"""
[ekz]
    Added options to In & Out w/ROC, from the following thread 
    thread:https://www.quantconnect.com/forum/discussion/10763/reproducing-in-out-with-roc-v1-9/p1/comment-31828
[/ekz]

---
SEL(stock selection part)
Qual Up
Based on the 'Quality Companies in an Uptrand' strategy introduced by Chris Cain, 22 Nov 2019
adapted and recoded by Jonathon Tzu and Peter Guenther

https://www.quantconnect.com/forum/discussion/9678/quality-companies-in-an-uptrend/p1
https://www.quantconnect.com/forum/discussion/9632/amazing-returns-superior-stock-selection-strategy-superior-in-amp-out-strategy/p2

I/O(in & out part)
Option 1: The In & Out algo
Based on the 'In & Out' strategy introduced by Peter Guenther, 4 Oct 2020
expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, Thomas Chang, 
Mateusz Pulka, Derek Melchin (QuantConnect), Nathan Swenson, Goldie Yalamanchi, and Sudip Sil

https://www.quantopian.com/posts/new-strategy-in-and-out
https://www.quantconnect.com/forum/discussion/9597/the-in-amp-out-strategy-continued-from-quantopian/p1

Option 2: The Distilled Bear in & out algo
based on Dan Whitnable's 22 Oct 2020 algo on Quantopian. 
Dan's original notes:
"This is based on Peter Guenther great “In & Out” algo.
Included Tentor Testivis recommendation to use volatility adaptive calculation of WAIT_DAYS and RET.
Included Vladimir's ideas to eliminate fixed constants
Help from Thomas Chang"

https://www.quantopian.com/posts/new-strategy-in-and-out
https://www.quantconnect.com/forum/discussion/9597/the-in-amp-out-strategy-continued-from-quantopian/
"""

from OptionsManager import * 
from QuantConnect.Data.UniverseSelection import *
import math
import numpy as np
import pandas as pd
import scipy as sp

class QualUp_inout(QCAlgorithm):

    def Initialize(self):

        distFromPrice  = int(self.GetParameter('distFromPrice'))/100
        daysTillExpiry = int(self.GetParameter('daysTillExpiry'))
        
        self.OptionsManager  = OptionsManager(self, distFromPrice, daysTillExpiry)

        self.SetStartDate(2015, 1, 1)  #Set Start Date
        #self.SetEndDate(2015, 3, 31)  #Set End Date
        self.cap = 100000
        self.SetCash(self.cap)
        
        res = Resolution.Hour
        
        # Holdings
        ### 'Out' holdings and weights
        self.BND1 = self.AddEquity('TLT', res).Symbol #TLT; TMF for 3xlev
        self.Securities["TLT"].SetDataNormalizationMode(DataNormalizationMode.Raw)
        
        # Choose in & out algo
        self.go_inout_vs_dbear = 0 # 1=In&Out, 0=DistilledBear
        
        ##### In & Out parameters #####
        # Feed-in constants
        self.INI_WAIT_DAYS = 15  # out for 3 trading weeks
        self.wait_days = self.INI_WAIT_DAYS
        
        # Market and list of signals based on ETFs
        self.MRKT = self.AddEquity('SPY', res).Symbol  # market
        self.PRDC = self.AddEquity('XLI', res).Symbol  # production (industrials)
        self.METL = self.AddEquity('DBB', res).Symbol  # input prices (metals)
        self.NRES = self.AddEquity('IGE', res).Symbol  # input prices (natural res)
        self.DEBT = self.AddEquity('SHY', res).Symbol  # cost of debt (bond yield)
        self.USDX = self.AddEquity('UUP', res).Symbol  # safe haven (USD)
        self.GOLD = self.AddEquity('GLD', res).Symbol  # gold
        self.SLVA = self.AddEquity('SLV', res).Symbol  # vs silver
        #self.INFL = self.AddEquity('RINF', res).Symbol  # disambiguate GPLD/SLVA pair via inflaction expectations
        self.TIPS = self.AddEquity('TIP', res).Symbol  # disambiguate GPLD/SLVA pair via inflaction expectations; Treasury Yield = TIPS Yield + Expected Inflation
        self.UTIL = self.AddEquity('XLU', res).Symbol  # utilities
        self.INDU = self.PRDC  # vs industrials
        self.SHCU = self.AddEquity('FXF', res).Symbol  # safe haven currency (CHF)
        self.RICU = self.AddEquity('FXA', res).Symbol  # vs risk currency (AUD)

