chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
5008 changed files with 910633 additions and 0 deletions
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+# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from AlgorithmImports import *
+
+from keras.models import *
+from tensorflow import keras
+from keras import Sequential
+from keras.layers import Dense, Activation
+from keras.optimizers import SGD
+
+class KerasNeuralNetworkAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2019, 1, 1)   # Set Start Date
+        self.set_end_date(2020, 4, 1)     # Set End Date
+        self.set_cash(100000)            # Set Strategy Cash
+
+        self._model_by_symbol = {}
+
+        for ticker in ["SPY", "QQQ", "TLT"]:
+            symbol = self.add_equity(ticker).symbol
+
+            # Read the model saved in the ObjectStore
+            for kvp in self.object_store:
+                key = f'{symbol}_model'
+                if not (key == kvp.key and kvp.value):
+                    continue
+                file_path = self.object_store.get_file_path(kvp.key)
+                self._model_by_symbol[symbol] = keras.models.load_model(file_path)
+                self.debug(f'Model for {symbol} sucessfully retrieved. File {file_path}. Size {len(kvp.value)}. Weights {self._model_by_symbol[symbol].get_weights()}')
+
+        # Look-back period for training set
+        self._lookback = 30
+
+        # Train Neural Network every monday
+        self.train(
+            self.date_rules.every(DayOfWeek.MONDAY),
+            self.time_rules.after_market_open("SPY"),
+            self.neural_network_training)
+
+        # Place trades on Monday, 30 minutes after the market is open
+        self.schedule.on(
+            self.date_rules.every_day("SPY"),
+            self.time_rules.after_market_open("SPY", 30),
+            self.trade)
+
+    def on_end_of_algorithm(self) -> None:
+        ''' Save the data and the mode using the ObjectStore '''
+        for symbol, model in self._model_by_symbol.items():
+            key = f'{symbol}_model.keras'
+            file = self.object_store.get_file_path(key)
+            model.save(file)
+            self.object_store.save(key)
+            self.debug(f'Model for {symbol} sucessfully saved in the ObjectStore')
+
+    def neural_network_training(self) -> None:
+        '''Train the Neural Network and save the model in the ObjectStore'''
+        symbols = self.securities.keys()
+
+        # Daily historical data is used to train the machine learning model
+        history = self.history(symbols, self._lookback + 1, Resolution.DAILY)
+        history = history.open.unstack(0)
+
+        for symbol in symbols:
+            if symbol not in history:
+                continue
+
+            predictor = history[symbol][:-1]
+            predictand = history[symbol][1:]
+
+            # build a neural network from the 1st layer to the last layer
+            model = Sequential()
+
+            model.add(Dense(10, input_dim = 1))
+            model.add(Activation('relu'))
+            model.add(Dense(1))
+
+            sgd = SGD(learning_rate = 0.01)   # learning rate = 0.01
+
+            # choose loss function and optimizing method
+            model.compile(loss='mse', optimizer=sgd)
+
+            # pick an iteration number large enough for convergence
+            for step in range(200):
+                # training the model
+                cost = model.train_on_batch(predictor, predictand)
+
+            self._model_by_symbol[symbol] = model
+
+    def trade(self) -> None:
+        '''
+        Predict the price using the trained model and out-of-sample data
+        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
+        '''
+        target = 1 / len(self.securities)
+
+        for symbol, model in self._model_by_symbol.items():
+            if symbol not in self.current_slice.bars:
+                continue
+            
+            # Get the out-of-sample history
+            history = self.history(symbol, self._lookback, Resolution.DAILY)
+            history = history.open.unstack(0)[symbol]
+
+            # Get the final predicted price
+            prediction = model.predict(history)[0][-1]
+            history_std = np.std(history)
+
+            holding = self.portfolio[symbol]
+            open_price = self.current_slice.bars[symbol].open
+
+            # Follow the trend
+            if holding.invested:
+                if open_price < prediction - history_std:
+                    self.liquidate(symbol)
+            else:
+                if open_price > prediction + history_std:
+                    self.set_holdings(symbol, target)