#region imports
from AlgorithmImports import *
import pandas as pd
import copy
import pickle
import calendar
import pytz

# import jsonpickle
#endregion


class ReportManager():
    
    # ==========================================
    # Constructor. Accepts algo Object
    # ==========================================
    def __init__(self, algo):
        self.algo                   = algo
        self.algo.OnEndOfAlgorithm  = self.OnEndOfAlgorithm
        self.projectID              = "17556876"

    # ==================================================
    def OnEndOfAlgorithm(self):

        closedTrades = self.algo.TradeBuilder.ClosedTrades

        # create an empty dataframe
        df = pd.DataFrame()

        # loop through the list of Trade objects and add one row for each trade object to the dataframe
        for trade in closedTrades:
            

            # Monday is 0 and Sunday is 6

            pctReturn =  round( ((trade.ExitPrice - trade.EntryPrice) / trade.EntryPrice),2)
            
            # Define the Eastern timezone
            eastern = pytz.timezone('US/Eastern')

            # Convert the original datetime to Eastern time
            tradeEntryTime = trade.EntryTime.astimezone(eastern)
            
            # dayOfWeek = tradeEntryTime.weekday()
            dayOfWeek = tradeEntryTime.strftime('%A')
            hourOfDay = tradeEntryTime.hour
            epochExit = calendar.timegm(trade.ExitTime.timetuple())
            genericObject = {   'symbol'    : str(trade.Symbol),
                                'entryPrice': str(trade.EntryPrice),
                                'quantity'  : str(trade.Quantity),
                                'entryPrice': str(trade.EntryPrice),
                                'exitPrice': str(trade.ExitPrice),
                                'direction' : str(trade.Direction),
                                'duration'  : str(trade.Duration.total_seconds()), 
                                'pctReturn' : str(pctReturn),
                                'mae'       : str(trade.MAE), 
                                'mfe'       : str(trade.MFE),
                                'exitTime'  : str(trade.ExitTime),
                                'epochExit' : str(epochExit),
                                'hourOfDay' : str(hourOfDay),
                                'dayOfWeek' : str(dayOfWeek)}
            # # use the deepcopy function to clone the Trade object into a generic object
            # generic_object = copy.deepcopy(trade)

            # # convert the generic object to a dictionary
            # dictObject = dict(generic_object)

            # # create a TradeWithDict object using the properties of the Trade object
            # trade_with_dict = TradeWithDict(**trade.__dict__)
            # df = df.append(genericObject, ignore_index=True)''
            # df = pd.concat([df, genericObject], ignore_index=True)
            genericObject_df = pd.DataFrame.from_records([genericObject])
            df = pd.concat([df, genericObject_df], ignore_index=True)            


        # JsonPickle Approach        
        # ---------------
        # serialized   = jsonpickle.encode(closedTrades)
        # self.algo.ObjectStore.SaveBytes(f"{self.projectID}/CompletedTrades", serialized)

        # storedObject = self.algo.ObjectStore.ReadBytes(f"{self.projectID}/CompletedTrades")
        # deserialized = jsonpickle.decode(bytearray(storedObject))
        
        
        # Pickle Approach        
        # ---------------
        # serialized   = pickle.dumps(closedTrades)
        # self.algo.ObjectStore.SaveBytes(f"{self.projectID}/CompletedTrades", serialized)

        # storedObject = self.algo.ObjectStore.ReadBytes(f"{self.projectID}/CompletedTrades")
        # deserialized = pickle.loads(bytearray(storedObject))


        # JSON Approach
        # ------------
        # serialized   = json.dumps(df)
        self.algo.ObjectStore.Save(f"{self.projectID}/CompletedTrades", df.to_json(date_unit='ns'))
            
        retrieved = self.algo.ObjectStore.Read(f"{self.projectID}/CompletedTrades")
        restored  = pd.read_json(retrieved)
        pass
        '''
        # Read back data from object store 
        restored_history = pd.read_json(qb.ObjectStore.Read("data"))

        # Restore the indices
        restored_history['time'] = pd.to_datetime(restored_history['time'])
        restored_history['symbol'] = restored_history['symbol'].apply(lambda x: qb.Symbol(x))
        restored_history.set_index(['symbol', 'time'], inplace=True)
        restored_history
        '''

        # deserialized = json.loads(retrieved)

        # pass
        # self.algo.ObjectStore.SaveBytes('OS_signal_dens', pickle.dumps(self.signal_dens))

        # https://www.quantconnect.com/forum/discussion/14176/object-store-upgrades-and-behavior-change/p1
        # Add logic: only do this if we are NOT optimizing, and we are NOT live.
        # self.algo.ObjectStore.SaveBytes(f"{self.projectID}/CompletedTrades", closedTrades)
        
