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+\section{XAUUSD Trading AI: A Machine Learning Approach Using Smart
+Money
+Concepts}\label{xauusd-trading-ai-a-machine-learning-approach-using-smart-money-concepts}
+
+\textbf{Author: Jonus Nattapong Tapachom}\\
+\textbf{Date: September 18, 2025}
+
+\subsection{Abstract}\label{abstract}
+
+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.
+
+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.
+
+\textbf{Keywords}: Algorithmic Trading, Machine Learning, Smart Money
+Concepts, XAUUSD, XGBoost, Technical Analysis
+
+\subsection{1. Introduction}\label{introduction}
+
+\subsubsection{1.1 Background}\label{background}
+
+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.
+
+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.
+
+\subsubsection{1.2 Problem Statement}\label{problem-statement}
+
+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.
+
+\subsubsection{1.3 Research Objectives}\label{research-objectives}
+
+\begin{enumerate}
+\def\labelenumi{\arabic{enumi}.}
+\tightlist
+\item
+  Develop a comprehensive feature set combining SMC and technical
+  indicators
+\item
+  Implement and optimize an XGBoost-based prediction model
+\item
+  Validate performance through rigorous backtesting
+\item
+  Analyze model robustness across different market conditions
+\item
+  Provide a reproducible framework for algorithmic trading research
+\end{enumerate}
+
+\subsubsection{1.4 Contributions}\label{contributions}
+
+\begin{itemize}
+\tightlist
+\item
+  Novel integration of SMC concepts with machine learning
+\item
+  Comprehensive feature engineering methodology
+\item
+  Robust backtesting framework with yearly performance analysis
+\item
+  Open-source implementation for research community
+\item
+  Empirical validation of SMC effectiveness in algorithmic trading
+\end{itemize}
+
+\subsection{2. Literature Review}\label{literature-review}
+
+\subsubsection{2.1 Algorithmic Trading in FX
+Markets}\label{algorithmic-trading-in-fx-markets}
+
+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.
+
+\subsubsection{2.2 Smart Money Concepts}\label{smart-money-concepts}
+
+SMC methodology, popularized by ICT (Inner Circle Trader) concepts,
+focuses on identifying institutional trading behavior through market
+structure analysis. Key SMC elements include:
+
+\begin{itemize}
+\tightlist
+\item
+  \textbf{Order Blocks}: Areas where significant buying/selling occurred
+\item
+  \textbf{Fair Value Gaps}: Price imbalances between candles
+\item
+  \textbf{Liquidity Concepts}: Understanding where institutional orders
+  are placed
+\item
+  \textbf{Market Structure}: Recognition of higher-timeframe trends
+\end{itemize}
+
+\subsubsection{2.3 Machine Learning in
+Trading}\label{machine-learning-in-trading}
+
+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.
+
+\subsubsection{2.4 Gold Price Prediction}\label{gold-price-prediction}
+
+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.
+
+\subsection{3. Methodology}\label{methodology}
+
+\subsubsection{3.1 Data Collection}\label{data-collection}
+
+\paragraph{3.1.1 Data Source}\label{data-source}
+
+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.
+
+\paragraph{3.1.2 Data Preprocessing}\label{data-preprocessing}
+
+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
+
+\subsubsection{3.2 Feature Engineering}\label{feature-engineering}
+
+\paragraph{3.2.1 Technical Indicators}\label{technical-indicators}
+
+Traditional technical indicators were calculated using the TA-Lib
+library:
+
+\textbf{Trend Indicators:} - Simple Moving Averages (SMA): 20-day and
+50-day periods - Exponential Moving Averages (EMA): 12-day and 26-day
+periods
+
+\textbf{Momentum Indicators:} - Relative Strength Index (RSI): 14-day
+period - Moving Average Convergence Divergence (MACD): Standard
+parameters
+
+\textbf{Volatility Indicators:} - Bollinger Bands: 20-day period, 2
+standard deviations
+
+\paragraph{3.2.2 SMC Feature
+Implementation}\label{smc-feature-implementation}
+
+\textbf{Fair Value Gaps (FVG):}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\KeywordTok{def}\NormalTok{ calculate\_fvg(df):}
+\NormalTok{    gaps }\OperatorTok{=}\NormalTok{ []}
+    \ControlFlowTok{for}\NormalTok{ i }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(}\DecValTok{1}\NormalTok{, }\BuiltInTok{len}\NormalTok{(df)}\OperatorTok{{-}}\DecValTok{1}\NormalTok{):}
+        \ControlFlowTok{if}\NormalTok{ df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{][i] }\OperatorTok{\textgreater{}}\NormalTok{ df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{][i}\OperatorTok{{-}}\DecValTok{1}\NormalTok{] }\KeywordTok{and}\NormalTok{ df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{][i] }\OperatorTok{\textgreater{}}\NormalTok{ df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{][i}\OperatorTok{+}\DecValTok{1}\NormalTok{]:}
+            \CommentTok{\# Bullish FVG}
+\NormalTok{            gap\_size }\OperatorTok{=}\NormalTok{ df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{][i] }\OperatorTok{{-}} \BuiltInTok{max}\NormalTok{(df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{][i}\OperatorTok{{-}}\DecValTok{1}\NormalTok{], df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{][i}\OperatorTok{+}\DecValTok{1}\NormalTok{])}
+\NormalTok{            gaps.append(\{}\StringTok{\textquotesingle{}type\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}bullish\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}size\textquotesingle{}}\NormalTok{: gap\_size, }\StringTok{\textquotesingle{}index\textquotesingle{}}\NormalTok{: i\})}
+        \ControlFlowTok{elif}\NormalTok{ df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{][i] }\OperatorTok{\textless{}}\NormalTok{ df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{][i}\OperatorTok{{-}}\DecValTok{1}\NormalTok{] }\KeywordTok{and}\NormalTok{ df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{][i] }\OperatorTok{\textless{}}\NormalTok{ df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{][i}\OperatorTok{+}\DecValTok{1}\NormalTok{]:}
+            \CommentTok{\# Bearish FVG}
+\NormalTok{            gap\_size }\OperatorTok{=} \BuiltInTok{min}\NormalTok{(df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{][i}\OperatorTok{{-}}\DecValTok{1}\NormalTok{], df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{][i}\OperatorTok{+}\DecValTok{1}\NormalTok{]) }\OperatorTok{{-}}\NormalTok{ df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{][i]}
+\NormalTok{            gaps.append(\{}\StringTok{\textquotesingle{}type\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}bearish\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}size\textquotesingle{}}\NormalTok{: gap\_size, }\StringTok{\textquotesingle{}index\textquotesingle{}}\NormalTok{: i\})}
+    \ControlFlowTok{return}\NormalTok{ gaps}
+\end{Highlighting}
+\end{Shaded}
+
+\textbf{Order Blocks:} Order blocks were identified by analyzing
+significant price movements and volume spikes, representing areas where
+institutional accumulation or distribution occurred.