        self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.SHCU, self.RICU, self.TIPS] #self.INFL
        self.SIGNALS = [self.PRDC, self.METL, self.NRES, self.DEBT, self.USDX]
        self.pairlist = ['G_S', 'U_I', 'C_A']
        
        # Initialize variables
        ## 'In'/'out' indicator
        self.be_in = 1 #-1 #initially, set to an arbitrary value different from 1 (in) and 0 (out)
        self.be_in_prior = 0 #-1 #initially, set to an arbitrary value different from 1 (in) and 0 (out)
        ## Day count variables
        self.dcount = 0  # count of total days since start
        self.outday = (-self.INI_WAIT_DAYS+1)  # setting ensures universe updating at algo start
        ## Flexi wait days
        self.WDadjvar = self.INI_WAIT_DAYS
        self.adjwaitdays = self.INI_WAIT_DAYS
        ## For inflation gauge
        self.debt1st = []
        self.tips1st = []
        
        ##### Distilled Bear parameters (note: some signals shared with In & Out) #####
        self.DISTILLED_BEAR = 1 #-1
        self.VOLA_LOOKBACK = 126
        self.WAITD_CONSTANT = 85
        
        ##### Qual Up parameters #####
        self.UniverseSettings.Resolution = res
        self.AddUniverse(self.UniverseCoarseFilter, self.UniverseFundamentalsFilter)
        self.num_coarse = 500
        self.num_screener = 250
        self.num_stocks = 20
        self.formation_days = 126
        self.lowmom = False
        self.data = {}
        self.setrebalancefreq = int(self.GetParameter('reb_freq')) # X days, update universe and momentum calculation
        self.updatefinefilter = 0
        self.symbols = None
        self.reb_count = 0
        
        self.Schedule.On(
            self.DateRules.EveryDay(),
            self.TimeRules.AfterMarketOpen('SPY', 30),
            self.rebalance_when_out_of_the_market)
        
        self.Schedule.On(
            self.DateRules.EveryDay(), 
            self.TimeRules.BeforeMarketClose('SPY', 0), 
            self.record_vars)  
        
        # Benchmarks
        self.QQQ = self.AddEquity('QQQ', res).Symbol
        self.benchmarks = []
        self.year = self.Time.year #for resetting benchmarks annually if applicable
        
        # Setup daily consolidation
        symbols = [self.MRKT] + self.SIGNALS + self.FORPAIRS + [self.QQQ]
        for symbol in symbols:
            self.consolidator = TradeBarConsolidator(timedelta(days=1))
            self.consolidator.DataConsolidated += self.consolidation_handler
            self.SubscriptionManager.AddConsolidator(symbol, self.consolidator)
        
        # Warm up history
        if self.go_inout_vs_dbear==1: self.lookback = 252
        if self.go_inout_vs_dbear==0: self.lookback = 126
        self.history = self.History(symbols, self.lookback, Resolution.Daily)
        if self.history.empty or 'close' not in self.history.columns:
            return
        self.history = self.history['close'].unstack(level=0).dropna()
        
    def UniverseCoarseFilter(self, coarse):
        if not (((self.dcount-self.reb_count)==self.setrebalancefreq) or (self.dcount == self.outday + self.adjwaitdays - 1)):
            self.updatefinefilter = 0
            return Universe.Unchanged
        
        self.updatefinefilter = 1
            
        # drop stocks which have no fundamental data or have too low prices
        selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)]
        # rank the stocks by dollar volume 
        filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)
        return [x.Symbol for x in filtered[:self.num_coarse]]
        
