Colton Sellers
d2d99b1f10
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* Implement scheduled event sampling solution * Use UTC time, only update daily portfolio value once a day * For daily resolutions sample chart always * Cleanup * Drop resample daily all together * Force final sample * Regression updates * FIx LiveResultHandler to update portfolio and benchmark values outside of sampling event * Name the daily sampling event * Address review pt 1 * Drop force and use reference wrapper * Adjust tests * Fix warning for Benchmark Timezone Misalignment and also add test * Fix for daily resolution orders and test adjustments * Also warn on universe settings with daily resolution * Update missed regression * Fix reference wrapper use * Update regression after rebase * Add values back in for Daylight Algo * Have statistics builder skip day 1 performance * Regression adjustments * Test adjustments * Update regression unit test * Adjust some regressions starts to show performance values * Add hourly algorithm for beta comparison * Address missing Python regression changes * Remove null comment
50 lines
2.3 KiB
Python
50 lines
2.3 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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from Portfolio.SectorWeightingPortfolioConstructionModel import SectorWeightingPortfolioConstructionModel
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### <summary>
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### This example algorithm defines its own custom coarse/fine fundamental selection model
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### with sector weighted portfolio.
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### </summary>
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class SectorWeightingFrameworkAlgorithm(QCAlgorithm):
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'''This example algorithm defines its own custom coarse/fine fundamental selection model
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with sector weighted portfolio.'''
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def Initialize(self):
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# Set requested data resolution
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self.UniverseSettings.Resolution = Resolution.Daily
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self.SetStartDate(2014, 4, 2)
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self.SetEndDate(2014, 4, 6)
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self.SetCash(100000)
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# set algorithm framework models
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self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.SelectCoarse, self.SelectFine))
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self.SetAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(1)))
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self.SetPortfolioConstruction(SectorWeightingPortfolioConstructionModel())
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def OnOrderEvent(self, orderEvent):
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if orderEvent.Status == OrderStatus.Filled:
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self.Debug(f"Order event: {orderEvent}. Holding value: {self.Securities[orderEvent.Symbol].Holdings.AbsoluteHoldingsValue}")
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def SelectCoarse(self, coarse):
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# IndustryTemplateCode of AAPL, IBM and GOOG is N, AIG is I, BAC is B. SPY have no fundamentals
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tickers = ["AAPL", "AIG", "IBM"] if self.Time.date() < date(2014, 4, 4) else [ "GOOG", "BAC", "SPY" ]
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return [Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers]
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def SelectFine(self, fine):
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return [f.Symbol for f in fine]
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