NSE Live Tick Data Collector

@asifdwan

As a highly experienced web scraping specialist, I am confident that my skills align perfectly with your NSE Live Tick Data Collector project. Having worked on countless projects involving the extraction of data from complex and even protected websites, I'm well-versed in navigating the intricacies of such tasks. I am proficient in Python and have successfully scraped and delivered structured data in various formats including CSV, Excel and JSON which perfectly matches your requirements. Additionally, my expertise extends to database management using SQL, which directly aligns with your need for an SQL based storage solution for this project. Leveraging my knowledge and skill of SQLAlchemy ORM, I can design a robust schema that ensures fast inserts while facilitating seamless larger data queries. This once again resonates with your preference for PostgreSQL and SQLAlchemy ORM. Lastly, my commitment to quality work under tight schedules perfectly aligns with your requirement of the tool running from 9:15 AM IST until market closing at 3:30 PM IST without any interruptions. From setting up NSE credentials/API keys to the appropriate restart behaviors if any connection drops, you will find my work more than satisfactory. Let's collaborate to create a powerful data collection solution.

@vishal1145

Your tick collector will fail under load if you write every incoming tick synchronously to PostgreSQL—NSE sends bursts of 500+ ticks per second during volatile periods, and blocking I/O will create a backlog that drops data. Quick questions - are you planning to run this on a single instance or do you need horizontal scaling for multiple stock universes? And do you have existing NSE API credentials or are you expecting the solution to include feed procurement? Here is the architectural approach: - PYTHON + SQLALCHEMY: Build an async ingestion pipeline using asyncio and asyncpg with a ring buffer that decouples network I/O from database commits, preventing tick loss during write spikes. - DATABASE DESIGN: Partition tables by trading day and stock symbol with BRIN indexes on timestamp columns to support fast bulk inserts (10K+ rows/sec) and efficient range queries for post-market analysis. - NSE API INTEGRATION: Implement WebSocket reconnection logic with exponential backoff and state recovery so the collector resumes from the last committed tick if the feed drops mid-session. I've built similar real-time data pipelines for 2 fintech clients processing 50M+ events daily with zero data loss. Let's schedule a 20-minute call to align on your NSE feed source and failover requirements before I architect the schema.

@Algokage

Hello, I hope you're doing well. I am an experienced Python developer with over 4 years of expertise in building data collection and processing systems, including real-time market data feeds. I’ve successfully developed similar tools that capture and store high-frequency financial data, ensuring accuracy and reliability. For your NSE Live Tick Data Collector, I’ll create a Python service that connects to the NSE equity feed, captures every tick as specified, and stores the raw data in an SQL database using SQLAlchemy for portability. The code will be clean, well-documented, and structured for Git collaboration, with a README detailing setup, schema design, and usage. I’ll provide a working proof, such as a sample DB dump, to confirm the collector meets all requirements. Message me to discuss further or to review relevant samples. Thanks, Adegoke M.

@Ritesh11gupta

I see you're looking for a Python service to connect to the NSE equity feed starting at 9:15 AM. I have solid experience with API integrations and can help you set this up smoothly. What specific features do you have in mind?

@MrDeaDpool707

hey, i've done many NSE option chain , banknifty and others to fetch their table data and other info into an oracle database

@Geetam00

Drawing on my extensive experience in AI and backend engineering, I am exceptionally well-qualified to deliver your NSE Live Tick Data Collector project. First and foremost, I am astute in Python and SQL - the very technologies you require for the entire development lifecycle, from designing a schema for fast inserts and later querying to leveraging SQLAlchemy for ORM and connection handling. Furthermore, the use of PostgreSQL is within my production workflow wheelhouse. Moreover, as a developer versed in building scalable web applications and automation systems, your brief is easily achievable for me. I can guarantee that the raw stream for each stock's data will be preserved exactly as received with no rule-based filtering. This way, you will be able to examine every tick of data logged from stock symbol to precise timestamp, LTP and LTQ, cumulative traded volume and much more using your allowed criterion at a later stage. Finally, my competence in efficient Git-tracking structure will ensure clean well-commented source code so that anyone can understand it including yourself for maintenance or optimization. You can count on me to provide more than just the delivery; I vow for a long-term growth mindset where I offer continuous improvement and ongoing support to your new service by closely studying its performance metrics while keeping updated with NSE's equity feed changes.

