Overall Statistics
Total Trades
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Net Profit
0%
Sharpe Ratio
0
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
0
Tracking Error
0
Treynor Ratio
0
Total Fees
$0.00
# 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.Framework")
AddReference("QuantConnect.Indicators")
AddReference("QuantConnect.Common")

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

class HistoricalReturnsAlphaModel:
    '''Uses Historical returns to create insights.'''

    def __init__(self, *args, **kwargs):
        '''Initializes a new default instance of the HistoricalReturnsAlphaModel class.
        Args:
            lookback(int): Historical return lookback period
            resolution: The resolution of historical data'''
        self.lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
        self.resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.Daily
        self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), self.lookback)
        self.symbolDataBySymbol = {}

    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.CanEmit:

                direction = InsightDirection.Flat
                magnitude = symbolData.Return
                if magnitude > 0: direction = InsightDirection.Up
                if magnitude < 0: direction = InsightDirection.Down

                insights.append(Insight.Price(symbol, self.predictionInterval, direction,  0.00001, None))

        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'''

        # clean up data for removed securities
        for removed in changes.RemovedSecurities:
            symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None)
            if symbolData is not None:
                symbolData.RemoveConsolidators(algorithm)

        # initialize data for added securities
        symbols = [ x.Symbol for x in changes.AddedSecurities ]
        history = algorithm.History(symbols, self.lookback, self.resolution)
        if history.empty: return

        tickers = history.index.levels[0]
        for ticker in tickers:
            symbol = SymbolCache.GetSymbol(ticker)

            if symbol not in self.symbolDataBySymbol:
                symbolData = SymbolData(symbol, self.lookback)
                self.symbolDataBySymbol[symbol] = symbolData
                symbolData.RegisterIndicators(algorithm, self.resolution)
                symbolData.WarmUpIndicators(history.loc[ticker])


class SymbolData:
    '''Contains data specific to a symbol required by this model'''
    def __init__(self, symbol, lookback):
        self.Symbol = symbol
        self.ROC = RateOfChange('{}.ROC({})'.format(symbol, lookback), lookback)
        self.Consolidator = None
        self.previous = 0

    def RegisterIndicators(self, algorithm, resolution):
        self.Consolidator = algorithm.ResolveConsolidator(self.Symbol, resolution)
        algorithm.RegisterIndicator(self.Symbol, self.ROC, self.Consolidator)

    def RemoveConsolidators(self, algorithm):
        if self.Consolidator is not None:
            algorithm.SubscriptionManager.RemoveConsolidator(self.Symbol, self.Consolidator)

    def WarmUpIndicators(self, history):
        for tuple in history.itertuples():
            self.ROC.Update(tuple.Index, tuple.close)

    @property
    def Return(self):
        return float(self.ROC.Current.Value)

    @property
    def CanEmit(self):
        if self.previous == self.ROC.Samples:
            return False

        self.previous = self.ROC.Samples
        return self.ROC.IsReady

    def __str__(self, **kwargs):
        return '{}: {:.2%}'.format(self.ROC.Name, (1 + self.Return)**252 - 1)                        
# 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.Framework")
AddReference("QuantConnect.Indicators")
AddReference("QuantConnect.Common")

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

class HistoricalReturnsAlphaModel_copy:
    '''Uses Historical returns to create insights.'''

    def __init__(self, *args, **kwargs):
        '''Initializes a new default instance of the HistoricalReturnsAlphaModel class.
        Args:
            lookback(int): Historical return lookback period
            resolution: The resolution of historical data'''
        self.lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
        self.resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.Daily
        self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), self.lookback)
        self.symbolDataBySymbol = {}

    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.CanEmit:

                direction = InsightDirection.Flat
                magnitude = symbolData.Return
                if magnitude > 0: direction = InsightDirection.Up
                if magnitude < 0: direction = InsightDirection.Down

                insights.append(Insight.Price(symbol, self.predictionInterval, direction,  0.00001, None))

        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'''

        # clean up data for removed securities
        for removed in changes.RemovedSecurities:
            symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None)
            if symbolData is not None:
                symbolData.RemoveConsolidators(algorithm)

