The main stock market index of United States, the US500, rose to 6680 points on October 20, 2025, gaining 0.23% from the previous session. Over the past month, the index has declined 0.21%, though it remains 14.10% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Japan's main stock market index, the JP225, fell to 47582 points on October 17, 2025, losing 1.44% from the previous session. Over the past month, the index has climbed 5.03% and is up 22.06% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on October of 2025.

United Kingdom's main stock market index, the GB100, fell to 9355 points on October 17, 2025, losing 0.86% from the previous session. Over the past month, the index has climbed 1.37% and is up 11.92% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on October of 2025.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Algorithmic Trading

1. Algorithmic Trading means using computers to make investment desicions.
2. There are man different types of algorithmic trading, which mainly differ in speed of execution.
3. Algorithmic Trading process:
   1. Collect Data
   2. Develop hypothesis for a strategy
   3. Backtest that strategy
   4. Implement that strategy in production
4. S&P 500 is the world's most popular stock market index. It is market capitalization-weighted; i.e. large companies get correspondingly larger weight in the index.
5. Momentum investing means investing in the assets that have increased in price the most.
6. Value investing means investing in stocks that are trading below perceived intrinsic value. Multiples are calculated by dividing a company's stock price by some measure of the company's worth like earnings or assets.
   1. Price-to-earnings
   2. Price-to-book-value
   3. Price-to-free-cash-flow

These indices collected from PACER Plus provide a measure of restrictiveness of trade in services in Pacific Island Countries and Territories.

Find more Pacific data on PDH.stat.

This data set contains data on the stock index DAX in Germany – the Deutscher Aktien Index or the GER40. It represents 40 of the largest and most liquid German companies that trade on the Frankfurt Exchange.

The data is publicly available from many economics and financial news sites such as Trading View and Trading Economics.

This particular dataset records the Open, High, Low, Close, Adj Close and Volume from September 2022 to September 2023.

Sweden's main stock market index, the Stockholm 30, fell to 2701 points on October 17, 2025, losing 1.47% from the previous session. Over the past month, the index has climbed 2.53% and is up 4.13% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Sweden. Sweden Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

The Trade Sentiment Index (TSI) dataset provides a real-time measure of how global trade narratives, tariff announcements, and geopolitical developments drive market sentiment across asset classes. Built using advanced natural language processing, the TSI ingests headlines from thousands of global sources, scoring them by tone, polarity, volume, and intensity. By quantifying narrative flows, the TSI offers investors a forward-looking lens on volatility at a time when traditional economic indicators often lag. The dataset captures how sentiment shocks around tariffs and trade align with price action in equities, commodities, FX, and rates, enabling systematic traders, portfolio managers, and strategists to: Detect leading indicators of cross-asset volatility. Anticipate market rotations in response to tariff rhetoric and trade negotiations. Stress test portfolios against geopolitical and trade-driven shocks. Enhance long-term allocation strategies with a sentiment-aware overlay. Key findings from recent analysis show: S&P 500 demonstrates the strongest correlation with trade sentiment, providing a useful proxy for global equity risk appetite. Copper has emerged as a geopolitical barometer, reacting sharply to tariff-driven disruptions. Gold, USD, and Treasuries show fragmented safe-haven behaviour, responding selectively to trade headlines. The TSI is available via API for seamless integration into trading, research, and risk workflows. It empowers investors to anticipate macro dislocations, tactical shifts, and regime changes in today’s sentiment-led markets.

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.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

The trade intensity index uses similar logic to that of revealed comparative advantage, but for markets rather than products. It indicates whether a reporter exports more, as a percentage, to a partner than the world does on average. It is measured as country i's exports to country j relative to its total exports divided by the world’s exports to country j relative to the world’s total exports.

Disclaimer!!! Data uploaded here are collected from the internet. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either monetary or any favor) for this dataset.

The NIFTY FMCG Index is designed to reflect the behaviour and performance of FMCGs (Fast Moving Consumer Goods) which are non-durable, mass consumption products and available off the shelf. The NIFTY FMCG Index comprises of 15 stocks from FMCG sector listed on the National Stock Exchange (NSE).

NIFTY FMCG Index is computed using free float market capitalization method, wherein the level of the index reflects the total free float market value of all the stocks in the index relative to particular base market capitalization value. NIFTY FMCG Index can be used for a variety of purposes such as benchmarking of fund portfolios, launching of index funds, ETFs and structured products.

In general, the stock prices of the same industry have a similar trend, but those of different industries do not. When investing in stocks of different industries, one should select the optimal model from lots of trading models for each industry because any model may not be suitable for capturing the stock trends of all industries. However, the study has not been carried out at present. In this paper, firstly we select 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) as the research objects from 2010 to 2017, divide them into 9 industries such as finance and energy respectively. Secondly, we apply 12 widely used machine learning algorithms to generate stock trading signals in different industries and execute the back-testing based on the trading signals. Thirdly, we use a non-parametric statistical test to evaluate whether there are significant differences among the trading performance evaluation indicators (PEI) of different models in the same industry. Finally, we propose a series of rules to select the optimal models for stock investment of every industry. The analytical results on SPICS and CSICS show that we can find the optimal trading models for each industry based on the statistical tests and the rules. Most importantly, the PEI of the best algorithms can be significantly better than that of the benchmark index and “Buy and Hold” strategy. Therefore, the algorithms can be used for making profits from industry stock trading.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Development of the volume indices is presented using 2021 as reference year. Volume indices are adjusted for working days and seasonal variations.

France's main stock market index, the FR40, rose to 8211 points on October 20, 2025, gaining 0.45% from the previous session. Over the past month, the index has climbed 4.86% and is up 8.95% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on October of 2025.

The trade complementarity index indicates to what extent the export profile of the reporter matches, or complements, the import profile of the partner. A high index may indicate that two countries would stand to gain from increased trade, and may be particularly useful in evaluating prospective bilateral or regional trade agreements Note: This dataset uses mirrored data instead of country reported data.

Time series data for the statistic Net barter terms of trade index (2000 = 100) and country Sierra Leone. Indicator Definition:Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year's trade values as weights.The indicator "Net barter terms of trade index (2000 = 100)" stands at 103.60 as of 12/31/2023. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -6.75 percent compared to the value the year prior.The 1 year change in percent is -6.75.The 3 year change in percent is 0.7782.The 5 year change in percent is -6.24.The 10 year change in percent is -14.31.The Serie's long term average value is 139.69. It's latest available value, on 12/31/2023, is 25.84 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2015, to it's latest available value, on 12/31/2023, is +3.60%.The Serie's change in percent from it's maximum value, on 12/31/2000, to it's latest available value, on 12/31/2023, is -53.57%.