TripleLeverageETFPairVolatilityDecayAlpha.py

# 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.Common")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Algorithm.Framework")

from System import *
from QuantConnect import *
from QuantConnect.Data.UniverseSelection import *
from QuantConnect.Orders.Fees import ConstantFeeModel
from QuantConnect.Algorithm.Framework.Alphas import *
from QuantConnect.Algorithm.Framework.Portfolio import EqualWeightingPortfolioConstructionModel
from QuantConnect.Algorithm.Framework.Selection import ManualUniverseSelectionModel
from datetime import timedelta

#
# Leveraged ETFs (LETF) promise a fixed leverage ratio with respect to an underlying asset or an index.
# A Triple-Leveraged ETF allows speculators to amplify their exposure to the daily returns of an underlying index by a factor of 3.
#
# Increased volatility generally decreases the value of a LETF over an extended period of time as daily compounding is amplified.
#
# This alpha emits short-biased insight to capitalize on volatility decay for each listed pair of TL-ETFs, by rebalancing the
# ETFs with equal weights each day.
#
# This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open sourced so the community and client funds can see an example of an alpha.
#

class TripleLeverageETFPairVolatilityDecayAlpha(QCAlgorithm):

    def Initialize(self):

        self.SetStartDate(2018, 1, 1)

        self.SetCash(100000)

        # Set zero transaction fees
        self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0)))

        # 3X ETF pair tickers
        ultraLong = Symbol.Create("UGLD", SecurityType.Equity, Market.USA)
        ultraShort = Symbol.Create("DGLD", SecurityType.Equity, Market.USA)

        # Manually curated universe
        self.UniverseSettings.Resolution = Resolution.Daily
        self.SetUniverseSelection(ManualUniverseSelectionModel([ultraLong, ultraShort]))

        # Select the demonstration alpha model
        self.SetAlpha(RebalancingTripleLeveragedETFAlphaModel(ultraLong, ultraShort))

        ## Set Equal Weighting Portfolio Construction Model
        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())

        ## Set Immediate Execution Model
        self.SetExecution(ImmediateExecutionModel())

        ## Set Null Risk Management Model
        self.SetRiskManagement(NullRiskManagementModel())


class RebalancingTripleLeveragedETFAlphaModel(AlphaModel):
    '''
        Rebalance a pair of 3x leveraged ETFs and predict that the value of both ETFs in each pair will decrease.
    '''

    def __init__(self, ultraLong, ultraShort):
        # Giving an insight period 1 days.
        self.period = timedelta(1)

        self.magnitude = 0.001
        self.ultraLong = ultraLong
        self.ultraShort = ultraShort

        self.Name = "RebalancingTripleLeveragedETFAlphaModel"

    def Update(self, algorithm, data):

        return Insight.Group(
            [
                Insight.Price(self.ultraLong, self.period, InsightDirection.Down, self.magnitude),
                Insight.Price(self.ultraShort, self.period, InsightDirection.Down, self.magnitude)
            ] )