Dataset Overview

This dataset contains comprehensive stock market data for June 2025, capturing daily trading information across multiple companies and sectors. The dataset represents a substantial collection of market data with detailed financial metrics and trading statistics.

Basic Dataset Information

• Time Period: June 1-21, 2025 (21 trading days)
• Total Records: Approximately 11,600+ entries
• Companies Covered: 500+ unique stocks
• Data Type: Daily stock market trading data with fundamental metrics

Markdown Table Format

Column Name	Data Type	Description	Example Values
Date	Date	Trading date in DD-MM-YYYY format	01-06-2025, 02-06-2025
Ticker	String	Stock ticker symbol (3-4 characters)	AAPL, GOOGL, TSLA
Open Price	Float	Opening price of the stock	34.92, 206.5, 125.1

Dataset Information Table

Dataset Overview

Attribute	Details
Dataset Name	Stock Market Data - June 2025
File Format	CSV
File Size	~2.5 MB
Number of Records	11,600+
Number of Features	13
Time Period	June 1-21, 2025

Data Schema

Column Name	Data Type	Description	Example Values
Date	Date	Trading date in DD-MM-YYYY format	01-06-2025, 02-06-2025
Ticker	String	Stock ticker symbol (3-4 characters)	AAPL, GOOGL, TSLA, SLH
Open Price	Float	Opening price of the stock	34.92, 206.5, 125.1
Close Price	Float	Closing price of the stock	34.53, 208.45, 124.03
High Price	Float	Highest price during the trading day	35.22, 210.51, 127.4
Low Price	Float	Lowest price during the trading day	34.38, 205.12, 121.77
Volume Traded	Integer	Number of shares traded	2,966,611, 1,658,738
Market Cap	Float	Market capitalization in dollars	57,381,363,838.88
PE Ratio	Float	Price-to-Earnings ratio	29.63, 13.03, 29.19
Dividend Yield	Float	Dividend yield percentage	2.85, 2.73, 2.64
EPS	Float	Earnings per Share	1.17, 16.0, 4.25
52 Week High	Float	Highest price in the last 52 weeks	39.39, 227.38, 138.35
52 Week Low	Float	Lowest price in the last 52 weeks	28.44, 136.79, 100.69
Sector	String	Industry sector classification	Industrials, Energy, Healthcare

Market Capitalization Tiers

• Mega Cap (>$1T): 6 companies (AAPL, MSFT, NVDA, AMZN, GOOGL, META)
• Large Cap ($200B-$1T): 28 companies
• Mid Cap ($50B-$200B): 47 companies

Key Market Characteristics

Price Volatility by Sector

• Technology: Higher volatility (±3.5% daily range)
• Energy: High volatility (±4.0% daily range)
• Utilities: Lower volatility (±1.5% daily range)
• Healthcare/Financials: Moderate volatility (±2.5% daily range)

Trading Volume Patterns

• Mega Cap: 25M - 90M shares daily
• Large Cap: 8M - 35M shares daily
• Mid Cap: 2M - 15M shares daily
• Small Cap: 500K - 5M shares daily

Financial Metrics Distribution

• Average P/E Ratio: 25.9 (market-wide)
• Average Dividend Yield: 1.25%
• Price Range: $19 (T) to $3,850 (BKNG)
• EPS Range: $1.50 to $70.00

Notable Market Features

High-Value Stocks

• BKNG (Booking Holdings): $3,650-$3,850 range
• AVGO (Broadcom): $1,650-$1,750 range
• REGN (Regeneron): $1,050-$1,150 range
• LLY (Eli Lilly): $920-$980 range

High-Dividend Yielders

• T (AT&T): 7.1% dividend yield
• VZ (Verizon): 6.2% dividend yield
• PFE (Pfizer): 5.8% dividend yield

Growth & Technology Leaders

• NOW (ServiceNow): P/E ratio of 85
• NVDA (NVIDIA): P/E ratio of 45
• TSLA (Tesla): P/E ratio of 55

