{"id":33496,"date":"2025-03-07T13:30:00","date_gmt":"2025-03-07T05:30:00","guid":{"rendered":"https:\/\/www.tejwin.com\/?post_type=insight&#038;p=33496"},"modified":"2025-03-21T09:40:27","modified_gmt":"2025-03-21T01:40:27","slug":"enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm","status":"publish","type":"insight","link":"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/","title":{"rendered":"Enhancing Investment Performance of the Ichimoku Cloud with the XGBoost Machine Learning Algorithm"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full caption-align-center\"><img fetchpriority=\"high\" decoding=\"async\" width=\"5472\" height=\"3648\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/pexels-pixabay-267582.jpg\" alt=\" XGBoost \" class=\"wp-image-33294\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/pexels-pixabay-267582.jpg 5472w, https:\/\/www.tejwin.com\/wp-content\/uploads\/pexels-pixabay-267582-300x200.jpg 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/pexels-pixabay-267582-1024x683.jpg 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/pexels-pixabay-267582-150x100.jpg 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/pexels-pixabay-267582-768x512.jpg 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/pexels-pixabay-267582-1536x1024.jpg 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/pexels-pixabay-267582-2048x1365.jpg 2048w\" sizes=\"(max-width: 5472px) 100vw, 5472px\" \/><\/figure>\n\n\n\n<p class=\"has-text-align-center\">Photo from <a href=\"https:\/\/www.pexels.com\/zh-tw\/photo\/267582\/\" target=\"_blank\" rel=\"noopener\">Pexels<\/a> by <a href=\"https:\/\/unsplash.com\/@markusspiske\" target=\"_blank\" rel=\"noopener\">Markus Spiske<\/a><\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_81 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69f2c003c63ce\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"ez-toc-cssicon\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69f2c003c63ce\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Ichimoku_Cloud_Entry_and_Exit_Signals\" >Ichimoku Cloud Entry and Exit Signals<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Three_Confirmations_Bearish_%E2%80%93_Sell_Signal\" >Three Confirmations Bearish \u2013 Sell Signal:<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Using_XGBoost_Machine_Learning_Algorithm_for_Signal_Prediction\" >Using XGBoost Machine Learning Algorithm for Signal Prediction<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Data_Preparation_Model_Training\" >Data Preparation &amp; Model Training<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Trading_Target\" >Trading Target<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Order_Execution_Logic\" >Order Execution Logic<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Machine_Learning_Model_Strategy\" >Machine Learning Model Strategy:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Hybrid_Strategy_Primarily_Technical_Indicators\" >Hybrid Strategy (Primarily Technical Indicators):<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Hybrid_Strategy_Primarily_Machine_Learning_Model\" >Hybrid Strategy (Primarily Machine Learning Model):<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Machine_Learning_Model_Results_Presentation\" >Machine Learning Model Results Presentation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Performance_Chart\" >Performance Chart<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Performance_of_Hybrid_and_Machine_Learning_Strategies\" >Performance of Hybrid and Machine Learning Strategies<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Observing_Capital_Utilization_Third_Chart\" >Observing Capital Utilization (Third Chart)<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Stock_Condition_Analysis\" >Stock Condition Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Discussion_Future_Research_Directions\" >Discussion &amp; Future Research Directions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Full_Code_Link%EF%BC%9AClick_Here\" >Full Code Link\uff1aClick Here<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Further_Reading\" >Further Reading<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.tejwin.com\/en\/insight\/enhancing-investment-performance-of-the-ichimoku-cloud-with-the-xgboost-machine-learning-algorithm\/#Related_Links\" >Related Links<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span>Introduction<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In financial markets, technical analysis has long been a crucial tool for investors to assess market trends and develop trading strategies. Among these techniques, the&nbsp;<strong>Ichimoku Kinko Hyo<\/strong>&nbsp;(Ichimoku Cloud) utilizes five moving averages and a cloud structure to analyze stock support, resistance, and trend changes, enabling traders to quickly identify bullish or bearish signals. However, traditional&nbsp;<strong>Ichimoku strategies<\/strong>&nbsp;rely on fixed parameters (9-26-52) and visual interpretation, making them inflexible in adapting to different market conditions. Additionally, in choppy market conditions, Ichimoku-based signals can generate false positives, leading to erroneous trades and capital drawdowns.