+
+\textbf{Recovery Patterns:} Implemented as pullbacks within trending
+markets, identifying potential continuation patterns.
+
+\paragraph{3.2.3 Lag Features}\label{lag-features}
+
+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
+
+\subsubsection{3.3 Target Variable
+Construction}\label{target-variable-construction}
+
+The prediction target was defined as binary classification for 5-day
+ahead price direction:
+
+\begin{verbatim}
+Target = 1 if Close[t+5] > Close[t] else 0
+\end{verbatim}
+
+This represents whether the price will be higher or lower in 5 trading
+days.
+
+\subsubsection{3.4 Model Development}\label{model-development}
+
+\paragraph{3.4.1 XGBoost Implementation}\label{xgboost-implementation}
+
+XGBoost was selected for its proven performance in financial prediction
+tasks. Key hyperparameters were optimized through grid search:
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{model\_params }\OperatorTok{=}\NormalTok{ \{}
+    \StringTok{\textquotesingle{}n\_estimators\textquotesingle{}}\NormalTok{: }\DecValTok{200}\NormalTok{,}
+    \StringTok{\textquotesingle{}max\_depth\textquotesingle{}}\NormalTok{: }\DecValTok{7}\NormalTok{,}
+    \StringTok{\textquotesingle{}learning\_rate\textquotesingle{}}\NormalTok{: }\FloatTok{0.2}\NormalTok{,}
+    \StringTok{\textquotesingle{}scale\_pos\_weight\textquotesingle{}}\NormalTok{: }\FloatTok{1.17}\NormalTok{,  }\CommentTok{\# Class balancing}
+    \StringTok{\textquotesingle{}objective\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}binary:logistic\textquotesingle{}}\NormalTok{,}
+    \StringTok{\textquotesingle{}eval\_metric\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}logloss\textquotesingle{}}
+\NormalTok{\}}
+\end{Highlighting}
+\end{Shaded}
+
+\paragraph{3.4.2 Class Balancing}\label{class-balancing}
+
+Given the slight class imbalance (54\% down, 46\% up),
+scale\_pos\_weight was calculated as:
+
+\begin{verbatim}
+scale_pos_weight = negative_samples / positive_samples = 0.54 / 0.46 ≈ 1.17
+\end{verbatim}
+
+\paragraph{3.4.3 Cross-Validation}\label{cross-validation}
+
+3-fold time-series cross-validation was implemented to prevent data
+leakage while maintaining temporal order.
+
+\subsubsection{3.5 Backtesting Framework}\label{backtesting-framework}
+
+\paragraph{3.5.1 Strategy Implementation}\label{strategy-implementation}
+
+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)
+
+\paragraph{3.5.2 Performance Metrics}\label{performance-metrics}
+
+\begin{itemize}
+\tightlist
+\item
+  Win Rate: Percentage of profitable trades
+\item
+  Total Return: Cumulative portfolio return
+\item
+  Sharpe Ratio: Risk-adjusted return measure
+\item
+  Maximum Drawdown: Largest peak-to-trough decline
+\end{itemize}
+
+\subsection{4. System Architecture and Data
+Flow}\label{system-architecture-and-data-flow}
+
+\subsubsection{4.1 Dataset Flow Diagram}\label{dataset-flow-diagram}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{graph TD}
+\NormalTok{    A[Yahoo Finance API\textless{}br/\textgreater{}GC=F Ticker] {-}{-}\textgreater{} B[Raw Data Collection\textless{}br/\textgreater{}2000{-}2020]}
+\NormalTok{    B {-}{-}\textgreater{} C[Data Preprocessing\textless{}br/\textgreater{}Missing Values, Outliers]}
+\NormalTok{    C {-}{-}\textgreater{} D[Feature Engineering\textless{}br/\textgreater{}23 Features]}
+    
+\NormalTok{    D {-}{-}\textgreater{} E[Technical Indicators]}
+\NormalTok{    D {-}{-}\textgreater{} F[SMC Features]}
+\NormalTok{    D {-}{-}\textgreater{} G[Lag Features]}
+    
+\NormalTok{    E {-}{-}\textgreater{} H[Target Creation\textless{}br/\textgreater{}5{-}Day Ahead Direction]}
+\NormalTok{    F {-}{-}\textgreater{} H}
+\NormalTok{    G {-}{-}\textgreater{} H}
+    
+\NormalTok{    H {-}{-}\textgreater{} I[Train/Test Split\textless{}br/\textgreater{}80/20 Temporal]}
+\NormalTok{    I {-}{-}\textgreater{} J[XGBoost Training\textless{}br/\textgreater{}Hyperparameter Optimization]}
+\NormalTok{    J {-}{-}\textgreater{} K[Model Validation\textless{}br/\textgreater{}Cross{-}Validation]}
+\NormalTok{    K {-}{-}\textgreater{} L[Backtesting\textless{}br/\textgreater{}2015{-}2020]}
+\NormalTok{    L {-}{-}\textgreater{} M[Performance Analysis\textless{}br/\textgreater{}Risk Metrics, Returns]}
+    
+\NormalTok{    style A fill:\#e1f5fe}
+\NormalTok{    style M fill:\#c8e6c9}
+\end{Highlighting}
+\end{Shaded}
+
+\subsubsection{4.2 Model Architecture
+Diagram}\label{model-architecture-diagram}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{graph TD}
+\NormalTok{    A[Input Features\textless{}br/\textgreater{}23 Dimensions] {-}{-}\textgreater{} B[Feature Scaling\textless{}br/\textgreater{}StandardScaler]}
+\NormalTok{    B {-}{-}\textgreater{} C[XGBoost Ensemble\textless{}br/\textgreater{}200 Trees]}
+    
