Popular Libraries
Tslearn
Create Subscriptions
In the Initialize
method, subscribe to some data so you can train the tslearn
model.
tickers = ["SPY", "QQQ", "DIA", "AAPL", "MSFT", "TSLA", "IEF", "TLT", "SHV", "SHY", "GLD", "IAU", "SLV", "USO", "XLE", "XOM"] symbols = [self.AddEquity(ticker, Resolution.Daily).Symbol for ticker in tickers]
Build Models
In this example, train a model that clusters the universe of Equities into distinct groups and then allocate an equal portion of the portfolio to each cluster. To cluster the securities, instead of using a real-time comparison, apply Dynamic Time Wrapping Barycenter Averaging (DBA) to their historical prices and then run a k-means clustering algorithm. DBA is a technique of averaging a few time-series into a single one without losing much of their information. Since not all time-series move efficiently like in ideal EMH assumption, this technique allows similarity analysis of different time-series with sticky lags. The following image shows a visualization of the process. For more information about the technical details, see Dynamic Time Warping in the tslearn documentation.

To perform DBA and then cluster the securities by k-means, create a TimeSeriesKMeans model:
self.model = TimeSeriesKMeans(n_clusters=6, # We have 6 main groups metric="dtw")
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.
training_length = 252 self.training_data = {} history = self.History(self.symbols, training_length, Resolution.Daily).unstack(0).close for symbol in self.symbols: self.training_data[symbol] = RollingWindow[float](training_length) for close_price in history[symbol]: self.training_data[symbol].Add(close_price)
Define a Training Method
To train the model, define a method that fits the model with the training data.
def get_features(self): close_price = pd.DataFrame({symbol: list(data)[::-1] for symbol, data in self.training_data.items()}) log_price = np.log(close_price) log_normal_price = (log_price - log_price.mean()) / log_price.std() return log_normal_price def my_training_method(self): features = self.get_features() self.model.fit(features.T.values)
Set Training Schedule
To train the model at the beginning of your algorithm, in the Initialize
method, call the Train
method.
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.
# Train the model every Sunday at 8:00 AM self.Train(self.DateRules.Every(DayOfWeek.Sunday), self.TimeRules.At(8, 0), self.my_training_method)
Update Training Data
To update the training data as the algorithm executes, in the OnData
method, add the current TradeBar
to the RollingWindow
that holds the training data.
def OnData(self, slice: Slice) -> None: for kvp in slice.Bars: self.training_data[kvp.Key].Add(kvp.Value.Close)
Predict Labels
To predict the labels of new data, in the OnData
method, get the most recent set of features and then call the predict
method.
features = self.get_features() self.labels = self.model.predict(features.T.values)
You can use the label prediction to place orders.
for i in set(self.labels): assets_in_cluster = features.columns[[n for n, k in enumerate(self.labels) if k == i]] size = 1/6/len(assets_in_cluster) self.SetHoldings([PortfolioTarget(symbol, size) for symbol in assets_in_cluster])
Save Models
Follow these steps to save tslearn
models into the ObjectStore:
- Set the key name of the model to be stored in the ObjectStore.
- Call the
GetFilePath
method with the key. - Call the
to_hdf5
method with the file path.
model_key = "model"
file_name = self.ObjectStore.GetFilePath(model_key)
This method returns the file path where the model will be stored.
self.model.to_hdf5(file_name + ".hdf5")
Load Models
You can load and trade with pre-trained tslearn
models that saved in ObjectStore. To load a tslearn
model from the ObjectStore, in the Initialize
method, get the file path to the saved model and then call the from_hdf5
method.
def Initialize(self) -> None: if self.ObjectStore.ContainsKey(model_key): file_name = self.ObjectStore.GetFilePath(model_key) self.model = TimeSeriesKMeans.from_hdf5(file_name + ".hdf5")
The ContainsKey
method returns a boolean that represents if the model_key
is in the ObjectStore. If the ObjectStore does not contain the model_key
, save the model using the model_key
before you proceed.