# Popular Libraries

## Aesera

### 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 | Normalized 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 these steps to prepare the data:

- Obtain the close price and return direction series.
- Loop through the
`close`

Series and collect the features. - Convert the lists of features and labels into
`numpy`

arrays. - Split the data into training and testing periods.

close = history['close'] returns = data['close'].pct_change().shift(-1)[lookback*2-1:-1].reset_index(drop=True) labels = pd.Series([1 if y > 0 else 0 for y in returns]) # binary class

lookback = 5 lookback_series = [] for i in range(1, lookback + 1): df = data['close'].shift(i)[lookback:-1] df.name = f"close-{i}" lookback_series.append(df) X = pd.concat(lookback_series, axis=1) # Normalize using the 5 day interval X = MinMaxScaler().fit_transform(X.T).T[4:]

X = np.array(features) y = np.array(labels)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

### 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 Logistic Regression model with log loss cross entropy and square error as cost function. Follow these steps to create the model:

- Generate a dataset.
- Initialize variables.
- Construct the model graph.
- Compile the model.
- Train the model with training dataset.

# D = (input_values, target_class) D = (np.array(X_train), np.array(y_train))

# 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(X.shape[1]), 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

train = aesara.function( inputs=[x, y], outputs=[prediction, xent], updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb))) predict = aesara.function(inputs=[x], outputs=prediction)

pred, err = train(D[0], D[1]) # We can also inspect the final outcome print("Final model:") print(w.get_value()) print(b.get_value()) print("target values for D:") print(D[1]) print("prediction on D:") print(predict(D[0])) # whether > 0.5 or not

### Test Models

You need to build and train the model before you test its performance. If you have trained the model, test it on the out-of-sample data. Follow these steps to test the model:

- Call the
`predict`

method with the features of the testing period. - Plot the actual and predicted labels of the testing period.
- Calculate the prediction accuracy.

y_hat = predict(np.array(X_test))

df = pd.DataFrame({'y': y_test, 'y_hat': y_hat}).astype(int) df.plot(title='Model Performance: predicted vs actual return direction in closing price', figsize=(12, 5))

correct = sum([1 if x==y else 0 for x, y in zip(y_test, y_hat)]) print(f"Accuracy: {correct}/{y_test.shape[0]} ({correct/y_test.shape[0]}%)")

### Store Models

You can save and load `aesera`

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
`dump`

method with the model and file path.

model_key = "model"

file_name = qb.object_store.get_file_path(model_key)

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

joblib.dump(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 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:

- Call the
`ContainsKey`

`contains_key`

method with the model key. - Call
`GetFilePath`

with the key. - Call
`load`

with the file path.

qb.object_store.contains_key(model_key)

This 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.

file_name = qb.object_store.get_file_path(model_key)

This method returns the path where the model is stored.

loaded_model = joblib.load(file_name)

This method returns the saved model.