Popular Libraries
Tensorflow
Introduction
This page explains how to build, train, deploy and store Tensorflow
v1 models. To view the tutorial on Tensorflow 2, see Keras.
Import Libraries
Import the tensorflow
libraries.
from AlgorithmImports import * import tensorflow.compat.v1 as tf from google.protobuf import json_format import json5 tf.disable_v2_behavior()
You need the google.protobuf
and json5
libraries to store and load models.
Disable tensorflow
v2 behaviors in order to deploy a v1 model.
Create Subscriptions
In the Initialize
method, subscribe to some data so you can train the tensorflow
model and make predictions.
self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
Build Models
In this example, build a neural-network 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:

Follow these steps to create a method to build the model:
- Create a method to build the model for the algorithm class.
- Instantiate the model, input layers, output layer, and optimizer and then save them as class variables.
- Call the
run
method with the result from theglobal_variables_initializer
method.
def BuildModel(self): # Instantiate a tensorflow session sess = tf.Session() # Declare the number of factors and then create placeholders for the input and output layers. num_factors = 5 X = tf.placeholder(dtype=tf.float32, shape=[None, num_factors], name='X') Y = tf.placeholder(dtype=tf.float32, shape=[None]) # Set up the weights and bias initializers for each layer. weight_initializer = tf.variance_scaling_initializer(mode="fan_avg", distribution="uniform", scale=1) bias_initializer = tf.zeros_initializer() # Create hidden layers that use the Relu activator. num_neurons_1 = 32 num_neurons_2 = 16 num_neurons_3 = 8 W_hidden_1 = tf.Variable(weight_initializer([num_factors, num_neurons_1])) bias_hidden_1 = tf.Variable(bias_initializer([num_neurons_1])) hidden_1 = tf.nn.relu(tf.add(tf.matmul(X, W_hidden_1), bias_hidden_1)) W_hidden_2 = tf.Variable(weight_initializer([num_neurons_1, num_neurons_2])) bias_hidden_2 = tf.Variable(bias_initializer([num_neurons_2])) hidden_2 = tf.nn.relu(tf.add(tf.matmul(hidden_1, W_hidden_2), bias_hidden_2)) W_hidden_3 = tf.Variable(weight_initializer([num_neurons_2, num_neurons_3])) bias_hidden_3 = tf.Variable(bias_initializer([num_neurons_3])) hidden_3 = tf.nn.relu(tf.add(tf.matmul(hidden_2, W_hidden_3), bias_hidden_3)) # Create the output layer and give it a name, so it is accessible after saving and loading the model. W_out = tf.Variable(weight_initializer([num_neurons_3, 1])) bias_out = tf.Variable(bias_initializer([1])) output = tf.transpose(tf.add(tf.matmul(hidden_3, W_out), bias_out), name='outer') # Set up the loss function and optimizers for gradient descent optimization and backpropagation. # This example uses mean-square error as the loss function because the close price is a continuous data and uses Adam as the optimizer because of its adaptive step size. loss = tf.reduce_mean(tf.squared_difference(output, Y)) optimizer = tf.train.AdamOptimizer().minimize(loss) return sess, X, Y, output, optimizer
self.model, self.X, self.Y, self.output, self.optimizer = self.BuildModel(features, labels)
self.model.run(tf.global_variables_initializer())
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[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 = list(self.training_data)[::-1] features = [] labels = [] for i in range(len(close_prices)-n_steps): features.append(close_prices[i:i+n_steps]) labels.append(close_prices[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.run(self.optimizer, feed_dict={self.X: features, self.Y: labels})
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 close price 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)
Predict Labels
To predict the labels of new data, in the OnData
method, get the most recent set of features and then call the run
method with new features.
new_features, __ = self.get_features_and_labels() prediction = self.model.run(self.output, feed_dict={self.X: new_features[-1].reshape(1, -1)}) prediction = float(prediction.flatten()[-1])
You can use the label prediction to place orders.
if prediction > slice[self.symbol].Price: self.SetHoldings(self.symbol, 1) else: self.SetHoldings(self.symbol, -1)
Save Models
Follow these steps to save Tensorflow
models into the ObjectStore:
- Export the
TensorFlow
graph as a JSON object. - Export the
TensorFlow
weights as a JSON object. - Save the graph and weights to the
ObjectStore
.
graph_definition = tf.compat.v1.train.export_meta_graph() json_graph = json_format.MessageToJson(graph_definition)
weights = self.model.run(tf.compat.v1.trainable_variables()) weights = [w.tolist() for w in weights] json_weights = json5.dumps(weights)
self.ObjectStore.Save('graph', json_graph) self.ObjectStore.Save('weights', json_weights)
Load Models
You can load and trade with pre-trained tensorflow
models that you saved in the ObjectStore. To load a tensorflow
model from the ObjectStore, in the Initialize
method, get the file path to the saved model and then recall the graph and weights of the model.
def Initialize(self) -> None: if self.ObjectStore.ContainsKey('graph') and self.ObjectStore.ContainsKey('weights'): json_graph = self.ObjectStore.Read('graph') json_weights = self.ObjectStore.Read('weights') # Restore the tensorflow graph from JSON objects tf.reset_default_graph() graph_definition = json_format.Parse(json_graph, tf.MetaGraphDef()) self.model = tf.Session() tf.train.import_meta_graph(graph_definition) # Select the input, output tensors and optimizer self.X = tf.get_default_graph().get_tensor_by_name('X:0') self.Y = tf.get_default_graph().get_tensor_by_name('Y:0') self.output = tf.get_default_graph().get_tensor_by_name('outer:0') self.optimizer = tf.get_default_graph().get_collection('Variable/Adam') # Restore the model weights from the JSON object. weights = [np.asarray(x) for x in json5.loads(json_weights)] assign_ops = [] feed_dict = {} vs = tf.trainable_variables() zipped_values = zip(vs, weights) for var, value in zipped_values: value = np.asarray(value) assign_placeholder = tf.placeholder(var.dtype, shape=value.shape) assign_op = var.assign(assign_placeholder) assign_ops.append(assign_op) feed_dict[assign_placeholder] = value self.model.run(assign_ops, feed_dict=feed_dict)
The ContainsKey
method returns a boolean that represents if the graph
and weights
is in the ObjectStore. If the ObjectStore does not contain the keys, save the model using them before you proceed.