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	</head>
	<body>
	<nav id="TOC" role="doc-toc">
	<ul>
	<li><a href="#xauusd-trading-ai-a-machine-learning-approach-using-smart-money-concepts" id="toc-xauusd-trading-ai-a-machine-learning-approach-using-smart-money-concepts"><span class="toc-section-number">1</span> XAUUSD Trading AI: A Machine
	Learning Approach Using Smart Money Concepts</a>
	<ul>
	<li><a href="#abstract" id="toc-abstract"><span class="toc-section-number">1.1</span> Abstract</a></li>
	<li><a href="#introduction" id="toc-introduction"><span class="toc-section-number">1.2</span> 1. Introduction</a>
	<ul>
	<li><a href="#background" id="toc-background"><span class="toc-section-number">1.2.1</span> 1.1 Background</a></li>
	<li><a href="#problem-statement" id="toc-problem-statement"><span class="toc-section-number">1.2.2</span> 1.2 Problem Statement</a></li>
	<li><a href="#research-objectives" id="toc-research-objectives"><span class="toc-section-number">1.2.3</span> 1.3 Research Objectives</a></li>
	<li><a href="#contributions" id="toc-contributions"><span class="toc-section-number">1.2.4</span> 1.4 Contributions</a></li>
	</ul></li>
	<li><a href="#literature-review" id="toc-literature-review"><span class="toc-section-number">1.3</span> 2. Literature Review</a>
	<ul>
	<li><a href="#algorithmic-trading-in-fx-markets" id="toc-algorithmic-trading-in-fx-markets"><span class="toc-section-number">1.3.1</span> 2.1 Algorithmic Trading in FX
	Markets</a></li>
	<li><a href="#smart-money-concepts" id="toc-smart-money-concepts"><span class="toc-section-number">1.3.2</span> 2.2 Smart Money
	Concepts</a></li>
	<li><a href="#machine-learning-in-trading" id="toc-machine-learning-in-trading"><span class="toc-section-number">1.3.3</span> 2.3 Machine Learning in
	Trading</a></li>
	<li><a href="#gold-price-prediction" id="toc-gold-price-prediction"><span class="toc-section-number">1.3.4</span> 2.4 Gold Price
	Prediction</a></li>
	</ul></li>
	<li><a href="#methodology" id="toc-methodology"><span class="toc-section-number">1.4</span> 3. Methodology</a>
	<ul>
	<li><a href="#data-collection" id="toc-data-collection"><span class="toc-section-number">1.4.1</span> 3.1 Data Collection</a></li>
	<li><a href="#feature-engineering" id="toc-feature-engineering"><span class="toc-section-number">1.4.2</span> 3.2 Feature Engineering</a></li>
	<li><a href="#target-variable-construction" id="toc-target-variable-construction"><span class="toc-section-number">1.4.3</span> 3.3 Target Variable
	Construction</a></li>
	<li><a href="#model-development" id="toc-model-development"><span class="toc-section-number">1.4.4</span> 3.4 Model Development</a></li>
	<li><a href="#backtesting-framework" id="toc-backtesting-framework"><span class="toc-section-number">1.4.5</span> 3.5 Backtesting
	Framework</a></li>
	</ul></li>
	<li><a href="#system-architecture-and-data-flow" id="toc-system-architecture-and-data-flow"><span class="toc-section-number">1.5</span> 4. System Architecture and Data
	Flow</a>
	<ul>
	<li><a href="#dataset-flow-diagram" id="toc-dataset-flow-diagram"><span class="toc-section-number">1.5.1</span> 4.1 Dataset Flow
	Diagram</a></li>
	<li><a href="#model-architecture-diagram" id="toc-model-architecture-diagram"><span class="toc-section-number">1.5.2</span> 4.2 Model Architecture
	Diagram</a></li>
	<li><a href="#buysell-workflow-diagram" id="toc-buysell-workflow-diagram"><span class="toc-section-number">1.5.3</span> 4.3 Buy/Sell Workflow
	Diagram</a></li>
	</ul></li>
	<li><a href="#discussion" id="toc-discussion"><span class="toc-section-number">1.6</span> 7. Discussion</a>
	<ul>
	<li><a href="#position-sizing-and-risk-management" id="toc-position-sizing-and-risk-management"><span class="toc-section-number">1.6.1</span> 5.1 Position Sizing and Risk
	Management</a></li>
	<li><a href="#risk-adjusted-performance-metrics" id="toc-risk-adjusted-performance-metrics"><span class="toc-section-number">1.6.2</span> 5.2 Risk-Adjusted Performance
	Metrics</a></li>
	<li><a href="#advanced-smc-implementation-techniques" id="toc-advanced-smc-implementation-techniques"><span class="toc-section-number">1.6.3</span> 5.3 Advanced SMC Implementation
	Techniques</a></li>
	<li><a href="#ensemble-signal-confirmation-framework" id="toc-ensemble-signal-confirmation-framework"><span class="toc-section-number">1.6.4</span> 5.4 Ensemble Signal Confirmation
	Framework</a></li>
	</ul></li>
	<li><a href="#experimental-results" id="toc-experimental-results"><span class="toc-section-number">1.7</span> 6. Experimental Results</a>
	<ul>
	<li><a href="#model-performance" id="toc-model-performance"><span class="toc-section-number">1.7.1</span> 6.1 Model Performance</a></li>
	<li><a href="#backtesting-results" id="toc-backtesting-results"><span class="toc-section-number">1.7.2</span> 6.2 Backtesting Results</a></li>
	<li><a href="#robustness-analysis" id="toc-robustness-analysis"><span class="toc-section-number">1.7.3</span> 6.3 Robustness Analysis</a></li>
	<li><a href="#performance-visualization" id="toc-performance-visualization"><span class="toc-section-number">1.7.4</span> 6.4 Performance
	Visualization</a></li>
	<li><a href="#key-findings" id="toc-key-findings"><span class="toc-section-number">1.7.5</span> 7.1 Key Findings</a></li>
	<li><a href="#limitations" id="toc-limitations"><span class="toc-section-number">1.7.6</span> 7.2 Limitations</a></li>
	<li><a href="#future-research-directions" id="toc-future-research-directions"><span class="toc-section-number">1.7.7</span> 7.3 Future Research
	Directions</a></li>
	</ul></li>
	<li><a href="#conclusion" id="toc-conclusion"><span class="toc-section-number">1.8</span> 8. Conclusion</a></li>
	<li><a href="#acknowledgments" id="toc-acknowledgments"><span class="toc-section-number">1.9</span> Acknowledgments</a>
	<ul>
	<li><a href="#development" id="toc-development"><span class="toc-section-number">1.9.1</span> Development</a></li>
	<li><a href="#declaration-of-competing-interests" id="toc-declaration-of-competing-interests"><span class="toc-section-number">1.9.2</span> Declaration of Competing
	Interests</a></li>
	<li><a href="#data-and-code-availability" id="toc-data-and-code-availability"><span class="toc-section-number">1.9.3</span> Data and Code
	Availability</a></li>
	</ul></li>
	<li><a href="#references" id="toc-references"><span class="toc-section-number">1.10</span> References</a></li>
	<li><a href="#appendix-a-feature-definitions" id="toc-appendix-a-feature-definitions"><span class="toc-section-number">1.11</span> Appendix A: Feature
	Definitions</a>
	<ul>
	<li><a href="#technical-indicators-1" id="toc-technical-indicators-1"><span class="toc-section-number">1.11.1</span> Technical Indicators</a></li>
	<li><a href="#smc-features" id="toc-smc-features"><span class="toc-section-number">1.11.2</span> SMC Features</a></li>
	</ul></li>
	<li><a href="#appendix-b-model-hyperparameters" id="toc-appendix-b-model-hyperparameters"><span class="toc-section-number">1.12</span> Appendix B: Model
	Hyperparameters</a></li>
	<li><a href="#appendix-c-backtesting-code-snippet" id="toc-appendix-c-backtesting-code-snippet"><span class="toc-section-number">1.13</span> Appendix C: Backtesting Code
	Snippet</a></li>
	</ul></li>
	</ul>
	</nav>
	<h1 data-number="1" id="xauusd-trading-ai-a-machine-learning-approach-using-smart-money-concepts"><span class="header-section-number">1</span> XAUUSD Trading AI: A Machine
	Learning Approach Using Smart Money Concepts</h1>
	<p><strong>Author: Jonus Nattapong Tapachom</strong><br />
	<strong>Date: September 18, 2025</strong></p>
	<h2 data-number="1.1" id="abstract"><span class="header-section-number">1.1</span> Abstract</h2>
	<p>This paper presents a comprehensive machine learning framework for
	predicting XAUUSD (Gold vs US Dollar) price movements using Smart Money
	Concepts (SMC) strategy elements. The proposed system achieves an 85.4%
	win rate in backtesting across six years of historical data (2015-2020),
	demonstrating the effectiveness of combining technical analysis with
	advanced machine learning techniques.</p>
	<p>The model utilizes XGBoost classification to predict 5-day ahead
	price direction, incorporating 23 features including traditional
	technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands) and
	SMC-specific features (Fair Value Gaps, Order Blocks, Recovery
	patterns). The system addresses class imbalance through strategic
	weighting and achieves robust performance across different market
	conditions.</p>
	<p><strong>Keywords</strong>: Algorithmic Trading, Machine Learning,
	Smart Money Concepts, XAUUSD, XGBoost, Technical Analysis</p>
	<h2 data-number="1.2" id="introduction"><span class="header-section-number">1.2</span> 1. Introduction</h2>
	<h3 data-number="1.2.1" id="background"><span class="header-section-number">1.2.1</span> 1.1 Background</h3>
	<p>Algorithmic trading has revolutionized financial markets, enabling
	systematic execution of trading strategies with speed and precision
	previously unattainable by human traders. The foreign exchange (FX)
	market, particularly currency pairs involving commodities like gold
	(XAUUSD), presents unique challenges due to its 24/5 operation and
	sensitivity to global economic events.</p>
	<p>Smart Money Concepts (SMC) represent a relatively new paradigm in
	technical analysis, focusing on identifying institutional trading
	patterns rather than retail-driven price action. SMC principles
	emphasize understanding market structure, liquidity concepts, and
	institutional order flow.</p>
	<h3 data-number="1.2.2" id="problem-statement"><span class="header-section-number">1.2.2</span> 1.2 Problem Statement</h3>
	<p>Traditional technical analysis indicators often fail to capture the
	sophisticated strategies employed by institutional traders. This
	research addresses the gap by developing a machine learning model that
	incorporates SMC principles alongside conventional technical indicators
	to predict short-term price movements in XAUUSD.</p>
	<h3 data-number="1.2.3" id="research-objectives"><span class="header-section-number">1.2.3</span> 1.3 Research Objectives</h3>
	<ol type="1">
	<li>Develop a comprehensive feature set combining SMC and technical
	indicators</li>
	<li>Implement and optimize an XGBoost-based prediction model</li>
	<li>Validate performance through rigorous backtesting</li>
	<li>Analyze model robustness across different market conditions</li>
	<li>Provide a reproducible framework for algorithmic trading
	research</li>
	</ol>
	<h3 data-number="1.2.4" id="contributions"><span class="header-section-number">1.2.4</span> 1.4 Contributions</h3>
	<ul>
	<li>Novel integration of SMC concepts with machine learning</li>
	<li>Comprehensive feature engineering methodology</li>
	<li>Robust backtesting framework with yearly performance analysis</li>
	<li>Open-source implementation for research community</li>
	<li>Empirical validation of SMC effectiveness in algorithmic
	trading</li>
	</ul>
	<h2 data-number="1.3" id="literature-review"><span class="header-section-number">1.3</span> 2. Literature Review</h2>
	<h3 data-number="1.3.1" id="algorithmic-trading-in-fx-markets"><span class="header-section-number">1.3.1</span> 2.1 Algorithmic Trading in FX
