Level 4 — ApplicationEntry, Exit & Trade Management

Entry, Exit & Trade Management

⏱ 60 minutes60 marksprintable — key stays hidden on paper

Level 4 — Application (Novel Problems, No Hints) Time Limit: 60 minutes Total Marks: 60

Instructions: Show all working. Round currency to two decimals and R-multiples to two decimals unless stated otherwise. Use $...$ notation where helpful.

Question 1 — Setup, Entry & Stop Construction (12 marks)

A trader defines a long swing setup on stock XYZ with the following rules:

Entry only on a breakout close above the 20-day high of $₹248.00$ , confirmed by volume $>1.5×$ the 20-day average volume.
Initial stop is placed at the most recent swing low of $₹236.50$ (structure stop).
Minimum acceptable risk-reward ratio is $1:2$ .

On the signal day, XYZ closes at $₹250.00$ on volume of $1.8×$ average. The trader enters at the next day's open of $₹251.20$ .

(a) State whether the entry is valid per the confirmation rules, and justify. (2) (b) Compute the per-share risk (R) using the actual entry price and structure stop. (3) (c) Determine the minimum target price that satisfies the $1:2$ RRR from the entry. (3) (d) The trader worries the structure stop is "too far." Explain one consequence of instead tightening the stop to $₹247.00$ on both the R value and the probability of premature exit. (4)

Question 2 — ATR Stop vs Fixed Stop (12 marks)

Stock ABC is entered long at $₹1,000.00$ . The 14-period ATR is $₹18.00$ . The trader considers two stop methods:

Method A (ATR): stop at entry $-\;2.5 \times \text{ATR}$ .
Method B (Fixed %): stop at entry $-\;3\%$ .

(a) Compute the stop price and per-share risk for each method. (4) (b) Trading capital is $₹5,00,000$ and the trader risks $1\%$ of capital per trade. Compute the position size (shares, rounded down) for each method. (4) (c) A week later ABC's ATR expands to $₹30.00$ due to earnings volatility. Explain, with reference to the numbers, why the ATR stop is generally preferred over the fixed stop in changing volatility regimes. (4)

Question 3 — R-Multiples & Expectancy (14 marks)

A trader's last 10 trades produced the following results in R-multiples (loss = negative R):

$-1,\ +2.5,\ -1,\ +3,\ -0.4,\ +1.8,\ -1,\ +2,\ -1,\ +0.5$

(Note: a $-0.4R$ occurs when a partial-loss exit closes before the full stop; a $+0.5R$ is a scratched partial winner.)

(a) Compute the win rate. (2) (b) Compute the average win (in R) and average loss (in R). (4) (c) Compute the expectancy per trade in R. (4) (d) If the trader takes 200 such trades in a year and risks $₹2,000$ per R, estimate the expected annual profit. (2) (e) The trader wants to raise expectancy to $+0.75R$ without changing entries. State one exit-management change and justify quantitatively how it could help. (2)

Question 4 — Scaling Out & Trade Management (12 marks)

A trader goes long 300 shares of stock PQR at $₹500.00$ , initial stop $₹485.00$ (so 1R = $₹15$ /share). The plan:

Scale out 100 shares at +1R, then move stop to break-even.
Scale out 100 shares at +2R.
Trail the final 100 shares with a stop $1R$ below price, exiting when hit.

The trade plays out: price hits $+1R$ , then $+2R$ , then peaks at $+3.5R$ before reversing and hitting the trailing stop.

(a) Compute the exit price at each of the three scale-out/exit events. (3) (b) Compute the total rupee profit of the whole trade. (5) (c) Express the total profit as a blended R-multiple on the original full position (1R risk = $300 \times ₹15$ ). (2) (d) Explain how moving to break-even after the first scale-out changed the worst-case outcome of the remaining position. (2)

Question 5 — Cutting Losers & Break-Even Logic (10 marks)

A trader has a mechanical rule: "Cut the loser if price closes below the entry candle's low; otherwise hold to target." Consider two trades:

Trade 1: Entry $₹80$ , stop $₹76$ (1R = $₹4$ ), target $₹88$ . Price never breaks the entry low, reaches target.
Trade 2: Entry $₹80$ , stop $₹76$ , target $₹88$ . Price closes below entry-candle low ( $₹78$ ) after two days and the trader exits at $₹78.20$ .

