input: Write a function to create an AI-based system in Python for predicting stock market movements using a deep learning model. The function should import necessary libraries, load data from a CSV file, split the data into training and testing sets, build a deep learning model, compile and train the model, and evaluate its performance. If the input data file is not found, return a message indicating the issue.
  assert predict_stock_market("data.csv")==None
  assert predict_stock_market("nonexistent.csv")=="File not found"
output: def predict_stock_market(file_path):
      try:
          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 tensorflow.keras.optimizers import Adam
  
          data = pd.read_csv(file_path)
          X = data.iloc[:, :-1]
          y = data.iloc[:, -1]
          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(), loss='mean_squared_error')
          model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
          loss = model.evaluate(X_test, y_test, verbose=0)
          return None
      except FileNotFoundError:
          return "File not found"