        # else:
        #     if self.algo.ObjectStore.ContainsKey(str(self.spy)):
        #         date = self.algo.ObjectStore.Read(str(self.spy))
        #         date = parser.parse(date)

        # that = self.algo.ObjectStore.ReadBytes(f"{self.projectID}/CompletedTrades")
        # so = 0


# class TradeWithDict(Trade):
#     @property
#     def __dict__(self):
#         # get a dictionary that contains the properties and their values
#         props = vars(self)
#         return props

        

#region imports
from AlgorithmImports import *
#endregion


class TrailingStopHelper():
    
    def __init__(self, algo, symbol, volaIndicator=None, trailStopCoeff=2, initialStopCoeff=1, activationCoeff=2):

    
        # Track state and indicators 
        self.algo                   = algo
        self.symbol                 = symbol 
        self.entryPrice             = 0
        self.ExitMessage            = ""
        self.systemActivated        = False
        self.volaIndicator          = volaIndicator
        self.volaIndicator.Updated += self.OnATRIndicatorUpdated
        
        # Stop Loss States
        self.trailStopCoeff     = trailStopCoeff
        self.initialStopCoeff   = initialStopCoeff
        self.activationCoeff    = activationCoeff
        self.ResetStopLosses()
            

    @property
    def lastPrice(self):
        if (self.symbol in self.algo.Securities \
            and self.algo.Securities[self.symbol] is not None):

            return self.algo.Securities[self.symbol].Price
        
        return 0

    ## -----------------------------------------------
    def OnATRIndicatorUpdated(self, sender, updated):        
        self.PlotCharts()

    
    # Trailing Stop Exit    
    # This method updates the trailing stop
    # ============================================
    def TrailingExitSignalFired(self):
        if( not self.volaIndicator.IsReady ):
            return False   

        # If trailing stop is NOT set, get last price, and set it
        # --------------------------------------------------------
        if( not self.stopsLossActivated ):
            self.highestPrice       = self.lastPrice
            self.trailingStopLoss   = self.lastPrice - (self.volaIndicator.Current.Value * self.trailStopCoeff)

            
            self.stopsLossActivated = True
            
            # Recursively call this function to check for stops
            # again, now that the trailing stop has been activated
            # and the stop loss value has been updated.
            # --------------------------------------------------
            return self.TrailingExitSignalFired()            
            
        # If trailing stop loss is activated, check if price closed below it.
        # If it did, then exit. If not, update the trailing stop loss. 
        else: 
            if self.PriceIsBelowStopLoss():              
                return True

            else:
                # If price has gone up
                if self.lastPrice > self.highestPrice:

                    # If price is above the trail activation price, update trailing stop
                    if self.lastPrice > self.activationPrice:

                        self.highestPrice       = self.lastPrice
                        newTrailingStopLoss     = self.highestPrice -  (self.volaIndicator.Current.Value * self.trailStopCoeff)                           
                        self.trailingStopLoss   = max (self.trailingStopLoss, newTrailingStopLoss, self.activationPrice)
                                        
                    # check again just in case price ends up below the new trailing stop level
                    if self.PriceIsBelowStopLoss():
                        return True

        return False 

    ## Check if price is below trailing stop loss or regular stop loss
    ## ---------------------------------------------------------------
    def PriceIsBelowStopLoss(self):
            
            if self.lastPrice > self.activationPrice:
                if( self.lastPrice < self.trailingStopLoss ):
                    self.ExitMessage = "Trailing Stop Loss"
                    return True
            else:
                if( self.lastPrice < self.initialStopLoss ):
                    self.ExitMessage = "Initial Stop Loss"
                    return True

            self.PlotCharts()

            return False

    ## Logic to run immediately after a new position is opened.
    ## We track entry price and set initial stop loss values
    ## ---------------------------------------------------------
    def Activate(self, entryPrice):            
    
        self.entryPrice         = entryPrice
        self.systemActivated    = True
        self.SetInitialStops()
        return
        
    ## Set initial stop and activation level. Called after new position opened.
    ## ------------------------------------------------------------------------
    def SetInitialStops(self):
        ## TODO: Use onOrderEvent to set this, because the actual price may be different
        self.entryPrice        = self.lastPrice
        
        self.initialStopLoss   = self.entryPrice - (self.volaIndicator.Current.Value * self.initialStopCoeff)  
        self.activationPrice   = self.entryPrice + (self.volaIndicator.Current.Value * self.activationCoeff)  

