Stock Price Prediction with LSTM

This project aims to predict stock prices using a Long Short-Term Memory (LSTM) neural network. The project includes data preprocessing, feature engineering, model training, and evaluation. Note: Changed the column names from Japanese to English (included here).

Project Structure

• pre.py: Contains functions for data preprocessing.
• train.py: Contains functions for feature engineering and model training.
• evaluate.py: Contains functions for model evaluation.
• main.py: For executing the prediction system sequentially.
• README.md: Project documentation.

Requirements

• Python 3.x
• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• tensorflow

Usage

Data Preprocessing

The pre.py file contains the preprocess_data function, which preprocesses the stock price data.

Feature Engineering

The train.py file contains the add_features function, which adds moving averages and volatility as features.

Model Training

The train.py file contains the train_model function, which trains the LSTM model.

Model Evaluation

The evaluate.py file contains the evaluate_model function, which evaluates the trained model.

Example

Here is an example of how to run the entire pipeline:

python pre.py
python train.py
python evaluate.py

or you may run the main.py to run it all sequentially.

python main.py

Acknowledgements

• The LSTM model is built using TensorFlow and Keras.
• Data preprocessing and feature engineering are done using pandas and scikit-learn.
• Visualization is done using matplotlib and seaborn.