Popular Libraries

Import the aesera and sklearn libraries.

from AlgorithmImports import *
import aesara
import aesara.tensor as at
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import joblib

You need the joblib library to store models.

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

self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol

In this example, build a logistic regression prediction model that uses the following features and labels:

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

Follow the below steps to build the model:

1. Initialize variables.

# Declare Aesara symbolic variables
x = at.dmatrix("x")
y = at.dvector("y")

# initialize the weight vector w randomly using share so model coefficients keep their values
# between training iterations (updates)
rng = np.random.default_rng(100)
w = aesara.shared(rng.standard_normal(5), name="w")

# initialize the bias term
b = aesara.shared(0., name="b")

2. Construct the model graph.

# Construct Aesara expression graph
p_1 = 1 / (1 + at.exp(-at.dot(x, w) - b))       # Logistic transformation
prediction = p_1 > 0.5                          # The prediction thresholded
xent = y * at.log(p_1) - (1 - y) * at.log(1 - p_1)  # Cross-entropy log-loss function
cost = xent.mean() + 0.01 * (w ** 2).sum()      # The cost to minimize (MSE)
gw, gb = at.grad(cost, [w, b])                  # Compute the gradient of the cost

3. Compile the model.

self.train = aesara.function(
          inputs=[x, y],
          outputs=[prediction, xent],
          updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)))
self.predict = aesara.function(inputs=[x], outputs=prediction)

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.

training_length = 252*2
self.training_data = RollingWindow[TradeBar](training_length)
history = self.history[TradeBar](self.symbol, training_length, Resolution.DAILY)
for trade_bar in history:
    self.training_data.add(trade_bar)

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):
    training_df = self.PandasConverter.GetDataFrame[TradeBar](list(self.training_data)[::-1])['close']

    features = []
    for i in range(1, n_steps + 1):
        close = training_df.shift(i)[n_steps:-1]
        close.name = f"close-{i}"
        features.append(close)
    features = pd.concat(features, axis=1)
    # Normalize using the 5 day interval
    features = MinMaxScaler().fit_transform(features.T).T[4:]
    
    Y = training_df.pct_change().shift(-1)[n_steps*2-1:-1].reset_index(drop=True)
    labels = np.array([1 if y > 0 else 0 for y in Y])   # binary class

    return features, labels

def my_training_method(self):
    features, labels = self.get_features_and_labels()
    D = (features, labels)
    self.train(D[0], D[1])

Set Training Schedule

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

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.

def on_data(self, slice: Slice) -> None:
    if self.symbol in slice.bars:
        self.training_data.add(slice.bars[self.symbol])

To predict the labels of new data, in the OnDataon_data method, get the most recent set of features and then call the predict method.

features, _ = self.get_features_and_labels()
prediction = self.predict(features[-1].reshape(1, -1))
prediction = float(prediction)

You can use the label prediction to place orders.

if prediction == 1:
    self.set_holdings(self.symbol, 1)
elif prediction == 0:            
    self.set_holdings(self.symbol, -1)

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

1. Set the key name you want to store the model under in the Object Store.

model_key = "model"

2. Call the GetFilePathget_file_path method with the key.

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.

joblib.dump(self.predict, 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 sklearn models that you saved in the Object Store. To load a sklearn model from the Object Store, in the Initializeinitialize 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 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.