Data Quality & Realism Features

✅ Authentic Price Ranges: Based on realistic 2025 market projections ✅ Sector-Appropriate Volatility: Different volatility patterns by industry ✅ Correlated Metrics: P/E ratios, dividend yields, and EPS align with market caps ✅ Realistic Trading Volumes: Volume scaled appropriately to market cap ✅ Temporal Consistency: Logical price progression over 53-day period ✅ Market Cap Accuracy: Daily fluctuations reflect actual price movements

Intended Use Cases

• Financial Analysis & Modeling: Portfolio optimization, risk assessment
• Machine Learning Applications: Predictive modeling, algorithmic trading
• Educational Purposes: Finance courses, data science training
• Algorithm Development: Backtesting trading strategies
• Market Research: Sector analysis, correlation studies
• Visualization Projects: Interactive dashboards, market trend analysis

This dataset provides a comprehensive foundation for quantitative finance research, offering both breadth across market sectors and depth in daily trading dynamics while maintaining statistical realism throughout the observation period...

Dataset

This dataset was created by Kamran Ansari

Released under Database: Open Database, Contents: Database Contents

Contents

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The data set focuses on the Debt and Money markets. The Fixed Income Market includes the debt and money markets. Investments in fixed-income securities are traded on the fixed income market. The fixed-income market comprises trading in securities such as Treasury Bills with various maturities, floating rate bonds, perpetual bonds, commercial paper, certificates of deposit, STRIPS, and debentures.

The information is gathered from the website amfiindia(Association of Mutual Funds in India) between June 2021 and June 2022.

Cleaning was necessary because the data came from the aforementioned website's data dump. The data has been partially cleaned, but much more cleaning is required.

Disclaimer!!! Data uploaded here are collected from the internet. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either monetary or any favor) for this dataset.

Overview

This dataset contains historical daily prices for Nifty 100 stocks and indices currently trading on the Indian Stock Market. - Data samples are of 15-minute intervals and the availability of data is from Jan 2015 to Feb 2022. - Along with OHLCV (Open, High, Low, Close, and Volume) data, we have created 55 technical indicators. - More details about these technical indicators are provided in the Data description file.

Content

The whole dataset is around 5 GB, and 100 stocks (Nifty 100 stocks) and 2 indices (Nifty 50 and Nifty Bank indices) are present in this dataset. Details about each file are - - OHLCV (Open, High, Low, Close, and Volume) data

Inspiration

• Data is uploaded for Research and Educational purposes.

Possible problem statements

• Univariate and Multi-variate time series forecasting of stock prices and index prices
• Multi-variate data can be used to predict the trend of the stock price (Buy or Sell or Hold)
• Different intraday or positional trading strategies can be built out of this multivariate data. [technical indicators are already added here]
• EDA on time series data

Index Name	Index Name	Index Name	Index Name
NIFTY BANK	NIFTY 50	NIFTY 100	NIFTY COMMODITIES
NIFTY CONSUMPTION	NIFTY FIN SERVICE	NIFTY IT	NIFTY INFRA
NIFTY ENERGY	NIFTY FMCG	NIFTY AUTO	NIFTY 200
NIFTY ALPHA 50	NIFTY 500	NIFTY CPSE	NIFTY GS COMPSITE
NIFTY HEALTHCARE	NIFTY CONSR DURBL	NIFTY LARGEMID250	NIFTY INDIA MFG
NIFTY IND DIGITAL

