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This page explains how to build, train, deploy and store Hmmlearn models.

Import Libraries

Import the hmmlearn and joblib libraries.

from AlgorithmImports import *
from hmmlearn import hmm
import joblib

You need the joblib library to store models.

Create Subscriptions

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

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

Build Models

In this example, assume the market has only 2 regimes and the market returns follow a Gaussian distribution. Therefore, create a 2-component Hidden Markov Model with Gaussian emissions, which is equivalent to a Gaussian mixture model with 2 means.

To build the model, call the GaussianHMM constructor with the number of components, a covariance type, and the number of iterations:

self.model = hmm.GaussianHMM(n_components=2, covariance_type="full", n_iter=100)

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 Initializeinitialize 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:

Define a Training Method

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

def get_features(self):
    training_df = np.array(list(self.training_data)[::-1])
    daily_pct_change = (np.roll(training_df, 1) - training_df) / training_df

    return daily_pct_change[1:].reshape(-1, 1)

def my_training_method(self):
    features = self.get_features()

Set Training Schedule

To train the model at the beginning of your algorithm, in the Initializeinitialize method, call the Traintrain 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 close price to the RollingWindow that holds the training data.

def on_data(self, slice: Slice) -> None:
    if self._symbol in slice.bars:

Predict Labels

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.

new_feature = self.get_features()
prediction = self.model.predict(new_feature)
prediction = float(prediction[-1])

You can use the label prediction to place orders.

if prediction == 1:
    self.set_holdings(self._symbol, 1)

Save Models

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

  1. Set the key name you want to store the model under in the Object Store.
  2. model_key = "model.hmm"
  3. Call the GetFilePathget_file_path method with the key.
  4. file_name = self.object_store.get_file_path(model_key)

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

  5. Call the dump method the file path.
  6. 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 hmmlearn models that you saved in the Object Store. To load a hmmlearn 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.

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