from AlgorithmImports import *
import numpy as np
from typing import List, Dict, Deque
from collections import deque
from scipy.optimize import minimize

class SymbolData:
    def __init__(self, period: int) -> None:
        self.closes: Deque = deque(maxlen=period)
        self.times: Deque = deque(maxlen=period)
        
    def update(self, time: datetime, close: float) -> None:
        self.times.append(time)
        self.closes.append(close)
        
    def is_ready(self) -> bool:
        return len(self.closes) == self.closes.maxlen and len(self.times) == self.times.maxlen

    def get_prices(self, list_period: List[datetime.date]) -> float:
        return_prices: List[float] = []
        
        for time, close in zip(self.times, self.closes):
            if time in list_period:
                return_prices.append(close)
                
        # check if there are enough data for performance calculation
        if len(return_prices) < 2:
            return None
            
        return (return_prices[-1] - return_prices[0]) / return_prices[0]

# Custom fee model
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters: OrderFeeParameters) -> OrderFee: 
        fee: float = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))
        
# 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: QCAlgorithm, long_size: int, short_size: int, holding_period: int) -> None:
        self.algorithm: QCAlgorithm = algorithm  # algorithm to execute orders in.
        
        self.long_size: int = long_size
        self.short_size: int = short_size
        
        self.long_len: int = 0
        self.short_len: int = 0
    
        # Arrays of ManagedSymbols
        self.symbols: List[Symbol] = []
        
        self.holding_period: int = holding_period    # Days of holding.
    
    # Add stock symbol object
    def Add(self, symbol: Symbol, long_flag: bool) -> None:
        # Open new long trade.
        managed_symbol: ManagedSymbol = 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, 1 / self.long_size)
                self.long_len += 1
            else:
                self.algorithm.Log("There's not place for additional trade.")

        # 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, - 1 / self.short_size)
                self.short_len += 1
            else:
                self.algorithm.Log("There's not place for additional trade.")
   
    # Decrement holding period and liquidate symbols.
    def TryLiquidate(self) -> None:
        symbols_to_delete: List[ManagedSymbol] = []
        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)
    
    def LiquidateTicker(self, ticker: str) -> None:
        symbol_to_delete: Union[None, ManagedSymbol] = None
        for managed_symbol in self.symbols:
            if managed_symbol.symbol.Value == ticker:
                self.algorithm.Liquidate(managed_symbol.symbol)
                symbol_to_delete = managed_symbol
                if managed_symbol.long_flag: self.long_len -= 1
                else: self.short_len -= 1
                
                break
        
        if symbol_to_delete: self.symbols.remove(symbol_to_delete)
        else: self.algorithm.Debug("Ticker is not held in portfolio!")
    
class ManagedSymbol():
    def __init__(self, symbol: Symbol, days_to_liquidate: int, long_flag: bool) -> None:
        self.symbol: Symbol = symbol
        self.days_to_liquidate: int = days_to_liquidate
        self.long_flag: bool = long_flag

# https://quantpedia.com/strategies/reversal-in-post-earnings-announcement-drift/
#
# The investment universe consists of all stocks from NYSE, AMEX, and NASDAQ with active options market (so mostly large-cap stocks). 
# Each day investor selects stocks which would have earnings announcement during the next working day. He then checks the abnormal 
# performance of these stocks during the previous earnings announcement. Investor goes long decile of stocks with the lowest abnormal
# past earnings announcement performance and goes short stocks with the highest abnormal past performance. Stocks are held for two 
# days, and the portfolio is weighted equally.
#
# QC Implementation changes:
#   - Universe consist of stock, which have earnings dates in Quantpedia data.

from data_tools import SymbolData, CustomFeeModel, TradeManager
from AlgorithmImports import *
import numpy as np
from collections import deque
from typing import Dict, List
from pandas.core.frame import DataFrame
from pandas.tseries.offsets import BDay
from dateutil.relativedelta import relativedelta

class ReversalPostEarningsAnnouncementDrift(QCAlgorithm):

    def Initialize(self) -> None:
        self.SetStartDate(2009, 1, 1) # earnings dates starts in 2010
        self.SetCash(100_000)

        self.long_symbols: int = 10
        self.short_symbols: int = 10
        self.holding_period: int = 2
        self.lookback_period: int = 3
        
        self.leverage: int = 5
        self.ear_period: int = 30
        self.prev_month_year: int = -1
        self.prev_month: int = -1
        self.percentiles: List[int] = [10, 90]
                
        self.data: Dict[Symbol, SymbolData] = {}

        # EAR last quarter data
        self.ear_data: Dict[Symbol, List[datetime.date, float]] = {}

        self.earnings_data: Dict[datetime.date, List[str]] = {}
        self.eps_data: Dict[int, Dict[int, Dict[str, Dict[datetime.date, float]]]] = {}
        
        self.first_date: Union[datetime.date, None] = None

        earnings_data: str = self.Download('data.quantpedia.com/backtesting_data/economic/earnings_dates_eps.json')
        earnings_data_json: List[dict] = json.loads(earnings_data)
        
        for obj in earnings_data_json:
            date: datetime.date = datetime.strptime(obj['date'], '%Y-%m-%d').date()
            year: int = date.year
            month: int = date.month

            self.earnings_data[date] = []

            if not self.first_date: self.first_date = date
            
            for stock_data in obj['stocks']:
                ticker: str = stock_data['ticker']

                self.earnings_data[date].append(ticker)

                if stock_data['eps'] == '':
                    continue

                if year not in self.eps_data:
                    self.eps_data[year] = {}

                if month not in self.eps_data[year]:
                    self.eps_data[year][month] = {}

                if ticker not in self.eps_data[year][month]:
                    self.eps_data[year][month][ticker] = {}

                self.eps_data[year][month][ticker][date] = float(stock_data['eps'])
        
