input: def predict_stock_movements(data_path: str) -> float:
      """ Predict stock market movements using a deep learning model. This function reads a CSV file, 
      splits the data into train and test sets, builds a deep learning model, and evaluates its performance.
      The function returns the mean squared error (MSE) score of the model on the test set.
      
      Example:
      >>> predict_stock_movements('data.csv')
      123.45
      """
output:     import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from sklearn.metrics import mean_squared_error
  
      # Load the data
      data = pd.read_csv(data_path)
  
      # Split the data into features and target
      X = data.drop('target', axis=1)
      y = data['target']
  
      # Split the data into train and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Build the deep learning model
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Calculate the mean squared error
      mse = mean_squared_error(y_test, y_pred)
  
      return mse