Sugar rose to 16.56 USd/Lbs on July 11, 2025, up 1.83% from the previous day. Over the past month, Sugar's price has risen 1.84%, but it is still 13.76% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Sugar - values, historical data, forecasts and news - updated on July of 2025.

Sugar Price Index in World decreased to 103.70 Index Points in June from 109.40 Index Points in May of 2025. This dataset includes a chart with historical data for World Sugar Price Index.

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Raw Sugar Market Size 2025-2029

The raw sugar market size is forecast to increase by USD 152.7 million, at a CAGR of 2.3% between 2024 and 2029.

The market is witnessing significant growth, driven primarily by the increasing demand for raw sugar in various food and beverage applications. This trend is being fueled by the expanding food industry, particularly in emerging economies, where sugar consumption is on the rise. Additionally, the emergence of e-commerce platforms has facilitated easier access to raw sugar for consumers and manufacturers, further boosting market growth. However, the high production cost of raw sugar poses a significant challenge for market participants. Producers must navigate this obstacle through efficient production methods, cost optimization, and strategic pricing to remain competitive in the market.
Companies seeking to capitalize on market opportunities and navigate challenges effectively should focus on innovation, cost reduction, and supply chain optimization. By staying agile and responsive to market trends, they can position themselves for long-term success in the dynamic the market.

What will be the Size of the Raw Sugar Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with various factors shaping its dynamics. Sugarcane and sugar beet supply and demand, production costs, and sustainability are key elements influencing market activities. Biofuel production from sugarcane bagasse and sugar beet residues adds complexity to the market. Sugarcane diseases and pests, as well as transportation challenges, can impact yields and prices. Sugarcane consumption is driven by various applications, including food and beverage industries, ethanol production, and pharmaceuticals. Organic sugar and fair trade sugar are gaining popularity, adding to the market's diversity. Sugarcane juice and molasses are used to produce syrups and other value-added products.

Sugarcane syrup and turbinado sugar cater to specific market segments. Sugarcane cultivation and harvesting techniques, as well as irrigation and fertilizer usage, influence production costs and quality. Sugarcane and sugar beet prices fluctuate based on supply and demand, with imports and exports playing a role in market equilibrium. Traceability and sustainability concerns are increasingly important, influencing consumer preferences and regulations. Sugarcane and sugar beet varieties, processing methods, and storage techniques also impact market trends. Overall, the market remains dynamic, with ongoing shifts in production, consumption, and market conditions.

How is this Raw Sugar Industry segmented?

The raw sugar industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Liquid sugar
  Crystallized sugar


Type

  Conventional
  Organic


Base

  Sugarcane-based
  Beet-based


Application

  Food & Beverage Industry
  Biofuel Production
  Pharmaceuticals
  Animal Feed
  Chemicals


End-use Industry

  Food Processing
  Beverage Production
  Ethanol Production
  Pharmaceutical & Personal Care
  Chemical Manufacturing


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Product Insights

The liquid sugar segment is estimated to witness significant growth during the forecast period.

Liquid sugar, derived from raw sugar through the addition of water, is a popular choice among manufacturers due to its convenience and versatility. The sweetener's ability to dissolve quickly and evenly makes it an ideal ingredient for large-scale production of beverages, including carbonated soft drinks, sports drinks, and juices. Additionally, it is widely used in the baking industry for creating cakes, cookies, and pastries. The consistency and stability of liquid sugar enable manufacturers to control the texture and flavor of their products effectively. The sugar beet industry and sugarcane industry serve as the primary sources for raw sugar production.

Sugarcane cultivation, which includes irrigation, fertilization, and pest management, incurs significant production costs. Sugarcane diseases and pests pose challenges to the industry, affecting both yield and quality. Sugarcane bagasse and molasses are by-products used in biofuel production and ethanol manufacturing. Sugar beet cultivation, on the other hand, is practiced in regions with cooler climates. Sugarcane and su

📈 Daily Historical Stock Price Data for Dhampur Sugar Mills Limited (2002–2025)

A clean, ready-to-use dataset containing daily stock prices for Dhampur Sugar Mills Limited from 2002-07-01 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Dhampur Sugar Mills Limited Ticker Symbol: DHAMPURSUG.NS Date Range: 2002-07-01 to 2025-05-28 Frequency: Daily Total Records:… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-dhampur-sugar-mills-limited-20022025.

This statistic depicts the average annual prices for sugar from 2014 through 2026*. In 2023, the average price for sugar stood at 0.46 nominal U.S. dollars per kilogram.

This year, world sugar production is expected to rise by 3% to 193M tons. The growth will be encouraged by favorable weather conditions in most of the largest producing countries, as well as the providing exemptions on the neonicotinoid usage against plant diseases and blights in France, Germany and the UK. The drop in sugar production and shipments from Brazil will be offset by large volumes coming from Thailand, which will boost global exports by +2.6% y-o-y. The increased supply in the world market is to keep sugar prices relatively stable over the next two years.

From January to August 2021, Brazil’s exported 23.7M tons, which was 26% larger than in the same period of 2020. This year, China’s sugar purchases from Brazil have doubled, reaching 4M tons. Shipments to Algeria, Nigeria, Saudi Arabia, Malaysia, Canada, and the United Arab Emirates have also grown sharply. Last year, sugar exports from Brazil hit record 27M tons, jumping by +67% y-o-y. In value terms, exports constituted $7.4B. China, Algeria and Bangladesh were the largest importers of Brazilian sugar in 2020.