+\NormalTok{    C {-}{-}\textgreater{} D[Tree 1\textless{}br/\textgreater{}Max Depth 7]}
+\NormalTok{    C {-}{-}\textgreater{} E[Tree 2\textless{}br/\textgreater{}Max Depth 7]}
+\NormalTok{    C {-}{-}\textgreater{} F[Tree N\textless{}br/\textgreater{}Max Depth 7]}
+    
+\NormalTok{    D {-}{-}\textgreater{} G[Weighted Voting\textless{}br/\textgreater{}Gradient Boosting]}
+\NormalTok{    E {-}{-}\textgreater{} G}
+\NormalTok{    F {-}{-}\textgreater{} G}
+    
+\NormalTok{    G {-}{-}\textgreater{} H[Probability Output\textless{}br/\textgreater{}0.0 {-} 1.0]}
+\NormalTok{    H {-}{-}\textgreater{} I[Decision Threshold\textless{}br/\textgreater{}Dynamic Adjustment]}
+\NormalTok{    I {-}{-}\textgreater{} J[Trading Signal\textless{}br/\textgreater{}Buy/Sell/Hold]}
+    
+\NormalTok{    J {-}{-}\textgreater{} K[Position Sizing\textless{}br/\textgreater{}Risk Management]}
+\NormalTok{    K {-}{-}\textgreater{} L[Order Execution\textless{}br/\textgreater{}Backtrader Framework]}
+    
+\NormalTok{    style C fill:\#fff3e0}
+\NormalTok{    style J fill:\#c8e6c9}
+\end{Highlighting}
+\end{Shaded}
+
+\subsubsection{4.3 Buy/Sell Workflow
+Diagram}\label{buysell-workflow-diagram}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{graph TD}
+\NormalTok{    A[Market Data\textless{}br/\textgreater{}Real{-}time] {-}{-}\textgreater{} B[Feature Calculation\textless{}br/\textgreater{}23 Features]}
+\NormalTok{    B {-}{-}\textgreater{} C[Model Prediction\textless{}br/\textgreater{}XGBoost Probability]}
+\NormalTok{    C {-}{-}\textgreater{} D\{Probability \textgreater{} Threshold?\}}
+    
+\NormalTok{    D {-}{-}\textgreater{}|Yes| E[Signal Strength Check]}
+\NormalTok{    D {-}{-}\textgreater{}|No| F[Hold Position\textless{}br/\textgreater{}No Action]}
+    
+\NormalTok{    E {-}{-}\textgreater{} G\{Strong Signal?\}}
+\NormalTok{    G {-}{-}\textgreater{}|Yes| H[Calculate Position Size\textless{}br/\textgreater{}Risk Management]}
+\NormalTok{    G {-}{-}\textgreater{}|No| I[Reduce Position Size\textless{}br/\textgreater{}Conservative Approach]}
+    
+\NormalTok{    H {-}{-}\textgreater{} J\{Existing Position?\}}
+\NormalTok{    I {-}{-}\textgreater{} J}
+    
+\NormalTok{    J {-}{-}\textgreater{}|No Position| K[Enter New Trade]}
+\NormalTok{    J {-}{-}\textgreater{}|Long Position| L\{Prediction Direction\}}
+\NormalTok{    J {-}{-}\textgreater{}|Short Position| M\{Prediction Direction\}}
+    
+\NormalTok{    L {-}{-}\textgreater{}|Bullish| N[Hold Long]}
+\NormalTok{    L {-}{-}\textgreater{}|Bearish| O[Close Long\textless{}br/\textgreater{}Enter Short]}
+    
+\NormalTok{    M {-}{-}\textgreater{}|Bearish| P[Hold Short]}
+\NormalTok{    M {-}{-}\textgreater{}|Bullish| Q[Close Short\textless{}br/\textgreater{}Enter Long]}
+    
+\NormalTok{    K {-}{-}\textgreater{} R[Order Execution\textless{}br/\textgreater{}Market Order]}
+\NormalTok{    O {-}{-}\textgreater{} R}
+\NormalTok{    Q {-}{-}\textgreater{} R}
+    
+\NormalTok{    R {-}{-}\textgreater{} S[Position Monitoring\textless{}br/\textgreater{}Stop Loss Check]}
+\NormalTok{    S {-}{-}\textgreater{} T\{Stop Loss Hit?\}}
+\NormalTok{    T {-}{-}\textgreater{}|Yes| U[Emergency Close\textless{}br/\textgreater{}Risk Control]}
+\NormalTok{    T {-}{-}\textgreater{}|No| V[Continue Holding\textless{}br/\textgreater{}Next Bar]}
+    
+\NormalTok{    U {-}{-}\textgreater{} W[Trade Logging\textless{}br/\textgreater{}Performance Tracking]}
+\NormalTok{    V {-}{-}\textgreater{} W}
+\NormalTok{    F {-}{-}\textgreater{} W}
+    
+\NormalTok{    style D fill:\#fff3e0}
+\NormalTok{    style R fill:\#c8e6c9}
+\end{Highlighting}
+\end{Shaded}
+
+\subsection{7. Discussion}\label{discussion}
+
+\subsubsection{5.1 Position Sizing and Risk
+Management}\label{position-sizing-and-risk-management}
+
+\paragraph{5.1.1 Kelly Criterion
+Adaptation}\label{kelly-criterion-adaptation}
+
+The position sizing incorporates a modified Kelly Criterion for optimal
+capital allocation:
+
+\begin{verbatim}
+Position Size = Account Balance × Risk Percentage × Win Rate Adjustment
+\end{verbatim}
+
+Where: - \textbf{Account Balance}: Current portfolio value (\$10,000
+initial) - \textbf{Risk Percentage}: 1\% per trade (conservative
+approach) - \textbf{Win Rate Adjustment}: √(Win Rate) for volatility
+scaling
+
+\textbf{Calculated Position Size}: \$10,000 × 0.01 × √(0.854) ≈ \$260
+per trade
+
+\paragraph{5.1.2 Kelly Fraction Formula}\label{kelly-fraction-formula}
+
+\begin{verbatim}
+Kelly Fraction = (Win Rate × Odds) - Loss Rate
+\end{verbatim}
+
+Where: - \textbf{Win Rate (p)}: 0.854 - \textbf{Odds (b)}: Average
+Win/Loss Ratio = 1.45 - \textbf{Loss Rate (q)}: 1 - p = 0.146
+
+\textbf{Kelly Fraction}: (0.854 × 1.45) - 0.146 = 1.14 (adjusted to 20\%
+for safety)
+
+\subsubsection{5.2 Risk-Adjusted Performance
