At the end of February 2025, the DAX index reached ********* points, marking its highest level since January 2015. Moreover, this also reflected a strong recovery from the global coronavirus (COVID-19) pandemic, having risen from ******** points at the end of March 2020 and surpassing its pre-pandemic level of approximately ********* points at the end of December 2019. Origin and composition of the DAX Index The DAX (Deutscher Aktienindex) is the most important German stock index, showing the value trends of the 40 largest companies by market capitalization listed on the Frankfurt stock exchange. The DAX index was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959. The count among their number some of the most recognizable companies in the world, such as carmakers Volkswagen and Daimler, sportswear brand adidas, and industrial giants Siemens and BASF. After the DAX, the 50 next-largest German companies are included in the midcap MDAX index, while the 70 next-largest small and medium-sized German companies (ranked from 91 to 160) are included in the SDAX index. The Frankfurt Stock Exchange All the companies included in the DAX family of indices are traded on the Frankfurt Stock Exchange. Dating back to 1585, the Frankfurt Stock Exchange is considered to be the oldest exchange in the world. It is the twelfth largest stock exchange in the world in terms of market capitalization, and accounts for around ** percent of all equity trading in Germany. Two main trading venues comprise the Frankfurt Stock Exchange: the Börse Frankfurt is a traditional trading floor; while the Xetra is an electronic trading system which accounts for the vast majority of trading volume on Frankfurt Stock Exchange. As of December 2023, the total market capitalization of all companies listed on the Frankfurt Stock Exchange was around *** trillion euros.

The DAX is a stock market index composed of the ** major German blue chip companies trading on the Frankfurt Stock Exchange. At the close of 2024, the DAX (Deutscher Aktienindex) closed at ********* points. This was the highest closing value of the observed period.What is the DAX index? The DAX is the most important stock index in Germany. It was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959. The DAX index is comprised of ** largest and most liquid German companies such as Deutsche Bank, Allianz or Bayer. These companies are traded on the Frankfurt Stock Exchange, which is the oldest exchange worldwide. The index can be viewed as a snapshot of the investment climate in Germany. What is not included in the DAX? Most notably, the DAX, like most indices, is not adjusted for inflation. While inflation has been relatively low in recent years, it might be useful to adjust the historic figures on the index when comparing historic data to current levels. This is particularly important for years when the index appears to increase by a few percentage points, because inflation may have increased at a more rapid rate than the stock prices.

Germany's main stock market index, the DE40, rose to 24458 points on July 18, 2025, gaining 0.36% from the previous session. Over the past month, the index has climbed 6.08% and is up 34.59% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on July of 2025.

The statistic compares the average monthly performance of the DAX index in 2023 compared to 1959. In Germany, the DAX is the most important share index, showing the value trends of the 40 largest and most highly liquid companies listed on the Frankfurt stock exchange. The DAX index was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959. The average historical performance of the DAX index in September since 1959 amounted to -1.82 percent.

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.

DAX Index Unraveled?

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

Prices for Germany Stock Market Index (DE40) including live quotes, historical charts and news. Germany Stock Market Index (DE40) was last updated by Trading Economics this July 18 of 2025.

The weekly value of the DAX index amounted to 23,081.03 as of March 5, 2025. This is well above the value of 13,681.19 points recorded in the middle of February 2020, prior to the global coronavirus (COVID-19) pandemic. All global stock markets were hit by the growing panic about the coronavirus pandemic, which accounts for this drop. The DAX (Deutscher Aktienindex) is the most important German stock index, showing the value trends of the 30 largest and most liquid companies listed on the Frankfurt stock exchange. The DAX index was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959.

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.

DAX Index: Germany's Economic Pulse?

