Real
Account name
Gird Zombie by bodyz
Broker
Exness
Currency / Leverage
USC · 1:2000
Account status
Not verified
Started
Sep 22, 2026
Trading style
—
Strategy
—
Last synced
10/9/26, 3:23 PM (2 hours ago)
Order type
EA + manualEA 99%648 orders
Manual 1%3 orders
Determined from the magic number on the real orders — an order opened by an EA carries the EA's identifier, one opened by hand does not
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AI analysis
1.5
/10
Accuracy
6.4
Profitability
1.3
Risk control
0
Consistency
0
Recovery
0
Trader shorts XAUUSDc exclusively across 109 trades with a 67.89% win rate, but the average loss (-¢44.65) exceeds the average gain (¢32.87) and the Recovery Factor is a low 0.89 — cumulative profit of ¢869.20 remains fragile due to 27 instances of overleveraging.
Strengths
3Consistently high win rate
67.89% win rate across 109 trades; shorts win 66.98% from 106 trades.
Strong early-morning profits
06:00–09:00 session: +¢434.90 from 23 trades, 65% win rate.
100% accuracy on longs
All 3 long orders won, though the sample size is very small.
Weaknesses
3Losses outweigh gains
Average loss -¢44.65 per trade vs. average gain ¢32.87 per trade.
Frequent overleveraging
Volume exceeded 2 SD above the 20-trade mean on 27 out of 109 trades.
Recovery Factor below 1
Recovery Factor 0.89 — profits earned do not justify the drawdown endured.
Risks
2Account blow-up from consecutive oversized trades
Worst trade -¢243.50; longest losing streak 7 trades — if this repeats during overleveraged periods, the account could be wiped out.
Heavy concentration on Tuesdays alone
107 of 109 trades placed on a Tuesday — if Tuesday sees abnormal volatility, there are no other days to spread the risk.
Suggested improvements
3- 1
Halve the lot size after 3 consecutive losses
The average loss exceeds the average gain, and the trader has previously lost 7 trades in a row.
- 2
Set a hard max lot cap at 0.20
27 overleveraging incidents — the largest trade at 0.46 lot carries severe drawdown risk.
- 3
Expand trading to 3–4 days per week
107 trades clustered on Tuesday — reduce single-day dependency risk.
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