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 collections import deque
+
+### <summary>
+### Demonstrates how to create a custom indicator and register it for automatic updated
+### </summary>
+### <meta name="tag" content="indicators" />
+### <meta name="tag" content="indicator classes" />
+### <meta name="tag" content="custom indicator" />
+class CustomIndicatorAlgorithm(QCAlgorithm):
+    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)
+
+        # The python custom class must inherit from PythonIndicator to enable Updated event handler
+        self._custom.updated += self.custom_updated
+
+        self._custom_window = RollingWindow(5)
+        self.register_indicator("SPY", self._custom, Resolution.MINUTE)
+        self.plot_indicator('CSMA', self._custom)
+
+    def custom_updated(self, sender: object, updated: IndicatorDataPoint) -> None:
+        self._custom_window.add(updated)
+
+    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}")
+
+        # Regression test: test fails with an early quit
+        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) -> 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: str, period: int) -> None:
+        super().__init__()
+        self.name = name
+        self.value = 0
+        self._queue = deque(maxlen=period)
+
+    # Update method is mandatory
+    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