EA has a live track record with 4.5 years of stable trading with low drawdown, it was designed to exploit existing market inefficiencies. Therefore it is not a simple "hit and miss" system which only survives by using grid. Instead it uses real market mechanics to its advantage to make profit
During the month of June 2025, our mean reversion strategy was actively applied across six major forex pairs. The strategy aimed to exploit price deviations from statistical means, identifying high-probability reversal zones. Trades were taken with fixed lot sizes depending on volatility and risk per symbol.
While the overall performance was mixed, CHFJPY emerged as the most profitable pair, delivering a 100% win rate. Other symbols such as EURGBP, GBPJPY, and GBPNZD underperformed, highlighting key areas for refinement.
Symbol
Lot Size
Profit (USD)
Trades Executed
Win Rate
CHFJPY
0.5
+503
2
100%
EURCHF
0.0
0
0
0%
EURGBP
1.5
-572
2
50%
GBPJPY
0.5
-313
3
33.33%
GBPNZD
0.5
-237
1
0%
USDCHF
0.0
0
0
0%
Symbol-Specific Insights
CHFJPY
Performance: Excellent
Observations: Both trades executed resulted in profit, confirming high efficiency of the mean reversion signals on this pair.
Actionable Insight: Continue monitoring CHFJPY closely; consider increasing lot size gradually under proper risk management.
EURCHF & USDCHF
Performance: No trades taken
Observations: Market conditions likely remained neutral or outside set parameters for mean reversion entry.
Actionable Insight: No immediate action required. Strategy filters appear effective in avoiding low-opportunity conditions.
EURGBP
Performance: Poor
Observations: Despite a higher lot size (1.5), only one out of two trades was successful, leading to a net loss.
Actionable Insight: Reassess entry timing or widen standard deviation threshold to reduce false signals.
GBPJPY
Performance: Weak
Observations: Volatile behavior may have led to premature reversals or stop-outs. One win in three trades.
Actionable Insight: Consider filtering trades with additional volatility filter or waiting for confluence with higher timeframes.
GBPNZD
Performance: Loss
Observations: Only one trade executed, which ended in a loss.
Actionable Insight: Sample size is too small for conclusions, but caution advised in low liquidity conditions.
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