Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.
--- Dataset description provided by original source is as follows ---
Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.
There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.
Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.
A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.
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https://www.visualcapitalist.com/wp-content/uploads/2020/03/mm3_black_swan_events_shareable.jpg">
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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.
Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.
The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)
Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.
Mining and updating of this dateset will depend upon Yahoo Finance .
Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
CRB Index rose to 368.08 Index Points on June 27, 2025, up 0.52% from the previous day. Over the past month, CRB Index's price has risen 2.57%, and is up 8.00% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on July of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains the essential files for conducting a dynamic stock market analysis using Power BI. The data is sourced from Yahoo Finance and includes historical stock prices, which can be dynamically updated by adding new stock codes to the provided Excel sheet.
Files Included: Power BI Report (.pbix): The interactive Power BI report that includes various visualizations such as Candle Charts, Line Charts for Support and Resistance, and Technical Indicators like SMA, EMA, Bollinger Bands, and RSI. The report is designed to provide a comprehensive analysis of stock performance over time.
Stock Data Excel Sheet (.xlsx): This Excel sheet is connected to the Power BI report and allows for dynamic data loading. By adding new stock codes to this sheet, the Power BI report automatically refreshes to include the new data, enabling continuous updates without manual intervention.
Overview and Chart Pages Snapshots for better understanding about the Report.
Key Features: Dynamic Data Loading: Easily update the dataset by adding new stock codes to the Excel sheet. The Power BI report will automatically pull the corresponding data from Yahoo Finance. Comprehensive Visualizations: Analyze stock trends using Candle Charts, identify key price levels with Support and Resistance lines, and explore market behavior through various technical indicators. Interactive Analysis: The Power BI report includes slicers and navigation buttons to switch between different time periods and visualizations, providing a tailored analysis experience. Use Cases: Ideal for financial analysts, traders, or anyone interested in conducting a detailed stock market analysis. Can be used to monitor the performance of individual stocks or compare trends across multiple stocks over time. Tags: Stock Market Power BI Financial Analysis Yahoo Finance Data Visualization
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Cobalt traded flat at 33,335 USD/T on June 26, 2025. Over the past month, Cobalt's price has fallen 1.08%, but it is still 22.78% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Cobalt - values, historical data, forecasts and news - updated on July of 2025.
https://www.gnu.org/licenses/gpl-3.0.htmlhttps://www.gnu.org/licenses/gpl-3.0.html
This dataset consists of five CSV files that provide detailed data on a stock portfolio and related market performance over the last 5 years. It includes portfolio positions, stock prices, and major U.S. market indices (NASDAQ, S&P 500, and Dow Jones). The data is essential for conducting portfolio analysis, financial modeling, and performance tracking.
This file contains the portfolio composition with details about individual stock positions, including the quantity of shares, sector, and their respective weights in the portfolio. The data also includes the stock's closing price.
Ticker
: The stock symbol (e.g., AAPL, TSLA) Quantity
: The number of shares in the portfolio Sector
: The sector the stock belongs to (e.g., Technology, Healthcare) Close
: The closing price of the stock Weight
: The weight of the stock in the portfolio (as a percentage of total portfolio)This file contains historical pricing data for the stocks in the portfolio. It includes daily open, high, low, close prices, adjusted close prices, returns, and volume of traded stocks.
Date
: The date of the data point Ticker
: The stock symbol Open
: The opening price of the stock on that day High
: The highest price reached on that day Low
: The lowest price reached on that day Close
: The closing price of the stock Adjusted
: The adjusted closing price after stock splits and dividends Returns
: Daily percentage return based on close prices Volume
: The volume of shares traded that dayThis file contains historical pricing data for the NASDAQ Composite index, providing similar data as in the Portfolio Prices file, but for the NASDAQ market index.
Date
: The date of the data point Ticker
: The stock symbol (for NASDAQ index, this will be "IXIC") Open
: The opening price of the index High
: The highest value reached on that day Low
: The lowest value reached on that day Close
: The closing value of the index Adjusted
: The adjusted closing value after any corporate actions Returns
: Daily percentage return based on close values Volume
: The volume of shares tradedThis file contains similar historical pricing data, but for the S&P 500 index, providing insights into the performance of the top 500 U.S. companies.
Date
: The date of the data point Ticker
: The stock symbol (for S&P 500 index, this will be "SPX") Open
: The opening price of the index High
: The highest value reached on that day Low
: The lowest value reached on that day Close
: The closing value of the index Adjusted
: The adjusted closing value after any corporate actions Returns
: Daily percentage return based on close values Volume
: The volume of shares tradedThis file contains similar historical pricing data for the Dow Jones Industrial Average, providing insights into one of the most widely followed stock market indices in the world.
Date
: The date of the data point Ticker
: The stock symbol (for Dow Jones index, this will be "DJI") Open
: The opening price of the index High
: The highest value reached on that day Low
: The lowest value reached on that day Close
: The closing value of the index Adjusted
: The adjusted closing value after any corporate actions Returns
: Daily percentage return based on close values Volume
: The volume of shares tradedThis data is received using a custom framework that fetches real-time and historical stock data from Yahoo Finance. It provides the portfolio’s data based on user-specific stock holdings and performance, allowing for personalized analysis. The personal framework ensures the portfolio data is automatically retrieved and updated with the latest stock prices, returns, and performance metrics.
