REKT AUTOPSY
FORENSIC TRADING ANALYSIS
BEHAVIORAL AUTOPSY / 5cs59HeEs1hAsoKot162jGP5xBxCCVHLgmZTu2U7VFBr
F
Behavioral Grade
F
20 / 100
Wallet Death Certificate
At your current pace, your capital halves every 0 days.
5cs59HeEs1hAsoKot162jGP5xBxCCVHLgmZTu2U7VFBr
74% of your losses are behavioral — not bad picks, bad habits. That's 1143.978 SOL.
Your brain cost you
1.1k SOL
≈ $89.0k behavioral damage
Survival prob.
0%
139 wins to break even
Capital half-life
0d
Bleed rate
11.4 SOL/hr
Win rate
2%
Net P&L
−1.4k SOL
Win rate
2%
Profit factor
0.1
Tokens
767
SOL in
1.7k
SOL out
303.9
Primary
Diagnosis
74% of attributed damage came from recurring behavior, not market noise — tilt, timing, sizing.
§ 03 · Compound Intelligence
THE NUMBERS THAT MATTER.
Avoidable Loss Score
Most losses were behavioral, not random.
74%
BEHAVIOR-DRIVEN
NOISE

Estimated share of total attributed loss caused by repeatable actions: re-entry after loss, average-down escalation, and holding through terminal drawdown.

Recovered Perf.
+1.1k SOL
Catchable Losses
512 / 750
Behavioral Drag
74%
Behavioral Kill Chain
The repeatable sequence of destruction.

The autopsy does not just rank mistakes — it identifies the recurring sequence that converts a normal loss into a terminal drawdown.

Loss
750 losses
→
Fast re-entry
317x
→
Bigger size
34 tokens
→
Late entry
→
Held collapse
Stages Fired
3 / 5
Fast Re-entries
317x
BEHAVIORAL DAMAGE REPORT
😤
Revenge Trading
-600.1 SOL($46.7k)
317 trades within 10 min of a loss
Win rate on these: 3%
⏰
Late Night Trading
-502.4 SOL($39.1k)
248 trades between 11PM–5AM
Best hours: 12PM
😴
Session Fatigue
-234.2 SOL($18.2k)
After trade #29, avg P&L drops to -2.6467 SOL
Trade #1 avg: -1.9719 SOL
📈
Averaging Down
-216.8 SOL($16.9k)
34 tokens where later buys were 1.5x larger
Later buys on losing positions
TOTAL BEHAVIORAL DRAG
-1.6k SOL($120.8k)
CIRCADIAN PROFILE
WORST: 2AM▾
12a51
1a36
2a46
3a31
4a39
5a48
6a34
7a30
8a27
9a16
10a11
11a4
12p9
1p21
2p20
3p29
4p26
5p26
6p41
7p49
8p38
9p50
10p40
11p45
Night (11PM–5AM): -2.0257 SOL/trade · Day (8AM–8PM): -1.5098 SOL/trade
DISPOSITION EFFECT
0x▾
WINNER HOLD
1.3d
LOSER HOLD
6m
You hold losers 0x longer than winners.
SESSION FATIGUE
177 SESSIONS▾
#1
-1.9719
#2
-1.9927
#3
-1.176
#4
-1.537
#5
-1.7472
#6
-1.6857
#7
-2.219
#8
-2.2689
Avg session: 4.3 trades. Dominant pattern: Loss from the Start (93%)
POST-LOSS VELOCITY
NORMAL▾
POST-LOSS GAP
12m
BASELINE GAP
12m
317 trades opened within 10 min of a loss. Loss rate on these: 97%
EQUITY CURVE
DISCIPLINED: +1.1k SOL▾
Peak: +0.0000 SOL at token #1
Max drawdown: 1.4k SOL
- - - = you without 512 behavioral trades
P&L SUMMARY
▾
ADVANCED METRICS
▾
LOSS CONCENTRATION
▾
DEX ANALYSIS
▾
YOUR PRESCRIPTION
Rules derived from your trading data that would have saved you 2.5k SOL (160% of total losses)
1
STOP BUYING ON PUMP_FUN
732 tokens from PUMP_FUN: 1% WR, -1371.130 SOL net
→ Would save ~822.7 SOL
2
WAIT 30 MIN AFTER A LOSS
Post-loss trades lose 97% vs 98% baseline
→ Would save ~600.1 SOL
3
DON'T TRADE 2AM–5AM
Late-night avg: -2.0257 SOL vs -1.5098 daytime
→ Would save ~502.4 SOL
4
NEVER ADD TO A LOSING POSITION
Later buys on losers are 1.5x larger than first buy
→ Would save ~316.1 SOL
5
DON'T INCREASE SIZE AFTER A LOSS
Avg size after loss: 2.228 SOL vs 1.625 after win (1.37x)
→ Would save ~225.8 SOL
Select a token for AI-narrated forensic analysis
Individual Token Autopsies
Forensic breakdown of 5 autopsied tokens — winners and losers. Open any for the full report, or trade directly.
HDeY...pump
Token went to zero. Wallet went to therapy.
The Rug Victim
−11.0 SOL
-100.0%
6.0/10
Full autopsy →
HJ1y...pump
Token went to zero. Wallet went to therapy.
The Rug Victim
−9.879 SOL
-100.0%
7.0/10
Full autopsy →
5E4j...pump
Token went to zero. Wallet went to therapy.
The Rug Victim
−9.041 SOL
-100.0%
7.0/10
Full autopsy →
7QSb...pump
Token went to zero. Wallet went to therapy.
The Rug Victim
−9.028 SOL
-100.0%
6.0/10
Full autopsy →
3dyk...pump
Token went to zero. Wallet went to therapy.
The Rug Victim
−8.933 SOL
-100.0%
7.0/10
Full autopsy →
NOT FINANCIAL ADVICE. JUST THE MATH.