Advanced Algo Trading Series | Episode 2: How To Create A Scanner & Scanning Option Chain | Dhan

Summary of “Advanced Algo Trading Series | Episode 2: How To Create A Scanner & Scanning Option Chain | Dhan”

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Main Financial Strategies and Business Trends Presented

• Algorithmic Trading Automation: Differentiating between scanners (which identify entry points) and algos (which manage orders, trailing stops, and exits).
• Indicator-Based Scanning: Using technical indicators such as RSI and Super Trend to create buy/sell signals.
• Candlestick and Chart Pattern Scanning: Incorporating price action patterns like Bullish/Bearish Engulfing for refined entry/exit conditions.
• Advanced Chart Types: Creating and using Heikin Ashi and Renko charts for alternative price visualization and scanning.
• Option Chain Scanning: Real-time scanning of option chains using APIs to identify strikes based on parameters like LTP (Last Traded Price), volume, open interest (OI), and Greeks.
• Put-Call Ratio (PCR) Analysis: Calculating PCR from volume and OI to gauge market sentiment and potential trend direction.
• Debugging and Testing Methodology: Emphasizing the importance of print statements and stepwise debugging to ensure scanner and algo accuracy before live deployment.
• Handling Real-World Data Issues: Managing exceptions such as missing data or symbol changes gracefully in code to avoid runtime errors.

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Methodology / Step-by-Step Guide

1. Creating a Scanner for Indicators and Patterns

• Import chart data (candlestick, Heikin Ashi, Renko).
• Apply technical indicators (e.g., RSI, Super Trend).
• Define entry conditions based on indicator values (e.g., RSI > 60, Super Trend green).
• Define candlestick pattern conditions (e.g., Bullish Engulfing = 100).
• Combine multiple buy/sell conditions using logical AND.
• Print or log signals for debugging.
• Test and debug by printing intermediate condition results.
• Temporarily disable difficult conditions to isolate issues.
• Adjust timeframes (e.g., 5 min, 15 min) for better signal detection during testing.
• Validate signals by cross-checking chart data manually.

2. Detecting Indicator Crossovers (Example: Super Trend Color Change)

• Compare current candle’s Super Trend color with previous candle’s color.
• Entry signal when color changes from red to green (buy) or green to red (sell).
• Code logic involves referencing current and previous candle Super Trend values.

3. Handling Errors and Exceptions

• Use try-except blocks to catch errors during data fetch or calculations.
• Use pass in exception handling to skip problematic symbols and continue scanning.

4. Scanning Option Chain Data

• Use API to fetch option chain data for a given underlying and expiry.
• Specify number of strikes above and below ATM to scan.
• Extract columns such as OI, volume, Greeks, strike price.
• Filter strikes based on criteria like LTP less than a threshold.
• Find strike with maximum/minimum LTP, volume, or open interest using Pandas methods (max(), min(), sort()).
• Calculate Put-Call Ratio (PCR) using sums of volumes or OI for puts and calls.
• Use PCR as a sentiment indicator (PCR < 1 may indicate bullishness).

5. Testing and Validation

• Run scanners during off-market hours using longer timeframes (e.g., 15 minutes) to generate signals.
• Cross-verify scanner outputs with chart data to confirm accuracy.
• Use print/debug statements extensively to understand which conditions are passing or failing.
• Adjust scanning parameters and conditions iteratively for better performance.

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Tools and Technologies Used

• Dhan API: For market data, charting, and option chain access.
• Python (Pandas): For data manipulation, filtering, sorting, and calculations.
• Technical Indicators: RSI, Super Trend.
• Candlestick Patterns: Bullish/Bearish Engulfing.
• Chart Types: Candlestick, Heikin Ashi, Renko.
• Debugging Techniques: Print statements, exception handling.

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Key Takeaways

• Scanners identify potential entry points but do not place or manage orders.
• Combining multiple indicators and price patterns improves signal reliability.
• Real-time option chain data enables advanced option strategy development.
• PCR is a useful metric for gauging market sentiment.
• Robust error handling and debugging are essential for reliable algo trading.
• Testing on different timeframes and conditions is critical before live deployment.
• Pandas simplifies complex data filtering and sorting operations for scanning.

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Presenter / Source

• Presenter: Dhan (Advanced Algo Trading Series)
• The session is part of a multi-episode series on advanced algorithmic trading using the Dhan platform and API.

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This summary encapsulates the financial strategies, technical methods, and practical coding approaches discussed in the video, providing a clear framework for building and testing algorithmic trading scanners and option chain scanners.