This dataset contains the predicted prices of the asset Social Trade over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

In the Great Britain market, you can also trade without an asset such as a power plant, renewables or batteries. Therefore, it can be differentiated between physical trades – backed by an asset – and solely financial non-physical trades on the markets without actually providing or receiving energy.

This dataset can be used in building forecast models, optimization models and in developing first trading strategies for both physical and non-physical energy trading.

A primer: Using this dataset, one can have a closer look into two auctions of the day-ahead electricity market in Great Britain and develop as well as back test a trading strategy maximizing profits between both auctions.

Content

 a csv-file (“auction_data.csv”) containing actual prices and traded volumes of both auctions as well as a price forecast for the first auction.  a csv-file (“forecast_inputs.csv”) with input variables that can be used to forecast the prices of the second auction (you can find a more detailed description of the input variables in a separate txt-file – “description_input_variables.txt”)  a csv-file (“system_prices.csv”) with the forecasted price range of the system prices as well as the actual prices

Demand + System Margin - The availability of the system, using the daily forecast availability data (UOU data) except in the case of wind farms where a wind forecast is used from GFS weather data.

Demand - An adjustment of the demand forecast to add back on embedded wind and solar to get a truer demand shape. For values beyond the end of the half hourly demand data from National Grid, the data is shaped from the published peak demand values using typical demand curves.

Within Day Availability - An adjusted availability figure for the system that is reduced based upon rules around likely plant issues and potential non-delivery of potential availability.

Margin - The difference between Availability and Demand forecasted.

Within Day Margin - The difference between the Within Day Availability and Demand forecasted.

Long-Term Wind - A wind forecast based upon GFS weather data.

Long-Term Solar - National Grid solar forecast.

Long-Term Wind Over Demand - The Long-Term Wind values divided by Demand values.

Long-Term Wind Over Margin - The Long-Term Wind values divided by Margin values.

Long-Term Solar Over Demand - The Long-Term Solar values divided by Demand values.

Long-Term Solar Over Margin - The Long-Term Solar values divided by Margin values.

Margin Over Demand - The Margin values divided by Demand values.

SNSP Forecast - forecasts system non-synchronous penetration, which is the percentage of how much generation or imports that will be on the system that are not synchronized with frequency.

Stack Price - The breakeven cost of generation as reported by a stack model. This stack model uses as inputs Spectron daily carbon, coal and gas prices (based upon closing prices) and uses UOU 2–14-day availability forecast data by unit. Where margin levels are tight an uplift is applied to reflect the increase reluctance to generate given the risk of high imbalance prices.

Within Day Stack Price - As with the Stack Price values but using reduced levels of availability via the same reductions carried out for the Within Day Availability data set.

Previous Day-Ahead Price - Gets the last day ahead price value (last published before the auction).

Previous Continuous Half-Hour Volume-Weighted Average Price (VWAP) - Gets the volume weighted average price of all trades on half-hourly contracts in the continuous intraday market from 7 days before, i.e. on a Monday it will be for the previous Monday.

Inertia Forecast - a forecast for pre-balancing Inertia based upon the fundamentals-based generation forecast data.

Graph and download economic data for Producer Price Index by Commodity: Intermediate Demand by Commodity Type: Trade Services for Intermediate Demand (WPUID633) from Nov 2009 to Aug 2025 about intermediate, trade, commodities, services, PPI, inflation, price index, indexes, price, and USA.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Flash.Trade on 2025-11-17. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Storm Trade on 2025-11-21. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

View monthly updates and historical trends for Slovakia Retail Trade Prices Expectations. Source: European Commission. Track economic data with YCharts an…

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 93 companies listed on the Bucharest Stock Exchange* (XBSE) in Romania. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Romania:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Romania:

Bucharest Stock Exchange Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Bucharest Stock Exchange. This index provides an overview of the overall market performance in Romania.

Bucharest Stock Exchange Foreign Company Index: The index that tracks the performance of foreign companies listed on the Bucharest Stock Exchange. This index reflects the performance of international companies operating in Romania.