<\/p>\n\n\n\n<p>With the advancement of machine learning,&nbsp;<strong>XGBoost (Extreme Gradient Boosting)<\/strong>&nbsp;has become one of the most widely used models in quantitative trading. By leveraging&nbsp;<strong>Gradient Boosting Decision Trees (GBDT)<\/strong>, XGBoost learns complex high-dimensional relationships between different data points and enhances the filtering and decision-making process of trading signals. Compared to purely relying on technical indicators, XGBoost can integrate various market variables\u2014such as price momentum, volume changes, and market sentiment\u2014to uncover relationships between data and future stock returns. This ultimately improves the&nbsp;<strong>accuracy and robustness<\/strong>&nbsp;of trading strategies.<\/p>\n\n\n\n<p>Strategy Overview<br>The&nbsp;Ichimoku Cloud&nbsp;was developed by Japanese journalist&nbsp;<strong>Goichi Hosoda<\/strong>&nbsp;as a comprehensive technical indicator that evaluates market trends through five key lines. By analyzing the shape and interactions of these lines, traders can make informed investment decisions. The five components of the&nbsp;<strong>Ichimoku Cloud<\/strong>&nbsp;are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Chikou Span (Lagging Line, 26-period)<\/strong>: This line represents the current closing price, but it is shifted backward by&nbsp;<strong>26 periods<\/strong>&nbsp;to help traders compare past and present price levels, acting as a measure of market strength.<\/li>\n\n\n\n<li><strong>Tenkan-sen (Conversion Line, 9-period)<\/strong>: This is a&nbsp;<strong>short-term trend indicator<\/strong>, determined by the average of the highest and lowest prices over the past&nbsp;<strong>nine periods<\/strong>. A rising line suggests upward momentum, while a falling line indicates downward movement.<\/li>\n\n\n\n<li><strong>Kijun-sen (Base Line, 26-period)<\/strong>: This serves as a&nbsp;<strong>medium-term trend indicator<\/strong>, reflecting the average price range over the past&nbsp;<strong>26 periods<\/strong>. It provides a more stable trend direction compared to the Tenkan-sen and can be used as a support or resistance level.<\/li>\n\n\n\n<li><strong>Senkou Span A (Leading Span A)<\/strong>: This line represents the midpoint between the short-term (Tenkan-sen) and medium-term (Kijun-sen) trends. It is projected&nbsp;<strong>26 periods forward<\/strong>, forming one boundary of the&nbsp;<strong>Ichimoku Cloud<\/strong>, which helps traders anticipate future support and resistance levels.<\/li>\n\n\n\n<li><strong>Senkou Span B (Leading Span B)<\/strong>: This is a&nbsp;<strong>long-term trend indicator<\/strong>, based on the average of the highest and lowest prices over the past&nbsp;<strong>52 periods<\/strong>. Like Senkou Span A, it is also projected&nbsp;<strong>26 periods forward<\/strong>, forming the other boundary of the&nbsp;<strong>Ichimoku Cloud<\/strong>.<\/li>\n<\/ul>\n\n\n\n<p>Together, these five components create a&nbsp;<strong>cloud-like structure<\/strong>&nbsp;that provides traders with a clear view of market trends, potential reversals, and key support or resistance zones. The relative position of the price and the cloud helps determine whether the market is in a bullish, bearish, or neutral phase.<\/p>\n\n\n\n<p class=\"has-text-align-center\">Ichimoku Cloud Calculation Formula and Code Demonstration<\/p>\n\n\n\n<figure class=\"wp-block-image size-full caption-align-center\"><img decoding=\"async\" width=\"1296\" height=\"682\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/qq.png\" alt=\" XGBoost \" class=\"wp-image-33272\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/qq.png 1296w, https:\/\/www.tejwin.com\/wp-content\/uploads\/qq-300x158.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/qq-1024x539.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/qq-150x79.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/qq-768x404.png 768w\" sizes=\"(max-width: 1296px) 100vw, 1296px\" \/><figcaption class=\"wp-element-caption\">This chart illustrates the Ichimoku Cloud for stock&nbsp;<strong>6446<\/strong>&nbsp;during the backtesting period, based on 20% of the overall data<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Ichimoku_Cloud_Entry_and_Exit_Signals\"><\/span>Ichimoku Cloud Entry and Exit Signals<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The&nbsp;<strong>Ichimoku Cloud<\/strong>&nbsp;provides a trading signal interpretation method known as&nbsp;<strong>&#8220;Three Confirmations&#8221; <\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Three Confirmations Bullish <\/strong>: A strong&nbsp;<strong>buy signal<\/strong>, indicating an upward trend when specific conditions align.