Popular Libraries
XGBoost
Create Subscriptions
In the Initializeinitialize method, subscribe to some data so you can train the xgboost model and make predictions.
# Subscribe to security data and store symbol for referencing in the algorithm.
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
Build Models
In this example, build a gradient boost tree regression prediction model that uses the following features and labels:
| Data Category | Description |
|---|---|
| Features | The last 5 closing prices |
| Labels | The following day's closing price |
The following image shows the time difference between the features and labels:
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 Initializeinitialize method, make a history request.
# Create a 2 year (252 trading days per year) period.
training_length = 252*2
# Create a rolling window to store training data.
self.training_data = RollingWindow[float](training_length)
# Get 2 years of daily data.
history = self.history[TradeBar](self._symbol, training_length, Resolution.DAILY)
for trade_bar in history:
# Populate the training_data window with the close price data.
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.
# Prepare feature and label data for training by processing rolling window data to create time-series sequences for model training.
def get_features_and_labels(self, n_steps=5):
# Create a list of close prices from the rolling window.
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)
# Standardize the features and labels arrays.
features = (features - features.mean()) / features.std()
labels = (labels - labels.mean()) / labels.std()
# Load the NumPy array into a DMatrix object.
d_matrix = xgb.DMatrix(features, label=labels)
return d_matrix
def my_training_method(self):
# Instantiate the DMatrix object with the features and labels array.
d_matrix = self.get_features_and_labels()
# Instantiate the parameters for the XGBoost model.
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,
}
# Create the model and fit it with the training data.
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 Initializeinitialize method, call the Traintrain method.
# 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 Initializeinitialize method, call the Traintrain method as a Scheduled Event.
# 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 OnDataon_data method, add the current TradeBar to the RollingWindow that holds the training data.
# Add the latest bar to 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:
# Update the training data with new most recent close price.
self.training_data.add(slice.bars[self._symbol].close)
Predict Labels
To predict the labels of new data, in the OnDataon_data method, get the most recent set of features and then call the predict method.
# Generate feature set and predict with the latest data for current market decisions. 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.
# Use label prediction to place orders based on forecasted market direction.
if float(prediction[-1]) > float(prediction[-2]):
self.SetHoldings(self._symbol, 1)
else:
self.SetHoldings(self._symbol, -1)
Save Models
Follow these steps to save xgboost models into the Object Store:
- Set the key name of the model to be stored in the Object Store.
- Call the
GetFilePathget_file_pathmethod with the key. - Call the
dumpmethod the file path.
# Set the key to store the model in Object Store for reuse across sessions. model_key = "model"
# 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.
# Serialize Python objects into a file to save the model's state for other runs. 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.
Load Models
You can load and trade with pre-trained xgboost models that saved in Object Store. To load a xgboost model from the Object Store, in the Initializeinitialize method, get the file path to the saved model and then call the load method.
# Load the xgboost model from Object Store to use its saved state and update with new data if needed.
def initialize(self) -> None:
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 ContainsKeycontains_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.
You can also see our Videos. You can also get in touch with us via Discord.
Did you find this page helpful?
