Initialize
initialize
method, call the Train
train
method.
self.train(self.my_training_method)
To periodically re-train the model as your algorithm executes, in the Initialize
initialize
method, call the Train
train
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 OnData
on_data
method, add the current TradeBar
to the RollingWindow
that holds the training data.
def on_data(self, slice: Slice) -> None: if self.symbol in slice.Bars: self.training_data.Add(slice.Bars[self.symbol].Close)
Predict Labels
To predict the labels of new data, in the OnData
on_data
method, get the most recent set of features and pass it to the model.
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.
if prediction > slice[self.symbol].Price: self.SetHoldings(self.symbol, 1) elif prediction < slice[self.symbol].Price: self.SetHoldings(self.symbol, -1)
Save Models
Follow these steps to save PyTorch
models into the Object Store:
- Set the key name of the model to be stored in the Object Store.
- Call the
GetFilePath
get_file_path
method with the key. - Call the
dump
method the file path.
model_key = "model"
file_name = self.object_store.get_file_path(model_key)
This method returns the file path where the model will be stored.
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.
Load Models
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 Initialize
initialize
method, get the file path to the saved model and then call the load
method.
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 ContainsKey
contains_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.