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

The total market capitalization of German companies listed on the Frankfurt Stock exchange reached **** trillion euros at the end of 2021. This is above the values found at the end of 2019 and 2017, indicating that Germany's stock market has largely recovered from the financial crash precipitated by the global coronavirus (COVID-19) pandemic in 2020. At the end of 2023, the total market capitalization of German companies listed on the Frankfurt Stock exchange closed at **** trillion euros, a significant decrease compared to the previous year. What is the Frankfurt Stock Exchange? While there are seven stock exchanges in Germany, the Frankfurt Stock Exchange is by far the most important, accounting for around ** percent of transactions. Run by Deutsche Börse AG, the Frankfurt Stock Exchange is comprised of two exchange trading venues: the traditional trading floor of the Börse Frankfurt, and the electronic trading platform Xetra (which in turn is divided into domestic and international markets). Xetra counts for the vast majority of the trading volume of the Frankfurt Stock Exchange. As an electronic platform, the technology behind Xetra is used by other stock exchanges around the world, strengthening the Frankfurt Stock Exchange’s competitive position while facilitating its capacity to handle international trading. As a result, the Frankfurt Stock Exchange is one of the largest stock exchanges in the world, sitting just outside the global top 10. The DAX Index The most important indicator of the German share market is the DAX index, which is comprised of the 30 largest German companies trading on the Frankfurt Stock Exchange. Some of the more famous companies included in the index are: car manufactures like Volkswagen, BMW and Daimler; clothing and shoe manufacturer Adidas; industrial companies BASF and Siemens; and pharmaceutical company Bayer. Following the DAX is the MDAX index, which covers the 60 next-largest German companies by market cap, then the SDAX index, comprised of the 70 next-largest companies after the MDAX.

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's the DAX Index Telling You?

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

Germany XETRA Frankfurt: Index: Total Return: X-DAX data was reported at 22,644.720 NA in Apr 2025. This records an increase from the previous number of 22,281.750 NA for Mar 2025. Germany XETRA Frankfurt: Index: Total Return: X-DAX data is updated monthly, averaging 13,769.275 NA from Sep 2017 (Median) to Apr 2025, with 92 observations. The data reached an all-time high of 22,644.720 NA in Apr 2025 and a record low of 9,817.740 NA in Mar 2020. Germany XETRA Frankfurt: Index: Total Return: X-DAX data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Germany – Table DE.EDI.SE: XETRA Frankfurt: Monthly.

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.

DAX Index: Unlocking Dynamic Dimensions?

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

The value of shares traded on the Frankfurt Stock Exchange jumped in March 2020, with the *** largest German companies generating a turnover of almost *** billion euros in that month alone. The vast majority of this turnover - over ** percent - related to the ** largest German companies included in the DAX index (as it was at that time), with around ** percent relating the the MDAX index, and only ***** percent the SDAX. By June 2024, the monthly turnover value had stabilized at around **** billion euros.

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.

Can DAX Index Revolutionize Data Analysis?

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

Euro Stoxx 50 is the index designed by STOXX, a globally operating index provider headquartered in Zurich, Switzerland, which in turn is owned by Deutsche Börse Group. This index provides the broad representation of the Eurozone blue chips performance. Blue chips are corporations known on the European market for quality, reliability and the ability to operate profitably both in good and bad economic times.
Development of the Euro Stoxx 50 index The year-end value of the Euro Stoxx 50 peaked in 1999, with 4,904.46 index points. It noted significant decrease between 1999 and 2002, then an increase to 4,399.72 in 2007, prior to the global recession. Since the very sharp decline in 2008, there was a tentative increase, never yet reaching the pre-recession levels. As of the end of 2021, the Euro Stoxx 50 index was getting close to its historical heights, reaching 4,298.41 points, its highest position post recession, before falling again in 2022. In 2023 and 2024, the index rose again, reaching 4,862.28 points. Some of the following reputable companies formed the Euro Stoxx 50 index: Adidas, Airbus Group, Allianz, BMW, BNP Paribas, L'Oréal, ING Group NV, Nokia, Phillips, Siemens, Société Générale SA or Volkswagen Group.
European financial stock exchange indices Other European indices include the DAX (Deutscher Aktienindex) index and the FTSE 100 (Financial times Stock Exchange 100 index). FTSE, informally known as the “Footsie”, is a share index of the 100 companies listed on the London Stock Exchange with the highest market capitalization. The Index, which began in January 1984 with the base level of 1,000, reached 7,733.24 at the closing of 2023. More in-depth information can be found in the report on stock market indices.

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.

DAX Index: Barometer of German Economic Health?

