On January 25, 2023, a short-seller report dropped after US market hours, targeting one of India’s largest conglomerates. By the time NSE opened the next morning, the news had spread to every corner of Indian financial media. Within minutes of the 9:15 open, multiple stocks in the group hit their lower circuit limits, trades halted, prices frozen, order books locked. For developers whose apps were displaying live portfolio values to thousands of retail investors, the experience was disorienting: the data feed went completely quiet not because of a network failure, but because the market itself had activated its emergency stops.<\/p>\n\n\n\n
That event is a good introduction to what makes NSE and BSE data technically interesting to work with. India is simultaneously the world’s fastest-growing retail equity market (demat accounts grew from 35 million in 2020 to over 175 million by 2025) and one of the most structurally complex from a data engineering perspective. Circuit halts. A pre-open call auction. A half-hour timezone offset. Two major exchanges listing many of the same stocks. Market-wide index circuit breakers that can pause all trading simultaneously across NSE and BSE.<\/p>\n\n\n\n
This guide walks you through an Indian trading day from a developer’s perspective, what the data looks like at each phase, what can go wrong if you don’t account for it, and how to build a pipeline using Infoway API that handles all of it cleanly.<\/p>\n\n\n\n
Before writing a single line of code, it helps to have a mental model of what NSE’s trading day actually looks like:<\/p>\n\n\n\n