Context

The National Stock Exchange of India Ltd. (NSE) is an Indian stock exchange located at Mumbai, Maharashtra, India. National Stock Exchange (NSE) was established in 1992 as a demutualized electronic exchange. It was promoted by leading financial institutions on request of the Government of India. It is India’s largest exchange by turnover. In 1994, it launched electronic screen-based trading. Thereafter, it went on to launch index futures and internet trading in 2000, which were the first of its kind in the country.

With the help of NSE, you can trade in the following segments:

• Equities

• Indices

• Mutual Funds

• Exchange Traded Funds

• Initial Public Offerings

• Security Lending and Borrowing Scheme

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Companies on successful IPOs gets their Stocks traded over different Stock Exchnage platforms. NSE is one important platofrm in India. There are thousands of companies trading their stocks in NSE. But, I have chosen two popular and high rated IT service companies of India; TCS and INFOSYS. and the third one is the benchmark for Indian IT companies , i.e. NIFTY_IT_INDEX .

Content

The dataset contains three csv files. Each resembling to INFOSYS, NIFTY_IT_INDEX, and TCS, respectively. One can easily identify that by the name of CSV files.

Timeline of Data recording : 1-1-2015 to 31-12-2015.

Source of Data : Official NSE website.

Method : We have used the NSEpy api to fetch the data from NSE site. I have also mentioned my approach in this Kernel - "**WebScraper to download data for NSE**". Please go though that to better understand the nature of this dataset.

Shape of Dataset:

INFOSYS - 248 x 15 || NIFTY_IT_INDEX - 248 x 7 || **TCS - 248 x 15

• Colum Descriptors:

• Date: date on which data is recorded

• Symbol: NSE symbol of the stock

• Series: Series of that stock | EQ - Equity

OTHER SERIES' ARE:

EQ: It stands for Equity. In this series intraday trading is possible in addition to delivery.

BE: It stands for Book Entry. Shares falling in the Trade-to-Trade or T-segment are traded in this series and no intraday is allowed. This means trades can only be settled by accepting or giving the delivery of shares.

BL: This series is for facilitating block deals. Block deal is a trade, with a minimum quantity of 5 lakh shares or minimum value of Rs. 5 crore, executed through a single transaction, on the special “Block Deal window”. The window is opened for only 35 minutes in the morning from 9:15 to 9:50AM.

BT: This series provides an exit route to small investors having shares in the physical form with a cap of maximum 500 shares.

GC: This series allows Government Securities and Treasury Bills to be traded under this category.

IL: This series allows only FIIs to trade among themselves. Permissible only in those securities where maximum permissible limit for FIIs is not breached.

• Prev Close: Last day close point

• Open: current day open point

• High: current day highest point

• Low: current day lowest point

• Last: the final quoted trading price for a particular stock, or stock-market index, during the most recent day of trading.

• Close: Closing point for the current day

• VWAP: volume-weighted average price is the ratio of the value traded to total volume traded over a particular time horizon

• Volume: the amount of a security that was traded during a given period of time. For every buyer, there is a seller, and each transaction contributes to the count of total volume.

• Turnover: Total Turnover of the stock till that day

• Trades: Number of buy or Sell of the stock.

• Deliverable: Volumethe quantity of shares which actually move from one set of people (who had those shares in their demat account before today and are selling today) to another set of people (who have purchased those shares and will get those shares by T+2 days in their demat account).

• %Deliverble: percentage deliverables of that stock

Acknowledgements

I woul dlike to acknowledge all my sincere thanks to the brains behind NSEpy api, and in particular SWAPNIL JARIWALA , who is also maintaining an amazing open source github repo for this api.

Inspiration

I have also built a starter kernel for this dataset. You can find that right here .

I am so excited to see your magical approaches for the same dataset.

THANKS!

Goals: Determine SOA yield of OH radical oxidation of VOC ... Summary: Alkanes were reacted with OH radicals in the presence of NOx in a 5900 L PTFE environmental chamber filled with clean, dry air (< 5 ppbv hydrocarbons, < 1% RH) at 25 C and atmospheric pressure. The reaction mixture contained 200 - 400 ug/m^3 of dioctyl sebacate (DOS) seed particles added from an evaporation condensation aerosol generator and 1 (0.5 heptadecane), 10, and 10 ppmv alkane, methyl nitrite, and NO. Reactions were initiated by turning on blacklights to form OH radicals by methyl nitrite photolysis. The average OH radical concentration for 60 minutes of reaction was 3 x 107 cm^-3 , and 50-85% of the alkane reacted. Organization: Ziemann Lab Affiliation: University of Colorado, Boulder, Boulder, CO, USA Chamber: UC Riverside (APRC) Experiment Category: Gas phase chemical reaction, Photolysis, any phase, Aerosol formation, Aerosol aging Oxidant: Hydroxyl radical Reactants: Octane, Methyl nitrite, Nitric oxide Reaction Type: Photooxidation Relative Humidity: 1 Temperature: 25 Seed Name: DOS Pressure: 740 Torr

This dataset contains Idealized Interdecadal Pacific Oscillation (IPO) pacemaker NetCDF data.

This dataset is an annual reconstruction of the Interdecadal Pacific Oscillation (IPO), a decadal-scale mode of variability in the Pacific Ocean which has climate impacts across the Pacific Basin. This data is a time series spanning CE 1-2011 inclusive (ie, the Common Era). The time series is reconstructed from three primary annually-resolved proxy series from the Law Dome ice core. These three series are the log-transformed seasonal sea salt concentration for the cool season (June to November), the log-transformed seasonal sea salt concentration for the warm season (December to May) and the annual snowfall accumulation rate. The reconstruction uses a Gaussian kernel correlation reconstruction method (Roberts et al., 2019) with 2000 ensemble members, which provides a mean IPO index value for each year, as well as upper and lower quartiles. The reconstruction target time series was the observed Interdecadal Pacific Oscillation spanning 1870-2020, which had been smoothed using a Gaussian window of 13 years. This Gaussian kernel correlation reconstruction is an evolution/replacement of the method and reconstruction presented in Vance et al., (2015) to reconstruct the IPO. This is now our preferred dataset for the Law Dome IPO reconstruction, and supersedes that published by Vance et al., (2015). The time series (dataset) consists of three columns with column headings as follows: Year – where year is the year from the beginning of the Common Era, ie, ‘436.0’ means the year CE 436, and ‘2009.0’ means the year 2009. IPO (mean) – the mean of the IPO reconstruction index value Std Dev) – the standard deviation of the index value for each year.