input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should include importing necessary libraries, importing data, splitting the data into train and test sets, building and compiling a deep learning model, training the model, and evaluating the model's performance. If the input data file is not found, the function should print "Data file not found" and exit.
  assert predict_stock_movements('data.csv')=='Model trained successfully'
  assert predict_stock_movements('nonexistent.csv')=='Data file not found'
output: def predict_stock_movements(file_path):
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
      
      try:
          data = pd.read_csv(file_path)
      except FileNotFoundError:
          print("Data file not found")
          return 'Data file not found'
      
      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)
      
      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='sigmoid'))
      
      model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])
      
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
      
      loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
      
      return 'Model trained successfully'