# https://quantpedia.com/strategies/reversal-during-earnings-announcements/
#
# The investment universe consists of stocks listed at NYSE, AMEX, and NASDAQ, whose daily price data are available at the CRSP database. 
# Earnings-announcement dates are collected from Compustat. Firstly, the investor sorts stocks into quintiles based on firm size. Then he
# further sorts the stocks in the top quintile (the biggest) into quintiles based on their average returns in the 3-day window between 
# t-4 and t-2, where t is the day of the earnings announcement. The investor goes long on the bottom quintile (past losers) and short on
# the top quintile (past winners) and holds the stocks during the 3-day window between t-1, t, and t+1. Stocks in the portfolios are 
# weighted equally.

import fk_tools
import numpy as np
from collections import deque

class ReversalDuringEarningsAnnouncements(QCAlgorithm):
    
    def Initialize(self):
        self.SetStartDate(2014, 1, 1)
        self.SetCash(100000)

        self.ear_period = 3
        self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
        
        # Daily price data.
        self.data = {}
        
        # Monthly selected universe.
        self.last_coarse = []
        self.coarse_count = 1000
        
        # Import earnigns data.
        self.earnings_data = {}
        
        # Available symbols from earning_dates.csv.
        self.symbols = set()
        
        self.first_date = None
        csv_string_file = self.Download('data.quantpedia.com/backtesting_data/economic/earning_dates.csv')
        lines = csv_string_file.split('\r\n')
        for line in lines:
            line_split = line.split(';')
            date = datetime.strptime(line_split[0], "%Y-%m-%d").date()
            
            if not self.first_date: self.first_date = date
            
            self.earnings_data[date] = []
            for i in range(1, len(line_split)):
                symbol = line_split[i]
                self.earnings_data[date].append(symbol)
                self.symbols.add(symbol)
                
        # EAR history for previous quarter used for statistics. 
        self.ear_previous_quarter = []
        self.ear_actual_quarter = []
        
        # 5 equally weighted brackets for traded symbols. - 20 symbols long , 20 for short, 3 days of holding.
        self.trade_manager = fk_tools.TradeManager(self, 20, 20, 3)
        
        self.month = 12
        self.selection_flag = False
        self.rebalance_flag = False
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction)
        
        self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)

    def OnSecuritiesChanged(self, changes):
        for security in changes.AddedSecurities:
            symbol = security.Symbol
            security.SetFeeModel(fk_tools.CustomFeeModel(self))

            if symbol not in self.data:
                self.data[symbol] = deque(maxlen = self.ear_period)

        for security in changes.RemovedSecurities:
            symbol = security.Symbol

            if symbol in self.data:
                del self.data[symbol]

    def CoarseSelectionFunction(self, coarse):
        if not self.selection_flag:
            return Universe.Unchanged
    
        selected = sorted([x for x in coarse if x.HasFundamentalData and x.Market == 'usa' and x.Price > 5 and x.Symbol.Value in self.symbols],
            key=lambda x: x.DollarVolume, reverse=True)
        
        self.selection_flag = False
        
        return [x.Symbol for x in selected[:self.coarse_count]]

    def OnData(self, data):
        date_to_lookup = (self.Time + timedelta(days=1)).date()
        

        # Liquidate opened symbols after three days.
        self.trade_manager.TryLiquidate()
        
        ret_t4_t2 = {}
        
        for symbol in self.data:
            if symbol.Value == 'SPY': continue
            
            # Store daily data for universe.
            if self.Securities.ContainsKey(symbol):
                price = self.Securities[symbol].Price
                if price != 0:
                    self.data[symbol].append(price)
                else:
                    # Append latest price as a next one in case there's 0 as price.
                    if len(self.data[symbol]) > 0:
                        last_price = self.data[-1]
                        self.data[symbol].append(last_price)
            
                # Data is ready.
                if len(self.data[symbol]) == self.data[symbol].maxlen:
                    if date_to_lookup in self.earnings_data:
                        # Earnings is in next two day for the symbol.
                        if symbol.Value in self.earnings_data[date_to_lookup]:
                            closes = [x for x in self.data[symbol]]
                            # Calculate t-4 to t-2 return.
                            ret = fk_tools.Return(closes)
                            ret_t4_t2[symbol] = ret
                            
                            # Store return in this month's history.
                            self.ear_actual_quarter.append(ret)
            
        # Wait until we have history data for previous three months.
        if len(self.ear_previous_quarter) != 0:
            # Sort by EAR.
            ear_values = self.ear_previous_quarter
            top_ear_quintile = np.percentile(ear_values, 80)
            bottom_ear_quintile = np.percentile(ear_values, 20)
            
            # Store symbol to set.
            long = [x[0] for x in ret_t4_t2.items() if x[1] <= bottom_ear_quintile]
            short = [x[0] for x in ret_t4_t2.items() if x[1] >= top_ear_quintile]
            
