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
開源腳本
秉持TradingView一貫精神，這個腳本的創作者將其設為開源，以便交易者檢視並驗證其功能。向作者致敬！您可以免費使用此腳本，但請注意，重新發佈代碼需遵守我們的社群規範。
免責聲明
這些資訊和出版物並非旨在提供，也不構成TradingView提供或認可的任何形式的財務、投資、交易或其他類型的建議或推薦。請閱讀使用條款以了解更多資訊。
開源腳本
秉持TradingView一貫精神，這個腳本的創作者將其設為開源，以便交易者檢視並驗證其功能。向作者致敬！您可以免費使用此腳本，但請注意，重新發佈代碼需遵守我們的社群規範。
免責聲明
這些資訊和出版物並非旨在提供，也不構成TradingView提供或認可的任何形式的財務、投資、交易或其他類型的建議或推薦。請閱讀使用條款以了解更多資訊。