	Markets</h3>
	<p>Research in algorithmic trading has evolved from simple rule-based
	systems to sophisticated machine learning approaches. Studies by Kearns
	and Nevmyvaka (2013) demonstrated that machine learning techniques can
	significantly outperform traditional technical analysis in forex
	markets. More recent work by Dixon et al. (2020) shows that deep
	learning models can capture complex market dynamics.</p>
	<h3 data-number="1.3.2" id="smart-money-concepts"><span class="header-section-number">1.3.2</span> 2.2 Smart Money Concepts</h3>
	<p>SMC methodology, popularized by ICT (Inner Circle Trader) concepts,
	focuses on identifying institutional trading behavior through market
	structure analysis. Key SMC elements include:</p>
	<ul>
	<li><strong>Order Blocks</strong>: Areas where significant
	buying/selling occurred</li>
	<li><strong>Fair Value Gaps</strong>: Price imbalances between
	candles</li>
	<li><strong>Liquidity Concepts</strong>: Understanding where
	institutional orders are placed</li>
	<li><strong>Market Structure</strong>: Recognition of higher-timeframe
	trends</li>
	</ul>
	<h3 data-number="1.3.3" id="machine-learning-in-trading"><span class="header-section-number">1.3.3</span> 2.3 Machine Learning in
	Trading</h3>
	<p>XGBoost has emerged as a powerful tool for financial prediction
	tasks. Chen and Guestrin (2016) demonstrated its effectiveness in
	various domains, including finance. Studies by Kraus and Feuerriegel
	(2017) show that gradient boosting methods outperform traditional
	statistical models in stock price prediction.</p>
	<h3 data-number="1.3.4" id="gold-price-prediction"><span class="header-section-number">1.3.4</span> 2.4 Gold Price
	Prediction</h3>
	<p>XAUUSD presents unique characteristics as both a commodity and
	currency pair. Research by Baur and Lucey (2010) highlights gold’s
	safe-haven properties during market stress. Studies by Pierdzioch et
	al. (2016) demonstrate that gold prices are influenced by multiple
	factors including interest rates, inflation expectations, and
	geopolitical events.</p>
	<h2 data-number="1.4" id="methodology"><span class="header-section-number">1.4</span> 3. Methodology</h2>
	<h3 data-number="1.4.1" id="data-collection"><span class="header-section-number">1.4.1</span> 3.1 Data Collection</h3>
	<h4 data-number="1.4.1.1" id="data-source"><span class="header-section-number">1.4.1.1</span> 3.1.1 Data Source</h4>
	<p>Historical XAUUSD data was obtained from Yahoo Finance using the
	ticker symbol “GC=F” (Gold Futures). The dataset spans from January 2000
	to December 2020, providing approximately 21 years of daily price
	data.</p>
	<h4 data-number="1.4.1.2" id="data-preprocessing"><span class="header-section-number">1.4.1.2</span> 3.1.2 Data
	Preprocessing</h4>
	<p>Raw data included Open, High, Low, Close prices and Volume.
	Preprocessing steps included: - Removal of missing values and outliers -
	Adjustment for corporate actions (minimal for futures) - Calculation of
	returns and volatility measures - Data quality validation</p>
	<h3 data-number="1.4.2" id="feature-engineering"><span class="header-section-number">1.4.2</span> 3.2 Feature Engineering</h3>
	<h4 data-number="1.4.2.1" id="technical-indicators"><span class="header-section-number">1.4.2.1</span> 3.2.1 Technical
	Indicators</h4>
	<p>Traditional technical indicators were calculated using the TA-Lib
	library:</p>
	<p><strong>Trend Indicators:</strong> - Simple Moving Averages (SMA):
	20-day and 50-day periods - Exponential Moving Averages (EMA): 12-day
	and 26-day periods</p>
	<p><strong>Momentum Indicators:</strong> - Relative Strength Index
	(RSI): 14-day period - Moving Average Convergence Divergence (MACD):
	Standard parameters</p>
	<p><strong>Volatility Indicators:</strong> - Bollinger Bands: 20-day
	period, 2 standard deviations</p>
	<h4 data-number="1.4.2.2" id="smc-feature-implementation"><span class="header-section-number">1.4.2.2</span> 3.2.2 SMC Feature
	Implementation</h4>
	<p><strong>Fair Value Gaps (FVG):</strong></p>
	<div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> calculate_fvg(df):</span>
	<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a> gaps <span class="op">=</span> []</span>
	<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> i <span class="kw">in</span> <span class="bu">range</span>(<span class="dv">1</span>, <span class="bu">len</span>(df)<span class="op">-</span><span class="dv">1</span>):</span>
	<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> df[<span class="st">'Low'</span>][i] <span class="op">></span> df[<span class="st">'High'</span>][i<span class="op">-</span><span class="dv">1</span>] <span class="kw">and</span> df[<span class="st">'Low'</span>][i] <span class="op">></span> df[<span class="st">'High'</span>][i<span class="op">+</span><span class="dv">1</span>]:</span>
	<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a> <span class="co"># Bullish FVG</span></span>
	<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a> gap_size <span class="op">=</span> df[<span class="st">'Low'</span>][i] <span class="op">-</span> <span class="bu">max</span>(df[<span class="st">'High'</span>][i<span class="op">-</span><span class="dv">1</span>], df[<span class="st">'High'</span>][i<span class="op">+</span><span class="dv">1</span>])</span>
	<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a> gaps.append({<span class="st">'type'</span>: <span class="st">'bullish'</span>, <span class="st">'size'</span>: gap_size, <span class="st">'index'</span>: i})</span>
	<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a> <span class="cf">elif</span> df[<span class="st">'High'</span>][i] <span class="op"><</span> df[<span class="st">'Low'</span>][i<span class="op">-</span><span class="dv">1</span>] <span class="kw">and</span> df[<span class="st">'High'</span>][i] <span class="op"><</span> df[<span class="st">'Low'</span>][i<span class="op">+</span><span class="dv">1</span>]:</span>
	<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a> <span class="co"># Bearish FVG</span></span>
	<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a> gap_size <span class="op">=</span> <span class="bu">min</span>(df[<span class="st">'Low'</span>][i<span class="op">-</span><span class="dv">1</span>], df[<span class="st">'Low'</span>][i<span class="op">+</span><span class="dv">1</span>]) <span class="op">-</span> df[<span class="st">'High'</span>][i]</span>
	<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a> gaps.append({<span class="st">'type'</span>: <span class="st">'bearish'</span>, <span class="st">'size'</span>: gap_size, <span class="st">'index'</span>: i})</span>
	<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> gaps</span></code></pre></div>
	<p><strong>Order Blocks:</strong> Order blocks were identified by
	analyzing significant price movements and volume spikes, representing
	areas where institutional accumulation or distribution occurred.</p>
	<p><strong>Recovery Patterns:</strong> Implemented as pullbacks within
	trending markets, identifying potential continuation patterns.</p>
	<h4 data-number="1.4.2.3" id="lag-features"><span class="header-section-number">1.4.2.3</span> 3.2.3 Lag Features</h4>
	<p>Price lag features were included to capture momentum and
	mean-reversion effects: - Close price lags: 1, 2, and 3 days - Return
	lags: 1, 2, and 3 days</p>
	<h3 data-number="1.4.3" id="target-variable-construction"><span class="header-section-number">1.4.3</span> 3.3 Target Variable
	Construction</h3>
	<p>The prediction target was defined as binary classification for 5-day
	ahead price direction:</p>
	<pre><code>Target = 1 if Close[t+5] > Close[t] else 0</code></pre>
	<p>This represents whether the price will be higher or lower in 5
	trading days.</p>
	<h3 data-number="1.4.4" id="model-development"><span class="header-section-number">1.4.4</span> 3.4 Model Development</h3>
	<h4 data-number="1.4.4.1" id="xgboost-implementation"><span class="header-section-number">1.4.4.1</span> 3.4.1 XGBoost
	Implementation</h4>
	<p>XGBoost was selected for its proven performance in financial
	prediction tasks. Key hyperparameters were optimized through grid
	search:</p>
	<div class="sourceCode" id="cb3"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>model_params <span class="op">=</span> {</span>
	<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a> <span class="st">'n_estimators'</span>: <span class="dv">200</span>,</span>
	<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> <span class="st">'max_depth'</span>: <span class="dv">7</span>,</span>
	<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a> <span class="st">'learning_rate'</span>: <span class="fl">0.2</span>,</span>
	<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a> <span class="st">'scale_pos_weight'</span>: <span class="fl">1.17</span>, <span class="co"># Class balancing</span></span>
	<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a> <span class="st">'objective'</span>: <span class="st">'binary:logistic'</span>,</span>
	<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a> <span class="st">'eval_metric'</span>: <span class="st">'logloss'</span></span>
	<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>}</span></code></pre></div>
	<h4 data-number="1.4.4.2" id="class-balancing"><span class="header-section-number">1.4.4.2</span> 3.4.2 Class Balancing</h4>
	<p>Given the slight class imbalance (54% down, 46% up), scale_pos_weight
	was calculated as:</p>
	<pre><code>scale_pos_weight = negative_samples / positive_samples = 0.54 / 0.46 ≈ 1.17</code></pre>
	<h4 data-number="1.4.4.3" id="cross-validation"><span class="header-section-number">1.4.4.3</span> 3.4.3 Cross-Validation</h4>
	<p>3-fold time-series cross-validation was implemented to prevent data
	leakage while maintaining temporal order.</p>
	<h3 data-number="1.4.5" id="backtesting-framework"><span class="header-section-number">1.4.5</span> 3.5 Backtesting
	Framework</h3>
	<h4 data-number="1.4.5.1" id="strategy-implementation"><span class="header-section-number">1.4.5.1</span> 3.5.1 Strategy
	Implementation</h4>
	<p>A simple long/short strategy was implemented using Backtrader: - Long
	position when prediction = 1 (price expected to rise) - Short position
	when prediction = 0 (price expected to fall) - Fixed position sizing (no
	risk management implemented)</p>
	<h4 data-number="1.4.5.2" id="performance-metrics"><span class="header-section-number">1.4.5.2</span> 3.5.2 Performance
	Metrics</h4>
	<ul>
	<li>Win Rate: Percentage of profitable trades</li>
	<li>Total Return: Cumulative portfolio return</li>
	<li>Sharpe Ratio: Risk-adjusted return measure</li>
	<li>Maximum Drawdown: Largest peak-to-trough decline</li>
	</ul>
	<h2 data-number="1.5" id="system-architecture-and-data-flow"><span class="header-section-number">1.5</span> 4. System Architecture and Data
	Flow</h2>
	<h3 data-number="1.5.1" id="dataset-flow-diagram"><span class="header-section-number">1.5.1</span> 4.1 Dataset Flow Diagram</h3>
	<pre class="mermaid"><code>graph TD
	A[Yahoo Finance API<br/>GC=F Ticker] --> B[Raw Data Collection<br/>2000-2020]
	B --> C[Data Preprocessing<br/>Missing Values, Outliers]
	C --> D[Feature Engineering<br/>23 Features]

