Deltain Trader

A Python application for fetching historical cryptocurrency data from Delta Exchange India API with configurable timeframes.

🚀 Features

• Configurable Timeframes: Fetch data with multiple resolutions (1m, 5m, 1h, 1d, etc.)
• Multiple Symbols: Support for Bitcoin, Ethereum, Solana, and other perpetual futures
• Smart Rate Limiting: Handles API rate limits and pagination automatically
• Data Export: Saves data to CSV files with proper formatting
• Technical Indicators: Calculate RSI, SMA-50, EMA Crossover, ATR, MACD, and more
• Comprehensive Logging: Detailed logs for monitoring and debugging
• Error Handling: Robust error handling and retry mechanisms

📊 Supported Timeframes

Resolution	Description
1m	1 minute candles
3m	3 minute candles
5m	5 minute candles
15m	15 minute candles
30m	30 minute candles
1h	1 hour candles
2h	2 hour candles
4h	4 hour candles
6h	6 hour candles
1d	Daily candles
1w	Weekly candles

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• Internet connection for API access

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/Deltain-trader.git
   cd Deltain-trader

2. Create and activate virtual environment:

   # Create virtual environment
   python -m venv venv
   
   # Activate (Windows)
   venv\Scripts\activate
   
   # Activate (Linux/Mac)
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

📋 Configuration

The project uses a central configuration file (config.py) with the following settings:

API Settings

• Base URL: https://api.india.delta.exchange
• API Version: v2
• Rate Limiting: 5 requests per second

Data Configuration

• Default Symbols: BTCUSD, ETHUSD, SOLUSD
• Time Zone: Asia/Kolkata (IST)
• Default Lookback: 10 days
• Default Resolution: 1 minute candles

Output Settings

• Data Directory: data/
• Log Directory: logs/
• CSV Format: {symbol}_{resolution}_{start_date}_{end_date}.csv

🚀 Usage

Interactive Mode

Run the main script for interactive resolution selection:

python main.py

The script will:

1. Display available timeframe options
2. Prompt you to select a resolution
3. Fetch data for all configured symbols
4. Save results to CSV files

Programmatic Usage

from data_fetcher import DeltaExchangeDataFetcher

# Fetch 1-minute data
minute_fetcher = DeltaExchangeDataFetcher(resolution="1m")
minute_data = minute_fetcher.fetch_symbol_data("BTCUSD")

# Fetch hourly data
hourly_fetcher = DeltaExchangeDataFetcher(resolution="1h")
hourly_data = hourly_fetcher.fetch_symbol_data("BTCUSD")

# Fetch multiple symbols
symbols = ["BTCUSD", "ETHUSD", "SOLUSD"]
results = hourly_fetcher.fetch_multiple_symbols(symbols)

Examples

Run the example script to see different usage patterns:

python examples/timeframe_examples.py

📁 Project Structure

Deltain-trader/
├── config.py              # Configuration settings
├── data_fetcher.py        # Core data fetching functionality
├── technical_indicators.py # Technical analysis indicators calculator
├── main.py                # Interactive script entry point
├── requirements.txt       # Python dependencies
├── README.md              # This documentation
├── data/                  # Directory for saved CSV files
│   ├── RSI/               # RSI indicator data
│   ├── SMA50/             # SMA-50 indicator data
│   ├── EMA_CROSSOVER/     # EMA 9-21 crossover data
│   ├── ATR/               # Average True Range data
│   ├── MACD/              # MACD indicator data
│   ├── BBANDS/            # Bollinger Bands data
│   ├── VWAP/              # Volume Weighted Average Price data
│   ├── OBV/               # On-Balance Volume data
│   ├── ADX/               # Average Directional Index data
│   └── consolidated/      # Combined indicators data
├── logs/                  # Directory for log files
└── examples/              # Example scripts
    └── timeframe_examples.py

📊 Technical Indicators

The technical_indicators.py module is a comprehensive technical analysis engine that calculates and organizes multiple technical indicators for cryptocurrency market analysis.

📈 Supported Indicators

Indicator	Description	Parameters
RSI-14	Relative Strength Index	Period: 14, Overbought: 60, Oversold: 40
SMA-50	Simple Moving Average	Period: 50 days
EMA 9-21 Crossover	Exponential Moving Average Crossover	Fast: 9 periods, Slow: 21 periods
ATR-14	Average True Range	Period: 14
MACD	Moving Average Convergence Divergence	Fast: 12, Slow: 26, Signal: 9
Bollinger Bands	Volatility Bands	Period: 20, Deviations: 2
VWAP	Volume Weighted Average Price	Calculated per session
OBV	On-Balance Volume	Uses price and volume relationship
ADX	Average Directional Index	Period: 14, Threshold: 25

🔍 Features

• Organized Data Storage: Each indicator is saved in its dedicated directory
• Consolidated Dataset: Creates a combined file with all indicators
• CSV Output: All data is exported to CSV format for easy integration
• Custom Signals: Generates trading signals based on indicator values
• Statistical Analysis: Provides current market condition insights
• Batch Processing: Process multiple symbols and timeframes at once