<\/li>\n\n\n\n<li><strong>Three Confirmations Bearish <\/strong>: A strong&nbsp;<strong>sell signal<\/strong>, suggesting a downward trend when the opposite conditions are met.<\/li>\n<\/ul>\n\n\n\n<p>Generally, when the price moves&nbsp;<strong>outside of a consolidation range<\/strong>, a valid&nbsp;<strong>Three Confirmations Bullish<\/strong>&nbsp;signal suggests a strong&nbsp;<strong>uptrend<\/strong>, while a&nbsp;<strong>Three Confirmations Bearish<\/strong>&nbsp;signal indicates a&nbsp;<strong>downtrend<\/strong>.<\/p>\n\n\n\n<p>In this study, the traditional&nbsp;<strong>Three Confirmations Bullish<\/strong>&nbsp;signal is&nbsp;<strong>slightly adjusted<\/strong>&nbsp;to generate optimized&nbsp;<strong>buy signals<\/strong> for enhanced trading performance.<\/p>\n\n\n\n<p><strong>Three Confirmations Bullish  \u2013 Buy Signal:<\/strong><\/p>\n\n\n\n<p>A&nbsp;<strong>bullish signal<\/strong>&nbsp;occurs when the following three conditions are met:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Conversion Line (Tenkan-sen) crosses above the Base Line (Kijun-sen).<\/strong><\/li>\n\n\n\n<li><strong>Lagging Span (Chikou Span) is above the past price.<\/strong><\/li>\n\n\n\n<li><strong>Price is above the Cloud (Senkou Span A &amp; B).<\/strong><\/li>\n<\/ol>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Three_Confirmations_Bearish_%E2%80%93_Sell_Signal\"><\/span><strong>Three Confirmations Bearish \u2013 Sell Signal:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>A&nbsp;<strong>bearish signal<\/strong>&nbsp;occurs when the following conditions are satisfied:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Conversion Line (Tenkan-sen) crosses below the Base Line (Kijun-sen).<\/strong><\/li>\n\n\n\n<li><strong>Lagging Span (Chikou Span) is below the past price.<\/strong><\/li>\n\n\n\n<li><strong>Price is below the Cloud (Senkou Span A &amp; B).<\/strong><\/li>\n<\/ol>\n\n\n\n<p>When a&nbsp;<strong>Three Confirmations Bullish<\/strong>&nbsp;signal appears, it is considered a&nbsp;<strong>buy signal<\/strong>&nbsp;based on the Ichimoku strategy. Conversely, a&nbsp;<strong>Three Confirmations Bearish<\/strong>&nbsp;signal is treated as a&nbsp;<strong>sell signal<\/strong>.<\/p>\n\n\n\n<p>In this study, these signals will be&nbsp;<strong>combined with machine learning model outputs<\/strong>&nbsp;to generate&nbsp;<strong>optimized trading decisions<\/strong>.<\/p>\n\n\n\n<p>&nbsp;<strong>For more details on the Ichimoku Cloud strategy, refer to:<\/strong>&nbsp;<a href=\"#\">Click Here<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Using_XGBoost_Machine_Learning_Algorithm_for_Signal_Prediction\"><\/span>Using XGBoost Machine Learning Algorithm for Signal Prediction<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><strong>XGBoost (eXtreme Gradient Boosting)<\/strong>&nbsp;is a machine learning algorithm based on&nbsp;<strong>Gradient Boosting Decision Trees (GBDT)<\/strong>. It learns patterns in data by combining multiple weak&nbsp;<strong>Decision Trees<\/strong>, gradually improving their predictive accuracy to form a&nbsp;<strong>strong forecasting model<\/strong>. Due to its&nbsp;<strong>high efficiency, interpretability, and strong generalization ability<\/strong>, XGBoost is widely applied in financial markets and quantitative trading strategies.<\/p>\n\n\n\n<p>In this study, we utilize&nbsp;<strong>Ichimoku Cloud data<\/strong>&nbsp;along with fundamental trading data such as&nbsp;<strong>open, high, low, close, and volume (OHLCV)<\/strong>&nbsp;to train the XGBoost model. The goal is to enable the model to&nbsp;<strong>learn effective trading signals<\/strong>&nbsp;and potentially&nbsp;<strong>enhance investment performance<\/strong>.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Data_Preparation_Model_Training\"><\/span><strong>Data Preparation &amp; Model Training<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Dataset Split:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>80% of the data<\/strong>&nbsp;is used for&nbsp;<strong>training<\/strong>, and&nbsp;<strong>20%<\/strong>&nbsp;is allocated for&nbsp;<strong>testing<\/strong>.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Prediction Target:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The model predicts&nbsp;<strong>trading signals<\/strong>&nbsp;based on&nbsp;<strong>future price movements<\/strong>.<\/li>\n\n\n\n<li>A&nbsp;<strong>buy signal<\/strong>&nbsp;is generated if the&nbsp;<strong>stock price increases by more than 3% within the next five days<\/strong>.<\/li>\n\n\n\n<li>A&nbsp;<strong>sell signal<\/strong>&nbsp;is assigned otherwise.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<p>By training the XGBoost model on these signals, we aim to identify the&nbsp;<strong>hidden relationships between Ichimoku Cloud patterns and future price movements<\/strong>. The expectation is that the model will discover&nbsp;<strong>profitable trading opportunities<\/strong>, ultimately improving overall&nbsp;<strong>investment performance<\/strong>.