Solving Gold Market Overfitting: A Predictive Machine Learning Approach – Trading Systems – 17 February 2026

Fixing Gold Market Overfitting: A Predictive Machine Studying Strategy with ONNX and Gradient Boosting

Case Research: The “Golden Gauss” Structure

Writer: Daglox Kankwanda

ORCID: 0009-0000-8306-0938
Technical Paper: Zenodo Repository (DOI: 10.5281/zenodo.18646499)

Contents

1. Introduction
2. The Core Issues in Algorithmic Buying and selling
3. Methodology
4. System Structure
5. Characteristic Engineering
6. Validation and Outcomes
7. Commerce Administration
8. Sincere Limitations
9. Conclusion
10. Implementation & Availability
11. References

1. Introduction

The algorithmic buying and selling area, notably in retail markets, faces a elementary credibility downside. The sample is predictable and pervasive: methods display spectacular backtest efficiency, adopted by speedy degradation in ahead testing, culminating in account destruction throughout stay deployment. This failure mode stems from a single root trigger—optimization for in-sample efficiency with out rigorous out-of-sample validation.

The mathematical actuality is simple: given enough levels of freedom, any mannequin can “memorize” historic value patterns. Such memorization produces spectacular backtest metrics whereas offering zero predictive energy for future market habits. The mannequin has discovered the noise, not the sign.

Past overfitting, conventional indicator-based approaches undergo from a elementary timing deficiency. Technical indicators, by development, are reactive—they course of historic knowledge to generate indicators after value actions have already begun.

Core Thesis: A very helpful buying and selling system should establish the circumstances previous important value exercise, not the exercise itself. The purpose is prediction, not affirmation.

This text presents a technique that synthesizes machine studying analysis insights right into a sensible, deployable buying and selling system for XAUUSD (Gold) markets, demonstrated by way of the “Golden Gauss” structure.

2. The Core Issues in Algorithmic Buying and selling

2.1 The Overfitting Disaster

The proliferation of “AI-powered” buying and selling methods in retail markets has created a credibility disaster, with most methods exhibiting catastrophic failure when deployed on unseen knowledge as a consequence of extreme overfitting.

Determine 1: Conceptual illustration of the standard Knowledgeable Advisor lifecycle. Fashions optimized for historic efficiency often fail catastrophically when deployed on unseen market circumstances.

2.2 The Latency Drawback in Technical Evaluation

Technical indicators are inherently reactive:

• By the point RSI crosses the overbought threshold, the value has already moved considerably
• By the point a MACD crossover confirms, the optimum entry window has handed
• By the point a breakout is “confirmed,” stop-loss necessities have expanded considerably

Determine 2: Comparability of timing between reactive technical indicators and predictive machine studying approaches. Conventional indicators verify strikes after optimum entry has handed, whereas predictive methods establish setup circumstances earlier than execution.

2.3 Literature Context

The appliance of machine studying to monetary time-series prediction has developed considerably. A number of constant findings are related:

3. Methodology

3.1 The Predictive Labeling Methodology

Commonplace approaches to coaching buying and selling fashions label knowledge on the level the place value motion happens. This creates a elementary downside: if the mannequin learns options calculated from the identical bars which are labeled, it successfully learns to acknowledge strikes which are already occurring quite than strikes which are about to occur.

The Golden Gauss structure employs a technique that maintains temporal separation between function calculation and label placement:

• The labeling course of identifies worthwhile zones the place value moved considerably in a particular route
• All options are calculated from market knowledge that occurred earlier than the labeled zone begins

Determine 3: Handbook labeling interface exhibiting XAUUSD value motion with recognized directional zones. The labeled BUY and SELL areas signify worthwhile strikes used as coaching targets; the mannequin learns to foretell these strikes utilizing options calculated from previous market knowledge.

Implications: This temporal separation ensures the mannequin learns to acknowledge preconditions—the market microstructure patterns that precede important strikes—quite than traits of the strikes themselves.