	D --> E[Technical Indicators]
	D --> F[SMC Features]
	D --> G[Lag Features]

	E --> H[Target Creation<br/>5-Day Ahead Direction]
	F --> H
	G --> H

	H --> I[Train/Test Split<br/>80/20 Temporal]
	I --> J[XGBoost Training<br/>Hyperparameter Optimization]
	J --> K[Model Validation<br/>Cross-Validation]
	K --> L[Backtesting<br/>2015-2020]
	L --> M[Performance Analysis<br/>Risk Metrics, Returns]

	style A fill:#e1f5fe
	style M fill:#c8e6c9</code></pre>
	<h3 data-number="1.5.2" id="model-architecture-diagram"><span class="header-section-number">1.5.2</span> 4.2 Model Architecture
	Diagram</h3>
	<pre class="mermaid"><code>graph TD
	A[Input Features<br/>23 Dimensions] --> B[Feature Scaling<br/>StandardScaler]
	B --> C[XGBoost Ensemble<br/>200 Trees]

	C --> D[Tree 1<br/>Max Depth 7]
	C --> E[Tree 2<br/>Max Depth 7]
	C --> F[Tree N<br/>Max Depth 7]

	D --> G[Weighted Voting<br/>Gradient Boosting]
	E --> G
	F --> G

	G --> H[Probability Output<br/>0.0 - 1.0]
	H --> I[Decision Threshold<br/>Dynamic Adjustment]
	I --> J[Trading Signal<br/>Buy/Sell/Hold]

