# 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 AlgorithmImports import *

class EmaCrossMagnitudeAlphaModel(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 = []
        for symbol, symbolData in self.symbolDataBySymbol.items():
            if symbolData.Fast.IsReady and symbolData.Slow.IsReady:


                magnitude = (abs(symbolData.Fast.Current.Value*symbolData.Slow.Current.Value))**0.5/symbolData.Security.Price**2
                if symbolData.FastIsOverSlow:
                    if symbolData.Slow > symbolData.Fast:
                        insights.append(Insight.Price(symbolData.Symbol, self.predictionInterval, InsightDirection.Down, magnitude=-magnitude)) #Placeholder magnitudes

                elif symbolData.SlowIsOverFast:
                    if symbolData.Fast > symbolData.Slow:
                        insights.append(Insight.Price(symbolData.Symbol, self.predictionInterval, InsightDirection.Up, magnitude=magnitude)) #Placeholder magnitudes

            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

# 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 AlgorithmImports import *

class MacdMagnitudeAlphaModel(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
            
            magnitude = (abs(sd.MACD.Histogram.Current.Value * sd.MACD.Signal.Current.Value))**0.5 / sd.Security.Price**2

            if normalized_signal > self.bounceThresholdPercent:
                direction = InsightDirection.Up
            elif normalized_signal < -self.bounceThresholdPercent:
                direction = InsightDirection.Down
                magnitude *= -1.0 #Placeholder magnitudes

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

            insight = Insight.Price(sd.Security.Symbol, self.insightPeriod, direction, magnitude=magnitude) #Placeholder magnitudes
            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)
        algorithm.WarmUpIndicator(security.Symbol, self.MACD, resolution)

        self.PreviousDirection = None

from Execution.ImmediateExecutionModel import ImmediateExecutionModel
from Portfolio.BlackLittermanOptimizationPortfolioConstructionModel import BlackLittermanOptimizationPortfolioConstructionModel
from Risk.MaximumDrawdownPercentPerSecurity import MaximumDrawdownPercentPerSecurity

from MacdMagnitudeAlphaModel import MacdMagnitudeAlphaModel
from EmaCrossMagnitudeAlphaModel import EmaCrossMagnitudeAlphaModel

class PensiveBrownMule(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2021, 9, 7)  # Set Start Date
        self.SetCash(100000)  # Set Strategy Cash
        self.AddAlpha(EmaCrossAlphaModel(50, 200, Resolution.Minute))

        self.AddAlpha(MacdAlphaModel(12, 26, 9, MovingAverageType.Simple, Resolution.Minute))

        self.SetExecution(ImmediateExecutionModel())

        self.SetPortfolioConstruction(BlackLittermanOptimizationPortfolioConstructionModel())

        self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.01))

        self.UniverseSettings.Resolution = Resolution.Minute

        symbols = [ 
        Symbol.Create("SPY", SecurityType.Equity, Market.USA), 
        Symbol.Create("AAPL", SecurityType.Equity, Market.USA), 
        Symbol.Create("UVXY", SecurityType.Equity, Market.USA), 
        Symbol.Create("SOXL", SecurityType.Equity, Market.USA), 
        
        ]
        self.AddUniverseSelection(ManualUniverseSelectionModel(symbols))




    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
            Arguments:
                data: Slice object keyed by symbol containing the stock data
        '''