3.2 High quality-Filtered Coaching Labels

Not all value actions are significant or tradeable. Many are:

• Too small to beat transaction prices (unfold + fee)
• Too erratic to execute cleanly
• A part of bigger consolidation patterns with out directional follow-through

The labeling course of applies strict filtering standards, figuring out solely zones the place value moved with enough magnitude and directional consistency. This ensures the mannequin learns solely from setups that exceeded minimal profitability thresholds.

3.3 Twin-Mannequin Directional Structure

Market dynamics exhibit elementary asymmetry between bullish and bearish habits:

• Accumulation patterns differ structurally from distribution patterns
• Concern-driven promoting sometimes executes quicker than greed-driven shopping for
• Assist habits differs from resistance habits
• Quantity traits differ between advances and declines

To respect this asymmetry, the structure employs two impartial binary fashions:

Every mannequin is a binary classifier detecting solely its respective directional setup. This prevents the confusion that happens when a single mannequin makes an attempt to study contradictory patterns concurrently.

3.4 Stroll-Ahead Validation Protocol

Commonplace machine studying cross-validation, which shuffles knowledge randomly, is inappropriate for monetary time-series as a consequence of temporal dependencies and lookahead bias dangers.

The system makes use of strict walk-forward validation with full chronological separation:

• Coaching knowledge extends by way of December 31, 2024
• All architectural choices, hyperparameters, and have engineering decisions had been finalized utilizing solely this knowledge
• The mannequin was then frozen and validated on a 13-month out-of-sample interval (January 2025 by way of January 2026)

Determine 4: Temporal knowledge separation for walk-forward validation. Coaching knowledge extends by way of finish of 2024; all 2025-2026 analysis represents strictly out-of-sample efficiency on knowledge not used for coaching.

Crucial Guidelines:

• No shuffling of time-series knowledge
• Analysis interval evaluation solely in spite of everything mannequin choices finalized
• No iterative “peeking” at analysis outcomes to regulate parameters

4. System Structure

The system contains two distinct however built-in elements:

1. Coaching Pipeline — applied in Python for mannequin growth and validation
2. Execution Engine — applied in MQL5 for real-time deployment inside MetaTrader 5

Determine 5: Excessive-level structure of the system. The coaching pipeline (high) processes historic knowledge by way of function engineering and mannequin coaching, exporting by way of ONNX. The execution engine (backside) calculates options instantaneously, obtains chance scores, and applies commerce administration logic for place execution.

4.1 Mannequin Structure Choice

The selection of mannequin structure was pushed by empirical analysis in opposition to standards particular to monetary time-series prediction:

Primarily based on in depth testing, Gradient Boosting Choice Bushes (GBDT) had been chosen. This alternative aligns with constant findings within the machine studying literature that GBDT architectures outperform deep studying approaches on structured monetary knowledge.

Why Not Neural Networks?

Whereas “Neural Community” generates advertising and marketing enchantment, the technical actuality for tabular monetary knowledge:

• GBDTs deal with function interactions naturally with out specific specification
• GBDTs are extra sturdy to noise and outliers in monetary knowledge
• GBDTs require considerably much less coaching knowledge
• GBDTs present interpretable function significance rankings
• GBDTs practice quicker, enabling extra in depth hyperparameter search

4.2 ONNX Deployment

The mannequin is exported by way of ONNX (Open Neural Community Change) for platform-agnostic deployment, enabling Python-trained fashions to execute at C++ speeds inside MT5.

A important requirement is training-serving parity: function calculations in MQL5 have to be mathematically similar to these carried out throughout Python coaching. Any discrepancy creates “training-serving skew” that degrades mannequin efficiency.

4.3 The MQL5-ONNX Interface

The bridge between Python coaching and MQL5 execution depends on the native ONNX API launched in MetaTrader 5 Construct 3600. The first engineering problem is making certain the enter tensor form matches the Python export precisely, and accurately deciphering the classifier’s dual-output construction.

