REKT AUTOPSY
FORENSIC TRADING ANALYSIS
BEHAVIORAL AUTOPSY / 5YVBBvxvbiw9JmdVvdXix7MdXiNSrnNMeNTt2pG8w4qk
F
Behavioral Grade
F
26 / 100
Wallet Death Certificate
The math gives you a 99% chance of going to zero.
5YVBBvxvbiw9JmdVvdXix7MdXiNSrnNMeNTt2pG8w4qk
At your current pace, your capital halves every 0.9 days.
Your brain cost you
133.5 SOL
≈ $10.4k behavioral damage
Survival prob.
0%
106 wins to break even
Capital half-life
0.9d
Bleed rate
0.306 SOL/hr
Win rate
38.2%
Net P&L
−69.8 SOL
Win rate
38.2%
Profit factor
0.56
Tokens
348
SOL in
214.9
SOL out
145.1
Primary
Diagnosis
85% of attributed damage came from recurring behavior, not market noise — sizing, timing, tilt.
§ 03 · Compound Intelligence
THE NUMBERS THAT MATTER.
Avoidable Loss Score
Most losses were behavioral, not random.
85%
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.
+133.5 SOL
Catchable Losses
130 / 204
Behavioral Drag
85%
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
204 losses
→
Fast re-entry
47x
→
Bigger size
39 tokens
→
Late entry
→
Held collapse
Stages Fired
3 / 5
Fast Re-entries
47x
BEHAVIORAL DAMAGE REPORT
😴
Session Fatigue
-112.9 SOL($8.8k)
After trade #9, avg P&L drops to -1.5159 SOL
Trade #1 avg: -0.1498 SOL
📈
Averaging Down
-77.7 SOL($6.0k)
39 tokens where later buys were 101.8x larger
Later buys on losing positions
⏰
Late Night Trading
-24.0 SOL($1.9k)
80 trades between 11PM–5AM
Best hours: 6AM
😤
Revenge Trading
-19.8 SOL($1.5k)
47 trades within 10 min of a loss
Win rate on these: 13%
TOTAL BEHAVIORAL DRAG
-234.3 SOL($18.2k)
CIRCADIAN PROFILE
WORST: 1AM▾
12a1
1a6
2a6
3a29
4a26
5a16
6a1
7a21
8a17
9a10
10a12
11a8
12p18
1p24
2p22
3p12
4p20
5p13
6p7
7p26
8p15
9p13
10p13
11p12
Night (11PM–5AM): -0.2995 SOL/trade · Day (8AM–8PM): -0.0778 SOL/trade
DISPOSITION EFFECT
0.2x▾
WINNER HOLD
3.2d
LOSER HOLD
13.5h
You hold losers 0.2x longer than winners.
SESSION FATIGUE
149 SESSIONS▾
#1
-0.1498
#2
+0.0386
#3
-0.4429
#4
-0.4517
#5
+0.0803
#6
-1.2851
#7
-1.0147
#8
-0.7073
Avg session: 2.3 trades. Dominant pattern: Loss from the Start (41%)
POST-LOSS VELOCITY
NORMAL▾
POST-LOSS GAP
1.1h
BASELINE GAP
1.1h
47 trades opened within 10 min of a loss. Loss rate on these: 87%
EQUITY CURVE
DISCIPLINED: +133.5 SOL▾
Peak: +42.1 SOL at token #106
Max drawdown: 111.9 SOL
- - - = you without 130 behavioral trades
P&L SUMMARY
▾
ADVANCED METRICS
▾
LOSS CONCENTRATION
▾
DEX ANALYSIS
▾
YOUR PRESCRIPTION
Rules derived from your trading data that would have saved you 212.2 SOL (135% of total losses)
1
NEVER ADD TO A LOSING POSITION
Later buys on losers are 101.8x larger than first buy
→ Would save ~66.3 SOL
2
STOP BUYING ON DFLOW
136 tokens from DFLOW: 12% WR, -67.042 SOL net
→ Would save ~40.2 SOL
3
DON'T INCREASE SIZE AFTER A LOSS
Avg size after loss: 0.7728 SOL vs 0.4032 after win (1.92x)
→ Would save ~37.5 SOL
4
STOP AFTER 3 TRADES PER SESSION
Trade #1 avg: -0.1498 SOL → Trade #9 avg: -1.5159 SOL
→ Would save ~36.9 SOL
5
STOP AFTER 3 CONSECUTIVE LOSSES
P(loss after loss) = 82% vs 62% baseline
→ Would save ~31.3 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.
5Ksq...pump
Kept buying. It kept dumping. They kept buying.
The Repeat Offender
−9.822 SOL
-63.8%
4.5/10
Full autopsy →
Aa3M...JN3P
Never sold. Still holding. Still hoping.
The Bag Holder
−8.008 SOL
-100.0%
8.0/10
Full autopsy →
Vf7G...pump
Token went to zero. Wallet went to therapy.
The Rug Victim
−6.793 SOL
-99.3%
6.0/10
Full autopsy →
2yey...pump
Never sold. Still holding. Still hoping.
The Bag Holder
−6.035 SOL
-100.0%
8.0/10
Full autopsy →
hpvo...pump
Never sold. Still holding. Still hoping.
The Bag Holder
−3.570 SOL
-100.0%
7.0/10
Full autopsy →
NOT FINANCIAL ADVICE. JUST THE MATH.