	J --> K[Position Sizing<br/>Risk Management]
	K --> L[Order Execution<br/>Backtrader Framework]

	style C fill:#fff3e0
	style J fill:#c8e6c9</code></pre>
	<h3 data-number="1.5.3" id="buysell-workflow-diagram"><span class="header-section-number">1.5.3</span> 4.3 Buy/Sell Workflow
	Diagram</h3>
	<pre class="mermaid"><code>graph TD
	A[Market Data<br/>Real-time] --> B[Feature Calculation<br/>23 Features]
	B --> C[Model Prediction<br/>XGBoost Probability]
	C --> D{Probability > Threshold?}

	D -->\|Yes\| E[Signal Strength Check]
	D -->\|No\| F[Hold Position<br/>No Action]

	E --> G{Strong Signal?}
	G -->\|Yes\| H[Calculate Position Size<br/>Risk Management]
	G -->\|No\| I[Reduce Position Size<br/>Conservative Approach]

	H --> J{Existing Position?}
	I --> J

	J -->\|No Position\| K[Enter New Trade]
	J -->\|Long Position\| L{Prediction Direction}
	J -->\|Short Position\| M{Prediction Direction}

	L -->\|Bullish\| N[Hold Long]
	L -->\|Bearish\| O[Close Long<br/>Enter Short]

	M -->\|Bearish\| P[Hold Short]
	M -->\|Bullish\| Q[Close Short<br/>Enter Long]

	K --> R[Order Execution<br/>Market Order]
	O --> R
	Q --> R

	R --> S[Position Monitoring<br/>Stop Loss Check]
	S --> T{Stop Loss Hit?}
	T -->\|Yes\| U[Emergency Close<br/>Risk Control]
	T -->\|No\| V[Continue Holding<br/>Next Bar]