+Metrics}\label{risk-adjusted-performance-metrics}
+
+\paragraph{5.2.1 Sharpe Ratio
+Calculation}\label{sharpe-ratio-calculation}
+
+\begin{verbatim}
+Sharpe Ratio = (Rp - Rf) / σp
+\end{verbatim}
+
+Where: - \textbf{Rp}: Portfolio return (18.2\%) - \textbf{Rf}: Risk-free
+rate (0\% for simplicity) - \textbf{σp}: Portfolio volatility (12.9\%)
+
+\textbf{Result}: 18.2\% / 12.9\% = 1.41
+
+\paragraph{5.2.2 Sortino Ratio (Downside
+Deviation)}\label{sortino-ratio-downside-deviation}
+
+\begin{verbatim}
+Sortino Ratio = (Rp - Rf) / σd
+\end{verbatim}
+
+Where: - \textbf{σd}: Downside deviation (8.7\%)
+
+\textbf{Result}: 18.2\% / 8.7\% = 2.09
+
+\paragraph{5.2.3 Maximum Drawdown
+Formula}\label{maximum-drawdown-formula}
+
+\begin{verbatim}
+MDD = max_{t∈[0,T]} (Peak_t - Value_t) / Peak_t
+\end{verbatim}
+
+\textbf{2018 MDD Calculation}: - Peak Value: \$10,000 (Jan 2018) -
+Trough Value: \$9,130 (Dec 2018) - MDD: (\$10,000 - \$9,130) / \$10,000
+= 8.7\%
+
+\paragraph{5.2.4 Calmar Ratio}\label{calmar-ratio}
+
+\begin{verbatim}
+Calmar Ratio = Annual Return / Maximum Drawdown
+\end{verbatim}
+
+\textbf{Result}: 3.0\% / 8.7\% = 0.34 (moderate risk-adjusted return)
+
+\subsubsection{5.3 Advanced SMC Implementation
+Techniques}\label{advanced-smc-implementation-techniques}
+
+\paragraph{5.3.1 Fair Value Gap Detection
+Algorithm}\label{fair-value-gap-detection-algorithm}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\KeywordTok{def}\NormalTok{ advanced\_fvg\_detection(prices\_df, volume\_df, lookback}\OperatorTok{=}\DecValTok{5}\NormalTok{):}
+    \CommentTok{"""}
+\CommentTok{    Advanced FVG detection with volume confirmation}
+\CommentTok{    """}
+\NormalTok{    fvgs }\OperatorTok{=}\NormalTok{ []}
+    
+    \ControlFlowTok{for}\NormalTok{ i }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(lookback, }\BuiltInTok{len}\NormalTok{(prices\_df) }\OperatorTok{{-}}\NormalTok{ lookback):}
+        \CommentTok{\# Identify potential gap}
+        \ControlFlowTok{if}\NormalTok{ prices\_df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{].iloc[i] }\OperatorTok{\textgreater{}}\NormalTok{ prices\_df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{].iloc[i}\OperatorTok{{-}}\DecValTok{1}\NormalTok{]:}
+            \CommentTok{\# Check for imbalance}
+\NormalTok{            left\_max }\OperatorTok{=} \BuiltInTok{max}\NormalTok{(prices\_df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{].iloc[i}\OperatorTok{{-}}\NormalTok{lookback:i])}
+\NormalTok{            right\_max }\OperatorTok{=} \BuiltInTok{max}\NormalTok{(prices\_df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{].iloc[i}\OperatorTok{+}\DecValTok{1}\NormalTok{:i}\OperatorTok{+}\NormalTok{lookback}\OperatorTok{+}\DecValTok{1}\NormalTok{])}
+            
+            \ControlFlowTok{if}\NormalTok{ prices\_df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{].iloc[i] }\OperatorTok{\textgreater{}}\NormalTok{ left\_max }\KeywordTok{and}\NormalTok{ prices\_df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{].iloc[i] }\OperatorTok{\textgreater{}}\NormalTok{ right\_max:}
+                \CommentTok{\# Volume confirmation}
+\NormalTok{                avg\_volume }\OperatorTok{=}\NormalTok{ volume\_df.iloc[i}\OperatorTok{{-}}\NormalTok{lookback:i].mean()}
+                \ControlFlowTok{if}\NormalTok{ volume\_df.iloc[i] }\OperatorTok{\textgreater{}}\NormalTok{ avg\_volume }\OperatorTok{*} \FloatTok{0.8}\NormalTok{:  }\CommentTok{\# Moderate volume}
+\NormalTok{                    fvgs.append(\{}
+                        \StringTok{\textquotesingle{}type\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}bullish\textquotesingle{}}\NormalTok{,}
+                        \StringTok{\textquotesingle{}size\textquotesingle{}}\NormalTok{: prices\_df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{].iloc[i] }\OperatorTok{{-}} \BuiltInTok{max}\NormalTok{(left\_max, right\_max),}
+                        \StringTok{\textquotesingle{}index\textquotesingle{}}\NormalTok{: i,}
+                        \StringTok{\textquotesingle{}strength\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}strong\textquotesingle{}} \ControlFlowTok{if}\NormalTok{ volume\_df.iloc[i] }\OperatorTok{\textgreater{}}\NormalTok{ avg\_volume }\OperatorTok{*} \FloatTok{1.2} \ControlFlowTok{else} \StringTok{\textquotesingle{}moderate\textquotesingle{}}
+\NormalTok{                    \})}
+    
+    \ControlFlowTok{return}\NormalTok{ fvgs}
+\end{Highlighting}
+\end{Shaded}
+
+\paragraph{5.3.2 Order Block Detection with Volume
+Profile}\label{order-block-detection-with-volume-profile}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\KeywordTok{def}\NormalTok{ advanced\_order\_block\_detection(prices\_df, volume\_df, lookback}\OperatorTok{=}\DecValTok{20}\NormalTok{):}
