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.

Index: TSE: 1st Section: MA: Real Estate data was reported at 1,520.779 04Jan1968=100 in Jun 2018. This records a decrease from the previous number of 1,559.857 04Jan1968=100 for May 2018. Index: TSE: 1st Section: MA: Real Estate data is updated monthly, averaging 925.960 04Jan1968=100 from Dec 1987 (Median) to Jun 2018, with 367 observations. The data reached an all-time high of 2,363.700 04Jan1968=100 in Dec 1989 and a record low of 402.363 04Jan1968=100 in Apr 2003. Index: TSE: 1st Section: MA: Real Estate data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

The NYSE U.S. 100 Index tracks the largest U.S. companies traded on the New York Stock Exchange. This statistic shows the leading 20 companies on the NYSE U.S. 100 Index by market capitalization. As of January 28, 2024 the multinational conglomerate company ****************** ranked as the first, with a market capitalization of over *** billion euros. This was followed by ********* and ***************, with market capitalizations amounting to *** billion and *** billion euros respectively. NYSE U.S. 100 Index vs. Nasdaq 100 Index The New York Stock Exchange and the Nasdaq are the largest two stock exchanges in the world, but they differ in the kinds of companies they list. The NYSE is known to list stable and long-lasting firms, commonly referred to as “blue-chip” companies. In contrast, the Nasdaq is renowned for listing the world’s biggest companies, mainly from the tech industry. Similar to the NYSE U.S. 100 Index, the Nasdaq 100 Index tracks the 100 largest non-financial companies listed on the Nasdaq exchange, including both U.S. and non-U.S. companies. The leader of the NYSE U.S. 100 index: Berkshire Hathaway Berkshire Hathaway, the leader of the NYSE U.S. 100 Index, was also among the world's largest companies by revenue in 2023. The company is a multinational conglomerate and holding company with insurance as its core business and interests in other sectors such as railroad, utilities and energy, finance. In fact, Berkshire was the world's biggest insurance company by revenue in 2023. As a holding company, it has significant stakes in some of the world’s largest companies, including Apple, Bank of America and Coca-Cola. With its diverse background in various businesses and industries, Berkshire Hathaway had a total revenue of *** billion U.S. dollars in 2023.

Japan Index: TSE: 1st Section: MA: Banks data was reported at 178.548 06Jan1992=1000 in Jun 2018. This records a decrease from the previous number of 187.154 06Jan1992=1000 for May 2018. Japan Index: TSE: 1st Section: MA: Banks data is updated monthly, averaging 210.372 06Jan1992=1000 from Jan 1997 (Median) to Jun 2018, with 258 observations. The data reached an all-time high of 645.418 06Jan1992=1000 in Jul 1997 and a record low of 100.428 06Jan1992=1000 in Nov 2011. Japan Index: TSE: 1st Section: MA: Banks data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

Japan Index: TSE 1st Section Composite data was reported at 1,667.450 04Jan1968=100 in Nov 2018. This records an increase from the previous number of 1,646.120 04Jan1968=100 for Oct 2018. Japan Index: TSE 1st Section Composite data is updated monthly, averaging 1,133.125 04Jan1968=100 from Feb 1970 (Median) to Nov 2018, with 586 observations. The data reached an all-time high of 2,881.370 04Jan1968=100 in Dec 1989 and a record low of 148.350 04Jan1968=100 in Dec 1970. Japan Index: TSE 1st Section Composite data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

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.

Japan Index: TSE: 1st Section: Iron and Steel data was reported at 515.460 04Jan1968=100 in Jun 2018. This records a decrease from the previous number of 536.970 04Jan1968=100 for May 2018. Japan Index: TSE: 1st Section: Iron and Steel data is updated monthly, averaging 565.595 04Jan1968=100 from Jan 1994 (Median) to Jun 2018, with 292 observations. The data reached an all-time high of 1,749.000 04Jan1968=100 in Jul 2007 and a record low of 262.540 04Jan1968=100 in Dec 2002. Japan Index: TSE: 1st Section: Iron and Steel data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

1) Data Introduction • The AI Global Index Dataset is a comprehensive index that benchmarks 62 countries based on the level of AI investment, innovation, and implementation, including seven key indicators (human resources, infrastructure, operational environment, research, development, government strategy, commercialization) and general information by country (region, cluster, income group, political system).

