This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines Forex Pair Headline Sentiment Explanation GBPUSD Diminishing bets for a move to 12400 Neutral Lack of strong sentiment in either direction GBPUSD No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft Positive Positive sentiment towards GBPUSD (Cable) in the near term GBPUSD When are the UK jobs and how could they affect GBPUSD Neutral Poses a question and does not express a clear sentiment JPYUSD Appropriate to continue monetary easing to achieve 2% inflation target with wage growth Positive Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply USDJPY Dollar rebounds despite US data. Yen gains amid lower yields Neutral Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other USDJPY USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains Negative USDJPY is expected to reach a lower value, with the USD losing value against the JPY AUDUSD RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

Positive Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD. Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

The global Trading Platforms System Software market is experiencing robust growth, driven by the increasing popularity of online trading and investment, fueled by factors such as rising internet penetration, smartphone usage, and the democratization of financial markets. The market, currently estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by the end of the forecast period. This expansion is propelled by several key trends: the rising adoption of cloud-based trading platforms offering scalability and accessibility; the increasing demand for sophisticated charting tools and analytical capabilities; and the growing need for advanced order management systems and risk management features. Furthermore, the expanding fintech sector and the emergence of innovative trading strategies are contributing to market growth. While regulatory changes and cybersecurity concerns pose challenges, the overall market outlook remains positive, with significant opportunities for established players and new entrants alike. The market is segmented by application (Finance, Retail, Education, Other) and type (Cloud-Based, On-Premises), with the cloud-based segment demonstrating particularly strong growth due to its cost-effectiveness and flexibility. North America and Europe currently dominate the market, but significant growth potential exists in the Asia-Pacific region driven by increasing adoption of online trading in rapidly developing economies such as India and China. The competitive landscape is characterized by a mix of established financial institutions offering proprietary platforms and specialized software vendors providing comprehensive trading solutions. Key players like MetaQuotes Software, NinjaTrader, Interactive Brokers, and others continuously innovate to enhance their offerings, integrating advanced technologies such as AI and machine learning to improve trading efficiency and user experience. The market’s future hinges on the ongoing evolution of trading technologies, evolving regulatory frameworks, and the ever-changing preferences of individual and institutional investors. The focus on enhancing security, improving user interfaces, and integrating broader financial data sources will be crucial in driving future market growth. The adoption of blockchain technology and decentralized finance (DeFi) also presents exciting opportunities for the development of novel trading platform solutions.

China BOC: Source of Foreign Fund: Foreign Currency Trading data was reported at 29.843 USD bn in 2004. This records an increase from the previous number of 5.518 USD bn for 2003. China BOC: Source of Foreign Fund: Foreign Currency Trading data is updated yearly, averaging 5.518 USD bn from Dec 1996 (Median) to 2004, with 9 observations. The data reached an all-time high of 29.843 USD bn in 2004 and a record low of -7.145 USD bn in 2000. China BOC: Source of Foreign Fund: Foreign Currency Trading data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under Global Database’s China – Table CN.KE: Bank of China (BOC): Source and Use of Fund.

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  USDJPY Tick Data Dataset





  Data Source

This dataset is generated from historical tick-level data of the USDJPY forex pair, collected from major forex trading platforms. The data has been cleaned and organized by year, with each CSV file representing one year of tick data.

  Data Content

Each row represents a single tick event, with the following fields:

timestamp: Transaction timestamp (millisecond precision) ask: Ask price (price to buy the pair) bid:… See the full description on the dataset page: https://huggingface.co/datasets/huggingXG/forex_USDJPY.

We have collected every tick of EUR_USD live from multiple sources since 1999. The high frequency tick by tick data is filtered to get rid of bad quotes and a fine series of hourly closing data is generated. The data is available directly online on this platform.

Timestamp format: dd.mm.yyyy,HH:00:00 Timestamp Zone: GMT

Czech Republic Electricity Balance: Foreign Exchange: ow Domestic Sources data was reported at 64,552.000 GWh in 2022. This records a decrease from the previous number of 67,468.000 GWh for 2021. Czech Republic Electricity Balance: Foreign Exchange: ow Domestic Sources data is updated yearly, averaging 63,762.500 GWh from Dec 1993 (Median) to 2022, with 30 observations. The data reached an all-time high of 67,468.000 GWh in 2021 and a record low of 52,880.000 GWh in 1993. Czech Republic Electricity Balance: Foreign Exchange: ow Domestic Sources data remains active status in CEIC and is reported by Czech Statistical Office. The data is categorized under Global Database’s Czech Republic – Table CZ.RB006: Electricity Balance and Trade.