Company Name	Company Name	Company Name	Company Name
ABB India Ltd.	Adani Energy Solutions Ltd.	Adani Enterprises Ltd.	Adani Green Energy Ltd.
Adani Ports and SEZ Ltd.	Adani Power Ltd.	Ambuja Cements Ltd.	Apollo Hospitals Enterprise Ltd.
Asian Paints Ltd.	Avenue Supermarts Ltd.	Axis Bank Ltd.	Bajaj Auto Ltd.
Bajaj Finance Ltd.	Bajaj Finserv Ltd.	Bajaj Holdings & Investment Ltd.	Bajaj Housing Finance Ltd.
Bank of Baroda	Bharat Electronics Ltd.	Bharat Petroleum Corporation Ltd.	Bharti Airtel Ltd.
Bosch Ltd.	Britannia Industries Ltd.	CG Power and Industrial Solutions Ltd.	Canara Bank
Cholamandalam Inv. & Fin. Co. Ltd.	Cipla Ltd.	Coal India Ltd.	DLF Ltd.
Dabur India Ltd.	Divi's Laboratories Ltd.	Dr. Reddy's Laboratories Ltd.	Eicher Motors Ltd.
Eternal Ltd.	GAIL (India) Ltd.	Godrej Consumer Products Ltd.	Grasim Industries Ltd.
HCL Technologies Ltd.	HDFC Bank Ltd.	HDFC Life Insurance Co. Ltd.	Havells India Ltd.
Hero MotoCorp Ltd.	Hindalco Industries Ltd.	Hindustan Aeronautics Ltd.	Hindustan Unilever Ltd.
Hyundai Motor India Ltd.	ICICI Bank Ltd.	ICICI Lombard General Insurance Ltd.	ICICI Prudential Life Insurance Ltd.
ITC Ltd.	Indian Hotels Co. Ltd.	Indian Oil Corporation Ltd.	I...

Debt-To-Assets-Ratio Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.

Coca-Cola (KO) Stock Price Data (1919-2025).

Overview:

This dataset provides historical stock price data for The Coca-Cola Company (NYSE: KO) from September 6, 1919, to January 31, 2025. Extracted from Yahoo Finance, this dataset is valuable for stock market analysis, long-term trend evaluation, and financial modeling.

Dataset Features:

Date: The trading date in YYYY-MM-DD format.

Open: Opening price of Coca-Cola stock on the respective day.

High: Highest price recorded during the trading session.

Low: Lowest price recorded during the trading session.

Close: Closing price of the stock at the end of the trading session.

Adj Close: Adjusted closing price, accounting for stock splits and dividends.

Volume: Total number of shares traded on that day.

Dataset Statistics:

Total Records: 15,877 rows.

Number of Features: 7 columns.

Time Span: Over 100 years of stock data (1919-2025)

Potential Use Cases:

Long-Term Market Trend Analysis – Analyze Coca-Cola’s stock performance over a century. Financial Forecasting – Train machine learning models to predict future stock prices. Volatility Analysis – Assess price fluctuations over different market cycles. Investment Strategy Development – Backtest various trading strategies.

Data Source:

This dataset has been extracted from Yahoo Finance.

License & Citation:

This dataset is publicly available for educational and research purposes. Please cite Yahoo Finance and Muhammad Atif Latif when using it in any analysis.

More Stocks Datasets.

Click here for more Datasets

Symbol: This acts as a unique identifier for a particular stock on a specific exchange. Just like AAPL represents Apple Inc. on the NASDAQ exchange. Name: This is the full name of the company that issued the stock. Currency: This indicates the currency in which the stock is traded. Examples include USD (US Dollar), EUR (Euro), and JPY (Japanese Yen). Exchange: This refers to the stock exchange where the stock is traded. NASDAQ and NYSE are some well-known exchanges. MIC Code: This stands for Market Identifier Code and is used to uniquely identify a specific exchange or trading venue. Country: This specifies the country of incorporation of the company that issued the stock. Type: the type of the st0ck

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Masonite (DOOR) Stock: A Solid Investment?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Stock Price Time Series for Neptune Group Ltd. Rich Goldman Holdings Limited, an investment holding company, engages in the money lending business in the People Republic of China, and Hong Kong. The company involved in the money lending activities; hotel operations; and property investment and leasing business. The company was formerly known as Neptune Group Limited and changed its name to Rich Goldman Holdings Limited in September 2017. Rich Goldman Holdings Limited was incorporated in 1972 and is headquartered in Central, Hong Kong.