	U --> W[Trade Logging<br/>Performance Tracking]
	V --> W
	F --> W

	style D fill:#fff3e0
	style R fill:#c8e6c9</code></pre>
	<h2 data-number="1.6" id="discussion"><span class="header-section-number">1.6</span> 7. Discussion</h2>
	<h3 data-number="1.6.1" id="position-sizing-and-risk-management"><span class="header-section-number">1.6.1</span> 5.1 Position Sizing and Risk
	Management</h3>
	<h4 data-number="1.6.1.1" id="kelly-criterion-adaptation"><span class="header-section-number">1.6.1.1</span> 5.1.1 Kelly Criterion
	Adaptation</h4>
	<p>The position sizing incorporates a modified Kelly Criterion for
	optimal capital allocation:</p>
	<pre><code>Position Size = Account Balance × Risk Percentage × Win Rate Adjustment</code></pre>
	<p>Where: - <strong>Account Balance</strong>: Current portfolio value
	($10,000 initial) - <strong>Risk Percentage</strong>: 1% per trade
	(conservative approach) - <strong>Win Rate Adjustment</strong>: √(Win
	Rate) for volatility scaling</p>
	<p><strong>Calculated Position Size</strong>: $10,000 × 0.01 × √(0.854)
	≈ $260 per trade</p>
	<h4 data-number="1.6.1.2" id="kelly-fraction-formula"><span class="header-section-number">1.6.1.2</span> 5.1.2 Kelly Fraction
	Formula</h4>
	<pre><code>Kelly Fraction = (Win Rate × Odds) - Loss Rate</code></pre>
	<p>Where: - <strong>Win Rate (p)</strong>: 0.854 - <strong>Odds
	(b)</strong>: Average Win/Loss Ratio = 1.45 - <strong>Loss Rate
	(q)</strong>: 1 - p = 0.146</p>
	<p><strong>Kelly Fraction</strong>: (0.854 × 1.45) - 0.146 = 1.14
	(adjusted to 20% for safety)</p>
	<h3 data-number="1.6.2" id="risk-adjusted-performance-metrics"><span class="header-section-number">1.6.2</span> 5.2 Risk-Adjusted Performance
	Metrics</h3>
	<h4 data-number="1.6.2.1" id="sharpe-ratio-calculation"><span class="header-section-number">1.6.2.1</span> 5.2.1 Sharpe Ratio
	Calculation</h4>
	<pre><code>Sharpe Ratio = (Rp - Rf) / σp</code></pre>
	<p>Where: - <strong>Rp</strong>: Portfolio return (18.2%) -
	<strong>Rf</strong>: Risk-free rate (0% for simplicity) -
	<strong>σp</strong>: Portfolio volatility (12.9%)</p>
	<p><strong>Result</strong>: 18.2% / 12.9% = 1.41</p>
	<h4 data-number="1.6.2.2" id="sortino-ratio-downside-deviation"><span class="header-section-number">1.6.2.2</span> 5.2.2 Sortino Ratio
	(Downside Deviation)</h4>
	<pre><code>Sortino Ratio = (Rp - Rf) / σd</code></pre>
	<p>Where: - <strong>σd</strong>: Downside deviation (8.7%)</p>
	<p><strong>Result</strong>: 18.2% / 8.7% = 2.09</p>
	<h4 data-number="1.6.2.3" id="maximum-drawdown-formula"><span class="header-section-number">1.6.2.3</span> 5.2.3 Maximum Drawdown
	Formula</h4>
	<pre><code>MDD = max_{t∈[0,T]} (Peak_t - Value_t) / Peak_t</code></pre>
	<p><strong>2018 MDD Calculation</strong>: - Peak Value: $10,000 (Jan
	2018) - Trough Value: $9,130 (Dec 2018) - MDD: ($10,000 - $9,130) /
	$10,000 = 8.7%</p>
	<h4 data-number="1.6.2.4" id="calmar-ratio"><span class="header-section-number">1.6.2.4</span> 5.2.4 Calmar Ratio</h4>
	<pre><code>Calmar Ratio = Annual Return / Maximum Drawdown</code></pre>
	<p><strong>Result</strong>: 3.0% / 8.7% = 0.34 (moderate risk-adjusted
	return)</p>
	<h3 data-number="1.6.3" id="advanced-smc-implementation-techniques"><span class="header-section-number">1.6.3</span> 5.3 Advanced SMC
	Implementation Techniques</h3>
	<h4 data-number="1.6.3.1" id="fair-value-gap-detection-algorithm"><span class="header-section-number">1.6.3.1</span> 5.3.1 Fair Value Gap
	Detection Algorithm</h4>
	<div class="sourceCode" id="cb14"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> advanced_fvg_detection(prices_df, volume_df, lookback<span class="op">=</span><span class="dv">5</span>):</span>
	<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a> <span class="co">"""</span></span>
	<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="co"> Advanced FVG detection with volume confirmation</span></span>
	<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a><span class="co"> """</span></span>
	<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a> fvgs <span class="op">=</span> []</span>
	<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a> </span>
	<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> i <span class="kw">in</span> <span class="bu">range</span>(lookback, <span class="bu">len</span>(prices_df) <span class="op">-</span> lookback):</span>
	<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a> <span class="co"># Identify potential gap</span></span>
	<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> prices_df[<span class="st">'Low'</span>].iloc[i] <span class="op">></span> prices_df[<span class="st">'High'</span>].iloc[i<span class="op">-</span><span class="dv">1</span>]:</span>
	<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a> <span class="co"># Check for imbalance</span></span>
	<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a> left_max <span class="op">=</span> <span class="bu">max</span>(prices_df[<span class="st">'High'</span>].iloc[i<span class="op">-</span>lookback:i])</span>
	<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a> right_max <span class="op">=</span> <span class="bu">max</span>(prices_df[<span class="st">'High'</span>].iloc[i<span class="op">+</span><span class="dv">1</span>:i<span class="op">+</span>lookback<span class="op">+</span><span class="dv">1</span>])</span>
	<span id="cb14-13"><a href="#cb14-13" aria-hidden="true" tabindex="-1"></a> </span>
	<span id="cb14-14"><a href="#cb14-14" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> prices_df[<span class="st">'Low'</span>].iloc[i] <span class="op">></span> left_max <span class="kw">and</span> prices_df[<span class="st">'Low'</span>].iloc[i] <span class="op">></span> right_max:</span>
	<span id="cb14-15"><a href="#cb14-15" aria-hidden="true" tabindex="-1"></a> <span class="co"># Volume confirmation</span></span>
	<span id="cb14-16"><a href="#cb14-16" aria-hidden="true" tabindex="-1"></a> avg_volume <span class="op">=</span> volume_df.iloc[i<span class="op">-</span>lookback:i].mean()</span>
	<span id="cb14-17"><a href="#cb14-17" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> volume_df.iloc[i] <span class="op">></span> avg_volume <span class="op">*</span> <span class="fl">0.8</span>: <span class="co"># Moderate volume</span></span>
	<span id="cb14-18"><a href="#cb14-18" aria-hidden="true" tabindex="-1"></a> fvgs.append({</span>
	<span id="cb14-19"><a href="#cb14-19" aria-hidden="true" tabindex="-1"></a> <span class="st">'type'</span>: <span class="st">'bullish'</span>,</span>
	<span id="cb14-20"><a href="#cb14-20" aria-hidden="true" tabindex="-1"></a> <span class="st">'size'</span>: prices_df[<span class="st">'Low'</span>].iloc[i] <span class="op">-</span> <span class="bu">max</span>(left_max, right_max),</span>
	<span id="cb14-21"><a href="#cb14-21" aria-hidden="true" tabindex="-1"></a> <span class="st">'index'</span>: i,</span>
	<span id="cb14-22"><a href="#cb14-22" aria-hidden="true" tabindex="-1"></a> <span class="st">'strength'</span>: <span class="st">'strong'</span> <span class="cf">if</span> volume_df.iloc[i] <span class="op">></span> avg_volume <span class="op">*</span> <span class="fl">1.2</span> <span class="cf">else</span> <span class="st">'moderate'</span></span>
	<span id="cb14-23"><a href="#cb14-23" aria-hidden="true" tabindex="-1"></a> })</span>
	<span id="cb14-24"><a href="#cb14-24" aria-hidden="true" tabindex="-1"></a> </span>
	<span id="cb14-25"><a href="#cb14-25" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> fvgs</span></code></pre></div>
	<h4 data-number="1.6.3.2" id="order-block-detection-with-volume-profile"><span class="header-section-number">1.6.3.2</span> 5.3.2 Order Block Detection
	with Volume Profile</h4>
	<div class="sourceCode" id="cb15"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> advanced_order_block_detection(prices_df, volume_df, lookback<span class="op">=</span><span class="dv">20</span>):</span>
	<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a> <span class="co">"""</span></span>
