#region imports
from AlgorithmImports import *
#endregion
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from clr import AddReference
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Algorithm.Framework")
AddReference("QuantConnect.Indicators")

from QuantConnect import *
from QuantConnect.Indicators import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Alphas import *
import numpy as np


class EmaCrossAlphaModel(AlphaModel):
    '''Alpha model that uses an EMA cross to create insights'''

    def __init__(self,
                 fastPeriod = 12,
                 slowPeriod = 26,
                 resolution = Resolution.Daily):
        '''Initializes a new instance of the EmaCrossAlphaModel class
        Args:
            fastPeriod: The fast EMA period
            slowPeriod: The slow EMA period'''
        self.fastPeriod = fastPeriod
        self.slowPeriod = slowPeriod
        self.resolution = resolution
        self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(resolution), fastPeriod)
        self.symbolDataBySymbol = {}

        resolutionString = Extensions.GetEnumString(resolution, Resolution)
        self.Name = '{}({},{},{})'.format(self.__class__.__name__, fastPeriod, slowPeriod, resolutionString)


    def Update(self, algorithm, data):
        '''Updates this alpha model with the latest data from the algorithm.
        This is called each time the algorithm receives data for subscribed securities
        Args:
            algorithm: The algorithm instance
            data: The new data available
        Returns:
            The new insights generated'''
        insights = []
        pos = 0
        neg = 0.0
        for symbol, symbolData in self.symbolDataBySymbol.items():
            if symbolData.Fast.IsReady and symbolData.Slow.IsReady:
                mag = (symbolData.Fast.Current.Value-symbolData.Slow.Current.Value )/\
                np.mean([symbolData.Fast.Current.Value,symbolData.Slow.Current.Value])

                if symbolData.FastIsOverSlow:
                    if symbolData.Slow > symbolData.Fast:
                        neg += -mag

                elif symbolData.SlowIsOverFast:
                    if symbolData.Fast > symbolData.Slow:
                        pos += mag
        tot = pos + neg                
        for symbol, symbolData in self.symbolDataBySymbol.items():
            if symbolData.Fast.IsReady and symbolData.Slow.IsReady:
                mag = (symbolData.Fast.Current.Value-symbolData.Slow.Current.Value )/\
                np.mean([symbolData.Fast.Current.Value,symbolData.Slow.Current.Value])

                if symbolData.FastIsOverSlow:
                    if symbolData.Slow > symbolData.Fast:
                        insights.append(Insight.Price(symbolData.Symbol, 
                        self.predictionInterval, 
                        InsightDirection.Down, 
                        mag,-mag,
                        "MyAlphaModel",-mag/tot))

                elif symbolData.SlowIsOverFast:
                    if symbolData.Fast > symbolData.Slow:
                        insights.append(Insight.Price(symbolData.Symbol, 
                        self.predictionInterval, 
                        InsightDirection.Up,
                        mag,
                        mag,
                        "MyAlphaModel",mag/tot))
                        
            symbolData.FastIsOverSlow = symbolData.Fast > symbolData.Slow

        return insights

    def OnSecuritiesChanged(self, algorithm, changes):
        '''Event fired each time the we add/remove securities from the data feed
        Args:
            algorithm: The algorithm instance that experienced the change in securities
            changes: The security additions and removals from the algorithm'''
        for added in changes.AddedSecurities:
            symbolData = self.symbolDataBySymbol.get(added.Symbol)
            if symbolData is None:
                # create fast/slow EMAs
                symbolData = SymbolData(added)
                symbolData.Fast = algorithm.EMA(added.Symbol, self.fastPeriod, self.resolution)
                symbolData.Slow = algorithm.EMA(added.Symbol, self.slowPeriod, self.resolution)
                self.symbolDataBySymbol[added.Symbol] = symbolData
            else:
                # a security that was already initialized was re-added, reset the indicators
                symbolData.Fast.Reset()
                symbolData.Slow.Reset()


class SymbolData:
    '''Contains data specific to a symbol required by this model'''
    def __init__(self, security):
        self.Security = security
        self.Symbol = security.Symbol
        self.Fast = None
        self.Slow = None

