This Dataset contains the Stock prices of Apple 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).

Apple D1 Daily timeframe

   datetime open high  low close   volume
0 1998-01-02 0.12 0.14 0.12  0.14 170539824
1 1998-01-05 0.14 0.14 0.13  0.14 152723900
2 1998-01-06 0.14 0.17 0.13  0.16 433041952
3 1998-01-07 0.16 0.16 0.15  0.15 251914152
4 1998-01-08 0.15 0.16 0.15  0.16 188994988


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

  datetime  open  high   low  close  volume


6634 2024-03-08 169.12 173.70 168.95 170.98 53335094
6635 2024-03-09 170.99 171.01 170.77 170.79   59796
6636 2024-03-11 172.94 174.38 172.05 172.75 44605588
6637 2024-03-12 173.15 174.03 171.01 173.21 37477359
6638 2024-03-13 172.77 173.19 170.76 171.12 31607988

Apple H1 Hourly timeframe

       datetime open high  low close  volume
0 1998-01-02 16:00:00 0.12 0.12 0.12  0.12 14512400
1 1998-01-02 17:00:00 0.12 0.13 0.12  0.12 52987312
2 1998-01-02 18:00:00 0.12 0.13 0.12  0.13 23746800
3 1998-01-02 19:00:00 0.13 0.13 0.13  0.13 21644000
4 1998-01-02 20:00:00 0.13 0.13 0.13  0.13 11933600


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

       datetime  open  high   low  close  volume


46746 2024-03-13 19:00:00 171.04 171.14 170.85 171.02 3019206
46747 2024-03-13 20:00:00 171.02 171.53 171.01 171.50 3736110
46748 2024-03-13 21:00:00 171.50 171.80 171.44 171.65 2899620
46749 2024-03-13 22:00:00 171.65 171.74 171.03 171.15 6318538
46750 2024-03-13 23:00:00 171.14 171.16 171.11 171.12  21317

This Dataset contains the Stock prices of Mastercard 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).

Mastercard D1 Daily timeframe

   datetime  open  high  low close  volume
0 2006-05-25 41.00 46.05 40.20 46.00 30181700
1 2006-05-26 46.30 46.74 44.11 44.92  9604700
2 2006-05-30 44.97 44.98 42.85 44.15  4908600
3 2006-05-31 44.35 45.36 44.35 44.93  2949300
4 2006-06-01 45.00 48.10 44.90 47.54  6169300


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

  datetime  open  high   low  close  volume


4522 2024-03-08 467.59 471.62 467.10 469.27  983015
4523 2024-03-09 469.28 469.34 469.23 469.26  294160
4524 2024-03-11 469.00 469.37 464.69 469.16  776020
4525 2024-03-12 470.53 474.37 468.71 472.87  873902
4526 2024-03-13 474.23 476.16 472.78 475.61 1028658

Mastercard H1 Hourly timeframe

       datetime  open  high  low close  volume
0 2006-05-25 17:00:00 41.00 44.16 40.20 44.15 14531500
1 2006-05-25 18:00:00 44.15 45.00 43.30 43.70  5178500
2 2006-05-25 19:00:00 43.70 43.91 43.11 43.84  2304100
3 2006-05-25 20:00:00 43.84 44.15 43.40 44.12  1950700
4 2006-05-25 21:00:00 44.15 44.83 44.03 44.63  2415400


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

       datetime  open  high   low  close volume


31969 2024-03-13 19:00:00 475.16 476.01 474.64 475.17  65395
31970 2024-03-13 20:00:00 475.25 475.49 474.80 475.33  58739
31971 2024-03-13 21:00:00 475.35 475.51 474.46 474.46  52727
31972 2024-03-13 22:00:00 474.55 475.98 473.36 475.78 158985
31973 2024-03-13 23:00:00 475.77 475.77 475.61 475.61 409383

sayanroy058/TESLA-Stock-Price-Prediction-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset presents historical stock price information for Zomato, a leading online food delivery and restaurant aggregator company. The dataset is compiled with data collected over a specific time period, showcasing the fluctuation in Zomato's stock prices over days, weeks, or months, depending on the granularity of the dataset.

Key Features:

Date: The date of the recorded stock price. Open Price: The opening price of Zomato's stock on the given date. Close Price: The closing price of Zomato's stock on the given date. High Price: The highest price of Zomato's stock reached during the trading day. Low Price: The lowest price of Zomato's stock reached during the trading day. Volume: The total volume of Zomato's stock traded on the given date.

This dataset is valuable for analysts, researchers, and investors interested in studying the historical performance and trends of Zomato's stock in the financial markets. It can be utilized for various purposes such as technical analysis, trend forecasting, and quantitative modeling to make informed decisions related to investments or understanding market dynamics.

The Yahoo finance stock price prediction dataset contains various financial metrics.

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.

Can we predict stock market using machine learning? (FZO 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

AI-powered price forecasts for PLRZ stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

This Dataset contains the Stock prices of TESLA Company the opening price, closing price, low price etc.. Stock Details of the Year 29/09/2021 to 29/09/2022.

Use these Data and Predict the Stock Prices for upcoming years.

This dataset contains the predicted prices of the asset Stock 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.

AI-powered price forecasts for ASST stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

AI-powered price forecasts for FRNT.V stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

US stock prices listed on Standard & Poor’s 500 (S&P 500) Stock Index and UK stock prices from the London Stock Exchange (LSE)

This dataset contains several daily features of NASDAQ Composite, Dow Jones Industrial Average, and NYSE Composite from 2010 to 2024. It covers features from various categories of technical indicators, futures contracts, price of commodities, important indices of markets around the world, price of major companies in the U.S. market, and treasury bill rates. Sources and thorough description of features have been mentioned in the paper of "CNNpred: CNN-based stock market prediction using a diverse set of variables" published at Expert Systems with Applications. This dataset has been used in "SAMBA: A Graph-Mamba Approach for Stock Price Prediction" published at ICASSP 2025. Link to Code: https://github.com/Ali-Meh619/SAMBA

Results of ANOVA analysis of the difference in accuracy between stock price predictions using image characteristics.

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.

NSE GABRIEL Target Price 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

AI-powered price forecasts for TE stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

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

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.

The Dow Jones U.S. Completion Total Stock Market Index

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

AI-powered price forecasts for GLD stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

AI-powered price forecasts for ET-PE stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.