Alexandre Catarino
2b0fd2e607
* Fixes Double to Decimal Cast in GetAnnualPerformance `GetAnnualPerformance` raises an exception if the `AnnualPerformance` calculation returns a double that cannot be cast to decimal (smaller than `decimal.MinValue` or bigger than `decimal.MaxValue`). See `ProbabilisticSharpeRatio` where the same solution was applied. * Updates SPY Market Data SPY is a key asset since it is the default benchmark, and any change can lead to different `Alpha` and `Beta` * Updates Unit Tests to Reflect Data Update * Updates Regression Tests to Reflect Data Update I Most of the regression tests change because of updated data (market and factors) of SPY (default benchmark) while the total trade remain the same. * Updates Regression Tests to Reflect Data Update II The following regression tests were changed to adapt to adjusted prices and keep the total trades: - `BacktestingBrokerageRegressionAlgorithm` - `LimitIfTouchedRegressionAlgorithm` - `PortfolioRebalanceOnCustomFuncRegressionAlgorithm` - `SetAccountCurrencySecurityMarginModelRegressionAlgorithm` - `StopLossOnOrderEventRegressionAlgorithm` - `TimeInForceAlgorithm` The following regression tests have more trades since adjusted prices allowed more 1-2 shares trades that were rounded down to zero before: - `FreePortfolioValueRegressionAlgorithm` 2 -> 3 - `PortfolioRebalanceOnDateRulesRegressionAlgorithm` 291 -> 298 - `TrailingStopRiskFrameworkAlgorithm` 5 -> 7 Especial cases: - `AutoRegressiveIntegratedMovingAverageRegressionAlgorithm` 65 -> 52 - ARIMA model sensibility - `BlackLittermanPortfolioOptimizationFrameworkAlgorithm` 18 -> 19 - BLM model sensibility - `ExtendedMarketHoursHistoryRegressionAlgorithm` 20 -> 18 - Less minute bars before market opens * Addresses Peer-Review Fix `BacktestingBrokerageRegressionAlgorithm` to use `CalculateOrderQuantity` and round down `quantity` to an even number to pass a value assertion and update the expected value from 50 to 52. The quantity calculated by `CalculateOrderQuantity` has changed from 50 to 53 because of factor file update.
87 lines
4.1 KiB
Python
87 lines
4.1 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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Algorithm.Framework")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Orders import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Securities import *
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from QuantConnect.Algorithm.Framework import *
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from QuantConnect.Algorithm.Framework.Alphas import *
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from QuantConnect.Algorithm.Framework.Execution import *
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from QuantConnect.Algorithm.Framework.Portfolio import *
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from QuantConnect.Algorithm.Framework.Selection import *
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from datetime import timedelta
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### <summary>
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### Regression algorithm testing portfolio construction model control over rebalancing,
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### specifying a custom rebalance function that returns null in some cases, see GH 4075.
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### </summary>
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class PortfolioRebalanceOnCustomFuncRegressionAlgorithm(QCAlgorithm):
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def Initialize(self):
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''' Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.UniverseSettings.Resolution = Resolution.Daily
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self.SetStartDate(2015, 1, 1)
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self.SetEndDate(2018, 1, 1)
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self.Settings.RebalancePortfolioOnInsightChanges = False;
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self.Settings.RebalancePortfolioOnSecurityChanges = False;
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self.SetUniverseSelection(CustomUniverseSelectionModel("CustomUniverseSelectionModel", lambda time: [ "AAPL", "IBM", "FB", "SPY", "AIG", "BAC", "BNO" ]))
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self.SetAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, None));
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(self.RebalanceFunction))
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self.SetExecution(ImmediateExecutionModel())
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self.lastRebalanceTime = self.StartDate
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def RebalanceFunction(self, time):
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# for performance only run rebalance logic once a week, monday
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if time.weekday() != 0:
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return None
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if self.lastRebalanceTime == self.StartDate:
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# initial rebalance
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self.lastRebalanceTime = time;
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return time;
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deviation = 0;
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count = sum(1 for security in self.Securities.Values if security.Invested)
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if count > 0:
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self.lastRebalanceTime = time;
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portfolioValuePerSecurity = self.Portfolio.TotalPortfolioValue / count;
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for security in self.Securities.Values:
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if not security.Invested:
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continue
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reservedBuyingPowerForCurrentPosition = (security.BuyingPowerModel.GetReservedBuyingPowerForPosition(
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ReservedBuyingPowerForPositionParameters(security)).AbsoluteUsedBuyingPower
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* security.BuyingPowerModel.GetLeverage(security)) # see GH issue 4107
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# we sum up deviation for each security
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deviation += (portfolioValuePerSecurity - reservedBuyingPowerForCurrentPosition) / portfolioValuePerSecurity;
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# if securities are deviated 1.5% from their theoretical share of TotalPortfolioValue we rebalance
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if deviation >= 0.015:
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return time
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return None
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def OnOrderEvent(self, orderEvent):
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if orderEvent.Status == OrderStatus.Submitted:
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if self.UtcTime != self.lastRebalanceTime or self.UtcTime.weekday() != 0:
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raise ValueError(f"{self.UtcTime} {orderEvent.Symbol}")
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