    
    ## Logic to run immediately after a position is closed
    ## Reset exit message, stop loss values.
    ## ---------------------------------------------------
    def Deactivate(self):            
        self.PlotCharts()
        self.ExitMessage      = "No Exit Message"
        self.systemActivated  = False
        self.ResetStopLosses()
        
    ## Reset stop losses    
    ## -------------------------------------------------
    def ResetStopLosses(self):
        self.stopsLossActivated   = False
        self.initialStopLoss      = 0
        self.activationPrice      = 0
        self.trailingStopLoss     = 0
        

    ## Plot Price, Stop losses & activation levels
    ## -------------------------------------------
    def PlotCharts(self):    
        return
        self.algo.Plot(f"{self.symbol} Trailing stop", "Price", self.lastPrice)        
        self.algo.Plot(f"{self.symbol} Trailing stop", "Initial Stop", self.initialStopLoss)
        self.algo.Plot(f"{self.symbol} Trailing stop", "Acivation Pt", self.activationPrice)
        self.algo.Plot(f"{self.symbol} Trailing stop", "TrailingStop", self.trailingStopLoss)
                
        return 

from AlgorithmImports import *
from ReportManager import *
from TrailingStopHelper import *
from io import StringIO 
import pandas as pd 
import pytz

class CSVSignalImporter(QCAlgorithm):

    def Initialize(self):     
        self.InitParams()   
        self.InitBacktest()   
        self.ImportSignals()
        self.InitIndicators()


    ## Initialize config parameters, state tracking,etc.
    ## ------------------------------------------------
    def InitParams(self):
    
        # Data parameters
        self.timeCol    = "date_time"
        self.timezone   = pytz.timezone('UTC')  
        self.newTimeCol = "NewTime"
        self.ticker     = "BTCUSDT"

        # Trading System parameters
        self.slATRCoeff  = int(self.get_parameter("slATRCoeff")) # 2  # ATR coefficient for stop loss
        self.rrRatio     = float(self.get_parameter("rrRatio"))  # 2  # risk-reward ratio for take profit
        self.sizeAcctPct = 1 # Pct of the cash available to use for trade
        self.threshold   = float(self.get_parameter("thresh")) # Prediction threshold
        self.signal_exp  = 30   # Signal expiry in seconds
        self.hold_dur    = 4320     # Duration (in minutes) for which to hold position
        self.exitMethod  = 4        # 0 exits after specified duration
                                    # 1 exits after specified duration + Stop loss
                                    # 2 exits after ATR TP/SL
                                    # 3 exits after ATR Trailing Stop 
                                    # 4 exits after min holding period and ATR Trailing Stop 
        
        self.useMLVola   = (1 == int(self.get_parameter("useMLVola"))) # When meauring tp/sl, use vola from the ML features

        # State Tracking
        self.direction   = None  # Keep track of trade direction
        self.entryMsg    = ""     
        self.exitMsg     = ""  
        self.signalTime  = ""  
        self.mlVola      = 0.0


    ## Init backtest properties / configs / managers
    ## ---------------------------------------------
    def InitBacktest(self):
        
        # Dates
        self.SetStartDate(2022, 7, 17)
        # self.SetEndDate(2022, 8, 1)
        self.SetEndDate(2023, 11, 1)
        
        # Data
        self.SetAccountCurrency("USDT")
        self.SetBrokerageModel(BrokerageName.Kraken, AccountType.Margin)
        self.symbol = self.AddCrypto(self.ticker, Resolution.Minute, Market.KRAKEN).Symbol
        self.SetBenchmark(self.symbol)

        # Custom reporting
        self.ReportManager = ReportManager(self)

    ## Init indicators used for tracking volatility and trailing stops
    ## ---------------------------------------------------------------
    def InitIndicators(self):
        
        self.atr = self.ATR(self.symbol, 14, MovingAverageType.Simple, Resolution.Hour)
        
        trailStopCoeff     = float(self.get_parameter("trailStopCoeff"))
        initialStopCoeff   = float(self.get_parameter("initialStopCoeff")) 
        activationCoeff    = initialStopCoeff * self.rrRatio 

        self.trailingStop  = TrailingStopHelper(self,self.symbol,self.atr,trailStopCoeff,initialStopCoeff,)


    ## Logic for ingesting and parsing the CSV with model signals 
    ## ----------------------------------------------------------
    def ImportSignals(self):

