import numpy as np
from scipy import stats
from statsmodels.distributions.empirical_distribution import ECDF
from scipy.stats import kendalltau, pearsonr, spearmanr
from scipy.optimize import minimize
from scipy.integrate import quad
import sys
from collections import deque


class CopulaPairsTradingAlgorithm(QCAlgorithm):
    
    def Initialize(self):
        '''Initialize algorithm and add universe'''
        
        self.SetStartDate(2010, 1, 1)
        self.SetEndDate(2019, 9, 1)
        self.SetCash(100000)
        
        self.numdays = 1000       # length of formation period which determine the copula we use
        self.lookbackdays = 250   # length of history data in trading period
        self.cap_CL = 0.95        # cap confidence level
        self.floor_CL = 0.05      # floor confidence level
        self.weight_v = 0.5       # desired holding weight of asset v in the portfolio, adjusted to avoid insufficient buying power
        self.coef = 0             # to be calculated: requested ratio of quantity_u / quantity_v
        self.window = {}          # stores historical price used to calculate trading day's stock return
        
        self.day = 0              # keep track of current day for daily rebalance
        self.month = 0            # keep track of current month for monthly recalculation of optimal trading pair
        self.pair = []            # stores the selected trading pair
        
        # Select optimal trading pair into the universe
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse('PairUniverse', self.PairSelection)


    def OnData(self, slice):
        '''Main event handler. Implement trading logic.'''

        self.SetSignal(slice)     # only executed at first day of each month

        # Daily rebalance
        if self.Time.day == self.day:
            return
        
        long, short = self.pair[0], self.pair[1]

        # Update current price to trading pair's historical price series
        for kvp in self.Securities:
            symbol = kvp.Key
            if symbol in self.pair:
                price = kvp.Value.Price
                self.window[symbol].append(price)

        if len(self.window[long]) < 2 or len(self.window[short]) < 2:
            return
        
        # Compute the mispricing indices for u and v by using estimated copula
        MI_u_v, MI_v_u = self._misprice_index()

        # Placing orders: if long is relatively underpriced, buy the pair
        if MI_u_v < self.floor_CL and MI_v_u > self.cap_CL:
            
            self.SetHoldings(short, -self.weight_v, False, f'Coef: {self.coef}')
            self.SetHoldings(long, self.weight_v * self.coef * self.Portfolio[long].Price / self.Portfolio[short].Price)

        # Placing orders: if short is relatively underpriced, sell the pair
        elif MI_u_v > self.cap_CL and MI_v_u < self.floor_CL:

            self.SetHoldings(short, self.weight_v, False, f'Coef: {self.coef}')
            self.SetHoldings(long, -self.weight_v * self.coef * self.Portfolio[long].Price / self.Portfolio[short].Price)
        
        self.day = self.Time.day


    def SetSignal(self, slice):
        '''Computes the mispricing indices to generate the trading signals.
        It's called on first day of each month'''

        if self.Time.month == self.month:
            return
        
        ## Compute the best copula
        
        # Pull historical log returns used to determine copula
        logreturns = self._get_historical_returns(self.pair, self.numdays)
        x, y = logreturns[str(self.pair[0])], logreturns[str(self.pair[1])]

        # Convert the two returns series to two uniform values u and v using the empirical distribution functions
        ecdf_x, ecdf_y  = ECDF(x), ECDF(y)
        u, v = [ecdf_x(a) for a in x], [ecdf_y(a) for a in y]
        
        # Compute the Akaike Information Criterion (AIC) for different copulas and choose copula with minimum AIC
        tau = kendalltau(x, y)[0]  # estimate Kendall'rank correlation
        AIC ={}  # generate a dict with key being the copula family, value = [theta, AIC]
        
        for i in ['clayton', 'frank', 'gumbel']:
            param = self._parameter(i, tau)
            lpdf = [self._lpdf_copula(i, param, x, y) for (x, y) in zip(u, v)]
            # Replace nan with zero and inf with finite numbers in lpdf list
            lpdf = np.nan_to_num(lpdf) 
            loglikelihood = sum(lpdf)
            AIC[i] = [param, -2 * loglikelihood + 2]
            
        # Choose the copula with the minimum AIC
        self.copula = min(AIC.items(), key = lambda x: x[1][1])[0]
        
        ## Compute the signals
        
        # Generate the log return series of the selected trading pair
        logreturns = logreturns.tail(self.lookbackdays)
        x, y = logreturns[str(self.pair[0])], logreturns[str(self.pair[1])]
        
        # Estimate Kendall'rank correlation
        tau = kendalltau(x, y)[0] 
        
        # Estimate the copula parameter: theta
        self.theta = self._parameter(self.copula, tau)
        
        # Simulate the empirical distribution function for returns of selected trading pair
        self.ecdf_x, self.ecdf_y  = ECDF(x), ECDF(y) 
        
        # Run linear regression over the two history return series and return the desired trading size ratio
        self.coef = stats.linregress(x,y).slope
        
        self.month = self.Time.month
        

    def PairSelection(self, date):
        '''Selects the pair of stocks with the maximum Kendall tau value.
        It's called on first day of each month'''
        
        if date.month == self.month:
            return Universe.Unchanged
        
        symbols = [ Symbol.Create(x, SecurityType.Forex, Market.Oanda) 
                    for x in ["EURUSD","GBPUSD","USDCAD", "AUDUSD", "NZDUSD",
                        "EURCAD", "USDCHF", "GBPCAD"] ]
        #history = self.History(symbols, 4, Resolution.Daily).close
        

        logreturns = self._get_historical_returns(symbols, self.lookbackdays)
        
        tau = 0
        for i in range(0, len(symbols), 2):
               
            x = logreturns[str(symbols[i])]
            
            y = logreturns[str(symbols[i+1])]
            