        
    def UniverseFundamentalsFilter(self, fundamental):
        if self.updatefinefilter == 0:
            return Universe.Unchanged
            
        rank_cash_return = sorted(fundamental, key=lambda x: x.ValuationRatios.CashReturn, reverse=True)
        rank_fcf_yield  = sorted(fundamental, key=lambda x: x.ValuationRatios.FCFYield, reverse=True)
        rank_roic = sorted(fundamental, key=lambda x: x.OperationRatios.ROIC.Value, reverse=True)
        rank_ltd_to_eq = sorted(fundamental, key=lambda x: x.OperationRatios.LongTermDebtEquityRatio.Value, reverse=True)
        
        combo_rank = {}
        for i,ele in enumerate(rank_cash_return):
            rank1 = i
            rank2 = rank_fcf_yield.index(ele)
            score = sum([rank1*0.5,rank2*0.5])
            combo_rank[ele] = score
        
        rank_value = dict(sorted(combo_rank.items(), key=lambda item:item[1], reverse=False))
        
        stock_dict = {}
        
        # assign a score to each stock, you can also change the rule of scoring here.
        for i,ele in enumerate(rank_roic):
            rank1 = i
            rank2 = rank_ltd_to_eq.index(ele)
            rank3 = list(rank_value.keys()).index(ele)
            score = sum([rank1*0.33,rank2*0.33,rank3*0.33])
            stock_dict[ele] = score
        
        # sort the stocks by their scores
        self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=True)
        self.sorted_symbol = [self.sorted_stock[i][0] for i in range(len(self.sorted_stock))]
        top= self.sorted_symbol[:self.num_screener]
        self.symbols = [x.Symbol for x in top]
        
        #self.Log("100 fine-filtered stocks\n" + str(sorted([str(i.Value) for i in self.symbols])))
        self.updatefinefilter = 0
        self.reb_count = self.dcount
        return self.symbols

    
    def OnSecuritiesChanged(self, changes):
        
        addedSymbols = []
        for security in changes.AddedSecurities:
            if( security.Symbol.HasUnderlying ): 
                continue # if this is an options security, dont process it. skip it

            addedSymbols.append(security.Symbol)
            if security.Symbol not in self.data:
                self.data[security.Symbol] = SymbolData(security.Symbol, self.formation_days, self)
   
        if len(addedSymbols) > 0:
            history = self.History(addedSymbols, 1 + self.formation_days, Resolution.Daily).loc[addedSymbols]
            for symbol in addedSymbols:
                try:
                    self.data[symbol].Warmup(history.loc[symbol])
                except:
                    self.Debug(str(symbol))
                    continue
    
    def consolidation_handler(self, sender, consolidated):
        self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close
        self.history = self.history.iloc[-self.lookback:]
        if self.go_inout_vs_dbear==1: self.update_history_shift()
    
    def update_history_shift(self):
        self.history_shift = self.history.rolling(11, center=True).mean().shift(60)
        
    def derive_vola_waitdays(self):
        volatility = 0.6 * np.log1p(self.history[[self.MRKT]].pct_change()).std() * np.sqrt(252)
        wait_days = int(volatility * self.WAITD_CONSTANT)
        returns_lookback = int((1.0 - volatility) * self.WAITD_CONSTANT)
        return wait_days, returns_lookback
    
    def signalcheck_inout(self):
        ##### In & Out signal check logic #####
        
        # Returns sample to detect extreme observations
        returns_sample = (self.history / self.history_shift - 1)
        # Reverse code USDX: sort largest changes to bottom
        returns_sample[self.USDX] = returns_sample[self.USDX] * (-1)
        # For pairs, take returns differential, reverse coded
        returns_sample['G_S'] = -(returns_sample[self.GOLD] - returns_sample[self.SLVA])
        returns_sample['U_I'] = -(returns_sample[self.UTIL] - returns_sample[self.INDU])
        returns_sample['C_A'] = -(returns_sample[self.SHCU] - returns_sample[self.RICU])   