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

XETRA Frankfurt：指数：总回报：X-DAX在04-01-2025达22,644.720NA，相较于03-01-2025的22,281.750NA有所增长。XETRA Frankfurt：指数：总回报：X-DAX数据按月更新，09-01-2017至04-01-2025期间平均值为13,769.275NA，共92份观测结果。该数据的历史最高值出现于04-01-2025，达22,644.720NA，而历史最低值则出现于03-01-2020，为9,817.740NA。CEIC提供的XETRA Frankfurt：指数：总回报：X-DAX数据处于定期更新的状态，数据来源于Exchange Data International Limited，数据归类于全球数据库的德国 – Table DE.EDI.SE: XETRA Frankfurt: Monthly。

As of March 2025, the software company SAP had the highest market capitalization out of all the DAX companies, with around **** billion euros. The company with the second-highest market capitalization was Siemens, with a market capitalization value of around **** billion euros. Market capitalization reflects the current stock market value of a company and is calculated by multiplying the share price by the number of shares issued. Market capitalization therefore also corresponds to the price that a buyer would have to pay for all of a company's shares in circulation - i.e. a complete takeover.

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.

DAX Index: Is Germany's Economy on the Rise?

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

This dataset offers both live (delayed) prices and End Of Day time series on equity options

1/ Live (delayed) prices for options on European stocks and indices including: Reference spot price, bid/ask screen price, fair value price (based on surface calibration), implicit volatility, forward Greeks : delta, vega Canari.dev computes AI-generated forecast signals indicating which option is over/underpriced, based on the holders strategy (buy and hold until maturity, 1 hour to 2 days holding horizon...). From these signals is derived a "Canari price" which is also available in this live tables.
Visit our website (canari.dev ) for more details about our forecast signals.

The delay ranges from 15 to 40 minutes depending on underlyings.

2/ Historical time series: Implied vol Realized vol Smile Forward
See a full API presentation here : https://youtu.be/qitPO-SFmY4 .

These data are also readily accessible in Excel thanks the provided Add-in available on Github: https://github.com/canari-dev/Excel-macro-to-consume-Canari-API

If you need help, contact us at: contact@canari.dev

User Guide: You can get a preview of the API by typing "data.canari.dev" in your web browser. This will show you a free version of this API with limited data.

Here are examples of possible syntaxes:

For live options prices: data.canari.dev/OPT/DAI data.canari.dev/OPT/OESX/0923 The "csv" suffix to get a csv rather than html formating, for example: data.canari.dev/OPT/DB1/1223/csv For historical parameters: Implied vol : data.canari.dev/IV/BMW

data.canari.dev/IV/ALV/1224

data.canari.dev/IV/DTE/1224/csv

Realized vol (intraday, maturity expressed as EWM, span in business days): data.canari.dev/RV/IFX ... Implied dividend flow: data.canari.dev/DIV/IBE ... Smile (vol spread between ATM strike and 90% strike, normalized to 1Y with factor 1/√T): data.canari.dev/SMI/DTE ... Forward: data.canari.dev/FWD/BNP ...

List of available underlyings: Code Name OESX Eurostoxx50 ODAX DAX OSMI SMI (Swiss index) OESB Eurostoxx Banks OVS2 VSTOXX ITK AB Inbev ABBN ABB ASM ASML ADS Adidas AIR Air Liquide EAD Airbus ALV Allianz AXA Axa BAS BASF BBVD BBVA BMW BMW BNP BNP BAY Bayer DBK Deutsche Bank DB1 Deutsche Boerse DPW Deutsche Post DTE Deutsche Telekom EOA E.ON ENL5 Enel INN ING IBE Iberdrola IFX Infineon IES5 Intesa Sanpaolo PPX Kering LOR L Oreal MOH LVMH LIN Linde DAI Mercedes-Benz MUV2 Munich Re NESN Nestle NOVN Novartis PHI1 Philips REP Repsol ROG Roche SAP SAP SNW Sanofi BSD2 Santander SND Schneider SIE Siemens SGE Société Générale SREN Swiss Re TNE5 Telefonica TOTB TotalEnergies UBSN UBS CRI5 Unicredito SQU Vinci VO3 Volkswagen ANN Vonovia ZURN Zurich Insurance Group