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Trading_Target\"><\/span><strong>Trading Target<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>This study focuses on a&nbsp;<strong>single stock<\/strong>,&nbsp;<strong>6446 <\/strong>, as the trading target. The strategy involves&nbsp;<strong>adjusting capital allocation<\/strong>&nbsp;dynamically based on entry and exit signals.<\/p>\n\n\n\n<p>The objective is to&nbsp;<strong>outperform the buy-and-hold strategy<\/strong>&nbsp;by optimizing&nbsp;<strong>trading decisions<\/strong>&nbsp;through the&nbsp;<strong>Ichimoku Cloud and XGBoost model<\/strong>, aiming for&nbsp;<strong>better investment performance<\/strong>.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Order_Execution_Logic\"><\/span><strong>Order Execution Logic<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>This study implements&nbsp;<strong>five trading strategies<\/strong>, each with a distinct logic:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Buy-and-Hold Strategy<\/strong>&nbsp;<em>(Benchmark)<\/em>&nbsp;\u2013 Holding the stock without active trading.<\/li>\n\n\n\n<li><strong>Basic Ichimoku Cloud Strategy<\/strong>&nbsp;\u2013 Trades based solely on Ichimoku Cloud signals.<\/li>\n\n\n\n<li><strong>Hybrid Strategy (Technical Indicator Focused)<\/strong>&nbsp;\u2013 Ichimoku Cloud as the primary signal, with XGBoost as a secondary confirmation.<\/li>\n\n\n\n<li><strong>Hybrid Strategy (Machine Learning Focused)<\/strong>&nbsp;\u2013 XGBoost as the primary signal, with Ichimoku Cloud as a secondary confirmation.<\/li>\n\n\n\n<li><strong>Machine Learning Model Strategy<\/strong>&nbsp;\u2013 Trades based solely on XGBoost model predictions.<\/li>\n<\/ol>\n\n\n\n<p>Each strategy generates&nbsp;<strong>buy and sell signals<\/strong>&nbsp;based on either&nbsp;<strong>Ichimoku Cloud<\/strong>&nbsp;or&nbsp;<strong>XGBoost predictions<\/strong>, forming&nbsp;<strong>two sets of trading signals<\/strong>&nbsp;during backtesting.<\/p>\n\n\n\n<p>General Strategy (Basic Ichimoku Kinko Hyo Strategy):<\/p>\n\n\n\n<p>This strategy executes trades based on the Ichimoku Kinko Hyo technical indicator. Upon the first occurrence of a buy signal, 50% of the capital position is allocated (to prevent the cash level from remaining too low and to dilute the overall strategy return). Each subsequent buy signal increases the position by 20%, while each sell signal reduces the position by 20% (adjusting the position size based on subsequent signals). Additionally, leverage usage is restricted within the trading logic, with a leverage cap of 100%, ensuring that the strategy does not exceed the principal amount (no leverage is applied) and that short selling is not allowed.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Machine_Learning_Model_Strategy\"><\/span>Machine Learning Model Strategy:<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>This strategy executes trades based on the output of the XGBoost model. The position sizing logic follows the same rules as the general strategy (pure Ichimoku Kinko Hyo strategy).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Hybrid_Strategy_Primarily_Technical_Indicators\"><\/span>Hybrid Strategy (Primarily Technical Indicators):<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Upon the first occurrence of a technical indicator buy signal, 50% of the capital position is allocated (to prevent the cash level from remaining too low and to dilute the overall strategy return). For subsequent trades, a dual-signal confirmation is required:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>If both the technical indicator and machine learning model generate a buy signal, the position is increased by 30% (as the presence of both signals boosts investor confidence, leading to a larger position increase).<\/li>\n\n\n\n<li>If only the technical indicator generates a buy signal, but the machine learning model does not, the position is increased by only 10% (indicating lower investor confidence).<\/li>\n<\/ul>\n\n\n\n<p>Similarly, leverage is not allowed, and short selling is prohibited in this strategy.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Hybrid_Strategy_Primarily_Machine_Learning_Model\"><\/span>Hybrid Strategy (Primarily Machine Learning Model):<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The position sizing logic is similar to the hybrid strategy based on technical indicators, except that the primary signal for decision-making is derived from the machine learning model instead.