Merge PythonSlice into Slice (#8707)

- To simplify stub generation, merge PythonSlice into Slice, following
  existing pattern too, see QCAlgorithm.Python.cs
- Minor sintax fixes in example python algorithms

Algorithm.Python/ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm.py

@@ -36,8 +36,8 @@ class ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm(QCAlgorithm):
         # bars and the values of this one
         self._rsi_timedelta = RelativeStrengthIndex("Second", 15, MovingAverageType.WILDERS)
         self._values = {}
-        self.count = 0;
-        self._indicators_compared = False;
+        self.count = 0
+        self._indicators_compared = False
 
     def on_data(self, data: Slice):
         if self.is_warming_up:

Algorithm.Python/ConsolidateRegressionAlgorithm.py

@@ -32,8 +32,8 @@ class ConsolidateRegressionAlgorithm(QCAlgorithm):
                                                                              self.start_date,
                                                                              self.end_date,
                                                                              self._future.exchange.time_zone,
-                                                                             False)));
-        self._expected_consolidation_counts = [];
+                                                                             False)))
+        self._expected_consolidation_counts = []
 
         self.consolidate(symbol, Calendar.MONTHLY, lambda bar: self.update_monthly_consolidator(bar, -1)) # shouldn't consolidate
 

Algorithm.Python/CustomDataUniverseRegressionAlgorithm.py

@@ -23,7 +23,7 @@ class CustomDataUniverseRegressionAlgorithm(QCAlgorithm):
         self.set_end_date(2014, 3, 31)
 
         self.current_underlying_symbols = set()
-        self.universe_settings.resolution = Resolution.DAILY;
+        self.universe_settings.resolution = Resolution.DAILY
         self.add_universe(CoarseFundamental, "custom-data-universe", self.selection)
 
         self._selection_time = [datetime(2014, 3, 24), datetime(2014, 3, 25), datetime(2014, 3, 26),
@@ -57,7 +57,7 @@ class CustomDataUniverseRegressionAlgorithm(QCAlgorithm):
             if len(custom_data) > 0:
                 for symbol in sorted(self.current_underlying_symbols, key=lambda x: x.id.symbol):
                     if not self.securities[symbol].has_data:
-                        continue;
+                        continue
                     self.set_holdings(symbol, 1 / len(self.current_underlying_symbols))
 
                     if len([x for x in custom_data.keys() if x.underlying == symbol]) == 0:

Algorithm.Python/CustomDataUniverseScheduledRegressionAlgorithm.py

@@ -24,7 +24,7 @@ class CustomDataUniverseScheduledRegressionAlgorithm(QCAlgorithm):
 
         self.current_underlying_symbols = []
         self._selection_time = [datetime(2014, 3, 25), datetime(2014, 3, 27), datetime(2014, 3, 29)]
-        self.universe_settings.resolution = Resolution.DAILY;
+        self.universe_settings.resolution = Resolution.DAILY
         self.universe_settings.schedule.on(self.date_rules.on(self._selection_time))
         self.add_universe(CoarseFundamental, "custom-data-universe", self.universe_settings, self.selection)
 

Algorithm.Python/CustomSignalExportDemonstrationAlgorithm.py

@@ -48,12 +48,12 @@ from requests import post
 class CustomSignalExport:
     def send(self, parameters: SignalExportTargetParameters) -> bool:
         targets = [PortfolioTarget.percent(parameters.algorithm, x.symbol, x.quantity) 
-                   for x in parameters.targets] ;
+                   for x in parameters.targets]
         data = [ {'symbol' : x.symbol.value, 'quantity': x.quantity} for x in targets ]
         response = post("http://localhost:5000/", json = data)
         result = response.json()
         success = result.get('success', False)
-        parameters.algorithm.log(f"Send #{len(parameters.targets)} targets. Success: {success}");
+        parameters.algorithm.log(f"Send #{len(parameters.targets)} targets. Success: {success}")
         return success      
 
     def dispose(self):

Algorithm.Python/IndicatorExtensionsSMAWithCustomIndicatorsRegressionAlgorithm.py

@@ -47,7 +47,7 @@ class RangeIndicator(PythonIndicator):
         self.name = name
         self.time = datetime.min
         self.value = 0
-        self.is_ready = False;
+        self.is_ready = False
 
     @property
     def is_ready(self):
@@ -72,7 +72,7 @@ class RangeIndicator2(PythonIndicator):
         self.name = name
         self.time = datetime.min
         self.value = 0
-        self._is_ready = False;
+        self._is_ready = False
 
     @property
     def is_ready(self):

Algorithm.Python/IndicatorHistoryAlgorithm.py

@@ -31,7 +31,7 @@ class IndicatorHistoryAlgorithm(QCAlgorithm):
         # Let's keep BB values for a 20 day period
         self.bollinger_bands.window.size = 20
         # Also keep the same period of data for the middle band
-        self.bollinger_bands.middle_band.window.size = 20;
+        self.bollinger_bands.middle_band.window.size = 20
 
     def on_data(self, slice: Slice):
         # Let's wait for our indicator to fully initialize and have a full window of history data

