import decimal as d
import numpy as np
import pandas as pd
import math
import datetime
import json

class DropboxBaseDataUniverseSelectionAlgorithm(QCAlgorithm):

    def Initialize(self):

        self.UniverseSettings.Resolution = Resolution.Minute;
        
        self.SetStartDate(2019,1,8)
        self.SetEndDate(2019,1,30)
        self.SetCash(100000)
        
        spy = self.AddEquity("SPY", Resolution.Minute) # add equity spy with minute resolution
        spy.SetDataNormalizationMode(DataNormalizationMode.Raw)
            
        
        self.AddUniverse(StockDataSource, "my-stock-data-source", self.stockDataSource) # add stock tickers from csv
        self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.BeforeMarketClose("SPY", 10), self.EveryDayBeforeMarketClose) 
        # on every trading day? why need this
        
        self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw

    def stockDataSource(self, data): # This will grab for each date and hour the different tickers in the csv and add them to the universe
        list = []
        for item in data:
            for symbol in item["Symbols"]:
                list.append(symbol)
        #self.Debug(str(self.Time))
        #self.Debug(str(list))
        return list

        
    def TradeOptions(self, contracts, ticker):
        # run CoarseSelection method and get a list of contracts expire within 5 days from now on
        # and the strike price between rank -1 to rank 1, rank being the step of the contract
        
        filtered_contracts = self.CoarseSelection(ticker, contracts, -1, 1, 0, 15)
        
        if len(filtered_contracts) >0:
            
            ## select one pair of long / put option with same strike price and closest expiry
            expiry = sorted(filtered_contracts,key = lambda x: x.ID.Date, reverse=False)[0].ID.Date # Take the closest expiry
            
            # filter the call options from the contracts expire on that date
            call = [i for i in filtered_contracts if i.ID.Date == expiry and i.ID.OptionRight == 0]
            # sorted the contracts according to their strike prices
            call_contracts = sorted(call,key = lambda x: x.ID.StrikePrice)
            self.call = call_contracts[0]
            
            for i in filtered_contracts:
                if i.ID.Date == expiry and i.ID.OptionRight == 1 and i.ID.StrikePrice ==call_contracts[0].ID.StrikePrice:
                    self.put = i
    
            ''' Before trading the specific contract, you need to add this option contract
                AddOptionContract starts a subscription for the requested contract symbol '''
        
            
            # self.call is the symbol of a contract 
            self.AddOptionContract(self.call, Resolution.Minute)
            self.AddOptionContract(self.put, Resolution.Minute)
            
            if not self.Portfolio.Invested:
                self.SetHoldings(self.call.Value, 0.1)
                self.SetHoldings(self.put.Value, 0.1)

            # Some Logging  
            self.Log("Strike Price : "+str(self.call.ID.StrikePrice))
            self.Log("Expiry : "+str(self.call.ID.Date))
            self.Log("Call Mid-Point : "+str(self.Securities[self.call].Price))
            
            #self.Log("IV : "+str(self.call.ImpliedVolatility))
            

        else:
            pass
        
    def OnData(self, data):
        if not self.Portfolio.Invested:
            for symbol in data.Keys:
                
                if symbol.Value == "SPY": continue
            
                self.Log(symbol)
                self.Log(symbol.SecurityType)
      
                if symbol.SecurityType == 1:
                    #self.SetHoldings(key, 0.1)            
                    stk = self.AddEquity(symbol.Value, Resolution.Minute) # add underlying equity
                    stk.SetDataNormalizationMode(DataNormalizationMode.Raw)
                    
                    contracts = self.OptionChainProvider.GetOptionContractList(symbol, self.Time.date()) # Get list of strikes and expiries
                    self.TradeOptions(contracts, symbol.Value) # Select the right strikes/expiries and trade

        
        
    def EveryDayBeforeMarketClose(self):
        #self.Debug("############## Closing Position " + str(self.Time.date()) + " " + str(self.Time) + "############## ")
        self.Liquidate()
        self.Debug("Positions closed")
        
    def CoarseSelection(self, underlyingsymbol, symbol_list, min_strike_rank, max_strike_rank, min_expiry, max_expiry):

        ''' This method implements the coarse selection of option contracts
            according to the range of strike price and the expiration date,
            this function will help you better choose the options of different moneyness '''
      
        
        ## step 1: filter the contracts based on the expiry range
        contract_list = [i for i in symbol_list if min_expiry <= (i.ID.Date.date() - self.Time.date()).days < max_expiry]
        self.Log("Ticker Und : " + str(underlyingsymbol))
        self.Log("Nb of contract found : " + str(len(contract_list)))
        self.Log("Underlying price : "+str(self.Securities[underlyingsymbol].Price))
        
        ## step 2: filter the contracts based on the strike price range
        # find the strike price of ATM option
        # It seems like sometimes OptionChainProvider.GetOptionContractList is bugging and returns nothing, so let's try/except
        try :
            atm_strike = sorted(contract_list,
                                key = lambda x: abs(x.ID.StrikePrice - self.Securities[underlyingsymbol].Price))[0].ID.StrikePrice
            strike_list = sorted(set([i.ID.StrikePrice for i in contract_list]))
            
            # find the index of ATM strike in the sorted strike list
            atm_strike_rank = strike_list.index(atm_strike)
            try: 
                min_strike = strike_list[atm_strike_rank + min_strike_rank]
                max_strike = strike_list[atm_strike_rank + max_strike_rank]
            except:
                min_strike = strike_list[0]
                max_strike = strike_list[-1]
            # filter the contracts based on the range of the strike price rank
            filtered_contracts = [i for i in contract_list if i.ID.StrikePrice >= min_strike and i.ID.StrikePrice <= max_strike]
        
        except:
            self.Debug("NO CONTRACT RETURNED -------")
            filtered_contracts = None
            
        
        return filtered_contracts 
        
class StockDataSource(PythonData):
    
    def GetSource(self, config, date, isLiveMode):
        url = "https://www.dropbox.com/s/2az14r5xbx4w5j6/daily-stock-picker-live.csv?dl=1" if isLiveMode else \
            "https://www.dropbox.com/s/ofzgxsp2b27pkri/quantconnect_triggers.csv?dl=1"

        return SubscriptionDataSource(url, SubscriptionTransportMedium.RemoteFile)
    
    def Reader(self, config, line, date, isLiveMode):
        #if not (line.strip() and line[0].isdigit()): return None
        
        stocks = StockDataSource()
        stocks.Symbol = config.Symbol
        
        csv = line.rstrip(',').split(',') # rstrip is essential because quantconnect throws an empty element error (extra commas at the end of the csv)
        if isLiveMode:
            stocks.Time = date
            stocks["Symbols"] = csv
        else:
            stocks.Time = datetime.datetime.combine(datetime.datetime.strptime(csv[0], "%Y%m%d"), 
                          datetime.time(9, 31))
            stocks["Symbols"] = csv[1:]
        return stocks