Engineering deep-dives, benchmarks, and unhedged opinions on Indian-market data infrastructure. Updated when there's something to say.
What it took to backfill NSE end-of-day prices from 2010 to today: two URL schemes, two CSV schemas, a SEBI format cutover in the middle, and a Go SQL driver that hates NULL.
Splits, bonuses, dividends, ISIN drift across mergers. How TejHQ Phase 4 actually shipped: the adjustment math, the per-ISIN reverse cumprod, the post-merger ISIN resolver, and the 89% Yahoo reconciliation.
Twelve-minus-one momentum, monthly rebalance, top decile, 2024-Q1 to 2026-Q1. From raw Bhavcopy to equity curve in one script. Full code + results.
Why retail quants in India pay anywhere from zero to twenty-four lakh rupees a year for the same OHLCV. A breakdown of who charges what, and what they're actually selling.
Same data, same query, two engines. Polars wins on pure scan speed; DuckDB wins on remote Parquet pruning. Numbers, code, and the cases where each loses.
End-of-day market data for India costs anywhere from ₹0 to ₹24 lakh a year. We pay zero. Here's the SEBI CMTS Bhavcopy pipeline that powers tej-bazaar.