Algorithm.Python/InsightTagAlphaRegressionAlgorithm.py

@@ -85,4 +85,4 @@ class OneTimeAlphaModel(AlphaModel):
 
     @staticmethod
     def generate_insight_tag(symbol: Symbol) -> str:
-        return f"Insight generated for {symbol}";
+        return f"Insight generated for {symbol}"

Algorithm.Python/LongAndShortButterflyPutStrategiesAlgorithm.py

@@ -78,4 +78,4 @@ class LongAndShortButterflyPutStrategiesAlgorithm(OptionStrategyFactoryMethodsBa
 
     def liquidate_strategy(self) -> None:
         # We should be able to close the position using the inverse strategy (a short butterfly put)
-        self.buy(self._short_butterfly_put, 2);
+        self.buy(self._short_butterfly_put, 2)

Algorithm.Python/NoUniverseSelectorRegressionAlgorithm.py

@@ -22,7 +22,7 @@ class NoUniverseSelectorRegressionAlgorithm(QCAlgorithm):
         self.set_start_date(2014, 3, 24)
         self.set_end_date(2014, 3, 31)
 
-        self.universe_settings.resolution = Resolution.DAILY;
+        self.universe_settings.resolution = Resolution.DAILY
         self.add_universe(CoarseFundamental)
         self.changes = None
 

Algorithm.Python/NullBuyingPowerOptionBullCallSpreadAlgorithm.py

@@ -27,7 +27,7 @@ class NullBuyingPowerOptionBullCallSpreadAlgorithm(QCAlgorithm):
         self.set_cash(200000)
 
         self.set_security_initializer(lambda security: security.set_margin_model(SecurityMarginModel.NULL))
-        self.portfolio.set_positions(SecurityPositionGroupModel.NULL);
+        self.portfolio.set_positions(SecurityPositionGroupModel.NULL)
 
         equity = self.add_equity("GOOG")
         option = self.add_option(equity.symbol)

Algorithm.Python/OptionChainApisConsistencyRegressionAlgorithm.py

@@ -41,7 +41,7 @@ class OptionChainApisConsistencyRegressionAlgorithm(QCAlgorithm):
 
         if len(option_chain_from_algorithm_api) != len(option_chain_from_provider_api):
             raise AssertionError(f"Expected {len(option_chain_from_provider_api)} options in chain from provider API, "
-                                 f"but got {len(option_chain_from_algorithm_api)}");
+                                 f"but got {len(option_chain_from_algorithm_api)}")
 
         for i in range(len(option_chain_from_algorithm_api)):
             symbol1 = option_chain_from_algorithm_api[i]

Algorithm.Python/OptionChainConsistencyRegressionAlgorithm.py

@@ -30,8 +30,8 @@ class OptionChainConsistencyRegressionAlgorithm(QCAlgorithm):
         self.set_start_date(2015,12,24)
         self.set_end_date(2015,12,24)
 
-        self.equity = self.add_equity(self.underlying_ticker);
-        self.option = self.add_option(self.underlying_ticker);
+        self.equity = self.add_equity(self.underlying_ticker)
+        self.option = self.add_option(self.underlying_ticker)
 
         # set our strike/expiry filter for this option chain
         self.option.set_filter(self.universe_func)

Algorithm.Python/RangeConsolidatorAlgorithm.py

@@ -24,11 +24,11 @@ class RangeConsolidatorAlgorithm(QCAlgorithm):
         return 100
 
     def initialize(self) -> None:
-        self.set_start_and_end_dates();
+        self.set_start_and_end_dates()
         self.add_equity("SPY", self.get_resolution())
         range_consolidator = self.create_range_consolidator()
         range_consolidator.data_consolidated += self.on_data_consolidated
-        self._first_data_consolidated = None;
+        self._first_data_consolidated = None
 
         self.subscription_manager.add_consolidator("SPY", range_consolidator)
 

Algorithm.Python/ShortableProviderOrdersRejectedRegressionAlgorithm.py

@@ -47,7 +47,7 @@ class ShortableProviderOrdersRejectedRegressionAlgorithm(QCAlgorithm):
 
             response = order_ticket.update_quantity(-999) # should be allowed, we are reducing the quantity we want to short
             if not response.is_success:
-                raise ValueError("Order update should of succeeded!");
+                raise ValueError("Order update should of succeeded!")
 
             self.initialized = True
             return

Algorithm.Python/StochasticIndicatorWarmsUpProperlyRegressionAlgorithm.py

@@ -24,7 +24,7 @@ class StochasticIndicatorWarmsUpProperlyRegressionAlgorithm(QCAlgorithm):
         self.set_end_date(2020, 2, 1)
         self.set_cash(100000)
 
-        self.data_points_received = False;
+        self.data_points_received = False
         self.spy = self.add_equity("SPY", Resolution.HOUR).symbol
 
         self.daily_consolidator = TradeBarConsolidator(timedelta(days=1))