(a) Compute the R-multiple result of each trade. (4) (b) Compare the outcome of Trade 2 under the "cut early" rule versus holding to the full stop, in both R and the emotional/statistical rationale. (4) (c) A trader claims "moving to break-even guarantees no loss." State one scenario where a break-even stop still results in a small loss. (2)

Answer keyMark scheme & solutions

Question 1 (12 marks)

(a) [2] Entry is valid. Breakout close ( $₹250.00 > ₹248.00$ ) confirmed AND volume $1.8× > 1.5×$ average. Both rule conditions met. (1 for close condition, 1 for volume condition)

(b) [3] Risk per share $= \text{Entry} - \text{Stop} = 251.20 - 236.50 = ₹14.70$ . (2 for setup, 1 for answer)

(c) [3] Reward needed $= 2 \times R = 2 \times 14.70 = ₹29.40$ . Minimum target $= 251.20 + 29.40 = ₹280.60$ . (2 for method, 1 for answer)

(d) [4]

Tighter stop at $₹247.00$ : new R $= 251.20 - 247.00 = ₹4.20$ (much smaller). (1)
Smaller R allows larger position / smaller rupee risk per share. (1)
BUT stop now sits above the structure swing low, inside normal noise range → much higher probability of premature exit on retest. (1)
Trade-off: tighter stops improve RRR arithmetic but degrade win rate; structure stop respects market mechanics. (1)

Question 2 (12 marks)

(a) [4]

Method A: stop $= 1000 - 2.5 \times 18 = 1000 - 45 = ₹955.00$ ; risk $= ₹45.00$ /share. (2)
Method B: stop $= 1000 \times (1-0.03) = ₹970.00$ ; risk $= ₹30.00$ /share. (2)

(b) [4] Rupee risk budget $= 1\% \times 5,00,000 = ₹5,000$ .

Method A: $5000 / 45 = 111.11 \Rightarrow$ 111 shares. (2)
Method B: $5000 / 30 = 166.67 \Rightarrow$ 166 shares. (2)

(c) [4]

ATR stop adapts to volatility: as ATR rises $18 \to 30$ , the stop distance widens ( $2.5 \times 30 = 75$ ), keeping the stop outside expanded noise. (2)
Fixed 3% stop stays at $₹30$ risk regardless — in high volatility it is now too tight, causing whipsaw stop-outs. (1)
ATR method keeps risk proportional to market conditions and automatically reduces position size when risk expands (via wider R). (1)

Question 3 (14 marks)

Data: $-1, +2.5, -1, +3, -0.4, +1.8, -1, +2, -1, +0.5$

(a) [2] Wins (positive R): $+2.5, +3, +1.8, +2, +0.5 = 5$ wins. Win rate $= 5/10 = 50\%$ . (2)

(b) [4]

Sum of wins $= 2.5+3+1.8+2+0.5 = 9.8$ ; average win $= 9.8/5 = +1.96R$ . (2)
Sum of losses $= -1-1-0.4-1-1 = -4.4$ ; average loss $= -4.4/5 = -0.88R$ . (2)

(c) [4] Expectancy $= (\text{WinRate} \times \text{AvgWin}) + (\text{LossRate} \times \text{AvgLoss})$ $= (0.5 \times 1.96) + (0.5 \times -0.88) = 0.98 - 0.44 = +0.54R$ . (2 for formula, 2 for answer) (Check: total R $= 9.8 - 4.4 = 5.4$ ; $5.4/10 = 0.54R$ .) ✓

(d) [2] Expected annual profit $= 200 \times 0.54R \times ₹2000 = 200 \times 0.54 \times 2000 = ₹2,16,000$ . (2)

(e) [2] Example: let winners run further / trail more loosely to raise average win, OR cut losers faster to make average loss smaller (e.g., $-0.7R$ ). Quantitative: reducing avg loss from $-0.88$ to $-0.5$ gives expectancy $= 0.98 - 0.25 = +0.73R \approx$ target. (1 change + 1 justification)

Question 4 (12 marks)

1R $= ₹15$ /share, entry $₹500$ .

