The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

This Dataset contains the Stock prices of Bank of America Company the opening price, closing price, low price etc.. Use these Data and Predict the Stock Prices for upcoming years. Available timeframes: Monthly(MN1), Weekly(W1), Daily(D1), 4-Hourly(H4), Hourly(H1), 30-Minutes(M30), 15-Minutes(M15), 10-Minutes(M10), 5-Minutes(M5).

Bank of America D1 Daily timeframe

   datetime  open  high  low close  volume
0 1998-01-02 30.19 30.50 29.73 30.38 2089631
1 1998-01-05 31.65 31.78 30.87 31.22 5821768
2 1998-01-06 31.68 31.76 30.65 30.81 8081564
3 1998-01-07 31.69 31.98 30.25 31.00 8945955
4 1998-01-08 30.48 31.36 30.25 30.69 9085504


... ... ... ... ... ... ... ...

  datetime  open  high  low close  volume


6634 2024-03-08 35.62 36.13 35.50 35.59 38412259
6635 2024-03-09 35.60 35.61 35.59 35.60  3632079
6636 2024-03-11 35.39 35.93 35.27 35.89 29377764
6637 2024-03-12 35.90 36.15 35.78 35.96 24420397
6638 2024-03-13 35.96 36.45 35.96 36.08 34379011

Bank of America H1 Hourly timeframe

       datetime  open  high  low close volume
0 1998-01-02 16:00:00 30.19 30.50 30.19 30.27 123618
1 1998-01-02 17:00:00 30.25 30.27 29.86 29.94 392911
2 1998-01-02 18:00:00 29.94 30.04 29.73 29.76 316560
3 1998-01-02 19:00:00 29.78 30.01 29.73 30.01 394851
4 1998-01-02 20:00:00 30.01 30.07 29.99 30.04 119012


... ... ... ... ... ... ... ...

       datetime  open  high  low close  volume


46741 2024-03-13 19:00:00 36.35 36.36 36.13 36.15 3342356
46742 2024-03-13 20:00:00 36.15 36.25 36.11 36.20 3289569
46743 2024-03-13 21:00:00 36.20 36.21 36.13 36.14 1942775
46744 2024-03-13 22:00:00 36.14 36.19 36.00 36.07 7260742
46745 2024-03-13 23:00:00 36.07 36.09 36.07 36.08 6681580

Common-Stock Time Series for American Assets Trust Inc. American Assets Trust, Inc. is a full service, vertically integrated and self-administered real estate investment trust (REIT), headquartered in San Diego, California. The company has over 55 years of experience in acquiring, improving, developing and managing premier office, retail, and residential properties throughout the United States in some of the nation's most dynamic, high-barrier-to-entry markets primarily in Southern California, Northern California, Washington, Oregon, Texas and Hawaii. The company's office portfolio comprises approximately 4.3 million rentable square feet, and its retail portfolio comprises approximately 2.4 million rentable square feet. In addition, the company owns one mixed-use property (including approximately 94,000 rentable square feet of retail space and a 369-room all-suite hotel) and 2,302 multifamily units. In 2011, the company was formed to succeed to the real estate business of American Assets, Inc., a privately held corporation founded in 1967 and, as such, has significant experience, long-standing relationships and extensive knowledge of its core markets, submarkets and asset classes.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

When there is a vast variety of metrics and tools available to gain market insight, Insider trading offers valuable clues to investors related to future share performance. We at Smart Insider provide global insider trading data and analysis on share transactions made by directors & senior staff in the shares of their own companies.

Monitoring all the insider trading activity is a huge task, we identify 'Smart Insiders' through specialist desktop and quantitative feeds that enable our clients to generate alpha.

Our experienced analyst team use quantitative and qualitative methods to identify the stocks most likely to outperform based on deep analysis of insider trades, and the insiders themselves. Using our easy-to-read derived data we help our clients better understand insider transactions activity to make informed investment decisions.

We provide full customization of reports delivered by desktop, through feeds, or alerts. Our quant clients can receive data in a variety of formats such as XML, XLSX or API via SFTP or Snowflake.

Sample dataset for Desktop Service has been provided with some proprietary fields concealed. Upon request, we can provide a detailed Quant sample.

Tags: Stock Market Data, Equity Market Data, Insider Transactions Data, Insider Trading Intelligence, Insider Trading Data, Investment Management, Alternative Investment, Asset Management, Equity Research, Market Analysis, United Sates of America, Canada, North America

An important point in our data is that the data must be cleaned and the valume column is better because there is a lot of data noise in it.

Common-Stock Time Series for Papa John's International Inc. Papa John's International, Inc. operates and franchises pizza delivery and carryout restaurants under the Papa Johns trademark in the United States, Canada, and internationally. It operates through four segments: Domestic Company-Owned Restaurants, North America Franchising, North America Commissaries, and International. The company also operates dine-in and delivery restaurants under the Papa Johns trademark internationally. It offers pizza and other food and beverage products. In addition, the company supplies pizza sauce, dough, food products, paper products, smallware, and cleaning supplies to restaurants. Papa John's International, Inc. was founded in 1984 and is based in Louisville, Kentucky.