        # initialize data for added securities
        symbols = [ x.Symbol for x in changes.AddedSecurities ]
        history = algorithm.History(symbols, self.lookback, self.resolution)
        if history.empty: return

        tickers = history.index.levels[0]
        for ticker in tickers:
            symbol = SymbolCache.GetSymbol(ticker)

            if symbol not in self.symbolDataBySymbol:
                symbolData = SymbolData(symbol, self.lookback)
                self.symbolDataBySymbol[symbol] = symbolData
                symbolData.RegisterIndicators(algorithm, self.resolution)
                symbolData.WarmUpIndicators(history.loc[ticker])


class SymbolData:
    '''Contains data specific to a symbol required by this model'''
    def __init__(self, symbol, lookback):
        self.Symbol = symbol
        self.ROC = RateOfChange('{}.ROC({})'.format(symbol, lookback), lookback)
        self.Consolidator = None
        self.previous = 0

    def RegisterIndicators(self, algorithm, resolution):
        self.Consolidator = algorithm.ResolveConsolidator(self.Symbol, resolution)
        algorithm.RegisterIndicator(self.Symbol, self.ROC, self.Consolidator)

    def RemoveConsolidators(self, algorithm):
        if self.Consolidator is not None:
            algorithm.SubscriptionManager.RemoveConsolidator(self.Symbol, self.Consolidator)

    def WarmUpIndicators(self, history):
        for tuple in history.itertuples():
            self.ROC.Update(tuple.Index, tuple.close)

    @property
    def Return(self):
        return float(self.ROC.Current.Value)

    @property
    def CanEmit(self):
        if self.previous == self.ROC.Samples:
            return False

        self.previous = self.ROC.Samples
        return self.ROC.IsReady

    def __str__(self, **kwargs):
        return '{}: {:.2%}'.format(self.ROC.Name, (1 + self.Return)**252 - 1)                        
# 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("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Indicators")

from System import *
from QuantConnect import *
from QuantConnect.Indicators import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Alphas import *
from QuantConnect.Algorithm.Framework.Portfolio import PortfolioTarget
from datetime import timedelta
import numpy as np
import pandas as pd
from scipy.optimize import minimize

### <summary>
### Provides an implementation of Mean-Variance portfolio optimization based on modern portfolio theory.
### The interval of weights in optimization method can be changed based on the long-short algorithm.
### The default model uses the last three months daily price to calculate the optimal weight 
### with the weight range from -1 to 1 and minimize the portfolio variance with a target return of 2%
### </summary>
class MeanVarianceOptimizationPortfolioConstructionModel:
    def __init__(self, *args, **kwargs):
        """Initialize the model
        Args:
            lookback(int): Historical return lookback period
            period(int): The time interval of history price to calculate the weight 
            resolution: The resolution of the history price 
            minimum_weight(float): The lower bounds on portfolio weights
            maximum_weight(float): The upper bounds on portfolio weights
            target_return(float): The target portfolio return
            optimization_method(method): Method used to compute the portfolio weights"""
        self.lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
        self.period = kwargs['period'] if 'period' in kwargs else 63
        self.resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.Daily
        self.minimum_weight = kwargs['minimum_weight'] if 'minimum_weight' in kwargs else -1
        self.maximum_weight = kwargs['maximum_weight'] if 'maximum_weight' in kwargs else 1
        self.target_return = kwargs['target_return'] if 'target_return' is kwargs else 0.02
        self.optimization_method = kwargs['optimization_method'] if 'optimization_method' in kwargs else self.minimum_variance
        self.symbolDataBySymbol = {}
        self.pendingRemoval = []

    def CreateTargets(self, algorithm, insights):
        """ 
        Create portfolio targets from the specified insights
        Args:
            algorithm: The algorithm instance
            insights: The insights to create portoflio targets from
        Returns: 
            An enumerable of portoflio targets to be sent to the execution model
        """
        for symbol in self.pendingRemoval:
            yield PortfolioTarget.Percent(algorithm, symbol, 0)

        self.pendingRemoval.clear()

        symbols = [insight.Symbol for insight in insights]
        if len(symbols) == 0:
            return []

        for insight in insights:
            symbolData = self.symbolDataBySymbol.get(insight.Symbol)
            if insight.Magnitude is None:
                algorithm.SetRunTimeError(ArgumentNullException('MeanVarianceOptimizationPortfolioConstructionModel does not accept \'None\' as Insight.Magnitude. Please checkout the selected Alpha Model specifications.'))
            symbolData.Add(algorithm.Time, insight.Magnitude)

