Professional Gold Trading Expert Advisor
Advanced algorithmic trading system designed for XAUUSD with robust risk management and consistent performance across market conditions.
"A model that looks too good on historical data is almost certainly wrong."
In quantitative finance, genuine edges are small. Our methodology is designed to detect and eliminate any artificial inflation of performance metrics, accepting modest but real predictive power over spectacular but illusory results.
Consistency across market regimes, realistic out-of-sample metrics, and robustness under transaction costs. Every design decision prioritises real-world viability over backtest aesthetics.
Any result that exceeds established plausibility thresholds is automatically flagged for review. Features without economic rationale are excluded regardless of their in-sample predictive power.
Traditional train/test splits or k-fold cross-validation randomly shuffle temporal data, allowing the model to "peek" at future information. Walk-forward validation respects the arrow of time: the model is always trained on past data and tested on strictly future, unseen data — exactly as it would operate in live trading.
Window 1: [====== TRAIN ======] [TEST] Window 2: [========= TRAIN =========] [TEST] Window 3: [============ TRAIN ============] [TEST] ... ... Window N: [==================== TRAIN ====================] [TEST] Each [TEST] = strictly unseen future data. No shuffling. No overlap.
Within each walk-forward window, the following operations are performed independently, using only that window's training data:
Data leakage occurs when information from the future inadvertently contaminates the training process. Even subtle forms — a feature computed from an unclosed candle, a cross-timeframe alignment error — can produce dramatically inflated backtest results that collapse in live trading.
Trade entries use prices available at the moment of the signal, never prices that would only be known after the fact.
Every feature is computed using data with timestamps strictly before the event timestamp. Multi-timeframe features use dedicated offsets ensuring only completed bars are used.
All reference levels are derived from fully completed periods. No intra-period data is used for level calculation.
Features mechanically correlated with the target variable — not because they capture market dynamics, but because they encode trade outcome information — are permanently removed.
Strict deduplication rules ensure no market event generates multiple correlated training samples, which would artificially inflate apparent model performance.
| # | Check | Method | Status |
|---|---|---|---|
| 1 | Walk-forward temporal validation (no random splits) | Structural | ✓ PASS |
| 2 | Entry price uses Open (not Close) of signal bar | Code audit | ✓ PASS |
| 3 | All features use data with timestamp < event time (past only) | Code audit | ✓ PASS |
| 4 | Higher-timeframe features use completed-bar offsets | Code audit | ✓ PASS |
| 5 | Reference levels from completed periods only | Code audit | ✓ PASS |
| 6 | Feature selection computed per-window on training data only | Structural | ✓ PASS |
| 7 | Event deduplication prevents correlated sample inflation | Structural | ✓ PASS |
| 8 | Mechanically predictive features removed from pipeline | Manual review | ✓ PASS |
| 9 | Low-sample event categories removed (no statistical significance) | Statistical | ✓ PASS |
Multiple regularisation techniques constrain model complexity, preventing memorisation of noise and forcing generalisable patterns.
From dozens of candidates, only top-ranked features are selected per window using statistical relevance computed exclusively on training data.
Every feature must have a plausible economic explanation. Features with statistical significance but no logical mechanism are excluded.
Models learn slowly and incrementally, reducing sensitivity to individual training examples and improving generalisation.
The 2016–2026 validation period spans fundamentally different market environments. The model is evaluated across all of these regimes, ensuring it does not rely on a single market condition.
Why we believe this is not overfitted:
Why this matters: Many of our clients already trade with popular XAUUSD Expert Advisors like The Gold Phantom or Mad Turtle. A common concern is whether adding Karat Killer to their setup would create overlapping trades, correlated drawdowns, or redundant exposure to the same market conditions.
This correlation analysis is provided for informational purposes only. Its sole objective is to demonstrate that Karat Killer can be deployed alongside two of the most popular XAUUSD Expert Advisors on the MQL5 marketplace — The Gold Phantom and Mad Turtle — within the same trading account or multi-strategy setup, without significant overlap in trading signals or equity curve behavior.
The low correlation coefficients (avg. 0.20) indicate that these strategies operate on different market mechanics and timeframes, making them technically compatible for simultaneous execution. This analysis does not constitute investment advice, portfolio allocation recommendations, or any guarantee of future diversification benefits.
Data source: All equity curves were obtained from backtests conducted on IC Trading broker data. Each strategy was configured with comparable risk parameters to ensure a fair comparison across similar exposure levels.
| Karat Killer | Mad Turtle | Gold Phantom | |
|---|---|---|---|
| Karat Killer | 1.00 | 0.21 | 0.26 |
| Mad Turtle | 0.21 | 1.00 | 0.13 |
| Gold Phantom | 0.26 | 0.13 | 1.00 |
| Karat Killer | Mad Turtle | Gold Phantom | |
|---|---|---|---|
| Karat Killer | 1.00 | 0.24 | 0.22 |
| Mad Turtle | 0.24 | 1.00 | 0.12 |
| Gold Phantom | 0.22 | 0.12 | 1.00 |
| Metric | Karat_Killer (XAUUSD) | Mad Turtle (XAUUSD) | The Gold Phantom (XAUUSD) |
|---|---|---|---|
| EA Name | Karat_Killer | Mad Turtle | The Gold Phantom |
| Symbol | XAUUSD | XAUUSD | XAUUSD |
| Period | 2016-01-01 – 2026-01-28 | 2016-01-01 – 2026-02-03 | 2016-01-01 – 2026-02-03 |
| Currency | EUR | EUR | EUR |
| Initial Deposit | €10,000.00 | €10,000.00 | €10,000.00 |
| Total Trades | 988 | 1982 | 8758 |
| Net Profit | +€722,988.07 | +€365,656.43 | +€619,719.62 |
| Win Rate | 66.0% | 58.8% | 63.9% |
| Profit Factor | 3.31 | 1.76 | 2.41 |
| Expectancy | +€731.77 | +€184.49 | +€70.76 |
| Equity DD % | 11.2% | 22.6% | 13.4% |
| Balance DD % | 8.7% | 20.4% | 7.8% |
| Max DD Days | 217d | 953d | 81d |
| Max Stagnation | 217d | 953d | 81d |
| Sharpe Ratio | 10.00 | 2.94 | 4.58 |
| Sortino Ratio | 8.17 | 2.88 | 5.09 |
| Calmar Ratio | 9.31 | 2.99 | 6.53 |
| Recovery Factor | 22.24 | 13.79 | 22.25 |
| Risk/Reward | 1.68 | 1.24 | 1.37 |
| Max Win Streak | 18 | 14 | 44 |
| Max Loss Streak | 5 | 10 | 25 |
| Avg Days/Trade | 4.16 | 1.85 | 0.42 |