"""
Based on 'In & Out' strategy by Peter Guenther 10-04-2020
expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, and Thomas Chang.

https://www.quantopian.com/posts/new-strategy-in-and-out
"""

# Import packages
import numpy as np
import pandas as pd
import scipy as sc


class InOut(QCAlgorithm):

    def Initialize(self):

        self.SetStartDate(2008, 1, 1)  # Set Start Date
        self.SetCash(10000)  # Set Strategy Cash
        self.UniverseSettings.Resolution = Resolution.Daily
        res = Resolution.Minute
        
        #margin_leverage = 1 == No margin
        self.max_margin_leverage = 2
        self.margin_leverage = 1.5
        
        # stock selection and enter condition
        self.STKSEL = {
            self.AddEquity('QLD', res).Symbol: {"margin_leverage": 1.95, "holding": .5, "last_exit_price": 0},
            self.AddEquity('SSO', res).Symbol: {"margin_leverage": 1.95, "holding": .5, "last_exit_price": 0}
        }
        
        self.STKSEL_FOR_HEDGE = {
            self.AddEquity('TLT', res).Symbol: {"margin_leverage": 1.95, "holding": .35, "last_exit_price": 0},
            self.AddEquity('IEF', res).Symbol: {"margin_leverage": 1.95, "holding": .35, "last_exit_price": 0},
            self.AddEquity('IEI', res).Symbol: {"margin_leverage": 1.95, "holding": .2, "last_exit_price": 0},
            self.AddEquity('UUP', res).Symbol: {"margin_leverage": 1.95, "holding": .1, "last_exit_price": 0}
        }
        
        # Calcuate current price 
        self.cal_current_price = True
        self.max_drop_percent = 0.15
        self.max_drop_check = False
        self.max_drop_in = False
        
        self.current_price = {}
        
        # Feed-in constants
        self.INI_WAIT_DAYS = 15  # out for 3 trading weeks

        self.MRKT = self.AddEquity('SPY', res).Symbol

        # Market and list of signals based on ETFs
        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.UTIL = self.AddEquity('XLU', res).Symbol  # utilities
        self.SHCU = self.AddEquity('FXF', res).Symbol  # safe haven (CHF)
        self.RICU = self.AddEquity('FXA', res).Symbol  # risk currency (AUD)
        self.INDU = self.PRDC  # vs industrials

        self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.SHCU, self.RICU]
        self.SIGNALS = [self.PRDC, self.METL, self.NRES, self.DEBT, self.USDX]

        # 'In' and 'out' holdings incl. weights
        self.HLD_IN = {}
        for symbol in self.STKSEL:
            self.HLD_IN[symbol] = self.STKSEL[symbol]['holding'] * self.STKSEL[symbol]['margin_leverage']
            
        self.HLD_OUT = {}
        for symbol in self.STKSEL_FOR_HEDGE:
            self.HLD_OUT[symbol] = self.STKSEL_FOR_HEDGE[symbol]['holding'] * self.STKSEL_FOR_HEDGE[symbol]['margin_leverage']
        
        # Initialize variables
        ## 'In'/'out' indicator
        self.be_in = 1
        ## Day count variables
        self.dcount = 0  # count of total days since start
        self.outday = 0  # dcount when self.be_in=0
        ## Flexi wait days
        self.WDadjvar = self.INI_WAIT_DAYS
        
        ## Setup Margin ##
        self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin)
        
        ## Add 2x Margin
        for symbol in self.HLD_IN:
            self.Securities[symbol].SetLeverage(self.max_margin_leverage)
            #self.Securities[symbol].MarginModel = PatternDayTradingMarginModel()
            
        for symbol in self.HLD_OUT:
            self.Securities[symbol].SetLeverage(self.max_margin_leverage)
            #self.Securities[symbol].MarginModel = PatternDayTradingMarginModel()

        self.Schedule.On(
            self.DateRules.EveryDay(),
            self.TimeRules.AfterMarketOpen('SPY', 120),
            self.rebalance_when_out_of_the_market
        )

        self.Schedule.On(
            self.DateRules.WeekEnd(),
            self.TimeRules.AfterMarketOpen('SPY', 120),
            self.rebalance_when_in_the_market
        )

    def OnData(self, data):
        if self.cal_current_price and data != None:
            for symbol in self.HLD_IN:
                if data.ContainsKey(symbol) and data[symbol] != None:
                    self.current_price[symbol] = data[symbol].Close
            
            for symbol in self.HLD_OUT:
                if data.ContainsKey(symbol) and data[symbol] != None:
                    self.current_price[symbol] = data[symbol].Close
                    
            for symbol in self.SIGNALS + self.FORPAIRS:
                if data.ContainsKey(symbol) and data[symbol] != None:
                    self.current_price[symbol] = data[symbol].Close
        
    def rebalance_when_out_of_the_market(self):
        # Returns sample to detect extreme observations
        hist = self.History(
            self.SIGNALS + [self.MRKT] + self.FORPAIRS, 252, Resolution.Daily)['close'].unstack(level=0).dropna()

