Stock Market Prediction: Can AI REALLY Beat the Market? (LSTM Deep Learning)

Download the source code from here: https://onepagecode.substack.com/ In this comprehensive tutorial, we delve into the exciting world of Deep Learning and its application in predicting stock opening prices. We'll guide you through a step-by-step process using Long Short-Term Memory (LSTM) networks implemented in Keras. Here's what you'll learn: Data Acquisition & Preprocessing: Discover how to gather historical stock data and prepare it for machine learning. Normalization Techniques: Understand the importance of data normalization and how to achieve it using scikit-learn. Building the LSTM Model: Craft an LSTM model in Keras specifically designed for time series forecasting of stock prices. Training & Evaluation: Explore the training process and evaluate the model's performance using root mean squared error (RMSE). Visualization & Future Predictions: Learn to visualize the results and harness the model for real-time stock price predictions. Whether you're a financial analyst, data scientist, or simply curious about AI in finance, this video equips you with the skills to build your own Deep Learning stock prediction model! pen_spark 00:00:00 Imports libraries for data preprocessing 00:02:15 Assigns value 'AMZN' to variable 'ticker' 00:02:52 Defines a variable named `ticker` 00:03:34 Defines a variable with value 'UBER' 00:04:03 Assigns the value 'TSLA' to ticker 00:04:34 Retrieve stock data for specified period 00:05:20 Displays the dimensions of a DataFrame 00:05:55 Changes plot style to dark theme 00:06:26 Plotting time series opening prices 00:07:44 Calculates training data length 00:08:33 Normalize dataset values between 0 and 1 00:09:22 Splits data into training sequences 00:10:58 Convert lists x_train, y_train to arrays 00:11:44 Reshape x_train to add dimension 00:12:47 Create LSTM neural network model 00:14:49 Configure model with optimizer and loss 00:15:51 Train a model for 3 epochs 00:17:07 Creates test data sequences 00:18:22 Convert x_test to a numpy array 00:19:14 Reshapes the x_test data 00:19:53 Generate and inverse transformation of predictions 00:21:18 Calculates and prints root mean square error 00:22:13 Set matplotlib style to dark 00:22:55 Visualising stock price predictions for TESLA company 00:24:29 Returns the last 15 elements 00:25:26 Retrieve and predict stock price 00:27:00 Retrieve and predict stock opening prices