            # Estimate Kendall rank correlation for each pair
            tau_ = kendalltau(x, y)[0]
            
            if tau > tau_:
                continue

            tau = tau_
            self.pair = symbols[i:i+2]
        
        return [x.Value for x in self.pair]


    def OnSecuritiesChanged(self, changes):
        '''Warms up the historical price for the newly selected pair.
        It's called when current security universe changes'''
        
        for security in changes.RemovedSecurities:
            symbol = security.Symbol
            self.window.pop(symbol)
            if security.Invested:
                self.Liquidate(symbol, "Removed from Universe")
        
        for security in changes.AddedSecurities:
            symbol=security.Symbol
            self.window[symbol] = deque(maxlen = 2)
        
         #Get historical prices
            history = self.History(list(self.window.keys()), 2, Resolution.Daily)
        
            if not history.empty:
                history = history.close.unstack(level=0)
                for symbol in self.window:
                    self.window[symbol].append(history[str(symbol)][0])

        
    def _get_historical_returns(self, symbols, period):
        '''Get historical returns for a given set of symbols and a given period
        '''
        
        history = self.History(symbols, period, Resolution.Daily)
        history = history[~history.index.duplicated(keep='first')]
        history = history.close.unstack(level=0)
        return (np.log(history) - np.log(history.shift(1))).dropna()
        
        
    def _parameter(self, family, tau):
        ''' Estimate the parameters for three kinds of Archimedean copulas
        according to association between Archimedean copulas and the Kendall rank correlation measure
        '''
        
        if  family == 'clayton':
            return 2 * tau / (1 - tau)
        
        elif family == 'frank':
            
            '''
            debye = quad(integrand, sys.float_info.epsilon, theta)[0]/theta  is first order Debye function
            frank_fun is the squared difference
            Minimize the frank_fun would give the parameter theta for the frank copula 
            ''' 
            
            integrand = lambda t: t / (np.exp(t) - 1)  # generate the integrand
            frank_fun = lambda theta: ((tau - 1) / 4.0  - (quad(integrand, sys.float_info.epsilon, theta)[0] / theta - 1) / theta) ** 2
            
            return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x 
        
        elif family == 'gumbel':
            return 1 / (1 - tau)
            

    def _lpdf_copula(self, family, theta, u, v):
        '''Estimate the log probability density function of three kinds of Archimedean copulas
        '''
        
        if  family == 'clayton':
            pdf = (theta + 1) * ((u ** (-theta) + v ** (-theta) - 1) ** (-2 - 1 / theta)) * (u ** (-theta - 1) * v ** (-theta - 1))
            
        elif family == 'frank':
            num = -theta * (np.exp(-theta) - 1) * (np.exp(-theta * (u + v)))
            denom = ((np.exp(-theta * u) - 1) * (np.exp(-theta * v) - 1) + (np.exp(-theta) - 1)) ** 2
            pdf = num / denom
            
        elif family == 'gumbel':
            A = (-np.log(u)) ** theta + (-np.log(v)) ** theta
            c = np.exp(-A ** (1 / theta))
            pdf = c * (u * v) ** (-1) * (A ** (-2 + 2 / theta)) * ((np.log(u) * np.log(v)) ** (theta - 1)) * (1 + (theta - 1) * A ** (-1 / theta))
            
        return np.log(pdf)


    def _misprice_index(self):
        '''Calculate mispricing index for every day in the trading period by using estimated copula
        Mispricing indices are the conditional probability P(U < u | V = v) and P(V < v | U = u)'''
        
        return_x = np.log(self.window[self.pair[0]][-1] / self.window[self.pair[0]][-2])
        return_y = np.log(self.window[self.pair[1]][-1] / self.window[self.pair[1]][-2])
        
        # Convert the two returns to uniform values u and v using the empirical distribution functions
        u = self.ecdf_x(return_x)
        v = self.ecdf_y(return_y)
        
        if self.copula == 'clayton':
            MI_u_v = v ** (-self.theta - 1) * (u ** (-self.theta) + v ** (-self.theta) - 1) ** (-1 / self.theta - 1) # P(U<u|V=v)
            MI_v_u = u ** (-self.theta - 1) * (u ** (-self.theta) + v ** (-self.theta) - 1) ** (-1 / self.theta - 1) # P(V<v|U=u)
    
        elif self.copula == 'frank':
            A = (np.exp(-self.theta * u) - 1) * (np.exp(-self.theta * v) - 1) + (np.exp(-self.theta * v) - 1)
            B = (np.exp(-self.theta * u) - 1) * (np.exp(-self.theta * v) - 1) + (np.exp(-self.theta * u) - 1)
            C = (np.exp(-self.theta * u) - 1) * (np.exp(-self.theta * v) - 1) + (np.exp(-self.theta) - 1)
            MI_u_v = B / C
            MI_v_u = A / C
        
        elif self.copula == 'gumbel':
            A = (-np.log(u)) ** self.theta + (-np.log(v)) ** self.theta
            C_uv = np.exp(-A ** (1 / self.theta))   # C_uv is gumbel copula function C(u,v)
            MI_u_v = C_uv * (A ** ((1 - self.theta) / self.theta)) * (-np.log(v)) ** (self.theta - 1) * (1.0 / v)
            MI_v_u = C_uv * (A ** ((1 - self.theta) / self.theta)) * (-np.log(u)) ** (self.theta - 1) * (1.0 / u)
            
        return MI_u_v, MI_v_u