What is paper trading?

The output of a paper-trading phase is not the simulated balance. It is the journal. The balance is a by-product with a known upward bias, for reasons the next two sections make precise; the journal is unbiased evidence about the only questions a simulator can truly answer: can you define a rule, execute it without deviation, and review it honestly. A simulation run without controls produces neither, which is why most paper trading is a pleasant way to learn nothing.

Five controls separate a simulation from a game. First, trade the instrument you intend to trade live: an index future, a liquid large cap and a small cap are different markets, with different spreads, depth and gap behaviour, and skill in one does not transfer automatically. Second, use the size you would really use, derived from the capital you will actually deploy. A fantasy account of one crore teaches sizing reflexes that collapse on two lakh; set the paper capital equal to the real capital and size every trade from a fixed risk budget. Third, use the same order types you will use live: decide whether the entry is a limit or a market order, and whether the stop is a stop-loss market or stop-loss limit instruction, because that choice changes live outcomes and should be rehearsed, not improvised. Fourth, write the entry, stop, target and a one-sentence reason before the trade, never after; a record reconstructed afterwards is fiction with a memory of being right. Fifth, run long enough to cross regimes: a rule simulated only in a trending month is untested, so carry it through a choppy stretch, an expiry week and at least one gap open before concluding anything.

The journal that captures all of this needs a fixed schema, because ad hoc notes decay into commentary. Each field below exists to close a specific escape hatch.

A simulator answers one question with high fidelity: what would my rules have decided. It answers a second question with almost none: what would the market have given me for those decisions. The distance between the two is not vague. It is five specific subsidies, each of which biases the paper result upward.

First, fills at the touch. Exchange matching runs on price-time priority: a limit order to buy at ₹500 joins the back of the queue of every order already resting at ₹500, and the price touching your level means only that the front of that queue traded. Touched is not filled. The bias is worse than a coin flip, because limit orders suffer adverse selection: when price kisses your level and reverses, only the front of the queue is filled and you miss the trade that would have worked; when price slices through the level, the whole queue fills, you included, so you reliably own the trades that are already going wrong. A simulator that fills you at the touch, in full, every time, is granting the single most valuable privilege that exists on an exchange: queue immunity. Partial fills, the other everyday reality of limit orders, usually do not exist on paper at all.

Second, zero slippage. A stop-loss is a trigger, not a price: when the last traded price crosses the trigger, a stop-loss market order is released and takes the next available price. Triggers fire, by construction, at moments when the market is moving against you, so the next available price is systematically worse than the trigger, and in a gap it can be far worse. Simulators execute the trigger price itself. Every stop-out in a paper log is therefore recorded at the kindest price the live market could conceivably have offered, and stops are exactly where the money is decided.

Third, zero costs. The Indian cost stack on a real round trip has seven lines, several of them revised as recently as October 2024, and the next section prices it in rupees.

Fourth, infinite liquidity at the quote. The best bid and offer on the screen are quotations for a finite quantity, the top of the order book. An order larger than that quantity walks the book, consuming successive price levels, which is market impact: your own order moves the price against you. The simulator fills any quantity at the last traded price. On a front-month index future or a large cap, small retail size rarely notices; on a mid cap, a small cap or a far out-of-the-money option, spread and impact are the difference between a strategy and a story about a strategy.

Fifth, zero consequence, which gets its own section below, because it is the one gap that does not shrink when you trade liquid instruments in small size.

Take the cleanest possible test: an intraday round trip on a stock near ₹500, buying 200 shares for ₹1,00,000 and selling them later the same day at the same price. The simulator reports zero. The market does not, and the arithmetic is worth doing line by line, at the rates in force since 1 October 2024, when both the securities transaction tax on derivatives and the exchanges' transaction charges were revised.

