Popular Libraries

Scikit-Learn

Introduction

This page explains how to build, train, deploy and store Scikit-Learn models.

Import Libraries

Import the sklearn and joblib libraries.

from AlgorithmImports import *
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
import joblib

You need the joblib library to store models.

Create Subscriptions

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

self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol

Build Models

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

Data CategoryDescription
FeaturesDaily percent change of the open, high, low, close, and volume of the SPY over the last 5 days
LabelsDaily percent return of the SPY over the next day

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

To build the model, call the GridSearchCV constructor with the SVR model, the parameter grid, a scoring method, the number of cross-validation folds:

param_grid = {'C': [.05, .1, .5, 1, 5, 10], 
            'epsilon': [0.001, 0.005, 0.01, 0.05, 0.1], 
            'gamma': ['auto', 'scale']}
self.model = GridSearchCV(SVR(), param_grid, scoring='neg_mean_squared_error', cv=5)

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*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])
    daily_pct_change = training_df.pct_change().dropna()

    features = []
    labels = []
    for i in range(len(daily_pct_change)-n_steps):
        features.append(daily_pct_change.iloc[i:i+n_steps].values.flatten())
        labels.append(daily_pct_change['close'].iloc[i+n_steps])
    features = np.array(features)
    labels = np.array(labels)

    return features, labels

def my_training_method(self):
    features, labels = self.get_features_and_labels()
    self.model = self.model.fit(features, labels).best_estimator_

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:
    if self.symbol in slice.Bars:
        self.training_data.Add(slice.Bars[self.symbol])

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_and_labels()
prediction = self.model.predict(features[-1].reshape(1, -1))
prediction = float(prediction)

You can use the label prediction to place orders.

if prediction > 0:
    self.SetHoldings(self.symbol, 1)
elif prediction < 0:            
    self.SetHoldings(self.symbol, -1)

Save Models

Follow these steps to save sklearn models into the ObjectStore:

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

  7. Call the Save method with the key.
  8. self.ObjectStore.Save(model_key)

Load Models

You can load and trade with pre-trained sklearn models that you saved in the ObjectStore. To load a sklearn model from the ObjectStore, in the Initialize method, get the file path to the saved model and then call the load method.

def Initialize(self) -> None:
    if self.ObjectStore.ContainsKey(model_key):
        file_name = self.ObjectStore.GetFilePath(model_key)
        self.model = joblib.load(file_name)

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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