TL;DR: Expiry day trading data means slicing your F&O results by where each trade sat in the expiry cycle — expiry day itself, the day before, the rest of the week — instead of reading one blended P&L. Almost every options trader believes expiry day is their most profitable day, because the wins are vivid and the charges are invisible. When you separate the numbers, three things usually appear: expiry-day gross profit is real, expiry-day net profit is much smaller, and the losses cluster in a specific two-hour window. This guide covers what to log, how to slice it, a worked example with real figures, and the mistakes that make expiry data lie to you.
Last updated: 6 August 2026 · 9 min read
By Pulkit Mangal — F&O trader since 2017, founder of TradeDiary. Traded index options across Zerodha, Kotak and Dhan; spent two years certain that expiry day was carrying my account before the data told me it was funding everyone else's.
Ask any Indian options trader which day of the week makes them money and you'll get the same answer without hesitation: expiry day. It's the day the premiums melt, the day a ₹4 option becomes ₹40, the day the screenshots get taken.
I believed it too. For about two years I treated expiry as my edge — bigger size, more trades, later hours. My reasoning was the reasoning everyone uses: I could remember the good expiry days. The 4,000-point BANKNIFTY move where a far OTM put went up eleven times. The Thursday I made more in ninety minutes than in the previous three weeks. Those days are unforgettable, and unforgettable is not the same as profitable.
When I finally tagged every F&O trade with its position in the expiry cycle and totalled them separately, expiry day showed a gross profit of ₹1,74,300 across fourteen months — and a net of ₹31,800. The other ₹1,42,500 had gone to brokerage, STT, exchange charges and GST, because expiry day was also the day I placed 61% of my trades. I was making money in the way a shop makes money by discounting everything: real revenue, almost no margin.
That's what expiry day trading data is for. Not more numbers — the same numbers, cut along the one axis that changes how you'd behave.
💡 Short on time? Start a free trading journal, import your F&O trades from any Indian broker, and every trade is automatically dated against its expiry. You'll see expiry-day net P&L, win rate and cost drag as separate rows — so you find out whether expiry is your edge or your leak.
What expiry day trading data actually means
Expiry day trading data is your own trade history, grouped by where each trade sat in the expiry cycle rather than by date alone. Not market-wide open interest. Not an options chain screenshot. Your fills, tagged by cycle position.
The minimum useful split is three buckets:
- Expiry day — trades opened on the day the contract expires
- Expiry eve — the session before, where positioning starts
- Rest of cycle — everything else
Most traders never make this split because their broker's P&L statement is organised by scrip and date, and "date" doesn't tell you that 31 July was an expiry and 30 July wasn't. So expiry performance sits mixed into a single monthly figure, where a genuinely excellent premium-selling month can be masked by an expiry-day habit that costs more than it earns — or the reverse.
A useful piece of context on how easy this is to get wrong: SEBI's study published in September 2024 found that 93% of individual F&O traders lost money over FY22–FY24, with aggregate losses of around ₹1.81 lakh crore. That statistic gets quoted as proof that F&O is a casino. It isn't quite that. What it's evidence of is that a very large number of people are trading a product whose costs they have never measured — and expiry day is where those costs concentrate hardest, because that's where the trade count spikes.
Why the expiry cycle matters more in India than most places
Four reasons this axis is unusually important for an Indian trader.
Weekly expiries make it a recurring weekly decision, not a monthly one. Since SEBI's rationalisation in late 2024, each exchange offers weekly expiries on a single benchmark index — NIFTY on the NSE, SENSEX on the BSE — rather than a different index expiring every day of the week. That was a real reduction in noise, but it also means whichever weekday your index expires on is now a load-bearing day in your week. If your behaviour changes on that day, it changes 52 times a year.
Costs scale with trade count, and expiry day is when trade count explodes. Options STT is charged on the sell side of the premium, and it was raised to 0.1% of premium from October 2024. Add exchange transaction charges, brokerage, stamp duty and 18% GST on the brokerage-and-exchange portion, and every additional round trip on expiry day carries a fixed toll. Ten extra scalps on expiry is not ten extra chances at profit; it's ten guaranteed charges against an uncertain edge.
