Prices for DXY Dollar Index including live quotes, historical charts and news. DXY Dollar Index was last updated by Trading Economics this July 24 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.

What is forex trading?

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

Graph and download economic data for Nominal Emerging Market Economies U.S. Dollar Index (DTWEXEMEGS) from 2006-01-02 to 2025-06-13 about trade-weighted, emerging markets, exchange rate, currency, goods, services, rate, indexes, and USA.

Graph and download economic data for Nominal Advanced Foreign Economies U.S. Dollar Index (DTWEXAFEGS) from 2006-01-02 to 2025-07-18 about trade-weighted, foreign, exchange rate, currency, goods, services, rate, indexes, and USA.

The global market for forex trading apps is experiencing robust growth, driven by increasing smartphone penetration, rising internet usage, and the democratization of financial markets. The ease of access and user-friendly interfaces offered by these apps have attracted a significant number of both individual and enterprise traders. While precise market sizing data is unavailable, considering a conservative CAGR (let's assume 15% based on industry trends) and a 2025 market value of approximately $5 billion (a reasonable estimate given the presence of major players and the expanding user base), the market is projected to surpass $10 billion by 2033. Key drivers include the growing popularity of mobile trading, technological advancements enabling sophisticated trading tools on mobile devices, and the expansion of the retail investor base. The segment breakdown reveals a significant contribution from both individual and enterprise users, with Android and iOS platforms sharing the majority market share. The competitive landscape is characterized by established players like IG, Saxo, and CMC Markets alongside emerging fintech companies. Regional variations exist, with North America and Europe currently dominating the market. However, Asia-Pacific is expected to witness significant growth in the coming years driven by increasing mobile adoption and economic expansion. Regulatory changes and cybersecurity concerns present potential restraints to market growth. Regulations aimed at protecting investors might increase compliance costs for app providers, and instances of data breaches could erode user trust and hinder market expansion. Future growth will likely be influenced by the development of innovative trading tools, advancements in artificial intelligence (AI) integration, personalized trading experiences, and the increasing adoption of cryptocurrencies and other digital assets within forex trading platforms. The market is projected to be highly competitive, requiring continuous innovation and adaptation to technological advancements and shifting regulatory landscapes. Continued focus on user experience, security, and regulatory compliance will be crucial for success in this dynamic market.

Real Effective Exchange Rate Index: 2010=100 data was reported at 100.490 2010=100 in Oct 2018. This records an increase from the previous number of 100.190 2010=100 for Sep 2018. Real Effective Exchange Rate Index: 2010=100 data is updated monthly, averaging 99.530 2010=100 from Apr 2005 (Median) to Oct 2018, with 163 observations. The data reached an all-time high of 116.220 2010=100 in Jun 2005 and a record low of 93.600 2010=100 in Feb 2016. Real Effective Exchange Rate Index: 2010=100 data remains active status in CEIC and is reported by Taipei Foreign Exchange Market Development Foundation. The data is categorized under Global Database’s Taiwan – Table TW.M009: Effective Exchange Rate Index: Taipei Foreign Exchange Market Development Foundation.

Gain exclusive access to specialist Foreign Exchange (FX) data, and the tools to manage trading analysis, risk and operations with LSEG's FX Pricing Data.

Twelve Data is a technology-driven company that provides financial market data, financial tools, and dedicated solutions. Large audiences - from individuals to financial institutions - use our products to stay ahead of the competition and success.

At Twelve Data we feel responsible for where the markets are going and how people are able to explore them. Coming from different technological backgrounds, we see how the world is lacking the unique and simple place where financial data can be accessed by anyone, at any time. This is what distinguishes us from others, we do not only supply the financial data but instead, we want you to benefit from it, by using the convenient format, tools, and special solutions.

We believe that the human factor is still a very important aspect of our work and therefore our ethics guides us on how to treat people, with convenient and understandable resources. This includes world-class documentation, human support, and dedicated solutions.

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.

Forex Trading: Understanding the Dynamics of the Global Currency Market

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 Inflation Nowcast: Contribution: Foreign Exchange Rates: Trade Weighted Dollar Index: Nominal: Broad Dollar Index data was reported at 0.108 % in 12 May 2025. This stayed constant from the previous number of 0.108 % for 05 May 2025. United States Inflation Nowcast: Contribution: Foreign Exchange Rates: Trade Weighted Dollar Index: Nominal: Broad Dollar Index data is updated weekly, averaging 0.016 % from Jun 2020 (Median) to 12 May 2025, with 259 observations. The data reached an all-time high of 6.547 % in 18 Apr 2022 and a record low of 0.000 % in 17 Feb 2025. United States Inflation Nowcast: Contribution: Foreign Exchange Rates: Trade Weighted Dollar Index: Nominal: Broad Dollar Index data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Inflation: Headline.