        # Extreme observations; statist. significance = 1%
        pctl_b = np.nanpercentile(returns_sample, 1, axis=0)
        extreme_b = returns_sample.iloc[-1] < pctl_b
        
        # Re-assess/disambiguate double-edged signals
        if self.dcount==0:
            self.debt1st = self.history[self.DEBT]
            self.tips1st = self.history[self.TIPS]
        self.history['INFL'] = (self.history[self.DEBT]/self.debt1st - self.history[self.TIPS]/self.tips1st)
        median = np.nanmedian(self.history, axis=0)
        abovemedian = self.history.iloc[-1] > median
        ### Interest rate expectations (cost of debt) may increase because the economic outlook improves (showing in rising input prices) = actually not a negative signal
        extreme_b.loc[[self.DEBT]] = np.where((extreme_b.loc[[self.DEBT]].any()) & (abovemedian[[self.METL, self.NRES]].any()), False, extreme_b.loc[[self.DEBT]])
        ### GOLD/SLVA differential may increase due to inflation expectations which actually suggest an economic improvement = actually not a negative signal
        extreme_b.loc['G_S'] = np.where((extreme_b.loc[['G_S']].any()) & (abovemedian.loc[['INFL']].any()), False, extreme_b.loc['G_S'])

        # Determine waitdays empirically via safe haven excess returns, 50% decay
        self.WDadjvar = int(
            max(0.50 * self.WDadjvar,
                self.INI_WAIT_DAYS * max(1,
                                         np.where((returns_sample[self.GOLD].iloc[-1]>0) & (returns_sample[self.SLVA].iloc[-1]<0) & (returns_sample[self.SLVA].iloc[-2]>0), self.INI_WAIT_DAYS, 1),
                                         np.where((returns_sample[self.UTIL].iloc[-1]>0) & (returns_sample[self.INDU].iloc[-1]<0) & (returns_sample[self.INDU].iloc[-2]>0), self.INI_WAIT_DAYS, 1),
                                         np.where((returns_sample[self.SHCU].iloc[-1]>0) & (returns_sample[self.RICU].iloc[-1]<0) & (returns_sample[self.RICU].iloc[-2]>0), self.INI_WAIT_DAYS, 1)
                                         ))
        )
        self.adjwaitdays = min(60, self.WDadjvar)
        
        return (extreme_b[self.SIGNALS + self.pairlist]).any()
    
    def signalcheck_dbear(self):
        ##### Distilled Bear signal check logic #####
        
        self.adjwaitdays, returns_lookback = self.derive_vola_waitdays()
        
        ## Check for Bears
        returns = self.history.pct_change(returns_lookback).iloc[-1]
    
        silver_returns = returns[self.SLVA]
        gold_returns = returns[self.GOLD]
        industrials_returns = returns[self.INDU]
        utilities_returns = returns[self.UTIL]
        metals_returns = returns[self.METL]
        dollar_returns = returns[self.USDX]
        
        DISTILLED_BEAR = (((gold_returns > silver_returns) and
                       (utilities_returns > industrials_returns)) and 
                       (metals_returns < dollar_returns)
                       )
        
        return DISTILLED_BEAR
    
        
    def rebalance_when_out_of_the_market(self):
        
        if self.go_inout_vs_dbear==1: out_signal = self.signalcheck_inout()
        if self.go_inout_vs_dbear==0: out_signal = self.signalcheck_dbear()
            
        ##### Determine whether 'in' or 'out' of the market. Perform out trading if applicable #####
        
        if out_signal:
            self.be_in = False
            self.outday = self.dcount
            
            if self.no_position_held(self.BND1):
                # Liquidate all except BND1
                for symbol, symbol_holding in self.Portfolio.items():
                    if symbol != self.BND1 and (symbol.Underlying != self.BND1):
                        self.close_position(symbol)

                self.open_position(self.BND1, 0.85)                        

        
        if (self.dcount >= self.outday + self.adjwaitdays):
            self.be_in = True
        
