# region imports
from AlgorithmImports import *
from io import StringIO
import pandas as pd
from dateutil import parser

class SquareTanBaboon(QCAlgorithm):

    def Initialize(self):
        self.SetTimeZone(TimeZones.NewYork)
        self.SetStartDate(2019, 2, 1)  # Set Start Date
        #self.SetEndDate(2022, 3, 1)
        self.SetCash(100000)  # Set Strategy Cash
        self.spx = self.AddIndex("SPX", Resolution.Minute).Symbol
      
        self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose(self.spx, 0), self.print_close)
     

        url_2022_2023 = "https://www.dropbox.com/s/sv3hb4nwhth03fl/Download%20Data%20-%20INDEX_US_S%26P%20US_SPX%20%281%29.csv?dl=1"
        url_2021_2022 = "https://www.dropbox.com/s/2yhmh8ti2ky09vg/downloaddatapartial%20%281%29.csv?dl=1"
        url_2020_2021 = "https://www.dropbox.com/s/zlmf47x3seq0tbm/Download%20Data%20-%20INDEX_US_S%26P%20US_SPX%20%282%29.csv?dl=1"
        url_2019_2020 = "https://www.dropbox.com/s/ikr3seg8qsyz4pn/Download%20Data%20-%20INDEX_US_S%26P%20US_SPX%20%283%29.csv?dl=1"


        urls = [url_2019_2020, url_2020_2021, url_2021_2022, url_2022_2023]
        self.data_frames = {}

        for url in urls:
            file = self.Download(url)
            df = pd.read_csv(StringIO(file))
            first_date = df.iloc[-1]['Date']
            last_date = df.iloc[0]['Date']
            self.data_frames[first_date] = (df, first_date, last_date)
     
    def print_close(self):
        
        selected_df = None
        format_1 = parser.parse(self.Time.strftime("%m/%d/%Y"))
        

        for date, df in self.data_frames.items():
            if format_1 >= parser.parse(df[1]):
                if format_1 <= parser.parse(df[2]):
                    selected_df = df[0]
                    break
        if selected_df is not None:
            date_to_search = self.Time.strftime("%m/%d/%Y")
            if date_to_search in selected_df['Date'].values:
                row = selected_df.loc[selected_df['Date'] == date_to_search]
                close_price = row['Close'].values[0]
                string_without_commas = float(close_price.replace(",", ""))
                qc_close = self.Securities[self.spx].Close
                difference = abs(string_without_commas - qc_close)
                if difference >= 1:
                    self.Debug(f"{self.Time} QC {qc_close}  MarketWatch {string_without_commas} Difference {difference}")
                
            else:
                date_to_search = self.Time.strftime("%m.%d.%Y")
                if date_to_search in selected_df['Date'].values:
                    row = selected_df.loc[selected_df['Date'] == date_to_search]
                    close_price = row['Close'].values[0]
                    string_without_commas = float(close_price.replace(",", ""))
                    qc_close = self.Securities[self.spx].Close
                    difference = abs(string_without_commas - qc_close)
                    if difference >= 1:
                        self.Debug(f"{self.Time} QC {qc_close}  MarketWatch {string_without_commas} Difference {difference}")
    


    def OnData(self, data: Slice):
        pass
        # if self.Time.year == 2022 and self.Time.month == 2 and self.Time.day == 25:
        #     self.Debug(f"{self.Time} {self.Securities[self.spx].Close}")