chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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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 *
+
+### <summary>
+### In this algorithm we demonstrate how to perform some technical analysis as
+### part of your coarse fundamental universe selection
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="indicators" />
+### <meta name="tag" content="universes" />
+### <meta name="tag" content="coarse universes" />
+class EmaCrossUniverseSelectionAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+
+        self.set_start_date(2010,1,1)  #Set Start Date
+        self.set_end_date(2015,1,1)    #Set End Date
+        self.set_cash(100000)           #Set Strategy Cash
+
+        self.universe_settings.resolution = Resolution.DAILY
+        self.universe_settings.leverage = 2
+
+        self.coarse_count = 10
+        self.averages = { }
+
+        # this add universe method accepts two parameters:
+        # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
+        self.add_universe(self.coarse_selection_function)
+
+
+    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
+    def coarse_selection_function(self, coarse):
+
+        # We are going to use a dictionary to refer the object that will keep the moving averages
+        for cf in coarse:
+            if cf.symbol not in self.averages:
+                self.averages[cf.symbol] = SymbolData(cf.symbol)
+
+            # Updates the SymbolData object with current EOD price
+            avg = self.averages[cf.symbol]
+            avg.update(cf.end_time, cf.adjusted_price)
+
+        # Filter the values of the dict: we only want up-trending securities
+        values = list(filter(lambda x: x.is_uptrend, self.averages.values()))
+
+        # Sorts the values of the dict: we want those with greater difference between the moving averages
+        values.sort(key=lambda x: x.scale, reverse=True)
+
+        for x in values[:self.coarse_count]:
+            self.log('symbol: ' + str(x.symbol.value) + '  scale: ' + str(x.scale))
+
+        # we need to return only the symbol objects
+        return [ x.symbol for x in values[:self.coarse_count] ]
+
+    # this event fires whenever we have changes to our universe
+    def on_securities_changed(self, changes):
+        # liquidate removed securities
+        for security in changes.removed_securities:
+            if security.invested:
+                self.liquidate(security.symbol)
+
+        # we want 20% allocation in each security in our universe
+        for security in changes.added_securities:
+            self.set_holdings(security.symbol, 0.1)
+
+
+class SymbolData(object):
+    def __init__(self, symbol):
+        self._symbol = symbol
+        self.tolerance = 1.01
+        self.fast = ExponentialMovingAverage(100)
+        self.slow = ExponentialMovingAverage(300)
+        self.is_uptrend = False
+        self.scale = 0
+
+    def update(self, time, value):
+        if self.fast.update(time, value) and self.slow.update(time, value):
+            fast = self.fast.current.value
+            slow = self.slow.current.value
+            self.is_uptrend = fast > slow * self.tolerance
+
+        if self.is_uptrend:
+            self.scale = (fast - slow) / ((fast + slow) / 2.0)