Module NSEDownload.stocks

Expand source code 
import datetime

import pandas as pd

from NSEDownload.scraper import scrape_data, scrape_bonus_splits, scrape_symbol

pd.options.mode.chained_assignment = None


def get_data(symbol, full_data=False, start_date=None, end_date=None, series="EQ"):
    """
    Function to get un-adjusted data for stocks

    Args:
        symbol (str): Scrip or Stock symbol in uppercase only
        full_data (bool, optional): Parameter to get complete data since inception. Defaults to False.
        start_date (datetime, optional): start date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.
        end_date (datetime, optional): end date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.
        series (str, optional): By default set to EQ, but can choose any series or All.

    Raises:
        ValueError: If no dates are provided/ Incorrect format of dates
        ValueError: If start date > end date

    Returns:
        DataFrame: Data for stocksymbol for given date range

    ##Example
    ```
    # Providing date range
    df = stocks.get_data(stockSymbol='RELIANCE', start_date='15-9-2021', end_date='1-10-2021')
    ```
    Output
    ```
    | Date                | Symbol   | Series   |   Prev Close |   Open Price |   High Price |   Low Price |   Last Price |   Close Price |   Average Price |   Total Traded Quantity |    Turnover |   No. of Trades |   Deliverable Qty |   % Dly Qt to Traded Qty |
    |:--------------------|:---------|:---------|-------------:|-------------:|-------------:|------------:|-------------:|--------------:|----------------:|------------------------:|------------:|----------------:|------------------:|-------------------------:|
    | 2021-09-15 | RELIANCE | EQ       |      2368.45 |      2368.5  |      2395.75 |     2368.5  |      2379.4  |       2378.3  |         2380.39 |                 4186300 | 9.96505e+09 |          168130 |           2310144 |                    55.18 |
    | 2021-09-16 | RELIANCE | EQ       |      2378.3  |      2381.55 |      2436.75 |     2367    |      2424    |       2428.2  |         2408.55 |                 6206657 | 1.49491e+10 |          214010 |           2473588 |                    39.85 |
    | 2021-09-17 | RELIANCE | EQ       |      2428.2  |      2446    |      2455.85 |     2375.6  |      2387    |       2390.55 |         2410.13 |                16098099 | 3.87986e+10 |          278098 |           9460717 |                    58.77 |
    | 2021-09-20 | RELIANCE | EQ       |      2390.55 |      2372.1  |      2418.35 |     2370    |      2391.85 |       2394.35 |         2398.57 |                 5436385 | 1.30396e+10 |          171011 |           3042705 |                    55.97 |
    | 2021-09-21 | RELIANCE | EQ       |      2394.35 |      2405    |      2416.6  |     2384    |      2400    |       2404.7  |         2401.93 |                 4576111 | 1.09915e+10 |          149803 |           2365643 |                    51.7  |
    | 2021-09-22 | RELIANCE | EQ       |      2404.7  |      2408    |      2442    |     2398.25 |      2430.8  |       2430.5  |         2426.47 |                 5074612 | 1.23134e+10 |          179090 |           2811116 |                    55.4  |

    ```

    ```
    # Using full_data argument
    df = stocks.get_data(stockSymbol='RELIANCE', full_data=True)

    # Here ANGELONE isn't cross-checked as a correct scrip symbol
    df = stocks.get_data(stockSymbol='ANGELONE', full_data=True, check_stockSymbol=False)

    ```

    """

    symbol = symbol.replace('&', '%26')
    symbol_count = scrape_symbol(symbol)

    if full_data is True:
        parsed_start_date = datetime.datetime.strptime('1-1-1992', "%d-%m-%Y")
        parsed_end_date = datetime.datetime.today()

    else:

        if start_date is None or end_date is None:
            raise ValueError("Provide start and end date.")

        parsed_start_date = parse_date(start_date)
        parsed_end_date = parse_date(end_date)

        if parsed_start_date > parsed_end_date:
            raise ValueError("Starting date is greater than end date.")

    result = scrape_data(
        parsed_start_date, parsed_end_date, 'stock', stock_symbol=symbol, symbol_count=symbol_count, series=series)
    return result


def get_adjusted_data(symbol, df):
    """
    Given a dataframe of data and symbol, the price is adjusted for events after 2010.