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.

Sugar Futures Signal Potential Price Volatility for CRB Commodities 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

After two years of decline, the Nepalese sugar market increased by 2.1% to $97M in 2024. In general, consumption posted a modest increase. Sugar consumption peaked at $190M in 2018; however, from 2019 to 2024, consumption remained at a lower figure.

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.

Is the Sugar Index a Sweet Spot for Investors?

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

Raizen stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

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.

Is the Sugar Index a Reliable Indicator of TR/CC CRB Performance?

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

In 2024, the Asian sugar market increased by 12% to $54B, rising for the fourth consecutive year after three years of decline. Over the period under review, consumption recorded a relatively flat trend pattern. Over the period under review, the market hit record highs in 2024 and is expected to retain growth in years to come.

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.

Sugar Index Poised for Volatility, Say Analysts

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 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.

Will the Sugar Index Sweeten Your Portfolio?

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 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.

DJ Commodity Sugar index projects moderate gains.

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 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.

TR/CC CRB Sugar index forecast: Upward trend anticipated.

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

The SADC sugar market stood at $3.7B in 2024, surging by 4.2% against the previous year. The market value increased at an average annual rate of +1.8% from 2012 to 2024; the trend pattern indicated some noticeable fluctuations being recorded in certain years. The level of consumption peaked at $3.9B in 2013; however, from 2014 to 2024, consumption stood at a somewhat lower figure.

Overview
This study uses a case study approach to demonstrate the potential to estimate farm shares and price spreads in Australia using a relatively simple methodology developed by the United States Department of Agriculture Economic Research Service. In this instance, the methodology has been applied to Australian sugar price data.

Key Points
• The study demonstrates that data is available that allows an analysis of farm share and price spread for raw sugar exports and refined sugar sold at retail outlets. • The analysis shows that trends in farm shares of retail and export prices were relatively flat between 1984-85 and 2014-15. So too were trends in farm-to-retail and farm-to-export price spreads. • If it is assumed that the emergence of market power beyond the farm gate is likely to be reflected in changes in trends in farm share and price spread, then these results suggest that there has been no obvious change in market power within the sugar industry over this period.

According to Cognitive Market Research, the global Sugar Confectionery market size will be USD 1954.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 4.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 781.6 million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 586.26 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 449.4 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 97.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 39.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.9% from 2024 to 2031.
The Hard-boiled Sweets Product Type category is the fastest growing segment of the Sugar Confectionery industry

Market Dynamics of Sugar Confectionery Market

Key Drivers for Sugar Confectionery Market

Urbanization and Lifestyle Changes to Boost Market Growth

The rapid urbanization, particularly in developing economies, has significantly fueled the demand for sugar confectionery. By 2022, the global urban population was estimated to have reached 56.9%. This figure is generally higher in developed regions, where 79.7% of the population resided in urban areas in 2022, compared to 52.3% in developing countries. In Least Developed Countries (LDCs), only 35.8% of people live in urban areas, making urban dwellers a minority. Over the past decade, urbanization has been especially pronounced in developing economies, with Asia and Oceania experiencing an increase from 44.0% in 2012 to 50.6% in 2022, while Africa saw a 4.6 percentage point rise during the same period. Today, around 56% of the world’s population, approximately 4.4 billion people, live in cities. This trend is expected to continue, with the urban population projected to more than double by 2050, at which point nearly 70% of people will reside in cities. Urban dwellers often lead busier lifestyles, increasing the demand for convenient, on-the-go snacks like candies and chewing gums, which drives further growth in the sugar confectionery market.

Snacking as a New Eating Trend to Drive Market Growth

Snacking between meals is increasingly becoming a global trend, particularly among younger consumers. Snacking continues to be a daily habit for many, with 71% of consumers globally snacking at least twice a day. Those under the age of 35 are significantly more likely to snack throughout the day compared to individuals aged 65 and older. Younger consumers are over twice as likely to snack in the late morning or early afternoon, and six times more likely in the early morning, and ten times more likely late at night. Notably, 37% of them snack after 11 p.m. Sugar confectionery items like hard candies, jelly sweets, and gummies are popular choices due to their variety and convenience, further driving demand as preferred snack options.

Restraint Factor for the Sugar Confectionery Market

Volatility in Raw Material Prices Will Limit Market Growth

The sugar confectionery market is highly dependent on the cost and availability of raw materials, particularly sugar. Sugar prices are volatile and can fluctuate due to several factors such as changing weather conditions, crop failures, trade restrictions, and changes in demand from other industries (e.g., biofuel production). High sugar prices increase the production costs for confectionery manufacturers, which can lead to higher retail prices and reduced demand from cost-conscious consumers. Apart from sugar, other ingredients like cocoa, dairy, and various flavoring agents are essential for producing confectionery products. The prices of these commodities can also be volatile due to supply chain disruptions, climate change, and geopolitical factors, further impacting profit margins for manufacturers. This challenge is particularly significant for premium confectionery brands that rely on high-quality raw materials.

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