# Popular Libraries

## Tslearn

### Introduction

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

### Import Libraries

Import the tslearn libraries.

from AlgorithmImports import *
from tslearn.barycenters import softdtw_barycenter
from tslearn.clustering import TimeSeriesKMeans

### 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:

1. Set the key name of the model to be stored in the ObjectStore.
2. model_key = "model"
3. Call the GetFilePath method with the key.
4. file_name = self.ObjectStore.GetFilePath(model_key)

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

5. Call the to_hdf5 method with the file path.
6. self.model.to_hdf5(file_name + ".hdf5")

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.

### Clone Example Algorithm

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