Theta and gamma both peak, which flatters and punishes at the same time. Premium decay is fastest on expiry day, which is what makes selling attractive. Gamma is also at its highest, which is what makes a losing short position deteriorate faster than your stop can react. Both facts are true simultaneously, and a blended P&L number tells you which one dominated your trading only if you separate the day out.
Expiry-day option premiums are small in rupee terms, which quietly distorts your R-multiples. A ₹6 option that goes to ₹18 is a 3x, and traders log it as a spectacular win. In rupee terms it might be ₹9,000 on one lot — less than a single bad position-sized loss earlier in the week. If you're tracking performance in percentages rather than rupees, expiry day will look like your best day almost mechanically. Working in R-multiples rather than percentages fixes this, and our R-multiple calculator will do the arithmetic if you want to check a few trades by hand first.
What to log so the data can answer the question
You need six fields per trade. Most brokers give you five of them in the tradebook; the sixth you add yourself.
| Field | Why it matters |
|---|---|
| Entry and exit timestamp | Expiry day performance is heavily time-of-day dependent |
| Expiry date of the contract | The whole axis depends on this — without it there is no cycle position |
| Strike and option type | ATM and far-OTM behave completely differently on expiry |
| Net premium in and out | Gross P&L in rupees, not percentage |
| All charges, itemised | STT, brokerage, exchange, stamp, GST — this is where expiry day is decided |
| Your reason for the trade | Distinguishes a planned expiry strategy from boredom |
The one that's routinely missing is charges. If your journal shows gross P&L only, expiry day will always look good, because the cost of the extra trades is exactly the thing being hidden. Any honest expiry analysis is a net analysis. If you import from a broker P&L statement, check whether it carries per-trade charges — some do, some don't, and the ones that don't will silently make you look better than you are.
The framework: four slices, in order
Run these in sequence. Each one narrows the question.
1. Expiry day versus the rest of the cycle, net. Two rows, net P&L, trade count and win rate. This is the headline. If expiry-day net is negative while total net is positive, the rest of your trading is subsidising a habit, and you've found the single most valuable fact in your journal.
2. Cost drag as a percentage of gross. Divide total charges by gross profit for each bucket separately. On a healthy positional book this might be 8–15%. On an expiry-day scalping habit it is routinely 40–80%, and above 100% means you're trading for your broker and the exchange. This number is more diagnostic than P&L, because it's stable — it doesn't swing with one lucky trade.
3. Time of day, on expiry day only. Split into three blocks: open to 11:30, 11:30 to 14:00, and 14:00 to close. Almost everyone has one block that's clearly worst, and for the majority of the traders I've compared notes with it's the final block, when premiums are lowest, the temptation to "make the day back" is highest, and a ₹5 option feels like it can't lose much. It can lose all of it.
4. Strike distance from spot at entry. ATM, one to three strikes out, and far OTM. Far-OTM expiry-day buying is the single most common leak in Indian retail options trading — a low-cost, low-probability bet that feels cheap and, repeated fifty times a year, isn't.
A worked example
Here is a real fourteen-month breakdown from a trader I went through this with — index options only, NIFTY and BANKNIFTY, figures rounded slightly for anonymity.
| Bucket | Trades | Gross P&L | Charges | Net P&L | Win rate |
|---|---|---|---|---|---|
| Expiry day | 412 | ₹1,74,300 | ₹1,42,500 | ₹31,800 | 44% |
| Expiry eve | 96 | ₹61,700 | ₹22,400 | ₹39,300 | 51% |
| Rest of cycle | 168 | ₹2,08,900 | ₹41,600 | ₹1,67,300 | 47% |
Three things jump out that a single ₹2.38L net figure would never have shown.
Expiry day was 61% of his trades and 13% of his profit. Not a loss — which is why it survived unexamined for so long — but a rounding error in exchange for the majority of his screen time and nearly all of his stress.
Cost drag on expiry day was 82% of gross. On the rest of the cycle it was 20%. He wasn't a worse trader on expiry day; his win rate barely moved. He was simply paying four times as much to express the same edge.
Expiry eve was quietly his best day per trade. ₹409 net per trade versus ₹77 on expiry day. He had no idea, because he'd never separated it out — and he'd been steadily reducing eve activity to save capital for expiry.