@techlinis

After examining your project requirements, I firmly believe that my expertise in API development and Python is perfect for the job at hand. Being the co-owner of Techlinis (OPC) Private Limited, I have successfully delivered numerous complex projects that relied heavily on data collection and processing. This has honed my skills in developing clean and efficient code, even when faced with large volumes of data. My familiarity with SQLAlchemy and PostgreSQL ensures the resulting code will be as portable as you need it to be, while guaranteeing fast inserts and smooth querying on your preferred database engine. Moreover, my ability to provide clean, well-commented source code in a Git-friendly structure aligns perfectly with your stipulated requirements. Lastly, what sets me apart is my dedication to producing high quality work that surpasses expectations. I will meticulously document the setup process for NSE credentials and API keys, provide clear instructions on launching the collector (including restart behavior), designing a schema for efficient storage, and an easy way to replay or inspect the captured data. Let's get started on this project today!

@sugamkumar27

Hi, I can build the Python-based NSE tick data collector with SQLAlchemy and PostgreSQL support, keeping the raw tick stream intact for later analysis. The service can capture the symbol, precise timestamp, LTP, LTQ, cumulative volume, buy/sell quantities, top five bid/ask levels with quantities, and stock-universe identifier. I’ll structure the database for efficient high-frequency inserts and later querying, with connection recovery and restart handling for feed interruptions. The source will include a clean Git-friendly structure, database schema/migration, credential configuration, collector startup instructions, and a simple method to replay or inspect captured session data. Regards, Sugam

@MuhammadAkif8

Hi, before starting first let me ask that, which NSE authorized API/feed are you using for the live tick stream (WebSocket, broker API, or another provider)? I’ll configure the collector to run automatically from 9:00 AM to 5:00 PM IST, with automatic connection recovery if the feed disconnects. The service will preserve the raw tick stream during the configured session window and safely stop after 5:00 PM IST. I can build this as a Python + SQLAlchemy PostgreSQL collector with raw tick preservation, including LTP/LTQ, volume, buy/sell quantities and top-5 bid/ask levels. I can provide the Git ready source, SQL schema/migrations, README and a replay/inspection utility. If you share the feed/API documentation, I can confirm the exact implementation approach before starting. Best, AkifH

@LehloM

Your request for a Python-based NSE Live Tick Data Collector is well understood. Capturing every equity tick without filtering is essential for your post-processing needs, and ensuring robust data logging will set a strong foundation for your analysis. I will focus on creating a reliable service that connects promptly at market opening and maintains a seamless data stream until closing. The SQL database schema will be designed for efficient inserts and querying, utilizing SQLAlchemy for portability. You’ll receive clean, well-commented code in a Git-friendly structure, along with a comprehensive README covering setup, launch instructions, and data inspection. To ensure we’re aligned, could you clarify if there are any specific requirements for the logging format or additional metrics you'd like to capture? You can view my portfolio [here](#) for similar projects that showcase my commitment to delivering high-quality solutions.

@aman179102

I have solid experience writing Python data collection services with SQLAlchemy and PostgreSQL, including real-time stream processing with automatic reconnection and restart-safe behaviour. I'll build the NSE tick collector to run continuously from 9:15 AM to 3:30 PM IST, capturing every tick unfiltered: symbol, precise timestamp, LTP/LTQ, cumulative volume, total buy/sell quantities, and the top-5 bid/ask levels with quantities. I'll design a fast-insert schema (daily-partitioned tables with composite index on symbol+timestamp) that stays portable across PostgreSQL/MySQL via SQLAlchemy, and deliver clean, well-commented, Git-friendly source with a README covering credentials setup, launch/restart behaviour, the migration file, and a replay script to inspect a day's captured data. I can record a live working session and provide a sample DB dump as acceptance proof.

@Jeeldhola01

Hello, With 5+ years in Python and database work, I can build your NSE tick data collector easily. I will connect to the NSE feed at market open, capture every tick with no data loss, and store all fields (LTP, LTQ, volume, top 5 bid/ask levels) in a fast SQL database using SQLAlchemy. The design will work well with PostgreSQL and support auto-restart if the connection drops. You get clean, commented code, a clear README, and a sample data dump as proof. I have built similar real-time data pipelines before. Let's talk about details, and I can start right away.