This part of the dataset would typically involve data specific to a particular user’s stock positions, weights, and performance, which can be integrated with the other files for portfolio performance analysis.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Full historical data for the S&P 500 (ticker ^GSPC), sourced from Yahoo Finance (https://finance.yahoo.com/).
Including Open, High, Low and Close prices in USD + daily volumes.
Info about S&P 500: https://en.wikipedia.org/wiki/S%26P_500
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Coffee fell to 307.33 USd/Lbs on June 27, 2025, down 0.20% from the previous day. Over the past month, Coffee's price has fallen 12.58%, but it is still 35.10% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on June of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Urals Oil fell to 63.77 USD/Bbl on June 26, 2025, down 0.76% from the previous day. Over the past month, Urals Oil's price has risen 10.62%, but it is still 20.34% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Urals Crude.
https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/
This report analyses movements in the Chicago Board Options Exchange (CBOE) Volatility Index. Known by its ticker symbol VIX, the CBOE Volatility Index is a real-time market index that indicates the stock market's expectation of volatility and is derived from the price inputs of the S&P 500 Index options - the S&P 500 is a US stock market index based on the market capitalisation of 500 large companies having common stock listed on the New York Stock Exchange (NYSE), the Nasdaq Stock Market (NASDAQ), or the Cboe BZX Exchange. Effectively, the VIX measures the degree of variation in S&P 500 stocks' trading price observed over a period of time. The data is sourced from Yahoo Finance, which ultimately derives from the CBOE, in addition to estimates by IBISWorld. The figures represent the average daily unadjusted close value of the index over the UK financial year (i.e. April through March).
https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/
This report analyses the All Ordinaries index. The All Ordinaries index is a share price index, which comprises the 500 largest companies listed on the Australian Securities Exchange. Companies are ranked by market capitalisation, which is the only requirement for inclusion in the index. The All Ordinaries is a non-float adjusted, market capitalisation weighted, price index. The data for this report is sourced from Yahoo Finance and is represented by an average of the daily index points at close over each financial year.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This Dataset is being collected Two Sources 1. Yahoo Finance 2. Alternative.me
This dataset specifically includes daily closing prices of Bitcoin, as well as daily volumes of Bitcoin, and the Fear and Greed Index values for the overall crypto market. This dataset presents a unique opportunity for researchers and analysts to explore the relationship between the prices and volumes of Bitcoin, as well as the sentiment of the overall crypto market. By conducting thorough analysis of this dataset, researchers and analysts can gain valuable insights into the behavior and trends of the cryptocurrency market. This includes examining the daily closing prices and volumes of Bitcoin, as well as the Fear and Greed Index values for the overall crypto market. Through comprehensive analysis, potential patterns, trends, and correlations between price movements, trading volumes, and market sentiment can be identified. These insights can inform investment strategies and decision-making, providing a more nuanced understanding of the dynamics of the cryptocurrency market. This data presents a unique opportunity for researchers and analysts to uncover valuable information that can contribute to a deeper understanding of the cryptocurrency market and its potential implications for investment decision-making.
The data collection strategy for this dataset involves gathering daily market closing prices and volume data of Bitcoin and collection daily crypto market fear and greed index.
To understand the methodology behind measuring the Fear and Greed Index, please refer to the official link at https://alternative.me/crypto/fear-and-greed-index/
A part of this dataset is produced and maintained by the administrators of https://alternative.me/crypto/fear-and-greed-index/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Lithium rose to 61,300 CNY/T on June 30, 2025, up 0.25% from the previous day. Over the past month, Lithium's price has risen 1.66%, but it is still 33.01% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lithium - values, historical data, forecasts and news - updated on June of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Baltic Dry fell to 1,489 Index Points on June 30, 2025, down 2.10% from the previous day. Over the past month, Baltic Dry's price has risen 4.71%, but it is still 31.00% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on July of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Nigeria's main stock market index, the NSE-All Share, fell to 119979 points on June 30, 2025, losing 0.01% from the previous session. Over the past month, the index has climbed 7.10% and is up 19.95% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Nigeria. Nigeria Stock Market NSE - values, historical data, forecasts and news - updated on July of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Morocco's main stock market index, the CFG 25, rose to 18475 points on June 26, 2025, gaining 0.38% from the previous session. Over the past month, the index has climbed 2.35% and is up 38.72% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Morocco. Morocco Stock Market MASI - values, historical data, forecasts and news - updated on June of 2025.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.
--- Dataset description provided by original source is as follows ---
Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.
There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.
Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.
A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.
#
#
https://www.visualcapitalist.com/wp-content/uploads/2020/03/mm3_black_swan_events_shareable.jpg">
#
#
New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.
Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.
The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)
Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.
Mining and updating of this dateset will depend upon Yahoo Finance .
Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting
--- Original source retains full ownership of the source dataset ---