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: Badakhshan, Badghis, Baghlan, Balkh, Bamyan, Daykundi, Farah, Faryab, Paktya, Ghazni, Ghor, Hilmand, Hirat, Nangarhar, Jawzjan, Kabul, Kandahar, Kapisa, Khost, Kunar, Kunduz, Laghman, Logar, Wardak, Nimroz, Nuristan, Paktika, Panjsher, Parwan, Samangan, Sar-e-pul, Takhar, Uruzgan, Zabul, Market Average, Armavir, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Lori, Vayotz Dzor, Yerevan, Kayanza, Ruyigi, Bubanza, Karuzi, Bujumbura Mairie, Muramvya, Gitega, Rumonge, Bururi, Kirundo, Cankuzo, Cibitoke, Muyinga, Rutana, Bujumbura Rural, Makamba, Ngozi, Mwaro, SAHEL, CASCADES, SUD-OUEST, EST, BOUCLE DU MOUHOUN, CENTRE-NORD, PLATEAU-CENTRAL, HAUTS-BASSINS, CENTRE, NORD, CENTRE-SUD, CENTRE-OUEST, CENTRE-EST, Khulna, Chittagong, Barisal, Rajshahi, Dhaka, Rangpur, Sylhet, Mymensingh, Ouaka, Mbomou, Bangui, Nana-Mambéré, Ouham, Sangha-Mbaéré, Ombella M'Poko, Mambéré-Kadéï, Vakaga, Ouham Pendé, Lobaye, Haute-Kotto, Kémo, Nana-Gribizi, Bamingui-Bangoran, Haut-Mbomou, Nord, Extrême-Nord, Ouest, Nord-Ouest, Adamaoua, Sud-Ouest, Est, Littoral, Centre, Haut-Uele, Nord-Kivu, Ituri, Tshopo, Kwilu, Kasai, Sud-Kivu, Kongo-Central, Nord-Ubangi, Sud-Ubangi, Kasai-Central, Bas-Uele, Tanganyika, Lualaba, Kasai-Oriental, Kwango, Haut-Lomami, Haut-Katanga, Maniema, Kinshasa, Equateur, Lomami, Likouala, Brazzaville, Point-Noire, Pool, Bouenza, Cuvette, Lekoumou, Nzerekore, Boke, Kindia, Kankan, Faranah, Mamou, Labe, Kanifing Municipal Council, Central River, Upper River, West Coast, North Bank, Lower River, Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, North, South, Artibonite, South-East, Grande'Anse, North-East, West, North-West, SULAWESI UTARA, SUMATERA UTARA, KALIMANTAN UTARA, JAWA BARAT, NUSA TENGGARA BARAT, NUSA TENGGARA TIMUR, SULAWESI SELATAN, JAMBI, JAWA TIMUR, KALIMANTAN SELATAN, BALI, BANTEN, JAWA TENGAH, RIAU, SUMATERA BARAT, KEPULAUAN RIAU, PAPUA, SULAWESI BARAT, BENGKULU, MALUKU UTARA, DAERAH ISTIMEWA YOGYAKARTA, KALIMANTAN BARAT, KALIMANTAN TENGAH, PAPUA BARAT, SUMATERA SELATAN, MALUKU, KEPULAUAN BANGKA BELITUNG, ACEH, DKI JAKARTA, SULAWESI TENGGARA, KALIMANTAN TIMUR, LAMPUNG, GORONTALO, SULAWESI TENGAH, Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, Coast, North Eastern, Nairobi, Rift Valley, , Eastern, Central, Nyanza, Attapeu, Bokeo, Bolikhamxai, Champasack, Houaphan, Khammouan, Louangphabang, Louangnamtha, Oudomxai, Phongsaly, Salavan, Savannakhet, Sekong, Vientiane Capital, Vientiane, Xaignabouly, Xiengkhouang, Akkar, Mount Lebanon, Baalbek-El Hermel, Beirut, Bekaa, El Nabatieh, Nimba, Grand Kru, Grand Cape Mount, Gbarpolu, Grand Bassa, Rivercess, Montserrado, River Gee, Lofa, Bomi, Bong, Sinoe, Maryland, Margibi, Grand Gedeh, East, North Central, Uva, Western, Sabaragamuwa, Southern, Northern, North Western, Kidal, Gao, Tombouctou, Bamako, Kayes, Koulikoro, Mopti, Segou, Sikasso, Yangon, Rakhine, Shan (North), Kayin, Kachin, Shan (South), Mon, Tanintharyi, Mandalay, Kayah, Shan (East), Chin, Magway, Sagaing, Zambezia, Cabo_Delgado, Tete, Manica, Sofala, Maputo, Gaza, Niassa, Inhambane, Maputo City, Nampula, Hodh Ech Chargi, Hodh El Gharbi, Brakna, Adrar, Assaba, Guidimakha, Gorgol, Trarza, Tagant, Dakhlet-Nouadhibou, Nouakchott, Tiris-Zemmour, Central Region, Southern Region, Northern Region, Tillaberi, Tahoua, Agadez, Zinder, Dosso, Niamey, Maradi, Diffa, Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Adamawa, Cordillera Administrative region, Region XIII, Region VI, Region V, Region III, Autonomous region in Muslim Mindanao, Region IV-A, Region VIII, Region VII, Region X, Region II, Region IV-B, Region XII, Region XI, Region I, National Capital region, Region IX, North Darfur, Blue Nile, Nile, Eastern Darfur, West Kordofan, Gedaref, West Darfur, North Kordofan, South Kordofan, Kassala, Khartoum, White Nile, South Darfur, Red Sea, Sennar, Al Gezira, Central Darfur, Tambacounda, Diourbel, Ziguinchor, Kaffrine, Dakar, Saint Louis, Fatick, Kolda, Louga, Kaolack, Kedougou, Matam, Thies, Sedhiou, Shabelle Hoose, Juba Hoose, Bay, Banadir, Shabelle Dhexe, Gedo, Hiraan, Woqooyi Galbeed, Awdal, Bari, Juba Dhexe, Togdheer, Nugaal, Galgaduud, Bakool, Sanaag, Mudug, Sool, Warrap, Unity, Jonglei, Northern Bahr el Ghazal, Upper Nile, Central Equatoria, Western Bahr el Ghazal, Eastern Equatoria, Western Equatoria, Lakes, Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Ouaddai, Salamat, Wadi Fira, Sila, Ennedi Est, Batha, Tibesti, Logone Oriental, Logone Occidental, Guera, Hadjer Lamis, Lac, Mayo Kebbi Est, Chari Baguirmi, Ennedi Ouest, Borkou, Tandjile, Mandoul, Moyen Chari, Mayo Kebbi Ouest, Kanem, Barh El Gazal, Ndjaména, Al Dhale'e, Aden, Al Bayda, Al Maharah, Lahj, Al Jawf, Raymah, Al Hudaydah, Hajjah, Amran, Shabwah, Dhamar, Ibb, Sana'a, Al Mahwit, Marib, Hadramaut, Sa'ada, Amanat Al Asimah, Socotra, Taizz, Abyan

