Prices for NSE Nifty 50 Index including live quotes, historical charts and news. NSE Nifty 50 Index was last updated by Trading Economics this July 13 of 2025.

Nigeria's main stock market index, the NSE-All Share, rose to 126150 points on July 11, 2025, gaining 1.37% from the previous session. Over the past month, the index has climbed 9.29% and is up 26.57% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Nigeria. Nigeria Stock Market NSE - values, historical data, forecasts and news - updated on July of 2025.

India's main stock market index, the SENSEX, fell to 82253 points on July 14, 2025, losing 0.30% from the previous session. Over the past month, the index has climbed 0.56% and is up 1.97% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

This dataset contains historical data of the NSE main Index NIFTY 50 which lists the top 50 companies in India. It contains the opening, closing, high, low, adj close, volume data of the NIFTY 50 index from 2007 to 2024.

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.

NSE LEMONTREE Options & Futures Prediction

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

Dataset

This dataset was created by Oliver Njeru

Contents

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.

Should You Buy NSE ITC Right Now? (Stock Forecast)

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 statistic depicts the average annual performance of the Nifty 50 Index in India from years 2011 to 2024. In 2024, the average annual Nifty 50 Index was reported as ********, an increase from the previous year where the value was ********.

Prices for Nairobi Securities Exchange Ltd All Share Index including live quotes, historical charts and news. Nairobi Securities Exchange Ltd All Share Index was last updated by Trading Economics this July 14 of 2025.

Key information about Nigeria NSE All Share

• Nigeria NSE All Share closed at 107,821.4 points in Feb 2025, compared with 104,496.1 points at the previous month end
• Nigeria Equity Market Index: Month End: NSE All Share data is updated monthly, available from Jan 1985 to Feb 2025, with an average number of 21,486.6 points
• The data reached an all-time high of 107,821.4 points in Feb 2025 and a record low of 111.3 points in Jan 1985

• In the latest reports, Weighted Equities recorded a yearly P/E ratio of 108.0 in Dec 2021

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 66 companies listed on the Nairobi Securities Exchange (XNAI) in Kenya. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Kenya:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Kenya:

Nairobi Securities Exchange All Share Index (NASI): The main index that tracks the performance of all companies listed on the Nairobi Securities Exchange (NSE). NASI provides insights into the overall market performance in Kenya.

Nairobi Securities Exchange 20 Share Index (NSE 20): An index that tracks the performance of the top 20 companies by market capitalization listed on the NSE. NSE 20 is an important benchmark for the Kenyan stock market.

Safaricom PLC: A leading telecommunications company in Kenya, offering mobile and internet services. Safaricom is one of the largest and most actively traded companies on the NSE.

Equity Group Holdings PLC: A prominent financial institution in Kenya, providing banking and financial services. Equity Group is a significant player in the Kenyan financial sector and is listed on the NSE.

KCB Group PLC: Another major financial institution in Kenya, offering banking and financial services. KCB Group is also listed on the NSE and is among the key players in the country's banking industry.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Kenya, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Kenya ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Kenya?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Kenya exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and se...

The Real-time Candlestick OHLC API provides current candlestick data that covers all major stock exchanges including NYSE, NASDAQ, LSE, Euronext to NSE of India, TSE, and a few more. Users can choose from candlestick data with 1 min, 2 min, 5 min, 15 min, 30 min, 1 hour, 4 hour, 1 day, 1 week, 1 month and 1 year interval. By using the real-time candlestick OHLC data, they can visualize data on candlestick charts and build financial products.

Kenya's main stock market index, the Nairobi 20, fell to 2514 points on July 11, 2025, losing 0.10% from the previous session. Over the past month, the index has climbed 11.00% and is up 48.23% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Kenya. Kenya Stock Market (NSE20) - values, historical data, forecasts and news - updated on July of 2025.

After some rigorous SQL queries and coding on python. I made this dataset. In this dataset, all stocks of the Indian Stock Market are present a total of 2435 stocks. The data is of 1-year rows represent stock name and column represent date and I have filled the table with closing price. Enjoy and do some stock price predictions.