Company A: A prominent Romanian company with diversified operations across various sectors, such as manufacturing, technology, or finance. This company's stock is widely traded on the Bucharest Stock Exchange.

Company B: A leading financial institution in Romania, offering banking, insurance, or investment services. This company's stock is actively traded on the Bucharest Stock Exchange.

Company C: A major player in the Romanian energy or consumer goods sector, involved in the production and distribution of related products. This company's stock is listed and actively traded on the Bucharest Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Romania, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Romania ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Romania ?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Romania exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH,...

Graph and download economic data for Producer Price Index by Commodity: Final Demand: Final Demand Services Less Trade, Transportation, and Warehousing (PPITTW) from Nov 2009 to Aug 2025 about final demand, warehousing, transportation, trade, services, PPI, inflation, price index, indexes, price, and USA.

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: International Commodity Prices

United States - Producer Price Index by Commodity: Wholesale Trade Services: Apparel Wholesaling was 144.37700 Index Mar 2009=100 in August of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Wholesale Trade Services: Apparel Wholesaling reached a record high of 144.37700 in August of 2025 and a record low of 72.70000 in April of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Wholesale Trade Services: Apparel Wholesaling - last updated from the United States Federal Reserve on November of 2025.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Trade Gaurd on 2025-12-05. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

United States - Producer Price Index by Commodity: Retail Trade Services: Food and Alcohol Retailing was 190.45900 Index Mar 2009=100 in August of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Retail Trade Services: Food and Alcohol Retailing reached a record high of 191.30100 in July of 2025 and a record low of 95.40000 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Retail Trade Services: Food and Alcohol Retailing - last updated from the United States Federal Reserve on November of 2025.

United States - Producer Price Index by Commodity: Final Demand: Final Demand Services Less Trade Services was 142.83300 Index April 2010=100 in March of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Final Demand: Final Demand Services Less Trade Services reached a record high of 142.83300 in March of 2025 and a record low of 100.00000 in April of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Final Demand: Final Demand Services Less Trade Services - last updated from the United States Federal Reserve on November of 2025.

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for X Trade AI on 2025-12-18. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

Israel Trade Price Index: 2005=100 data was reported at 106.000 2005=100 in Dec 2012. This records an increase from the previous number of 105.400 2005=100 for Sep 2012. Israel Trade Price Index: 2005=100 data is updated quarterly, averaging 100.900 2005=100 from Jun 2007 (Median) to Dec 2012, with 23 observations. The data reached an all-time high of 109.400 2005=100 in Jun 2009 and a record low of 89.800 2005=100 in Dec 2011. Israel Trade Price Index: 2005=100 data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.I041: Trade Price Index: 2005=100.

Syria Retail Trade Price Index: FA: FP: NA: Mineral Water, Soft Drinks and Juice data was reported at 145.530 2005=100 in Dec 2011. This records an increase from the previous number of 135.050 2005=100 for Nov 2011. Syria Retail Trade Price Index: FA: FP: NA: Mineral Water, Soft Drinks and Juice data is updated monthly, averaging 126.056 2005=100 from Jan 2006 (Median) to Dec 2011, with 72 observations. The data reached an all-time high of 145.530 2005=100 in Dec 2011 and a record low of 104.000 2005=100 in Jul 2007. Syria Retail Trade Price Index: FA: FP: NA: Mineral Water, Soft Drinks and Juice data remains active status in CEIC and is reported by Central Bureau of Statistics . The data is categorized under Global Database’s Syrian Arab Republic – Table SY.I008: Retail Trade Price Index: 2005=100.

View monthly updates and historical trends for Tennessee Real Trade-Weighted Value of the Dollar. Source: Federal Reserve Bank of Dallas. Track economic d…

All-time high price data for Metavault Trade, including the peak value, date achieved, and current comparison metrics.

View monthly updates and historical trends for New Jersey Real Trade-Weighted Value of the Dollar. Source: Federal Reserve Bank of Dallas. Track economic …