Context The StockNet dataset, introduced by Xu and Cohen at ACL 2018, is a benchmark for measuring the effectiveness of textual information in stock market prediction. While the original dataset provides valuable price and news data, it requires significant pre-processing and feature engineering to be used effectively in advanced machine learning models.

This dataset was created to bridge that gap. We have taken the original data for 87 stocks and performed extensive feature engineering, creating a rich, multi-modal feature repository.

A key contribution of this work is a preliminary statistical analysis of the news data for each stock. Based on the consistency and volume of news, we have categorized the 87 stocks into two distinct groups, allowing researchers to choose the most appropriate modeling strategy:

joint_prediction_model_set: Stocks with rich and consistent news data, ideal for building complex, single models that analyze all stocks jointly.

panel_data_model_set: Stocks with less consistent news data, which are better suited for traditional panel data analysis.

Content and File Structure The dataset is organized into two main directories, corresponding to the two stock categories mentioned above.

1.joint_prediction_model_set This directory contains stocks suitable for sophisticated, news-aware joint modeling.

-Directory Structure: This directory contains a separate sub-directory for each stock suitable for joint modeling (e.g., AAPL/, MSFT/, etc.).

-Folder Contents: Inside each stock's folder, you will find a set of files, each corresponding to a different category of engineered features. These files include:

-News Graph Embeddings: A NumPy tensor file (.npy) containing the encoded graph embeddings from daily news. Its shape is (Days, N, 128), where N is the number of daily articles.

-Engineered Features: A CSV file containing fundamental features derived directly from OHLCV data (e.g., intraday_range, log_return).

-Technical Indicators: A CSV file with a wide array of popular technical indicators (e.g., SMA, EMA, MACD, RSI, Bollinger Bands).

-Statistical & Time Features: A CSV file with rolling statistical features (e.g., volatility, skew, kurtosis) over an optimized window, plus cyclical time-based features.

-Advanced & Transformational Features: A CSV file with complex features like lagged variables, wavelet transform coefficients, and the Hurst Exponent.

2.panel_data_model_set This directory contains stocks that are more suitable for panel data models, based on the statistical properties of their associated news data.

-Directory Structure: Similar to the joint prediction set, this directory also contains a separate sub-directory for each stock in this category.

-Folder Contents: Inside each stock's folder, you will find the cleaned and structured price and news text data. This facilitates the application of econometric models or machine learning techniques designed for panel data, where observations are tracked for the same subjects (stocks) over a period of time.

-Further Information: For a detailed breakdown of the statistical analysis used to separate the stocks into these two groups, please refer to the data_preview.ipynb notebook located in the TRACE_ACL18_raw_data directory.

Methodology The features for the joint_prediction_model_set were generated systematically for each stock:

-News-to-Graph Pipeline: Daily news headlines were processed to extract named entities. These entities were then used to query Wikidata and build knowledge subgraphs. A Graph Convolutional Network (GCN) model encoded these graphs into dense vectors.

-Feature Engineering: All other features were generated from the raw price and volume data. The process included basic calculations, technical analysis via pandas-ta, generation of statistical and time-based features, and advanced transformations like wavelet analysis.

Acknowledgements This dataset is an extension and transformation of the original StockNet dataset. We extend our sincere gratitude to the original authors for their contribution to the field.

Original Paper: "StockNet: A Probing Task for Measuring Stock Market Prediction" by Yumeng Xu and Mohit Bansal. (ACL 2018).

Original Data Repository: https://github.com/yumoxu/stocknet-dataset

Inspiration This dataset opens the door to numerous exciting research questions:

-Can you build a single, powerful joint model using the joint_prediction_model_set to predict movements for all stocks simultaneously?

-How does a sophisticated joint model compare against a traditional panel data model trained on the panel_data_model_set?

-What is the lift in predictive power from using news-based graph embeddings versus using only technical indicators?

-Can you apply transfer learning or multi-task learning, using the feature-rich joint set to improve predictions for the panel set?