Rupees eighty-two on a flat trade, with a flat-fee broker and before a single tick of slippage. Add one tick on each side, ten paise on 200 shares twice, and the true cost of the round trip is roughly ₹102. The simulator's breakeven is the entry price; the market's breakeven is the entry price plus about a tenth of a percent. Run a hundred paper trades and the log is silently about ₹8,200 per lakh kinder than the identical trades would have been live, which is more than enough to turn a marginal method's paper record from encouraging into meaningless.

Delivery is heavier, not lighter. Securities transaction tax on delivery equity is 0.1 percent on both legs, ₹200 on the same notional round trip, stamp duty rises to 0.015 percent on the buy, and the sell side attracts a depository charge: a flat fee per scrip for each day you debit shares from the demat account, levied in rupees rather than percentages, so it bites hardest on exactly the small positions a beginner should be trading. The debit itself rides on a standing DDPI authorisation or a per-sale OTP confirmation, one more piece of live plumbing no simulator rehearses. Derivatives took the October 2024 revision directly: STT on futures sales rose from 0.0125 to 0.02 percent, and on option sales from 0.0625 to 0.1 percent of premium, figures many older guides still quote at the pre-revision rates.

Every gap so far can be narrowed by modelling: charge yourself the stack, dock yourself a tick, refuse to count fills at the touch. The fifth gap cannot be modelled, because it lives in you. The loss-aversion literature that began with Kahneman and Tversky's prospect theory in 1979 keeps returning the same shape of result: a loss weighs roughly twice as much as an equal gain. Trading real money therefore runs on a loop that simulated money never starts. Fear of crystallising a loss makes traders hold losers and widen stops; the relief of a gain makes them cut winners early to bank the feeling; and a fresh stop-out makes the next trade bigger and faster than the plan allows, which is revenge sizing. On paper, none of it fires. Nothing is at stake, so the paper log measures your rules under laboratory conditions and says nothing about your ability to obey them under load.

The same weightlessness inflates paper results from a second direction: simulated traders take entries they would never fund with real money, because a toy account makes "why not" feel like a reason, and they sit through drawdowns no funded account would tolerate, because unrealised paper losses do not bleed. Both behaviours pad the record. The live base rate shows what waits on the other side: SEBI's study of individual trading in equity derivatives for FY25, published in July 2025, found that 91 percent of individual traders lost money, a net ₹1,05,603 crore between them. A simulator will not close that distance by itself; a disciplined bridge, set out below, at least stops you from trying to cross it in one leap.

Simulation itself has impeccable institutional credentials. NSE and BSE run periodic mock trading sessions, typically on Saturdays and announced by circular, in which members' systems fire orders at the live matching engine, including a planned mid-session switchover to the disaster-recovery site. The exchanges are explicit that mock trades create no rights, no liabilities, and no margin or settlement obligations. Notice what is being tested: order routing, risk checks, capacity, failover. No institution reads meaning into its mock-session profit and loss, and that is the correct posture toward every simulation: validate the machinery, ignore the score.

Around retail simulation, the regulator has spent two years drawing a careful boundary. A SEBI circular of 24 May 2024 barred exchanges, depositories and intermediaries from sharing real-time price data with third parties, a measure aimed squarely at platforms running virtual trading contests and fantasy stock games on live prices, and it permitted lagged data for investor education only where participation carries no monetary incentive. A second circular, dated 29 January 2025, regulated the educator side: a person engaged solely in education could not use market price data from the preceding three months while naming securities in any way that indicated future prices or a recommendation. In May 2026, SEBI harmonised the two into a single rule: a uniform 30-day lag for both the sharing and the use of price data for educational purposes, effective 1 July 2026. As this guide is written, that 30-day figure is days old, and any article still quoting the three-month rule is describing a superseded regime. The regulator's own securities-markets education institute, notably, was granted a one-day lag exclusively for its simulation lab, which is about as close as a regulator comes to endorsing simulation as a teaching instrument.

None of this restricts you. The circulars bind institutions redistributing data and people publishing education; a private individual paper trading against live quotes on their own screen is outside their scope. What the boundary tells you is what the regulator thinks simulation is for: prize contests dressed as practice were shut off, and teaching on lagged data was explicitly preserved. Practice, in other words, is education, not competition.