        # Update stock ranking/holdings, when swithing from 'out' to 'in' plus every X days when 'in' (set rebalance frequency)
        if (self.be_in and not self.be_in_prior) or (self.be_in and (self.dcount==self.reb_count)):
            self.rebalance()
                
        self.be_in_prior = self.be_in
        self.dcount += 1


    def rebalance(self):
            
        if self.symbols is None: return
        chosen_df = self.calc_return(self.symbols)
        chosen_df = chosen_df.iloc[:self.num_stocks]
        
        # Liquidate BND
        if not self.no_position_held(self.BND1):
            self.close_position(self.BND1)

        # Allocate equally to remaining securities
        weight = 0.85 / self.num_stocks
        for symbol, security in self.Securities.items():
            if symbol == self.BND1:
                continue
            if not self.CurrentSlice.ContainsKey(symbol) or self.CurrentSlice[symbol] is None:
                continue
            if symbol not in chosen_df.index:
                self.close_position(symbol)

            else:
                self.open_position(symbol, weight)

    def calc_return(self, stocks):
        
        ret = {}
        for symbol in stocks:
            try:
                ret[symbol] = self.data[symbol].Roc.Current.Value
            except:
                self.Debug(str(symbol))
                continue
            
        df_ret = pd.DataFrame.from_dict(ret, orient='index')
        df_ret.columns = ['return']
        sort_return = df_ret.sort_values(by = ['return'], ascending = self.lowmom)
        
        return sort_return
    
        
    def record_vars(self): 
        
        if self.dcount==1: self.benchmarks = [self.history[self.MRKT].iloc[-2], self.Portfolio.TotalPortfolioValue, self.history[self.QQQ].iloc[-2]]
        # reset portfolio value and qqq benchmark annually
        if self.Time.year!=self.year: self.benchmarks = [self.benchmarks[0], self.Portfolio.TotalPortfolioValue, self.history[self.QQQ].iloc[-2]]
        self.year = self.Time.year
        
        # SPY benchmark for main chart
        spy_perf = self.history[self.MRKT].iloc[-1] / self.benchmarks[0] * self.cap
        self.Plot('Strategy Equity', 'SPY', spy_perf)
        
        # Leverage gauge: cash level
        self.Plot('Cash level', 'cash', round(self.Portfolio.Cash+self.Portfolio.UnsettledCash, 0))
        
        # Annual saw tooth return comparison: Portfolio VS QQQ
        saw_portfolio_return = self.Portfolio.TotalPortfolioValue / self.benchmarks[1] - 1
        saw_qqq_return = self.history[self.QQQ].iloc[-1] / self.benchmarks[2] - 1
        self.Plot('Annual Saw Tooth Returns: Portfolio VS QQQ', 'Annual portfolio return', round(saw_portfolio_return, 4))
        self.Plot('Annual Saw Tooth Returns: Portfolio VS QQQ', 'Annual QQQ return', round(float(saw_qqq_return), 4))
        
        ### IN/Out indicator and wait days
        self.Plot("In Out", "in_market", int(self.be_in))
        self.Plot("Wait Days", "waitdays", self.adjwaitdays)


    ##################################################################
    def OnData(self, dataSlice):
        if bool(int(self.GetParameter('tradeOptions'))):
            pass #self.OptionsManager.DequeueOptionHoldings()        
            


    def open_position(self, holdingSymbol, holdingWeight):
        if(self.no_position_held(holdingSymbol)):
            if bool(int(self.GetParameter('tradeOptions'))):
                self.OptionsManager.TradeOption(holdingSymbol, holdingWeight) #QueueOptionHoldings(holdingSymbol, holdingWeight)
            else:
                self.SetHoldings(holdingSymbol, holdingWeight)

    def close_position(self, holdingSymbol): 
        if bool(int(self.GetParameter('tradeOptions'))):
            self.OptionsManager.LiquidateOptionsHoldings(holdingSymbol)
        else:
            self.Liquidate(holdingSymbol)

    def no_position_held(self, holdingSymbol):                
        if bool(int(self.GetParameter('tradeOptions'))):
            return not self.OptionsManager.PortfolioHasOptionsForThisSymbol(holdingSymbol)
        else:
            return not self.Securities[holdingSymbol].Invested

class SymbolData(object):
    def __init__(self, symbol, roc, algorithm):
        self.Symbol = symbol
        self.Roc = RateOfChange(roc)
        self.algorithm = algorithm
        
