Context

Extracted data from Investing .com by building a web scraper which could extract data if Start and End Date are given as parameters. You could find the scaper code here. Copper ,Gold, Crude Oil , Brent Oil , Natural Gas, Silver Prices variation of each of the commodities from the last 10 Years .

Copper

Copper prices are up 20% year to date, supported in part by a rebounding economy in the U.S. and other parts of the world as the pandemic comes under control. Improving economies are key to copper demand since it's an industrial metal that's a good conductor of electricity. Copper is found in a host of items from air conditioning units and televisions to cars. The base metal may also benefit from President Joe Biden's infrastructure plan and the growing appetite for electric vehicles. That's spurred investor interest in the red metal, but buyers need to do plenty of research. Commodities are unlike traditional stock and bond investing, as these markets have different fundamental drivers that affect pricing.

Gold

Gold has traditionally been regarded as a superior investment asset. It has become a safe haven for investors all around the world in recent years. Gold, in particular, possesses all of the characteristics that a traditional investor seeks in an asset class. Investing in gold has always shown to be a successful approach to combat inflation.

Crude Oil

Crude oil is at the heart of many global industries. It is the power that moves most vehicles, allows factories to operate and is used to generate electricity. Oil’s importance to mankind has made it a valuable commodity for many companies and countries. Along with its derivatives, crude oil is the most traded commodity in the world.

Brent Oil

Brent oil is a major benchmark price for purchases of oil worldwide. While Brent Crude oil is sourced from the North Sea the oil production coming from Europe, Africa and the Middle East flowing West tends to be priced relative to this oil. The Brent prices displayed in Trading Economics are based on over-the-counter (OTC) and contract for difference (CFD) financial instruments. Our market prices are intended to provide you with a reference only, rather than as a basis for making trading decisions. Trading Economics does not verify any data and disclaims any obligation to do so.

Natural Gas

The US Energy Information Administration says natural gas is the most widely used fuel for space heating in the US, and it has also started to beat out coal as the top fuel for power generation. Even so, demand for natural gas around the world can be volatile as it is very much dependent on the weather.

Silver

Silver may be used as an investment like other precious metals. It has been regarded as a form of money and store of value for more than 4,000 years, although it lost its role as legal tender in developed countries when the use of the silver standard came to a final end in 1935.

Acknowledgements

For this dataset I depended upon Investing.com to scrape the data . It's the premier source for financial, economic, and alternative datasets, serving investment professionals. Investing’s platform is used by over 400,000 people, including analysts from the world’s top hedge funds, asset managers and investment banks.

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.

Commodity Index: The Future of Investing?

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 dataset contains detailed records of India’s principal commodity-wise exports to various countries from 2017–18 to 2022–23 (skipping 2020–21 due to COVID-related data gaps). The data is structured annually and provides insights into:

Exported commodity names Destination countries Export quantity Export value (in USD million) Measurement units

These records are sourced from https://www.data.gov.in/ and are valuable for analysts, researchers, and policymakers studying international trade trends, market demand, and sector-wise export performance.

📁 Files Included File Name Year Format Principal_Commodity_wise_export_201718.csv 2017–18 CSV Principal_Commodity_wise_export_201819.csv 2018–19 CSV Principal_Commodity_wise_export_201920.csv 2019–20 CSV Principal_Commodity_wise_export_202122.csv 2021–22 CSV Principal_Commodity_wise_export_202223.csv 2022–23 CSV

📌 Columns Each file includes the following columns: COMMODITY – Name of the exported product COUNTRY – Country to which the product was exported UNIT – Unit of measurement (e.g., KGS, NOS, LITRES) QUANTITY – Total exported quantity VALUE (US$ Million) – Export value in USD millions

💡 Use Cases Time-series analysis of export performance Identifying high-value export commodities Understanding trade relationships with countries Policy and strategy development for boosting exports Comparative analysis across years and regions

📊 Sample Questions You Can Explore What are the top 10 exported commodities from India over the last 5 years? How has India's export value to the USA evolved since 2017? Are there any emerging markets for Indian goods? What commodities saw a decline in exports after COVID-19?

Invest wisely in the booming Commodity Index Funds market! Discover market trends, leading players (BlackRock, Invesco, iShares), and regional insights in our comprehensive analysis. Projected to reach $1 trillion by 2033, explore the potential of this lucrative sector.

