strike project django get nearest strike

 import pandas as pd

from django.db import connection

from datetime import datetime, timedelta

import pytz

today_string=None

yesterday_string=None

tomorrow_string=None

def detect_day(today):

    global today_string, yesterday_string, tomorrow_string

    date_string = today

    parsed_date = datetime.strptime(date_string, "%Y-%m-%d")

    day_of_week = parsed_date.weekday()

    day_of_week_string = parsed_date.strftime("%A")

    # print(day_of_week)

    # print(day_of_week_string)

    date_string = today

    parsed_date = datetime.strptime(date_string, "%Y-%m-%d")

    yesterday = parsed_date - timedelta(days=1)

    yesterday_string = yesterday.strftime("%Y-%m-%d")

    # print("Yesterday's date:", yesterday_string)

    if day_of_week == 0:

        yesterday = parsed_date - timedelta(days=3)

        yesterday_string = yesterday.strftime("%Y-%m-%d")

        today_string = today

        tomorrow_string = parsed_date + timedelta(days=1)

        tomorrow_string = tomorrow_string.strftime("%Y-%m-%d")

    if day_of_week == 1:

        yesterday = parsed_date - timedelta(days=1)

        yesterday_string = yesterday.strftime("%Y-%m-%d")

        today_string = today

        tomorrow_string = parsed_date + timedelta(days=1)

        tomorrow_string = tomorrow_string.strftime("%Y-%m-%d")

    if day_of_week == 2:

        yesterday = parsed_date - timedelta(days=1)

        yesterday_string = yesterday.strftime("%Y-%m-%d")

        today_string = today

        tomorrow_string = parsed_date + timedelta(days=1)

        tomorrow_string = tomorrow_string.strftime("%Y-%m-%d")

    if day_of_week == 3:

        yesterday = parsed_date - timedelta(days=1)

        yesterday_string = yesterday.strftime("%Y-%m-%d")

        today_string = today

        tomorrow_string = parsed_date + timedelta(days=1)

        tomorrow_string = tomorrow_string.strftime("%Y-%m-%d")

    if day_of_week == 4:

        yesterday = parsed_date - timedelta(days=1)

        yesterday_string = yesterday.strftime("%Y-%m-%d")

        today_string = today

        tomorrow_string = parsed_date + timedelta(days=1)

        tomorrow_string = tomorrow_string.strftime("%Y-%m-%d")

    if day_of_week == 5:

        yesterday = parsed_date - timedelta(days=2)

        yesterday_string = yesterday.strftime("%Y-%m-%d")

        today_string = today

        today_string = datetime.strptime(today_string, "%Y-%m-%d")

        today_string = today_string - timedelta(days=1)

        today_string = today_string.strftime("%Y-%m-%d")

        tomorrow_string = parsed_date + timedelta(days=1)

        tomorrow_string = tomorrow_string.strftime("%Y-%m-%d")

    if day_of_week == 6:

        yesterday = parsed_date - timedelta(days=3)

        yesterday_string = yesterday.strftime("%Y-%m-%d")

        today_string = today

        today_string = datetime.strptime(today_string, "%Y-%m-%d")

        today_string = today_string - timedelta(days=2)

        today_string = today_string.strftime("%Y-%m-%d")

        tomorrow_string = parsed_date + timedelta(days=1)

        tomorrow_string = tomorrow_string.strftime("%Y-%m-%d")

class UserSelectedPrice:

    def round_to_nearest_5_minutes(ist_time):

        # Calculate the remainder of minutes when divided by 5

        minutes_remainder = ist_time.minute % 5

        # Subtract the remainder from the current minutes to round to the nearest 5 minutes

        rounded_ist_time = ist_time - timedelta(minutes=minutes_remainder)

        # Set seconds to 00

        rounded_ist_time = rounded_ist_time.replace(second=0)