        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        algorithm.RegisterIndicator(symbol, self.Roc, self.consolidator)
        
    def Warmup(self, history):
        for index, row in history.iterrows():
            self.Roc.Update(index, row['close'])

from QuantConnect.Securities.Option import OptionStrategies
from QuantConnect.Securities.Option import OptionPriceModels


class OptionsManager():
    def __init__(self, algo, distFromPrice = 0, daysTillExp = 120):
        self.algo           = algo
        self.OptionOrderQueue = []
        self.distFromPrice = distFromPrice  # distance of the strike price from trading price (0.10 = 10%) 
        self.daysTillExp   = daysTillExp    # how many days till expiration, for the contract
        algo.SetSecurityInitializer(lambda x: x.SetMarketPrice(algo.GetLastKnownPrice(x)))
        algo.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw


    def TradeOption(self, holdingSymbol, holdingWeight):
        # Search OptionChainProvider for option to select
        callStrike    = self.GetTargetStrikeByPctDist( holdingSymbol,self.distFromPrice )
        putStrike     = self.GetTargetStrikeByPctDist( holdingSymbol,(-1* self.distFromPrice))
        expiryDate    = self.GetTargetExpiryByDTE( self.daysTillExp )
        
        contract = self.SelectContract(holdingSymbol, callStrike, expiryDate, OptionRight.Call)
        if contract is None:
            return
        option_contract = self.algo.AddOptionContract(contract, Resolution.Minute)
        
        # Purchase
        self.algo.SetHoldings(option_contract.Symbol, holdingWeight)
        

    # ==============================================================
    # Queue an options order 
    # ==============================================================
    def QueueOptionHoldings(self, holdingSymbol, holdingWeight):
        #self.SubscribeToOptionFeed( holdingSymbol )
        self.OptionOrderQueue.append((holdingSymbol, holdingWeight)) 

    # ==============================================================
    # Dequeue a queued options order 
    # ==============================================================
    def DequeueOptionHoldings(self):
        if (self.OptionOrderQueue is not None and \
            len(self.OptionOrderQueue) > 0):
                
            # for holdingTuple in self.OptionsManager.OptionOrderQueue[:]:
            for index, holdingTuple in enumerate(self.OptionOrderQueue[:]):                
                holdingSymbol = holdingTuple[0]
                holdingWeight = holdingTuple[1]
                if(self.SetOptionsHoldings(holdingSymbol, holdingWeight)):    
                    self.OptionOrderQueue.remove(holdingTuple)            

    # ====================================================================
    # Execute an options Order -- set options holdings for given stock 
    # ====================================================================
    def SetOptionsHoldings(self, holdingSymbol, holdingWeight):

        callStrike    = self.GetTargetStrikeByPctDist( holdingSymbol,self.distFromPrice )
        putStrike     = self.GetTargetStrikeByPctDist( holdingSymbol,(-1* self.distFromPrice))
        expiryDate    = self.GetTargetExpiryByDTE( self.daysTillExp )

        # Buy a single Call Option
        # -------------------------------------------
        callContract, callOrderMsg = self.GetCallOrPut(holdingSymbol, callStrike, expiryDate, OptionRight.Call )  
        if( callContract is not None ):
            self.algo.SetHoldings(callContract, holdingWeight, False, callOrderMsg)
            return True
        else:
            self.algo.Debug(f"{self.algo.Time} - Failed to get buy calls for {holdingSymbol}")    
            return False

    # ==============================================================
    # Liquidate an Options Holdings for the given symbol 
    # ==============================================================
            
    def LiquidateOptionsHoldings(self, holdingsSymbol):
        self.LiquidateOptionsOfType(holdingsSymbol, OptionRight.Call)
        
