Fix bug/syntax in python examples (#8658)

* CustomDataRegressionAlgorithm

* DescendingCustomDataObjectStoreRegressionAlgorithm

* CustomDataPropertiesRegressionAlgorithm

* DateTime -> should be datetime

* KerasNeuralNetworkAlgorithm

* OptionIndicatorsMirrorContractsRegressionAlgorithm

* BybitCustomDataCryptoRegressionAlgorithm

* DropboxBaseDataUniverseSelectionAlgorithm

* UserDefinedUniverseAlgorithm

* CompleteOrderTagUpdateAlgorithm

* BasicTemplateOptionEquityStrategyAlgorithm hint

* ETFConstituentUniverseFrameworkRegressionAlgorithm

* FutureStopMarketOrderOnExtendedHoursRegressionAlgorithm

* SecurityDynamicPropertyPythonClassAlgorithm

* hint

* hinting

* CallbackCommandRegressionAlgorithm

* CustomWarmUpPeriodIndicatorAlgorithm

* CrunchDAOSignalExportDemonstrationAlgorithm

* ExpiryHelperAlphaModelFrameworkAlgorithm

* ClassicRenkoConsolidatorAlgorithm

* SmaCrossUniverseSelectionAlgorithm

* PEP8 Fix: Assigning to a Method

* SliceGetByTypeRegressionAlgorithm

* MarketOnCloseOrderBufferExtendedMarketHoursRegressionAlgorithm

* MarketOnCloseOrderBufferRegressionAlgorithm

* CustomIndicatorAlgorithm

* ScheduledQueuingAlgorithm

* ComboOrdersFillModelAlgorithm

* CustomIndicatorWithExtensionAlgorithm

* IndicatorWithRenkoBarsRegressionAlgorithm

* CoarseFineOptionUniverseChainRegressionAlgorithm

* NumeraiSignalExportDemonstrationAlgorithm

* DropboxUniverseSelectionAlgorithm

* WeeklyUniverseSelectionRegressionAlgorithm

* AutoRegressiveIntegratedMovingAverageRegressionAlgorithm

* DropboxBaseDataUniverseSelectionAlgorithm

* IronCondorStrategyAlgorithm

* LongAndShortButterflyPutStrategiesAlgorithm

* FutureStopMarketOrderOnExtendedHoursRegressionAlgorithm

* LongAndShortCallCalendarSpreadStrategiesAlgorithm

* KerasNeuralNetworkAlgorithm

* LongAndShortPutCalendarSpreadStrategiesAlgorithm

* OptionPriceModelForOptionStylesBaseRegressionAlgorithm

* TensorFlowNeuralNetworkAlgorithm

* MarketOnCloseOrderBufferRegressionAlgorithm

* MarketOnCloseOrderBufferExtendedMarketHoursRegressionAlgorithm

* typing

* ComboOrderTicketDemoAlgorithm

* PytorchNeuralNetworkAlgorithm

* MultipleSymbolConsolidationAlgorithm

* fixes

* revert getattr mypy syntax

* address peer review

* Addresses Peer-Review

---------

Co-authored-by: Alexandre Catarino <AlexCatarino@users.noreply.github.com>

Algorithm.Python/VolumeShareSlippageModelAlgorithm.py

@@ -18,8 +18,8 @@ from Orders.Slippage.VolumeShareSlippageModel import VolumeShareSlippageModel
 ### Example algorithm implementing VolumeShareSlippageModel.
 ### </summary>
 class VolumeShareSlippageModelAlgorithm(QCAlgorithm):
-    longs = []
-    shorts = []
+    _longs = []
+    _shorts = []
 
     def initialize(self) -> None:
         self.set_start_date(2020, 11, 29)
@@ -27,27 +27,24 @@ class VolumeShareSlippageModelAlgorithm(QCAlgorithm):
         # To set the slippage model to limit to fill only 30% volume of the historical volume, with 5% slippage impact.
         self.set_security_initializer(lambda security: security.set_slippage_model(VolumeShareSlippageModel(0.3, 0.05)))
 
-        # Create SPY symbol to explore its constituents.
-        spy = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
-        
         self.universe_settings.resolution = Resolution.DAILY
         # Add universe to trade on the most and least weighted stocks among SPY constituents.
-        self.add_universe(self.universe.etf(spy, universe_filter_func=self.selection))
+        self.add_universe(self.universe.etf("SPY", universe_filter_func=self.selection))
 
-    def selection(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
+    def selection(self, constituents: list[ETFConstituentUniverse]) -> list[Symbol]:
         sorted_by_weight = sorted(constituents, key=lambda c: c.weight)
         # Add the 10 most weighted stocks to the universe to long later.
-        self.longs = [c.symbol for c in sorted_by_weight[-10:]]
+        self._longs = [c.symbol for c in sorted_by_weight[-10:]]
         # Add the 10 least weighted stocks to the universe to short later.
-        self.shorts = [c.symbol for c in sorted_by_weight[:10]]
+        self._shorts = [c.symbol for c in sorted_by_weight[:10]]
 
-        return self.longs + self.shorts
+        return self._longs + self._shorts
 
     def on_data(self, slice: Slice) -> None:
         # Equally invest into the selected stocks to evenly dissipate capital risk.
         # Dollar neutral of long and short stocks to eliminate systematic risk, only capitalize the popularity gap.
-        targets = [PortfolioTarget(symbol, 0.05) for symbol in self.longs]
-        targets += [PortfolioTarget(symbol, -0.05) for symbol in self.shorts]
+        targets = [PortfolioTarget(symbol, 0.05) for symbol in self._longs]
+        targets += [PortfolioTarget(symbol, -0.05) for symbol in self._shorts]
 
         # Liquidate the ones not being the most and least popularity stocks to release fund for higher expected return trades.
         self.set_holdings(targets, liquidate_existing_holdings=True)