Real-Time DHAN (API) Market Depth FETCHING AND STORAGE using PYTHON

@UzeeTech

With over 9 years in the software industry and a specialization in Python, I am confident that I can create the high-concurrency data logger you need for your Dhan API project. My extensive experience working with websockets and CSV file storage combined with my ability to use the asyncio or multiprocessing libraries shows that I have the skill-set to handle the complexities of this task. I take great pride in delivering clean, well-commented code as evidenced by my previous clients' reviews. Your satisfaction is my top priority, so I ensure my work exceeds expectations. Additionally, my fluency in other languages such as Node.js and PHP allows me to be more versatile, adhering to any future modifications or enhancements your project may require. As a Full-Stack Developer, I believe I bring versatility that would benefit you throughout your entire project lifecycle. I possess strong problem-solving skills to ensure successful implementation even with challenging requirements. Lastly, I'm 100% committed to delivering quality work on time – a commitment that will reflect in your data logger's functionality and performance.

@Hemantt841

I understand you require a Python developer to build a high-concurrency data logger that maintains five parallel WebSocket connections for fetching 200-level market depth of futures and options instruments from the DHAN API. You need the script to handle auto-login with PYOTP and continuously write incoming order-book snapshots to a CSV file for later analysis. Ensuring non-blocking data capture using asyncio or multiprocessing is also a key part of your requirements. With over 15 years of experience and more than 200 projects completed, I specialize in Python development and API integration, which aligns well with your need for real-time data processing and handling multiple WebSocket streams efficiently. My expertise in writing clean, well-commented code will ensure your project is maintainable and easy to extend. For this project, I will implement asynchronous WebSocket connections using Python’s asyncio to manage the five allowed connections simultaneously, fetching live NIFTY and BANKNIFTY data, then deriving the option and future instruments as per your formulas. Data will be continuously logged into a structured CSV file with minimal latency. I anticipate delivering the initial version within a week for your review. Feel free to reach out so we can discuss your project details and timelines further.

@Thiran003

Hello, I can develop a Python script for high-concurrency data logging using the Dhan API. The solution will use asyncio/multiprocessing to maintain 5 parallel WebSocket connections, fetch 200-level market depth for F&O instruments, and continuously store structured data in CSV format for research and back-testing. I will also implement auto login using PYOTP and ensure the code is clean, stable, and well documented. Best regards, Thiran360AI

@Aannshu

Hello, I understand that you need a high-concurrency Python script to capture 200-level market depth data from the Dhan API using multiple WebSocket connections and store the order book data continuously in CSV for later analysis. I can build a clean and efficient solution using Python with asyncio or multiprocessing to maintain up to 5 parallel WebSocket connections as required by the Dhan API. The script will handle authentication using PYOTP, subscribe to NIFTY and BANKNIFTY live data, generate the required FUTURES and OPTIONS instruments based on your formulas, and capture full market depth updates. The system will continuously log structured order-book snapshots into CSV files so the data can easily be used for research, back-testing, or analysis later. The code will be well structured, commented, and optimized for stable long-running execution. Deliverables: • Clean and well-commented Python source code • Multi-WebSocket concurrent data capture system • Continuous CSV data logging • Simple configuration to change instruments later I have experience working with Python, data processing, and real-time data pipelines, and I can ensure the script runs reliably. Looking forward to working with you. Best regards, Anshu Agnihotri

@bhuvans39

Hello, I have experience in Python, API integration, and data processing. I can build a script to fetch real-time market data using WebSocket, handle multiple connections, and store the data continuously into CSV files. I understand working with async / multiprocessing and will write clean and well-structured code as required. I am ready to start immediately and complete the project on time.

@taofiq12

Hello, I can build a high-concurrency Python data logger for the Dhan API that maintains 5 parallel WebSocket connections, each subscribing to 200-level market depth for the required F&O instruments. I have experience working with async Python, WebSocket market data feeds, and high-frequency data logging, so the script will capture updates reliably without blocking. The solution will use asyncio for concurrent WebSocket handling, implement PYOTP-based auto login, dynamically generate the required FUTURE and OPTION instruments from NIFTY/BANKNIFTY live data, and continuously write the order-book snapshots into a structured CSV file suitable for later analysis and back-testing. The code will be clean, well-commented, and optimized for stability and continuous data capture. Estimated turnaround: about 1–2 days. Ready to start right away.

@gauravb787

Hi, this needs a reliable Python logger that can keep all 5 Dhan WebSocket connections running in parallel without dropping depth updates, while also handling dynamic instrument selection from live NIFTY and BANKNIFTY data. I’d build it with asyncio-based connection management, PYOTP auto-login, and a structured pipeline that computes the required FUTURE and OPTION instruments first, then subscribes each instrument to its own 200-level depth stream as allowed by Dhan. Every order-book snapshot and level update will be written continuously into clean CSV tables with timestamps and instrument metadata so the dataset is usable later for research and back-testing. The code will be well-structured, commented, and designed to recover from disconnects or API interruptions so data capture stays stable during market hours. I can keep the implementation focused on performance, clean storage format, and production-safe concurrency from the start.