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quantconnect--lean/Algorithm.Python/CompositeIndicatorWorksAsExpectedRegressionAlgorithm.py
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2026-07-13 13:02:50 +08:00

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Python

# 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 *
### <summary>
### This algorithm tests the functionality of the CompositeIndicator
### using either a lambda expression or a method reference.
### </summary>
class CompositeIndicatorWorksAsExpectedRegressionAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2013, 10, 4)
self.set_end_date(2013, 10, 5)
self.add_equity("SPY", Resolution.MINUTE)
close = self.identity("SPY", Resolution.MINUTE, Field.CLOSE)
low = self.min("SPY", 420, Resolution.MINUTE, Field.LOW)
self.composite_min_direct = CompositeIndicator("CompositeMinDirect", close, low, lambda l, r: IndicatorResult(min(l.current.value, r.current.value)))
self.composite_min_method = CompositeIndicator("CompositeMinMethod", close, low, self.composer)
self.data_received = False
def composer(self, l, r):
return IndicatorResult(min(l.current.value, r.current.value))
def on_data(self, data):
self.data_received = True
if self.composite_min_direct.current.value != self.composite_min_method.current.value:
raise AssertionError(f"Values of indicators differ: {self.composite_min_direct.current.value} | {self.composite_min_method.current.value}")
def on_end_of_algorithm(self):
if not self.data_received:
raise AssertionError("No data was processed during the algorithm execution.")