Beneath is the structural logic used to initialize and run inference with the Gradient Boosting mannequin throughout the Knowledgeable Advisor:

Mannequin Initialization

#useful resource "InformationBULLISH_Model.onnx" as uchar ExtModelBuy[]
lengthy g_onnx_buy;
const int SNIPER_FEATURES = 239;

bool InitializeONNXModels()
{
    Print("Loading ONNX fashions...");
    
    
    g_onnx_buy = OnnxCreateFromBuffer(ExtModelBuy, ONNX_DEFAULT);
    if(g_onnx_buy == INVALID_HANDLE)
    {
        Print("[FAIL] Didn't load BUY mannequin");
        return false;
    }
    
    
    ulong input_shape_buy[] = {1, SNIPER_FEATURES};
    if(!OnnxSetInputShape(g_onnx_buy, 0, input_shape_buy))
    {
        Print("[FAIL] Didn't set BUY mannequin enter form");
        return false;
    }
    
    Print("   [OK] BUY mannequin loaded efficiently");
    return true;
}

Chance Inference

The classifier outputs two tensors: predicted labels and sophistication chances. For probability-based execution, we extract the chance of the goal class:

bool GetBuyPrediction(const float &options[], double &chance)
{
    chance = 0.0;
    
    if(g_onnx_buy == INVALID_HANDLE)
    {
        Print("[FAIL] BUY mannequin not loaded");
        return false;
    }
    
    
    float input_data[];
    ArrayResize(input_data, SNIPER_FEATURES);
    ArrayCopy(input_data, options);
    
    
    
    
    
    lengthy output_labels[];      
    float output_probs[];      
    
    ArrayResize(output_labels, 1);
    ArrayResize(output_probs, 2);
    ArrayInitialize(output_labels, 0);
    ArrayInitialize(output_probs, 0.0f);
    
    
    if(!OnnxRun(g_onnx_buy, ONNX_NO_CONVERSION, input_data, output_labels, output_probs))
    {
        int error = GetLastError();
        Print("[FAIL] BUY ONNX inference failed: ", error);
        return false;
    }
    
    
    
    chance = (double)output_probs[0];
    
    return true;
}

Key Implementation Particulars:

• Twin-Output Construction: Gradient Boosting classifiers exported by way of ONNX produce two outputs—the anticipated label and the chance distribution throughout courses. The chance output is used for threshold-based execution.
• Class Mapping: Class 0 represents the goal situation (BULLISH for the BUY mannequin). The chance output_probs[0] instantly signifies mannequin confidence in an imminent bullish transfer.
• Form Validation: Strict form checking at initialization catches training-serving mismatches instantly quite than producing silent prediction errors throughout stay buying and selling.

4.4 Execution Configuration

5. Characteristic Engineering

The system processes 239 engineered options throughout a number of research-backed domains. These options had been developed by way of educational literature overview, area experience in market microstructure, and iterative empirical testing with strict validation protocols.

5.1 Characteristic Classes Overview

5.2 Key Driving Options

The next options constantly ranked among the many most influential in line with world SHAP significance evaluation:

• ADX Pattern Power (14-period): Measuring pattern power, impartial of route
• VWAP Volatility Deviation: Distance of value from intraday VWAP, normalized by current volatility
• Volatility Regime Classifier: ATR relative to its shifting common, indicating low-, normal-, or high-volatility states
• MACD Histogram Momentum: Capturing short-term momentum and potential reversals
• 60-minute Gold/DXY Rolling Correlation: Rolling correlation between XAUUSD and DXY returns
• 60-minute Gold/USDJPY Rolling Correlation: Rolling correlation between XAUUSD and USDJPY returns
• Directional Volatility Regime: Signed volatility function combining EMA-based pattern power with present ATR regime
• Order-Circulate Persistence: Proxy for a way lengthy directional strikes persist throughout current candles
• EMA Unfold Dynamics: Distances and slopes between quick and gradual EMAs

The presence of well-known indicators (ADX, MACD) alongside proprietary regime and correlation options demonstrates that the mannequin enhances, quite than replaces, established market relationships with higher-resolution timing indicators.

