Popular Libraries
TensorFlow
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 Category | Description |
---|---|
Features | The last 5 close price differencing to the current price |
Labels | The following day's price change |
Follow these steps to prepare the data:
- Loop through the DataFrame of historical prices and collect the features.
- Select the close column and then call the
shift
method to collect the labels. - Drop the first 5 samples and then call the
reset_index
method. - Split the data into training and testing datasets.
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}" lookback_series.append(df) X = pd.concat(lookback_series, axis=1).reset_index(drop=True).dropna() X
The following image shows the format of the features DataFrame:
![Features and labels for training](https://cdn.quantconnect.com/i/tu/ml-tf.png)
Y = data['close'].diff(-1)
Y = Y[lookback:-1].reset_index(drop=True)
This method aligns the history of the features and labels.
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:
- Set the number of layers, their number of nodes, the number of epoch and the learning rate.
- Create hidden layers with the set number of layer and their corresponding number of nodes.
- Select an optimizer.
- Define the loss function.
num_factors = X_test.shape[1] num_neurons_1 = 10 num_neurons_2 = 10 num_neurons_3 = 5 epochs = 20 learning_rate = 0.0001
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), tf.keras.layers.dense(1) ])
We're using Adam optimizer in this example. You may also consider others like SGD.
optimizer = tf.keras.optimizers.adam(learning_rate=learning_rate)
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.title(title) plt.xlabel("Time step") plt.ylabel("SPY Price") plt.legend() plt.show() test_model(y_test, "Test Set Results from Original Model", X_test)
![Tensorflow model performance](https://cdn.quantconnect.com/i/tu/tf-test.png)
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:
- Set the key name of the model to be stored in the Object Store.
- Call the
GetFilePath
get_file_path
method with the key. - Call the
save
method with the model and file path. - Save the model to the file path.
model_key = "model.keras"
Note that the model has to have the suffix .keras
.
file_name = qb.object_store.get_file_path(model_key)
This method returns the file path where the model will be stored.
model.save(file_name)
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:
- Get the file path from the Object Store.
- Restore the
TensorFlow
model from the saved path.
file_name = qb.object_store.get_file_path(model_key)
model = tf.keras.models.load_model(file_name)