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>
2025-04-14 20:43:03 +08:00
parent fe46e5ec3b
commit 020cf013df
55 changed files with 717 additions and 738 deletions
@@ -17,7 +17,7 @@ import torch.nn.functional as F

 class PytorchNeuralNetworkAlgorithm(QCAlgorithm):

-    def initialize(self):
+    def initialize(self) -> None:
        self.set_start_date(2013, 10, 7)  # Set Start Date
        self.set_end_date(2013, 10, 8) # Set End Date
        
@@ -27,14 +27,14 @@ class PytorchNeuralNetworkAlgorithm(QCAlgorithm):
        spy = self.add_equity("SPY", Resolution.MINUTE)
        self._symbols = [spy.symbol] # using a list can extend to condition for multiple symbols
        
-        self.lookback = 30 # days of historical data (look back)
+        self._lookback = 30 # days of historical data (look back)
        
        self.schedule.on(self.date_rules.every_day("SPY"), self.time_rules.after_market_open("SPY", 28), self.net_train) # train the NN
        self.schedule.on(self.date_rules.every_day("SPY"), self.time_rules.after_market_open("SPY", 30), self.trade)
    
-    def net_train(self):
+    def net_train(self) -> None:
        # Daily historical data is used to train the machine learning model
-        history = self.history(self._symbols, self.lookback + 1, Resolution.DAILY)
+        history = self.history(self._symbols, self._lookback + 1, Resolution.DAILY)
        
        # dicts that store prices for training
        self.prices_x = {} 
@@ -53,7 +53,6 @@ class PytorchNeuralNetworkAlgorithm(QCAlgorithm):
        for symbol in self._symbols:
            # if this symbol has historical data
            if symbol in self.prices_x:
-                
                net = Net(n_feature=1, n_hidden=10, n_output=1)     # define the network
                optimizer = torch.optim.SGD(net.parameters(), lr=0.2)
                loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss
@@ -79,28 +78,28 @@ class PytorchNeuralNetworkAlgorithm(QCAlgorithm):
            self.buy_prices[symbol] = net(y)[-1] + np.std(y.data.numpy())
            self.sell_prices[symbol] = net(y)[-1] - np.std(y.data.numpy())
        
-    def trade(self):
+    def trade(self) -> None:
        ''' 
        Enter or exit positions based on relationship of the open price of the current bar and the prices defined by the machine learning model.
        Liquidate if the open price is below the sell price and buy if the open price is above the buy price 
        ''' 
        for holding in self.portfolio.values():
-            if self.current_slice[holding.symbol].open < self.sell_prices[holding.symbol] and holding.invested:
+            bar = self.current_slice.bars.get(holding.symbol, None)
+            if bar and bar.open < self.sell_prices[holding.symbol] and holding.invested:
                self.liquidate(holding.symbol)
-            
-            if self.current_slice[holding.symbol].open > self.buy_prices[holding.symbol] and not holding.invested:
+            elif bar and bar.open > self.buy_prices[holding.symbol] and not holding.invested:
                self.set_holdings(holding.symbol, 1 / len(self._symbols))

            
        
 # class for Pytorch NN model
 class Net(torch.nn.Module):
-    def __init__(self, n_feature, n_hidden, n_output):
+    def __init__(self, n_feature: int, n_hidden: int, n_output: int) -> None:
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer
        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer
    
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = F.relu(self.hidden(x))      # activation function for hidden layer
        x = self.predict(x)             # linear output
        return x