Gold rose to 3,354.76 USD/t.oz on July 11, 2025, up 0.92% from the previous day. Over the past month, Gold's price has fallen 0.92%, but it is still 39.14% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on July of 2025.

Natural gas rose to 3.36 USD/MMBtu on July 11, 2025, up 0.58% from the previous day. Over the past month, Natural gas's price has fallen 3.89%, but it is still 44.10% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on July of 2025.

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.

Coinbase's Climb: Can it Maintain Momentum? (COIN)

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

United States SCE: Commodity Price Change Expectation: 1 Year Ahead: Food data was reported at 5.081 % in Apr 2025. This records a decrease from the previous number of 5.233 % for Mar 2025. United States SCE: Commodity Price Change Expectation: 1 Year Ahead: Food data is updated monthly, averaging 5.027 % from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 9.843 % in Mar 2022 and a record low of 3.766 % in Nov 2024. United States SCE: Commodity Price Change Expectation: 1 Year Ahead: Food data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.H080: Survey of Consumer Expectations: Commodity Price.

Analysis of ‘IMF Zinc Price Forecast Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yasserh/imf-zinc-price-forecast-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Description:

A simple yet challenging project, to forecast the IMF commodity price of Zinc, based on monthly totals zinc price recorded from 1980 to 2016. Can you overcome these obstacles & Forecast its future prices?

This data frame contains the following columns:

• Month : The month of observation
• Price : Average Prices of zinc in that particular month

Acknowledgement

This dataset is referred from Kaggle.

Objectives:

• Understand the Dataset & cleanup (if required).
• Perform the necessary checks like stationarity & DF on the Dataset.
• Build a forcasting model to forecast zinc prices.

--- Original source retains full ownership of the source dataset ---

Power Price Forecasting Solution Market Outlook

According to our latest research, the global Power Price Forecasting Solution market size reached USD 1.58 billion in 2024, and it is expected to grow at a robust CAGR of 13.2% over the forecast period, reaching USD 4.16 billion by 2033. This remarkable growth is primarily driven by the increasing integration of renewable energy sources, the growing complexity of electricity grids, and the rising demand for efficient energy trading and risk management solutions. The market’s expansion is further propelled by the need for accurate, real-time forecasting to support grid stability, optimize energy procurement, and enhance profitability for stakeholders across the energy value chain.

One of the key growth factors for the Power Price Forecasting Solution market is the rapid digital transformation sweeping across the global energy sector. Utilities and independent power producers are increasingly deploying advanced analytics, artificial intelligence, and machine learning technologies to predict price fluctuations with greater accuracy. The proliferation of distributed energy resources such as solar, wind, and battery storage has introduced unprecedented volatility and complexity into power markets, making traditional forecasting methods inadequate. As a result, there is a surge in demand for sophisticated forecasting platforms capable of processing massive datasets and delivering actionable insights in real time. This trend is expected to accelerate as more countries pursue aggressive decarbonization targets and grid modernization initiatives.

Another significant driver is the evolving regulatory landscape and market liberalization efforts in many regions. Deregulation of electricity markets, particularly in Europe and North America, has created a highly competitive environment where accurate price forecasting is crucial for market participants to maximize returns and minimize risks. In addition, the growing participation of energy traders, financial institutions, and industrial consumers in wholesale electricity markets has heightened the need for precision forecasting tools. These stakeholders rely on advanced forecasting solutions to inform bidding strategies, hedge against price volatility, and comply with regulatory requirements. The increasing availability of high-frequency market data and advancements in computational capabilities are further enabling the development of more precise and adaptive forecasting models.

The Power Price Forecasting Solution market is also benefiting from increased investments in smart grid infrastructure and IoT-enabled devices. Utilities are leveraging these technologies to enhance grid visibility, improve demand-side management, and facilitate the integration of variable renewable energy sources. The convergence of big data analytics, cloud computing, and edge technologies is empowering market participants to make data-driven decisions and respond proactively to market dynamics. As energy systems become more decentralized and interconnected, the ability to forecast power prices accurately will become an essential competitive differentiator. This is fostering a vibrant ecosystem of solution providers, technology vendors, and system integrators, all vying for a share of the rapidly expanding market.

From a regional perspective, North America currently leads the global Power Price Forecasting Solution market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of these regions can be attributed to their mature electricity markets, high penetration of renewables, and strong regulatory support for market transparency and innovation. Meanwhile, Asia Pacific is poised for the fastest growth over the forecast period, driven by rapid urbanization, expanding grid infrastructure, and increasing investments in clean energy projects. Latin America and the Middle East & Africa are also witnessing rising adoption of forecasting solutions, albeit at a slower pace, as they modernize their energy sectors and embrace digital transformation.

Component

CRB Index fell to 373.31 Index Points on July 14, 2025, down 0.01% from the previous day. Over the past month, CRB Index's price has fallen 1.86%, but it is still 10.05% higher than a year ago, 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.

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.

The Dow Jones U.S. Completion Total Stock Market 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

Cocoa fell to 7,949.69 USD/T on July 3, 2025, down 2.54% from the previous day. Over the past month, Cocoa's price has fallen 18.92%, but it is still 1.88% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Cocoa - values, historical data, forecasts and news - updated on July of 2025.

