This dataset contains the essential files for conducting a dynamic stock market analysis using Power BI. The data is sourced from Yahoo Finance and includes historical stock prices, which can be dynamically updated by adding new stock codes to the provided Excel sheet.

Files Included: Power BI Report (.pbix): The interactive Power BI report that includes various visualizations such as Candle Charts, Line Charts for Support and Resistance, and Technical Indicators like SMA, EMA, Bollinger Bands, and RSI. The report is designed to provide a comprehensive analysis of stock performance over time.

Stock Data Excel Sheet (.xlsx): This Excel sheet is connected to the Power BI report and allows for dynamic data loading. By adding new stock codes to this sheet, the Power BI report automatically refreshes to include the new data, enabling continuous updates without manual intervention.

Overview and Chart Pages Snapshots for better understanding about the Report.

Key Features: Dynamic Data Loading: Easily update the dataset by adding new stock codes to the Excel sheet. The Power BI report will automatically pull the corresponding data from Yahoo Finance. Comprehensive Visualizations: Analyze stock trends using Candle Charts, identify key price levels with Support and Resistance lines, and explore market behavior through various technical indicators. Interactive Analysis: The Power BI report includes slicers and navigation buttons to switch between different time periods and visualizations, providing a tailored analysis experience. Use Cases: Ideal for financial analysts, traders, or anyone interested in conducting a detailed stock market analysis. Can be used to monitor the performance of individual stocks or compare trends across multiple stocks over time. Tags: Stock Market Power BI Financial Analysis Yahoo Finance Data Visualization

Context

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the all_stocks_5yr.csv and corresponding folder) and a smaller version of the dataset (all_stocks_1yr.csv) with only the past year's stock data for those wishing to use something more manageable in size.

The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv and all_stocks_1yr.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd Open - price of the stock at market open (this is NYSE data so all in USD) High - Highest price reached in the day Low Close - Lowest price reached in the day Volume - Number of shares traded Name - the stock's ticker name

Acknowledgements

I scraped this data from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and The Market.

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

Czech Republic's main stock market index, the PX, fell to 2155 points on July 4, 2025, losing 0.49% from the previous session. Over the past month, the index has declined 0.11%, though it remains 36.74% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Czech Republic. Czech Republic Stock Market (SE PX) - values, historical data, forecasts and news - updated on July of 2025.

The Real-time Candlestick OHLC API provides current candlestick data that covers all major stock exchanges including NYSE, NASDAQ, LSE, Euronext to NSE of India, TSE, and a few more. Users can choose from candlestick data with 1 min, 2 min, 5 min, 15 min, 30 min, 1 hour, 4 hour, 1 day, 1 week, 1 month and 1 year interval. By using the real-time candlestick OHLC data, they can visualize data on candlestick charts and build financial products.

Dataset Description:

The myusabank.csv dataset contains daily financial data for a fictional bank (MyUSA Bank) over a two-year period. It includes various key financial metrics such as interest income, interest expense, average earning assets, net income, total assets, shareholder equity, operating expenses, operating income, market share, and stock price. The data is structured to simulate realistic scenarios in the banking sector, including outliers, duplicates, and missing values for educational purposes.

Potential Student Tasks:

1. Data Cleaning and Preprocessing:

   • Handle missing values, duplicates, and outliers to ensure data integrity.
   • Normalize or scale data as needed for analysis.

2. Exploratory Data Analysis (EDA):

   • Visualize trends and distributions of financial metrics over time.
   • Identify correlations between different financial indicators.

3. Calculating Key Performance Indicators (KPIs):

   • Compute metrics such as Net Interest Margin (NIM), Return on Assets (ROA), Return on Equity (ROE), and Cost-to-Income Ratio using calculated fields.
   • Analyze the financial health and performance of MyUSA Bank based on these KPIs.

4. Building Tableau Dashboards:

   • Design interactive dashboards to present insights and trends.
   • Include summary cards, bar charts, line charts, and pie charts to visualize financial performance metrics.

5. Forecasting and Predictive Modeling:

   • Use historical data to forecast future financial performance.
   • Apply regression or time series analysis to predict market share or stock price movements.

6. Business Insights and Reporting:

   • Interpret findings to derive actionable insights for bank management.
   • Prepare reports or presentations summarizing key findings and recommendations.

Educational Goals:

The dataset aims to provide hands-on experience in data preprocessing, analysis, and visualization within the context of banking and finance. It encourages students to apply data science techniques to real-world financial data, enhancing their skills in data-driven decision-making and strategic analysis.

Coffee fell to 293.47 USd/Lbs on July 3, 2025, down 0.72% from the previous day. Over the past month, Coffee's price has fallen 15.05%, but it is still 31.39% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on July of 2025.