+    \CommentTok{"""}
+\CommentTok{    Advanced Order Block detection with volume profile analysis}
+\CommentTok{    """}
+\NormalTok{    order\_blocks }\OperatorTok{=}\NormalTok{ []}
+
+    \ControlFlowTok{for}\NormalTok{ i }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(lookback, }\BuiltInTok{len}\NormalTok{(prices\_df) }\OperatorTok{{-}} \DecValTok{5}\NormalTok{):}
+        \CommentTok{\# Volume analysis}
+\NormalTok{        avg\_volume }\OperatorTok{=}\NormalTok{ volume\_df.iloc[i}\OperatorTok{{-}}\NormalTok{lookback:i].mean()}
+\NormalTok{        current\_volume }\OperatorTok{=}\NormalTok{ volume\_df.iloc[i]}
+
+        \CommentTok{\# Price action analysis}
+\NormalTok{        high\_swing }\OperatorTok{=}\NormalTok{ prices\_df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{].iloc[i}\OperatorTok{{-}}\NormalTok{lookback:i].}\BuiltInTok{max}\NormalTok{()}
+\NormalTok{        low\_swing }\OperatorTok{=}\NormalTok{ prices\_df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{].iloc[i}\OperatorTok{{-}}\NormalTok{lookback:i].}\BuiltInTok{min}\NormalTok{()}
+\NormalTok{        current\_range }\OperatorTok{=}\NormalTok{ prices\_df[}\StringTok{\textquotesingle{}High\textquotesingle{}}\NormalTok{].iloc[i] }\OperatorTok{{-}}\NormalTok{ prices\_df[}\StringTok{\textquotesingle{}Low\textquotesingle{}}\NormalTok{].iloc[i]}
+
+        \CommentTok{\# Order block criteria}
+\NormalTok{        volume\_spike }\OperatorTok{=}\NormalTok{ current\_volume }\OperatorTok{\textgreater{}}\NormalTok{ avg\_volume }\OperatorTok{*} \FloatTok{1.5}
+\NormalTok{        range\_expansion }\OperatorTok{=}\NormalTok{ current\_range }\OperatorTok{\textgreater{}}\NormalTok{ (high\_swing }\OperatorTok{{-}}\NormalTok{ low\_swing) }\OperatorTok{*} \FloatTok{0.5}
+\NormalTok{        price\_rejection }\OperatorTok{=} \BuiltInTok{abs}\NormalTok{(prices\_df[}\StringTok{\textquotesingle{}Close\textquotesingle{}}\NormalTok{].iloc[i] }\OperatorTok{{-}}\NormalTok{ prices\_df[}\StringTok{\textquotesingle{}Open\textquotesingle{}}\NormalTok{].iloc[i]) }\OperatorTok{\textgreater{}}\NormalTok{ current\_range }\OperatorTok{*} \FloatTok{0.6}
+
+        \ControlFlowTok{if}\NormalTok{ volume\_spike }\KeywordTok{and}\NormalTok{ range\_expansion }\KeywordTok{and}\NormalTok{ price\_rejection:}
+\NormalTok{            direction }\OperatorTok{=} \StringTok{\textquotesingle{}bullish\textquotesingle{}} \ControlFlowTok{if}\NormalTok{ prices\_df[}\StringTok{\textquotesingle{}Close\textquotesingle{}}\NormalTok{].iloc[i] }\OperatorTok{\textgreater{}}\NormalTok{ prices\_df[}\StringTok{\textquotesingle{}Open\textquotesingle{}}\NormalTok{].iloc[i] }\ControlFlowTok{else} \StringTok{\textquotesingle{}bearish\textquotesingle{}}
+\NormalTok{            order\_blocks.append(\{}
+                \StringTok{\textquotesingle{}index\textquotesingle{}}\NormalTok{: i,}
+                \StringTok{\textquotesingle{}direction\textquotesingle{}}\NormalTok{: direction,}
+                \StringTok{\textquotesingle{}entry\_price\textquotesingle{}}\NormalTok{: prices\_df[}\StringTok{\textquotesingle{}Close\textquotesingle{}}\NormalTok{].iloc[i],}
+                \StringTok{\textquotesingle{}volume\_ratio\textquotesingle{}}\NormalTok{: current\_volume }\OperatorTok{/}\NormalTok{ avg\_volume,}
+                \StringTok{\textquotesingle{}strength\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}strong\textquotesingle{}}
+\NormalTok{            \})}
+
+    \ControlFlowTok{return}\NormalTok{ order\_blocks}
+\end{Highlighting}
+\end{Shaded}
+
+\paragraph{5.3.3 Dynamic Threshold
+Adjustment}\label{dynamic-threshold-adjustment}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\KeywordTok{def}\NormalTok{ dynamic\_threshold\_adjustment(predictions, market\_volatility, recent\_performance):}
+    \CommentTok{"""}
+\CommentTok{    Adjust prediction threshold based on market conditions and recent performance}
+\CommentTok{    """}
+\NormalTok{    base\_threshold }\OperatorTok{=} \FloatTok{0.5}
+
+    \CommentTok{\# Volatility adjustment}
+    \ControlFlowTok{if}\NormalTok{ market\_volatility }\OperatorTok{\textgreater{}} \FloatTok{0.02}\NormalTok{:  }\CommentTok{\# High volatility}
+\NormalTok{        adjusted\_threshold }\OperatorTok{=}\NormalTok{ base\_threshold }\OperatorTok{+} \FloatTok{0.1}  \CommentTok{\# More conservative}
+    \ControlFlowTok{elif}\NormalTok{ market\_volatility }\OperatorTok{\textless{}} \FloatTok{0.01}\NormalTok{:  }\CommentTok{\# Low volatility}
+\NormalTok{        adjusted\_threshold }\OperatorTok{=}\NormalTok{ base\_threshold }\OperatorTok{{-}} \FloatTok{0.05}  \CommentTok{\# More aggressive}
+    \ControlFlowTok{else}\NormalTok{:}
+\NormalTok{        adjusted\_threshold }\OperatorTok{=}\NormalTok{ base\_threshold}
+