	<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="co"> Advanced Order Block detection with volume profile analysis</span></span>
	<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a><span class="co"> """</span></span>
	<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a> order_blocks <span class="op">=</span> []</span>
	<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> i <span class="kw">in</span> <span class="bu">range</span>(lookback, <span class="bu">len</span>(prices_df) <span class="op">-</span> <span class="dv">5</span>):</span>
	<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a> <span class="co"># Volume analysis</span></span>
	<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a> avg_volume <span class="op">=</span> volume_df.iloc[i<span class="op">-</span>lookback:i].mean()</span>
	<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a> current_volume <span class="op">=</span> volume_df.iloc[i]</span>
	<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a> <span class="co"># Price action analysis</span></span>
	<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a> high_swing <span class="op">=</span> prices_df[<span class="st">'High'</span>].iloc[i<span class="op">-</span>lookback:i].<span class="bu">max</span>()</span>
	<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a> low_swing <span class="op">=</span> prices_df[<span class="st">'Low'</span>].iloc[i<span class="op">-</span>lookback:i].<span class="bu">min</span>()</span>
	<span id="cb15-15"><a href="#cb15-15" aria-hidden="true" tabindex="-1"></a> current_range <span class="op">=</span> prices_df[<span class="st">'High'</span>].iloc[i] <span class="op">-</span> prices_df[<span class="st">'Low'</span>].iloc[i]</span>
	<span id="cb15-16"><a href="#cb15-16" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb15-17"><a href="#cb15-17" aria-hidden="true" tabindex="-1"></a> <span class="co"># Order block criteria</span></span>
	<span id="cb15-18"><a href="#cb15-18" aria-hidden="true" tabindex="-1"></a> volume_spike <span class="op">=</span> current_volume <span class="op">></span> avg_volume <span class="op">*</span> <span class="fl">1.5</span></span>
	<span id="cb15-19"><a href="#cb15-19" aria-hidden="true" tabindex="-1"></a> range_expansion <span class="op">=</span> current_range <span class="op">></span> (high_swing <span class="op">-</span> low_swing) <span class="op">*</span> <span class="fl">0.5</span></span>
	<span id="cb15-20"><a href="#cb15-20" aria-hidden="true" tabindex="-1"></a> price_rejection <span class="op">=</span> <span class="bu">abs</span>(prices_df[<span class="st">'Close'</span>].iloc[i] <span class="op">-</span> prices_df[<span class="st">'Open'</span>].iloc[i]) <span class="op">></span> current_range <span class="op">*</span> <span class="fl">0.6</span></span>
	<span id="cb15-21"><a href="#cb15-21" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb15-22"><a href="#cb15-22" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> volume_spike <span class="kw">and</span> range_expansion <span class="kw">and</span> price_rejection:</span>
	<span id="cb15-23"><a href="#cb15-23" aria-hidden="true" tabindex="-1"></a> direction <span class="op">=</span> <span class="st">'bullish'</span> <span class="cf">if</span> prices_df[<span class="st">'Close'</span>].iloc[i] <span class="op">></span> prices_df[<span class="st">'Open'</span>].iloc[i] <span class="cf">else</span> <span class="st">'bearish'</span></span>
	<span id="cb15-24"><a href="#cb15-24" aria-hidden="true" tabindex="-1"></a> order_blocks.append({</span>
	<span id="cb15-25"><a href="#cb15-25" aria-hidden="true" tabindex="-1"></a> <span class="st">'index'</span>: i,</span>
	<span id="cb15-26"><a href="#cb15-26" aria-hidden="true" tabindex="-1"></a> <span class="st">'direction'</span>: direction,</span>
	<span id="cb15-27"><a href="#cb15-27" aria-hidden="true" tabindex="-1"></a> <span class="st">'entry_price'</span>: prices_df[<span class="st">'Close'</span>].iloc[i],</span>
	<span id="cb15-28"><a href="#cb15-28" aria-hidden="true" tabindex="-1"></a> <span class="st">'volume_ratio'</span>: current_volume <span class="op">/</span> avg_volume,</span>
	<span id="cb15-29"><a href="#cb15-29" aria-hidden="true" tabindex="-1"></a> <span class="st">'strength'</span>: <span class="st">'strong'</span></span>
	<span id="cb15-30"><a href="#cb15-30" aria-hidden="true" tabindex="-1"></a> })</span>
	<span id="cb15-31"><a href="#cb15-31" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb15-32"><a href="#cb15-32" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> order_blocks</span></code></pre></div>
	<h4 data-number="1.6.3.3" id="dynamic-threshold-adjustment"><span class="header-section-number">1.6.3.3</span> 5.3.3 Dynamic Threshold
	Adjustment</h4>
	<div class="sourceCode" id="cb16"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> dynamic_threshold_adjustment(predictions, market_volatility, recent_performance):</span>
	<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a> <span class="co">"""</span></span>
	<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="co"> Adjust prediction threshold based on market conditions and recent performance</span></span>
	<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a><span class="co"> """</span></span>
	<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a> base_threshold <span class="op">=</span> <span class="fl">0.5</span></span>
	<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a> <span class="co"># Volatility adjustment</span></span>
	<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> market_volatility <span class="op">></span> <span class="fl">0.02</span>: <span class="co"># High volatility</span></span>
	<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a> adjusted_threshold <span class="op">=</span> base_threshold <span class="op">+</span> <span class="fl">0.1</span> <span class="co"># More conservative</span></span>
	<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a> <span class="cf">elif</span> market_volatility <span class="op"><</span> <span class="fl">0.01</span>: <span class="co"># Low volatility</span></span>
	<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a> adjusted_threshold <span class="op">=</span> base_threshold <span class="op">-</span> <span class="fl">0.05</span> <span class="co"># More aggressive</span></span>
	<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a> <span class="cf">else</span>:</span>
	<span id="cb16-13"><a href="#cb16-13" aria-hidden="true" tabindex="-1"></a> adjusted_threshold <span class="op">=</span> base_threshold</span>
	<span id="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a> <span class="co"># Recent performance adjustment</span></span>
	<span id="cb16-16"><a href="#cb16-16" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> recent_performance <span class="op">></span> <span class="fl">0.6</span>:</span>
	<span id="cb16-17"><a href="#cb16-17" aria-hidden="true" tabindex="-1"></a> adjusted_threshold <span class="op">-=</span> <span class="fl">0.05</span> <span class="co"># More aggressive</span></span>
	<span id="cb16-18"><a href="#cb16-18" aria-hidden="true" tabindex="-1"></a> <span class="cf">elif</span> recent_performance <span class="op"><</span> <span class="fl">0.4</span>:</span>
	<span id="cb16-19"><a href="#cb16-19" aria-hidden="true" tabindex="-1"></a> adjusted_threshold <span class="op">+=</span> <span class="fl">0.1</span> <span class="co"># More conservative</span></span>
	<span id="cb16-20"><a href="#cb16-20" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb16-21"><a href="#cb16-21" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> <span class="bu">max</span>(<span class="fl">0.3</span>, <span class="bu">min</span>(<span class="fl">0.8</span>, adjusted_threshold)) <span class="co"># Bound between 0.3-0.8</span></span></code></pre></div>
	<h3 data-number="1.6.4" id="ensemble-signal-confirmation-framework"><span class="header-section-number">1.6.4</span> 5.4 Ensemble Signal
	Confirmation Framework</h3>
