2 datasets found
  1. Cocoa Futures Show Bullish Trend, DJ Commodity Cocoa Index Forecasts Rise...

    • kappasignal.com
    Updated Apr 24, 2025
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    KappaSignal (2025). Cocoa Futures Show Bullish Trend, DJ Commodity Cocoa Index Forecasts Rise (Forecast) [Dataset]. https://www.kappasignal.com/2025/04/cocoa-futures-show-bullish-trend-dj.html
    Explore at:
    Dataset updated
    Apr 24, 2025
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Cocoa Futures Show Bullish Trend, DJ Commodity Cocoa Index Forecasts Rise

    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

  2. Monthly cocoa price worldwide 2016-2025

    • statista.com
    Updated Aug 26, 2025
    + more versions
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    Statista (2025). Monthly cocoa price worldwide 2016-2025 [Dataset]. https://www.statista.com/statistics/498496/global-cocoa-price/
    Explore at:
    Dataset updated
    Aug 26, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2016 - Apr 2025
    Area covered
    Worldwide
    Description

    Between 2016 and 2025, the monthly price of cocoa worldwide peaked in January 2025 at about ****** U.S. dollars per metric ton. Global cocoa industry Global cocoa production is expected to reach about *** million tons in the 2023/2024 crop year. Most of the world’s cocoa beans are grown in Africa; in 2023/2024, about *** million tons of cocoa beans were produced there, while about *** million tons were grown in the Americas. Within Africa, Côte d'Ivoire and Ghana were the countries with the highest production of cocoa beans. Chocolate retail worldwide In 2016, Mars controlled a **** percent share of the chocolate market worldwide, making it the single biggest player in the chocolate industry. Mars owns such chocolate brands as M&Ms, Snickers, and Dove. The countries with the highest per capita chocolate consumption are Switzerland, Austria, and Germany.

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Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
KappaSignal (2025). Cocoa Futures Show Bullish Trend, DJ Commodity Cocoa Index Forecasts Rise (Forecast) [Dataset]. https://www.kappasignal.com/2025/04/cocoa-futures-show-bullish-trend-dj.html
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Cocoa Futures Show Bullish Trend, DJ Commodity Cocoa Index Forecasts Rise (Forecast)

Explore at:
Dataset updated
Apr 24, 2025
Dataset authored and provided by
KappaSignal
License

https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

Description

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.

Cocoa Futures Show Bullish Trend, DJ Commodity Cocoa Index Forecasts Rise

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

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