2) Data Utilization (1) AI Global Index Dataset has characteristics that: • This dataset consists of a total of 13 columns with 5 categorical variables (regions, clusters, etc.) and 8 numerical variables (scores for each indicator), covering 62 countries. • The seven key indicators are classified into three pillars: △ implementation (human resources/infrastructure/operational environment) △ innovation (R&D) △ investment (government strategy/commercialization), and assess each country's overall AI ecosystem capabilities in multiple dimensions. (2) AI Global Index Dataset can be used to: • Global AI leadership pattern analysis: Correlation analysis between seven indicators can identify AI strengths and weaknesses by country and perform group comparisons by region and income level. • Machine learning-based predictive model: It can be used for data science education and application, such as country-specific index prediction through regression analysis or classification of AI development types through clustering.

Graph and download economic data for Volatility of Stock Price Index for Mongolia (DDSM01MNA066NWDB) from 1999 to 2021 about Mongolia, volatility, stocks, price index, indexes, and price.

Japan Index: TSE: 1st Section: Metal Products data was reported at 1,159.060 04Jan1968=100 in Nov 2018. This records an increase from the previous number of 1,146.790 04Jan1968=100 for Oct 2018. Japan Index: TSE: 1st Section: Metal Products data is updated monthly, averaging 980.360 04Jan1968=100 from Jan 1994 (Median) to Nov 2018, with 297 observations. The data reached an all-time high of 1,794.290 04Jan1968=100 in Jan 1994 and a record low of 576.940 04Jan1968=100 in Feb 2009. Japan Index: TSE: 1st Section: Metal Products data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

Kazakhstan Equity Market Index: USD data was reported at 86.690 2010=100 in 2025. This records an increase from the previous number of 84.792 2010=100 for 2024. Kazakhstan Equity Market Index: USD data is updated yearly, averaging 51.135 2010=100 from Dec 2000 (Median) to 2025, with 26 observations. The data reached an all-time high of 165.313 2010=100 in 2007 and a record low of 5.591 2010=100 in 2001. Kazakhstan Equity Market Index: USD data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kazakhstan – Table KZ.World Bank.GEM: Equity Market Index: Annual. Local equity market index valued in US$ terms

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Monthly (MYD13C2) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the MYD13A2 products that overlap the month and employs a weighted temporal average. Global MYD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.Improvments/Changes from Previous Version The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).

This index tracks premium connected timepieces including TAG Heuer Connected series, Montblanc Summit, Louis Vuitton Tambour Horizon, Frederique Constant Horological Smartwatch, Alpina AlpinerX, Tissot T-Touch Connect Solar and more. It measures change in luxury brand adaptation with the digital age, traditional manufacture technology integration, and collector acceptance of connected luxury.

The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.

Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.

Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).

Taiwan TWSE: Equity Market Index: Chemical data was reported at 99.750 29Jun2007=100 in Oct 2018. This records a decrease from the previous number of 111.960 29Jun2007=100 for Sep 2018. Taiwan TWSE: Equity Market Index: Chemical data is updated monthly, averaging 106.640 29Jun2007=100 from Jul 2007 (Median) to Oct 2018, with 136 observations. The data reached an all-time high of 152.140 29Jun2007=100 in Jul 2011 and a record low of 53.200 29Jun2007=100 in Oct 2008. Taiwan TWSE: Equity Market Index: Chemical data remains active status in CEIC and is reported by Taiwan Stock Exchange Corporation. The data is categorized under Global Database’s Taiwan – Table TW.Z001: Taiwan Stock Exchange (TWSE): Indices.

In April 2020, the global consumer confidence index of ** countries worldwide dropped to **** following the outbreak of the COVID-19 pandemic. It then slowly increased until July 2021, when it reached an index score of ****. Global consumer confidence dropped in the latter half of 2022 following rising inflation rates, but has been increasing since November that year.

This dataset provides air quality index (AQI) data from major cities worldwide, covering the years 2015 to 2025. It is compiled from various sources, including government monitoring stations, environmental agencies, and open APIs.

The dataset includes daily AQI values along with major pollutants such as PM2.5, PM10, NO2, SO2, CO, and O3. Additional meteorological data such as temperature, humidity, and wind speed are also included to support deeper analysis.

