Project Description

Stock Trading Prediction Model This project aims to build a machine learning model that predicts stock price movements based on historical data. By analyzing features such as daily returns, moving averages, volatility, and trading volume, the model provides insights into whether a stock's closing price will increase or decrease the next day.

The project is designed to serve as a practical exercise in data preprocessing, feature engineering, model training, and evaluation using Python and popular libraries like pandas, scikit-learn, and matplotlib. The final model can help individuals explore the potential of machine learning in stock trading strategies.

Key Features: Data Preprocessing: Clean and organize historical stock data for machine learning. Feature Engineering: Generate features like daily returns, moving averages, and volatility to capture trends and patterns. Model Training: Train a classification model (Random Forest) to predict price movements. Evaluation: Assess model performance using accuracy, classification reports, and feature importance visualizations. Visualization: Plot feature importance to understand key drivers of predictions. This project provides a hands-on approach for beginners and intermediate developers to learn the fundamentals of machine learning and its application to financial markets. It is structured to be modular, making it easy to extend and experiment with additional features, algorithms, or datasets.

Prerequisites

1. Libraries: Install the following Python libraries using pip or conda:

• pandas (for data manipulation)
• numpy (for numerical operations)
• scikit-learn (for machine learning)
• matplotlib (for data visualization) Install them via: pip install pandas numpy scikit-learn matplotlib

2. Data Requirements: Historical Stock Data: A CSV file containing columns such as Date, Open, High, Low, Close, and Volume. If you do not have a dataset, you can download one from APIs like Alpha Vantage, Yahoo Finance, or Quandl.

Installation Instructions

Follow the steps below to set up the project on your local machine.

Step 1: Clone or Download the Project Clone the repository from GitHub (if hosted online):

bash git clone https://github.com/Sharif-Sallam/stock_trading_prediction.git

Step 2: Navigate to the Project Directory Use your terminal or command prompt to navigate to the project folder:

bash cd stock_trading_prediction

Step 3: Set Up a Virtual Environment (Optional but Recommended) Create a virtual environment: bash python -m venv venv

Activate the virtual environment: Windows: bash venv\Scripts\activate macOS/Linux: bash source venv/bin/activate

Step 4: Install Dependencies Install the required Python libraries listed in requirements.txt:

bash pip install -r requirements.txt

Step 5: Prepare the Dataset Place your stock data CSV file (e.g., stock_data.csv) in the data/ folder. Ensure the CSV file has the following columns: Date, Open, High, Low, Close, Volume.

Step 6: Verify the Installation Run the following command to check if the installation was successful: bash python src/main.py

Note: If the setup is correct, the script will start processing the data, train the model, and display the evaluation results.

Step 7: Run Unit Tests (Optional) To verify the individual modules, run the tests:

bash pytest tests/

Step 8: (Optional) Customize the Project Modify the src/main.py file to adjust parameters like the test size, model type, or additional features. Add new data sources or experiment with other machine learning algorithms.

You are now ready to use the stock trading prediction model! For further usage instructions, refer to the Usage Guide section in the README.md.

Explanation of Each Component

1. data/ Purpose: Store raw and processed datasets. stock_data.csv: Placeholder for the historical stock data used in the project. README.md: Instructions for acquiring or generating the required datasets.

2. src/ Purpose: Contains the core functionality of the project. data_loader.py: Contains functions to load and preprocess the stock data.

3. tests/ Purpose: Contains unit tests for individual modules, ensuring each function works as expected. Use pytest or unittest to write test cases.

4. .gitignore Purpose: Ensures unnecessary files are not tracked by version control.

5. README.md Purpose: Provides an overview of the project and guides users on setting it up. Include sections like: Project Description Prerequisites Installation Instructions Usage Guide Future Work

6. requirements.txt Purpose: Lists all Python libraries required for the project.

7. setup.py (Optional) Purpose: Allows the project to be installed as a Python package.

Usage Guide

1. Place the stock_data.csv in the data/ folder.
2. Run main.py in the src/ folder to execute the end-to-end workflow.
3. Use pytest to validate the functionality of each module in the tests/ folder.

Project Structure

stock_trading_prediction/ │ ├── data/ │ ├── stock_data.csv # Historical stock data (input file) │ └── README.md # Instructions for obtaining or preparing data │ ├── src/ │ ├── init.py # Indicates this folder is a package │ ├── data_loader.py # Handles loading and preprocessing of data │ ├── feature_engineering.py # Feature engineering functions │ ├── model_training.py # Functions for model training and evaluation │ └── visualization.py # Visualization utilities (e.g., feature importance) │ ├── tests/ │ ├── init.py # Indicates this folder is a package │ ├── test_data_loader.py # Tests for data loading │ ├── test_feature_engineering.py # Tests for feature engineering functions │ └── test_model_training.py # Tests for model training and evaluation │ ├── .gitignore # Files and folders to ignore in version control ├── README.md # Project description and instructions ├── requirements.txt # Python dependencies (pandas, scikit-learn, matplotlib, etc.) ├── main.py # Entry point for the project └── setup.py # For packaging and installing the project (optional)