        # True if the fast is above the slow, otherwise false.
        # This is used to prevent emitting the same signal repeatedly
        self.FastIsOverSlow = False

    @property
    def SlowIsOverFast(self):
        return not self.FastIsOverSlow

#region imports
from AlgorithmImports import *
#endregion

class HighPERatioUniverseSelectionModel(FineFundamentalUniverseSelectionModel):
    def __init__(self, universe_settings: UniverseSettings = None) -> None:
        super().__init__(self.SelectCoarse, self.SelectFine, universe_settings)

    def SelectCoarse(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
        selected = [c for c in coarse if c.HasFundamentalData]
        sorted_by_dollar_volume = sorted(selected, key=lambda c: c.DollarVolume, reverse=True)
        return [c.Symbol for c in sorted_by_dollar_volume[:100]]

    def SelectFine(self, fine: List[FineFundamental]) -> List[Symbol]:
        sorted_by_pe_ratio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)
        return [c.Symbol for c in sorted_by_pe_ratio[:10]]

#region imports
from AlgorithmImports import *
#endregion

class LowPERatioUniverseSelectionModel(FineFundamentalUniverseSelectionModel):
    def __init__(self, universe_settings: UniverseSettings = None) -> None:
        super().__init__(self.SelectCoarse, self.SelectFine, universe_settings)

    def SelectCoarse(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
        selected = [c for c in coarse if c.HasFundamentalData]
        sorted_by_dollar_volume = sorted(selected, key=lambda c: c.DollarVolume, reverse=True)
        return [c.Symbol for c in sorted_by_dollar_volume[:100]]

    def SelectFine(self, fine: List[FineFundamental]) -> List[Symbol]:
        sorted_by_pe_ratio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=False)
        return [c.Symbol for c in sorted_by_pe_ratio[:10]]

#region imports
from AlgorithmImports import *
#endregion
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from clr import AddReference
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Algorithm.Framework")
AddReference("QuantConnect.Indicators")

from QuantConnect import *
from QuantConnect.Indicators import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Alphas import *


class MacdAlphaModel(AlphaModel):
    '''Defines a custom alpha model that uses MACD crossovers. The MACD signal line
    is used to generate up/down insights if it's stronger than the bounce threshold.
    If the MACD signal is within the bounce threshold then a flat price insight is returned.'''

    def __init__(self,
                 fastPeriod = 12,
                 slowPeriod = 26,
                 signalPeriod = 9,
                 movingAverageType = MovingAverageType.Exponential,
                 resolution = Resolution.Daily):
        ''' Initializes a new instance of the MacdAlphaModel class
        Args:
            fastPeriod: The MACD fast period
            slowPeriod: The MACD slow period</param>
            signalPeriod: The smoothing period for the MACD signal
            movingAverageType: The type of moving average to use in the MACD'''
        self.fastPeriod = fastPeriod
        self.slowPeriod = slowPeriod
        self.signalPeriod = signalPeriod
        self.movingAverageType = movingAverageType
        self.resolution = resolution
        self.insightPeriod = Time.Multiply(Extensions.ToTimeSpan(resolution), fastPeriod)
        self.bounceThresholdPercent = 0.01
        self.symbolData = {}

        resolutionString = Extensions.GetEnumString(resolution, Resolution)
        movingAverageTypeString = Extensions.GetEnumString(movingAverageType, MovingAverageType)
        self.Name = '{}({},{},{},{},{})'.format(self.__class__.__name__, fastPeriod, slowPeriod, signalPeriod, movingAverageTypeString, resolutionString)


    def Update(self, algorithm, data):
        ''' Determines an insight for each security based on it's current MACD signal
        Args:
            algorithm: The algorithm instance
            data: The new data available
        Returns:
            The new insights generated'''
        insights = []

        for key, sd in self.symbolData.items():
            if sd.Security.Price == 0:
                continue

            direction = InsightDirection.Flat
            normalized_signal = sd.MACD.Signal.Current.Value / sd.Security.Price

            if normalized_signal > self.bounceThresholdPercent:
                direction = InsightDirection.Up
            elif normalized_signal < -self.bounceThresholdPercent:
                direction = InsightDirection.Down

