REKT AUTOPSY
FORENSIC TRADING ANALYSIS
BEHAVIORAL AUTOPSY / 99i9uVA7Q56bY22ajKKUfTZTgTeP5yCtVGsrG9J4pDYQ
F
Behavioral Grade
F
7 / 100
Wallet Death Certificate
The math gives you a 99% chance of going to zero.
99i9uVA7Q56bY22ajKKUfTZTgTeP5yCtVGsrG9J4pDYQ
At your current pace, your capital halves every 0 days.
Your brain cost you
749.8 SOL
$58.1k behavioral damage
Survival prob.
0%
101 wins to break even
Capital half-life
0d
Bleed rate
22.8 SOL/hr
Win rate
6%
Net P&L
663.1 SOL
Win rate
6%
Profit factor
0.19
Tokens
397
SOL in
1.4k
SOL out
687.1
Primary
Diagnosis
91% 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.
91%
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.
+749.8 SOL
Catchable Losses
324 / 358
Behavioral Drag
91%
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
358 losses
Fast re-entry
282x
Bigger size
3 tokens
Late entry
Held collapse
3.9x hold
Stages Fired
4 / 5
Fast Re-entries
282x
BEHAVIORAL DAMAGE REPORT
😤
Revenge Trading
-550.0 SOL($42.6k)
282 trades within 10 min of a loss
Win rate on these: 7%
Late Night Trading
-383.0 SOL($29.7k)
209 trades between 11PM–5AM
Best hours: 10PM
💎
Diamond Hand Delusion
-108.2 SOL($8.4k)
Held losers 3.9x longer than winners
Winners: 5m · Losers: 18m
📈
Averaging Down
-91.6 SOL($7.1k)
3 tokens where later buys were 1.1x larger
Later buys on losing positions
😴
Session Fatigue
-24.7 SOL($1.9k)
After trade #57, avg P&L drops to -2.2489 SOL
Trade #1 avg: -1.365 SOL
TOTAL BEHAVIORAL DRAG
-1.2k SOL($89.7k)
CIRCADIAN PROFILE
WORST: 5AM
12a60
1a36
2a46
3a27
4a7
5a2
4p1
5p10
6p30
7p27
8p51
9p44
10p23
11p33
Night (11PM–5AM): -1.8326 SOL/trade · Day (8AM–8PM): -1.9111 SOL/trade
DISPOSITION EFFECT
3.9x
WINNER HOLD
5m
LOSER HOLD
18m
You hold losers 3.9x longer than winners. This is extreme. Cut losers faster.
SESSION FATIGUE
22 SESSIONS
#1
-1.365
#2
-1.8269
#3
-2.0607
#4
-2.2351
#5
-1.3697
#6
-1.2383
#7
-2.3037
#8
-2.6858
Avg session: 18 trades. Dominant pattern: Loss from the Start (71%)
POST-LOSS VELOCITY
NORMAL
POST-LOSS GAP
3m
BASELINE GAP
3m
282 trades opened within 10 min of a loss. Loss rate on these: 93%
EQUITY CURVE
DISCIPLINED: +749.8 SOL
Peak: +26.4 SOL at token #10
Max drawdown: 689.4 SOL
- - - = you without 324 behavioral trades
P&L SUMMARY
ADVANCED METRICS
LOSS CONCENTRATION
DEX ANALYSIS
YOUR PRESCRIPTION
Rules derived from your trading data that would have saved you 1.6k SOL (194% of total losses)
1
WAIT 30 MIN AFTER A LOSS
Post-loss trades lose 93% vs 94% baseline
→ Would save ~550.0 SOL
2
DON'T TRADE 5AM–8AM
Late-night avg: -1.8326 SOL vs -1.9111 daytime
→ Would save ~383.0 SOL
3
STOP BUYING ON PUMP_FUN
336 tokens from PUMP_FUN: 1% WR, -628.726 SOL net
→ Would save ~377.2 SOL
4
NEVER ADD TO A LOSING POSITION
Later buys on losers are 1.1x larger than first buy
→ Would save ~227.9 SOL
5
AVOID TRADING ON TUES
TUE avg: -2.3225 SOL across 32 trades
→ Would save ~52.0 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.
EPjF...Dt1v
Never sold. Still holding. Still hoping.
The Bag Holder
47.2 SOL
-100.0%
7.0/10
Full autopsy →
D9pj...pump
Kept buying. It kept dumping. They kept buying.
The Repeat Offender
13.0 SOL
-28.1%
4.5/10
Full autopsy →
FAXP...1LqV
Panic sold within minutes. Fear won.
The Paper Hand
9.808 SOL
-63.1%
5.5/10
Full autopsy →
GU4X...XG4d
Token went to zero. Wallet went to therapy.
The Rug Victim
7.940 SOL
-100.0%
8.0/10
Full autopsy →
DQGJ...aJUf
Kept buying. It kept dumping. They kept buying.
The Repeat Offender
7.498 SOL
-38.7%
4.5/10
Full autopsy →
NOT FINANCIAL ADVICE. JUST THE MATH.