Position sizing — the single most important habit
Why this lesson exists
Most retail traders blow up for one reason: they trade too big. Not because they picked the wrong direction, not because the strategy was bad — because a normal losing trade ate more of the account than it should have, and the next trade had to be bigger to recover, and the one after that bigger still.
Position sizing is the habit that breaks this loop. It is one short formula and a 5-second calculation before every entry. Skip it and nothing else in this module saves you. Use it and your worst week looks like a paper cut instead of an evacuation.
This lesson covers the formula, why it is leverage-agnostic, why 1% risk per trade survives where 5% does not, and how to apply it on a ₹5,00,000 xtree Standard account.
The formula
Position size is the answer to one question: if my stop hits, how much do I lose? You decide the rupee answer first. Then you back out the size.
position_size = risk_rupees / stop_distance_rupees
position_size = risk_rupees ÷ stop_distance_rupees
That is it. Three numbers. The order they get decided in matters:
- Account size. On Standard, ₹5,00,000.
- Risk per trade as a fraction. Pick something between 0.5% and 2%. Most professionals sit at 1%.
- Stop distance. This comes from the chart, not from your account. Wherever the trade idea is invalidated — below the swing low for a long, above the prior swing high for a short — that is the stop.
Multiply (1) by (2) to get your risk in rupees. Divide by (3) to get the size.
Walking through a real entry
Standard account, ₹5,00,000. Risk per trade: 1% = ₹5,000.
You like a BTC long on the 1h chart. Mid-mark is ₹85,00,000. The most recent swing low — your invalidation point — is ₹84,50,000. Stop distance: ₹50,000 per BTC.
position_size = ₹5,000 / ₹50,000 = 0.1 BTC
Notional exposure is 0.1 × ₹85,00,000 = ₹8,50,000. At 5× leverage, the margin you actually have to post is ₹1,70,000. That fits comfortably inside a ₹5L account.
If BTC drops to ₹84,50,000 and your stop fires, the loss is 0.1 BTC × ₹50,000 = ₹5,000. Exactly what you planned. Not ₹50,000, not ₹500. The number you wrote down before you clicked.
Why leverage is not in the formula
Look at the formula again. There is no leverage term. This is not a mistake.
Leverage controls how much margin you have to post to hold the position. It does not change how much you make or lose per ₹ of price movement. (Lesson 1.3 P&L mechanics derives this — P&L is always size × price_move, never multiplied by leverage.)
So leverage adapts the margin requirement to the size you already chose. Run the BTC example at different leverages:
| Leverage | Margin needed | Risk if stop hits | |---|---|---| | 1× | ₹8,50,000 | ₹5,000 | | 5× | ₹1,70,000 | ₹5,000 | | 10× | ₹85,000 | ₹5,000 |
The risk number does not move. What moves is whether the trade physically fits on your account. At 1× the trade is impossible on a ₹5L account — you do not have ₹8.5L of margin. At 5× it fits. At 10× it leaves room for two or three more positions. Pick the lowest leverage that lets the trade fit, no more. (See Leverage, margin, and liquidation for the survivable-range trade-off.)
On xtree's Standard tier, the asset cap is 10× on xBTC/xETH/xGOLD and 5× on xOIL. These caps exist so that the position-sizing formula always has a feasible leverage that fits the account — you should never need to fight the cap.
Why 1% — the compounding argument
A 1% rule sounds conservative. Run the math on a 10-trade losing streak — which every trader hits eventually — and the conservatism becomes survival.
Starting balance: ₹5,00,000. Risk per trade kept constant as a fraction.
| Risk per trade | Account after 10 straight losses | Drawdown | |---|---|---| | 1% | ₹4,52,386 | 9.5% | | 2% | ₹4,08,931 | 18.2% | | 5% | ₹2,99,374 | 40.1% | | 10% | ₹1,74,339 | 65.1% |
At 1% risk, ten losses in a row barely scratch the account. The trader can keep working. At 10% risk, ten losses leave them with a third of their starting capital and no realistic path back — they need a 187% gain to recover.
This is the asymmetry: losses compound geometrically against you, and the larger each loss is, the faster the curve bends. A 50% drawdown requires a 100% gain to recover. A 75% drawdown requires a 300% gain. (Lesson 2.3 MLL digs into this drawdown-recovery math in detail.)