        # hist_shift = hist.rolling(66).apply(lambda x: x[:11].mean())

        hist_shift = hist.apply(lambda x: (x.shift(65) + x.shift(64) + x.shift(63) + x.shift(62) + x.shift(
            61) + x.shift(60) + x.shift(59) + x.shift(58) + x.shift(57) + x.shift(56) + x.shift(55)) / 11)

        returns_sample = (hist / hist_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])    
        self.pairlist = ['G_S', 'U_I', 'C_A']

        # Determine waitdays empirically via safe haven excess returns, 50% decay
        self.WDadjvar = int(
            max(0.50 * self.WDadjvar,
                self.INI_WAIT_DAYS * max(1,
                                         #returns_sample[self.GOLD].iloc[-1] / returns_sample[self.SLVA].iloc[-1],
                                         #returns_sample[self.UTIL].iloc[-1] / returns_sample[self.INDU].iloc[-1],
                                         #returns_sample[self.SHCU].iloc[-1] / returns_sample[self.RICU].iloc[-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.Debug('Wait Day Before: {}'.format(self.WDadjvar))
        adjwaitdays = min(60, self.WDadjvar)
        #self.Debug('Wait Day After: {}'.format(self.WDadjvar))
        
        # Remove unrelated pairs first
        returns_sample[self.FORPAIRS] = 0
        
        # Extreme observations; statist. significance = 1%
        pctl_b = np.nanpercentile(returns_sample, 1, axis=0)
        extreme_b = returns_sample.iloc[-1] < pctl_b

        # Determine whether 'in' or 'out' of the market
        if (extreme_b[self.SIGNALS + self.pairlist]).any():
            self.be_in = False
            self.outday = self.dcount
    
        if self.dcount >= self.outday + adjwaitdays:
            self.be_in = True
            
        self.dcount += 1    
        
        # Swap to 'out' assets if applicable
        if not self.be_in and not self.max_drop_in:
            # Close 'In' holdings
            for asset, weight in self.HLD_IN.items():
                self.SetHoldings(asset, 0)

            for asset, weight in self.HLD_OUT.items():
                self.SetHoldings(asset, weight)
                
            #Calculate last exit price
            if self.cal_current_price:
                for symbol in self.STKSEL:
                    self.STKSEL[symbol]['last_exit_price'] = self.current_price[symbol]
                    #self.Plot( "Stock Out Price", "Price ({})".format(symbol), self.STKSEL[symbol]['last_exit_price'])
                
                #for symbol in self.STKSEL_FOR_HEDGE:
                #    self.Plot( "Stock In Price", "Price ({})".format(symbol), self.current_price[symbol])
                
            
        self.Plot("In Out", "in_market", int(self.be_in))
        self.Plot("In Out", "num_out_signals", extreme_b[self.SIGNALS + self.pairlist].sum())
        self.Plot("Wait Days", "waitdays", adjwaitdays)
        
        for symbol in self.HLD_IN:
            self.Plot("Stock Price", "Price ({})".format(symbol), self.current_price[symbol])
            
        for symbol in self.HLD_OUT:
            self.Plot("Stock Price", "Price ({})".format(symbol), self.current_price[symbol])
            
        for symbol in self.SIGNALS:
            self.Plot("Signal Index", "Index ({})".format(symbol), self.current_price[symbol])
            
        for symbol in self.FORPAIRS:
            self.Plot("Compare Index", "Index ({})".format(symbol), self.current_price[symbol])

    def rebalance_when_in_the_market(self):
        #Max Drop In Hold for 1 week and check again
        if self.max_drop_check and self.max_drop_in:
            self.max_drop_in = False
        
         # Enter the market if any sel stock drop more than xxx%
        if self.max_drop_check and self.cal_current_price and not self.be_in and not self.max_drop_in:
            for symbol in self.STKSEL:
                if self.current_price[symbol] != 0:
                    if self.STKSEL[symbol]['last_exit_price'] == 0:
                        self.STKSEL[symbol]['last_exit_price'] = self.current_price[symbol]
                        
                    if  (1- (self.current_price[symbol] / self.STKSEL[symbol]['last_exit_price'])) >= self.max_drop_percent:
                        self.be_in = True
                        self.max_drop_in = True
                        self.Plot("In Out", "max_drop_in", int(True))
        else:
            self.Plot("In Out", "max_drop_in", int(False))
            
        # Swap to 'in' assets if applicable
        if self.be_in:
            # Close 'Out' holdings
            for asset, weight in self.HLD_OUT.items():
                self.SetHoldings(asset, 0)

            for asset, weight in self.HLD_IN.items():
                self.SetHoldings(asset, weight)
                
            #if self.cal_current_price:
                #for symbol in self.STKSEL:
                #    self.Plot( "Stock In Price", "Price ({})".format(symbol), self.current_price[symbol])
                
                #for symbol in self.STKSEL_FOR_HEDGE:
                #    self.Plot( "Stock Out Price", "Price ({})".format(symbol), self.current_price[symbol])