Serbia Banking Sector Open Forex Positions: FX Risk Indicator: Gross Principle data was reported at 3.180 % in Jun 2018. This records a decrease from the previous number of 3.470 % for Mar 2018. Serbia Banking Sector Open Forex Positions: FX Risk Indicator: Gross Principle data is updated quarterly, averaging 3.925 % from Sep 2008 (Median) to Jun 2018, with 40 observations. The data reached an all-time high of 8.280 % in Sep 2008 and a record low of 2.600 % in Jun 2017. Serbia Banking Sector Open Forex Positions: FX Risk Indicator: Gross Principle data remains active status in CEIC and is reported by National Bank of Serbia. The data is categorized under Global Database’s Serbia – Table RS.KB011: Banking Sector Performance Indicators. Starting from Q4 2011, National Bank of Serbia (NBS) changed the calculation methodology of the FX ratio. It is now calculated on a gross basis where NBS adds up all the gross long and short positions, regardless of the net position in each currency; the larger of the two is put in relation to regulatory capital. Prior to that, the FX ratio has been calculated on a net basis where in each currency the NBS determined the net position first (i.e. net long/short) and consequently all net long and short positions were summed up. The larger of two has been put in relation to regulatory capital.

Key information about European Union Foreign Exchange Reserves

• European Union Foreign Exchange Reserves was measured at 297.5 USD bn in Jan 2025, compared with 298.7 USD bn in the previous month
• EU Foreign Exchange Reserves: USD mn data is updated monthly, available from Dec 1999 to Jan 2025
• The data reached an all-time high of 319.1 USD bn in Jul 2021 and a record low of 165.4 USD bn in Mar 2006

• In the latest reports, EU Foreign Exchange Reserves equaled 1.3 Months of Import in Nov 2024.
• Its Money Supply M2 increased 16,118.6 USD bn YoY in Dec 2024.
• EU Domestic Credit reached 25,343.8 USD bn in Nov 2024, representing an increased of 0.7 % YoY.
• Household Debt of EU reached 44.4 % in Sep 2024, accounting for 44.4 % of the country's Nominal GDP.

deerfieldgreen/fx-technical-indicators-4hour dataset hosted on Hugging Face and contributed by the HF Datasets community

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

Bosnia and Herzegovina Banking Sector: Forex Liabilities to Total Financial Liabilities data was reported at 40.026 % in Dec 2024. This records a decrease from the previous number of 40.721 % for Sep 2024. Bosnia and Herzegovina Banking Sector: Forex Liabilities to Total Financial Liabilities data is updated quarterly, averaging 61.866 % from Jun 2003 (Median) to Dec 2024, with 87 observations. The data reached an all-time high of 70.737 % in Jun 2009 and a record low of 40.026 % in Dec 2024. Bosnia and Herzegovina Banking Sector: Forex Liabilities to Total Financial Liabilities data remains active status in CEIC and is reported by Central Bank of Bosnia and Herzegovina. The data is categorized under Global Database’s Bosnia and Herzegovina – Table BA.KB007: Banking Sector: Performance Indicators.

Bosnia and Herzegovina Banking Sector: Forex and Indexed Loans to Total Loans data was reported at 31.888 % in Dec 2024. This records a decrease from the previous number of 33.271 % for Sep 2024. Bosnia and Herzegovina Banking Sector: Forex and Indexed Loans to Total Loans data is updated quarterly, averaging 65.989 % from Jun 2003 (Median) to Dec 2024, with 87 observations. The data reached an all-time high of 74.525 % in Sep 2007 and a record low of 31.888 % in Dec 2024. Bosnia and Herzegovina Banking Sector: Forex and Indexed Loans to Total Loans data remains active status in CEIC and is reported by Central Bank of Bosnia and Herzegovina. The data is categorized under Global Database’s Bosnia and Herzegovina – Table BA.KB007: Banking Sector: Performance Indicators.

This thesis deals with theoretical and practical aspects of trading on financial markets and tries to create detailed description of trading strategy optimized for specific trading pairs. The main goal of this thesis is to design trading strategy based on technical analysis traded with trend. Important part of the thesis is to design suitable optimization of chosen parameters with purpose of maximizing profit and stability and lastly, comparison and evaluation of the results before and after optimization. Táto práca sa zaoberá teoretickými aj praktickými aspektami obchodovania na devízových trhoch a snaží sa vytvoriť podrobný popis obchodnej stratégie optimalizovanej na konkrétne menové páry. Hlavným cieľom tejto práce je návrh obchodnej stratégie založenej na technickej analýze obchodovanej do trendu. Dôležitou časťou práce je návrh vhodnej optimalizácie vybraných parametrov stratégie s cieľom maximalizácie zisku a stability a nakoniec porovnanie a vyhodnotenie výsledkov pred a po optimalizácii. D

deerfieldgreen/fx-technical-indicators-30min dataset hosted on Hugging Face and contributed by the HF Datasets community

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

Introducing our comprehensive economic calendar, your ultimate resource for tracking major global economic events and their impact on currency and stock market prices. With a vast array of fields including event name, country, previous and current values, and more, our calendar provides you with essential data to make informed financial decisions. Stay ahead of the curve with our real-time updates, ensuring you have access to the latest information every 15 minutes. With this powerful tool at your fingertips, you can confidently navigate the dynamic world of economic events and seize opportunities for success. Don't miss out on this essential resource for staying informed and making calculated moves in the market.

Context

Possible prediction of the next opening or closing price

Content

See Column_Description_GBPUSD.csv

Acknowledgements

Thanks to all who have made a contribution to this dataset

Georgia Commercial Banks: Consolidated Open FX Position data was reported at 276.785 GEL mn in Sep 2018. This records an increase from the previous number of 221.228 GEL mn for Aug 2018. Georgia Commercial Banks: Consolidated Open FX Position data is updated monthly, averaging 42.060 GEL mn from Jan 2002 (Median) to Sep 2018, with 201 observations. The data reached an all-time high of 18,405.889 GEL mn in Jul 2005 and a record low of -108.039 GEL mn in Aug 2015. Georgia Commercial Banks: Consolidated Open FX Position data remains active status in CEIC and is reported by National Bank of Georgia . The data is categorized under Global Database’s Georgia – Table GE.KB011: Financial Soundness Indicators: Commercial Banks.

Russia Sberbank: Main Indicators: Year to Date: Net Gain (Loss) from FX Revaluation & Trading Operations data was reported at 100,186.000 RUB mn in Dec 2018. This records an increase from the previous number of 83,592.000 RUB mn for Nov 2018. Russia Sberbank: Main Indicators: Year to Date: Net Gain (Loss) from FX Revaluation & Trading Operations data is updated monthly, averaging 11,969.000 RUB mn from Jun 2008 (Median) to Dec 2018, with 127 observations. The data reached an all-time high of 100,186.000 RUB mn in Dec 2018 and a record low of -33,792.000 RUB mn in Jun 2016. Russia Sberbank: Main Indicators: Year to Date: Net Gain (Loss) from FX Revaluation & Trading Operations data remains active status in CEIC and is reported by Sberbank of Russia. The data is categorized under Russia Premium Database’s Monetary and Banking Statistics – Table RU.KAK016: Sberbank: Main Indicators.