from QuantConnect.Algorithm import QCAlgorithm
from io import StringIO
import pandas as pd
from QuantConnect import Resolution
from datetime import timedelta
from datetime import datetime


class MyAlgorithm(QCAlgorithm):
    def Initialize(self):
        # Initialize algorithm settings (cash, start-end dates, etc.)
        self.SetCash(1000000)
        self.SetStartDate(2022, 1, 1)
        self.SetEndDate(2023, 12, 15)
        data=self.Download("https://docs.google.com/spreadsheets/d/e/2PACX-1vQJGBy6bzWWPkOUIUDUuddF_S8X2YIKtVifCtMfGCKqSMrDn--stcC_rWwEeoy_1kFooypdb9xHvOcr/pub?gid=2050710775&single=true&output=csv")
        #rows=[]
        try:
            headers = ['Start Date', 'Symbol']
            df = pd.read_csv(StringIO(data), header=1, names=headers)
            # print(df.head())
            self.Debug(f"Start Date types: {df['Start Date'].dtype}")
            
            df['Start Date'] = pd.to_datetime(df['Start Date'])
            self.Debug(f"Start Date types after conversion: {df['Start Date'].dtype}")
            df = df[df['Start Date'] >= self.StartDate]
            for symbol in df['Symbol'].unique():
                self.AddEquity(symbol, Resolution.Daily)
                # self.Debug(f"Added {symbol}")


            self.Debug("CSV file successfully loaded.")
        except Exception as e:
            self.Debug(f"Error loading CSV file: {str(e)}")
            return

    

        if 'Start Date' in df.columns:
            self.trading_schedule = df.groupby('Start Date')['Symbol'].apply(list).to_dict()
            self.Debug("Trading schedule successfully created.")

            # for symbol in df['Symbol'].unique():
            #     self.AddEquity(symbol, Resolution.Minute)
            # for date, symbols in self.trading_schedule.items():
            #     self.Debug(f"{date}: {symbols}")

        else:
            self.Debug("Error: 'Start Date' column not found in the data.")

    
    def OnData(self, data):
        current_date = self.Time
        if current_date.weekday() == 4:
            self.Liquidate()
            self.Debug(f"Liquidating all positions on {current_date}")
        if current_date in self.trading_schedule:    
            symbols_today = self.trading_schedule[current_date]
            if len(symbols_today) == 0:
                self.Debug(f"No symbols to trade on {current_date}")
                return
            cash_per_stock = self.Portfolio.Cash / len(symbols_today)
            for symbol_str in symbols_today:
                symbol = self.Symbol(symbol_str)
                for symbol in symbols_today:
                    if symbol in data and data[symbol] is not None and data[symbol].Price > 0:
                        quantity = int(cash_per_stock / data[symbol].Price)
                        self.Buy(symbol, quantity)
                        self.Debug(f"Buying {quantity} of {symbol} at {data[symbol].Price}")
                    else:
                        self.Debug(f"No price data available for {symbol} at {self.Time}")
        else:
            self.Debug(f"No trading schedule for {current_date}")
    
    
    # def ExecuteTrades(self):
    #     week_start_date = None
    #     week_end_date = None

    #     current_date = self.Time.strftime("%Y-%m-%d")
    #     if current_date in self.trading_schedule:
    #         for symbol in self.trading_schedule[current_date]:
            
    #             quantity = 100

    #             if self.Time.weekday() == 4:
    #                 week_start_date = self.Time
    #                 week_end_date = self.Time + timedelta(days=6)

    #                 self.SetHoldings(symbol, quantity)

    #             if week_start_date is not None and self.Time >= week_end_date:
    #                 self.Liquidate(symbol)
    #                 week_start_date = None
    #                 week_end_date = None