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/ScheduledQueuingAlgorithm.py

@@ -16,7 +16,7 @@ from queue import Queue
 
 class ScheduledQueuingAlgorithm(QCAlgorithm):
 
-    def initialize(self):
+    def initialize(self) -> None:
         self.set_start_date(2020, 9, 1)
         self.set_end_date(2020, 9, 2)
         self.set_cash(100000)
@@ -29,33 +29,33 @@ class ScheduledQueuingAlgorithm(QCAlgorithm):
         
         self.set_execution(ImmediateExecutionModel())
         
-        self.queue = Queue()
-        self.dequeue_size = 100
+        self._queue = Queue()
+        self._dequeue_size = 100
         
         self.add_equity("SPY", Resolution.MINUTE)
         self.schedule.on(self.date_rules.every_day("SPY"), self.time_rules.at(0, 0), self.fill_queue)
         self.schedule.on(self.date_rules.every_day("SPY"), self.time_rules.every(timedelta(minutes=60)), self.take_from_queue)
 
-    def coarse_selection_function(self, coarse):
+    def coarse_selection_function(self, coarse: list[CoarseFundamental]) -> list[Symbol]:
         has_fundamentals = [security for security in coarse if security.has_fundamental_data]
         sorted_by_dollar_volume = sorted(has_fundamentals, key=lambda x: x.dollar_volume, reverse=True)
         return [ x.symbol for x in sorted_by_dollar_volume[:self.__number_of_symbols] ]
     
-    def fine_selection_function(self, fine):
+    def fine_selection_function(self, fine: list[FineFundamental]) -> list[Symbol]:
         sorted_by_pe_ratio = sorted(fine, key=lambda x: x.valuation_ratios.pe_ratio, reverse=True)
         return [ x.symbol for x in sorted_by_pe_ratio[:self.__number_of_symbols_fine] ]
         
-    def fill_queue(self):
-        securities = [security for security in self.active_securities.values() if security.fundamentals is not None]
+    def fill_queue(self) -> None:
+        securities = [security for security in self.active_securities.values() if security.fundamentals]
         
         # Fill queue with symbols sorted by PE ratio (decreasing order)
-        self.queue.queue.clear()
+        self._queue.queue.clear()
         sorted_by_pe_ratio = sorted(securities, key=lambda x: x.fundamentals.valuation_ratios.pe_ratio, reverse=True)
         for security in sorted_by_pe_ratio:
-            self.queue.put(security.symbol)
+            self._queue.put(security.symbol)
         
-    def take_from_queue(self):
-        symbols = [self.queue.get() for _ in range(min(self.dequeue_size, self.queue.qsize()))]
+    def take_from_queue(self) -> None:
+        symbols = [self._queue.get() for _ in range(min(self._dequeue_size, self._queue.qsize()))]
         self.history(symbols, 10, Resolution.DAILY)
         
         self.log(f"Symbols at {self.time}: {[str(symbol) for symbol in symbols]}")