A downloaded workbook has fourteen columns. Most are self-explanatory, two are routinely confused with each other, and one is quietly blank more often than people notice. This is the reference for all of them, in the order they appear.
The columns, in order
| Column | What it is |
|---|---|
DATE | The trading session the row describes. Written as a real spreadsheet date, so sorting and charting work without conversion. |
SERIES | The market segment the stock traded in that day — EQ for normal rolling settlement, BE/BZ for trade-to-trade, and others. Explained in full here. |
OPEN | The first traded price of the session, established by the pre-open call auction rather than simply being the first trade of continuous trading. |
HIGH | Highest price traded during the session. |
LOW | Lowest price traded during the session. |
PREV. CLOSE | The prior session’s closing price, as the exchange reported it for this row. Note it can be restated — see below. |
LTP | Last traded price: the price of the final trade executed in continuous trading. |
CLOSE | The official closing price. Usually not the same number as LTP — see below. |
VWAP | Volume-weighted average price for the session: total traded value divided by total traded quantity. |
VOLUME | Total quantity of shares traded, in shares. |
VALUE | Total turnover for the session, in rupees. |
NO OF TRADES | Count of individual trades executed — a rough proxy for how many distinct participants were active. |
DELIVERY QTY | Shares that actually moved between demat accounts rather than being squared off intraday. |
DELIVERY % | Delivery quantity as a percentage of total traded quantity. How to read it. |
LTP is not CLOSE
This is the single most common misreading of Indian market data, and it is worth being precise about.
LTP is literally the last trade that happened: whatever price the final transaction of continuous trading printed at. A single small order in the last second sets it.
CLOSEis the official closing price, and it is computed rather than observed. NSE derives it as a volume-weighted average price of trades in the closing window — the final half hour of the session — specifically so that one stray trade cannot set the day’s official mark. Where a stock has no trades in that window, the last traded price is used as the close instead.
The practical consequences:
- Use CLOSE for analysis. Returns, moving averages, charts, and anything you would compare across days should be built on CLOSE, because that is the number the rest of the market uses for valuation, index calculation, and margining.
- Expect the two to differ, usually slightly. A gap between them is normal, not a data error.
- In thinly traded stocks the gap can be large, and LTP in particular becomes unreliable. If you are looking at a small-cap with a few hundred trades a day, treat both numbers with suspicion.
PREV. CLOSE is a reference, not just yesterday
Most of the time PREV. CLOSE on a row equals CLOSE on the row above it, and you can ignore the distinction. The exception matters a great deal.
When a corporate action takes effect — a stock split, a bonus issue — the exchange restates the reference price so that the day’s move is measured against a comparable base. On the ex-date, PREV. CLOSE can therefore be materially different from the previous row’s CLOSE.
That difference is genuinely useful: comparing the two columns is a quick way to flag sessions where a corporate action probably occurred, which is otherwise invisible in raw price data. The full method, and how to adjust the history afterwards, is in the corporate actions guide. Treat it as a detector that produces candidates to verify, not as proof.
VOLUME, VALUE and VWAP are one relationship
These three columns are not independent. Turnover is quantity times price, so:
VOLUMEis in shares;VALUEis in rupees;VWAPis approximatelyVALUE ÷ VOLUME.
Two things follow. First, comparing raw VOLUME across different stocks is close to meaningless — a hundred thousand shares of a ₹20 stock and a hundred thousand of a ₹4,000 stock are entirely different events. Compare VALUE instead when you want to know where money actually went.
Second, dividing VALUE by VOLUME yourself is a cheap sanity check on a row. If your computed figure sits far outside the day’s HIGH-LOW range, something is off with that record.
VWAP is also the honest answer to “what price did people actually trade at today?” — better than OPEN, CLOSE, or the midpoint, all of which ignore where the volume was.
NO OF TRADES adds texture to volume
Volume alone cannot distinguish one institution moving a million shares from ten thousand retail orders totalling the same. Trade count can.
Dividing VOLUME by NO OF TRADES gives an average trade size. A day where volume jumps but trade count barely moves implies a small number of large participants — a block, a fund building a position. A day where both jump together looks more like broad participation. Neither is inherently bullish or bearish; it is context about who was trading.
When columns come back empty
Real exports have gaps, and they are usually not bugs:
- Delivery columns are the most frequent blanks. Delivery data is published on a separate settlement cycle from price data, so the most recent sessions may show nothing. Older rows may also be missing it for particular series.
- Missing dates are usually just closures — weekends, exchange holidays, or a genuinely untraded day for an illiquid stock. The export contains sessions where the stock traded, not a continuous calendar.
- Rows before listing do not exist. A range starting before the company listed simply begins later than you asked.
Before concluding a number is wrong, check it against the exchange’s own published record for that date. The scope note covers what this dataset does and does not include.
Putting it to work
With the columns understood, the spreadsheet guide has ready-to-paste formulas for returns, moving averages, volatility and drawdown written against exactly this layout.
Keep reading
- NSE series codes: EQ, BE, BZ, SM and ST — Why your export sometimes has two rows for the same date, what each NSE series code means for how a stock trades, and which rows you probably want to keep.
- Delivery percentage: how to read it, and its limits — How NSE delivery quantity and delivery percentage are calculated, what high and low readings suggest about participation, and the cases where the number misleads.
- Working with your NSE export in Excel or Google Sheets — Ready-to-paste formulas for daily returns, moving averages, volatility and drawdown, written against the exact column layout this tool produces.
Or go straight to the download console and pull a file.