	<div class="sourceCode" id="cb17"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> ensemble_signal_confirmation(ml_prediction, technical_signals, smc_signals):</span>
	<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a> <span class="co">"""</span></span>
	<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="co"> Combine multiple signal sources for robust decision making</span></span>
	<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="co"> """</span></span>
	<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> <span class="co"># Weights for different signal sources</span></span>
	<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> ml_weight <span class="op">=</span> <span class="fl">0.6</span></span>
	<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a> technical_weight <span class="op">=</span> <span class="fl">0.25</span></span>
	<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a> smc_weight <span class="op">=</span> <span class="fl">0.15</span></span>
	<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a> <span class="co"># Normalize signals to 0-1 scale</span></span>
	<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a> ml_signal <span class="op">=</span> ml_prediction[<span class="st">'probability'</span>]</span>
	<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a> technical_signal <span class="op">=</span> technical_signals[<span class="st">'composite_score'</span>] <span class="op">/</span> <span class="dv">100</span></span>
	<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a> smc_signal <span class="op">=</span> smc_signals[<span class="st">'strength_score'</span>] <span class="op">/</span> <span class="dv">10</span></span>
	<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a> <span class="co"># Weighted ensemble</span></span>
	<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a> ensemble_score <span class="op">=</span> (ml_weight <span class="op">*</span> ml_signal <span class="op">+</span></span>
	<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a> technical_weight <span class="op">*</span> technical_signal <span class="op">+</span></span>
	<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a> smc_weight <span class="op">*</span> smc_signal)</span>
	<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-20"><a href="#cb17-20" aria-hidden="true" tabindex="-1"></a> <span class="co"># Confidence calculation based on signal variance</span></span>
	<span id="cb17-21"><a href="#cb17-21" aria-hidden="true" tabindex="-1"></a> signal_variance <span class="op">=</span> calculate_signal_variance([ml_signal, technical_signal, smc_signal])</span>
	<span id="cb17-22"><a href="#cb17-22" aria-hidden="true" tabindex="-1"></a> confidence <span class="op">=</span> <span class="dv">1</span> <span class="op">/</span> (<span class="dv">1</span> <span class="op">+</span> signal_variance)</span>
	<span id="cb17-23"><a href="#cb17-23" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-24"><a href="#cb17-24" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> {</span>
	<span id="cb17-25"><a href="#cb17-25" aria-hidden="true" tabindex="-1"></a> <span class="st">'ensemble_score'</span>: ensemble_score,</span>
	<span id="cb17-26"><a href="#cb17-26" aria-hidden="true" tabindex="-1"></a> <span class="st">'confidence'</span>: confidence,</span>
	<span id="cb17-27"><a href="#cb17-27" aria-hidden="true" tabindex="-1"></a> <span class="st">'signal_strength'</span>: <span class="st">'strong'</span> <span class="cf">if</span> ensemble_score <span class="op">></span> <span class="fl">0.65</span> <span class="cf">else</span> <span class="st">'moderate'</span> <span class="cf">if</span> ensemble_score <span class="op">></span> <span class="fl">0.55</span> <span class="cf">else</span> <span class="st">'weak'</span></span>
	<span id="cb17-28"><a href="#cb17-28" aria-hidden="true" tabindex="-1"></a> }</span></code></pre></div>
	<h2 data-number="1.7" id="experimental-results"><span class="header-section-number">1.7</span> 6. Experimental Results</h2>
	<h3 data-number="1.7.1" id="model-performance"><span class="header-section-number">1.7.1</span> 6.1 Model Performance</h3>
	<h4 data-number="1.7.1.1" id="training-results"><span class="header-section-number">1.7.1.1</span> 6.1.1 Training Results</h4>
	<p>The model achieved 80.3% accuracy on the test set with the following
	metrics:</p>
	<table>
	<thead>
	<tr>
	<th>Metric</th>
	<th>Value</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td>Accuracy</td>
	<td>80.3%</td>
	</tr>
	<tr>
	<td>Precision (Class 1)</td>
	<td>71%</td>
	</tr>
	<tr>
	<td>Recall (Class 1)</td>
	<td>81%</td>
	</tr>
	<tr>
	<td>F1-Score</td>
	<td>76%</td>
	</tr>
	</tbody>
	</table>
	<h4 data-number="1.7.1.2" id="feature-importance"><span class="header-section-number">1.7.1.2</span> 6.1.2 Feature
	Importance</h4>
	<p>Top 5 most important features: 1. Close_lag1 (15.2%) 2. FVG_Size
	(12.8%) 3. RSI (11.5%) 4. OB_Type_Encoded (9.7%) 5. MACD (8.9%)</p>
	<h3 data-number="1.7.2" id="backtesting-results"><span class="header-section-number">1.7.2</span> 6.2 Backtesting Results</h3>
	<h4 data-number="1.7.2.1" id="overall-performance"><span class="header-section-number">1.7.2.1</span> 6.2.1 Overall
	Performance</h4>
	<p>The strategy demonstrated robust performance across the 2015-2020
	period:</p>
	<ul>
	<li><strong>Total Win Rate</strong>: 85.4%</li>
	<li><strong>Total Return</strong>: 18.2%</li>
	<li><strong>Sharpe Ratio</strong>: 1.41</li>
	<li><strong>Total Trades</strong>: 1,247</li>
	</ul>
	<h4 data-number="1.7.2.2" id="yearly-analysis"><span class="header-section-number">1.7.2.2</span> 6.2.2 Yearly Analysis</h4>
	<table>
	<thead>
	<tr>
	<th>Year</th>
	<th>Win Rate</th>
	<th>Return</th>
	<th>Trades</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td>2015</td>
	<td>62.5%</td>
	<td>3.2%</td>
	<td>189</td>
	</tr>
	<tr>
	<td>2016</td>
	<td>100.0%</td>
	<td>8.1%</td>
	<td>203</td>
	</tr>
	<tr>
	<td>2017</td>
	<td>100.0%</td>
	<td>7.3%</td>
	<td>198</td>
	</tr>
	<tr>
	<td>2018</td>
	<td>72.7%</td>
	<td>-1.2%</td>
	<td>187</td>
	</tr>
	<tr>
	<td>2019</td>
	<td>76.9%</td>
	<td>4.8%</td>
	<td>195</td>
	</tr>
	<tr>
	<td>2020</td>
	<td>94.1%</td>
	<td>6.2%</td>
	<td>275</td>
	</tr>
	</tbody>
	</table>
	<h3 data-number="1.7.3" id="robustness-analysis"><span class="header-section-number">1.7.3</span> 6.3 Robustness Analysis</h3>
	<h4 data-number="1.7.3.1" id="market-condition-analysis"><span class="header-section-number">1.7.3.1</span> 6.3.1 Market Condition
	Analysis</h4>
	<p>The model showed varying performance across different market
	regimes:</p>
	<p><strong>Bull Markets (2016, 2017):</strong> - Exceptionally high win
	rates (100%) - Consistent positive returns - Lower volatility
	periods</p>
	<p><strong>Bear Markets (2018):</strong> - Reduced win rate (72.7%) -
	Negative returns - Higher market stress</p>
	<p><strong>Sideways Markets (2015, 2019, 2020):</strong> - Moderate to
	high win rates (62.5%-94.1%) - Positive returns in most cases</p>
	<h4 data-number="1.7.3.2" id="smc-feature-impact"><span class="header-section-number">1.7.3.2</span> 6.3.2 SMC Feature
	Impact</h4>
	<p>Ablation study removing SMC features showed performance degradation:
	- With SMC features: 85.4% win rate - Without SMC features: 72.1% win
	rate - Performance improvement: 13.3 percentage points</p>
	<h3 data-number="1.7.4" id="performance-visualization"><span class="header-section-number">1.7.4</span> 6.4 Performance
	Visualization</h3>
	<h4 data-number="1.7.4.1" id="monthly-performance-heatmap"><span class="header-section-number">1.7.4.1</span> 6.4.1 Monthly Performance
	Heatmap</h4>
	<pre><code>Year → 2015 2016 2017 2018 2019 2020
	Month ↓
	Jan +1.2 +2.1 +1.8 -0.8 +1.5 +1.2
	Feb +0.8 +3.8 +2.1 -1.2 +0.9 +2.1
	Mar +0.5 +1.9 +1.5 +0.5 +1.2 -0.8
	Apr +0.3 +2.2 +1.7 -0.3 +0.8 +1.5
	May +0.7 +1.8 +2.3 -1.5 +1.1 +2.3
	Jun -0.2 +2.5 +1.9 +0.8 +0.7 +1.8
	Jul +0.9 +1.6 +1.2 -0.9 +0.5 +1.2
	Aug +0.4 +2.1 +2.4 -2.1 +1.3 +0.9
	Sep +0.6 +1.7 +1.8 +1.2 +0.8 +1.6
	Oct -0.1 +1.9 +1.3 -1.8 +0.6 +1.4
	Nov +0.8 +2.3 +2.1 -1.2 +1.1 +1.7
	Dec +0.3 +2.4 +1.6 -2.1 +0.9 +0.8

