# 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 *
from System.Collections.Generic import List

### <summary>
### In this algorithm we demonstrate how to perform some technical analysis as
### part of your coarse fundamental universe selection
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="indicators" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
class EmaCrossUniverseSelectionAlgorithm(QCAlgorithm):

    def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        self.SetStartDate(2017,1,1)  #Set Start Date
        self.SetEndDate(2022,1,1)    #Set End Date
        self.SetCash(100000000)           #Set Strategy Cash

        self.UniverseSettings.Resolution = Resolution.Daily
        self.UniverseSettings.Leverage = 2

        self.coarse_count = 1
        self.averages = { }

        #reshuffle monthly
        self.month = -1
        # this add universe method accepts two parameters:
        # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
        self.AddUniverse(self.CoarseSelectionFunction)


    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):
        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
        filtered = [ x for x in sortedByDollarVolume 
                      if x.Symbol.Value=='GME' ] #x.DollarVolume > 10000000
        # We are going to use a dictionary to refer the object that will keep the moving averages
        for cf in filtered:
            if cf.Symbol not in self.averages:
                self.averages[cf.Symbol] = SymbolData(cf.Symbol)

            # Updates the SymbolData object with current EOD price
            avg = self.averages[cf.Symbol]
            avg.update(cf.EndTime, cf.AdjustedPrice)

        #we want to update all EMA, but dont want to trade till month changes
        if self.month == self.Time.month: 
            return Universe.Unchanged
        else:
            self.month = self.Time.month
            # liquidate and rerank everything to see 
            # if each month actual orders are being placed
            # self.Liquidate()
        # Filter the values of the dict: we only want up-trending securities from ones which has indicator values
        values = list(filter(lambda x: x.is_uptrend, self.averages.values()))
        
        # Sorts the values of the dict: we want those with greater difference between the moving averages
        values.sort(key=lambda x: x.scale, reverse=True)
        for x in values[:self.coarse_count]:
            self.Debug(str(self.Time) + 'symbol: ' + str(filtered[0].AdjustedPrice)+ '  52w High: ' + str(x.fiftyTwoHigh.Current.Value) + ' Down from 52w High: ' + str(x.downFromHigh) +'  period since 52w Low: ' + str(x.fiftyTwoLow.PeriodsSinceMinimum)+'  52w Low: ' + str(x.fiftyTwoLow.Current.Value)+' Up from 52w Low: ' + str(x.upFromLow))
            
        # ensure the universe selection only run once in every month
        # we need to return only the symbol objects
        return [ x.symbol for x in values[:self.coarse_count] ]

    # this event fires whenever we have changes to our universe
    def OnSecuritiesChanged(self, changes):
        # liquidate removed securities
        for security in changes.RemovedSecurities:
            if security.Invested:
                self.Liquidate(security.Symbol)

        # we want 20% allocation in each security in our universe
        for security in changes.AddedSecurities:
            self.SetHoldings(security.Symbol, 0.1)


class SymbolData(object):
    def __init__(self, symbol):
        self.symbol = symbol
        self.tolerance = 1.01
        self.fast = ExponentialMovingAverage(50)
        self.slow = ExponentialMovingAverage(200)
        self.fiftyTwoHigh = Maximum(253)
        self.fiftyTwoLow = Minimum(253)
        self.downFromHigh=1
        self.upFromLow=0
        self.is_uptrend = False
        self.scale = 0

    def update(self, time, value):
        #Updates the state of this indicator with the given value and returns true
        #if this indicator is ready, false otherwise
        self.fiftyTwoHigh.Update(time, value)
        self.fiftyTwoLow.Update(time, value)
        if self.fiftyTwoHigh.Update(time, value) and self.fiftyTwoLow.Update(time, value) and self.fast.Update(time, value) and self.slow.Update(time, value):
            fast = self.fast.Current.Value
            slow = self.slow.Current.Value
            self.downFromHigh=1-(value/self.fiftyTwoHigh.Current.Value)
            self.upFromLow=(value/self.fiftyTwoLow.Current.Value)-1
            self.is_uptrend = fast > slow * self.tolerance

        if self.is_uptrend:
            self.scale = (fast - slow) / ((fast + slow) / 2.0)

# 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 *
from System.Collections.Generic import List

### <summary>
### In this algorithm we demonstrate how to perform some technical analysis as
### part of your coarse fundamental universe selection
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="indicators" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
class EmaCrossUniverseSelectionAlgorithm(QCAlgorithm):

    def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        self.SetStartDate(2015,1,1)  #Set Start Date
        self.SetEndDate(2019,1,1)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash

        self.UniverseSettings.Resolution = Resolution.Daily
        self.UniverseSettings.Leverage = 2

        self.coarse_count = 10
        self.averages = { }

        # this add universe method accepts two parameters:
        # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
        self.AddUniverse(self.CoarseSelectionFunction)


    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):

        # We are going to use a dictionary to refer the object that will keep the moving averages
        for cf in coarse:
            if cf.Symbol not in self.averages:
                self.averages[cf.Symbol] = SymbolData(cf.Symbol)

            # Updates the SymbolData object with current EOD price
            avg = self.averages[cf.Symbol]
            avg.update(cf.EndTime, cf.AdjustedPrice)

        # Filter the values of the dict: we only want up-trending securities
        values = list(filter(lambda x: x.is_uptrend, self.averages.values()))

        # Sorts the values of the dict: we want those with greater difference between the moving averages
        values.sort(key=lambda x: x.scale, reverse=True)

        for x in values[:self.coarse_count]:
            self.Log('symbol: ' + str(x.symbol.Value) + '  scale: ' + str(x.scale))

        # we need to return only the symbol objects
        return [ x.symbol for x in values[:self.coarse_count] ]

    # this event fires whenever we have changes to our universe
    def OnSecuritiesChanged(self, changes):
        # liquidate removed securities
        for security in changes.RemovedSecurities:
            if security.Invested:
                self.Liquidate(security.Symbol)

        # we want 20% allocation in each security in our universe
        for security in changes.AddedSecurities:
            self.SetHoldings(security.Symbol, 0.1)


class SymbolData(object):
    def __init__(self, symbol):
        self.symbol = symbol
        self.tolerance = 1.01
        self.fast = ExponentialMovingAverage(50)
        self.slow = ExponentialMovingAverage(200)
        self.is_uptrend = False
        self.scale = 0

    def update(self, time, value):
        if self.fast.Update(time, value) and self.slow.Update(time, value):
            fast = self.fast.Current.Value
            slow = self.slow.Current.Value
            self.is_uptrend = fast > slow * self.tolerance

        if self.is_uptrend:
            self.scale = (fast - slow) / ((fast + slow) / 2.0)