Dhaka Stock Exchange Historical Data (1999-2025)

<h2>Dhaka Stock Exchange Historical Data Overview</h2>

<p>This dataset contains historical technical data from the Dhaka Stock Exchange (DSE), primarily collected from the official DSE website and supplemented with other publicly available online sources. It is intended solely for informational and research purposes. While every effort has been made to ensure the accuracy and completeness of the data, some inconsistencies or errors may still exist. Users are advised to independently verify any critical information before use.</p>

<h3>Data Summary:</h3>
<p>This dataset provides historical trading data for over 700 listed companies on the Dhaka Stock Exchange (DSE), covering the period from January 1999 to April 2025. The dataset consists of 1,684,249 rows and 7 columns, including the following fields:</p>
<ul>
<li><b>Trading Code:</b> Ticker symbol of the company</li>
<li><b>Date:</b> Trading date</li>
<li><b>Open:</b> Opening price</li>
<li><b>High:</b> Highest price during the day</li>
<li><b>Low:</b> Lowest price during the day</li>
<li><b>Close:</b> Closing price</li>
<li><b>Volume:</b> Total shares traded on that day</li>
</ul>

<h3>Notable Findings:</h3>
<p>The dataset reflects significant market cycles, including bullish and bearish trends, over two decades. Includes major economic events, such as:</p>
<ul>
<li>2008 global financial crisis impact on DSE</li>
<li>The 2010–11 market crash in Bangladesh</li>
<li>The effects of COVID-19 (2020–21) on trading volume and volatility</li>
<li>Historical price trajectories of major companies like BEXIMCO, SQUARE, GP, BATBC, etc., are well captured.</li>
</ul>

<h3>Value of the Data:</h3>
<ul>
<li>Offers a comprehensive, time-rich view of Bangladesh’s capital market over 25+ years.</li>
<li>Useful for quantitative finance, econometrics, and machine learning applications in time series forecasting.</li>
<li>Enables comparative studies across sectors like banking, pharmaceuticals, telecom, textiles, etc.</li>
<li>Suitable for academic research, policy analysis, and investment strategy development.</li>
<li>Acts as a benchmark dataset for algorithm testing, especially in emerging market scenarios.</li>
</ul>

<h3>Potential Use Cases:</h3>
<ul>
<li>Financial modeling and stock price forecasting using machine learning</li>
<li>Volatility and risk analysis across different timeframes</li>
<li>Impact studies of global/regional events on stock performance</li>
<li>Development of automated trading systems for the Bangladesh market</li>
<li>Training data for university courses in finance, statistics, or data science</li>
<li>Backtesting investment strategies and portfolio simulations</li>
<li>Data visualization projects to explore long-term market trends</li>
</ul>