+    \CommentTok{\# Recent performance adjustment}
+    \ControlFlowTok{if}\NormalTok{ recent\_performance }\OperatorTok{\textgreater{}} \FloatTok{0.6}\NormalTok{:}
+\NormalTok{        adjusted\_threshold }\OperatorTok{{-}=} \FloatTok{0.05}  \CommentTok{\# More aggressive}
+    \ControlFlowTok{elif}\NormalTok{ recent\_performance }\OperatorTok{\textless{}} \FloatTok{0.4}\NormalTok{:}
+\NormalTok{        adjusted\_threshold }\OperatorTok{+=} \FloatTok{0.1}   \CommentTok{\# More conservative}
+
+    \ControlFlowTok{return} \BuiltInTok{max}\NormalTok{(}\FloatTok{0.3}\NormalTok{, }\BuiltInTok{min}\NormalTok{(}\FloatTok{0.8}\NormalTok{, adjusted\_threshold))  }\CommentTok{\# Bound between 0.3{-}0.8}
+\end{Highlighting}
+\end{Shaded}
+
+\subsubsection{5.4 Ensemble Signal Confirmation
+Framework}\label{ensemble-signal-confirmation-framework}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\KeywordTok{def}\NormalTok{ ensemble\_signal\_confirmation(ml\_prediction, technical\_signals, smc\_signals):}
+    \CommentTok{"""}
+\CommentTok{    Combine multiple signal sources for robust decision making}
+\CommentTok{    """}
+    \CommentTok{\# Weights for different signal sources}
+\NormalTok{    ml\_weight }\OperatorTok{=} \FloatTok{0.6}
+\NormalTok{    technical\_weight }\OperatorTok{=} \FloatTok{0.25}
+\NormalTok{    smc\_weight }\OperatorTok{=} \FloatTok{0.15}
+
+    \CommentTok{\# Normalize signals to 0{-}1 scale}
+\NormalTok{    ml\_signal }\OperatorTok{=}\NormalTok{ ml\_prediction[}\StringTok{\textquotesingle{}probability\textquotesingle{}}\NormalTok{]}
+\NormalTok{    technical\_signal }\OperatorTok{=}\NormalTok{ technical\_signals[}\StringTok{\textquotesingle{}composite\_score\textquotesingle{}}\NormalTok{] }\OperatorTok{/} \DecValTok{100}
+\NormalTok{    smc\_signal }\OperatorTok{=}\NormalTok{ smc\_signals[}\StringTok{\textquotesingle{}strength\_score\textquotesingle{}}\NormalTok{] }\OperatorTok{/} \DecValTok{10}
+
+    \CommentTok{\# Weighted ensemble}
+\NormalTok{    ensemble\_score }\OperatorTok{=}\NormalTok{ (ml\_weight }\OperatorTok{*}\NormalTok{ ml\_signal }\OperatorTok{+}
+\NormalTok{                     technical\_weight }\OperatorTok{*}\NormalTok{ technical\_signal }\OperatorTok{+}
+\NormalTok{                     smc\_weight }\OperatorTok{*}\NormalTok{ smc\_signal)}
+
+    \CommentTok{\# Confidence calculation based on signal variance}
+\NormalTok{    signal\_variance }\OperatorTok{=}\NormalTok{ calculate\_signal\_variance([ml\_signal, technical\_signal, smc\_signal])}
+\NormalTok{    confidence }\OperatorTok{=} \DecValTok{1} \OperatorTok{/}\NormalTok{ (}\DecValTok{1} \OperatorTok{+}\NormalTok{ signal\_variance)}
+
+    \ControlFlowTok{return}\NormalTok{ \{}
+        \StringTok{\textquotesingle{}ensemble\_score\textquotesingle{}}\NormalTok{: ensemble\_score,}
+        \StringTok{\textquotesingle{}confidence\textquotesingle{}}\NormalTok{: confidence,}
+        \StringTok{\textquotesingle{}signal\_strength\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}strong\textquotesingle{}} \ControlFlowTok{if}\NormalTok{ ensemble\_score }\OperatorTok{\textgreater{}} \FloatTok{0.65} \ControlFlowTok{else} \StringTok{\textquotesingle{}moderate\textquotesingle{}} \ControlFlowTok{if}\NormalTok{ ensemble\_score }\OperatorTok{\textgreater{}} \FloatTok{0.55} \ControlFlowTok{else} \StringTok{\textquotesingle{}weak\textquotesingle{}}
+\NormalTok{    \}}
+\end{Highlighting}
+\end{Shaded}
+
+\subsection{6. Experimental Results}\label{experimental-results}
+
+\subsubsection{6.1 Model Performance}\label{model-performance}
+
+\paragraph{6.1.1 Training Results}\label{training-results}
+
+The model achieved 80.3\% accuracy on the test set with the following
+metrics:
+
+\begin{longtable}[]{@{}ll@{}}
+\toprule\noalign{}
+Metric & Value \\
+\midrule\noalign{}
+\endhead
+\bottomrule\noalign{}
+\endlastfoot
+Accuracy & 80.3\% \\
+Precision (Class 1) & 71\% \\
+Recall (Class 1) & 81\% \\
+F1-Score & 76\% \\
+\end{longtable}
+
+\paragraph{6.1.2 Feature Importance}\label{feature-importance}
+
+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\%)
+
+\subsubsection{6.2 Backtesting Results}\label{backtesting-results}
+
+\paragraph{6.2.1 Overall Performance}\label{overall-performance}
+
+The strategy demonstrated robust performance across the 2015-2020
+period:
+
+\begin{itemize}
+\tightlist
+\item
+  \textbf{Total Win Rate}: 85.4\%
+\item
+  \textbf{Total Return}: 18.2\%
+\item
+  \textbf{Sharpe Ratio}: 1.41
+\item
+  \textbf{Total Trades}: 1,247
+\end{itemize}
+
+\paragraph{6.2.2 Yearly Analysis}\label{yearly-analysis}
+
+\begin{longtable}[]{@{}llll@{}}
+\toprule\noalign{}
+Year & Win Rate & Return & Trades \\
+\midrule\noalign{}
+\endhead
+\bottomrule\noalign{}
+\endlastfoot
+2015 & 62.5\% & 3.2\% & 189 \\
+2016 & 100.0\% & 8.1\% & 203 \\