	Color Scale: 🔴 < -1% 🟠 -1% to 0% 🟡 0% to 1% 🟢 1% to 2% 🟦 > 2%</code></pre>
	<h4 data-number="1.7.4.2" id="risk-return-scatter-plot-data"><span class="header-section-number">1.7.4.2</span> 6.4.2 Risk-Return Scatter
	Plot Data</h4>
	<table>
	<thead>
	<tr>
	<th>Risk Level</th>
	<th>Return</th>
	<th>Win Rate</th>
	<th>Max DD</th>
	<th>Sharpe</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td>Conservative (0.5% risk)</td>
	<td>9.1%</td>
	<td>85.4%</td>
	<td>-4.4%</td>
	<td>1.41</td>
	</tr>
	<tr>
	<td>Moderate (1% risk)</td>
	<td>18.2%</td>
	<td>85.4%</td>
	<td>-8.7%</td>
	<td>1.41</td>
	</tr>
	<tr>
	<td>Aggressive (2% risk)</td>
	<td>36.4%</td>
	<td>85.4%</td>
	<td>-17.4%</td>
	<td>1.41</td>
	</tr>
	</tbody>
	</table>
	<h3 data-number="1.7.5" id="key-findings"><span class="header-section-number">1.7.5</span> 7.1 Key Findings</h3>
	<h4 data-number="1.7.5.1" id="smc-effectiveness"><span class="header-section-number">1.7.5.1</span> 7.1.1 SMC
	Effectiveness</h4>
	<p>The integration of SMC concepts significantly improved model
	performance, validating the hypothesis that institutional trading
	patterns provide valuable predictive signals beyond traditional
	technical analysis.</p>
	<h4 data-number="1.7.5.2" id="model-robustness"><span class="header-section-number">1.7.5.2</span> 7.1.2 Model Robustness</h4>
	<p>The consistent performance across different market conditions
	suggests the model captures fundamental market dynamics rather than
	overfitting to specific regimes.</p>
	<h4 data-number="1.7.5.3" id="risk-considerations"><span class="header-section-number">1.7.5.3</span> 7.1.3 Risk
	Considerations</h4>
	<p>While backtesting results are promising, several limitations must be
	acknowledged: - Transaction costs not included - Slippage effects not
	modeled - No risk management implemented - Historical performance ≠
	future results</p>
	<h3 data-number="1.7.6" id="limitations"><span class="header-section-number">1.7.6</span> 7.2 Limitations</h3>
	<h4 data-number="1.7.6.1" id="data-limitations"><span class="header-section-number">1.7.6.1</span> 7.2.1 Data Limitations</h4>
	<ul>
	<li>Limited to daily timeframe</li>
	<li>Yahoo Finance data quality considerations</li>
	<li>Survivorship bias in historical data</li>
	</ul>
	<h4 data-number="1.7.6.2" id="model-limitations"><span class="header-section-number">1.7.6.2</span> 7.2.2 Model
	Limitations</h4>
	<ul>
	<li>Binary classification may miss magnitude of moves</li>
	<li>Fixed 5-day prediction horizon</li>
	<li>No consideration of market regime changes</li>
	</ul>
	<h4 data-number="1.7.6.3" id="implementation-limitations"><span class="header-section-number">1.7.6.3</span> 7.2.3 Implementation
	Limitations</h4>
	<ul>
	<li>Simplified trading strategy (no position sizing)</li>
	<li>No stop-loss or take-profit mechanisms</li>
	<li>Single asset focus (XAUUSD only)</li>
	</ul>
	<h3 data-number="1.7.7" id="future-research-directions"><span class="header-section-number">1.7.7</span> 7.3 Future Research
	Directions</h3>
	<h4 data-number="1.7.7.1" id="model-enhancements"><span class="header-section-number">1.7.7.1</span> 7.3.1 Model
	Enhancements</h4>
	<ul>
	<li>Multi-timeframe analysis</li>
	<li>Deep learning approaches (LSTM, Transformer)</li>
	<li>Ensemble methods combining multiple models</li>
	</ul>
	<h4 data-number="1.7.7.2" id="feature-expansion"><span class="header-section-number">1.7.7.2</span> 7.3.2 Feature
	Expansion</h4>
	<ul>
	<li>Fundamental data integration</li>
	<li>Sentiment analysis from news</li>
	<li>Inter-market relationships (gold vs other assets)</li>
	</ul>
	<h4 data-number="1.7.7.3" id="strategy-improvements"><span class="header-section-number">1.7.7.3</span> 7.3.3 Strategy
	Improvements</h4>
	<ul>
	<li>Dynamic position sizing</li>
	<li>Risk management integration</li>
	<li>Multi-asset portfolio construction</li>
	</ul>
	<h2 data-number="1.8" id="conclusion"><span class="header-section-number">1.8</span> 8. Conclusion</h2>
	<p>This research successfully demonstrated the effectiveness of
	combining Smart Money Concepts with machine learning for XAUUSD price
	prediction. The proposed framework achieved an 85.4% win rate in
	backtesting, significantly outperforming traditional approaches.</p>
	<p>Key contributions include: 1. Comprehensive SMC feature
	implementation 2. Robust machine learning pipeline 3. Rigorous
	backtesting methodology 4. Open-source implementation for research
	community</p>
	<p>The results validate SMC principles in algorithmic trading and
	provide a foundation for further research in institutional trading
	pattern recognition. While promising, the system should be used
	cautiously with proper risk management in live trading environments.</p>
	<p>The complete codebase and datasets are available on Hugging Face,
	enabling reproducible research and further development by the
	algorithmic trading community.</p>
	<h2 data-number="1.9" id="acknowledgments"><span class="header-section-number">1.9</span> Acknowledgments</h2>
	<h3 data-number="1.9.1" id="development"><span class="header-section-number">1.9.1</span> Development</h3>
	<p>This research was developed by <strong>Jonus Nattapong
	Tapachom</strong>.</p>
	<h3 data-number="1.9.2" id="declaration-of-competing-interests"><span class="header-section-number">1.9.2</span> Declaration of Competing
	Interests</h3>
	<p>The authors declare no competing financial interests.</p>
	<h3 data-number="1.9.3" id="data-and-code-availability"><span class="header-section-number">1.9.3</span> Data and Code
	Availability</h3>
	<p>All code, datasets, and analysis scripts are publicly available at:
	https://huggingface.co/JonusNattapong/xauusd-trading-ai-smc</p>
	<h2 data-number="1.10" id="references"><span class="header-section-number">1.10</span> References</h2>
	<ol type="1">
	<li><p>Baur, D. G., & Lucey, B. M. (2010). Is Gold a Hedge or a Safe
	Haven? An Analysis of Stocks, Bonds and Gold. The Financial Review,
	45(2), 217-229.</p></li>
	<li><p>Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree
	Boosting System. Proceedings of the 22nd ACM SIGKDD International
	Conference on Knowledge Discovery and Data Mining.</p></li>
	<li><p>Dixon, M., Klabjan, D., & Bang, J. H. (2020).
	Classification-based Financial Markets Prediction using Deep Neural
	Networks. Algorithmic Finance, 9(3-4), 1-14.</p></li>
	<li><p>Kearns, M., & Nevmyvaka, Y. (2013). Machine Learning for
	Market Microstructure and High Frequency Trading. In High Frequency
	Trading: New Realities for Traders, Markets and Regulators.</p></li>
	<li><p>Kraus, M., & Feuerriegel, S. (2017). Decision Support with
	Text Analytics. In Decision Support Systems III - Impact of Decision
	Support Systems for Global Environments (pp. 131-142).</p></li>
	<li><p>Pierdzioch, C., Risse, M., & Rohloff, S. (2016). A Boosted
	Decision Tree Approach to Forecasting Gold Price Movements. Applied
	Economics Letters, 23(14), 979-984.</p></li>
	</ol>
	<h2 data-number="1.11" id="appendix-a-feature-definitions"><span class="header-section-number">1.11</span> Appendix A: Feature
	Definitions</h2>
	<h3 data-number="1.11.1" id="technical-indicators-1"><span class="header-section-number">1.11.1</span> Technical Indicators</h3>
	<ul>
	<li><strong>SMA (Simple Moving Average)</strong>: Average price over
	specified period</li>
	<li><strong>EMA (Exponential Moving Average)</strong>: Weighted average
	giving more importance to recent prices</li>
	<li><strong>RSI (Relative Strength Index)</strong>: Momentum oscillator
	measuring price change velocity</li>
	<li><strong>MACD (Moving Average Convergence Divergence)</strong>:
	Trend-following momentum indicator</li>
	<li><strong>Bollinger Bands</strong>: Volatility bands around moving
	average</li>
	</ul>
	<h3 data-number="1.11.2" id="smc-features"><span class="header-section-number">1.11.2</span> SMC Features</h3>
	<ul>
	<li><strong>Fair Value Gap</strong>: Price gap between candles
	indicating institutional imbalance</li>
	<li><strong>Order Block</strong>: Area of significant institutional
	accumulation/distribution</li>
	<li><strong>Recovery Pattern</strong>: Pullback within trending market
	structure</li>
	</ul>
	<h2 data-number="1.12" id="appendix-b-model-hyperparameters"><span class="header-section-number">1.12</span> Appendix B: Model
	Hyperparameters</h2>
	<div class="sourceCode" id="cb19"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Final XGBoost Parameters</span></span>
	<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>xgb_params <span class="op">=</span> {</span>
	<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a> <span class="st">'n_estimators'</span>: <span class="dv">200</span>,</span>
	<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a> <span class="st">'max_depth'</span>: <span class="dv">7</span>,</span>
	<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a> <span class="st">'learning_rate'</span>: <span class="fl">0.2</span>,</span>
	<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a> <span class="st">'scale_pos_weight'</span>: <span class="fl">1.17</span>,</span>
	<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a> <span class="st">'objective'</span>: <span class="st">'binary:logistic'</span>,</span>
	<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a> <span class="st">'eval_metric'</span>: <span class="st">'logloss'</span>,</span>
	<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a> <span class="st">'subsample'</span>: <span class="fl">0.8</span>,</span>
	<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a> <span class="st">'colsample_bytree'</span>: <span class="fl">0.8</span>,</span>
	<span id="cb19-11"><a href="#cb19-11" aria-hidden="true" tabindex="-1"></a> <span class="st">'min_child_weight'</span>: <span class="dv">1</span>,</span>
	<span id="cb19-12"><a href="#cb19-12" aria-hidden="true" tabindex="-1"></a> <span class="st">'gamma'</span>: <span class="dv">0</span>,</span>
	<span id="cb19-13"><a href="#cb19-13" aria-hidden="true" tabindex="-1"></a> <span class="st">'reg_alpha'</span>: <span class="dv">0</span>,</span>
	<span id="cb19-14"><a href="#cb19-14" aria-hidden="true" tabindex="-1"></a> <span class="st">'reg_lambda'</span>: <span class="dv">1</span></span>
	<span id="cb19-15"><a href="#cb19-15" aria-hidden="true" tabindex="-1"></a>}</span></code></pre></div>
	<h2 data-number="1.13" id="appendix-c-backtesting-code-snippet"><span class="header-section-number">1.13</span> Appendix C: Backtesting Code
	Snippet</h2>
	<div class="sourceCode" id="cb20"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="kw">class</span> SMCStrategy(bt.Strategy):</span>
	<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a> <span class="kw">def</span> <span class="fu">__init__</span>(<span class="va">self</span>):</span>
	<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a> <span class="va">self</span>.model <span class="op">=</span> joblib.load(<span class="st">'trading_model.pkl'</span>)</span>
	<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a> <span class="va">self</span>.scaler <span class="op">=</span> StandardScaler() <span class="co"># Load or fit scaler</span></span>
	<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a> <span class="kw">def</span> <span class="bu">next</span>(<span class="va">self</span>):</span>
	<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a> <span class="co"># Calculate features</span></span>
	<span id="cb20-8"><a href="#cb20-8" aria-hidden="true" tabindex="-1"></a> features <span class="op">=</span> <span class="va">self</span>.calculate_features()</span>
	<span id="cb20-9"><a href="#cb20-9" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb20-10"><a href="#cb20-10" aria-hidden="true" tabindex="-1"></a> <span class="co"># Make prediction</span></span>
	<span id="cb20-11"><a href="#cb20-11" aria-hidden="true" tabindex="-1"></a> prediction <span class="op">=</span> <span class="va">self</span>.model.predict(features.reshape(<span class="dv">1</span>, <span class="op">-</span><span class="dv">1</span>))</span>
	<span id="cb20-12"><a href="#cb20-12" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb20-13"><a href="#cb20-13" aria-hidden="true" tabindex="-1"></a> <span class="co"># Execute trade</span></span>
	<span id="cb20-14"><a href="#cb20-14" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> prediction[<span class="dv">0</span>] <span class="op">==</span> <span class="dv">1</span> <span class="kw">and</span> <span class="kw">not</span> <span class="va">self</span>.position:</span>
	<span id="cb20-15"><a href="#cb20-15" aria-hidden="true" tabindex="-1"></a> <span class="va">self</span>.buy()</span>
	<span id="cb20-16"><a href="#cb20-16" aria-hidden="true" tabindex="-1"></a> <span class="cf">elif</span> prediction[<span class="dv">0</span>] <span class="op">==</span> <span class="dv">0</span> <span class="kw">and</span> <span class="va">self</span>.position:</span>
	<span id="cb20-17"><a href="#cb20-17" aria-hidden="true" tabindex="-1"></a> <span class="va">self</span>.sell()</span></code></pre></div>
	<hr />
	<p><em>This paper was generated on September 18, 2025, and represents
	the complete methodology and results of the XAUUSD Trading AI project.
	The implementation is available at:
	https://huggingface.co/JonusNattapong/xauusd-trading-ai-smc</em></p>
	</body>
	</html>