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>
+### Constructs a displaced moving average ribbon and buys when all are lined up, liquidates when they all line down
+### Ribbons are great for visualizing trends
+### Signals are generated when they all line up in a paricular direction
+### A buy signal is when the values of the indicators are increasing (from slowest to fastest).
+### A sell signal is when the values of the indicators are decreasing (from slowest to fastest).
+### </summary>
+### <meta name="tag" content="charting" />
+### <meta name="tag" content="plotting indicators" />
+### <meta name="tag" content="indicators" />
+### <meta name="tag" content="indicator classes" />
+class DisplacedMovingAverageRibbon(QCAlgorithm):
+
+    # Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
+    def initialize(self):
+        self.set_start_date(2009, 1, 1)  #Set Start Date
+        self.set_end_date(2015, 1, 1)    #Set End Date
+        self._spy = self.add_equity("SPY", Resolution.DAILY).symbol
+        count = 6
+        offset = 5
+        period = 15
+        self._ribbon = []
+        # define our sma as the base of the ribbon
+        self._sma = SimpleMovingAverage(period)
+        
+        for x in range(count):
+            # define our offset to the zero sma, these various offsets will create our 'displaced' ribbon
+            delay = Delay(offset*(x+1))
+            # define an indicator that takes the output of the sma and pipes it into our delay indicator
+            delayed_sma = IndicatorExtensions.of(delay, self._sma)
+            # register our new 'delayed_sma' for automatic updates on a daily resolution
+            self.register_indicator(self._spy, delayed_sma, Resolution.DAILY)
+            # plot indicators each time they update using the plot_indicator function
+            self.plot_indicator("Ribbon", delayed_sma) 
+            self._ribbon.append(delayed_sma)
+        self._previous = datetime.min
+
+    # on_data event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+    def on_data(self, data):
+        
+        if not data[self._spy]: return
+        # wait for our entire ribbon to be ready
+        if not all(x.is_ready for x in self._ribbon): return
+        # only once per day
+        if self._previous.date() == self.time.date(): return
+        self.plot("Ribbon", "Price", data[self._spy].price)
+
+        # check for a buy signal
+        values = [x.current.value for x in self._ribbon]
+        holding = self.portfolio[self._spy]
+        if (holding.quantity <= 0 and self.is_ascending(values)):
+            self.set_holdings(self._spy, 1.0)
+        elif (holding.quantity > 0 and self.is_descending(values)):
+            self.liquidate(self._spy)
+        self._previous = self.time
+    
+    # Returns true if the specified values are in ascending order
+    def is_ascending(self, values):
+        last = None
+        for val in values:
+            if not last:
+                last = val
+                continue
+            if last < val:
+                return False
+            last = val
+        return True
+    
+    # Returns true if the specified values are in Descending order
+    def is_descending(self, values):
+        last = None
+        for val in values:
+            if not last:
+                last = val
+                continue
+            if last > val:
+                return False
+            last = val
+        return True