Popular Libraries
Aesera
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 logistic regression prediction model that uses the following features and labels:
Data Category | Description |
---|---|
Features | Normalized daily close price of the SPY over the last 5 days |
Labels | Return direction of the SPY over the next day |
The following image shows the time difference between the features and labels:

Follow the below steps to build the model:
- Initialize variables.
- Construct the model graph.
- Compile the model.
# Declare Aesara symbolic variables x = at.dmatrix("x") y = at.dvector("y") # initialize the weight vector w randomly using share so model coefficients keep their values # between training iterations (updates) rng = np.random.default_rng(100) w = aesara.shared(rng.standard_normal(5), name="w") # initialize the bias term b = aesara.shared(0., name="b")
# Construct Aesara expression graph p_1 = 1 / (1 + at.exp(-at.dot(x, w) - b)) # Logistic transformation prediction = p_1 > 0.5 # The prediction thresholded xent = y * at.log(p_1) - (1 - y) * at.log(1 - p_1) # Cross-entropy log-loss function cost = xent.mean() + 0.01 * (w ** 2).sum() # The cost to minimize (MSE) gw, gb = at.grad(cost, [w, b]) # Compute the gradient of the cost
self.train = aesara.function( inputs=[x, y], outputs=[prediction, xent], updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb))) self.predict = aesara.function(inputs=[x], outputs=prediction)
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])['close'] features = [] for i in range(1, n_steps + 1): close = training_df.shift(i)[n_steps:-1] close.name = f"close-{i}" features.append(close) features = pd.concat(features, axis=1) # Normalize using the 5 day interval features = MinMaxScaler().fit_transform(features.T).T[4:] Y = training_df.pct_change().shift(-1)[n_steps*2-1:-1].reset_index(drop=True) labels = np.array([1 if y > 0 else 0 for y in Y]) # binary class return features, labels def my_training_method(self): features, labels = self.get_features_and_labels() D = (features, labels) self.train(D[0], D[1])
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.predict(features[-1].reshape(1, -1)) prediction = float(prediction)
You can use the label prediction to place orders.
if prediction == 1: self.SetHoldings(self.symbol, 1) elif prediction == 0: self.SetHoldings(self.symbol, -1)
Save Models
Follow these steps to save sklearn
models into the Object Store:
- Set the key name you want to store the model under in the Object Store.
- Call the
GetFilePath
method with the key. - Call the
dump
method 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.
joblib.dump(self.predict, 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 sklearn
models that you saved in the Object Store. To load a sklearn
model from the Object Store, 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 Object Store. If the Object Store does not contain the model_key
, save the model using the model_key
before you proceed.