Risk Reward Ratio in Trading: How Indian Traders Set 1:2 Targets Without Overfitting

Any useful risk reward ratio trading India routine comes down to two distances on the chart. One is how far your stop is from your entry, which is your risk. The other is how far your target is from your entry, which is your reward. A lot of Indian retail traders are told that a fixed 1:2 ratio is the rule. It is a decent place to start, but it isn’t a law. In this guide we define risk and reward in rupees, show how expectancy ties win rate to R multiples, size positions from a fixed rupee risk, and explain why forcing 1:2 onto every Nifty or cash-equity setup overfits the chart and often backfires. Our risk management in trading pillar has the broader framework.

Reading risk and reward off an NSE chart

Risk is the rupee gap between your planned entry and your planned stop. Reward is the rupee gap between entry and your first realistic target. The risk reward ratio, usually written R:R, is simply reward divided by risk. So a 1:2 frame means you are going after twice as many rupees as you are prepared to lose if the stop is hit.

For a long cash-equity trade, risk is entry minus stop and reward is target minus entry. A short flips it: the stop sits above entry, so risk is stop minus entry and reward is entry minus target. Zerodha Varsity’s shorting chapter shows exactly this with a worked HCL Technologies intraday short: short price ₹1,202.80, stoploss ₹1,215, target ₹1,180. On those published figures, risk is ₹12.20 and reward ₹22.80 per share, a reward-to-risk of about 1.87:1. The chapter also makes the point that on a short, the stoploss is always above the short price.

Keep your units straight. Work out R:R in rupees per share, or points on Nifty futures, before you multiply by quantity. Percentages can hide a real problem: get the size wrong, and a wide stop on a cheap stock can still burn more capital than a tight stop on a liquid large-cap.

R:R is not a personality trait. It is two measurable distances: entry to stop, and entry to a target the market structure can actually reach.

Why 1:2 became the default

People like 1:2 because it is easy to remember and it makes you ask whether the upside is big enough to justify the stop. Risk ₹1 to make ₹2 and you can lose more often than you win and still come out with positive expectancy, as long as costs don’t eat the edge. That bit of arithmetic is why so many educators teach 1:2 as the default.

There’s nothing sacred about it, though. Zerodha Varsity’s Kelly Criterion chapter uses an intraday Tata Motors futures example with entry at ₹393.65, stop at ₹390 and a first target of at least ₹400. The stop distance there is ₹3.65 and the target distance ₹6.35, which the chapter calls a reward-to-risk of 1.7 and describes as “great for an intraday trade.” That is near 1:2, and nobody is pretending every setup has to hit exactly twice the risk.

Treat 1:2 as a filter, not a straitjacket. If the chart only gives you 1.2R to the next clear level, you have three choices: skip it, trade smaller with a clearer thesis, or accept the lower R:R only if your past win rate on that setup is high enough to keep expectancy positive.

Expectancy: win rate times R, minus loss rate times 1R

Expectancy asks something different from a single trade’s R:R. It asks what you should make on average per trade if you repeat the same rules many times.

Measured in R multiples, where a full stop loss is −1R and a full target at 2R is +2R, a simple version looks like this:

Expectancy (in R) ≈ (Win rate × Average win in R) − (Loss rate × Average loss in R)

If every win is +2R and every loss is −1R, expectancy ≈ (W × 2) − ((1−W) × 1). That turns positive once W is above one-third. A 40% win rate gives you roughly +0.2R per trade before costs, and 50% gives +0.5R. The same formula explains why a lower R:R can still pay: if your wins average only +1R but you win 60% of the time, expectancy also comes out around +0.2R.

Varsity’s Kelly chapter takes it a step further. It defines Kelly % = W − [(1−W)/R], where W is the winning probability (winning trades divided by total trades) and R is the win/loss ratio (average gain on winners divided by average loss on losers). In the chapter’s ten-trade Tata Motors sample, W is 0.6 and R is about 1.384, giving a Kelly percentage of 31%. The chapter then warns straight away against reading that as permission to risk 31% of capital on the next trade, and pairs Kelly with a capped percentage-risk budget instead.

For retail traders in India, two things follow:

• If you set a 1:2 target you almost never reach, you don’t have a 2R system. Your average win in R drops, and expectancy drops with it.
• A mean-reversion setup with a high win rate and a 1:1 target can beat a breakout system with a low win rate and a theoretical 1:3 that rarely plays out.