    # ==============================================================
    # Get Target Strike Given Pct Distnace 
    # ==============================================================
    def GetTargetStrikeByPctDist( self, symbolArg, pctDist):
        theCurrPrice   = self.algo.Securities[symbolArg].Price
        theStrikePrice = theCurrPrice * (1 + (pctDist) )
        return theStrikePrice
    
    # ==============================================================
    # Get Target Expiry Given DTE Days 
    # ==============================================================
    def GetTargetExpiryByDTE( self, dteDays):
        theExpiration = self.algo.Time + timedelta(days=dteDays)
        return theExpiration
    
    # ==============================================================
    # Get Strike & Expiration, fiven 
    # ==============================================================
    def GetStrikeAndExpiration( self, symbolArg, pctDist, dteDays ):
        theCurrPrice   = self.algo.Securities[symbolArg].Price
        theExpiration  = self.algo.Time + timedelta(days=dteDays)
        theStrikePrice = theCurrPrice * (1 + (pctDist) )
        return theStrikePrice , theExpiration
            
    # ==============================================================
    # Get Call or Put
    # ==============================================================
    def GetCallOrPut(self, symbolArg, callStrike, expiryDTE, optionRightArg = OptionRight.Call ):
    
        # retrive closest call contracts
        # -------------------------------
        selectedContract = self.SelectContract(symbolArg, callStrike, expiryDTE, optionRightArg)
        if( selectedContract is None):
            return None, None
        
        addedContract    = self.algo.AddOptionContract(selectedContract, Resolution.Minute)
    
        # construct orer message
        # -------------------
        underlyingPrice = self.algo.Securities[symbolArg].Price
        callOrPutString = "CALL" if (optionRightArg == OptionRight.Call) else "PUT"
        orderMsg        = f"[BUY {callOrPutString}] {symbolArg}-{str(round(addedContract.StrikePrice,0))} | Stock @ $ {str(underlyingPrice)}"

        return (selectedContract, orderMsg)


    # ==============================================================
    # Subscribe to the Option feed
    # ==============================================================
    # todo: explore how to unsubscribe from the option order feed
    def SubscribeToOptionFeed(self, symbolArg):
        
        option = self.algo.AddOption(symbolArg, Resolution.Minute)

        # set our strike/expiry filter for this option chain
        # ----------------------------------------------------------
        option.SetFilter(-5, +5, timedelta(0), timedelta(30))

        # for greeks and pricer (needs some warmup?) 
        # https://github.com/QuantConnect/Lean/blob/21cd972e99f70f007ce689bdaeeafe3cb4ea9c77/Common/Securities/Option/OptionPriceModels.cs#L81
        # ------------------------------------------------
        option.PriceModel = OptionPriceModels.CrankNicolsonFD()  # both European & American, automatically
        
    
    # ==============================================================
    # Select a Contract, given a symbol, desired strike and expiration
    # ==============================================================
    def SelectContract(self, symbolArg, strikePriceArg, expirationArg, optionRightArg):
    
        contracts = self.algo.OptionChainProvider.GetOptionContractList(symbolArg, self.algo.Time)
        
        if len( contracts ) == 0:
            return
            
        # get all contracts that match type
        # ------------------------------------
        filteredContracts = [symbol for symbol in contracts if symbol.ID.OptionRight == optionRightArg]
    
        # sort contracts by expiry dates and select expiration closest to desired expiration; check that it meets min time to expiry
        # --------------------------------------------
        contractsSortedByExpiration = sorted(filteredContracts, key=lambda p: abs(p.ID.Date - expirationArg), reverse=False)
        min_expiry = int(self.algo.GetParameter('reb_freq'))
        contractsSortedByExpiration = [p for p in contractsSortedByExpiration if ((p.ID.Date - self.algo.Time).days > min_expiry+5)]
        if len(contractsSortedByExpiration) == 0: return
        closestExpirationDate = contractsSortedByExpiration[0].ID.Date
    