5.3 Cross-Asset Intelligence

Gold (XAUUSD) doesn’t commerce in isolation. Its value motion is influenced by:

• US Greenback Dynamics: Usually inverse correlation; greenback power usually pressures gold costs
• Secure-Haven Flows: Correlation with different safe-haven belongings throughout risk-off durations
• Yield Expectations: Relationship with actual rate of interest proxies

The function set incorporates lagged returns from correlated devices, rolling correlations at a number of time scales, divergence detection, and regime change indicators.

6. Validation and Outcomes

The validation method follows a single precept: display generalization, not memorization. Any mannequin can obtain spectacular outcomes on knowledge it has seen. The one significant analysis is efficiency on strictly unseen knowledge.

6.1 Out-of-Pattern Efficiency

All 2025 efficiency represents true out-of-sample (OOS) outcomes. The mannequin structure, hyperparameters, and have set had been frozen earlier than any 2025 knowledge was evaluated.

Determine 6: Backtest fairness and stability curves from Jan 2021 to Jan 2026. The interval Jan 2021–Dec 2024 represents knowledge included in mannequin coaching; the interval Jan 2025–Jan 2026 constitutes strictly out-of-sample analysis.

Interpretation: The out-of-sample interval demonstrates continued profitability with metrics that degrade gracefully from the coaching interval:

• Win charge decreases from 88.71% to 83.67%—a managed 5% discount indicating the mannequin generalizes quite than memorizes
• Revenue issue stays above 1.50, confirming constructive expectancy on unseen knowledge
• The upper OOS Sharpe ratio (13.9 vs 9.90) offers robust proof in opposition to overfitting

This efficiency hole is anticipated and wholesome. The managed degradation confirms real sample generalization.

6.2 Chance Threshold Evaluation

The mannequin outputs steady chance scores. Evaluation reveals the connection between chance ranges and commerce outcomes:

Why 88% Minimal Threshold? The 88% threshold was decided by way of systematic analysis because the optimum entry level balancing commerce frequency in opposition to high quality. Beneath this threshold, false-positive charges enhance considerably.

6.3 Exit Composition Evaluation

The overwhelming majority of successful trades exit by way of the ratchet system, capturing income dynamically quite than ready for full TP.

6.4 Temporal Consistency

All years worthwhile with constant efficiency patterns throughout coaching and out-of-sample durations.

7. Commerce Administration

The system implements a complete commerce administration layer that extends past easy entry execution.

7.1 Chance-Primarily based Choice Making

Not like methods that generate discrete “purchase” or “promote” indicators, the structure calculates chance scores instantaneously on every new bar:

• Entry Choice: Chance should exceed 88% threshold earlier than place opening
• Route Choice: Increased chance between BUY and SELL fashions determines route
• Exit Timing: Chance modifications inform place closure choices
• Maintain/Shut Logic: Steady chance monitoring throughout open positions

7.2 Entry Validation and Filtering

• Twin-Mannequin Affirmation: Each BUY and SELL mannequin chances are assessed to verify directional bias and filter ambiguous circumstances
• Regime Filtering: Extra filters detect unfavorable market regimes (excessive volatility occasions, low liquidity durations)
• Conditional Execution: Commerce execution proceeds solely after chance thresholds are happy and regime filters verify favorable circumstances

7.3 Ratchet Revenue Safety

Drawback Addressed: Worth might transfer 80% towards the take-profit stage, then reverse—with out lively administration, this unrealized revenue could be misplaced.

