Compare
Manual vs automated market data collection for research teams.
The sources are public. The work of collecting them, cleaning them and keeping history is not small.
| Manual collection by analysts | Automated data agents | |
|---|---|---|
| Coverage | The names there is time for | The full universe in each run |
| Time | Days of copy-paste each month | A scheduled run |
| Cleaning | By hand, differently each time | The same written rules every run |
| Matching | VLOOKUP on names that do not match | By registration number or ISIN |
| History | Each pull overwrites the last | Every run versioned |
| Source changes | Noticed when something looks odd | Reported as an alert |
| Lineage | Rarely recorded | Source, time, run and version on every record |
| Output | A sheet that changes shape | The same export, plus an API |
When manual is fine
- A one-off question on a handful of names.
- A source that is checked rarely and changes little.
When to automate
- The same pull every day, week or month.
- A universe too large to cover by hand.
- Models or reports that depend on the data being complete and correct.
- A need to answer “what changed since last month?”.
Read the schema change detection playbook.
How Terminal X does it
Terminal X runs data agents on public market and regulatory sources for portfolio managers, brokers and research desks. Four standard agents cover the SEBI Portfolio Manager Monthly Report for every registered portfolio manager, PMS strategy performance from APMI against Nifty 50 or Nifty 500, NSE ETF liquidity, and Nifty strategy-index constituents. Each agent works the portal in a real browser with parallel windows, extracts every row and sub-report, turns text like “12.4%”, “(3.2)” and “--” into numbers or blanks, matches names by registration number or ISIN, reconciles counts, marks empty filings as “no data” rather than zero and compares each run with the last. Every record keeps its source, fetch time, run and previous version; layout changes at the source are reported, not swallowed. Results arrive as the same Excel export every time, a REST API and alerts on new data, and other sources can be added as custom agents.
This page describes data collection and research workflow only. It is not investment advice.
Questions
Should a research team automate data collection?
Yes for recurring pulls, large universes and data that feeds models or reports; manual collection is fine for one-off questions on a few names.
What does automated data collection add beyond speed?
Full coverage, the same cleaning rules every run, matching by identifier, version history, alerts on source changes and lineage on every record.
See it on your own data. Terminal X — Filings and market data, collected. Book a 30-minute working session with an engineer.