input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should include importing necessary libraries, reading data from a CSV file, splitting the data into training and testing sets, building a deep learning model using TensorFlow, compiling and training the model, and finally evaluating the model. If the input data file is missing, the function should handle the exception and return an appropriate message.
  assert predict_stock_movements(data.csv)==score
  assert predict_stock_movements('nonexistent.csv')=="Error: File not found"
output: def predict_stock_movements(file_path):
      try:
          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
  
          data = pd.read_csv(file_path)
          X = data.iloc[:, :-1].values
          y = data.iloc[:, -1].values
  
          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='linear'))
  
          model.compile(optimizer=Adam(learning_rate=0.001), loss='mean_squared_error')
          model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
  
          score = model.evaluate(X_test, y_test, verbose=0)
          return score
      except FileNotFoundError:
          return "Error: File not found"