What he changed was not dramatic. He capped expiry-day trades at six, stopped trading after 14:00 on expiry, and moved size to the eve session. The next nine months weren't magic, but net P&L rose while his total number of trades fell by roughly a third. Most improvements in trading look like this — subtraction, not a new strategy.
Common mistakes that make expiry data lie
Measuring gross instead of net. Expiry day is the one bucket where charges are big enough to invert the conclusion. Gross-only analysis will actively mislead you here.
Counting percentage returns instead of rupees. A 200% gain on a ₹4 option and a 12% gain on a ₹340 option are not comparable, and averaging them produces a number that means nothing.
Judging on too few cycles. One expiry with a 900-point move will dominate a small sample. You want at least 15–20 expiries before you trust the split — roughly four to five months of weeklies.
Forgetting that a stopped-out short is not a small loss. Gamma on expiry day means a short option can move against you several multiples before you exit. If your journal records the intended stop rather than the realised exit, your expiry-day losses will be understated.
Confusing "I remember winning" with "I won". This is the whole reason the exercise exists. Memory over-weights vivid outcomes, and nothing in trading is more vivid than an expiry-day multibagger. Your journal exists to disagree with your memory.
How TradeDiary helps
Every F&O trade imported into TradeDiary already carries its expiry date, so the cycle split happens without you tagging anything — expiry day, eve and the rest of the cycle become rows you can read. Charges come in from your broker statement where they're available and are estimated from your actual brokerage plan where they aren't, so the net figure is a net figure. You can then group the same trades by strategy, by implied volatility regime, or by the greeks you were exposed to, which is usually where the reason behind an expiry-day pattern turns up.
If you trade with Zerodha, the Zerodha journal import will pull your tradebook directly; every other major Indian broker is supported through file import. And if you're still deciding whether a dedicated journal beats a spreadsheet for this, we've compared them honestly — expiry-cycle analysis is one of the few places where the spreadsheet genuinely loses, because the expiry-date join is fiddly to maintain by hand.
Frequently asked questions
What is expiry day trading data? It's your own trade history grouped by position in the expiry cycle — expiry day, the session before, and the rest of the cycle — with net P&L, trade count, win rate and charges reported separately for each. It is not market-wide open-interest or options-chain data; it's your fills, sliced along the axis that most changes trader behaviour.
Is expiry day actually profitable for most traders? Gross, often yes. Net, frequently not. Expiry day is where trade count peaks, and charges scale with trade count, so cost drag of 40–80% of gross profit is common. SEBI's September 2024 study found 93% of individual F&O traders lost money over FY22–FY24, and unmeasured transaction costs are a substantial part of that picture. The only way to know your own answer is to separate the bucket and read the net.
How many expiries do I need before the data is meaningful? At least 15–20, which is roughly four to five months of weekly expiries. Below that, a single large directional move dominates the sample and you'll draw a confident conclusion from noise.
Should I stop trading on expiry day entirely? Usually not — the data more often supports trading it less rather than not at all. In the example above, capping trades at six and stopping after 14:00 preserved most of the profit while removing most of the cost. Cut the part of the day the data condemns, not the whole day.
Does this apply to stock options as well as index options? The framework does, but the numbers differ. Stock options have wider spreads and thinner liquidity near expiry, and physical settlement obligations on expiring in-the-money positions add a risk index options don't carry. If you trade both, split them before comparing — blending stock and index expiry data produces an average that describes neither.
The takeaway
Expiry day feels like an edge because it is memorable, fast and occasionally spectacular. Whether it is an edge is a question your own tradebook can answer in about twenty minutes, and the answer is frequently uncomfortable: real gross profit, thin net profit, and most of the difference paid away in charges you never saw itemised.
You don't need a new strategy to fix that. You need the same trades, grouped one level differently.
Start your free trading journal →
Risk disclaimer: This article is for educational purposes only and is not investment advice. Derivatives trading carries a substantial risk of loss and is not suitable for every investor. Figures quoted are illustrative and drawn from anonymised trader data; your results will differ. Please consult a SEBI-registered investment adviser before making trading decisions.