Pull average win and average loss in rupees from your own trade log, and don’t make them up. Until you have enough trades, use 1:2 for planning, not as a claim about performance.

Size the position from rupee risk, not conviction

R:R only protects you when position size is tied to the stop. The percentage-risk method in Varsity’s Kelly chapter spells out the professional thumb rule: traders don’t risk more than 1% to 3% of capital on any one trade. Its worked example uses 1.5% of a ₹5,00,000 account, or a maximum loss of ₹7,500. With a ₹3.65 stop and a 1,500-share lot, the loss per lot is ₹5,475, so the chapter takes one lot (7,500 ÷ 5,475 ≈ 1.36, rounded down to 1).

For cash equity, the same idea reads:

Quantity = Max rupee risk ÷ (Entry − Stop) for a long, or ÷ (Stop − Entry) for a short.

Illustrative example (not a live quote): say you have ₹2,00,000 of trading capital and risk 1% (₹2,000) on a liquid NSE large-cap. You plan a long at ₹1,000 with a stop at ₹980, which is ₹20 of risk per share. Quantity = 2,000 ÷ 20 = 100 shares. With a first target of ₹1,040, reward is ₹40 per share and the plan’s R:R is 2:1. But if the only clean resistance is at ₹1,015, reward shrinks to ₹15 and R:R to 0.75:1. The sizing formula still works; the trade just may not cover costs or pass your minimum expectancy filter.

Varsity’s position-sizing chapter adds the recovery numbers that make oversizing so costly. After a 5% drawdown you need about 5.3% just to get back to flat. After 10%, you need about 11.1%. After a 60% hole, about 150%. That lopsided maths is why the “I’m very sure, so I’ll take 5R of risk” impulse usually wrecks accounts rather than adding edge.

How a fixed 1:2 on every setup goes wrong

Overfitting here doesn’t mean some elaborate backtest. It means slapping the same 1:2 template on every candle pattern, every Nifty opening range and every stock tip, without checking whether the chart has room to reach the target.

We see the same mistakes on Indian screens again and again:

1. Chasing a far target past structure. You buy a pullback into support with a tight stop below the swing low, then put the target at exactly 2R, even though a prior day’s high, a round number or VWAP is sitting at 1.1R. Price reaches that level, sellers turn up, and you either hand the gains back or watch a winner become a scratch, while still calling it a “1:2” system.
2. Widening the stop to keep a favourite target. You want ₹40 of upside, so you push the stop from ₹15 away to ₹20 away to hold the ratio. Your rupee risk just went up, so quantity should come down. Plenty of traders keep the same size anyway and call it discipline.
3. Ignoring the kind of day it is. On a quiet range day, 2R breakout targets fail all the time. On a trend day, 1:1 mean-reversion fades can be the wrong tool. R:R without the market regime is a spreadsheet, not a strategy.
4. Forcing 1:2 on weekly options premiums. Premium decay and gap risk mean the R:R on the underlying is not the rupee R:R on the premium. Map your stop and target on the instrument you actually hold.

A fixed 1:2 that ignores the next real level on the chart is not risk management. It is a habit wearing a ratio’s clothes.

The fix is dull, and it works. First mark the invalidation level, where the setup is proven wrong. Then mark the first realistic take-profit level, where you expect opposing orders. Only then work out R:R. If the ratio is too low for the expectancy your system needs, skip the trade. Don’t invent a farther target just to make the journal look good.

Worked cash-equity and Nifty-style illustrations

The numbers below are labelled illustrations so you can follow the arithmetic. They are not live NSE quotes from this session. For live index and flow context, use the Stock Market Today India hub along with exchange data.

Illustration A — a cash long with room to run

• Capital: ₹3,00,000; risk per trade: 1% = ₹3,000.
• Entry ₹500; stop ₹490 (risk ₹10); target ₹520 (reward ₹30); planned R:R = 3:1.
• Quantity = 3,000 ÷ 10 = 300 shares.
• If stopped: −₹3,000 (−1R). If the target hits: +₹9,000 (+3R).

The chart offered more than 1:2 here, so take what it gives you instead of trimming the target back to 1:2 out of habit.