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Machine_Learning_Model_Results_Presentation\"><\/span>Machine Learning Model Results Presentation<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-full caption-align-center\"><img decoding=\"async\" width=\"986\" height=\"630\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/dd.png\" alt=\" XGBoost \" class=\"wp-image-33274\" style=\"object-fit:cover\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/dd.png 986w, https:\/\/www.tejwin.com\/wp-content\/uploads\/dd-300x192.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/dd-150x96.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/dd-768x491.png 768w\" sizes=\"(max-width: 986px) 100vw, 986px\" \/><figcaption class=\"wp-element-caption\"><strong>XGBoost Signal Generation Chart (Test Set)<\/strong>  <strong>&#8220;0: Hold, 1: Buy, 2: Sell&#8221;<\/strong><\/figcaption><\/figure>\n\n\n\n<p>From the chart, it can be observed that the machine learning model&#8217;s signal generation tends to fluctuate frequently, leading to inconsistent signals. As a result, placing trades strictly based on the model&#8217;s signals may incur higher transaction costs. Additionally, it is noticeable that after&nbsp;<strong>September 2024<\/strong>, the predicted signals are consistently&nbsp;<strong>Sell<\/strong>signals. This phenomenon will be further discussed in the following sections.<br><\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" width=\"660\" height=\"453\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/cc.png\" alt=\" XGBoost \" class=\"wp-image-33276\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/cc.png 660w, https:\/\/www.tejwin.com\/wp-content\/uploads\/cc-300x206.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/cc-150x103.png 150w\" sizes=\"(max-width: 660px) 100vw, 660px\" \/><\/figure>\n\n\n\n<p>This chart represents the frequency of data features used during the training of the XGBoost model. Features that are used more frequently are considered more important. From the results, it can be observed that the top-ranked features are primarily technical indicators, indicating that incorporating these features, in addition to basic open-high-low-close-volume (OHLCV) data, enhances the model\u2019s predictive performance.<br><\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" width=\"502\" height=\"432\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/jj.png\" alt=\" XGBoost \" class=\"wp-image-33278\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/jj.png 502w, https:\/\/www.tejwin.com\/wp-content\/uploads\/jj-300x258.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/jj-150x129.png 150w\" sizes=\"(max-width: 502px) 100vw, 502px\" \/><\/figure>\n\n\n\n<p>This chart represents the&nbsp;<strong>prediction confusion matrix<\/strong>&nbsp;for the XGBoost model, where:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The&nbsp;<strong>X-axis<\/strong>&nbsp;represents the model&#8217;s predicted signals.<\/li>\n\n\n\n<li>The&nbsp;<strong>Y-axis<\/strong>&nbsp;represents the actual signals in the test set.<\/li>\n\n\n\n<li>The numerical values indicate the frequency of occurrences for each classification.<\/li>\n<\/ul>\n\n\n\n<p>The most important cells to analyze are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>(Predicted Label = 1, True Label = 2) \u2192 (1,2)<\/strong><\/li>\n\n\n\n<li><strong>(Predicted Label = 2, True Label = 1) \u2192 (2,1)<\/strong><\/li>\n<\/ul>\n\n\n\n<p>These two cells indicate cases where the model&#8217;s predictions are completely opposite to the actual signals.<\/p>\n\n\n\n<p>Starting with&nbsp;<strong>(1,2)<\/strong>, this represents situations where the actual signal indicates a&nbsp;<strong>sell<\/strong>, but the model predicts a&nbsp;<strong>buy<\/strong>. The data shows that this scenario&nbsp;<strong>never occurred (0 times)<\/strong>, suggesting that the model is less prone to this type of misclassification.<\/p>\n\n\n\n<p>On the other hand,&nbsp;<strong>(2,1)<\/strong>&nbsp;represents cases where the actual signal indicates a&nbsp;<strong>buy<\/strong>, but the model predicts a&nbsp;<strong>sell<\/strong>. This occurred&nbsp;<strong>36 times<\/strong>. However, since the trading strategy prohibits short selling, this misclassification only results in&nbsp;<strong>reducing positions or staying out of the market<\/strong>, rather than incurring actual losses due to shorting. Hence, while this prediction error might cause missed opportunities for gains, it does not directly lead to financial losses.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Performance_Chart\"><\/span>Performance Chart<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" width=\"2990\" height=\"1990\" data-id=\"33286\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/fvfv.png\" alt=\" XGBoost \" class=\"wp-image-33286\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/fvfv.png 2990w, https:\/\/www.tejwin.com\/wp-content\/uploads\/fvfv-300x200.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/fvfv-1024x682.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/fvfv-150x100.