input: def predict_stock_movements() -> float:
      """ Create an AI-based system in Python for predicting stock market movements.
      The function reads historical stock market data from a CSV file and builds a deep learning model 
      using TensorFlow to predict stock movements. It trains the model and evaluates it on test data.
      The function returns the Mean Squared Error (MSE) of the model on the test data.
      >>> predict_stock_movements()
      0.0345
      """
output:     import pandas as pd
      import numpy as np
      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 tensorflow.keras.optimizers import Adam
      from sklearn.metrics import mean_squared_error
  
      # Load historical stock market data from CSV
      data = pd.read_csv('stock_data.csv')
  
      # Preprocess the data
      X = data.drop('target', axis=1).values
      y = data['target'].values
  
      # Split the data into training and testing 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([
          Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(32, activation='relu'),
          Dense(1)
      ])
  
      # Compile the model
      model.compile(optimizer=Adam(learning_rate=0.001), loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
  
      # Evaluate the model on test data
      y_pred = model.predict(X_test)
      mse = mean_squared_error(y_test, y_pred)
  
      return mse