    Args:
        symbol (str): Scrip or Stock symbol in uppercase only
        df (pandas dataframe): Data for given stock

    Returns:
        DataFrame: dataframe with adjusted data

    ##Example
    ```
    from NSEDownload import stocks
    df = stocks.get_data(stockSymbol = 'RELIANCE', start_date = '15-9-2015', end_date = '1-10-2021')
    df = stocks.get_adjusted_data('RELIANCE', df)
    ```
    Output
    ```
    RELIANCE
    BONUS  on :  07-Sep-2017  and ratio is :  2.0
    ```

    Adjusts can be easily check by plotting the closing price :

    |   Before Adjustment       |   After Adjustment    |
    | :--------:                |   :--------:          |
    |<img src = "Pre_adjustment.png">            |<img src = "Post_adjustment.png">    |


    """

    headers = ['Open Price', 'High Price', 'Low Price',
           'Last Price', 'Close Price', 'Average Price']

    symbol = symbol.replace('&', '%26')

    if df.empty:
        print("Please check data. Dataframe is empty")
        return df

    df.index = pd.to_datetime(df.index)
    df.sort_index(inplace=True)

    try:
        df = df.drop(['Prev Close'], axis=1)
    except KeyError:
        pass

    ratio, dates = scrape_bonus_splits(symbol)
    for index in range(len(dates)):

        date = datetime.datetime.strptime(dates[index], '%d-%b-%Y')
        changed_data = df.loc[df.index < date]
        same_data = df.loc[df.index >= date]

        for header_index in headers:
            try:
                changed_data.loc[:, header_index] = changed_data.loc[:, header_index] / ratio[index]
            except TypeError:
                pass

        df = pd.concat([changed_data, same_data])

    return df


def get_adjusted_stock(symbol, full_data=False, start_date=None, end_date=None):
    """
    Returns adjusted stock in 1 step. Combines get_data and get_adjusted_data

    Args:
        symbol (str): Scrip or Stock symbol in uppercase only
        full_data (bool, optional): Parameter to get complete data since inception. Defaults to False.
        start_date (str, optional): start date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.
        end_date (str, optional): end date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.

    Returns:
        DataFrame: DataFrame containing data for stocksymbol for given date range

    ##Example
    ```
    from NSEDownload import stocks
    df = stocks.get_adjusted_stock(stockSymbol='NMDC', full_data=True)
    df = stocks.get_adjusted_stock(stockSymbol='INFY', start_date="2010-11-12", end_date="2011-11-12")
    ```
    """

    df = get_data(symbol, full_data, start_date, end_date)
    df = get_adjusted_data(symbol, df)

    return df


def parse_date(text):
    """
    Parses date in either YYYY-MM-DD or DD-MM-YYYY format
    """

    for fmt in ('%Y-%m-%d', '%d-%m-%Y'):
        try:
            return datetime.datetime.strptime(text, fmt)
        except ValueError:
            pass

    raise ValueError('Dates should be in YYYY-MM-DD or DD-MM-YYYY format')

Functions

def get_adjusted_data(symbol, df)

Given a dataframe of data and symbol, the price is adjusted for events after 2010.

Args

symbol : str
Scrip or Stock symbol in uppercase only
df : pandas dataframe
Data for given stock

Returns

DataFrame
dataframe with adjusted data

Example

from NSEDownload import stocks
df = stocks.get_data(stockSymbol = 'RELIANCE', start_date = '15-9-2015', end_date = '1-10-2021')
df = stocks.get_adjusted_data('RELIANCE', df)

Output

RELIANCE
BONUS  on :  07-Sep-2017  and ratio is :  2.0

Adjusts can be easily check by plotting the closing price :

Before Adjustment After Adjustment
Expand source code 
def get_adjusted_data(symbol, df):
    """
    Given a dataframe of data and symbol, the price is adjusted for events after 2010.

    Args:
        symbol (str): Scrip or Stock symbol in uppercase only
        df (pandas dataframe): Data for given stock

    Returns:
        DataFrame: dataframe with adjusted data

    ##Example
    ```
    from NSEDownload import stocks
    df = stocks.get_data(stockSymbol = 'RELIANCE', start_date = '15-9-2015', end_date = '1-10-2021')
    df = stocks.get_adjusted_data('RELIANCE', df)
    ```
    Output
    ```
    RELIANCE
    BONUS  on :  07-Sep-2017  and ratio is :  2.0
    ```

    Adjusts can be easily check by plotting the closing price :

    |   Before Adjustment       |   After Adjustment    |
    | :--------:                |   :--------:          |
    |<img src = "Pre_adjustment.png">            |<img src = "Post_adjustment.png">    |


    """

    headers = ['Open Price', 'High Price', 'Low Price',
           'Last Price', 'Close Price', 'Average Price']

    symbol = symbol.replace('&', '%26')

    if df.empty:
        print("Please check data. Dataframe is empty")
        return df

    df.index = pd.to_datetime(df.index)
    df.sort_index(inplace=True)

    try:
        df = df.drop(['Prev Close'], axis=1)
    except KeyError:
        pass

    ratio, dates = scrape_bonus_splits(symbol)
    for index in range(len(dates)):

        date = datetime.datetime.strptime(dates[index], '%d-%b-%Y')
        changed_data = df.loc[df.index < date]
        same_data = df.loc[df.index >= date]

        for header_index in headers:
            try:
                changed_data.loc[:, header_index] = changed_data.loc[:, header_index] / ratio[index]
            except TypeError:
                pass

        df = pd.concat([changed_data, same_data])

    return df
def get_adjusted_stock(symbol, full_data=False, start_date=None, end_date=None)