A survey on Indonesians and their opinion on investing in cryptocurrencies found that **** percent of respondents were strongly agree that investing in crypto assets was better than investing in commodities. In comparison, *** percent of respondents stated that they were strongly disagree with that.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

Invest smarter with commodity index funds! Discover the booming $500 billion market, projected to reach $1.6 trillion by 2033. Learn about market drivers, trends, and top players like BlackRock & Invesco. Explore diverse segments including precious metals, energy, and agriculture. Diversify your portfolio and hedge against inflation.

Content

Gold Futures Historical Data Periode data : Nov 01, 2017 - Oct 16,2020 Source :https://www.investing.com/commodities/gold-historical-data

Disclaimer: According to Fusion Media (https://www.investing.com/commodities/gold-historical-data) that the data contained in their website is not necessarily real-time nor accurate. All CFDs (stocks, indexes, futures), cryptocurrencies, and Forex prices are not provided by exchanges but rather by market makers, and so prices may not be accurate and may differ from the actual market price, meaning prices are indicative and not appropriate for trading purposes.

Explore the opportunities and challenges of investing in corn prices, influenced by supply-demand dynamics, agricultural reports, and global economic factors. Understand how futures, ETFs, and stocks offer exposure in the agricultural commodities market.

Data available from 01-01-2020 to 31-12-2024

Organisation: Agricultural Marketing and Co-operation Department

This dataset contains information on the daily prices of all the commodities across all the market yards in the state of Telangana

Contains the below columns: | DDate | AmcCode | AmcName | YardCode | YardName | CommCode | CommName | VarityCode | VarityName | Arrivals | Minimum | Maximum | Modal |

Abbreviation:

DDate: Date AmcCode: Agricultural Market Committee Code AmcName: Agricultural Market Committee Name YardCode: Agricultural Market Yard Code YardName: Agricultural Market Yard Name CommCode: Commodity Code CommName: Commodity Name VarityCode: Commodity Variety Code VarityName: Commodity Variety Name Arrivals: Quantity in Quintals (Qtls) Minimum: Minimum Price per Quintal Maximum: Maximum Price per Quintal Model: Model Price is the price per quintal at which most transactions took place

Taken from

https://data.telangana.gov.in/dataset/daily-prices-market-yard-commodities-telangana

Note: All copyrights belongs to original owner data is extracted for educational purpose only

Other-Cashflows-From-Investing-Activities Time Series for Nanjing Red Sun Co Ltd. Nanjing Red Sun Co.,Ltd. engages in the research, manufacture, and sale of pesticides and other chemicals in China. The company offers herbicides, insecticides, fungicides, disinfectants, and three-drug intermediates, as well as fine chemicals. It also provides technical consultation and investment management and consultation services, as well as self-operated and agent Import and export business of commodities and technologies. Nanjing Red Sun Co.,Ltd. was founded in 1991 and is headquartered in Nanjing, China.

Dataset

This dataset was created by Murilo Faix Gonçalves

Contents

This dataset contains Historical Data extracted from Investing.com. It contains trading days for Brazilian Corn Futures traded in B3, the Brazilian Exchange, from January 2nd, 2014 to March 3rd, 2023. I collected the data and did some basic preprocessing, renaming some columns, dropping symbols, and converting data types to numerical values.

You may find some null values that I decided to keep, so users would decide by themselves how to handle missing data (removal, median imputation, mean imputation, etc.)

Brazilian Live Corn Future contracts are bimonthly. The switch to the next contract always happen at the 15th day of the month of the switching or the next trading day, in case there's no trading session at the 15th day.

Code and month for each contract are as below:

F: January H: March K: May N: July U: September X: November

Tick: CCM Tick size: BRL0.01

In case you have any doubt, feel free to leave a question.

This Dataset contains Historical Price of Gold in Indian Commodity Market . The data has been collected from https://in.investing.com/commodities/ using web scrapping . The script can be customized to suit the needs (like customizing frequency interval , commodity type etc ) Link to web scrapping script - https://github.com/Pritam3355/web_scrapping/blob/master/stock_price.py

Column contains - Date, Price ,Open , High ,Low ,Volume ,Chg% these columns can be sorted first in the website then use the url in script to download the data according to your need