Dark Mode
Daily Soft Commodity Price Index Data
Data Science and Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Provides an extensive and current assortment of futures related to crucial soft commodities. Futures are financial contracts that legally obligate a buyer to purchase, and a seller to sell, a specified amount of a particular commodity at a predetermined price on a set date in the future. The data focuses on key agricultural products including Cocoa, Coffee, Cotton, Lumber, Orange Juice, and Sugar futures, sourced from Yahoo Finance. Users should be aware of the importance of ethical sourcing and consumption, particularly concerning commodities like cocoa and coffee, which have known ethical concerns in their supply chains.
Columns
The dataset contains 8 distinct columns:
- Date: The date when the market data was recorded, following the YYYY-MM-DD format.
- Open: The opening market price for the day’s trading session.
- High: The maximum price achieved during the trading session.
- Low: The lowest traded price recorded during the session.
- Close: The market's concluding price at the end of the session.
- Volume: The total count of contracts traded throughout the session.
- Ticker: The distinct market quotation symbol used for the specific commodity future (e.g., CC=F, CT=F).
- Commodity: Specifies the type of soft agricultural product the futures contract represents (e.g., Cocoa, Coffee).
Distribution
The data is provided in a CSV file format, named all_agricultural_products_data.csv, with a file size of 2.65 MB. The structure includes 8 columns and holds approximately 30.9 thousand valid records. The expected update frequency for this dataset is daily, ensuring continuous market relevance.
Usage
Ideal applications and use cases for this financial dataset include:
- Price Forecasting: Leveraging machine learning models to predict the future price dynamics of specific commodities like cocoa and coffee, assisting financial stakeholders in strategic decision-making.
- Supply Chain Analysis: Evaluating the correlation between futures prices and major global economic or political events, offering predictive insights into potential supply chain disruptions.
- Demand Projections: Utilising deep learning techniques to correlate historical consumption patterns with price movements to project future market demand.
Coverage
The dataset covers a time range extending over two decades, beginning on 3 January 2000 and concluding on 24 June 2024. The data reflects global financial market activity related to the futures contracts of the six specified soft commodities.
License
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Who Can Use It
This data product is highly suitable for:
- Financial Analysts and Economists: For market research, trend analysis, and economic modelling related to agricultural markets.
- Machine Learning Engineers: For developing and training advanced predictive models focused on price volatility and time-series forecasting.
- Academic Researchers: For studying the relationship between soft commodity prices, global events, and supply chain fragility.
Dataset Name Suggestions
- Global Soft Commodity Futures History
- Agricultural Market Dynamics (2000–2024)
- Daily Soft Commodity Price Index Data
Attributes
Original Data Source: Daily Soft Commodity Price Index Data
QUALITYLoading...