Returns adjusted stock in 1 step. Combines get_data and get_adjusted_data

Args

symbol : str
Scrip or Stock symbol in uppercase only
full_data : bool, optional
Parameter to get complete data since inception. Defaults to False.
start_date : str, optional
start date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.
end_date : str, optional
end date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.

Returns

DataFrame
DataFrame containing data for stocksymbol for given date range

Example

from NSEDownload import stocks
df = stocks.get_adjusted_stock(stockSymbol='NMDC', full_data=True)
df = stocks.get_adjusted_stock(stockSymbol='INFY', start_date="2010-11-12", end_date="2011-11-12")
Expand source code 
def get_adjusted_stock(symbol, full_data=False, start_date=None, end_date=None):
    """
    Returns adjusted stock in 1 step. Combines get_data and get_adjusted_data

    Args:
        symbol (str): Scrip or Stock symbol in uppercase only
        full_data (bool, optional): Parameter to get complete data since inception. Defaults to False.
        start_date (str, optional): start date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.
        end_date (str, optional): end date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.

    Returns:
        DataFrame: DataFrame containing data for stocksymbol for given date range

    ##Example
    ```
    from NSEDownload import stocks
    df = stocks.get_adjusted_stock(stockSymbol='NMDC', full_data=True)
    df = stocks.get_adjusted_stock(stockSymbol='INFY', start_date="2010-11-12", end_date="2011-11-12")
    ```
    """

    df = get_data(symbol, full_data, start_date, end_date)
    df = get_adjusted_data(symbol, df)

    return df
def get_data(symbol, full_data=False, start_date=None, end_date=None, series='EQ')

Function to get un-adjusted data for stocks

Args

symbol : str
Scrip or Stock symbol in uppercase only
full_data : bool, optional
Parameter to get complete data since inception. Defaults to False.
start_date : datetime, optional
start date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.
end_date : datetime, optional
end date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.
series : str, optional
By default set to EQ, but can choose any series or All.

Raises

ValueError
If no dates are provided/ Incorrect format of dates
ValueError
If start date > end date

Returns

DataFrame
Data for stocksymbol for given date range

Example

# Providing date range
df = stocks.get_data(stockSymbol='RELIANCE', start_date='15-9-2021', end_date='1-10-2021')

Output

| Date                | Symbol   | Series   |   Prev Close |   Open Price |   High Price |   Low Price |   Last Price |   Close Price |   Average Price |   Total Traded Quantity |    Turnover |   No. of Trades |   Deliverable Qty |   % Dly Qt to Traded Qty |
|:--------------------|:---------|:---------|-------------:|-------------:|-------------:|------------:|-------------:|--------------:|----------------:|------------------------:|------------:|----------------:|------------------:|-------------------------:|
| 2021-09-15 | RELIANCE | EQ       |      2368.45 |      2368.5  |      2395.75 |     2368.5  |      2379.4  |       2378.3  |         2380.39 |                 4186300 | 9.96505e+09 |          168130 |           2310144 |                    55.18 |
| 2021-09-16 | RELIANCE | EQ       |      2378.3  |      2381.55 |      2436.75 |     2367    |      2424    |       2428.2  |         2408.55 |                 6206657 | 1.49491e+10 |          214010 |           2473588 |                    39.85 |
| 2021-09-17 | RELIANCE | EQ       |      2428.2  |      2446    |      2455.85 |     2375.6  |      2387    |       2390.55 |         2410.13 |                16098099 | 3.87986e+10 |          278098 |           9460717 |                    58.77 |
| 2021-09-20 | RELIANCE | EQ       |      2390.55 |      2372.1  |      2418.35 |     2370    |      2391.85 |       2394.35 |         2398.57 |                 5436385 | 1.30396e+10 |          171011 |           3042705 |                    55.97 |
| 2021-09-21 | RELIANCE | EQ       |      2394.35 |      2405    |      2416.6  |     2384    |      2400    |       2404.7  |         2401.93 |                 4576111 | 1.09915e+10 |          149803 |           2365643 |                    51.7  |
| 2021-09-22 | RELIANCE | EQ       |      2404.7  |      2408    |      2442    |     2398.25 |      2430.8  |       2430.5  |         2426.47 |                 5074612 | 1.23134e+10 |          179090 |           2811116 |                    55.4  |


