Contains stock prices and other details for stocks listed in NEPSE, categorized by date and stock.

All data herein were extracted by web-scraping the official website of the Nepal Stock Exchange (old website). NEPSE official website: http://www.nepalstock.com/

Company details were obtained by web-scraping the webpage at the following link. The data obtained can be found in the "companies_with_details.csv" file. "http://www.nepalstock.com/company">http://www.nepalstock.com/company

Stock Prices and other details for each day starting 2022-06-03 till 2022-07-08 were obtained by web-scraping webpage at the following link. The data obtained can be found in the "By_Date" folder. "http://www.nepalstock.com/todaysprice">http://www.nepalstock.com/todaysprice

Python and BeautifulSoup were used to do the scrapping. 2012-06-03 was used as the start date of data collection because this seems to be the oldest date where data exist at the above link. Non-Traded days have been excluded.

The data obtained thus was further combed through to categorize the data based on individual stocks. The data obtained can be found in the "By_Stock" folder. Note that a few filenames may not match exactly with their company names (as listed). For example, "&" in the listed company name has been replaced with "and" in the stock's filename. Similarly, a '/' in the company name has been replaced with '(underscore)' in the stock's filename. This was done because kaggle does not allow '&' in the filename and Mac OS did not allow '/' in the filename.

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

Scraping Grader Market Outlook

The global market size for the Scraping Grader market was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a CAGR of about 12.5% during the forecast period. This growth is primarily driven by the increasing need for accurate and timely data extraction across various industries.

One of the main growth factors for the Scraping Grader market is the escalating demand for data-driven decision-making in business operations. As industries grow more competitive, the need for real-time data extraction to inform strategic decisions has become imperative. This has led to an increased adoption of scraping and grading technologies that can efficiently process large volumes of data from various sources. Both large enterprises and SMEs are investing significantly in these technologies to stay ahead of the curve and maintain a competitive edge.

Another significant driver is the rise in digital transformation across industries. Companies are increasingly leveraging web scraping tools to gather critical market insights, conduct competitive analysis, and monitor pricing strategies. The exponential growth of e-commerce and online businesses has further augmented the demand for scraping graders, as these enterprises need to continuously analyze market trends, customer preferences, and competitor activities. The integration of advanced technologies like AI and machine learning into scraping solutions has enhanced their efficiency and accuracy, making them indispensable tools for modern businesses.

The expanding applications of scraping graders in diverse sectors such as BFSI, healthcare, and retail is also a noteworthy growth factor. In the financial sector, for instance, scraping graders are used for market analysis, monitoring stock prices, and collecting financial news. Similarly, in healthcare, these tools help in gathering patient data, tracking pharmaceutical prices, and monitoring market trends. Retailers use scraping graders for price monitoring, inventory management, and understanding customer behavior. This wide range of applications across multiple sectors is significantly boosting the demand for scraping grader solutions.

From a regional perspective, North America holds a dominant position in the Scraping Grader market due to the early adoption of advanced technologies and the presence of major market players in the region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This can be attributed to the rapid digital transformation in countries like China and India, growing e-commerce activities, and increasing investments in data-driven technologies. Europe and Latin America are also expected to experience substantial growth, driven by the rising demand for efficient data extraction solutions and the growing awareness of the benefits of data-driven decision-making.

Component Analysis

The Scraping Grader market by component is segmented into software, hardware, and services. The software segment dominates the market, accounting for a significant share due to the increasing adoption of advanced scraping tools and solutions. These software solutions offer a wide range of functionalities, including data extraction, processing, and analysis, which are essential for businesses to make informed decisions. The integration of AI and machine learning algorithms in these software solutions has further enhanced their efficiency and accuracy, making them highly sought after in the market.

The hardware segment, although smaller in comparison to software, plays a crucial role in the overall functioning of scraping grader solutions. High-performance hardware is required to support the complex algorithms and large-scale data processing needs of modern scraping tools. With advancements in computing technology, the hardware segment is expected to grow steadily, driven by the need for more powerful and efficient systems to handle the increasing volumes of data.

The services segment encompasses a range of offerings, including consulting, implementation, training, and support services. These services are critical for the successful deployment and operation of scraping grader solutions. Consulting services help organizations identify the right tools and strategies for their specific needs, while implementation services ensure seamless integration with existing systems. Training and support services are essential for maximizing the benefits of these solutions by ensuring that users are well-versed in t

NASDAQ (National Association of Securities Dealers Automated Quotation) is the world's second largest automated and electronic stock exchange and securities market in the United States, the first being the New York Stock Exchange, with more than 8,000 companies and corporations. It has more trading volume per hour than any other stock exchange in the world. More than 7,000 small and mid-cap stocks are traded on the NASDAQ. It is characterized by comprising high-tech companies in electronics, computers, telecommunications, biotechnology, and many others. This dataset was created as a result of an automatic extraction of open & public data available in nasdaq.com, using web scraping techniques. The only purpose of creating it was for academic reasons https://github.com/jadvani/NasdaqScraper

NEPSE Daily Index Data (2020-2025)

Subtitle: Daily NEPSE Index Values, Changes, and Percentages from 2020 to March 2025

This dataset provides a comprehensive collection of daily historical data for the Nepal Stock Exchange (NEPSE) Index, covering the period from January 1, 2020, to March 4, 2025 (with data available up to March 4, 2025, at the time of creation). The NEPSE Index is the primary benchmark for Nepal's stock market, reflecting the performance of listed companies. This dataset was scraped from publicly available financial data sources and is intended for financial analysis, time series forecasting, and economic research related to Nepal's capital markets.

Dataset Details

Columns:

• Date: The trading day in YYYY-MM-DD format (e.g., 2025-03-04).
• Index Value: The closing value of the NEPSE Index for the day, representing the market’s end-of-day performance.
• Absolute Change: The daily change in the index value (positive or negative), calculated as the difference from the previous trading day’s close.
• Percentage Change: The daily percentage change in the index value, reflecting the relative movement (e.g., -1.05 for a -1.05% drop).

Data Range:

• Start Date: January 1, 2020
• End Date: March 4, 2025 (latest available as of dataset creation)
• Frequency: Daily (trading days only; excludes weekends and public holidays as per NEPSE’s schedule)

Size:

• Approximately 1,200–1,500 rows (depending on trading days), covering over 5 years of data.

Source

The data was sourced from ShareSansar, a leading financial portal in Nepal providing NEPSE index history. It was scraped using Python with Selenium and processed into a clean CSV format.

Methodology

• Scraping: Automated web scraping extracted daily records from ShareSansar’s Index History Data page.
• Processing: Data was cleaned to ensure consistent date formatting and numeric values (removing commas and percentage signs).
• Tools: Python libraries including Selenium, BeautifulSoup, and pandas were used to compile this dataset.

Usage

This dataset is ideal for: - Time Series Analysis: Forecasting NEPSE trends using models like ARIMA, LSTM, or Prophet. - Financial Research: Studying Nepal’s stock market volatility, growth patterns, or economic correlations. - Data Visualization: Plotting index trends, daily changes, or comparative analyses with other markets. - Educational Purposes: Learning data analysis or financial modeling with real-world data.