🛠️ Usage

Command Line

Process all data files in the data directory:

python technical_indicators.py

Programmatic Usage

from technical_indicators import process_all_csv_files, process_csv_file, create_output_directories

# Process all CSV files in the data directory
results = process_all_csv_files()

# Process a specific file
from pathlib import Path
file_path = Path("data/BTCUSD_1h_20230101_20230131.csv")
dirs = create_output_directories()
stats = process_csv_file(file_path, dirs)

📁 Data Organization

The technical indicators module organizes data in the following structure:

• Individual Indicator Files: Each indicator is saved in its dedicated subfolder
• Naming Convention: {symbol}_{timeframe}_{start_date}_{end_date}_{indicator}.csv
• Consolidated File: All indicators combined in one file at data/consolidated/{symbol}_{timeframe}_{start_date}_{end_date}_ALL_INDICATORS.csv

📊 Signal Generation

The module provides trading signals based on each indicator:

• RSI Signals: Overbought (≥60), Oversold (≤40), Neutral (40-60)
• SMA Signals: Price Above/Below SMA, Rising/Falling Trend
• EMA Crossover: Bullish Cross (9 crosses above 21), Bearish Cross (9 crosses below 21)
• MACD Signals: Bullish (MACD > Signal), Bearish (MACD < Signal)
• Bollinger Bands: Above Bands, Below Bands, Inside Bands
• ADX Signals: Strong Trend (>25), Weak Trend (≤25)

📊 Data Output

CSV File Format

Each CSV file contains the following columns:

• symbol: Trading pair symbol (e.g., BTCUSD)
• datetime_ist: Timestamp in IST timezone
• datetime: UTC timestamp
• time: Unix timestamp
• open: Opening price
• high: Highest price
• low: Lowest price
• close: Closing price
• volume: Trading volume
• price_change: Price change (close - open)
• price_change_pct: Percentage price change

File Naming Convention

Files are saved with the following format:

• BTCUSD_1m_20250710_20250720.csv (1-minute data)
• ETHUSD_1h_20250710_20250720.csv (1-hour data)
• SOLUSD_1d_20250710_20250720.csv (daily data)

⚙️ Configuration Options

Customize Symbols

Edit config.py to add or remove trading pairs:

SYMBOLS = [
    "BTCUSD",    # Bitcoin perpetual
    "ETHUSD",    # Ethereum perpetual
    "SOLUSD",    # Solana perpetual
    "ADAUSD",    # Add Cardano
    "DOTUSD",    # Add Polkadot
]

Adjust Time Range

Change the data collection period:

DAYS_TO_FETCH = 30  # Fetch last 30 days instead of 10

Modify Rate Limiting

Adjust API request frequency:

MAX_REQUESTS_PER_SECOND = 3  # Slower rate limiting

🔧 Advanced Usage

Custom Resolution Fetching

from data_fetcher import DeltaExchangeDataFetcher

# Initialize with custom resolution
fetcher = DeltaExchangeDataFetcher(resolution="5m")

# Fetch data for specific symbol
df = fetcher.fetch_symbol_data("BTCUSD")

# Save with custom parameters
from datetime import datetime
fetcher.save_to_csv(df, "BTCUSD", datetime.now(), datetime.now())

Batch Processing

# Process different timeframes for analysis
resolutions = ["1m", "5m", "1h", "1d"]
symbol = "BTCUSD"

for resolution in resolutions:
    fetcher = DeltaExchangeDataFetcher(resolution=resolution)
    data = fetcher.fetch_symbol_data(symbol)
    print(f"{resolution}: {len(data)} records")

📋 Dependencies

• requests: HTTP library for API communication
• pandas: Data manipulation and CSV export
• python-dateutil: Date handling utilities
• pytz: Timezone support
• python-dotenv: Environment variable management

🐛 Troubleshooting

Common Issues

1. No data retrieved:

   • Check internet connection
   • Verify symbol names are correct
   • Check Delta Exchange API status

2. Rate limiting errors:

   • Increase REQUEST_DELAY in config.py
   • Reduce MAX_REQUESTS_PER_SECOND

3. File permission errors:

   • Ensure write permissions for data/ and logs/ directories
   • Run script with appropriate permissions

Enable Debug Logging

For detailed troubleshooting, modify the logging level:

import logging
logging.basicConfig(level=logging.DEBUG)

📜 API Documentation

This project uses the Delta Exchange India API. For complete API documentation, visit: https://docs.delta.exchange/#introduction

⚠️ Disclaimer

This software is for educational and research purposes only. Trading cryptocurrencies involves significant risk. Always conduct your own research and consider your risk tolerance before making any trading decisions.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📞 Support

If you encounter any issues or have questions:

1. Check the troubleshooting section
2. Review the logs in logs/data_fetcher.log
3. Open an issue on GitHub with detailed error information

🎯 Roadmap

• Real-time data streaming via WebSocket
• Technical indicators calculation
• Data visualization dashboard
• Database storage support
• Multiple exchange support
• Automated trading strategies

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Happy Trading! 📈