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This page explains how to build, train, test, and store Tensorflow models.

Import Libraries

Import the tensorflow, and sklearn libraries.

import tensorflow as tf
from sklearn.model_selection import train_test_split

You need the sklearn library to prepare the data.

Get Historical Data

Get some historical market data to train and test the model. For example, to get data for the SPY ETF during 2020 and 2021, run:

qb = QuantBook()
symbol = qb.add_equity("SPY", Resolution.DAILY).symbol
history = qb.history(symbol, datetime(2020, 1, 1), datetime(2022, 1, 1)).loc[symbol]

Prepare Data

You need some historical data to prepare the data for the model. If you have historical data, manipulate it to train and test the model. In this example, use the following features and labels:

Data CategoryDescription
FeaturesThe last 5 close price differencing to the current price
LabelsThe following day's price change

Follow these steps to prepare the data:

  1. Loop through the DataFrame of historical prices and collect the features.
  2. data = history
    lookback = 5
    lookback_series = []
    for i in range(1, lookback + 1):
        df = data['close'].diff(i)[lookback:-1]
        df.name = f"close-{i}"
    X = pd.concat(lookback_series, axis=1).reset_index(drop=True).dropna()

    The following image shows the format of the features DataFrame:

    Features and labels for training
  3. Select the close column and then call the shift method to collect the labels.
  4. Y = data['close'].diff(-1)
  5. Drop the first 5 samples and then call the reset_index method.
  6. Y = Y[lookback:-1].reset_index(drop=True)

    This method aligns the history of the features and labels.

  7. Split the data into training and testing datasets.
  8. For example, to use the last third of data to test the model, run:

    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, shuffle=False)

Train Models

You need to prepare the historical data for training before you train the model. If you have prepared the data, build and train the model. In this example, build a neural network model that predicts the future price of the SPY.

Build the Model

Follow these steps to build the model:

  1. Set the number of layers, their number of nodes, the number of epoch and the learning rate.
  2. num_factors = X_test.shape[1]
    num_neurons_1 = 10
    num_neurons_2 = 10
    num_neurons_3 = 5
    epochs = 20
    learning_rate = 0.0001
  3. Create hidden layers with the set number of layer and their corresponding number of nodes.
  4. In this example, we're constructing the model with the in-built Keras API, with Relu activator for non-linear activation of each tensors.

    model = tf.keras.sequential([
        tf.keras.layers.dense(num_neurons_1, activation=tf.nn.relu, input_shape=(num_factors,)),  # input shape required
        tf.keras.layers.dense(num_neurons_2, activation=tf.nn.relu),
        tf.keras.layers.dense(num_neurons_3, activation=tf.nn.relu),
  5. Select an optimizer.
  6. We're using Adam optimizer in this example. You may also consider others like SGD.

    optimizer = tf.keras.optimizers.adam(learning_rate=learning_rate)
  7. Define the loss function.
  8. In the context of numerical regression, we use MSE as our objective function. If you're doing classification, cross entropy would be more suitable.

    def loss_mse(target_y, predicted_y):
        return tf.reduce_mean(tf.square(target_y - predicted_y))

Train the Model

Iteratively train the model by the set epoch number. The model will train adaptively by the gradient provided by the loss function with the selected optimizer.

for i in range(epochs):
    with tf.gradient_tape() as t:
        loss = loss_mse(y_train, model(X_train))

    train_loss = loss_mse(y_train, model(X_train))
    test_loss = loss_mse(y_test, model(X_test))
    print(f"""Epoch {i+1}:
Training loss = {train_loss.numpy()}. Test loss = {test_loss.numpy()}""")

    jac = t.gradient(loss, model.trainable_weights)
    optimizer.apply_gradients(zip(jac, model.trainable_weights))

Test Models

To test the model, we'll setup a method to plot test set predictions ontop of the SPY price.

def test_model(actual, title, X):
    prediction = model(X).numpy()
    prediction = prediction.reshape(-1, 1)

    plt.figure(figsize=(16, 6))
    plt.plot(actual, label="Actual")
    plt.plot(prediction, label="Prediction")
    plt.xlabel("Time step")
    plt.ylabel("SPY Price")

test_model(y_test, "Test Set Results from Original Model", X_test)
Tensorflow model performance

Store Models

You can save and load TensorFlow models using the Object Store.

Save Models

Follow these steps to save models in the Object Store:

  1. Set the key name of the model to be stored in the Object Store.
  2. model_key = "model.keras"

    Note that the model has to have the suffix .keras.

  3. Call the GetFilePathget_file_path method with the key.
  4. file_name = qb.object_store.get_file_path(model_key)

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

  5. Call the save method with the model and file path.
  6. model.save(file_name)
  7. Save the model to the file path.
  8. qb.object_store.save(model_key)

Load Models

You must save a model into the Object Store before you can load it from the Object Store. If you saved a model, follow these steps to load it:

  1. Get the file path from the Object Store.
  2. file_name = qb.object_store.get_file_path(model_key)
  3. Restore the TensorFlow model from the saved path.
  4. model = tf.keras.models.load_model(file_name)

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