Borsa Bilge Analysis Automation Project - Readme.md

# Borsa Analysis Automation Project

## Project Description

The Borsa Analysis Automation Project is a comprehensive Python application that automatically fetches, analyzes, scores, and visualizes financial data for stocks listed on Borsa Istanbul (BIST). This project integrates with a PostgreSQL database and Flask web framework, aiming to assist in making informed investment decisions.

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## Features

- **Data Fetching:** Automatically fetches stock and balance sheet data via the Finnet API.
- **Database Management:** Securely stores and optimizes data using PostgreSQL.
- **Scoring System:** Scores stock financial performance based on 5-period data.
- **Portfolio Optimization:** Creates optimal portfolios using the PyPortfolioOpt library.
- **Graham Valuation:** Conducts Graham valuation for stock price analysis.
- **Percentage Return Analysis:** Calculates percentage gain or loss over specific periods.
- **Graphical Analysis:** Visualizes stock analysis with dynamic graphs using Plotly.
- **Database Cleanup:** Automatically removes redundant and duplicate data for optimization.

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## Installation

1. **Install Required Libraries**
   ```bash
   pip install -r requirements.txt

2. Create a .env File Create a .env file with the following structure:

   DB_NAME=...
   DB_USER=...
   DB_PASSWORD=...
   DB_HOST=...
   DB_PORT=...
   KULLANICI_ADI=...
   SIFRE=...
   

3. Setup the Database Initialize the database by running:

   python db_setup.py

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Usage

1. Fetch and Save Data Fetch financial data from the API and save it to the database:

   python main.py

2. Portfolio Optimization Create an optimal portfolio:

   python portfolio.py

3. Graphical Analysis Visualize stock analysis:

   python grafik_cizdir.py

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File Descriptions

• main.py: The main program file for data fetching, analysis, and reporting.
• fetching.py: Handles API requests for stock and balance sheet data.
• data_pipeline.py: Manages data processing workflows, including cleaning and saving.
• puanla.py: Contains algorithms for scoring stocks and analyzing financial performance.
• grafik_cizdir.py: Generates dynamic graphs using Plotly.
• portfolio.py: Performs portfolio optimization operations.
• db_save.py: Handles saving and updating data in the database.
• db_get.py: Fetches data from the database.
• db_cleaner.py: Cleans and optimizes the database by removing redundant entries.
• load.py: Processes and loads data for analysis.

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Future Development Goals

1. Diversified Target Price Methods: Implementing additional target price models and analysis techniques.
2. Database Optimization: Enhancing query and storage structures for faster data processing.
3. User-Friendly API Structure: Developing an API service for easier integration and data access.
4. Integration of Macro Financial Data: Incorporating global economic indicators (interest rates, inflation, etc.) into analyses.
5. Graphical User Interface (GUI): Creating a GUI to make analysis results easier to understand for users.
6. Artificial Intelligence and Prediction Algorithms: Adding machine learning algorithms to predict future stock performance.

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Developer

Fatih KÜÇÜKER

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License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Contact

For more information, visit the Borsa Bilge GitHub page.

This English version of the README provides a detailed overview of your project, including features, installation steps, usage instructions, file descriptions, and future goals. Let me know if there’s anything else you'd like to add or modify! 😊