🏦 Bank Nifty Scalping Bot

A high-frequency scalping bot for Bank Nifty options using the DhanHQ API. Built with Python's async/await architecture for efficient, event-driven trading.

⚠️ Disclaimer

This software is for educational purposes only. Trading in financial markets involves substantial risk of loss. Past performance is not indicative of future results. Use at your own risk.

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📋 Features

• High-Frequency Scalping - Optimized for quick entry/exit on Bank Nifty options
• Async Event-Driven Architecture - Efficient processing with asyncio queues
• Dual Momentum Strategy - Combines EMA, RSI, and MACD for signal generation
• Real-Time Candle Building - Constructs OHLCV candles from tick data
• WebSocket Market Feed - Live streaming data via DhanHQ WebSocket
• Risk Management - Configurable stop-loss, targets, and trailing stops
• Paper Trading Mode - Test strategies without risking real money
• Rate Limit Handling - Respects API rate limits (25 orders/second)

🏗️ Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  Market Feed    │───▶│  Candle Builder │───▶│  Alpha Engine   │
│  (Producer)     │    │  (Aggregator)   │    │  (Strategy)     │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                                       │
                                                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  Position Mgr   │◀───│  Order Manager  │◀───│  Signal Queue   │
│  (Risk)         │    │  (Executor)     │    │  (Consumer)     │
└─────────────────┘    └─────────────────┘    └─────────────────┘

📁 Project Structure

dhan-trader-bot/
├── main.py           # Core orchestrator and trading engine
├── auth.py           # OAuth authentication (run: python auth.py)
├── strategy.py       # Dual momentum scalping strategy
├── indicators.py     # Technical indicators (EMA, RSI, MACD, ATR)
├── candle_builder.py # Real-time OHLCV candle construction
├── order_manager.py  # Order execution and position management
├── market_feed.py    # DhanHQ WebSocket market data handler
├── config.py         # Configuration and environment settings
├── models.py         # Data models and structures
├── utils.py          # Helper utilities and logging
└── requirements.txt  # Python dependencies

🚀 Quick Start

Prerequisites

• Python 3.10 or higher
• DhanHQ trading account with API access
• TA-Lib C library (for technical indicators)

1. Clone the Repository

git clone https://github.com/vishwamartur/dhan-trader-bot.git
cd dhan-trader-bot

2. Install TA-Lib C Library

Windows: Download and install from TA-Lib Windows

macOS:

brew install ta-lib

Linux (Ubuntu/Debian):

sudo apt-get install ta-lib
# Or build from source:
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar -xzf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
sudo make install

3. Install Python Dependencies

pip install -r requirements.txt

4. Authenticate with Dhan

Option A: Easy OAuth Login (Recommended)

python auth.py

This opens your browser for Dhan login. After authentication, your token is saved automatically.

# Check authentication status
python auth.py --status

# Logout (remove saved token)
python auth.py --logout

Option B: Environment Variables

Set environment variables if you prefer manual configuration:

# Linux/macOS
export DHAN_CLIENT_ID="your_client_id"
export DHAN_ACCESS_TOKEN="your_access_token"

# Windows (PowerShell)
$env:DHAN_CLIENT_ID="your_client_id"
$env:DHAN_ACCESS_TOKEN="your_access_token"

Or create a .env file:

DHAN_CLIENT_ID=your_client_id
DHAN_ACCESS_TOKEN=your_access_token

5. Run the Bot

Paper Trading Mode (Recommended for Testing):

python main.py

Live Trading Mode:

python main.py --live

Test API Connection:

python main.py --test-connection

⚙️ Configuration

Edit config.py to customize:

Parameter	Default	Description
PAPER_TRADING	True	Enable/disable paper trading
LOT_SIZE	15	Bank Nifty lot size
NUM_LOTS	2	Number of lots per trade
STOP_LOSS_POINTS	20.0	Stop loss in points
TARGET_POINTS	40.0	Target profit in points
MAX_DAILY_LOSS	5000.0	Maximum daily loss (INR)
CANDLE_TIMEFRAME_SECONDS	60	Candle period (1 min)
EMA_PERIOD	9	EMA indicator period
RSI_PERIOD	14	RSI indicator period

📊 Strategy Overview

The bot uses a Dual Momentum Strategy:

1. EMA Crossover - Price crossing above/below 9-period EMA
2. RSI Filter - RSI > 60 for longs, RSI < 40 for shorts
3. MACD Confirmation - MACD histogram direction alignment

Entry Conditions

Signal	Conditions
LONG	Price > EMA(9) + RSI > 60 + MACD histogram positive
SHORT	Price < EMA(9) + RSI < 40 + MACD histogram negative

Exit Conditions

• Stop Loss - Fixed points below entry
• Target - Fixed points above entry
• Trailing Stop - Dynamic stop adjustment as price moves favorably

🔒 Risk Management

• Maximum 1 position at a time
• Fixed stop-loss and target levels
• Daily loss limit enforcement
• Rate limit compliance (25 orders/second)
• Graceful shutdown with position closing

📝 Logging

Logs are written to logs/trading.log with the following format:

2024-01-21 09:30:15 | INFO | 📊 Tick processor started
2024-01-21 09:30:15 | INFO | 🧠 Signal processor started
2024-01-21 09:30:15 | INFO | 💰 Order executor started

🛠️ Development

Running Tests

python -m pytest tests/

Code Structure

• Async Queues - Tick, Candle, and Signal queues for decoupled processing
• Event-Driven - Callbacks for candle completion and signal generation
• Graceful Shutdown - Signal handlers for clean termination

📚 API Reference

This bot uses the official DhanHQ Python SDK:

• DhanHQ API Documentation
• WebSocket Market Feed
• Order Placement

🤝 Contributing

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

📄 License

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

📧 Contact

• GitHub: @vishwamartur

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Made with ❤️ for algorithmic traders