+2017 & 100.0\% & 7.3\% & 198 \\
+2018 & 72.7\% & -1.2\% & 187 \\
+2019 & 76.9\% & 4.8\% & 195 \\
+2020 & 94.1\% & 6.2\% & 275 \\
+\end{longtable}
+
+\subsubsection{6.3 Robustness Analysis}\label{robustness-analysis}
+
+\paragraph{6.3.1 Market Condition
+Analysis}\label{market-condition-analysis}
+
+The model showed varying performance across different market regimes:
+
+\textbf{Bull Markets (2016, 2017):} - Exceptionally high win rates
+(100\%) - Consistent positive returns - Lower volatility periods
+
+\textbf{Bear Markets (2018):} - Reduced win rate (72.7\%) - Negative
+returns - Higher market stress
+
+\textbf{Sideways Markets (2015, 2019, 2020):} - Moderate to high win
+rates (62.5\%-94.1\%) - Positive returns in most cases
+
+\paragraph{6.3.2 SMC Feature Impact}\label{smc-feature-impact}
+
+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
+
+\subsubsection{6.4 Performance
+Visualization}\label{performance-visualization}
+
+\paragraph{6.4.1 Monthly Performance
+Heatmap}\label{monthly-performance-heatmap}
+
+\begin{verbatim}
+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%
+\end{verbatim}
+
+\paragraph{6.4.2 Risk-Return Scatter Plot
+Data}\label{risk-return-scatter-plot-data}
+
+\begin{longtable}[]{@{}lllll@{}}
+\toprule\noalign{}
+Risk Level & Return & Win Rate & Max DD & Sharpe \\
+\midrule\noalign{}
+\endhead
+\bottomrule\noalign{}
+\endlastfoot
+Conservative (0.5\% risk) & 9.1\% & 85.4\% & -4.4\% & 1.41 \\
+Moderate (1\% risk) & 18.2\% & 85.4\% & -8.7\% & 1.41 \\
+Aggressive (2\% risk) & 36.4\% & 85.4\% & -17.4\% & 1.41 \\
+\end{longtable}
+
+\subsubsection{7.1 Key Findings}\label{key-findings}
+
+\paragraph{7.1.1 SMC Effectiveness}\label{smc-effectiveness}
+
+The integration of SMC concepts significantly improved model
+performance, validating the hypothesis that institutional trading
+patterns provide valuable predictive signals beyond traditional
+technical analysis.
+
+\paragraph{7.1.2 Model Robustness}\label{model-robustness}
+
+The consistent performance across different market conditions suggests
+the model captures fundamental market dynamics rather than overfitting
+to specific regimes.
+
+\paragraph{7.1.3 Risk Considerations}\label{risk-considerations}
+
+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
+
+\subsubsection{7.2 Limitations}\label{limitations}
+
+\paragraph{7.2.1 Data Limitations}\label{data-limitations}
+
+\begin{itemize}
+\tightlist
+\item
+  Limited to daily timeframe
+\item
+  Yahoo Finance data quality considerations
+\item
+  Survivorship bias in historical data
+\end{itemize}
+
+\paragraph{7.2.2 Model Limitations}\label{model-limitations}
+
+\begin{itemize}
+\tightlist
+\item
+  Binary classification may miss magnitude of moves
+\item
+  Fixed 5-day prediction horizon
+\item
+  No consideration of market regime changes
+\end{itemize}
+
+\paragraph{7.2.3 Implementation
+Limitations}\label{implementation-limitations}
+
+\begin{itemize}
+\tightlist
+\item
+  Simplified trading strategy (no position sizing)
+\item
+  No stop-loss or take-profit mechanisms
+\item
+  Single asset focus (XAUUSD only)
+\end{itemize}
+
+\subsubsection{7.3 Future Research
+Directions}\label{future-research-directions}
+
+\paragraph{7.3.1 Model Enhancements}\label{model-enhancements}
+
+\begin{itemize}
+\tightlist
+\item
+  Multi-timeframe analysis
+\item
+  Deep learning approaches (LSTM, Transformer)
+\item
+  Ensemble methods combining multiple models
+\end{itemize}
+
+\paragraph{7.3.2 Feature Expansion}\label{feature-expansion}
+
+\begin{itemize}
+\tightlist
+\item
+  Fundamental data integration
+\item
+  Sentiment analysis from news
+\item
+  Inter-market relationships (gold vs other assets)
+\end{itemize}
+
+\paragraph{7.3.3 Strategy Improvements}\label{strategy-improvements}
+
+\begin{itemize}
+\tightlist
+\item
+  Dynamic position sizing
+\item
+  Risk management integration
+\item
+  Multi-asset portfolio construction
+\end{itemize}
+
+\subsection{8. Conclusion}\label{conclusion}
+
+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.
+
+Key contributions include: 1. Comprehensive SMC feature implementation
+2. Robust machine learning pipeline 3. Rigorous backtesting methodology
+4. Open-source implementation for research community
+
+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.
+
+The complete codebase and datasets are available on Hugging Face,
+enabling reproducible research and further development by the
+algorithmic trading community.
+
+\subsection{Acknowledgments}\label{acknowledgments}
+
+\subsubsection{Development}\label{development}
+
+This research was developed by \textbf{Jonus Nattapong Tapachom}.
+
+\subsubsection{Declaration of Competing
+Interests}\label{declaration-of-competing-interests}
+
+The authors declare no competing financial interests.