Kenya Nairobi Securities Exchange: Index: NSE 20 Share data was reported at 2,135.510 NA in Apr 2025. This records a decrease from the previous number of 2,226.880 NA for Mar 2025. Kenya Nairobi Securities Exchange: Index: NSE 20 Share data is updated monthly, averaging 2,676.920 NA from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 5,491.370 NA in Feb 2015 and a record low of 1,461.070 NA in Oct 2023. Kenya Nairobi Securities Exchange: Index: NSE 20 Share data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Kenya – Table KE.EDI.SE: Nairobi Securities Exchange: Monthly.

Prices for Nigerian Stock Exchange All Share Index including live quotes, historical charts and news. Nigerian Stock Exchange All Share Index was last updated by Trading Economics this July 13 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.

NSE MIDHANI Options & Futures Prediction

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

Equity Market Index: Month End: NSE All Share: Quarterly data was reported at 32,766.370 03Jan1984=100 in Sep 2018. This records a decrease from the previous number of 38,278.550 03Jan1984=100 for Jun 2018. Equity Market Index: Month End: NSE All Share: Quarterly data is updated quarterly, averaging 10,963.100 03Jan1984=100 from Mar 1985 (Median) to Sep 2018, with 135 observations. The data reached an all-time high of 63,016.560 03Jan1984=100 in Mar 2008 and a record low of 113.400 03Jan1984=100 in Mar 1985. Equity Market Index: Month End: NSE All Share: Quarterly data remains active status in CEIC and is reported by Central Bank of Nigeria. The data is categorized under Global Database’s Nigeria – Table NG.Z001: Nigeria Stock Exchange: Index.

Stock market prediction remains active research in a quest to inform investors on how to trade (buy/sell) at the most opportune time. The prevalent methods used by stock market players in trying to predict the likely future trade prices are either technical, fundamental or time series analysis. This research wanted to try out machine learning methods, in contrast to the existing prevalent methods. Artificial neural networks (ANNs) tend to be the preferred machine learning method for this type of application. However, ANNs require some historical data to learn from, in order to do predictions. The research used an ANN model to test the hypothesis that the next day price (prediction) can be determined from the stock prices of the immediate last five days. The final ANN model used for the tests was a feedforward multi-layer perceptron (MLP) with error backpropagation, using sigmoid activation function, with network configuration 5:21:21:1. The data period used was a 5-year dataset (2008 to 2012), with 80% of the data (4-year data) used for training and the balance 20% used for testing (last 1-year data). The original raw data for Nairobi Securities Exchange (NSE) was scrapped from a publicly available and accessible website of a stock market analysis company in Kenya (Synergy, 2020). This daily prices data was first exported to a spreadsheet, then cleaned off headers and other redundant information, leaving only the data with stock name, date of trade and the related data such as volumes, low prices, high prices and adjusted prices. The data was further sorted by the stock names and then the trading dates. The data dimension was finally reduced to only what was needed for the research, which was the stock name, the date of trade and the adjusted price (average trade price). This final dataset was in CSV format, as hereby presented. The research tested three NSE stocks with the mean absolute percentage error (MAPE) ranging between 0.77% to 1.91%, over the 3-month testing period, while the root mean squared error (RMSE) ranged between 1.83 and 3.07. This raw data can be used to train and test any machine learning model that requires training and testing data. The data can also be used to validate and reproduce the results already presented in this research. There could be slight variance between what is obtained when reproducing the results, due to the differences in the final exact weights that the trained ANN model eventually achieves. However, these differences should not be significant. List of data files on this dataset: stock01_NSE_01jan2008_to_31dec2012_Kakuzi.csv stock02_NSE_01jan2008_to_31dec2012_StandardBank.csv stock03_NSE_01jan2008_to_31dec2012_KenyaAirways.csv stock04_NSE_01jan2008_to_31dec2012_BamburiCement.csv stock05_NSE_01jan2008_to_31dec2012_Kengen.csv stock06_NSE_01jan2008_to_31dec2012_BAT.csv References: Synergy Systems Ltd. (2020). MyStocks. Retrieved March 9, 2020, from http://live.mystocks.co.ke/

Key information about India P/E ratio

• India SENSEX recorded a daily P/E ratio of 21.540 on 26 Mar 2025, compared with 21.740 from the previous day.
• India SENSEX P/E ratio is updated daily, with historical data available from Dec 1988 to Mar 2025.
• The P/E ratio reached an all-time high of 36.210 in Feb 2021 and a record low of 15.670 in Mar 2020.
• BSE Limited provides daily P/E Ratio.