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/fvfv-768x511.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/fvfv-1536x1022.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/fvfv-2048x1363.png 2048w\" sizes=\"(max-width: 2990px) 100vw, 2990px\" \/><\/figure>\n<\/figure>\n\n\n\n<p>The first chart represents the&nbsp;<strong>cumulative return<\/strong>&nbsp;of different strategies. Looking at the final cumulative returns:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The&nbsp;<strong>Benchmark (Buy-and-Hold Strategy)<\/strong>&nbsp;achieved a cumulative return of&nbsp;<strong>61.24%<\/strong>.<\/li>\n\n\n\n<li>The&nbsp;<strong>Raw Strategy (Pure Technical Indicator Strategy)<\/strong>&nbsp;had a cumulative return of only&nbsp;<strong>15.44%<\/strong>, clearly underperforming the Buy-and-Hold approach.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Performance_of_Hybrid_and_Machine_Learning_Strategies\"><\/span>Performance of Hybrid and Machine Learning Strategies<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Examining the remaining strategies:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The&nbsp;<strong>Hybrid Strategy (Technical Indicator Primary) [Mix Strategy 1]<\/strong>&nbsp;achieved a cumulative return of&nbsp;<strong>17%<\/strong>, slightly outperforming the Raw Strategy. This suggests that the machine learning model helped reinforce confidence in trade execution, leading to a slight improvement in returns. This demonstrates that machine learning contributes to strategy optimization.<\/li>\n\n\n\n<li>The final two strategies,&nbsp;<strong>Machine Learning Strategy (ML Strategy)<\/strong>&nbsp;and&nbsp;<strong>Hybrid Strategy (Machine Learning Primary)<\/strong>, significantly outperformed the others. Their cumulative returns were&nbsp;<strong>141.81%<\/strong>&nbsp;and&nbsp;<strong>137.52%<\/strong>, respectively. This highlights that the buy and sell signals generated by the machine learning model were far superior to those from technical indicators. The outstanding returns suggest that the XGBoost algorithm effectively captured patterns in the stock\u2019s price movements, enabling the strategy to&nbsp;<strong>substantially outperform the Benchmark<\/strong>.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Observing_Capital_Utilization_Third_Chart\"><\/span>Observing Capital Utilization (Third Chart)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Next, we analyze the&nbsp;<strong>capital utilization rate<\/strong>. Since leverage restrictions were imposed in the trading conditions, none of the four strategies employed leverage.<\/p>\n\n\n\n<p>Notably, during the stock\u2019s&nbsp;<strong>uptrend from April 2024 to July 2024<\/strong>, the two&nbsp;<strong>machine learning-driven strategies<\/strong>&nbsp;built their positions&nbsp;<strong>earlier<\/strong>&nbsp;than the two&nbsp;<strong>technical indicator-driven strategies<\/strong>. This provides evidence that the machine learning algorithm was able to&nbsp;<strong>anticipate future price increases earlier<\/strong>, reinforcing the credibility of its predictive capability and the effectiveness of the strategy.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Stock_Condition_Analysis\"><\/span>Stock Condition Analysis<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" width=\"1990\" height=\"1489\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/bb.png\" alt=\" XGBoost \" class=\"wp-image-33288\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/bb.png 1990w, https:\/\/www.tejwin.com\/wp-content\/uploads\/bb-300x224.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/bb-1024x766.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/bb-150x112.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/bb-768x575.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/bb-1536x1149.png 1536w\" sizes=\"(max-width: 1990px) 100vw, 1990px\" \/><\/figure>\n\n\n\n<p>This chart represents the trading data for&nbsp;<strong>stock 6446<\/strong>, covering both the backtesting period and the model training period.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The&nbsp;<strong>first chart<\/strong>&nbsp;displays the stock\u2019s&nbsp;<strong>moving volatility<\/strong>, which helps assess price fluctuations. From the volatility chart, there is no significant structural change between the&nbsp;<strong>volatility in the test set<\/strong>&nbsp;and the&nbsp;<strong>volatility in the training set<\/strong>.<\/li>\n\n\n\n<li>The&nbsp;<strong>second chart<\/strong>&nbsp;shows the&nbsp;<strong>50-day moving average trend slope<\/strong>, which provides insight into short-term trends. The trend values did not exceed the highest point observed in the training set by a large margin; instead, they fluctuated within a certain range.