Introduction:
In today’s rapidly changing stock market, predicting future stock prices is a challenging yet crucial task for investors. In this blog post, we’ll delve into the world of stock market forecasting using a Long Short-Term Memory (LSTM) neural network. We’ll walk through each step of the process, from data preparation to making predictions for the next 1, 2, and 3 days.
Step 1: Imports and Installation To get started, we install the yahoo_fin library and import essential modules for data manipulation, machine learning, and visualization.
Step 2: Settings Set up various parameters, including window size, lookup steps, stock ticker, and current date.
Step 3: Data Loading and Preparation Fetch historical stock data for the specified stock over the past three years, drop unnecessary columns, and scale closing prices
Step 4: Data Scaling Scale closing prices using Min-Max scaling to ensure effective training of the neural network.
Step 5: Data Preparation Function (PrepareData): Create sequences of historical data and corresponding target values for training the neural network.
Step 6: Model Training Function (GetTrainedModel): Define a simple LSTM-based neural network using Keras and train the model.
Step 7: Prediction Loop Loop through lookup steps (1, 2, 3 days), prepare data, and train the model for each step. Make predictions and print the results.
Step 8: Output Print the predicted prices for the next 1, 2, and 3 days.