        # Create a dictionary keyed by the symbols in the insights with an pandas.Series as value to create a data frame
        returns = { str(symbol) : data.Return for symbol, data in self.symbolDataBySymbol.items() if symbol in symbols }
        returns = pd.DataFrame(returns)

        # The optimization method processes the data frame
        opt, weights = self.optimization_method(returns)
        
        algorithm.Log('{}:\n\r{}'.format(algorithm.Time, weights))
        
        # Create portfolio targets from the specified insights
        for insight in insights:
            weight = weights[str(insight.Symbol)]
            yield PortfolioTarget.Percent(algorithm, insight.Symbol, weight)


    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'''

        # clean up data for removed securities
        for removed in changes.RemovedSecurities:
            self.pendingRemoval.append(removed.Symbol)
            symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None)
            if symbolData is not None:
                symbolData.RemoveConsolidators(algorithm)

        # initialize data for added securities
        symbols = [ x.Symbol for x in changes.AddedSecurities ]
        history = algorithm.History(symbols, self.lookback * self.period, self.resolution)
        if history.empty: return

        tickers = history.index.levels[0]
        for ticker in tickers:
            symbol = SymbolCache.GetSymbol(ticker)

            if symbol not in self.symbolDataBySymbol:
                symbolData = SymbolData(symbol, self.lookback, self.period)
                self.symbolDataBySymbol[symbol] = symbolData
                symbolData.RegisterIndicators(algorithm, self.resolution)
                symbolData.WarmUpIndicators(history.loc[ticker])


    def minimum_variance(self, returns):
        '''Minimum variance optimization method'''

        # Objective function
        fun = lambda weights: np.dot(weights.T, np.dot(returns.cov(), weights))

        # Constraint #1: The weights can be negative, which means investors can short a security.
        constraints = [{'type': 'eq', 'fun': lambda w: np.sum(w) - 1}]

        # Constraint #2: Portfolio targets a given return
        constraints.append({'type': 'eq', 'fun': lambda weights: np.dot(np.matrix(returns.mean()), np.matrix(weights).T).item() - self.target_return})

        size = returns.columns.size
        x0 = np.array(size * [1. / size])
        bounds = tuple((self.minimum_weight, self.maximum_weight) for x in range(size))

        opt = minimize(fun,                         # Objective function
                       x0,                          # Initial guess
                       method='SLSQP',              # Optimization method:  Sequential Least SQuares Programming
                       bounds = bounds,             # Bounds for variables 
                       constraints = constraints)   # Constraints definition

        return opt, pd.Series(opt['x'], index = returns.columns)


class SymbolData:
    '''Contains data specific to a symbol required by this model'''
    def __init__(self, symbol, lookback, period):
        self.Symbol = symbol
        self.ROC = RateOfChange('{}.ROC({})'.format(symbol, lookback), lookback)
        self.ROC.Updated += self.OnRateOfChangeUpdated
        self.Window = RollingWindow[IndicatorDataPoint](period)
        self.Consolidator = None

    def RegisterIndicators(self, algorithm, resolution):
        self.Consolidator = algorithm.ResolveConsolidator(self.Symbol, resolution)
        algorithm.RegisterIndicator(self.Symbol, self.ROC, self.Consolidator)

    def RemoveConsolidators(self, algorithm):
        if self.Consolidator is not None:
            algorithm.SubscriptionManager.RemoveConsolidator(self.Symbol, self.Consolidator)

    def WarmUpIndicators(self, history):
        for tuple in history.itertuples():
            self.ROC.Update(tuple.Index, tuple.close)

    def OnRateOfChangeUpdated(self, roc, value):
        if roc.IsReady:
            self.Window.Add(value)

    def Add(self, time, value):
        item = IndicatorDataPoint(self.Symbol, time, value)
        self.Window.Add(item)

