Trading Data Analyser 📊🚀

A robust data analysis pipeline for Binance trade history, designed to process, analyze, and visualize trading data. This project calculates key financial metrics, ranks portfolios, generates visual insights, and provides an automated trading report in PDF format..

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📌 Features

✅ Data Processing & Cleaning – Parses trade history, validates data, and removes inconsistencies.
✅ Financial Metrics Calculation – Computes ROI, PnL, Sharpe Ratio, MDD, Win Rate, and more.
✅ Portfolio Ranking – Scores and ranks top-performing portfolios based on profitability.
✅ Exploratory Data Analysis (EDA) – Generates key insights using Seaborn & Matplotlib visualizations.
✅ Automated PDF Reports – Summarizes key findings, top traders, and recommendations.
✅ Modular & Scalable – Supports future enhancements like real-time data streaming.

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📁 Project Structure

TradingDataAnalyser/
│── data/                      # Raw & processed trade history files
│   ├── trade_history.csv       # Input trade history data
│   
│── logs/                       # Logs for debugging & tracking execution
│── output/                     # Stores final CSV results & analysis
│── reports/                    # Generated reports & visualizations
│   ├── plots/                  # Trading insights visualizations
│   ├── trading_report.pdf      # Automated PDF report
│── scripts/                    # Core Python scripts
│   ├── main.py                 # Main execution pipeline
│   ├── trading_analyzer.py      # Data processing & analysis module
│   ├── eda.py                  # Exploratory Data Analysis (EDA) script
│   ├── generate_report.py       # Automated PDF report generator
│── README.md                    # Project documentation
│── requirements.txt              # Required Python dependencies
│── .gitignore                    # Ignore unnecessary files in Git

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🚀 Installation & Setup

🔹 Step 1: Clone the Repository

git clone https://github.com/Prathameshsci369/TradingDataAnalyser.git
cd TradingDataAnalyser

🔹 Step 2: Create a Virtual Environment (Optional)

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

🔹 Step 3: Install Dependencies

pip install -r requirements.txt

🔹Step 4: Unzip file

Befor the run code unzip the data_compressed.tar.xz file. That are contain one folder and that folder are have the one sample csv file for testing.

📌 How to Run the Project

🔹 1. Run the Main Pipeline

python scripts/main.py

This will clean data, compute financial metrics, rank portfolios, and generate results.

🔹 2. Perform Exploratory Data Analysis (EDA)

python scripts/eda.py

This will generate visual insights like:

• ROI Distribution
• Risk vs Return Analysis
• Win Rate vs Total Positions
• Portfolio Growth Over Time

🔹 3. Generate the Trading Report (PDF)

python scripts/generate_report.py

The final report will be saved in:

reports/trading_report.pdf

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📊 Key Financial Metrics Calculated

Metric	Description
ROI (%)	Return on Investment (Profit % based on initial capital)
PnL ($)	Total Profit/Loss generated
Sharpe Ratio	Risk-adjusted return metric
MDD (Max Drawdown)	Largest peak-to-trough portfolio loss
Win Rate (%)	Percentage of profitable trades
Profit Factor	Ratio of total profit to total loss

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📌 Example Output (top_portfolios.csv)

Port_ID	ROI (%)	PnL ($)	Sharpe Ratio	MDD	Win Rate (%)	Total Positions	Profit Factor
3826087012661391104	27.03	532.66	9.7	-7.6e+21	91.3	69	16.08
4029506971304830209	6.04	2413.65	23.81	0.0	60.0	5	1899.27
4037282461943784704	0.5	4760.37	24.78	0.0	78.16	174	2.98

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📌 Visual Insights Generated

🔹 Histogram of ROI (%)
🔹 Risk vs Return: Sharpe Ratio vs ROI Scatter Plot
🔹 Win Positions vs Total Positions (Bar Chart)
🔹 Portfolio Growth Over Time (Line Chart)

All visualizations are saved in:

reports/plots/

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🛠 Future Improvements

✅ Automate real-time trading data fetching from Binance API
✅ Integrate Streamlit for interactive visual dashboards
✅ Expand to include multi-asset portfolio analysis
✅ Machine Learning predictions for trade optimization

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📌 Contributing

We welcome contributions! If you'd like to improve the analysis, add new features, or fix bugs, follow these steps:

1. Fork the repository.
2. Create a new branch (feature-new-analysis).
3. Commit your changes (git commit -m "Added new feature")
4. Push to GitHub (git push origin feature-new-analysis)
5. Submit a Pull Request! 🎉

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📜 License

This project is licensed under the MIT License. You are free to use, modify, and distribute this project with proper attribution.

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📞 Contact

🔹 GitHub: Prathameshsci369
🔹 Email: prathameshsci963@gmail.com

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🚀 Ready to analyze your trading data? Let's get started!

🔥 Star the repo if you found this useful! 🌟
🔄 Fork & contribute to make it even better! 🚀

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