Japan's main stock market index, the JP225, rose to 40790 points on July 23, 2025, gaining 2.55% from the previous session. Over the past month, the index has climbed 5.15% and is up 4.18% 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 July of 2025.

One United States dollar was worth over ****** Indonesian rupiah in May 2024, the highest value in a comparison of over 50 different currencies worldwide. All countries and territories shown here are based on the Big Mac Index - a measurement of how much a single Big Mac is worth across different areas in the world. This exchange rate comparison reveals a strong position of the dollar in Asia and Latin America. Note, though, that several of the top currencies shown here do not rank among the most traded. The quarterly U.S. dollar exchange rate against the ten biggest forex currencies only contains the Korean won and the Japanese yen.

FOREX Rate Index: Month Avg: Bilateral: NM: Poland data was reported at 145.359 Dec2000=100 in Jun 2018. This records an increase from the previous number of 141.634 Dec2000=100 for May 2018. FOREX Rate Index: Month Avg: Bilateral: NM: Poland data is updated monthly, averaging 112.134 Dec2000=100 from Jan 1999 (Median) to Jun 2018, with 234 observations. The data reached an all-time high of 160.944 Dec2000=100 in Dec 2016 and a record low of 78.657 Dec2000=100 in Jul 2008. FOREX Rate Index: Month Avg: Bilateral: NM: Poland data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.M011: Nominal and Real Foreign Exchange Rate Index: Bilateral: by Country: IMF Approach.

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: Kidal, Gao, Tombouctou, Bamako, Kayes, Koulikoro, Mopti, Segou, Sikasso, Market Average

New Zealand Foreign Exchange Rate: Avg: Trade Weighted Index: 17 Group Currency: Historical data was reported at 71.670 Jun1979=100 in Oct 2018. This records a decrease from the previous number of 71.790 Jun1979=100 for Sep 2018. New Zealand Foreign Exchange Rate: Avg: Trade Weighted Index: 17 Group Currency: Historical data is updated monthly, averaging 64.180 Jun1979=100 from Jul 1986 (Median) to Oct 2018, with 388 observations. The data reached an all-time high of 80.930 Jun1979=100 in Jul 2014 and a record low of 46.880 Jun1979=100 in Nov 2000. New Zealand Foreign Exchange Rate: Avg: Trade Weighted Index: 17 Group Currency: Historical data remains active status in CEIC and is reported by Reserve Bank of New Zealand. The data is categorized under Global Database’s New Zealand – Table NZ.M017: New Zealand Dollar Trade Weighted Index.