Illustration B — forcing 1:2 through resistance

• Same entry at ₹500 and stop at ₹490.
• Clear supply sits at ₹508. An honest first target is ₹508 (reward ₹8, R:R 0.8:1).
• Even stretching the target to ₹510 only works if you imagine liquidity that isn’t there, yet many traders force the 1:2 anyway, write “target ₹520” and then wonder why the fill never comes.

Either skip it, or take a smaller, expectancy-aware scalp, and only if your log shows this setup wins often enough at ~0.8R after costs.

Illustration C — short geometry from Varsity’s published HCL example

Using the numbers from Varsity’s shorting chapter (not a live print): short ₹1,202.80, stop ₹1,215, target ₹1,180, quantity 350. Risk of ₹12.20 and reward of ₹22.80 per share give about 1.87:1. On those figures, full target profit is ₹22.80 × 350 = ₹7,980 and a full stop loss is ₹12.20 × 350 = ₹4,270. The chapter also points out that shorting equity in the spot market is strictly intraday and has to be squared off the same day, while futures shorts can be carried overnight with margin.

Illustration D — lower R:R, higher win rate

• The setup averages +1.0R on wins and −1.0R on losses.
• Its historical win rate in your log: 55%.
• Expectancy ≈ 0.55×1 − 0.45×1 = +0.10R per trade before costs.

That beats a “1:3 breakout” that wins only 20% of the time (expectancy ≈ 0.20×3 − 0.80×1 = −0.20R), even if the breakout looks far more exciting on social media.

When a lower risk reward ratio is still fine

A lower R:R is acceptable when all three of these hold:

1. Your measured win rate for that exact setup is high enough to keep expectancy positive after brokerage, taxes and slippage.
2. The stop is structural, placed beyond a swing, range or level, and not an arbitrary percentage.
3. You aren’t bumping up size to “make the day” after a loss. Varsity’s position-sizing chapter calls this the gambler’s-fallacy trap, where a run of stops convinces you the next trade is “due.”

Intraday mean reversion to a well-watched level, opening-range fades on quiet days, and some pair or spread trades often sit in the 1:1 to 1:1.5 zone. Trend pullbacks and breakouts more often justify aiming for 1:2 or better, but only when the path to the target is clear.

What SEBI says about leverage and guarantees

Getting the R:R maths right doesn’t cancel out leverage. SEBI’s investor-education note on understanding derivatives warns that the amount you pay for a derivatives position is fairly small next to the market price of the underlying, so profits or losses can be multiplied, and that a wrong speculative call can wipe out your net worth. The same page calls derivatives a zero-sum game and cites a SEBI study that found 9 out of 10 individual traders in the equity F&O segment made net losses in both FY 2018-19 and FY 2021-22.

SEBI’s Do’s and Don’ts for securities market investors is just as blunt: don’t borrow money to invest, don’t rely on hot tips, don’t deal with unregistered intermediaries, and dabba trading is illegal. A tidy 1:2 plan paid for with a personal loan, or lifted from an unverified tip channel, is still a bad plan.

Turned into desk rules, that means keeping risk per trade within a 1–3% band of capital, as Varsity’s professional thumb rule says, sizing from the stop, never moving a stop farther away to save a ratio, and treating any promise of “guaranteed” returns as a red flag rather than an edge.

Risk reward ratio trading in India: a pre-trade routine

1. Write down entry, stop and first target in rupees (or index points) before you click buy or sell.
2. Work out R:R from those three prices. Don’t reverse-engineer a target to force 1:2.
3. Size quantity as max rupee risk ÷ stop distance.
4. Check whether the target sits at a real level (prior high/low, round number, anchored average) or in empty space.
5. Test expectancy using your own win rate and average R for this type of setup.
6. In F&O, keep SEBI’s leverage and loss-rate warnings in mind; margin is not the same thing as risk.
7. Log both the planned R:R and the R you actually got. Systems break down in the gap between the two.
8. If the wider process still feels shaky, go back to the risk management in trading guide before adding more setups.

Done this way, risk reward ratio trading in India becomes a habit of sizing and filtering: define the risk first, insist on a reward the chart can actually deliver, size in rupees, and refuse to force a fixed 1:2 onto every candle. It is slower than chasing tips. That’s rather the point.

This article is for education only and is not investment advice. Trading in the cash and derivatives segments of Indian markets involves a risk of loss, including loss of capital.