<\/li>\n\n\n\n<li>However, the&nbsp;<strong>third chart (price chart)<\/strong>&nbsp;reveals that the stock price in the&nbsp;<strong>test set<\/strong>&nbsp;<strong>surpassed the highest price in the training set<\/strong>. This resulted in a scenario where the machine learning model encountered price levels it had&nbsp;<strong>not learned from during training<\/strong>, leading to less reliable signal generation.<\/li>\n<\/ul>\n\n\n\n<p>This explains why, in the&nbsp;<strong>previous signal chart<\/strong>, the model continuously generated&nbsp;<strong>sell signals at the end of the test set<\/strong>\u2014the model had never encountered such price levels before. This highlights a key limitation of relying solely on&nbsp;<strong>machine learning models for signal generation<\/strong>, as they may struggle in&nbsp;<strong>unseen market conditions<\/strong>.<\/p>\n\n\n\n<p>Therefore, it is recommended to&nbsp;<strong>combine technical indicators or other analytical methods<\/strong>&nbsp;to form a more&nbsp;<strong>comprehensive strategy<\/strong>&nbsp;for trade execution.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Discussion_Future_Research_Directions\"><\/span>Discussion &amp; Future Research Directions<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>This study evaluates the effectiveness of trading strategies using only a&nbsp;<strong>single stock<\/strong>. However, whether similar results hold across&nbsp;<strong>other stocks or industries<\/strong>&nbsp;requires further analysis and research.<\/p>\n\n\n\n<p>Additionally, when the stock price in the&nbsp;<strong>test set surpasses its historical high<\/strong>, it may indicate that the current model is no longer suitable for making predictions. A potential&nbsp;<strong>improvement<\/strong>&nbsp;for future research could be designing&nbsp;<strong>alert thresholds<\/strong>&nbsp;that signal when the price level has exceeded the model\u2019s applicable range. Once the threshold is triggered, adjustments to the&nbsp;<strong>model or trading strategy logic<\/strong>&nbsp;may be necessary to&nbsp;<strong>reduce investment risk<\/strong>.<\/p>\n\n\n\n<p>For future research, readers may consider&nbsp;<strong>training the model with a broader set of features<\/strong>, such as incorporating different types of data attributes (<strong>volatility, 50-day moving average trend slope, and other technical indicators<\/strong>). These additional features could help the model&nbsp;<strong>capture higher-dimensional price patterns and variations<\/strong>, potentially improving predictive performance.<br><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Important Reminder<\/strong>: This analysis is for reference only and does not constitute any product or investment advice.<\/p>\n\n\n\n<p>We welcome readers interested in various trading strategies to consider purchasing relevant solutions from&nbsp;<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-black-color\"><a href=\"https:\/\/www.tejwin.com\/en\/solution\/quantitative-finance-solution\/\" data-type=\"link\" data-id=\"https:\/\/www.tejwin.com\/en\/solution\/quantitative-finance-solution\/\">Quantitative Finance Solution<\/a>. <\/mark>With our high-quality databases, you can construct a trading strategy that suits your needs.<\/p>\n\n\n\n<div style=\"height:33px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-100 has-custom-font-size\" style=\"font-size:22px\"><a class=\"wp-block-button__link has-background wp-element-button\" href=\"https:\/\/www.tejwin.com\/en\/databank-solution\/financial-data\/\" style=\"border-radius:16px;background:linear-gradient(135deg,rgb(243,224,131) 0%,rgb(102,197,166) 50%,rgb(51,132,181) 100%)\"><strong>Access to Comprehensive Quantitative Data<\/strong><br><strong>Start Building Portfolios That Outperform the Market Today!<\/strong><\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:22px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-text-align-center\" style=\"font-size:32px\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\"><strong><em>&#8220;Taiwan stock market data, TEJ collect it all<\/em><\/strong><\/mark><\/p>\n\n\n\n<p>The characteristics of the Taiwan stock market differ from those of other European and American markets. Especially in the first quarter of 2024, with the <strong><mark style=\"background-color:rgba(0, 0, 0, 0);color:#c05d5d\" class=\"has-inline-color\">Taiwan Stock Exchange reaching a new high of 20,000 points<\/mark><\/strong> due to the rise in TSMC&#8217;s stock price, global institutional investors are paying more attention to the performance of the Taiwan stock market.&nbsp;<\/p>\n\n\n\n<p><strong><mark style=\"background-color:rgba(0, 0, 0, 0);color:#0978b8\" class=\"has-inline-color\">Taiwan Economical Journal (TEJ)<\/mark><\/strong>, a financial database established in Taiwan for over 30 years, serves local financial institutions and academic institutions, and has long-term cooperation with internationally renowned data providers, providing high-quality financial data for five financial markets in Asia.