#from riskManagement import * 
from datetime import timedelta
class EMAMomentumUniverse(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2015, 1, 1)
        self.SetEndDate(2015, 3, 1)
        #self.SetEndDate(2019, 4, 1)
        self.SetCash(100000)
        self.SetBenchmark("SPY")
        self.UniverseSettings.Resolution = Resolution.Minute
        self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw
        #setting the coarse filter for investment universe
        self.AddUniverse(self.CoarseSelectionFunction)
        #self.AddUniverseSelection(LiquidValueUniverseSelectionModel())        
        #self.AddRiskManagement( ProtecOptions() )
        #declaring dictionary averages
        #self.SetRiskManagement(MaximumDrawdownPercentPerSecurityCustom(0.10))
        self.SetExecution(ImmediateExecutionModel())        
        self.averages = { }
#        self.underlyingsymbol = equity.Symbol
        self.hist = RollingWindow[float](390*22)
        self.contract = None   
        self.SetSecurityInitializer(self.security_initializer)
        self.buys = []
        self.sells = []
        self.contract_by_equity = {}
        
    def security_initializer(self, security):
        if security.Type == SecurityType.Option:
            security.SetMarketPrice(self.GetLastKnownPrice(security))
            
    def CoarseSelectionFunction(self, universe):  
        #Main output, creating a list where the below applies
        selected = []
        #Sort by dollar volume using lambda function, declare universe as EQTY > $10
        universe = sorted(universe, key=lambda c: c.DollarVolume, reverse=True)  
        universe = [c for c in universe if c.Price > 10][:100]
        #loop for all stocks in universe, uses all coarse data
        for coarse in universe:
            symbol = coarse.Symbol
            #Check for instance of SelectionData for this symbol in averages dictionary
            if symbol not in self.averages:
                # 1. Call history to get an array of 200 days of history data
                history = self.History(symbol, 300, Resolution.Daily)
                if history.empty or 'close' not in history.columns:
                    continue
                #2. Create new instance of SelectionData with the 'history' result
                self.averages[symbol] = SelectionData(history.loc[symbol].close) 
            #Update symbol with latest coarse.AdjustedPrice data \\ accesing method and pass params
            self.averages[symbol].update(self.Time, coarse.AdjustedPrice)
            #Check if indicators are ready, and that the 50 day EMA is > the 200 day EMA; then add to list 'selected'
            #Access property of class as dictionary item
            if  self.averages[symbol].is_ready() and self.averages[symbol].fast > self.averages[symbol].slow:
                selected.append(symbol)
        #update the selected list with the top 10 results
        return selected[:10]
        
    def OnData(self, data):
        sells = self.sells.copy()
        for security in sells:
            self.Liquidate(security.Symbol) # Is this necessary??
            if security.Symbol in self.contract_by_equity:
                contract = self.contract_by_equity.pop(security.Symbol)    
                self.Liquidate(contract)
            self.sells.remove(security)
        buys = self.buys.copy()
        for security in buys:
            if data.ContainsKey(security.Symbol) and data[security.Symbol] is not None and security.Type == SecurityType.Equity:
                self.SetHoldings(security.Symbol, 0.05)
                self.contract_by_equity[security.Symbol] = self.BuyPut(security.Symbol)
                self.buys.remove(security)

    #Method for monitoring if universe has changed
    def OnSecuritiesChanged(self, changes):
        for security in changes.AddedSecurities:
            self.buys.append(security)
        for security in changes.RemovedSecurities:
            self.sells.append(security)

    def BuyPut(self, symbol):    
        contracts = self.OptionChainProvider.GetOptionContractList(symbol, self.Time)
        #contracts = self.OptionChainProvider.GetOptionChains(self.Symbol, self.Time.date())
        if len(contracts) == 0: return
        min_expiry = 0
        max_expiry = 40
        filtered_contracts = [i for i in contracts if min_expiry <= (i.ID.Date.date() - self.Time.date()).days <= max_expiry]
        put = [x for x in filtered_contracts if x.ID.OptionRight == 1] 
        if len(put) == 0: return
        price = self.Securities[symbol].Price
        # sorted the contracts according to their expiration dates and choose the ATM options
        self.contract = sorted(sorted(put, key = lambda x: abs(price - x.ID.StrikePrice)), 
                                        key = lambda x: x.ID.Date, reverse=True)[0]
        self.AddOptionContract(self.contract, Resolution.Minute)
        self.MarketOrder(self.contract, 1)
        return self.contract
        
class SelectionData(object):
    def __init__(self, closes):
        self.tolerance = 1.01
        self.fast = ExponentialMovingAverage(100)
        self.slow = ExponentialMovingAverage(300)
        self.is_uptrend = False
        self.scale = 0
        for time, close in closes.iteritems():
            self.fast.Update(time, close)
            self.slow.Update(time, close)
            
    def update(self, time, value):
        if self.fast.Update(time, value) and self.slow.Update(time, value):
            fast = self.fast.Current.Value
            slow = self.slow.Current.Value
            self.is_uptrend = fast > slow * self.tolerance
        if self.is_uptrend:
            self.scale = (fast - slow) / ((fast + slow) / 2.0)
            
    def is_ready(self):
        return self.slow.IsReady