United States - Sources of Revenue: Brokering and Dealing foreign Currency Fees - Wholesale for Commodity Contracts Dealing and Brokerage, All Establishments, Employer Firms was 432.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Sources of Revenue: Brokering and Dealing foreign Currency Fees - Wholesale for Commodity Contracts Dealing and Brokerage, All Establishments, Employer Firms reached a record high of 1361.00000 in January of 2014 and a record low of 233.00000 in January of 2016. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Sources of Revenue: Brokering and Dealing foreign Currency Fees - Wholesale for Commodity Contracts Dealing and Brokerage, All Establishments, Employer Firms - last updated from the United States Federal Reserve on June of 2025.

Algorithmic Trading Market size was valued at USD 16.37 Billion in 2024 and is projected to reach USD 31.90 Billion by 2032, growing at a CAGR of 10% from 2026 to 2032.

Global Algorithmic Trading Market Dynamics

The key market dynamics that are shaping the Algorithmic Trading Market include:

Key Market Drivers

Adoption of Algorithmic Trading by Financial Institutions: Algorithms are significantly lowering trading costs, headcount, and improving sales desk operations. They also help automate order sending to exchanges, eliminating the need for brokers for enhancing liquidity, pricing, and broker commissions. The increasing use of automated trading software by banking organizations is demanding for cloud-based solutions and market monitoring software, driving the market.

Integration of Artificial Intelligence (AI) and Machine Learning (ML): AI algorithms can react to market changes in milliseconds, executing trades at speeds far exceeding human capabilities. This is crucial for capitalizing on fleeting opportunities and minimizing losses in volatile markets.

Key Challenges:

High Chances of Error and Inconsistency in Data: Inaccurate or inconsistent data can lead to misinformed trading decisions. If trading algorithms are fed with erroneous data, they may generate incorrect signals, resulting in poor trade execution or losses. Errors in market data can increase operational and market risk. For example, if a trading algorithm relies on incorrect pricing data, it may execute trades at unfavorable prices, leading to increased losses or unexpected exposures.

Market Fragmentation and Liquidity Challenge: Automated trading systems face challenges due to liquidity dispersion across platforms and asset categories, resulting in higher execution costs and limited liquidity. To overcome these issues, market participants should develop advanced order routing algorithms, optimize execution methods, and access various liquidity pools.

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E-Brokerage Market Size 2025-2029

The e-brokerage market size is forecast to increase by USD 7.39 billion, at a CAGR of 7.9% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing proliferation of internet access worldwide. This expansion is fueled by the convenience and accessibility that e-brokerage platforms offer, enabling investors to manage their portfolios remotely and execute trades in real-time. Another key trend shaping the market is the rising demand for customization and personalization in e-brokerage solutions. As investors seek more tailored services to meet their unique needs, e-brokerage providers are responding by offering personalized investment advice, customizable interfaces, and a wide range of financial instruments. However, the market also faces notable challenges. With the increasing popularity of e-brokerage platforms, cybersecurity risks have become a significant concern. As more investors turn to digital channels for their financial needs, the threat of data breaches, hacking, and other cyber attacks grows. E-brokerage providers must invest heavily in robust cybersecurity measures to protect their platforms and their clients' sensitive information. Additionally, regulatory compliance remains a complex and ever-evolving challenge for e-brokerage firms, requiring significant resources and expertise to navigate the intricacies of various financial regulations. These challenges, while daunting, present opportunities for e-brokerage providers that can effectively address these issues and provide a secure, reliable, and personalized platform for their clients.

What will be the Size of the E-Brokerage 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.
Request Free SampleThe market continues to evolve, with dynamic market dynamics shaping its various sectors. Investment products and services are increasingly integrated, offering users a comprehensive platform for financial management. Mobile app development is a key focus, enabling seamless trading and real-time data access. Cryptocurrency trading is gaining popularity, requiring advanced technology and robust security protocols. Market data and educational resources are essential components, empowering users with the tools for fundamental analysis and financial modeling. User experience is paramount, with customer support, account management, and portfolio optimization ensuring client satisfaction. Order routing and management systems facilitate efficient trade execution, while fractional shares and commission structures cater to diverse investment strategies. Data analytics and technical analysis provide valuable insights, driving informed decisions. High-frequency trading and algorithmic trading require advanced API integration and direct market access. Risk management and tax optimization are crucial, with real-time data and automated trading offering enhanced control. Client onboarding and account minimums are essential considerations, with various brokerage services catering to different customer segments. Wealth management and retirement planning require a holistic approach, incorporating estate planning and dividend reinvestment. Security breaches and data encryption are ongoing concerns, with robust security protocols essential for safeguarding sensitive information. Investment products and trading platforms continue to expand, offering users a wide range of options, including futures trading and forex trading. Charting tools and social trading provide additional resources for informed decision-making. The market's continuous dynamism ensures a constantly evolving landscape, requiring adaptability and innovation.

