from Alphas.EmaCrossAlphaModel import EmaCrossAlphaModel

from Execution.ImmediateExecutionModel import ImmediateExecutionModel

from Risk.NullRiskManagementModel import NullRiskManagementModel

from Selection.QC500UniverseSelectionModel import QC500UniverseSelectionModel

from SlopeBasedEquityMomentumAlphaModel import SlopeBasedEquityMomentumAlphaModel

class BasicTemplateFrameworkAlgorithm(QCAlgorithmFramework):

    def Initialize(self):

        # Set requested data resolution
        self.UniverseSettings.Resolution = Resolution.Daily

        self.SetStartDate(2019, 3, 4)   #Set Start Date
        self.SetEndDate(2019, 3, 7)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash

        # selection will run on mon/tues/thurs at 00:00/06:00/12:00/18:00
        self.SetUniverseSelection(QC500UniverseSelectionModel())
        
        self.SetAlpha(SlopeBasedEquityMomentumAlphaModel())
        
        self.SetPortfolioConstruction(NullPortfolioConstructionModel()) 
        
        self.SetExecution(ImmediateExecutionModel())
        
        self.SetRiskManagement(NullRiskManagementModel())
        
    

    def OnOrderEvent(self, orderEvent):
        if orderEvent.Status == OrderStatus.Filled:
            # self.Debug("Purchased Stock: {0}".format(orderEvent.Symbol))
            pass

# 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 SlopeBasedEquityMomentumAlphaModel(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,
                 shortTermMomentumWindow = 60,
                 longTermMomentumWindow = 90,
                 minimumMomentum = 60,
                 indexAverageWindow = 100,
                 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.shortTermMomentumWindow = shortTermMomentumWindow,
        self.longTermMomentumWindow = longTermMomentumWindow,
        self.minimumMomentum = minimumMomentum,
        self.indexAverageWindow = indexAverageWindow,
        self.resolution = resolution
        self.symbolData = {}
        resolutionString = Extensions.GetEnumString(resolution, Resolution)
 
        
    def slope(ts):
        '''
        Args: 
            Price time series.
        Returns: 
            Annualized exponential regression slope, multipl
        '''
        x = np.arange(len(ts))
        log_ts = np.log(ts)
        slope, intercept, r_value, p_value, std_err = stats.linregress(x, log_ts)
        annualized_slope = (np.power(np.exp(slope), 250) - 1) * 100
        return annualized_slope * (r_value ** 2)
    
    def Update(self, algorithm, data):
        ''' Determines an insight for each security based on two annualized slopes
        Args:
            algorithm: The algorithm instance
            data: The new data available
        Returns:
            The new insights generated'''
        insights = []
        
        #shortTermBars = GetDailyClosesForAllSymbolsInUniverse(self.shortTermMomentumWindow,self.resolution)
        #longTermBars = GetDailyClosesForAllSymbolsInUniverse(self.longTermMomentumWindow,self.resolution)
        
        #...
        
        return insights