        # EAR quarters history
        self.current_quarter_ears: List[float] = []
        self.previous_quarter_ears: List[float] = []
        
        # equally weighted brackets for traded symbols - 10 symbols long and short, 2 days of holding
        self.trade_manager: TradeManager = TradeManager(self, self.long_symbols, self.short_symbols, self.holding_period)

        self.symbol: Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
        
        self.selection_flag: bool = False
        self.store_sales_data_flag: bool = True
        self.sales_growth_sort_flag: bool = False
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
        self.settings.daily_precise_end_time = False
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.FundamentalSelectionFunction)
        self.Schedule.On(self.DateRules.MonthStart(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)

    def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
        for security in changes.AddedSecurities:
            security.SetFeeModel(CustomFeeModel())
            security.SetLeverage(self.leverage)
    
    def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
        # update daily prices
        for stock in fundamental:
            symbol: Symbol = stock.Symbol
            
            if symbol in self.data:
                self.data[symbol].update(self.Time, stock.AdjustedPrice)
        
        # monthly selection
        if not self.selection_flag:
            return Universe.Unchanged
        self.selection_flag = False

        prev_month_date: datetime.date = (self.Time - relativedelta(months=1)).date()
        self.prev_month_year: int = prev_month_date.year
        self.prev_month: int = prev_month_date.month

        if self.prev_month_year not in self.eps_data or self.prev_month not in self.eps_data[self.prev_month_year]:
            return Universe.Unchanged

        # select every stock, which had earnings in previous month
        stocks_with_prev_month_eps: Dict[str, Dict[datetime.date, float]] = self.eps_data[self.prev_month_year][self.prev_month]
        selected_symbols: List[Symbol] = [x.Symbol for x in fundamental if x.Symbol.Value in stocks_with_prev_month_eps]
        
        for symbol in selected_symbols + [self.symbol]:
            if symbol not in self.data:   
                # warm up stock prices
                self.data[symbol] = SymbolData(self.ear_period)
                history: DataFrame = self.History(symbol, self.ear_period, Resolution.Daily)
                if history.empty:
                    continue
                
                closes: Series = history.loc[symbol].close
                for time, close in closes.items():
                    self.data[symbol].update(self.Time, close)

        for symbol in selected_symbols:
            if not self.data[symbol].is_ready():
                continue
            ticker: str = symbol.Value

            # get all stock's eps from previous month
            stock_prev_month_eps: Dict[datetime.date, float] = self.eps_data[self.prev_month_year][self.prev_month][ticker]
            # get the date of the latest eps in previous month
            stock_latest_eps_date: datetime.date = list(stock_prev_month_eps.keys())[-1]

            # get 4 days around earnings and calculate EAR
            date_from: datetime = stock_latest_eps_date - BDay(2)
            date_to: datetime = stock_latest_eps_date + BDay(1)
            
            market_return: float = self.data[self.symbol].get_prices([date_from, date_to])
            stock_return: float = self.data[symbol].get_prices([date_from, date_to])
            
            # check if returns are ready 
            if market_return and stock_return:
                ear: float = stock_return - market_return

                ear_data: List[datetime.date] = (stock_latest_eps_date, ear)
                self.ear_data[symbol] = ear_data
                    
                # store ear in this month's history
                self.current_quarter_ears.append(ear)
        
        # check if there are any symbols, which can be traded                
        if len(self.ear_data) == 0:
            return Universe.Unchanged
        
        # return symbols from self.ear_data, because they will be traded
        return list(self.ear_data.keys())

    def OnData(self, data: Slice) -> None:
        # open trades on earnings day
        date_to_lookup: datetime.date = self.Time.date()

        # if there is no earnings data yet
        if date_to_lookup < self.first_date:
            return

        # liquidate opened symbols after holding period
        self.trade_manager.TryLiquidate()
        
        # wait until we have history data for previous three months
        if len(self.previous_quarter_ears) == 0:
            return
        
        ear_values: List[float] = [x for x in self.previous_quarter_ears]
        top_ear_decile: float = np.percentile(ear_values, self.percentiles[1])
        bottom_ear_decile: float = np.percentile(ear_values, self.percentiles[0])

        # Open new trades.
        if date_to_lookup in self.earnings_data:
            symbols_to_trade: List[Symbol] = [symbol for symbol in self.ear_data if symbol.Value in self.earnings_data[date_to_lookup]]

            symbols_to_delete: List[Symbol] = []
            for symbol in symbols_to_trade:
                # last earnings was less than three months ago
                last_earnings_date: datetime.date = self.ear_data[symbol][0]

                if last_earnings_date >= (self.Time - relativedelta(months=self.lookback_period)).date():
                    if symbol in data and data[symbol]:
                        if self.ear_data[symbol][1] >= top_ear_decile:
                            self.trade_manager.Add(symbol, True)
                            symbols_to_delete.append(symbol)
                        elif self.ear_data[symbol][1] <= bottom_ear_decile:
                            self.trade_manager.Add(symbol, False)
                            symbols_to_delete.append(symbol)
            
            # delete already traded symbols from symbol to trade
            for symbol in symbols_to_delete:
                del self.ear_data[symbol]

    def Selection(self) -> None:
        self.selection_flag = True
        
        if self.Time.month % 3 == 0:
            # store previous quarter's history
            self.previous_quarter_ears = [x for x in self.current_quarter_ears]
            self.current_quarter_ears.clear()