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, and evaluating the model's performance.
  assert predict_stock_movements()
output: def predict_stock_movements():
      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, LSTM
      from sklearn.preprocessing import MinMaxScaler
  
      # Read data from CSV file
      data = pd.read_csv('stock_data.csv')
      data = data.dropna()
  
      # Preprocess data
      scaler = MinMaxScaler(feature_range=(0, 1))
      scaled_data = scaler.fit_transform(data[['Close']])
  
      # Split data into training and testing sets
      train_size = int(len(scaled_data) * 0.8)
      train_data, test_data = scaled_data[:train_size], scaled_data[train_size:]
  
      def create_dataset(dataset, time_step=1):
          X, Y = [], []
          for i in range(len(dataset) - time_step - 1):
              X.append(dataset[i:(i + time_step), 0])
              Y.append(dataset[i + time_step, 0])
          return np.array(X), np.array(Y)
  
      time_step = 60
      X_train, y_train = create_dataset(train_data, time_step)
      X_test, y_test = create_dataset(test_data, time_step)
  
      X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
      X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)
  
      # Build the model
      model = Sequential()
      model.add(LSTM(50, return_sequences=True, input_shape=(time_step, 1)))
      model.add(LSTM(50, return_sequences=False))
      model.add(Dense(25))
      model.add(Dense(1))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X_train, y_train, batch_size=1, epochs=1)
  
      # Evaluate the model
      predictions = model.predict(X_test)
      predictions = scaler.inverse_transform(predictions)
      y_test = scaler.inverse_transform(y_test.reshape(-1, 1))
  
      return predictions