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ANALYZING SQL STOCK: A COMPREHENSIVE EVALUATION OF GROWTH POTENTIAL AND INVESTMENT OPPORTUNITIES

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Stock Price Time Series for South Plains Financial Inc. South Plains Financial, Inc. operates as a bank holding company for City Bank that provides commercial and consumer financial services to small and medium-sized businesses and individuals. It offers deposit products, including demand deposit accounts, interest-bearing products, savings accounts, and certificate of deposits. The company also provides traditional trust products and services; debit and credit cards; retirement services and products, including real estate administration, family trust administration, revocable and irrevocable trusts, charitable trusts for individuals and corporations, and self-directed individual retirement accounts. In addition, it offers investment services, such as self-directed IRAs, money market funds, mutual funds, annuities and tax-deferred annuities, stocks and bonds, investments for non-U.S. residents, treasury bills, treasury notes and bonds, and tax-exempt municipal bonds. Further, the company provides commercial real estate loans; general and specialized commercial loans, including agricultural production and real estate, energy, finance, investment, and insurance loans, as well as loans to goods, services, restaurant and retail, construction, and other industries; residential construction loans; and 1-4 family residential loans, auto loans, and other loans for recreational vehicles or other purposes; and mortgage banking services. The company was founded in 1941 and is headquartered in Lubbock, Texas.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is now good time to invest? (FRE.DE Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Dataset

This dataset was created by Le Viet Thang

Contents

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

MOD Stock: A Bright Investment or a Bubble Waiting to Burst?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

BAM Stock: A Risky Investment

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is now good time to invest? (VBTX Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset contains the daily historical stock prices for Apple Inc. (AAPL) over the past year. The data includes key indicators for each trading day, providing insights into the company's stock performance and volatility. It is ideal for financial analysis, predictive modeling, and educational projects focused on time series forecasting, quantitative finance, and machine learning applications.

Data Columns:

-**Date:** The trading date (YYYY-MM-DD)

-**Open:** Stock price at market open (USD)

-**High:** Highest price during the trading day (USD)

-**Low:** Lowest price during the trading day (USD)

-**Close:** Price at market close (USD)

-**Volume:** Number of shares traded

Use Cases:

-Analyzing price trends and volatility for AAPL

-Building forecasting models for future stock prices

-Feature engineering for machine learning or statistical algorithms

-Comparing performance with other stocks or indices

Context

Predicting the stock market is one of the most commonly performed projects when someone is learning about ML and Data Science. After all, who wouldn't want to delegate the task of picking stocks to a model and reap the rewards for themselves? However, one of the most difficult and tedious steps to predict what stocks to invest in is actually gathering the data to use. There are so many options and it is important to get sufficient information for each. But, what if you can skip this step and just download a dataset that has all that information easily available for you? Look no further as this is the answer to this problem.

Content

This dataset contains information of 4447 stocks traded under Nasdaq across various exchanges. There is a file that contains information for all 4447 stocks but also has several null fields, which is why I labeled it as full_financial_stocks_raw.csv --it has minimal modifications to the values inside the rows. The second file, dividend_stocks_only.csv, is still a raw-ish style dataset but it only contains stocks that pay out dividends to its shareholders. Interestingly, it seems dividend-paying stocks have more information about them, which explains why this file has significantly fewer rows with null values.

Update: In the next 24 hours, I will be uploading an optimized, feature-engineered dataset that has fewer columns overall and fewer rows with null values. This dataset is intended to be a fully cleaned option to directly feed into ML/DL models.

Acknowledgements

I would like to thank the sources where I obtained my data, which are the FTP Nasdaq Trader website and the Yahoo Finance API.

Inspiration

Analyzing the stock market is one of the most intriguing endeavors I could think of as the ways it can be influenced are so broad and distinct from one another. A news article can influence how investors view a particular company, social media can directly fluctuate a company's share price, and there are numerous calculations and formulas that can show what stocks are worth investing in.