Dataset Features: Date: The date of AQI measurement (YYYY-MM-DD). City: Name of the city where the AQI is recorded. Country: Country of the city. AQI: The daily air quality index value. PM2.5 (µg/m³): Fine particulate matter concentration. PM10 (µg/m³): Larger particulate matter concentration. NO2 (ppb): Nitrogen dioxide concentration. SO2 (ppb): Sulfur dioxide concentration. CO (ppm): Carbon monoxide concentration. O3 (ppb): Ozone concentration. Temperature (°C): Daily average temperature. Humidity (%): Daily average humidity. Wind Speed (m/s): Daily average wind speed. Potential Use Cases: ✅ Data Science & Machine Learning: Predict air quality trends, create AQI forecasting models, and build environmental monitoring applications. ✅ Health & Epidemiology: Analyze correlations between air pollution and respiratory diseases, cardiovascular conditions, and general health. ✅ Climate & Environmental Research: Study pollution patterns, seasonal variations, and their relation to climate change. ✅ Urban Planning & Policy Making: Help city planners implement better pollution control strategies.

Why This Dataset? 📌 10-year coverage (2015-2025) for long-term trend analysis. 📌 Global scope with diverse geographical representation. 📌 Multiple pollutants & weather data for comprehensive insights. 📌 Ready-to-use for ML models, EDA, and research.

Renewable Energy Investment Index Market Outlook

According to our latest research, the global Renewable Energy Investment Index market size reached USD 485.2 billion in 2024, fueled by a robust policy push, technological advancements, and escalating demand for sustainable energy solutions. The market is growing at a steady CAGR of 8.1% and is forecasted to attain a value of USD 919.3 billion by 2033. This impressive growth trajectory is driven by aggressive decarbonization commitments, increased capital inflows from both public and private sectors, and the expanding portfolio of renewable energy projects worldwide. As per our latest research, the Renewable Energy Investment Index market continues to be a critical barometer for global energy transition and sustainability-driven investments.

The primary growth factor for the Renewable Energy Investment Index market lies in the global shift towards decarbonization and the urgent need to address climate change. Governments across the globe have set ambitious targets for net-zero emissions, and renewable energy is at the core of these strategies. The implementation of supportive regulatory frameworks, such as feed-in tariffs, tax incentives, and renewable portfolio standards, has significantly enhanced the attractiveness of renewable energy investments. Furthermore, the declining costs of renewable technologies, particularly in solar and wind, have made these sources increasingly competitive with traditional fossil fuels. This cost parity, coupled with heightened environmental awareness among consumers and corporations, is propelling sustained capital flows into the sector, making renewable energy investments a mainstream asset class.

Another significant driver is the rapid technological innovation within the renewable energy sector. Advancements in energy storage solutions, grid integration, and digitalization of energy management systems are enhancing the reliability and scalability of renewable projects. The rise of smart grids, artificial intelligence, and blockchain-based solutions for energy trading and management are further optimizing operational efficiency and transparency. These technological leaps are reducing operational risks for investors and enabling larger, more complex projects to come online. As a result, institutional investors, private equity, and venture capital are increasingly allocating funds to renewable energy, recognizing both the stable returns and the alignment with environmental, social, and governance (ESG) criteria.

Financial innovation and evolving investment models are also catalyzing growth in the Renewable Energy Investment Index market. The emergence of green bonds, yieldcos, and securitization of renewable energy assets has broadened the investor base and improved access to capital for project developers. Crowdfunding platforms and community-based investment schemes are democratizing participation in the renewable energy transition. Additionally, the growing influence of ESG investing has compelled asset managers and institutional investors to prioritize renewable energy assets within their portfolios. This convergence of financial innovation and sustainability imperatives is creating a virtuous cycle, accelerating the deployment of capital into renewable energy infrastructure globally.

The global push towards Renewable Energy is not only reshaping energy markets but also redefining economic landscapes. Countries are increasingly recognizing the strategic importance of energy independence and sustainability, driving significant investments in renewable infrastructure. This shift is fostering innovation in energy technologies and creating new industries and job opportunities. As nations strive to meet their climate commitments, renewable energy is becoming a cornerstone of national energy policies, promoting cleaner air, reduced carbon footprints, and enhanced energy security. The ripple effects of this transition are being felt across sectors, from manufacturing to finance, as the world embraces a more sustainable energy future.

Regionally, the Asia Pacific market is leading the charge, accounting for the largest share of global renewable energy investments, followed closely by Europe and North America. Asia PacificÂ’s dominance is underpinned by large-scale deployments in China and India, robust government po

The average for 2022 based on 188 countries was 108.5 index points. The highest value was in Senegal: 189.9 index points and the lowest value was in Malta: 53.8 index points. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

Global Economic Policy Uncertainty Index - Real GDP - Historical chart and current data through 2025.