Build a written trading plan — Stock-Market notes

WHY this works: Human memory is reconstructive, not photographic. In high-stress trading moments, your brain rewrites what you "meant to do" to justify what you did. A written document is immutable evidence that prevents this self-deception.

The Core Components: What Must Be Written

Each component converts "I think" into "I will" statements with measurable criteria.

1. Entry Rules: The Setup Checklist

ALL criteria must be true (logical AND). single "maybe" = NO TRADE.

Example Entry Rule (Momentum Breakout):

Price closes above 20-day high
Volume > 1.5× average daily volume
ATR (volatility) > 2% to ensure sufficient movement
Market (index) is not at -2% or worse today
I have < 3 open positions (concentration limit)

WHY checklist format? Converts subjective "looks good" into objective yes/no tests. The AND operator enforces discipline – you can't rationalize "4 out of 5 is close enough."

Criterion	Data	Pass?	Why This Step?
Price > 20D high	$50.10 close	✓	Confirms breakout timing
Volume	800K vs 600K avg	✓	Validates buyer conviction (1.33× is close, but rule says 1.5×)
STOP		✗	Volume fails → NO TRADE

Even though 4/5 criteria passed, the plan blocks the trade. This prevents the recency bias of "I'll just slightly bend this one rule..."

2. Exit Rules: Pre-Decided Outcomes

Where:

$R$ = risk per share (entry price - stop price)
$k$ = reward-to-risk ratio (typically 2–3)

WHY two exits? Protects against both loss aversion (holding losers) and premature exit (fear-based selling of winners).

Derivation from First Principles: Why do we need exits at all?

Asymetry of ruin: A 50% loss requires 100% gain to recover $\frac{P_{\text{final}}}{P_{\text{initial}}} = (1-0.5)(1+x) = 1 \implies x = 1.0 = 100\%$
Expected value without exits is undefined: $EV = \sum_{i=1}^{\infty} p_i \cdot \text{outcome}_i$ You can't calculate this if you never close positions (infinite hold times).
Stop loss bounds the $\max(\text{loss})$ : $\text{Account}_{\text{worst case}} = \text{Account}_{\text{today}} - n \cdot R$ Where $n$ = number of positions. This makes drawdown predictable.
Target ensures you realize gains before mean reversion: If price follows a random walk with drift, $P_t = P_0 + \mu t + \sigma W_t$ , the probability of reaching target $T$ before stop $S$ is: $P(\text{hit } T \text{ first}) = \frac{S}{S+T}$ (For no drift case). A2:1 target gives ~33% win rate breakeven.

Day 1: Price drops to $47.50.

Check stop: $47.50 <$ 48$ → EXIT immediately
Loss: $(50-47.50) \times 100 = -5$ 250$
WHY this step? The stop preserves $\$ 2300 $of remaining capital for next trade. Without it, you might hold to$ 40 (-$1000 loss = 5× worse).

Day 3(alternate timeline): Price rises to $54.25.

Check target: $54.25 >$ 54$ → EXIT immediately
Profit: $(54-50) \times 100 = +8$ 400$
WHY this step? Lock in the2R gain. Holding for "more" often leads to giving back profits when price reverses.

3. Position Sizing: The Math of Survival

Where:

$f^*$ = fraction of capital to risk
$p$ = probability of win
$q = 1-p$ = probability of loss
$b$ = reward-to-risk ratio

Derivation from scratch: Goal: Maximize long-term growth rate $G$ .

After $N$ trades with $W$ wins and $L$ losses: $\text{Capital} = C_0(1+fb)^W(1-f)^L$

Taking log (since we want exponential growth): $\ln(\text{Capital}) = \ln(C_0) + W\ln(1+fb) + L\ln(1-f)$

Growth rate per trade: $G = \frac{1}{N}[\ln(\text{Capital}) - \ln(C_0)] = p\ln(1+fb) + q\ln(1-f)$

Maximize $G$ by taking $\frac{dG}{df} = 0$ : $\frac{pb}{1+fb} - \frac{q}{1-f} = 0$

Solving for $f$ : $pb(1-f) = q(1+fb)$ $pb - pbf = q + qbf$ $pb - q = f(pb + qb) = fb(p+q) = fb$ $f^* = \frac{pb - q}{b}$

Practical rule: Use half-Kelly ( $f^*/2$ ) to reduce volatility.

Step 1: Calculate full Kelly $f^* = \frac{0.4 \times 2.5 - 0.6}{2.5} = \frac{1.0 - 0.6}{2.5} = \frac{0.4}{2.5} = 0.16$

WHY this step? Kelly tells us theoretically optimal risk fraction that maximizes growth without ruin.

Step 2: Apply half-Kelly safety margin $f_{\text{use}} = \frac{0.16}{2} = 0.08 = 8\%$

Step 3: Risk per trade $\text{Risk} = 0.08 \times \$10000 = \$800$

Step 4: Determine shares (if stop is $2 away from entry) $$\text{Shares} = \frac{\$ 800}{$2} = 400 \text{ shares}$$

WHY this step? This math guarantees you can survive 12 consecutive losses (1/0.08 ≈ 12) before account depletion – nearly impossible with40% win rate.

4. Market Condition Filters: When NOT to Trade

WHY filter conditions? They block trades when edge disappears or execution degrades.

Derivation: In high volatility, stop distances must widen to avoid noise-based exits: $\text{Stop distance} \propto \sigma_{\text{price}}$

If you keep fixed stops during VIX spikes, false stop-outs increase: $P(\text{stopped out}) = P(|P_t - P_0| > S) \approx 2\Phi\left(-\frac{S}{\sigma\sqrt{t}}\right)$

As $\sigma$ doubles, your stops get hit2× more often even without true trend changes.