        # get all contracts for selected expiration
        # ------------------------------------------------
        contractsFilteredByExpiration = [contract for contract in contractsSortedByExpiration if contract.ID.Date == closestExpirationDate]
        
        # sort contracts and select the one closest to desired strike
        # -----------------------------------------------------------
        contractsSortedByStrike = sorted(contractsFilteredByExpiration, key=lambda p: abs(p.ID.StrikePrice - strikePriceArg), reverse=False)
        theOptionContract = contractsSortedByStrike[0]
        
        return theOptionContract

    # ==================================================================
    # Liquidate all positions of the given type,for the given symbol.
    # ==================================================================
    def LiquidateOptionsOfType(self, symbolArg, optionRightArg = OptionRight.Call, orderMsgArg="Liquidated"):
        for symbolKey in self.algo.Securities.Keys:
            
            portfolioPosition = self.algo.Securities[symbolKey]
            
            if portfolioPosition.Invested:
                # Manage Option positions
                # --------------------------------
                if( portfolioPosition.Type == SecurityType.Option ) and \
                  ( portfolioPosition.Underlying.Symbol == symbolArg) and \
                  ( portfolioPosition.Right == optionRightArg):
                    
                    profitPct         = round(portfolioPosition.Holdings.UnrealizedProfitPercent * 100,2) 
                    orderMsgArg       = f"{orderMsgArg} | Profit: {profitPct}%"
                    self.algo.Debug(f"{self.algo.Time} - {orderMsgArg}")
                    self.algo.Liquidate(symbolKey, orderMsgArg)
                    self.algo.RemoveSecurity(symbolKey) # Remove option contract subsubscription


    # ==============================================================
    # Return True if we are holding options for this underlying
    # ==============================================================
    def PortfolioHasOptionsForThisSymbol(self, underlyingSymbol):
        
        # cycle through holdings and check if there are options 
        # positions open for this underlying.symbol.
        # ------------------------------------------------------
        optionInPortfolioForThisSecurity = False            

        for symbolKey in self.algo.Securities.Keys:
            
            currentHolding = self.algo.Securities[symbolKey]
            
            if currentHolding.Invested:
                try:
                    if (currentHolding.Underlying.Symbol == underlyingSymbol):
                        optionInPortfolioForThisSecurity = True
                        break
                except:
                    timestamp   = self.algo.Time.strftime('%b %d %Y')
                    # todo: Explore why this is ever hit. Happens a lot with NVDA in march 2018    
                    self.algo.Debug(f"[{timestamp}] [ERROR] PortfolioHasOptionsForThisSymbol(): Could not get underlying for  {symbolKey}") 
                    # self.Debug(f"[{timestamp}] [exception] It's possible that assignment has occured")
        
        return optionInPortfolioForThisSecurity

from bisect import bisect_left 

def GetIndexOfClosestValueFromList(myList, myNumber):

    """
    Assumes myList is sorted. Returns closest value to myNumber.

    If two numbers are equally close, return the smallest number.
    
    Credit from: 
    https://stackoverflow.com/questions/12141150/from-list-of-integers-get-number-closest-to-a-given-value/12141511#12141511
    """
    
    pos = bisect_left(myList, myNumber)
    if pos == 0:
        return 0
    if pos == len(myList):
        return len(myList)-1
    before = myList[pos - 1]
    after = myList[pos]
    if after - myNumber < myNumber - before:
       return pos
    else:
       return pos-1


def GetClosestContractFromList(myList, myNumber):
    """
    Assumes myList is sorted. Returns closest value to myNumber.

    If two numbers are equally close, return the smallest number.
    
    Credit from: 
    https://stackoverflow.com/questions/12141150/from-list-of-integers-get-number-closest-to-a-given-value/12141511#12141511
    """
    pos = bisect_left(myList, myNumber)
    if pos == 0:
        return myList[0]
    if pos == len(myList):
        return myList[-1]
    before = myList[pos - 1]
    after = myList[pos]
    if after - myNumber < myNumber - before:
       return after
    else:
       return before