wehub-resource-sync
82 lines
3.3 KiB
Python
82 lines
3.3 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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#
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# Leveraged ETFs (LETF) promise a fixed leverage ratio with respect to an underlying asset or an index.
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# A Triple-Leveraged ETF allows speculators to amplify their exposure to the daily returns of an underlying index by a factor of 3.
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#
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# Increased volatility generally decreases the value of a LETF over an extended period of time as daily compounding is amplified.
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#
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# This alpha emits short-biased insight to capitalize on volatility decay for each listed pair of TL-ETFs, by rebalancing the
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# ETFs with equal weights each day.
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#
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# 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.
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#
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class TripleLeverageETFPairVolatilityDecayAlpha(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2018, 1, 1)
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self.set_cash(100000)
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# Set zero transaction fees
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self.set_security_initializer(lambda security: security.set_fee_model(ConstantFeeModel(0)))
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# 3X ETF pair tickers
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ultra_long = Symbol.create("UGLD", SecurityType.EQUITY, Market.USA)
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ultra_short = Symbol.create("DGLD", SecurityType.EQUITY, Market.USA)
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# Manually curated universe
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self.universe_settings.resolution = Resolution.DAILY
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self.set_universe_selection(ManualUniverseSelectionModel([ultra_long, ultra_short]))
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# Select the demonstration alpha model
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self.set_alpha(RebalancingTripleLeveragedETFAlphaModel(ultra_long, ultra_short))
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## Set Equal Weighting Portfolio Construction Model
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self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
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## Set Immediate Execution Model
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self.set_execution(ImmediateExecutionModel())
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## Set Null Risk Management Model
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self.set_risk_management(NullRiskManagementModel())
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class RebalancingTripleLeveragedETFAlphaModel(AlphaModel):
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'''
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Rebalance a pair of 3x leveraged ETFs and predict that the value of both ETFs in each pair will decrease.
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'''
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def __init__(self, ultra_long, ultra_short):
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# Giving an insight period 1 days.
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self.period = timedelta(1)
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self.magnitude = 0.001
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self.ultra_long = ultra_long
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self.ultra_short = ultra_short
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self.name = "RebalancingTripleLeveragedETFAlphaModel"
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def update(self, algorithm, data):
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return Insight.group(
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[
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Insight.price(self.ultra_long, self.period, InsightDirection.DOWN, self.magnitude),
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Insight.price(self.ultra_short, self.period, InsightDirection.DOWN, self.magnitude)
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] )
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