        # Format the time with the desired format using str.format()

        formatted_time = "{}-{:02d}-{:02d} {}:{}:{}".format(

            rounded_ist_time.year,

            rounded_ist_time.month,

            rounded_ist_time.day,

            rounded_ist_time.hour,

            rounded_ist_time.minute,

            rounded_ist_time.second,

        )

        return formatted_time

    def convert_utc_to_ist(utc_time):

        utc_time_str = utc_time.strftime("%Y-%m-%d %H:%M:%S")

        utc_time = datetime.strptime(utc_time_str, "%Y-%m-%d %H:%M:%S")

        utc_time = pytz.utc.localize(utc_time)

        ist_time = utc_time.astimezone(pytz.timezone("Asia/Kolkata"))

        return ist_time.strftime("%Y-%m-%d %H:%M:%S")

    def custom_round(prices, symbol):

        rounded_prices = []

        for price in prices:

            if symbol == "NIFTY":

                last_two_digits = price % 100

                if last_two_digits < 25:

                    rounded_price = (price // 100) * 100

                elif last_two_digits < 75:

                    rounded_price = (price // 100) * 100 + 50

                else:

                    rounded_price = (price // 100 + 1) * 100

            elif symbol == "BANKNIFTY":

                last_two_digits = price % 100

                if last_two_digits < 50:

                    rounded_price = (price // 100) * 100

                else:

                    rounded_price = (price // 100 + 1) * 100

            else:

                rounded_price = price  # Handle the "else" case

            rounded_prices.append(rounded_price)

        return rounded_prices[0]

    def combined_oi_calculation(

        symbol,

        instrument,

        strikeprice,

        option_type,

        expiery,

        today,

        tomorrow,

    ):

        engine = connection

        df = pd.read_sql_query(

            f"""

            SELECT symbol, expiry_date, strike_price, created_at, ticker, instrument_name, option_type, open_interest FROM indiacharts.fno_prices fp 

            WHERE created_at > '{today}' AND created_at < '{tomorrow}'

            AND symbol = '{symbol}' AND instrument_name = '{instrument}' AND strike_price = {strikeprice}

            AND option_type = '{option_type}'

            ORDER BY created_at ASC

            """,

            con=engine,

        )

        df["created_at_ist"] = df["created_at"].apply(UserSelectedPrice.convert_utc_to_ist)

        df["created_at_ist"] = pd.to_datetime(df["created_at_ist"])

        df["rounded_created_at_ist"] = df["created_at_ist"].apply(

            UserSelectedPrice.round_to_nearest_5_minutes

        )

        # Group by rounded_created_at_ist and calculate the sum of open_interest

        oi_sum_df = (

            df.groupby("rounded_created_at_ist")["open_interest"].sum().reset_index()

        )

        df["rounded_created_at_ist"] = pd.to_datetime(df["rounded_created_at_ist"])

        oi_sum_df["rounded_created_at_ist"] = pd.to_datetime(

            oi_sum_df["rounded_created_at_ist"]

        )

        # Merge the DataFrames on 'rounded_created_at_ist'

        merged_df = pd.merge(df, oi_sum_df, on="rounded_created_at_ist", how="left")

        merged_df.rename(columns={"open_interest_y": "Combined OI"}, inplace=True)

        merged_df["expiry_date"] = merged_df["expiry_date"].astype(str)

        merged_df = merged_df[merged_df["expiry_date"] == expiery]

        merged_df.to_csv(f'{symbol}_{strikeprice}_{option_type}_{expiery}.csv', index=False)

        return merged_df

    def get_historical_eod_data(

        symbol, instrument, strikeprice, option_type, expiery, yesterday_string

    ):

        engine = connection

        df = pd.read_sql_query(

            f"""select symbol,expiry_date,strike_price,created_at,ticker, instrument_name,option_type,open_interest from indiacharts.fno_price_eod fpe 

            WHERE created_at ='{yesterday_string}'

            AND symbol = '{symbol}' AND instrument_name = '{instrument}' AND strike_price = {strikeprice}

            AND option_type = '{option_type}' 

            ORDER BY created_at ASC

                               """,

            con=engine,

        )

        # Calculate the sum of open_interest and add it as a new column

        combined_oi_eod = df["open_interest"].sum()

        df["Combined OI EOD"] = combined_oi_eod

        df.to_csv(f'{symbol}_{strikeprice}_{option_type}_{expiery}_eod.csv', index=False)

        return df