from datetime import timedelta
import numpy as np
import pandas as pd

class ModelA(AlphaModel): 
    
    def __init__(self, resolution, insightsTimeDelta ):

        self.symbolDataBySymbol =   {}
        self.modelResolution    =   resolution
        self.insightsTimeDelta  =   insightsTimeDelta
        
    def OnSecuritiesChanged(self, algorithm, changes):
            for added in changes.AddedSecurities:
                symbolData = self.symbolDataBySymbol.get(added.Symbol)
                if symbolData is None:
                    symbolData = SymbolData(added.Symbol, algorithm, self.modelResolution)
                    self.symbolDataBySymbol[added.Symbol] = symbolData
 
    def Update(self, algorithm, data):
        
        insights=[]
       
        
        for symbol, symbolData in self.symbolDataBySymbol.items():
 
            symbolData.getInsight(algorithm.Securities[symbol].Price) # Latest known price; we are at 12:00 and the last trade at 10.57 
            
            if symbolData.tradeSecurity:

                insights.append(Insight(symbol, self.insightsTimeDelta, InsightType.Price, symbolData.InsightDirection, 0.0025,None, "ModelA",None))
                algorithm.Log(f"{symbol}\tMOM\t[{symbolData.fmom}]\t{round(symbolData.mom.Current.Value,2)}\tKAMA\t[{symbolData.fkama}]\t{round(symbolData.kama.Current.Value,2)}\
                                    \tPrice\t{symbolData.price}\tROC\t[{symbolData.froc}]\t{round(symbolData.roc.Current.Value,4)}\tEMA\t[{symbolData.fema}]\tEMA-13\t{round(symbolData.ema13.Current.Value,2)}\
                                    \tEMA-63\t{round(symbolData.ema63.Current.Value,2)}\tEMA-150\t{round(symbolData.ema150.Current.Value,2)}\taction\t{symbolData.InsightDirection}")
        return insights

class FrameworkAlgorithm(QCAlgorithm):
    
    def Initialize(self):
        
        tickers             =   ["MSFT","MRNA","MELI","FSLY"]
        symbols             =   [Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers]
        resolution          =   Resolution.Hour  #10-11, etc Daily data is midnight to mifnight, 12AM EST 
        warmup              =   28
        insightsTimeDelta   =   timedelta(hours=1)
        fallback_barrier    =   1000
        self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.Every(TimeSpan.FromMinutes(60)), self.hourlyHousekeeping)

        self.SetStartDate(2020, 12, 12)   
        self.SetCash(10000)           
        self.SetBenchmark("SPY")
        self.UniverseSettings.Resolution = resolution
        self.SetWarmUp(timedelta(warmup)) 
        self.SetUniverseSelection(ManualUniverseSelectionModel(symbols))
        self.SetAlpha(ModelA(resolution,insightsTimeDelta))
        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
        #self.SetPortfolioConstruction(MeanVarianceOptimizationPortfolioConstructionModel(resolution,PortfolioBias.LongShort,1,63,resolution,0.02,MaximumSharpeRatioPortfolioOptimizer(0,1,0)))
        self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.02)) # drop in profit from the max / done daily > redo hourly?
        self.SetExecution(ImmediateExecutionModel())
        


    def hourlyHousekeeping(self):
        
        
            
            # Fail Safe - If our strategy is losing than acceptable (something is wrong)
            # Strategy suddenly losing moiney or logic problem/bug we did't carch i testing
       
            if self.LiveMode:
                if (self.Portfolio.UnrealizedProfit+self.Portfolio.TotalProfit < -1000):# does not work
                    self.Log(f"Fallback event triggered, liqudating {self.Portfolio.UnrealizedProfit} {self.Portfolio.TotalProfit}")
                    self.Liquidate()
                    self.Quit()
            
            dt=int(self.Time.hour)
            if dt >9 and dt<18:
                if (self.IsMarketOpen("SPY") and self.Portfolio.Invested):
                    self.Log("\n\nPortfolio")
                    summary = {}
    
                    invested = [ x.Symbol.Value for x in self.Portfolio.Values if x.Invested ]
                    for symbol in invested:
                    
                        hold_val    = round(self.Portfolio[symbol].HoldingsValue, 2)
                        abs_val     = round(self.Portfolio[symbol].AbsoluteHoldingsValue, 2)
                        pnl         = round(self.Portfolio[symbol].UnrealizedProfit, 2)
                        qty         = self.Portfolio[symbol].Quantity
                        price       = self.Portfolio[symbol].Price
                        
                        summary[symbol]=[hold_val,abs_val,pnl,qty,price]
                        
                    df=pd.DataFrame(summary)
                    df.index = ['hold_val', 'abs_val', 'pnl', 'qty','price']
                    df=df.T
                    hold_val_total= abs(df['hold_val']).sum()
                    df = df.assign(weight=abs(df['hold_val'])/hold_val_total)
                    self.Log(df)
                    self.Log("\n\n")

            
class SymbolData:
    
    def __init__(self, symbol, algorithm, resolution):
        
        self.symbol             = symbol
        self.price              = 0.00
        self.kama               = algorithm.KAMA(symbol, 10,2,30, resolution)
        self.kama_factor        = 1.01 # tolerance level to avoid buy and immediate sell scenario
        self.mom                = algorithm.MOM(symbol, 14, resolution)
        self.roc                = algorithm.ROC(symbol, 9, resolution) 
        self.ema13              = algorithm.EMA(symbol, 13, resolution)
        self.ema63              = algorithm.EMA(symbol, 63, resolution)
        self.ema150             = algorithm.EMA(symbol, 150, resolution)
        self.fkama              = False
        self.fmom               = False
        self.froc               = False
        self.fema               = False
        self.held               = False # to ensure we only sell what we own

    def getInsight(self, price):    
        
        self.price              = price
        self.fkama              = self.price>self.kama.Current.Value*self.kama_factor 
        self.fmom               = self.mom.Current.Value>0
        self.froc               = self.roc.Current.Value>0
        self.fema               = self.ema13.Current.Value>self.ema63.Current.Value>self.ema150.Current.Value
        self.tradeSecurity      = False # helps to avoid liquidating when InsightDirection.Flat
        
        self.InsightDirection   = InsightDirection.Flat # liqudates unless self.tradeSecurity flag is set to False
        
        if self.fmom and self.fkama and self.fema and self.froc:
            self.InsightDirection   =   InsightDirection.Up
            self.tradeSecurity      =   True 
            self.held               =   True

        if self.held and (not self.fmom or not self.fkama or not self.fema or not self.froc):
            self.InsightDirection = InsightDirection.Flat # liqudates position - work around InsightDirection.Down which may sell and then short  
            self.held               =   False