Almost every guide says "start small" and stops. A usable protocol has rungs, promotion rules and demotion triggers, and it changes exactly one variable at a time, which is the same experimental discipline the simulation began with.

Promotion out of paper is earned by process, never by the simulated return, because the five subsidies have already corrupted that number. A reasonable bar: a block of thirty or more journalled trades of one named setup, spanning at least two regimes, with rule breaks near zero. Then go live at the smallest viable size, defined by feel rather than formula: a full stop-out should be financially ignorable and emotionally noticeable. You are not there to earn; you are introducing one new variable, consequence, while holding the strategy constant. Keep the single setup. Expect the numbers to degrade relative to paper, because fills, costs and your own pulse are now real; the degradation is the data you came for. Review on a fixed cadence, weekly, against the same journal schema, comparing process metrics rather than money: stops honoured, entries matching the written setup, slippage and costs inside what you modelled.

Promotion up a size rung follows a clean block at the current size, one increment at a time, and each increment should be small, because every new size is a new emotional regime: the moment a red number equals a day's salary, it stops being abstract. Demotion is automatic and immediate: a pulled or widened stop, a revenge entry minutes after a stop-out, size creeping above plan, or gaps appearing in the journal each cost one rung, back to smaller size, or back to paper if the process itself has broken. A losing streak with rules followed is not a demotion trigger; at any realistic win rate, streaks are a statistical certainty, which is expectancy arithmetic, not a verdict. Demotion is cheap. Discovering at full size that your discipline was never tested is not.

Run properly, paper trading proves four things: that you can operate the machinery without error, that your rules are specific enough to be followed by the person you actually are, that your setup occurs often enough to be worth trading, and that you can keep honest records. It cannot prove that the edge survives the queue, the cost stack and impact, and it cannot prove that you will follow the rules when the loss is real. Its results should be read as evidence of readiness, never as a forecast.

The deeper point is that a simulator only rehearses what you bring to it. The setup worth testing, the level that invalidates it, and the sizing logic that survives being wrong are all upstream of any practice environment, and that upstream work is exactly what the method we teach is built around. Rehearse like an engineer, graduate on evidence, and let the journal, not the simulated balance, make every promotion decision.

• SEBI price-data norms for virtual trading and education. Circular SEBI/HO/MRD/MRD-PoD-3/P/CIR/2024/56 (24 May 2024) barred sharing of real-time prices with third parties, targeting virtual trading contests and fantasy stock games, and allowed lagged data for education with no monetary incentive; circular SEBI/HO/MIRSD/MIRSD-PoD-1/P/CIR/2025/11 (29 January 2025) set a three-month usage lag for educators; a May 2026 circular replaced both with a uniform 30-day lag, effective 1 July 2026. sebi.gov.in
• Exchange mock trading sessions. NSE and BSE circulars announcing periodic mock sessions, including disaster-recovery switchover drills; the exchanges state that mock trades create no rights, liabilities, margin or pay-in and pay-out obligations.
• The cost stack. STT rates including the 1 October 2024 revision under the Finance (No. 2) Act 2024 (futures 0.02 percent, option premium 0.1 percent, on sale), exchange transaction charges effective the same date, the SEBI turnover fee and GST treatment, as published in the exchanges' investor levies schedules. nseindia.com
• Uniform stamp duty. Indian Stamp Act amendments effective 1 July 2020: buy-side duty of 0.015 percent on delivery equity, 0.003 percent intraday, 0.002 percent on futures. pib.gov.in
• The behavioural evidence. Kahneman and Tversky, Prospect Theory: An Analysis of Decision under Risk (Econometrica, 1979), and the loss-aversion literature it began; SEBI's FY25 study of individual trading in equity derivatives (July 2025): 91 percent of individual traders lost money, a net ₹1,05,603 crore.