            # Open new trades.
            for symbol in long:
                self.trade_manager.Add(symbol, True)
            for symbol in short:
                self.trade_manager.Add(symbol, False)

    def Selection(self):
        # There is no earnings data yet.
        if self.Time.date() < self.first_date:
            return

        self.selection_flag = True
        
        # Every three months.    
        if self.month % 3 == 0:
            # Save quarter history.
            self.ear_previous_quarter = [x for x in self.ear_actual_quarter]
            self.ear_actual_quarter.clear()

        self.month += 1
        if self.month > 12:
            self.month = 1

import numpy as np
from scipy.optimize import minimize

sp100_stocks = ['AAPL','MSFT','AMZN','FB','BRKB','GOOGL','GOOG','JPM','JNJ','V','PG','XOM','UNH','BAC','MA','T','DIS','INTC','HD','VZ','MRK','PFE','CVX','KO','CMCSA','CSCO','PEP','WFC','C','BA','ADBE','WMT','CRM','MCD','MDT','BMY','ABT','NVDA','NFLX','AMGN','PM','PYPL','TMO','COST','ABBV','ACN','HON','NKE','UNP','UTX','NEE','IBM','TXN','AVGO','LLY','ORCL','LIN','SBUX','AMT','LMT','GE','MMM','DHR','QCOM','CVS','MO','LOW','FIS','AXP','BKNG','UPS','GILD','CHTR','CAT','MDLZ','GS','USB','CI','ANTM','BDX','TJX','ADP','TFC','CME','SPGI','COP','INTU','ISRG','CB','SO','D','FISV','PNC','DUK','SYK','ZTS','MS','RTN','AGN','BLK']

def MonthDiff(d1, d2):
    return (d1.year - d2.year) * 12 + d1.month - d2.month

def Return(values):
    return (values[-1] - values[0]) / values[0]
    
def Volatility(values):
    values = np.array(values)
    returns = (values[1:] - values[:-1]) / values[:-1]
    return np.std(returns)  

# Custom fee model
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))

# Quandl free data
class QuandlFutures(PythonQuandl):
    def __init__(self):
        self.ValueColumnName = "settle"

# Quandl short interest data.
class QuandlFINRA_ShortVolume(PythonQuandl):
    def __init__(self):
        self.ValueColumnName = 'SHORTVOLUME'    # also 'TOTALVOLUME' is accesible

# Quantpedia data
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class QuantpediaFutures(PythonData):
    def GetSource(self, config, date, isLiveMode):
        return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)

    def Reader(self, config, line, date, isLiveMode):
        data = QuantpediaFutures()
        data.Symbol = config.Symbol
        
        if not line[0].isdigit(): return None
        split = line.split(';')
        
        data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
        data['settle'] = float(split[1])
        data.Value = float(split[1])

        return data
        
# NOTE: Manager for new trades. It's represented by certain count of equally weighted brackets for long and short positions.
# If there's a place for new trade, it will be managed for time of holding period.
class TradeManager():
    def __init__(self, algorithm, long_size, short_size, holding_period):
        self.algorithm = algorithm  # algorithm to execute orders in.
        
        self.long_size = long_size
        self.short_size = short_size
        self.weight = 1 / (self.long_size + self.short_size)
        
        self.long_len = 0
        self.short_len = 0
    
        # Arrays of ManagedSymbols
        self.symbols = []
        
        self.holding_period = holding_period    # Days of holding.
    
    # Add stock symbol object
    def Add(self, symbol, long_flag):
        # Open new long trade.
        managed_symbol = ManagedSymbol(symbol, self.holding_period, long_flag)
        
        if long_flag:
            # If there's a place for it.
            if self.long_len < self.long_size:
                self.symbols.append(managed_symbol)
                self.algorithm.SetHoldings(symbol, self.weight)
                self.long_len += 1
        # Open new short trade.
        else:
            # If there's a place for it.
            if self.short_len < self.short_size:
                self.symbols.append(managed_symbol)
                self.algorithm.SetHoldings(symbol, - self.weight)
                self.short_len += 1
    
    # Decrement holding period and liquidate symbols.
    def TryLiquidate(self):
        symbols_to_delete = []
        for managed_symbol in self.symbols:
            managed_symbol.days_to_liquidate -= 1
            
            # Liquidate.
            if managed_symbol.days_to_liquidate == 0:
                symbols_to_delete.append(managed_symbol)
                self.algorithm.Liquidate(managed_symbol.symbol)
                if managed_symbol.long_flag: self.long_len -= 1
                else: self.short_len -= 1

        # Remove symbols from management.
        for managed_symbol in symbols_to_delete:
            self.symbols.remove(managed_symbol)

class ManagedSymbol():
    def __init__(self, symbol, days_to_liquidate, long_flag):
        self.symbol = symbol
        self.days_to_liquidate = days_to_liquidate
        self.long_flag = long_flag
        
        
class PortfolioOptimization(object):
    def __init__(self, df_return, risk_free_rate, num_assets):
        self.daily_return = df_return
        self.risk_free_rate = risk_free_rate
        self.n = num_assets # numbers of risk assets in portfolio
        self.target_vol = 0.05

    def annual_port_return(self, weights):
        # calculate the annual return of portfolio
        return np.sum(self.daily_return.mean() * weights) * 252

    def annual_port_vol(self, weights):
        # calculate the annual volatility of portfolio
        return np.sqrt(np.dot(weights.T, np.dot(self.daily_return.cov() * 252, weights)))

    def min_func(self, weights):
        # method 1: maximize sharp ratio
        return - self.annual_port_return(weights) / self.annual_port_vol(weights)
        
        # method 2: maximize the return with target volatility
        #return - self.annual_port_return(weights) / self.target_vol

    def opt_portfolio(self):
        # maximize the sharpe ratio to find the optimal weights
        cons = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
        bnds = tuple((0, 1) for x in range(2)) + tuple((0, 0.25) for x in range(self.n - 2))
        opt = minimize(self.min_func,                               # object function
                       np.array(self.n * [1. / self.n]),            # initial value
                       method='SLSQP',                              # optimization method
                       bounds=bnds,                                 # bounds for variables 
                       constraints=cons)                            # constraint conditions
                      
        opt_weights = opt['x']
 
        return opt_weights