        # tolu_apr_14_csv   = self.Download("https://docs.google.com/spreadsheets/d/1c0yd2udMUEZiAPFQAJN6Kl683xHxWU928HNbv-WABXM/export?format=csv")
        # furkan_jun_14_csv = self.Download("https://docs.google.com/spreadsheets/d/1uho1Q1hgbHc4Av7KegT1Pz-ot_gPQ02XN6elkPSfpSY/export?format=csv")
        # furkan_jun_25_csv   = self.Download("https://docs.google.com/spreadsheets/d/1H-FXAdPoMvvQocWOAPH-lvKWRfrbVfZSCzdAeT5Yku0/export?format=csv")
        # furkan_jun_26_csv   = self.Download("https://docs.google.com/spreadsheets/d/1X7W7vamKfowN1wukF2VkX2-bCdmrnTc2Jzmz1rIpwyk/export?format=csv")
        furkan_jun_30_csv   = self.Download("https://docs.google.com/spreadsheets/d/1ZcegNaSUgPNsxKkrBErG-YEkMzyHLfsZ3FKD7FIt86s/export?format=csv")
    
        signals_csv         = furkan_jun_30_csv

        self.df                  = pd.read_csv(StringIO(signals_csv))        
        self.df[self.newTimeCol] = pd.to_datetime(self.df[self.timeCol], format='%Y-%m-%d %H:%M:%S')
        self.df[self.newTimeCol] = self.df[self.newTimeCol].dt.to_pydatetime()
        self.df[self.newTimeCol] = pd.to_datetime(self.df[self.newTimeCol]).dt.tz_localize('UTC')

        # Strip rows that dont meet our prediction threshold
        self.df = self.df[self.df["primary_prediction"].notna() & (self.df["primary_prediction"] >= self.threshold)]
        
        # Sort df by date -- shouldnt be necessary though
        self.df = self.df.sort_values(self.newTimeCol)

        # Custom handling for Tolu's signal CSV
        if 'Quantity' in self.df.columns:

            # Remove rows with negative quantity (sell signals)
            self.df = self.df[self.df['Quantity'] >= 0]


    ## System method, called every time a new bar comes in.
    ## Here we check for entry / exit signals, and clean up data
    ## ----------------------------------------------------------
    def OnData(self, data: Slice):

        # First, make sure we have valid data coming in
        if (self.ticker in data ) and (data[self.ticker] is not None):
            
            currTimeStr     = self.Time.strftime("%Y-%m-%d %H:%M:%S")
            current_time    = self.Time.astimezone(self.timezone)

            # If not invested, check for entry signals
            if( not self.HasHoldings(self.symbol)):
            

                # If we have a signal, open position, set direction, set tops. 
                if self.EntrySignalFired():
                    second_diff = (current_time - self.signalTime).total_seconds()
                    self.SetHoldings(self.symbol, self.sizeAcctPct, tag=f"Signal fired {second_diff} seconds ago)")                
                    self.direction = PortfolioBias.LONG
                    self.SetStopLossTakeProfit()

            # If invested, check for exit signals
            else:
                if self.ExitSignalFired():
                    self.LiquidateWithMsg(self.exitMsg)
                    # self.Liquidate(tag=self.exitMsg)

            # remove expired signals (records older than x minutes ago). 
            self.df = self.df[self.df[self.newTimeCol] >= (current_time - timedelta(seconds=self.signal_exp))]


    ## Returns true if we found a recent valid entry signal (that has not expired).
    ## ----------------------------------------------------------------------------
    def EntrySignalFired(self):  
        current_time    = self.Time.astimezone(self.timezone)

        # Get most recent prediction record that hasnt expired (ie: within last x minutes)
        recent_df =  self.df[self.df[self.newTimeCol] <= (current_time - timedelta(seconds=self.signal_exp))]

        if not recent_df.empty:
            for index, row in recent_df.iterrows():
                rowTimeOne  = (row[self.newTimeCol])
                
                rowTime     = pd.to_datetime(row[self.newTimeCol]).astimezone(self.timezone)
                # rowTimeStr  = rowTime.strftime("%Y-%m-%d %H:%M:%S")
                # self.Debug(f"Entry Signal stamped {rowTimeStr} Fired On Day: {currTimeStr}")                    

                # Delete
                # rowTime     = pd.to_datetime(row[self.newTimeCol])
                # rowTime     = rowTime.to_pydatetime()
                # rowTime     = rowTime.astimezone(self.timezone)
                
