Popular Libraries

Import the XGBoost and joblib libraries.

from AlgorithmImports import *
import xgboost as xgb
import joblib

You need the joblib library to store models.

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

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

In this example, build a gradient boost tree regression prediction model that uses the following features and labels:

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

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[float](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.

def get_features_and_labels(self, n_steps=5):
    close_prices = np.array(list(self.training_data)[::-1])
    df = (np.roll(close_prices, -1) - close_prices) * 0.5 + close_prices * 0.5
    df = df[:-1]

    features = []
    labels = []
    for i in range(len(df)-n_steps):
        features.append(df[i:i+n_steps])
        labels.append(df[i+n_steps])

    features = np.array(features)
    labels = np.array(labels)
    features = (features - features.mean()) / features.std()
    labels = (labels - labels.mean()) / labels.std()

    d_matrix = xgb.DMatrix(features, label=labels)

    return d_matrix

def my_training_method(self):
    d_matrix = self.get_features_and_labels()
    params = {
        'booster': 'gbtree',
        'colsample_bynode': 0.8,
        'learning_rate': 0.1,
        'lambda': 0.1,
        'max_depth': 5,
        'num_parallel_tree': 100,
        'objective': 'reg:squarederror',
        'subsample': 0.8,
      }
    self.model = xgb.train(params, d_matrix, num_boost_round=10)

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].Close)

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

new_d_matrix = self.get_features_and_labels(df)
prediction = self.model.predict(new_d_matrix)
prediction = prediction.flatten()

You can use the label prediction to place orders.

if float(prediction[-1]) > float(prediction[-2]):
    self.SetHoldings(self.symbol, 1)
else:            
    self.SetHoldings(self.symbol, -1)

Follow these steps to save xgboost models into the ObjectStore:

1. Set the key name of the model to be stored in the ObjectStore.

model_key = "model"

2. Call the GetFilePath method with the key.

file_name = self.ObjectStore.GetFilePath(model_key)

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

3. Call the dump method the file path.

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.

You can load and trade with pre-trained xgboost models that saved in ObjectStore. To load a xgboost 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.