wehub-resource-sync
83 lines
3.5 KiB
Python
83 lines
3.5 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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from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
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from Execution.ImmediateExecutionModel import ImmediateExecutionModel
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### <summary>
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### Basic template framework algorithm uses framework components to define the algorithm.
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### Liquid ETF Competition template
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="trading and orders" />
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class LiquidETFUniverseFrameworkAlgorithm(QCAlgorithm):
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'''Basic template framework algorithm uses framework components to define the algorithm.'''
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def initialize(self):
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# Set Start Date so that backtest has 5+ years of data
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self.set_start_date(2014, 11, 1)
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# No need to set End Date as the final submission will be tested
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# up until the review date
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# Set $1m Strategy Cash to trade significant AUM
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self.set_cash(1000000)
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# Add a relevant benchmark, with the default being SPY
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self.set_benchmark('SPY')
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# Use the Alpha Streams Brokerage Model, developed in conjunction with
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# funds to model their actual fees, costs, etc.
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# Please do not add any additional reality modelling, such as Slippage, Fees, Buying Power, etc.
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self.set_brokerage_model(AlphaStreamsBrokerageModel())
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# Use the LiquidETFUniverse with minute-resolution data
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self.universe_settings.resolution = Resolution.MINUTE
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self.set_universe_selection(LiquidETFUniverse())
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# Optional
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self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
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self.set_execution(ImmediateExecutionModel())
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# List of symbols we want to trade. Set it in OnSecuritiesChanged
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self._symbols = []
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def on_data(self, slice):
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if all([self.portfolio[x].invested for x in self._symbols]):
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return
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# Emit insights
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insights = [Insight.price(x, timedelta(1), InsightDirection.UP)
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for x in self._symbols if self.securities[x].price > 0]
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if len(insights) > 0:
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self.emit_insights(insights)
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def on_securities_changed(self, changes):
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# Set symbols as the Inverse Energy ETFs
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for security in changes.added_securities:
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if security.symbol in LiquidETFUniverse.ENERGY.inverse:
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self._symbols.append(security.symbol)
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# Print out the information about the groups
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self.log(f'Energy: {LiquidETFUniverse.ENERGY}')
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self.log(f'Metals: {LiquidETFUniverse.METALS}')
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self.log(f'Technology: {LiquidETFUniverse.TECHNOLOGY}')
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self.log(f'Treasuries: {LiquidETFUniverse.TREASURIES}')
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self.log(f'Volatility: {LiquidETFUniverse.VOLATILITY}')
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self.log(f'SP500Sectors: {LiquidETFUniverse.SP_500_SECTORS}')
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