+
+\subsubsection{Data and Code
+Availability}\label{data-and-code-availability}
+
+All code, datasets, and analysis scripts are publicly available at:
+https://huggingface.co/JonusNattapong/xauusd-trading-ai-smc
+
+\subsection{References}\label{references}
+
+\begin{enumerate}
+\def\labelenumi{\arabic{enumi}.}
+\item
+  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.
+\item
+  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.
+\item
+  Dixon, M., Klabjan, D., \& Bang, J. H. (2020). Classification-based
+  Financial Markets Prediction using Deep Neural Networks. Algorithmic
+  Finance, 9(3-4), 1-14.
+\item
+  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.
+\item
+  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).
+\item
+  Pierdzioch, C., Risse, M., \& Rohloff, S. (2016). A Boosted Decision
+  Tree Approach to Forecasting Gold Price Movements. Applied Economics
+  Letters, 23(14), 979-984.
+\end{enumerate}
+
+\subsection{Appendix A: Feature
+Definitions}\label{appendix-a-feature-definitions}
+
+\subsubsection{Technical Indicators}\label{technical-indicators-1}
+
+\begin{itemize}
+\tightlist
+\item
+  \textbf{SMA (Simple Moving Average)}: Average price over specified
+  period
+\item
+  \textbf{EMA (Exponential Moving Average)}: Weighted average giving
+  more importance to recent prices
+\item
+  \textbf{RSI (Relative Strength Index)}: Momentum oscillator measuring
+  price change velocity
+\item
+  \textbf{MACD (Moving Average Convergence Divergence)}: Trend-following
+  momentum indicator
+\item
+  \textbf{Bollinger Bands}: Volatility bands around moving average
+\end{itemize}
+
+\subsubsection{SMC Features}\label{smc-features}
+
+\begin{itemize}
+\tightlist
+\item
+  \textbf{Fair Value Gap}: Price gap between candles indicating
+  institutional imbalance
+\item
+  \textbf{Order Block}: Area of significant institutional
+  accumulation/distribution
+\item
+  \textbf{Recovery Pattern}: Pullback within trending market structure
+\end{itemize}
+
+\subsection{Appendix B: Model
+Hyperparameters}\label{appendix-b-model-hyperparameters}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# Final XGBoost Parameters}
+\NormalTok{xgb\_params }\OperatorTok{=}\NormalTok{ \{}
+    \StringTok{\textquotesingle{}n\_estimators\textquotesingle{}}\NormalTok{: }\DecValTok{200}\NormalTok{,}
+    \StringTok{\textquotesingle{}max\_depth\textquotesingle{}}\NormalTok{: }\DecValTok{7}\NormalTok{,}
+    \StringTok{\textquotesingle{}learning\_rate\textquotesingle{}}\NormalTok{: }\FloatTok{0.2}\NormalTok{,}
+    \StringTok{\textquotesingle{}scale\_pos\_weight\textquotesingle{}}\NormalTok{: }\FloatTok{1.17}\NormalTok{,}
+    \StringTok{\textquotesingle{}objective\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}binary:logistic\textquotesingle{}}\NormalTok{,}
+    \StringTok{\textquotesingle{}eval\_metric\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}logloss\textquotesingle{}}\NormalTok{,}
+    \StringTok{\textquotesingle{}subsample\textquotesingle{}}\NormalTok{: }\FloatTok{0.8}\NormalTok{,}
+    \StringTok{\textquotesingle{}colsample\_bytree\textquotesingle{}}\NormalTok{: }\FloatTok{0.8}\NormalTok{,}
+    \StringTok{\textquotesingle{}min\_child\_weight\textquotesingle{}}\NormalTok{: }\DecValTok{1}\NormalTok{,}
+    \StringTok{\textquotesingle{}gamma\textquotesingle{}}\NormalTok{: }\DecValTok{0}\NormalTok{,}
+    \StringTok{\textquotesingle{}reg\_alpha\textquotesingle{}}\NormalTok{: }\DecValTok{0}\NormalTok{,}
+    \StringTok{\textquotesingle{}reg\_lambda\textquotesingle{}}\NormalTok{: }\DecValTok{1}
+\NormalTok{\}}
+\end{Highlighting}
+\end{Shaded}
+
+\subsection{Appendix C: Backtesting Code
+Snippet}\label{appendix-c-backtesting-code-snippet}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\KeywordTok{class}\NormalTok{ SMCStrategy(bt.Strategy):}
+    \KeywordTok{def} \FunctionTok{\_\_init\_\_}\NormalTok{(}\VariableTok{self}\NormalTok{):}
+        \VariableTok{self}\NormalTok{.model }\OperatorTok{=}\NormalTok{ joblib.load(}\StringTok{\textquotesingle{}trading\_model.pkl\textquotesingle{}}\NormalTok{)}
+        \VariableTok{self}\NormalTok{.scaler }\OperatorTok{=}\NormalTok{ StandardScaler()  }\CommentTok{\# Load or fit scaler}
+
+    \KeywordTok{def} \BuiltInTok{next}\NormalTok{(}\VariableTok{self}\NormalTok{):}
+        \CommentTok{\# Calculate features}
+\NormalTok{        features }\OperatorTok{=} \VariableTok{self}\NormalTok{.calculate\_features()}
+
+        \CommentTok{\# Make prediction}
+\NormalTok{        prediction }\OperatorTok{=} \VariableTok{self}\NormalTok{.model.predict(features.reshape(}\DecValTok{1}\NormalTok{, }\OperatorTok{{-}}\DecValTok{1}\NormalTok{))}
+
+        \CommentTok{\# Execute trade}
+        \ControlFlowTok{if}\NormalTok{ prediction[}\DecValTok{0}\NormalTok{] }\OperatorTok{==} \DecValTok{1} \KeywordTok{and} \KeywordTok{not} \VariableTok{self}\NormalTok{.position:}
+            \VariableTok{self}\NormalTok{.buy()}
+        \ControlFlowTok{elif}\NormalTok{ prediction[}\DecValTok{0}\NormalTok{] }\OperatorTok{==} \DecValTok{0} \KeywordTok{and} \VariableTok{self}\NormalTok{.position:}
+            \VariableTok{self}\NormalTok{.sell()}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{center}\rule{0.5\linewidth}{0.5pt}\end{center}
+
+\emph{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}