                                
                self.mlVola     = row['vol']
                self.signalTime = rowTime
                return True
                
        
        return False


    def SetStopLossTakeProfit(self):
        
        vola = self.mlVola
        self.EntryTime = self.Time 
        closePrice     = self.CurrentSlice[self.ticker].Close


        if (self.exitMethod == 1) or (self.exitMethod == 2) :

            if( self.useMLVola ): 
                # Set take profit/stop loss - Using Volatiliy from ML features
                self.stopLoss   = closePrice - (self.slATRCoeff * vola) 
                self.takeProfit = closePrice + (self.slATRCoeff * vola * self.rrRatio)
            else:
                # Set take profit/stop loss - Using ATR-measured Volatiliy
                self.stopLoss   = closePrice - (self.slATRCoeff * self.atr.Current.Value) 
                self.takeProfit = closePrice + (self.slATRCoeff * self.atr.Current.Value * self.rrRatio)

        elif(self.exitMethod == 3):
            # Set Trailstop
            self.trailingStop.Activate(closePrice)        

    # Check if exit signal has fired, due to 
    # Take Profit, Stop Loss, or time duration
    def ExitSignalFired(self):

        # Exit based on time. ie: Held position for x minutes
        if self.exitMethod == 0:
            if self.HeldForMinimumDuration():
                self.exitMsg = f"Exit After {self.hold_dur} minutes"
                return True
        
        # Exit based on time AND stop loss. 
        elif self.exitMethod == 1:
            closePrice = self.CurrentSlice[self.ticker].Close
            if self.HeldForMinimumDuration():
                self.exitMsg = f"Exit After {self.hold_dur} minutes"
                return True
            elif closePrice <= self.stopLoss:
                self.exitMsg = "Stop Loss Exit"
                return True

        # Take Profit / Stop loss exit
        elif self.exitMethod == 2:
            closePrice = self.CurrentSlice[self.ticker].Close
            if closePrice >= self.takeProfit:
                self.exitMsg = "Take Profit Exit"
                return True
            elif closePrice <= self.stopLoss:
                self.exitMsg = "Stop Loss Exit"
                return True
                    
        # Trailing stop exit
        elif self.exitMethod == 3:
            if  self.trailingStop.TrailingExitSignalFired():
                self.exitMsg = self.trailingStop.ExitMessage
                return True
    
        # Trailing stop exit after Min Hold Duration
        elif self.exitMethod == 4:
            if (self.HeldForMinimumDuration()):

                if not self.trailingStop.systemActivated:
                    self.trailingStop.Activate(self.securities[self.symbol].price)
                
                if  self.trailingStop.TrailingExitSignalFired():
                    self.exitMsg = self.trailingStop.ExitMessage
                    return True

        return False


    def HeldForMinimumDuration(self):
        return (self.Time - self.EntryTime) >= pd.Timedelta(minutes=self.hold_dur)


    # Convenience method to liquidate with a message
    def LiquidateWithMsg(self, exitReason):
        
        pnl         = round(100* self.Portfolio[self.symbol].UnrealizedProfitPercent,2)
        biasText    = 'Long' if (self.direction == PortfolioBias.LONG) else 'short'
        winlossText = 'win' if pnl > 0 else 'loss'        
        
        self.LiquidateCrypto(symbol=self.symbol, tag=f"[{pnl}% {winlossText}] {exitReason} Exiting {biasText} position with ")
        # self.Liquidate(tag=f"{exitReason} Exiting {biasText} position with {pnl}% {winlossText}")
        self.trailingStop.Deactivate()


    def LiquidateCrypto(self, symbol, tag):
        crypto = self.securities[symbol]
        base_currency = crypto.base_currency

        # Avoid negative amount after liquidate
        quantity = min(crypto.holdings.quantity, base_currency.amount)

        # Round down to observe the lot size
        lot_size = crypto.symbol_properties.lot_size;
        quantity = (round(quantity / lot_size) - 1) * lot_size

        if self.is_valid_order_size(crypto, quantity):
            self.market_order(symbol, -quantity, tag=tag)
            self.debug("liquidated succesfully")
        else:
            self.debug(f"Invalid order size: {quantity}")

    # Brokerages have different order size rules
    # Binance considers the minimum volume (price x quantity):
    def is_valid_order_size(self, crypto, quantity):
        return abs(crypto.price * quantity) > crypto.symbol_properties.minimum_order_size
    
    def HasHoldings(self, symbol):
        
        ## TODO: Explore a better way to do this. Not clear how base_currency.amount should be used
        crypto          = self.securities[symbol]
        base_currency   = crypto.base_currency
        quantity        = min(crypto.holdings.quantity, base_currency.amount)

        return (quantity > 0)

        # quantity = crypto.holdings.quantity 
        # return abs(crypto.price * quantity) > crypto.symbol_properties.minimum_order_size