Graph and download economic data for Trade Weighted U.S. Dollar Index: Other Important Trading Partners, Goods (DISCONTINUED) from 1995-01-04 to 2020-01-01 about trade-weighted, trade, exchange rate, currency, goods, rate, indexes, and USA.

Switzerland FOREX Rate Index: Month Avg: Effective: Real:(CPI) Consumer Price IndexBased data was reported at 108.423 Dec2000=100 in Jun 2018. This records an increase from the previous number of 106.641 Dec2000=100 for May 2018. Switzerland FOREX Rate Index: Month Avg: Effective: Real:(CPI) Consumer Price IndexBased data is updated monthly, averaging 99.395 Dec2000=100 from Jan 1973 (Median) to Jun 2018, with 546 observations. The data reached an all-time high of 128.074 Dec2000=100 in Aug 2011 and a record low of 72.851 Dec2000=100 in Jan 1973. Switzerland FOREX Rate Index: Month Avg: Effective: Real:(CPI) Consumer Price IndexBased data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.M010: Nominal and Real Foreign Exchange Rate Index: Effective: IMF Approach. Since 2000 is calculated against the trading partners with a share in Switzerland's foreign trade exceeding 0.2% from the following pool of 54 countries: Egypt, Argentina, Australia, Belgium, Brazil, Bulgaria, Chile, China, Denmark, Germany, Estonia, Finland, France, Greece, Hong Kong, India, Ireland, Israel, Italy, Japan, Jordan, Canada, Croatia, Latvia, Lithuania, Luxembourg, Malaysia, Malta, Mexico, New Zealand, Netherlands, Norway, Austria, Peru, Philippines, Poland, Portugal, Romania, Russian Federation, Saudi Arabia, Sweden, Singapore, Slovakia, Slovenia, Spain, South Africa, South Korea, Thailand, Czech Republic, Turkey, Hungary, United States, United Kingdom and Cyprus. Between 1973 and 1999 was calculated against the following 15 trading partners: Belgium/Luxembourg, Denmark, Germany, France, Italy, Japan, Canada, Netherlands, Norway, Austria, Portugal, Sweden, Spain, United States and the United Kingdom.

Effective FX Index: Trade: 6 Currencies: Real: 2021-22p data was reported at 101.651 2021-2022=100 in Jun 2024. This records an increase from the previous number of 100.248 2021-2022=100 for May 2024. Effective FX Index: Trade: 6 Currencies: Real: 2021-22p data is updated monthly, averaging 99.979 2021-2022=100 from Jun 2018 (Median) to Jun 2024, with 73 observations. The data reached an all-time high of 103.340 2021-2022=100 in Dec 2019 and a record low of 94.598 2021-2022=100 in Oct 2018. Effective FX Index: Trade: 6 Currencies: Real: 2021-22p data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.MC009: Effective Exchange Rate Index.

Switzerland FOREX Rate Index: Month Avg: Effective: Nominal data was reported at 151.474 Dec2000=100 in Jun 2018. This records an increase from the previous number of 148.625 Dec2000=100 for May 2018. Switzerland FOREX Rate Index: Month Avg: Effective: Nominal data is updated monthly, averaging 97.947 Dec2000=100 from Jan 1973 (Median) to Jun 2018, with 546 observations. The data reached an all-time high of 157.603 Dec2000=100 in Jun 2015 and a record low of 40.401 Dec2000=100 in Jan 1973. Switzerland FOREX Rate Index: Month Avg: Effective: Nominal data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.M010: Nominal and Real Foreign Exchange Rate Index: Effective: IMF Approach. Since 2000 is calculated against the trading partners with a share in Switzerland's foreign trade exceeding 0.2% from the following pool of 54 countries: Egypt, Argentina, Australia, Belgium, Brazil, Bulgaria, Chile, China, Denmark, Germany, Estonia, Finland, France, Greece, Hong Kong, India, Ireland, Israel, Italy, Japan, Jordan, Canada, Croatia, Latvia, Lithuania, Luxembourg, Malaysia, Malta, Mexico, New Zealand, Netherlands, Norway, Austria, Peru, Philippines, Poland, Portugal, Romania, Russian Federation, Saudi Arabia, Sweden, Singapore, Slovakia, Slovenia, Spain, South Africa, South Korea, Thailand, Czech Republic, Turkey, Hungary, United States, United Kingdom and Cyprus. Between 1973 and 1999 was calculated against the following 15 trading partners: Belgium/Luxembourg, Denmark, Germany, France, Italy, Japan, Canada, Netherlands, Norway, Austria, Portugal, Sweden, Spain, United States and the United Kingdom.