    @property
    def Return(self):
        data = [(1 + float(x.Value))**252 - 1 for x in self.Window]
        index = [x.EndTime for x in self.Window]
        return pd.Series(data, index=index)

    @property
    def IsReady(self):
        return self.Window.IsReady

    def __str__(self, **kwargs):
        return '{}: {:.2%}'.format(self.ROC.Name, (1 + self.Window[0])**252 - 1)                        
from System import *
from QuantConnect import *
from QuantConnect.Orders import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Alphas import *
from QuantConnect.Algorithm.Framework.Execution import * 
from QuantConnect.Algorithm.Framework.Risk import * 
from QuantConnect.Algorithm.Framework.Selection import *

from HistoricalReturnsAlphaModel import HistoricalReturnsAlphaModel
from HistoricalReturnsAlphaModel_copy import HistoricalReturnsAlphaModel_copy
from MeanVarianceOptimizationPortfolioConstructionModel import MeanVarianceOptimizationPortfolioConstructionModel
import numpy as np
import pandas as pd
from scipy.optimize import minimize

class MeanVarianceOptimizationAlgorithm(QCAlgorithmFramework):
    '''Mean Variance Optimization Algorithm.'''

    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.'''

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

        self.SetStartDate(2017, 4, 1)   #Set Start Date
        self.SetEndDate(2018, 4, 9)     #Set End Date
        self.SetCash(100000)            #Set Strategy Cash

        tickers = ['IWD', 'MTUM', 'IWN', 'IWM', 'EFA', 'EEM', 'IEF', 'SPY', 'LQD', 'TLT', 'DBC', 'GLD', 'VNQ']
        
        symbols = [ Symbol.Create(ticker, SecurityType.Equity, Market.USA) for ticker in tickers ]

        self.minimum_weight = -1
        self.maximum_weight = 1

        # set algorithm framework models
        self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
        
        #Alpha #1
        self.SetAlpha( HistoricalReturnsAlphaModel(resolution = Resolution.Daily) )
        #Alpha #2
        self.SetAlpha( HistoricalReturnsAlphaModel_copy(resolution = Resolution.Daily) )
        #Composite Alpha
        #self.SetAlpha( CompositeAlphaModel([HistoricalReturnsAlphaModel(resolution = Resolution.Daily), HistoricalReturnsAlphaModel_copy(resolution = Resolution.Daily)]) )
        
        self.SetPortfolioConstruction( MeanVarianceOptimizationPortfolioConstructionModel() )
        
        self.SetExecution( NullExecutionModel() )
        
        self.SetRiskManagement( NullRiskManagementModel() )


    #def OnOrderEvent(self, orderEvent):
    #    if orderEvent.Status == OrderStatus.Filled:
    #        self.Debug(orderEvent.ToString())

    def maximum_sharpe_ratio(self, returns):
        '''Maximum Sharpe Ratio optimization method'''

        # Objective function
        fun = lambda weights: -self.sharpe_ratio(returns, weights)
        
        # Constraint #1: The weights can be negative, which means investors can short a security.
        constraints = [{'type': 'eq', 'fun': lambda w: np.sum(w) - 1}]

        size = returns.columns.size
        x0 = np.array(size * [1. / size])
        bounds = tuple((self.minimum_weight, self.maximum_weight) for x in range(size))
        
        opt = minimize(fun,                         # Objective function
                       x0,                          # Initial guess
                       method='SLSQP',              # Optimization method:  Sequential Least SQuares Programming
                       bounds = bounds,             # Bounds for variables 
                       constraints = constraints)   # Constraints definition

        weights = pd.Series(opt['x'], index = returns.columns)

        return opt, weights

    def sharpe_ratio(self, returns, weights):
        annual_return = np.dot(np.matrix(returns.mean()), np.matrix(weights).T).item()
        annual_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov(), weights)))
        return annual_return/annual_volatility