Check filters:

VIX = 35 (Friday close was 22) → BLOCKED
Why? Your backtested win rate is 40% at VIX < 25, but only 28% at VIX > 30. The edge disappeared.

Even though the setup looks "perfect," the plan overides your judgment. This is pre-commitment working.

5. Review Process: The Feedback Loop

More frequent reviews = faster adaptation, but:

Daily: Too noisy (randomness dominates)
Monthly: Too slow (bad habits solidify)
Weekly: Optimal balance

Weekly review checklist:

Trade log: Did I follow the plan? (% compliance)
Error classification: Which rules did I break? Why?
P&L attribution: Wins/losses from plan vs. deviations
Plan adjustment: Does data support a rule change?

WHY weekly? Central Limit Theorem: With 20–40 trades/month, weekly samples (5–10 trades) provide enough data to detect patterns without overreacting to noise.

Trade #	Compliant?	Outcome	R-multiple
1	Yes	Win	+2.1R
2	Yes	Loss	-1.0R
3	No (chased)	Loss	-1.5R
4	Yes	Win	+2.3R
5	No (held past target)	Win	+1.2R (gave back 0.8R)
6	Yes	Loss	-1.0R
7	No (no stop)	Loss	-3.0R
8	Yes	Win	+2.0R

Analysis:

Compliant trades: 4W-1L = 80% win rate, avg +1.48R
Violations: 1W-2L = 33% win rate, avg -1.1R
Insight: Deviations cost 3.6R (= $360 if$ 100/R)

WHY this step? Quantifies the cost of indiscipline. Seeing "-$360 from not following plan" is more powerful than "I should be disciplined."

The Psychology: Why Plans Fail (And How to Fix It)

WHY this feels right: Confirmation bias shows you the trades that would have worked. You don't see the 10 similar setups that failed.

The fix:

Track "missed" trades separately (paper trade them)
After 30 occurrences, calculate if they would have improved your stats
If yes, add this setup to the plan (formal rule change)
If no, you've proven the plan is correct

Math: With sample size $n=30$ , confidence interval for win rate: $\text{CI} = p \pm 1.96\sqrt{\frac{p(1-p)}{n}}$

At $p=0.5$ , this gives ±18% margin. You need≥30 samples to distinguish a 50% rule from a 65% rule with statistical confidence.

WHY this feels right: You want the plan to be "perfect" before committing.

The fix:

Write version 1.0 TODAY with your current best guesses
Label it "Draft – update after20 trades"
Having a bad plan to revise > having no plan to drift

Analogy: Scientists don't wait for the perfect hypothesis. They test the current best guess and refine based on data.

Active Recall Checkpoints

Recall Explain This to a 12-Year-Old

Imagine you're playing a video game, but every time you make a mistake, you lose real money. Scary, right?

A trading plan is like writing down the game strategy BEFORE you start playing. You decide: "I'll only jump when I see THIS enemy" (entry rules), "I'll run away if my health drops below 20%" (stop loss), and "I'll quit after I collect 50 coins" (target).

Why write it down? Because when you're in the game, your heart is pounding and you might make a dumb move. But if you wrote "Never jump into lava" yesterday when you were calm, you can read that rule and save yourself.

The cool part: every week, you review your game replays. If you followed your rules, you usually win. If you ignored them, you usually lose. So paper isn't just advice – it's like a cheat code that only works if you follow it exactly.

Concept Map

Hinglish (regional understanding)

Intuition Hinglish mein samjho

Dekho, trading mein sabse bada dushman hamara apna dimaag hota hai, kyunki jab market bhaag rahi hoti hai tab hum emotional ho jaate hain aur galat decisions le lete hain. Iska solution hai ek written trading plan — matlab apni saari rules pehle se paper pe likh dena. Jab aap calm mind mein baithe ho tab jo decisions likhte ho, wo trading ke chaos wale moments mein aapko bachate hain. Yaad rakhna, humari memory photograph nahi hai — high-stress mein humara brain baad mein cheezein "rewrite" kar deta hai apni galtiyon ko justify karne ke liye. Ek likha hua document immutable evidence hai jo yeh self-deception rok deta hai.

Ab is plan mein kya-kya hona chahiye? Paanch main cheezein — entry rules (kaunse setup pe trade karna hai), exit rules (kab profit ya loss book karna hai), position sizing (kitna paisa ek trade mein lagana hai), market conditions (kab trade NAHI karna), aur review process (kaise improve karna hai). Entry ke liye checklist banao jismein SAARI conditions ek saath true honi chahiye (logical AND) — 5 mein se 4 pass ho toh bhi "NO TRADE". Yeh discipline enforce karta hai taaki aap "chalta hai, thoda bend kar deta hoon" wali rationalization na karo. Exit ke liye do system rakho: stop loss (loss ko bound karta hai) aur target (profit ko lock karta hai mean reversion se pehle).

Yeh sab why-matters isliye hai kyunki loss ki ek nasty asymmetry hai — 50% loss recover karne ke liye aapko 100% gain chahiye! Isliye stop loss lagana zaroori hai, warna ek hi bura trade aapka account tabaah kar sakta hai. Aur reward-to-risk ratio (jaise 2:1 ya 3:1) rakhne se aap kam win-rate pe bhi profitable reh sakte ho. Simple baat yeh hai — jab aap apne rules likh dete ho aur unhe follow karte ho, tab aapka trading ek predictable, measurable process ban jaata hai, na ki emotions pe chalne wala gamble. Yehi ek amateur aur professional trader ke beech ka farak hai.

Test yourself — Trading Psychology