Ratchet Resolution: As value strikes favorably, the system progressively locks in revenue by tightening exit circumstances, making certain that important favorable strikes are captured even when the total take-profit isn’t reached.

7.4 Ratchet Loss Minimization

Drawback Addressed: Even high-confidence predictions sometimes fail; ready for the fastened stop-loss leads to most loss on each dropping commerce.

Ratchet Resolution: When value strikes adversely, the system actively manages the exit to reduce loss quite than passively ready for stop-loss execution, lowering common loss per unsuccessful commerce.

8. Sincere Limitations

8.1 What This System Is NOT

• Not infallible: Roughly 15–18% of indicators end in suboptimal entries relying on market circumstances
• Not common: Educated solely for XAUUSD with its particular market microstructure and session dynamics
• Not static: Periodic retraining (3–6 months) is required as markets evolve
• Not assured: Out-of-sample validation demonstrates methodology soundness however doesn’t assure future efficiency

8.2 Recognized Danger Elements

8.3 Execution Assumptions

All reported outcomes are based mostly on historic simulations. No further slippage mannequin has been utilized, and real-world execution might result in materially completely different efficiency. These statistics must be interpreted as estimates beneath perfect execution circumstances.

9. Conclusion

This text offered a technique for fixing two elementary failures that characterize retail algorithmic buying and selling—overfitting to historic noise and reactive sign technology—by way of rigorous machine studying practices.

The core improvements demonstrated within the Golden Gauss structure embody:

• Predictive labeling that permits real anticipation of value strikes
• Twin-model directional specialization that respects market asymmetry
• Chance-driven execution that quantifies confidence earlier than commerce entry
• Clever commerce administration that minimizes losses when predictions show suboptimal

On strictly out-of-sample 2025 knowledge—collected in spite of everything mannequin choices had been finalized—the system demonstrates roughly 83.67% directional accuracy on the 88% chance threshold. The managed efficiency differential from coaching metrics signifies real sample studying quite than memorization.

Key Takeaways for Practitioners

1. By no means shuffle time-series knowledge throughout validation—this creates lookahead bias and knowledge leakage
2. Out-of-sample efficiency is the one significant metric for evaluating stay buying and selling potential
3. Chance thresholds allow accuracy/frequency tradeoffs—greater thresholds yield fewer however higher-quality indicators
4. Twin binary fashions respect the asymmetry between bullish and bearish market dynamics
5. Commerce administration amplifies edge—ratchet mechanisms maximize wins and decrease losses
6. All methods have limitations—sincere acknowledgment allows applicable deployment and threat administration

The retail algorithmic buying and selling business suffers from systematic misalignment between vendor incentives and person outcomes. The methodology offered right here—strict temporal separation, documented efficiency degradation, bounded confidence claims—presents a template for sincere system analysis that prioritizes sustainable operation over advertising and marketing enchantment.

Knowledgeable critique of the validation methodology and underlying assumptions is welcomed. Progress in algorithmic buying and selling requires methods designed to outlive scrutiny quite than keep away from it.

10. Implementation & Availability

The structure described on this paper—particularly the predictive labeling engine and the ONNX chance inference—has been totally applied within the Golden Gauss AI system.

To assist additional analysis and validation, the entire system is on the market for testing within the MQL5 Market. The package deal contains the “Visualizer” mode, which renders the chance cones and “Kill Zones” instantly on the chart, permitting merchants to look at the mannequin’s decision-making course of in real-time.

Danger Disclaimer: Buying and selling foreign exchange and CFDs entails substantial threat of loss and isn’t appropriate for all traders. Previous efficiency, whether or not in backtesting or stay buying and selling, doesn’t assure future outcomes. The validation outcomes offered signify historic evaluation beneath particular market circumstances that won’t persist. Merchants ought to solely use capital they’ll afford to lose and may contemplate their monetary state of affairs earlier than buying and selling.

References

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