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TradeTrack - Personal Trading Journal & PnL Dashboard 📈

Welcome to TradeTrack! This is a comprehensive, professional-grade trading journal and Profit & Loss (PnL) analysis dashboard built with Python and Streamlit. I designed this application to help traders and investors meticulously track their daily performance, analyze monthly trends, and manage their portfolio balances efficiently.

🌟 Key Features

  • Intuitive Dashboard: A sleek, dark/light mode responsive user interface built for fast data entry and clear visualization.
  • Monthly PnL Tracking: Easily input your daily profit/loss figures and instantly see your monthly statistics (Total PnL, Win Rate, Best/Worst Days).
  • Advanced Visualizations: Includes bar charts, trend lines, and cumulative PnL graphs using Plotly for deep dive analysis.
  • Portfolio Management: Track deposits, withdrawals, and current balance alongside your trading performance.
  • Local Data Storage: All data is stored securely in a local data.json file. No need for databases or complex setups.

🚀 Installation & Usage

  1. Prerequisites: Ensure you have Python 3.8+ installed on your system.
  2. Install Dependencies:
    pip install streamlit plotly
  3. Run the Application: Navigate to the project folder and run the provided batch file or execute the Streamlit command directly:
    streamlit run dashboard.py
    Or simply double-click the basla.bat script on Windows.

🛠️ Built With

  • Streamlit: For the interactive web application framework.
  • Plotly: For rich, interactive data visualizations.
  • Python: The core logic and data processing.

💡 Motivation

I built this tool because I needed a lightweight, visual, and highly customizable way to track my trading performance without relying on bloated spreadsheet software or expensive third-party subscriptions. It's designed to be clean, fast, and exactly what a trader needs.

📄 License

This project is open-source and available under the MIT License.

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A comprehensive, professional-grade trading journal and Profit & Loss (PnL) analysis dashboard built with Python and Streamlit.

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