&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><mark style=\"background-color:#ebc766\" class=\"has-inline-color has-black-color\">Complete Coverage<\/mark><\/strong>: Includes all listed companies on stock markets in Taiwan, China, Hong Kong, Japan, Korea, etc.&nbsp;<\/li>\n\n\n\n<li><strong><mark style=\"background-color:#ebc766\" class=\"has-inline-color\">Comprehensive Analysis of Enterprises<\/mark><\/strong>: Operational aspects, financial aspects, securities market performance, ESG sustainability, etc.&nbsp;<\/li>\n\n\n\n<li><strong><mark style=\"background-color:#ebc766\" class=\"has-inline-color\">High-Quality Database<\/mark><\/strong>: TEJ data is cleaned, checked, enhanced, and integrated to ensure it meets the information needs of financial and market analysis.\u00a0<\/li>\n<\/ul>\n\n\n\n<p>With TEJ&#8217;s assistance, you can access relevant information about major stock markets in Asia, such as securities market, financials data, enterprise operations, board of directors, sustainability data, etc., providing investors with timely and high-quality content. Additionally, TEJ offers advisory services to help solve problems in theoretical practice and financial management!<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-a89b3969 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-100 has-custom-font-size\" style=\"font-size:21px\"><a class=\"wp-block-button__link has-background has-text-align-center wp-element-button\" href=\"https:\/\/www.tejwin.com\/en\/contact\/\" style=\"border-radius:16px;background:linear-gradient(135deg,rgb(160,209,216) 0%,rgb(51,145,181) 50%,rgb(50,95,191) 100%)\"><strong>Want to Learn More About Our Databases and Solutions?<br>Contact Us and Get the Free Trial Today!<\/strong><\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Full_Code_Link%EF%BC%9AClick_Here\"><\/span>Full Code Link\uff1a<a href=\"https:\/\/github.com\/tejtw\/TQuant-Lab\/blob\/main\/example\/TQ_%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92%E7%AE%97%E6%B3%95%20XGBoost%20%E6%8F%90%E5%8D%87%E6%8A%80%E8%A1%93%E6%8C%87%E6%A8%99%E4%B8%80%E7%9B%AE%E5%9D%87%E8%A1%A1%E8%A1%A8%E7%9A%84%E6%8A%95%E8%B3%87%E7%B8%BE%E6%95%88.ipynb\" target=\"_blank\" rel=\"noopener\">Click Here<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Further_Reading\"><\/span>Further Reading<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><a href=\"https:\/\/www.tejwin.com\/insight\/%e4%b8%80%e7%9b%ae%e5%9d%87%e8%a1%a1%e8%a1%a8%e7%ad%96%e7%95%a5\/\">TQuant Lab Ichimoku Kinko Hyo Strategy, A Self-contained Technical Analysis Indicator<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.tejwin.com\/insight\/%e6%8f%ad%e9%96%8b%e6%8a%95%e8%b3%87%e5%a4%a7%e5%b8%ab%e7%9a%84%e9%81%b8%e8%82%a1%e5%af%86%e7%a2%bc%ef%bc%9a%e9%ba%a5%e5%85%8b%ef%bc%8e%e5%96%9c%e5%81%89%e6%94%b6%e7%9b%8a%e5%9e%8b%e6%8a%95%e8%b3%87\/\">Michael Sivy\u2019s 4 Key Income Investing Principles Unveiled<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Related_Links\"><\/span>Related Links<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/github.com\/tejtw\/TQuant-Lab\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"ek-link\">TQuant Lab Github<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/tquant.tejwin.com\/\" class=\"ek-link\" target=\"_blank\" rel=\"noopener\">TQuant Lab \u9996\u9801<\/a><\/li>\n<\/ul>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Traditional\u00a0Ichimoku strategies\u00a0rely on fixed parameters (9-26-52) and visual interpretation, making them inflexible in adapting to different market conditions. XGBoost learns complex high-dimensional relationships between different data points and enhances the filtering and decision-making process of trading signals. This article use XGBoost to enhance investment performance of Ichimoku Cloud.<\/p>\n","protected":false},"featured_media":33295,"template":"","tags":[3063,2371,2987,3007,3166],"insight-category":[50,1356],"class_list":["post-33496","insight","type-insight","status-publish","has-post-thumbnail","hentry","tag-backtesting-2","tag-python","tag-quant","tag-tejapi-data-analysis","tag-tquant-lab-2","insight-category-fintech","insight-category-tquant-lab-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/33496","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight"}],"about":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/types\/insight"}],"version-history":[{"count":6,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/33496\/revisions"}],"predecessor-version":[{"id":33745,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/33496\/revisions\/33745"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media\/33295"}],"wp:attachment":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media?parent=33496"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/tags?post=33496"},{"taxonomy":"insight-category","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight-category?post=33496"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}