As an essential part of daily life, the drastic fluctuations in agricultural commodity prices significantly impact producers’ motivation and consumers’ quality of life, further exacerbating market uncertainty and unsustainability. The ability to scientifically and effectively predict agricultural commodity prices is of great significance for the rational deployment of market mechanisms, the timely adjustment of supply chains, and the promotion of food policy adjustments. This paper proposes a sustainable hybrid model SV-PSO-BiLSTM which integrates Seasonal-Trend decomposition procedure based on Loess (STL), Variational Mode Decomposition (VMD), Particle Swarm Optimization (PSO), and Bidirectional Long Short-Term Memory (BiLSTM) neural networks. This innovative approach first performs seasonal decomposition of the original data using the STL method, then applies the VMD method for double decomposition of the residual components, reconstructs the data based on sample entropy, and finally predicts agricultural commodity market prices using the BiLSTM network model optimized by the PSO algorithm. This paper investigates the market price dynamics of four agricultural commodities (chili, garlic, ginger, and pork) and one agricultural financial derivative (soybean futures). The experimental results indicate that the proposed SV-PSO-BiLSTM hybrid model achieves average values of 0.2241 for root mean square error (RMSE), 0.1665 for mean absolute error (MAE), 0.0207 for mean absolute percentage error (MAPE), and 0.9851 for the coefficient of determination (R2). These results surpass those of other comparative models, demonstrating stronger generalization, reliability, and stability. The research findings can provide effective guidance for the reasonable regulation of agricultural commodity market prices and further promote the healthy and sustainable development of the agricultural commodity industry.

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 Natural Gas the Future?

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 global energy price index stood at around 101.5 in 2024. Energy prices were on a decreasing trend that year, and forecasts suggest the price index would decrease below 80 by 2026. Price indices show the development of prices for goods or services over time relative to a base year. Commodity prices may be dependent on various factors, from supply and demand to overall economic growth. Electricity prices around the world As with overall fuel prices, electricity costs for end users are dependent on power infrastructure, technology type, domestic production, and governmental levies and taxes. Generally, electricity prices are lower in countries with great coal and gas resources, as those have historically been the main sources for electricity generation. This is one of the reasons why electricity prices are lowest in resource-rich countries such as Iran, Qatar, and Russia. Meanwhile, many European governments that have introduced renewable surcharges to support the deployment of solar and wind power and are at the same time dependent on fossil fuel imports, have the highest household electricity prices. Benchmark oil prices One of the commodities found within the energy market is oil. Oil is the main raw material for all common motor fuels, from gasoline to kerosene. In resource-poor and remote regions such as the United States' states of Alaska and Hawaii, or the European country of Cyprus, it is also one of the largest sources for electricity generation. Benchmark oil prices such as Europe’s Brent, the U.S.' WTI, or the OPEC basket are often used as indicators for the overall energy price development.

In 2030, the price of lanthanum oxide is forecast to be 1,590 U.S. dollars per metric ton. There are 17 rare earth elements and although they may be fairly abundant in the Earth's crust, often they occur at sparse intervals are are less economically exploitable.

Coffee rose to 305.70 USd/Lbs on July 14, 2025, up 5.93% from the previous day. Over the past month, Coffee's price has fallen 11.39%, but it is still 26.62% 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 July of 2025.

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.

Putnam Premier Income (PPT): Is it Worth a Second Look?

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 Q1 2025, the North American carbon black market exhibited a largely stable price trend, with quotations fluctuating narrowly between USD 1980 to 2010 per MT FOB Texas during January and settling at around USD 2000/MT through February. The market was primarily shaped by an oversupply situation and a subdued demand outlook, prompting suppliers and traders to maintain steady prices while awaiting stronger buying signals. Despite this apparent stagnancy, cost-side pressures persisted throughout the quarter—driven notably by elevated natural gas and oil prices—which continued to influence the overall production cost structure.

The Nylon 6 market is projected to reach a market size of Billion USD by 2033, expanding at a CAGR of 27.25% over the forecast period (2025-2033). Key factors driving the growth of the market include the increasing demand for lightweight and durable materials in various end-use industries, such as automotive, electrical and electronics, and consumer goods. Additionally, the growing adoption of sustainable and eco-friendly materials is further fueling the demand for Nylon 6. The market is segmented based on market type into spot and futures, and by end-user into automotive, electrical and electronics, consumer goods, and industrial. The automotive segment is expected to account for the largest share of the market due to the increasing utilization of Nylon 6 in the production of automotive parts, such as airbags, seat belts, and interior components. Geographically, the market is segmented into North America, South America, Europe, Middle East & Africa, and Asia Pacific. Asia Pacific is projected to dominate the market due to the presence of a large automotive and electronics industry in the region. Recent developments include: , The Nylon 6 Quarterly Price Forecast Market is projected to grow significantly over the next decade, driven by rising demand from various end-use industries such as automotive, electrical and electronics, and consumer goods. The market is expected to witness a CAGR of around 27.25% from 2024 to 2032, reaching a valuation of USD 22.94 billion by 2032. This growth can be attributed to increasing urbanization, rising disposable income, and growing demand for lightweight and durable materials. Key market players are focusing on expanding their production capacities and investing in research and development to cater to the growing demand. Recent developments in the market include the launch of bio-based nylon 6 products and the adoption of sustainable production practices., Nylon 6 Quarterly Price Forecast Market Segmentation Insights, Nylon 6 Quarterly Price Forecast Market Market Type Outlook. Key drivers for this market are: Growing automotive industry Increasing demand from electronics sector Rising construction activities Expanding chemical industry Booming packaging sector.. Potential restraints include: Rising nylon 6 feedstock costs Growing automotive demand Increasing construction activities Fluctuating crude oil prices Supply chain disruptions.

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.

LON:BIG Target Price Prediction

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

Price prediction data for Market Dominance on 2025-08-11

Gasoline rose to 2.19 USD/Gal on July 11, 2025, up 1.65% from the previous day. Over the past month, Gasoline's price has risen 1.03%, but it is still 12.72% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on July of 2025.