Germany's main stock market index, the DE40, fell to 23787 points on July 4, 2025, losing 0.61% from the previous session. Over the past month, the index has declined 2.20%, though it remains 28.75% higher than a year ago, 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.

Germany's main stock market index, the DE40, rose to 23668 points on June 24, 2025, gaining 1.71% from the previous session. Over the past month, the index has declined 1.50%, though it remains 30.20% higher than a year ago, 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 June of 2025.

This dataset contains the futures market from the following countries:

Australia -> Aus200

Brazil -> Bra50 and MinDol

Spain -> Esp35

France -> Fra40

Germany -> Ger40

Hong Kong -> HkInd

Italy-> Ita40

Netherlands -> Neth25

Switzerland -> Swi20

United Kingdom -> UK100

United States -> Usa500, UsaTec and UsaRus

There's a csv file for all those markets and one with all in one.

Note: the MinDol, Swi20 and Neth25 data were taken by it's monthly contract, because MetaTrader5 don't have their historical series (like S&P 500, that has the 'Usa500' and 'Usa500Mar24'):

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17272056%2Fefa5c9f6d7841c496d20d467d4a1c874%2Ffutures_dailycontract.png?generation=1704756245532483&alt=media" alt="">

MT5 Library: (I used PyCharm because the i couldn't be able to use the mt5 library on GoogleColab)

import MetaTrader5 as mt5
import pandas as pd
import numpy as np
import pytz
from datetime import datetime

if not mt5.initialize(login= , server= "server", password=""):
# you can use your login and password if you have an account on a broker to use mt5
  print("initialize() failed, error code =", mt5.last_error())
  quit()


symbols = mt5.symbols_get()

list_symbols = []
for num in range(0, len(symbols)):
  list_symbols.append(symbols[num].name)

print(list_symbols)

list_futures = ['Aus200', 'Bra50', 'Esp35', 'Fra40', 'Ger40', 'HKInd', 'Ita40Mar24', 'Jp225', 'MinDolFeb24', 'Neth25Jan24', 'UK100', 'Usa500', 'UsaRus', 'UsaTec', 'Swi20Mar24']
time_frame = mt5.TIMEFRAME_D1
dynamic_vars = {}

time_zone = pytz.timezone('Etc/UTC')

time_start = datetime(2017, 1, 1, tzinfo= time_zone)
time_end = datetime(2023, 12, 31, tzinfo= time_zone)

for sym in list_futures:
  var = f'{sym}'
  rates = mt5.copy_rates_range(sym, time_frame, time_start, time_end)

  rates_frame = pd.DataFrame(rates)
  rates_frame['time'] = pd.to_datetime(rates_frame['time'], unit='s')
  rates_frame = rates_frame[['time', 'close']]
  rates_frame.rename(columns = {'close': var}, inplace = True)
  dynamic_vars[var] = rates_frame

  dynamic_vars[sym].to_csv(f'{sym}.csv', index = False)

Comprehensive dataset of 40 Roller skating clubs in Germany as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 40 Mining consultants in Germany as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

About this dataset

Context

The dataset tabulates the population of German Valley by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for German Valley. The dataset can be utilized to understand the population distribution of German Valley by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in German Valley. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for German Valley.

Key observations

Largest age group (population): Male # 0-4 years (44) | Female # 40-44 years (21). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the German Valley population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the German Valley is shown in the following column.
• Population (Female): The female population in the German Valley is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in German Valley for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for German Valley Population by Gender. You can refer the same here

This dataset provides information on 40 in Germany as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset is about books. It has 1 row and is filtered where the book is The Forty-eighters : political refugees of the German Revolution of 1848. It features 7 columns including author, publication date, language, and book publisher.

About this dataset

Context

The dataset tabulates the population of German town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for German town. The dataset can be utilized to understand the population distribution of German town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in German town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for German town.

Key observations

Largest age group (population): Male # 50-54 years (23) | Female # 40-44 years (12). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the German town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the German town is shown in the following column.
• Population (Female): The female population in the German town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in German town for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for German town Population by Gender. You can refer the same here

Used-car market monitoring was started in 2018, 03 January, when Federal Administrative Court in Leipzig (Germany) allowed cities and communes in Germany to impose bans for diesel cars in order to reduce the level of nitric oxide in the air. This data-set represent 5th year of market monitoring, 2022. First year monitoring (2018) data-set is available together with data article: Skribans, V. Used-car market dataset for Latvia 2018, (2019) Data in Brief, 22, pp. 859-862. doi: 10.1016/j.dib.2018.12.075. Methodology of data mining is available in research paper: SKRIBANS, V. and HEGERTY, S.W., 2019. Data mining application for used-car market analysis, IMCIC 2019 - 10th International Multi-Conference on Complexity, Informatics and Cybernetics, Proceedings 2019, pp. 35-40. Second year monitoring (2019) data-set is available there: Skribans, Valerijs (2019), “Latvian Used-car Market Announcements Monitoring in 2019”, Mendeley Data, V1, doi: 10.17632/kyf948x63n.1 Third year year monitoring (2020) data-set is available there: Skribans, Valerijs; Gulbis, Aivars; Krastins, Aivars (2020), “Latvian Used-car Market Announcements Monitoring in 2020”, Mendeley Data, V1, doi: 10.17632/hg38nn6c45.1 . First quarter of the fourth year of market monitoring, 2021. set is available there: Skribans, Valerijs; Gulbis, Aivars; Cevers, Aldis; Rudzitis, Normunds; Krastins, Aivars (2021), “Latvian Used-car Market Announcements Monitoring in 2021 Q1”, Mendeley Data, V1, doi: 10.17632/rh9zh9ncnk.1

Imports from Samoa in Germany decreased to 35 EUR Thousand in January from 40 EUR Thousand in December of 2023. This dataset includes a chart with historical data for Germany Imports from Samoa.

About this dataset

Context

The dataset tabulates the German Valley population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for German Valley. The dataset can be utilized to understand the population distribution of German Valley by age. For example, using this dataset, we can identify the largest age group in German Valley.

Key observations

The largest age group in German Valley, IL was for the group of age 40 to 44 years years with a population of 52 (10.46%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in German Valley, IL was the 80 to 84 years years with a population of 4 (0.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the German Valley is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of German Valley total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for German Valley Population by Age. You can refer the same here

The whole study comprises of two patient cohorts. Screening cohort: 40 patients of Germany; validation cohort: 40 patients from Asia. Further, bile duct and CCA cell lines have been analyzed. This dataset contains whole exome sequencing data of 37 tumor/normal pairs from the screening cohort plus an additional relapse tumor of one of those 37 patients. Data was generated on Illumina HiSeq 2000 device in paired-end mode and is stored in BAM file format.

Les prix de gros en Allemagne ont augmenté à 117,40 points en janvier contre 116,30 points en décembre 2024. Cette dataset fournit - Prix de gros en Allemagne - valeurs réelles, données historiques, prévisions, graphique, statistiques, calendrier économique et actualités.

Les prix de gros sur un an en Allemagne ont diminué à 0,40 % en mai contre 0,80 % en avril 2025. Cette dataset présente un graphique avec des données historiques sur l'Indice des prix de gros en Allemagne sur un an.