            # ignore signal for same direction as previous signal
            if direction == sd.PreviousDirection:
                continue

            insight = Insight.Price(sd.Security.Symbol, self.insightPeriod, direction,normalized_signal)
            sd.PreviousDirection = insight.Direction
            insights.append(insight)

        return insights


    def OnSecuritiesChanged(self, algorithm, changes):
        '''Event fired each time the we add/remove securities from the data feed.
        This initializes the MACD for each added security and cleans up the indicator for each removed security.
        Args:
            algorithm: The algorithm instance that experienced the change in securities
            changes: The security additions and removals from the algorithm'''
        for added in changes.AddedSecurities:
            self.symbolData[added.Symbol] = SymbolData(algorithm, added, self.fastPeriod, self.slowPeriod, self.signalPeriod, self.movingAverageType, self.resolution)

        for removed in changes.RemovedSecurities:
            data = self.symbolData.pop(removed.Symbol, None)
            if data is not None:
                # clean up our consolidator
                algorithm.SubscriptionManager.RemoveConsolidator(removed.Symbol, data.Consolidator)

class SymbolData:
    def __init__(self, algorithm, security, fastPeriod, slowPeriod, signalPeriod, movingAverageType, resolution):
        self.Security = security
        self.MACD = MovingAverageConvergenceDivergence(fastPeriod, slowPeriod, signalPeriod, movingAverageType)

        self.Consolidator = algorithm.ResolveConsolidator(security.Symbol, resolution)
        algorithm.RegisterIndicator(security.Symbol, self.MACD, self.Consolidator)

        self.PreviousDirection = None

#region imports
from AlgorithmImports import *
#endregion
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from clr import AddReference
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Algorithm.Framework")
AddReference("QuantConnect.Indicators")
AddReference("QuantConnect.Logging")
AddReference("QuantConnect.Common")

from QuantConnect import *
from QuantConnect.Indicators import *
from QuantConnect.Logging import Log
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Alphas import *
from datetime import timedelta
from enum import Enum

class RsiAlphaModel(AlphaModel):
    '''Uses Wilder's RSI to create insights.
    Using default settings, a cross over below 30 or above 70 will trigger a new insight.'''

    def __init__(self,
                 period = 14,
                 resolution = Resolution.Daily):
        '''Initializes a new instance of the RsiAlphaModel class
        Args:
            period: The RSI indicator period'''
        self.period = period
        self.resolution = resolution
        self.insightPeriod = Time.Multiply(Extensions.ToTimeSpan(resolution), period)
        self.symbolDataBySymbol ={}
        self.lowcutoff = 30.0
        self.highcutoff = 70.0
        self.buffer = 5.0

        resolutionString = Extensions.GetEnumString(resolution, Resolution)
        self.Name = '{}({},{})'.format(self.__class__.__name__, period, resolutionString)

    def Update(self, algorithm, data):
        '''Updates this alpha model with the latest data from the algorithm.
        This is called each time the algorithm receives data for subscribed securities
        Args:
            algorithm: The algorithm instance
            data: The new data available
        Returns:
            The new insights generated'''
        insights = []
        for symbol, symbolData in self.symbolDataBySymbol.items():
            rsi = symbolData.RSI
            previous_state = symbolData.State
            state = self.GetState(rsi, previous_state)

            if state != previous_state and rsi.IsReady:
                if state == State.TrippedLow:
                    insights.append(Insight.Price(symbol, self.insightPeriod, InsightDirection.Up, 0.01*(self.lowcutoff-rsi.Current.Value)/self.lowcutoff))
                if state == State.TrippedHigh:
                    insights.append(Insight.Price(symbol, self.insightPeriod, InsightDirection.Down, 0.01*(rsi.Current.Value-self.highcutoff)/(100-self.highcutoff)))

            symbolData.State = state

        return insights


    def OnSecuritiesChanged(self, algorithm, changes):
        '''Cleans out old security data and initializes the RSI for any newly added securities.
        Event fired each time the we add/remove securities from the data feed
        Args:
            algorithm: The algorithm instance that experienced the change in securities
            changes: The security additions and removals from the algorithm'''