How is this E-Brokerage Industry segmented?

The e-brokerage 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. Service TypeFull time brokerDiscounted brokerApplicationIndividual investorInstitutional investorOwnershipPrivately heldPublicly heldPlatformWeb-basedMobile appsDesktopAssest TypeEquitiesBondsDerivativesCryptocurrenciesGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyThe NetherlandsUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Service Type Insights

The full time broker segment is estimated to witness significant growth during the forecast period.In the dynamic world of E-brokerage, full-time brokers play a pivotal role in facilitating the trade of various financial securities for clients. These licensed professionals, regulated by bodies like the SEC and FCA, work closely with individuals, institutions, and corporations to understand t

India RBI: Money Stocks: Sources: Net FX Assets of Banking Sector data was reported at 29,212,194.013 INR mn in 23 Nov 2018. This records a decrease from the previous number of 29,809,344.013 INR mn for 09 Nov 2018. India RBI: Money Stocks: Sources: Net FX Assets of Banking Sector data is updated daily, averaging 3,989,995.000 INR mn from Jan 1980 (Median) to 23 Nov 2018, with 816 observations. The data reached an all-time high of 30,279,177.443 INR mn in 12 Oct 2018 and a record low of 11,560.000 INR mn in 23 Dec 1983. India RBI: Money Stocks: Sources: Net FX Assets of Banking Sector data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Daily Database’s Monetary – Table IN.KAA002: Money Stock: M3.

Foreign Exchange Reserves in Saudi Arabia increased to 1721072 SAR Million in May from 1647513 SAR Million in April of 2025. This dataset provides - Saudi Arabia Foreign Exchange Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

Currency exchange rate is an important metric to inform economic policy but traditional sources are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual rate trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes currency exchange rate estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Adamawa, Market Average

This dataset shows the Principal statistics of stock, share, commodity brokers and foreign exchange services, 1971 - 2017, Malaysia. Footnote: No survey were conducted in 1980, 1982, 1993, 1995, 1997, 1999, 2001, 2006, 2008, 2011-2014 and 2016. Commodity brokers were included in the coverage from year 1983 onwards. Money changers were included in the coverage from year 2004 onwards. For the year 2009, data refer to Stock, Share & Bond Brokers only. Source: Department of Statistics, Malaysia

Foreign Exchange Reserves in Pakistan increased to 16597.70 USD Million in May from 14759.40 USD Million in April of 2025. This dataset provides the latest reported value for - Pakistan Foreign Exchange Reserves - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

IMPACT OF FOREIGN EXCHANGE ON AGRICULTURE EXPORTS AND IMPORTS

Silver fell to 35.86 USD/t.oz on July 1, 2025, down 0.67% from the previous day. Over the past month, Silver's price has risen 3.16%, and is up 21.22% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Silver - values, historical data, forecasts and news - updated on July of 2025.

Uranium rose to 79.05 USD/Lbs on June 27, 2025, up 0.70% from the previous day. Over the past month, Uranium's price has risen 9.87%, but it is still 7.81% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Uranium - values, historical data, forecasts and news - updated on June of 2025.

The USD/COP exchange rate rose to 4,089.5000 on June 30, 2025, up 0.03% from the previous session. Over the past month, the Colombian Peso has strengthened 1.01%, and is up by 1.08% over the last 12 months. Colombian Peso - values, historical data, forecasts and news - updated on July of 2025.

中国银行：外国资金来源：外汇交易在12-01-2004达29.843十亿美元，相较于12-01-2003的5.518十亿美元有所增长。中国银行：外国资金来源：外汇交易数据按年更新，12-01-1996至12-01-2004期间平均值为5.518十亿美元，共9份观测结果。该数据的历史最高值出现于12-01-2004，达29.843十亿美元，而历史最低值则出现于12-01-2000，为-7.145十亿美元。CEIC提供的中国银行：外国资金来源：外汇交易数据处于定期更新的状态，数据来源于中国人民银行，数据归类于Global Database的中国 – Table CN.KE : 中国银行 : 信贷收支。