Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

Use our Stock Market dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

This dataset presents an extensive record of daily historical stock prices for Tesla, Inc. (TSLA), one of the world’s most innovative and closely watched electric vehicle and clean energy companies. The data was sourced from Yahoo Finance, a widely used and trusted provider of financial market data, and covers a significant period spanning from Tesla’s initial public offering (IPO) to the most recent date available at the time of extraction.

The dataset includes critical trading metrics for each market day, such as the opening price, highest and lowest prices of the day, closing price, adjusted closing price (accounting for dividends and splits), and total trading volume. This rich dataset supports a variety of use cases, including financial market analysis, investment research, time series forecasting, development and backtesting of trading algorithms, and educational projects in data science and finance.

Dataset Features

• Date: The calendar date for each trading session (in YYYY-MM-DD format)
• Open: The opening price of TSLA shares at the start of the trading day
• High: The highest price reached during the trading session
• Low: The lowest price reached during the trading session
• Close: The last price at which the stock traded during the day
• Adj Close: The closing price adjusted for corporate actions (splits, dividends, etc.)
• Volume: The total number of TSLA shares traded on that day

Source and Collection Details

• Source: Yahoo Finance - Tesla (TSLA) Historical Data
• Collection Method: Data was downloaded using Yahoo Finance's CSV export feature for accuracy and completeness.
• Time Range: Covers from Tesla’s IPO (June 2010) to the most recent available trading day.
• Data Integrity: Minimal cleaning was performed—dates were standardized, and any duplicate or empty rows were removed; all values remain as originally reported by Yahoo Finance.

Example Use Cases

• Stock Price Prediction: Train and test time series models (ARIMA, LSTM, Prophet, etc.) to forecast Tesla’s stock prices.
• Algorithmic Trading: Backtest and evaluate trading strategies using historical price and volume data.
• Market Trend Analysis: Analyze price trends, volatility, and return rates over different periods.
• Event Study: Investigate the impact of major announcements (e.g., product launches, earnings releases) on TSLA stock price.
• Educational Projects: Use as a hands-on resource for learning finance, statistics, or machine learning.

License & Acknowledgments

• Intended Use: This dataset is provided for academic, research, and personal projects. For commercial or investment use, please verify data accuracy and consult Yahoo Finance’s terms of use.
• Acknowledgment: Data sourced from Yahoo Finance. All trademarks and copyrights belong to their respective owners.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

The latest closing stock price for Microsoft as of June 18, 2025 is 480.24. An investor who bought $1,000 worth of Microsoft stock at the IPO in 1986 would have $8,056,718 today, roughly 8,057 times their original investment - a 25.94% compound annual growth rate over 39 years. The all-time high Microsoft stock closing price was 480.24 on June 18, 2025. The Microsoft 52-week high stock price is 481.00, which is 0.2% above the current share price. The Microsoft 52-week low stock price is 344.79, which is 28.2% below the current share price. The average Microsoft stock price for the last 52 weeks is 422.77. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

The latest closing stock price for Alphabet as of June 18, 2025 is 173.86. An investor who bought $1,000 worth of Alphabet stock at the IPO in 2004 would have $68,661 today, roughly 69 times their original investment - a 22.39% compound annual growth rate over 21 years. The all-time high Alphabet stock closing price was 205.89 on February 04, 2025. The Alphabet 52-week high stock price is 207.05, which is 19.1% above the current share price. The Alphabet 52-week low stock price is 140.53, which is 19.2% below the current share price. The average Alphabet stock price for the last 52 weeks is 172.15. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

Dataset Description

Overview:

This dataset contains the historical stock prices and related financial information for five major technology companies: Apple (AAPL), Microsoft (MSFT), Amazon (AMZN), Google (GOOGL), and Tesla (TSLA). The dataset spans a five-year period from January 1, 2019, to January 1, 2024. It includes key stock metrics such as Open, High, Low, Close, Adjusted Close, and Volume for each trading day.

Data Collection:

The data was sourced using the yfinance library in Python, which provides convenient access to historical market data from Yahoo Finance.

Contents:

The dataset contains the following columns:

Date: The trading date. Open: The opening price of the stock on that date. High: The highest price of the stock on that date. Low: The lowest price of the stock on that date. Close: The closing price of the stock on that date. Adj Close: The adjusted closing price, accounting for dividends and splits. Volume: The number of shares traded on that date. Ticker: The stock ticker symbol representing each company.

During the coronavirus (COVID-19) pandemic, AT&T have suffered the largest drop in share prices, falling from ***** U.S. dollars per share to ***** U.S. dollars. T-Mobile's share prices were boosted by the successful merger with Sprint Corp. on 1 April 2020.

The Dow Jones Industrial Average (DJIA) is a stock market index used to analyze trends in the stock market. While many economists prefer to use other, market-weighted indices (the DJIA is price-weighted) as they are perceived to be more representative of the overall market, the Dow Jones remains one of the most commonly-used indices today, and its longevity allows for historical events and long-term trends to be analyzed over extended periods of time. Average changes in yearly closing prices, for example, shows how markets developed year on year. Figures were more sporadic in early years, but the impact of major events can be observed throughout. For example, the occasions where a decrease of more than 25 percent was observed each coincided with a major recession; these include the Post-WWI Recession in 1920, the Great Depression in 1929, the Recession of 1937-38, the 1973-75 Recession, and the Great Recession in 2008.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Dataset Information

This dataset includes daily price data for various stocks.

