Fixing Gold Market Overfitting: A Predictive Machine Studying Method – Buying and selling Techniques – 17 February 2026

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

Case Research: The “Golden Gauss” Structure

Creator: 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. Function 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 house, significantly in retail markets, faces a basic credibility downside. The sample is predictable and pervasive: programs reveal spectacular backtest efficiency, adopted by speedy degradation in ahead testing, culminating in account destruction throughout reside 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 easy: given enough levels of freedom, any mannequin can “memorize” historic worth 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 endure from a basic timing deficiency. Technical indicators, by building, are reactive—they course of historic knowledge to generate alerts after worth actions have already begun.

Core Thesis: A really helpful buying and selling system should determine the situations previous important worth exercise, not the exercise itself. The aim is prediction, not affirmation.

This text presents a strategy that synthesizes machine studying analysis insights right into a sensible, deployable buying and selling system for XAUUSD (Gold) markets, demonstrated via 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 programs in retail markets has created a credibility disaster, with most programs exhibiting catastrophic failure when deployed on unseen knowledge attributable to extreme overfitting.

Determine 1: Conceptual illustration of the everyday Knowledgeable Advisor lifecycle. Fashions optimized for historic efficiency regularly fail catastrophically when deployed on unseen market situations.

2.2 The Latency Downside in Technical Evaluation

Technical indicators are inherently reactive:

• By the point RSI crosses the overbought threshold, the worth 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 affirm strikes after optimum entry has handed, whereas predictive programs determine setup situations 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

Normal approaches to coaching buying and selling fashions label knowledge on the level the place worth motion happens. This creates a basic downside: if the mannequin learns options calculated from the identical bars which might be labeled, it successfully learns to acknowledge strikes which might be already occurring moderately than strikes which might be about to occur.

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

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

Determine 3: Guide labeling interface displaying XAUUSD worth motion with recognized directional zones. The labeled BUY and SELL areas symbolize 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—moderately than traits of the strikes themselves.

3.2 High quality-Filtered Coaching Labels

Not all worth 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 worth 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 basic asymmetry between bullish and bearish habits:

• Accumulation patterns differ structurally from distribution patterns
• Worry-driven promoting usually 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

Normal machine studying cross-validation, which shuffles knowledge randomly, is inappropriate for monetary time-series attributable to temporal dependencies and lookahead bias dangers.

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

• Coaching knowledge extends via 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 via January 2026)

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

Essential 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 via characteristic 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 towards 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 enchantment, the technical actuality for tabular monetary knowledge:

• GBDTs deal with characteristic interactions naturally with out specific specification
• GBDTs are extra strong to noise and outliers in monetary knowledge
• GBDTs require considerably much less coaching knowledge
• GBDTs present interpretable characteristic 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 Alternate) for platform-agnostic deployment, enabling Python-trained fashions to execute at C++ speeds inside MT5.

A crucial requirement is training-serving parity: characteristic calculations in MQL5 should be mathematically an identical 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 guaranteeing the enter tensor form matches the Python export precisely, and appropriately decoding the classifier’s dual-output construction.

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

Mannequin Initialization

#useful resource "RecordsdataBULLISH_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] Did not load BUY mannequin");
        return false;
    }
    
    
    ulong input_shape_buy[] = {1, SNIPER_FEATURES};
    if(!OnnxSetInputShape(g_onnx_buy, 0, input_shape_buy))
    {
        Print("[FAIL] Did not 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 possibilities. 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] straight signifies mannequin confidence in an imminent bullish transfer.
• Form Validation: Strict form checking at initialization catches training-serving mismatches instantly moderately than producing silent prediction errors throughout reside buying and selling.

4.4 Execution Configuration

5. Function Engineering

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

5.1 Function Classes Overview

5.2 Key Driving Options

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

• ADX Pattern Energy (14-period): Measuring pattern power, impartial of course
• VWAP Volatility Deviation: Distance of worth from intraday VWAP, normalized by current volatility
• Volatility Regime Classifier: ATR relative to its transferring 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 characteristic combining EMA-based pattern power with present ATR regime
• Order-Stream Persistence: Proxy for the way lengthy directional strikes persist throughout current candles
• EMA Unfold Dynamics: Distances and slopes between quick and sluggish EMAs

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

5.3 Cross-Asset Intelligence

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

• US Greenback Dynamics: Sometimes inverse correlation; greenback power typically pressures gold costs
• Protected-Haven Flows: Correlation with different safe-haven property throughout risk-off intervals
• Yield Expectations: Relationship with actual rate of interest proxies

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

6. Validation and Outcomes

The validation method follows a single precept: reveal 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 steadiness 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 moderately 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 sturdy proof towards 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 via systematic analysis because the optimum entry level balancing commerce frequency towards high quality. Under this threshold, false-positive charges improve considerably.

6.3 Exit Composition Evaluation

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

6.4 Temporal Consistency

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

7. Commerce Administration

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

7.1 Chance-Primarily based Choice Making

In contrast to programs that generate discrete “purchase” or “promote” alerts, the structure calculates chance scores instantaneously on every new bar:

• Entry Choice: Chance should exceed 88% threshold earlier than place opening
• Course Choice: Larger chance between BUY and SELL fashions determines course
• 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 possibilities are assessed to verify directional bias and filter ambiguous situations
• Regime Filtering: Further filters detect unfavorable market regimes (excessive volatility occasions, low liquidity intervals)
• Conditional Execution: Commerce execution proceeds solely after chance thresholds are glad and regime filters affirm favorable situations

7.3 Ratchet Revenue Safety

Downside Addressed: Worth could transfer 80% towards the take-profit degree, then reverse—with out energetic administration, this unrealized revenue could be misplaced.

Ratchet Answer: As worth strikes favorably, the system progressively locks in revenue by tightening exit situations, guaranteeing that important favorable strikes are captured even when the total take-profit isn’t reached.

7.4 Ratchet Loss Minimization

Downside Addressed: Even high-confidence predictions often fail; ready for the mounted stop-loss leads to most loss on each shedding commerce.

Ratchet Answer: When worth strikes adversely, the system actively manages the exit to attenuate loss moderately than passively ready for stop-loss execution, decreasing common loss per unsuccessful commerce.

8. Sincere Limitations

8.1 What This System Is NOT

• Not infallible: Roughly 15–18% of alerts lead to suboptimal entries relying on market situations
• Not common: Skilled 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 Threat Components

8.3 Execution Assumptions

All reported outcomes are based mostly on historic simulations. No further slippage mannequin has been utilized, and real-world execution could result in materially totally different efficiency. These statistics needs to be interpreted as estimates beneath splendid execution situations.

9. Conclusion

This text offered a strategy for fixing two basic failures that characterize retail algorithmic buying and selling—overfitting to historic noise and reactive sign era—via rigorous machine studying practices.

The core improvements demonstrated within the Golden Gauss structure embrace:

• Predictive labeling that permits real anticipation of worth 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 moderately 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 reside buying and selling potential
3. Chance thresholds allow accuracy/frequency tradeoffs—increased thresholds yield fewer however higher-quality alerts
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 programs have limitations—trustworthy acknowledgment permits 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—gives a template for trustworthy system analysis that prioritizes sustainable operation over advertising enchantment.

Knowledgeable critique of the validation methodology and underlying assumptions is welcomed. Progress in algorithmic buying and selling requires programs designed to outlive scrutiny moderately 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 help additional analysis and validation, the entire system is out there for testing within the MQL5 Market. The package deal contains the “Visualizer” mode, which renders the chance cones and “Kill Zones” straight on the chart, permitting merchants to watch the mannequin’s decision-making course of in real-time.

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

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