        # clean up data for removed securities
        symbols = [ x.Symbol for x in changes.RemovedSecurities ]
        if len(symbols) > 0:
            for subscription in algorithm.SubscriptionManager.Subscriptions:
                if subscription.Symbol in symbols:
                    self.symbolDataBySymbol.pop(subscription.Symbol, None)
                    subscription.Consolidators.Clear()

        # initialize data for added securities

        addedSymbols = [ x.Symbol for x in changes.AddedSecurities if x.Symbol not in self.symbolDataBySymbol]
        if len(addedSymbols) == 0: return

        history = algorithm.History(addedSymbols, self.period, self.resolution)

        for symbol in addedSymbols:
            rsi = algorithm.RSI(symbol, self.period, MovingAverageType.Wilders, self.resolution)

            if not history.empty:
                ticker = SymbolCache.GetTicker(symbol)

                if ticker not in history.index.levels[0]:
                    Log.Trace(f'RsiAlphaModel.OnSecuritiesChanged: {ticker} not found in history data frame.')
                    continue

                for tuple in history.loc[ticker].itertuples():
                    rsi.Update(tuple.Index, tuple.close)

            self.symbolDataBySymbol[symbol] = SymbolData(symbol, rsi)


    def GetState(self, rsi, previous):
        ''' Determines the new state. This is basically cross-over detection logic that
        includes considerations for bouncing using the configured bounce tolerance.'''
        if rsi.Current.Value > self.highcutoff :
            return State.TrippedHigh
        if rsi.Current.Value < self.lowcutoff :
            return State.TrippedLow
        if previous == State.TrippedLow:
            if rsi.Current.Value > (self.lowcutoff+self.buffer):
                return State.Middle
        if previous == State.TrippedHigh:
            if rsi.Current.Value < (self.highcutoff-self.buffer):
                return State.Middle

        return previous


class SymbolData:
    '''Contains data specific to a symbol required by this model'''
    def __init__(self, symbol, rsi):
        self.Symbol = symbol
        self.RSI = rsi
        self.State = State.Middle


class State(Enum):
    '''Defines the state. This is used to prevent signal spamming and aid in bounce detection.'''
    TrippedLow = 0
    Middle = 1
    TrippedHigh = 2

#region imports
from AlgorithmImports import *
#endregion
from datetime import timedelta
from QuantConnect.Data.UniverseSelection import * 
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
from Risk.MaximumDrawdownPercentPerSecurity import MaximumDrawdownPercentPerSecurity
from HighPERatioUnivese import HighPERatioUniverseSelectionModel
from LowPERatioUniverse import LowPERatioUniverseSelectionModel
from MacdAlphaModel import MacdAlphaModel
from RsiAlphaModel import RsiAlphaModel

class MakretNeutralAlgoFramework(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2018, 1, 1)
        self.SetEndDate(2023, 1, 1)
        self.SetCash(1000000)

        self.UniverseSettings.Resolution = Resolution.Hour
        #self.SetSecurityInitializer(lambda x: x.SetMarketPrice(self.GetLastKnownPrice(x)))
        self.AddUniverseSelection(HighPERatioUniverseSelectionModel()) #Short Universe
        self.AddUniverseSelection(LowPERatioUniverseSelectionModel()) #Long Universe       
        self.AddAlpha(MacdAlphaModel(12, 26, 9, MovingAverageType.Simple, Resolution.Daily)) #Long Alpha
        self.AddAlpha(RsiAlphaModel(10, Resolution.Daily)) #Short Alpha
        
        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
        self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.01))
        self.SetExecution(ImmediateExecutionModel())
        self.SetWarmUp(300, Resolution.Hour)