OPEN-SOURCE SCRIPT
Fix Webhook Latency & DH-905 Errors (PineScript to Python Bridge
If you are routing TradingView alerts directly to an Indian broker API (like Dhan or Zerodha) and experiencing execution delays or DH-905 lot size rejections, the flaw is in your routing architecture.
The Problem: Dynamic vs. Static Data
PineScript generates dynamic data. A moving average crossover fires a signal based on a dynamic {{close}} price. However, broker APIs require static Exchange Tokens and rigid SEM_LOT_SIZE multipliers (e.g., Nifty = 65).
If you try to pass quantity: 1 directly to the Dhan API, or if your hardcoded exchange token moves out of the ATM range overnight, the exchange rejects the payload. Furthermore, standard third-party webhook routing adds 1 to 2 seconds of latency, destroying your entry price on fast breakouts.
The Middleware Solution
The PineScript above is Step 1. It formats a clean JSON payload containing the dynamic variables.
Step 2 is interception. You must not send this JSON to the broker. You must send it to a localized Python Flask server hosted on a headless Ubuntu VPS.
On startup, your Python server downloads the NSE Scrip Master directly into a Pandas DataFrame stored in RAM. When this PineScript webhook hits your VPS, the Pandas logic instantly snaps the dynamic {{close}} price to the nearest ATM strike, extracts the exact static Exchange Token, and routes the order to the API.
By removing the third-party webhook bottleneck and relying on RAM-cached token lookups, execution time drops to sub-50ms.
The Open-Source Architecture
I engineered this complete routing logic to eliminate my own Nifty options slippage. I have open-sourced the underlying Python Flask and Pandas routing logic so you can build out the middleware yourself.
Check the GitHub repository for the full Python architecture: github.com/codetradesalgo-cmyk/dhan-api-tradingview-webhook-python
The Problem: Dynamic vs. Static Data
PineScript generates dynamic data. A moving average crossover fires a signal based on a dynamic {{close}} price. However, broker APIs require static Exchange Tokens and rigid SEM_LOT_SIZE multipliers (e.g., Nifty = 65).
If you try to pass quantity: 1 directly to the Dhan API, or if your hardcoded exchange token moves out of the ATM range overnight, the exchange rejects the payload. Furthermore, standard third-party webhook routing adds 1 to 2 seconds of latency, destroying your entry price on fast breakouts.
The Middleware Solution
The PineScript above is Step 1. It formats a clean JSON payload containing the dynamic variables.
Step 2 is interception. You must not send this JSON to the broker. You must send it to a localized Python Flask server hosted on a headless Ubuntu VPS.
On startup, your Python server downloads the NSE Scrip Master directly into a Pandas DataFrame stored in RAM. When this PineScript webhook hits your VPS, the Pandas logic instantly snaps the dynamic {{close}} price to the nearest ATM strike, extracts the exact static Exchange Token, and routes the order to the API.
By removing the third-party webhook bottleneck and relying on RAM-cached token lookups, execution time drops to sub-50ms.
The Open-Source Architecture
I engineered this complete routing logic to eliminate my own Nifty options slippage. I have open-sourced the underlying Python Flask and Pandas routing logic so you can build out the middleware yourself.
Check the GitHub repository for the full Python architecture: github.com/codetradesalgo-cmyk/dhan-api-tradingview-webhook-python
오픈 소스 스크립트
트레이딩뷰의 진정한 정신에 따라, 이 스크립트의 작성자는 이를 오픈소스로 공개하여 트레이더들이 기능을 검토하고 검증할 수 있도록 했습니다. 작성자에게 찬사를 보냅니다! 이 코드는 무료로 사용할 수 있지만, 코드를 재게시하는 경우 하우스 룰이 적용된다는 점을 기억하세요.
면책사항
해당 정보와 게시물은 금융, 투자, 트레이딩 또는 기타 유형의 조언이나 권장 사항으로 간주되지 않으며, 트레이딩뷰에서 제공하거나 보증하는 것이 아닙니다. 자세한 내용은 이용 약관을 참조하세요.
오픈 소스 스크립트
트레이딩뷰의 진정한 정신에 따라, 이 스크립트의 작성자는 이를 오픈소스로 공개하여 트레이더들이 기능을 검토하고 검증할 수 있도록 했습니다. 작성자에게 찬사를 보냅니다! 이 코드는 무료로 사용할 수 있지만, 코드를 재게시하는 경우 하우스 룰이 적용된다는 점을 기억하세요.
면책사항
해당 정보와 게시물은 금융, 투자, 트레이딩 또는 기타 유형의 조언이나 권장 사항으로 간주되지 않으며, 트레이딩뷰에서 제공하거나 보증하는 것이 아닙니다. 자세한 내용은 이용 약관을 참조하세요.