  Instruments Included

7000+ US Stocks

  Dataset Columns

symbol: The symbol of the stock. date: The date of the data. open: The opening price of the stock. high: The highest price of the stock. low: The lowest price of the stock. close: The closing price of the stock. volume: The volume of the stock. adj_close: The adjusted closing price of the stock.

  Data Splits

The… See the full description on the dataset page: https://huggingface.co/datasets/paperswithbacktest/Stocks-Daily-Price.

We offer historical price data for equity indexes, ETFs and individual stocks in a Open/High/Low/Close (OHLC) format and can add almost any other required metric. We cover all major markets and many minor markets. Available for one-time purchase or with regular updates. Real-time/near-time (usually anything quicker than a 15min delay) requires an additional licence from the respective exchange, anything slower does not.

The Dow Jones Industrial Average is (DJIA) is possibly the most well-known and commonly used stock index in the United States. It is a price-weighted index that assesses the stock prices of 30 prominent companies, whose combined prices are then divided by a regularly-updated divisor (0.15199 in February 2021), which gives the index value. The companies included are rotated in and out on a regular basis; as of mid-2022, the longest mainstay on the list is Procter & Gamble, which was added in 1932; whereas Amgen, Salesforce, and Honeywell were all added in 2020. As one of the oldest indices for stock market analysis, the impact of major events, recessions, and economic shocks or booms can be tracked and contextualized over longer periods of time.

Due to inflation, unadjusted figures appear to be more sporadic in recent years, however the greatest fluctuations came in the earliest years of the index. In the given period, the greatest decline came in the wake of the Wall Street Crash in 1929; by 1932 average values had fallen to just one fifth of their 1929 average, from roughly 314 to 65.

About Dataset:

This dataset includes the daily historical stock prices for Google (GOOGL) spanning from 2020 to 2025. It features essential financial metrics such as opening and closing prices, daily highs and lows, adjusted close prices, and trading volumes. The information offers valuable insights into the stock's performance over a five-year timeframe.

Column Descriptions:

• Price: Date of the stock data (needs cleaning as the first two rows are headers).
• Adj Close: Adjusted closing price, accounting for events like dividends and splits.
• Close: Closing price of the stock at the end of the trading day.
• High: Highest price of the stock during the trading day.
• Low: Lowest price of the stock during the trading day.
• Open: Opening price of the stock at the start of the trading day.
• Volume: Number of shares traded during the day.

What Can You Achieve and Apply on This Data:

• Time Series Analysis: Examine trends and patterns over time.
• Stock Price Prediction: Use machine learning models to forecast future prices.
• Volatility Analysis: Measure the stock's price fluctuations.
• Technical Analysis: Calculate indicators like moving averages, RSI, and MACD.
• Correlation Analysis: Investigate the relationship between volume and price changes.
• Investment Strategy Backtesting: Test trading strategies like moving average crossovers.

Note: 1. This data is scraped from Yahoo Finance by me using python code. 2. Some of the About Data is generated from AI, but verified from me.

The latest closing stock price for Wells Fargo as of June 18, 2025 is 74.74. An investor who bought $1,000 worth of Wells Fargo stock at the IPO in 1972 would have $534,004 today, roughly 534 times their original investment - a 12.58% compound annual growth rate over 53 years. The all-time high Wells Fargo stock closing price was 80.58 on February 06, 2025. The Wells Fargo 52-week high stock price is 81.50, which is 9% above the current share price. The Wells Fargo 52-week low stock price is 50.15, which is 32.9% below the current share price. The average Wells Fargo stock price for the last 52 weeks is 67.36. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

During the Global Financial Crisis of 2007-2008, a number of systemically important financial institutions in the United States declared bankruptcy, sought takeovers to prevent financial failure, or turned to the U.S. government for bailouts. Two of these institutions, Fannie Mae and Freddie Mac, were government-sponsored enterprises (GSEs), meaning that they were set up by the federal government in order to steer credit towards lower income homebuyers through interventions in the secondary mortgage market. While both were chartered by the government, they were also publicly traded companies, with a majority of shares owned by private investors. The fall of Fannie Mae and Freddie Mac These GSEs' business model was based on buying mortgages from their originators (banks, mortgage brokers, etc.) and then packaging groups of these mortgages together as mortgage-backed securities (MBS), before selling these on again to private investors. While this allowed the expansion of mortgage credit, meaning that many Americans were able to buy houses who would not have in other cases, this also contributed to the growing speculation in the housing market and related financial derivatives, such as MBS. The lowering of mortgage lending standards by originators in the early 2000s, as well as the need for GSEs to compete with their private sector rivals, meant that Fannie Mae and Freddie Mac became caught up in the financial mania associated with the early 2000s U.S. housing bubble. As their losses mounted due to the bursting of the bubble in 2007, both companies came under increasing financial stress, finally being brought into government conservatorship in September 2008. Fannie Mae and Freddie Mac were eventually unlisted from stock exchanges in 2010.

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.

Machine Learning stock prediction: HD Stock Prediction

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

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

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.

LON:STG Stock: The Stock Market Bubble Is About 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