(a) [3]

$+1R$ exit: $500 + 15 = ₹515.00$ . (1)
$+2R$ exit: $500 + 30 = ₹530.00$ . (1)
Trailing stop $1R$ below peak of $+3.5R$ : peak price $= 500 + 3.5\times15 = ₹552.50$ ; trailing exit $= 552.50 - 15 = ₹537.50$ (i.e. $+2.5R$ ). (1)

(b) [5]

100 shares @ $+1R$ : $100 \times 15 = ₹1,500$ . (1)
100 shares @ $+2R$ : $100 \times 30 = ₹3,000$ . (1)
100 shares @ $+2.5R$ : $100 \times 37.50 = ₹3,750$ . (2)
Total $= 1500 + 3000 + 3750 = ₹8,250$ . (1)

(c) [2] Original 1R risk $= 300 \times 15 = ₹4,500$ . Blended R $= 8250 / 4500 = +1.83R$ . (2)

(d) [2] After scaling out at $+1R$ and moving to break-even, the remaining 200 shares' worst-case outcome became ₹0 loss (stop at entry) rather than $-1R$ on that portion — the trade was locked risk-free while still holding upside. (2)

Question 5 (10 marks)

(a) [4]

Trade 1: reaches target $₹88$ ; profit $= 88-80 = ₹8$ /share; R $= 8/4 = +2.00R$ . (2)
Trade 2: exit $₹78.20$ ; result $= 78.20 - 80 = -₹1.80$ /share; R $= -1.80/4 = -0.45R$ . (2)

(b) [4]

Under "cut early": loss $= -0.45R$ . (1)
Under full stop ( $₹76$ ): loss $= -1.00R$ . (1)
Cutting early saves $0.55R$ per such trade; over many trades this materially reduces average loss (raising expectancy). (1)
Rationale: exits weak trades before full stop, freeing capital and reducing drawdown; discipline avoids "hoping" a broken setup recovers. (1)

(c) [2] A break-even stop can still lose to slippage/gap: e.g., price gaps down through the break-even level overnight and fills below entry, or bid-ask spread + brokerage/costs make the net exit slightly negative. (2)

[
  {"claim":"Q1b risk per share = 14.70 and Q1c min target = 280.60","code":"entry=Rational('251.20'); stop=Rational('236.50'); R=entry-stop; target=entry+2*R; result=(R==Rational('14.70') and target==Rational('280.60'))"},
  {"claim":"Q2 position sizes: ATR->111 shares, Fixed->166 shares","code":"budget=5000; atr_risk=45; fix_risk=30; a=budget//atr_risk; b=budget//fix_risk; result=(a==111 and b==166)"},
  {"claim":"Q3 expectancy = 0.54R and annual profit = 216000","code":"Rs=[-1,Rational('2.5'),-1,3,Rational('-0.4'),Rational('1.8'),-1,2,-1,Rational('0.5')]; exp=sum(Rs)/len(Rs); profit=200*exp*2000; result=(exp==Rational('0.54') and profit==216000)"},
  {"claim":"Q4 total profit 8250 and blended 1.83R","code":"p=100*15+100*30+100*Rational('37.5'); blended=p/(300*15); result=(p==8250 and round(float(blended),2)==1.83)"},
  {"claim":"Q5 Trade2 R = -0.45","code":"r=(Rational('78.20')-80)/4; result=(r==Rational('-0.45'))"}
]