---
title: "Hmmlearn"
description: "This page explains how to build, train, deploy and store Hmmlearn models."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/hmmlearn
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/hmmlearn.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/31%20Machine%20Learning/03%20Popular%20Libraries/04%20Hmmlearn
modified: 2026-11-01
index: https://www.quantconnect.com/llms.txt
---

# Hmmlearn

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Machine Learning > [Popular Libraries](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries.md) > Hmmlearn

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## Introduction

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

## Import Libraries

Import the `hmmlearn` and `joblib` libraries.

```python
from AlgorithmImports import *
from hmmlearn import hmm
import joblib
```

You need the `joblib` library to store models.

## Create Subscriptions

In the `initialize` method, [subscribe to some data](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes.md) so you can train the `hmmlearn` model and make predictions.

```python
# Add a security and save a reference to its Symbol.
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:

```python
# Initialize GaussianHMM with 2 states for capturing patterns, full covariance to model complex relationships, and
# 100 iterations for sufficient training.
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 `initialize` method, make a [history request](https://www.quantconnect.com/docs/v2/writing-algorithms/historical-data/history-requests.md).

```python
# Fill a RollingWindow with 2 years of historical closing prices.
training_length = 252*2
self.training_data = RollingWindow(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.

```python
# Prepare feature and label data for training by processing the RollingWindow data into a time series.
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()
    self.model.fit(features)
```

### Set Training Schedule

To train the model at the beginning of your algorithm, in the `initialize` method, call the `train` method.

```python
# 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 `initialize` method, call the `train` method as a [Scheduled Event](https://www.quantconnect.com/docs/v2/writing-algorithms/scheduled-events.md).

```python
# 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 `on_data` method, add the current close price to the `RollingWindow` that holds the training data.

```python
# Add the latest bar to the 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)
```

## Predict Labels

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

```python
# Get the current feature set and make a prediction.
new_feature = self.get_features()
prediction = self.model.predict(new_feature)
prediction = float(prediction[-1])
```

You can use the label prediction to place orders.

```python
# Place orders based on the forecasted market direction.
if prediction == 1:
    self.set_holdings(self._symbol, 1)
else:
    self.liquidate(self._symbol)
```

## Save Models

Follow these steps to save `hmmlearn` models into the [Object Store](https://www.quantconnect.com/docs/v2/writing-algorithms/object-store.md):

1. Set the key name you want to store the model under in the Object Store.
   ```python
   # Set the key to store the model in the Object Store so you can use it later.
   model_key = f"{self.project_id}/model.hmm"
   ```
2. Call the `get_file_path` method with the key.
   ```python
   # 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.
   ```python
   # Serialize the model and save it to the file.
   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.

## Upload Models

If you train models locally or in another environment, you can upload the model files to the Object Store so your algorithms can use them. Use the model key that matches the key your algorithm expects when it calls `object_store.get_file_path`.

Follow one of these approaches to upload your model files:

### LEAN CLI

Run the [`lean cloud object-store set`](https://www.quantconnect.com/docs/v2/lean-cli/api-reference/lean-cloud-object-store-set.md) command to upload a local file to the Object Store.

```
$ lean cloud object-store set <projectId>/model.hmm <pathTo>/model.hmm
```

Replace `<projectId>` with your project Id and `<pathTo>` with the path to the local model file.

### Cloud API

Use the [Upload Object Store Files](https://www.quantconnect.com/docs/v2/cloud-platform/api-reference/object-store-management/upload-object-store-files.md) endpoint to upload a model file through the API.

## 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 `initialize` method, get the file path to the saved model and then call the `load` method.

```python
# Load the hmmlearn model from Object Store to use its saved state and update it with new data if needed.
def initialize(self) -> None:
    model_key = f"{self.project_id}/model.hmm"
    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 `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.

## Examples

The following examples demonstrate some common practices for using `Hmmlearn` library.

### Example 1: Hidden Markov Model Regime Detection

The below algorithm makes use of `Hmmlearn` library to predict the future market regime through a hidden markov model using return data. If the regime is upward market, we hold SPY. Otherwise, we liquidate the portfolio. The model is trained using rolling 2-year data. To ensure the model applicable to the current market environment, we recalibrate the model on every Sunday.

```python
from hmmlearn import hmm
import joblib

class HmmlearnExampleAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        self.set_cash(100000)
        # Request SPY data for model training, prediction and trading.
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol

        # Use the session rolling window to cache 2-year daily data for training.
        training_length = 252*2
        self.securities[self._symbol].session.size = training_length
        self.set_warm_up(training_length, Resolution.DAILY)

        # Retrieve already trained model from object store to use immediately.
        self._model_key = f"{self.project_id}/model.hmm"
        if self.object_store.contains_key(self._model_key):
            file_name = self.object_store.get_file_path(self._model_key)
            self.model = joblib.load(file_name)
        # Create a 2-regime model otherwise to predict different variance regime markets.
        else:
            self.model = hmm.GaussianHMM(n_components=2, covariance_type="full", n_iter=100)

    def on_warmup_finished(self) -> None:
        # Train the model to use the prediction right away.
        self.train(self.my_training_method)
        # Recalibrate the model weekly to ensure its accuracy on the updated domain.
        self.train(self.date_rules.every(DayOfWeek.SUNDAY), self.time_rules.at(8,0), self.my_training_method)

    def get_features(self) -> None:
        # Train and predict the return data, which is more normalized and stationary.
        training_df = np.array([x.close for x in self.securities[self._symbol].session][::-1])
        daily_pct_change = np.diff(training_df) / training_df[:-1]

        return daily_pct_change.reshape(-1, 1)

    def my_training_method(self) -> None:
        # Prepare the processed training data.
        features = self.get_features()
        # Recalibrate the model based on updated data.
        self.model.fit(features)

    def on_data(self, slice: Slice) -> None:
        if self.is_warming_up or self._symbol not in slice.bars:
            return

        # Get prediction by the updated features.
        new_feature = self.get_features()
        prediction = self.model.predict(new_feature)
        prediction = float(prediction[-1])

        # If the predicted direction is going upward, buy SPY.
        if prediction == 1:
            self.set_holdings(self._symbol, 1)
        # If the predicted direction is going downward, liquidate.
        else:
            self.liquidate(self._symbol)

    def on_end_of_algorithm(self) -> None:
        # Store the model to object store to retrieve it in other instances in case the algorithm stops.
        file_name = self.object_store.get_file_path(self._model_key)
        joblib.dump(self.model, file_name)
```