input: Write a function to create an AI-based system for predicting stock market movements using Python. The function should import necessary libraries, load data, split it into training and testing sets, build and compile a deep learning model, train the model, and evaluate its performance. If the input data is provided as 'data.csv', the function should return the evaluation score of the model.
  assert predict_stock_movements() == score
output: def predict_stock_movements():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from sklearn.preprocessing import StandardScaler
  
      data = pd.read_csv('data.csv')
      X = data.drop('target', axis=1)
      y = data['target']
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      model = Sequential([
          Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(32, activation='relu'),
          Dense(1, activation='sigmoid')
      ])
  
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)
  
      _, score = model.evaluate(X_test, y_test)
      return score