A tighter stop lets you size bigger
Here is the part that feels counter-intuitive. A tighter stop increases your position size, because the risk per BTC is smaller.
Same ₹5,00,000 account, same 1% (₹5,000) risk. ETH at ₹2,00,000, a 15m setup with a tight stop at ₹1,99,500 — ₹500 away.
position_size = ₹5,000 / ₹500 = 10 ETH
Notional 10 × ₹2,00,000 = ₹20,00,000. At 10× leverage, margin ₹2,00,000. That fits.
Same rupee risk as the BTC trade. Much larger notional. The tight stop earned the size. This is why scalpers can run apparently "huge" positions without taking apparently huge risk — their stops are tiny, so their notional is large but their loss-if-wrong is small.
The reverse holds too. A wide stop on a daily chart shrinks your size to almost nothing — and that is correct. The chart is telling you the trade is uncertain; the formula is telling you to size for that uncertainty.
Position sizing and the DLL — a structural fit
The position-size formula and xtree's Daily Loss Limit are designed to work together. With DLL opted in at ₹10,000 and a 1% risk rule (₹5,000 per trade), the DLL is exactly two consecutive losing trades. That's not a coincidence — it's the platform telling you that more than two full-risk losses in a single session is a sign your day-management has broken down, regardless of strategy.
If you find yourself routinely placing four or five 1%-risk trades per day and breaching DLL on bad sessions, the problem isn't the DLL — it's that you're sizing for activity rather than for the day. Most professional traders take one to three setups per session. Forced selectivity from a tight DLL is a feature, not a bug.
The same logic applies to the Maximum Loss Limit. The MLL cushion of ₹15,000 is three full-risk losing trades. If you can lose your entire MLL cushion in one trading session, your sizing is wrong for the platform.
Worked example
Standard ₹5,00,000 account. You opted into the DLL at signup so your day-stop is ₹10,000 and you pay ₹4,000/month.
You take three setups in one day, each sized at 1% risk:
| Trade | Entry | Stop | Stop distance | Size | Outcome | Realised P&L | |---|---|---|---|---|---|---| | 1. xBTC long | ₹85,00,000 | ₹84,50,000 | ₹50,000 | 0.1 BTC | Stopped | −₹5,000 | | 2. xETH long | ₹2,00,000 | ₹1,98,000 | ₹2,000 | 2.5 ETH | Stopped | −₹5,000 | | 3. xBTC short | ₹85,40,000 | ₹85,60,000 | ₹20,000 | 0.25 BTC | +1.5R | +₹7,500 |
End of day: −₹2,500. Nowhere near the ₹10,000 DLL. Two losing trades sized correctly cost less than one over-sized trade would have. The third trade — a win — covers most of the day's losses and proves nothing about your skill. The sizing did the heavy lifting.
Common misunderstanding
"My account is small so I need to use higher leverage to make meaningful money."
The leverage does not change how much you make. The size does. A ₹50,000 trader who uses 50× leverage to put on ₹25,00,000 notional has not increased their earning power — they have just made their account fragile to a 2% move. A 2% adverse move = liquidation. They will be wiped out by routine intraday noise.
The same trader sized using the formula — say ₹500 risk per trade (1% of ₹50K), 1% stop distance on BTC — would hold a position around ₹50,000 notional. Small? Yes. But survivable across the inevitable losing streak. The path to meaningful money is not bigger leverage on a small account; it is consistent sizing on a growing account.
Recap
- Position size = risk_rupees / stop_distance_rupees. Three inputs, one formula, every trade.
- Leverage is not in the formula. It only changes the margin you have to post.
- Risk 0.5%–2% per trade. 1% is the professional default.
- A tighter stop earns a larger size at the same rupee risk.
- Ten losing trades at 1% costs ~9.5% of the account; at 10% it costs 65%. Choose your survival curve.
Next up: how to evaluate whether a strategy is actually worth running — risk-to-reward, expectancy, and why "high win rate" is the wrong question.
Test yourself
Next lesson: Risk-to-reward and expectancy — why a 30% win rate can be profitable and a 70% win rate can be a slow bleed.