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/CustomIndicatorAlgorithm.py

@@ -21,54 +21,54 @@ from collections import deque
 ### <meta name="tag" content="indicator classes" />
 ### <meta name="tag" content="custom indicator" />
 class CustomIndicatorAlgorithm(QCAlgorithm):
-    def initialize(self):
+    def initialize(self) -> None:
         self.set_start_date(2013,10,7)
         self.set_end_date(2013,10,11)
         self.add_equity("SPY", Resolution.SECOND)
 
         # Create a QuantConnect indicator and a python custom indicator for comparison
         self._sma = self.sma("SPY", 60, Resolution.MINUTE)
-        self.custom = CustomSimpleMovingAverage('custom', 60)
+        self._custom = CustomSimpleMovingAverage('custom', 60)
 
         # The python custom class must inherit from PythonIndicator to enable Updated event handler
-        self.custom.updated += self.custom_updated
+        self._custom.updated += self._custom_updated
 
-        self.custom_window = RollingWindow[IndicatorDataPoint](5)
-        self.register_indicator("SPY", self.custom, Resolution.MINUTE)
-        self.plot_indicator('CSMA', self.custom)
+        self._custom_window = RollingWindow[IndicatorDataPoint](5)
+        self.register_indicator("SPY", self._custom, Resolution.MINUTE)
+        self.plot_indicator('CSMA', self._custom)
 
-    def custom_updated(self, sender, updated):
-        self.custom_window.add(updated)
+    def custom_updated(self, sender: object, updated: IndicatorDataPoint) -> None:
+        self._custom_window.add(updated)
 
-    def on_data(self, data):
+    def on_data(self, data: Slice) -> None:
         if not self.portfolio.invested:
             self.set_holdings("SPY", 1)
 
         if self.time.second == 0:
             self.log(f"   sma -> IsReady: {self._sma.is_ready}. Value: {self._sma.current.value}")
-            self.log(f"custom -> IsReady: {self.custom.is_ready}. Value: {self.custom.value}")
+            self.log(f"custom -> IsReady: {self._custom.is_ready}. Value: {self._custom.value}")
 
         # Regression test: test fails with an early quit
-        diff = abs(self.custom.value - self._sma.current.value)
+        diff = abs(self._custom.value - self._sma.current.value)
         if diff > 1e-10:
             self.quit(f"Quit: indicators difference is {diff}")
 
-    def on_end_of_algorithm(self):
-        for item in self.custom_window:
+    def on_end_of_algorithm(self) -> None:
+        for item in self._custom_window:
             self.log(f'{item}')
 
 # Python implementation of SimpleMovingAverage.
 # Represents the traditional simple moving average indicator (SMA).
 class CustomSimpleMovingAverage(PythonIndicator):
-    def __init__(self, name, period):
+    def __init__(self, name: str, period: int) -> None:
         super().__init__()
         self.name = name
         self.value = 0
-        self.queue = deque(maxlen=period)
+        self._queue = deque(maxlen=period)
 
     # Update method is mandatory
-    def update(self, input):
-        self.queue.appendleft(input.value)
-        count = len(self.queue)
-        self.value = np.sum(self.queue) / count
-        return count == self.queue.maxlen
+    def update(self, input: IndicatorDataPoint) -> bool:
+        self._queue.appendleft(input.value)
+        count = len(self._queue)
+        self.value = np.sum(self._queue) / count
+        return count == self._queue.maxlen