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.

BML^H Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 2)

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-Based-Compensation Time Series for American Electric Power Co Inc. American Electric Power Company, Inc., an electric public utility holding company, engages in the generation, transmission, and distribution of electricity for sale to retail and wholesale customers in the United States. It operates through Vertically Integrated Utilities, Transmission and Distribution Utilities, AEP Transmission Holdco, and Generation & Marketing segments. The company generates electricity using coal and lignite, natural gas, renewable, nuclear, hydro, solar, wind, and other energy sources; owns, operates, maintains, and invests in transmission infrastructure; and engages in wholesale energy trading and marketing business. It operates approximately 225,000 circuit miles of distribution lines that delivers electricity to 5.6 million customers; 40,000 circuit miles of transmission lines; and 23,000 MWs of regulated owned generating capacity. American Electric Power Company, Inc. was incorporated in 1906 and is headquartered in Columbus, Ohio.

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.

ITCB Itau CorpBanca American Depositary Shares (each representing 1500 shares of Common Stock no par value)

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

Corporate Profits in the United States increased to 3259.41 USD Billion in the second quarter of 2025 from 3252.44 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Bank of America (BAC) Historical Stock Prices (1978-2025)

Description:

This dataset provides daily historical stock price data for Bank of America (ticker: BAC) from March 1, 1978, to January 31, 2025. The data includes opening, high, low, closing, and adjusted closing prices, along with trading volume.

Columns:

Date: Trading date (1978-03-01).

Open: Opening price of the stock.

High: Highest price during the trading day.

Low: Lowest price during the trading day.

Close: Closing price of the stock.

Adj Close: Adjusted closing price, accounting for corporate actions (e.g., splits, dividends).

Volume: Number of shares traded during the day.

Key Notes:

Date Range Anomaly: The dataset includes dates up to January 31, 2025, which appears to be a placeholder for future data. Users should verify the latest entries.

Price Format: Prices are recorded in fractions (e.g., 1.453125), reflecting historical stock price conventions.

Missing/Zero Values: Some Open values are listed as 0.0, likely indicating non-trading days (e.g., weekends, holidays) or data gaps.

Adjusted Close: Adjusted for splits and dividends to reflect accurate historical performance.

Potential Use Cases:

Technical Analysis: Study trends, moving averages, or volatility.

Machine Learning: Train models to predict stock movements.

Historical Research: Analyze long-term performance and market cycles.

Backtesting: Validate trading strategies using historical data.

Dataset Source:

Data is compiled from historical market records. Adjusted close prices are calculated retroactively to ensure consistency.

License: Public Domain (CC0).

Suggested Citation: "Bank of America (BAC) Historical Stock Prices, 1978-2025."

Kaggle Tags:

finance, stocks, historical-data, banking, time-series-analysis

Acknowledgments:

This dataset is intended for educational and research purposes. Always verify data accuracy before making financial decisions.

More Dataset

This dataset is scrape by Muhammad Atif Latif.

If you want to explore more datasets then CLICK HERE

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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.

American Well Answers: Is AMWL Stock Set to Surge?

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.

Bank of America: A Preferred Investment? (BML-J)

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

Context

The dataset presentes changes of some stocks, like Google,Apple,Amazon,Alibaba,Facebook,Tencent.

Content

It contains the date, the start price, the end price and other informations in 2017.1-2018.1; Fetched it from Yahoo finance used pandas_reader.

Acknowledgements

Thanks everyone encouraging me.

Inspiration

How do you analyze a dataset by python?

This dataset contains the predicted prices of the asset American Express Tokenized Stock (Ondo) over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Stock-Based-Compensation Time Series for eQ Oyj. eQ Oyj is a publicly owned investment manager. The firm through its subsidiaries provides asset management, corporate finance and investments. The firm specializing in fund of funds investments and secondary transactions. It seeks to make primary investments to funds being raised and to acquire commitments in the secondary market. The firm invests in venture capital and middle market funds, buyout funds, and private equity funds in the technology sector. It seeks to invest in funds based in Baltic States, Northern Europe, North America, South America, Southern Europe, CEE/SEE, Africa, United States, Western Europe, EU, Finland, Benelux, Asia, Germany, Switzerland, United Kingdom, France, Russia/CIS, Nordic Region, the former Soviet republics, and Eastern Europe. In its own funds, it invests predominantly in Northern European and North-American funds that are in the size bracket of "50 and "500 million. In its client mandates, it invests in European buyout funds of any size. Typically the funds we invest in acquire majority positions in target companies. The firm was formerly known as Amanda Capital Oyj. eQ Oyj was founded in 2000 and is based in Helsinki, Finland.

Lingcod (Ophiodon elongatus) populations along the West Coast of North America have recovered from overfishing, but the status of genetically distinct lingcod in Puget Sound, Washington is less clear. This project will use small-scale lingcod releases to learn about the benefits and risks of using stock enhancement as a tool to help rebuild marine fish populations. We have conducted experiments...