# Using full_data argument
df = stocks.get_data(stockSymbol='RELIANCE', full_data=True)

# Here ANGELONE isn't cross-checked as a correct scrip symbol
df = stocks.get_data(stockSymbol='ANGELONE', full_data=True, check_stockSymbol=False)
Expand source code 
def get_data(symbol, full_data=False, start_date=None, end_date=None, series="EQ"):
    """
    Function to get un-adjusted data for stocks

    Args:
        symbol (str): Scrip or Stock symbol in uppercase only
        full_data (bool, optional): Parameter to get complete data since inception. Defaults to False.
        start_date (datetime, optional): start date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.
        end_date (datetime, optional): end date of date range in YYYY-MM-DD or DD-MM-YYYY format. Defaults to None.
        series (str, optional): By default set to EQ, but can choose any series or All.

    Raises:
        ValueError: If no dates are provided/ Incorrect format of dates
        ValueError: If start date > end date

    Returns:
        DataFrame: Data for stocksymbol for given date range

    ##Example
    ```
    # Providing date range
    df = stocks.get_data(stockSymbol='RELIANCE', start_date='15-9-2021', end_date='1-10-2021')
    ```
    Output
    ```
    | Date                | Symbol   | Series   |   Prev Close |   Open Price |   High Price |   Low Price |   Last Price |   Close Price |   Average Price |   Total Traded Quantity |    Turnover |   No. of Trades |   Deliverable Qty |   % Dly Qt to Traded Qty |
    |:--------------------|:---------|:---------|-------------:|-------------:|-------------:|------------:|-------------:|--------------:|----------------:|------------------------:|------------:|----------------:|------------------:|-------------------------:|
    | 2021-09-15 | RELIANCE | EQ       |      2368.45 |      2368.5  |      2395.75 |     2368.5  |      2379.4  |       2378.3  |         2380.39 |                 4186300 | 9.96505e+09 |          168130 |           2310144 |                    55.18 |
    | 2021-09-16 | RELIANCE | EQ       |      2378.3  |      2381.55 |      2436.75 |     2367    |      2424    |       2428.2  |         2408.55 |                 6206657 | 1.49491e+10 |          214010 |           2473588 |                    39.85 |
    | 2021-09-17 | RELIANCE | EQ       |      2428.2  |      2446    |      2455.85 |     2375.6  |      2387    |       2390.55 |         2410.13 |                16098099 | 3.87986e+10 |          278098 |           9460717 |                    58.77 |
    | 2021-09-20 | RELIANCE | EQ       |      2390.55 |      2372.1  |      2418.35 |     2370    |      2391.85 |       2394.35 |         2398.57 |                 5436385 | 1.30396e+10 |          171011 |           3042705 |                    55.97 |
    | 2021-09-21 | RELIANCE | EQ       |      2394.35 |      2405    |      2416.6  |     2384    |      2400    |       2404.7  |         2401.93 |                 4576111 | 1.09915e+10 |          149803 |           2365643 |                    51.7  |
    | 2021-09-22 | RELIANCE | EQ       |      2404.7  |      2408    |      2442    |     2398.25 |      2430.8  |       2430.5  |         2426.47 |                 5074612 | 1.23134e+10 |          179090 |           2811116 |                    55.4  |

    ```

    ```
    # Using full_data argument
    df = stocks.get_data(stockSymbol='RELIANCE', full_data=True)

    # Here ANGELONE isn't cross-checked as a correct scrip symbol
    df = stocks.get_data(stockSymbol='ANGELONE', full_data=True, check_stockSymbol=False)

    ```

    """

    symbol = symbol.replace('&', '%26')
    symbol_count = scrape_symbol(symbol)

    if full_data is True:
        parsed_start_date = datetime.datetime.strptime('1-1-1992', "%d-%m-%Y")
        parsed_end_date = datetime.datetime.today()

    else:

        if start_date is None or end_date is None:
            raise ValueError("Provide start and end date.")

        parsed_start_date = parse_date(start_date)
        parsed_end_date = parse_date(end_date)

        if parsed_start_date > parsed_end_date:
            raise ValueError("Starting date is greater than end date.")

    result = scrape_data(
        parsed_start_date, parsed_end_date, 'stock', stock_symbol=symbol, symbol_count=symbol_count, series=series)
    return result
def parse_date(text)

Parses date in either YYYY-MM-DD or DD-MM-YYYY format

Expand source code 
def parse_date(text):
    """
    Parses date in either YYYY-MM-DD or DD-MM-YYYY format
    """

    for fmt in ('%Y-%m-%d', '%d-%m-%Y'):
        try:
            return datetime.datetime.strptime(text, fmt)
        except ValueError:
            pass

    raise ValueError('Dates should be in YYYY-MM-DD or DD-MM-YYYY format')