Martin-Molinero
03f56481d4
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* Python research import improvements - Improve start.py for research env - Remove unrequired imports * Centralize algorithm imports * Add regression test GH action * Unit test python import clean up * Join research and main imports * More python import clean up * Fix failing skipped regression algorithm
96 lines
4.1 KiB
Python
96 lines
4.1 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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### <summary>
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### Constructs a displaced moving average ribbon and buys when all are lined up, liquidates when they all line down
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### Ribbons are great for visualizing trends
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### Signals are generated when they all line up in a paricular direction
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### A buy signal is when the values of the indicators are increasing (from slowest to fastest).
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### A sell signal is when the values of the indicators are decreasing (from slowest to fastest).
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### </summary>
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### <meta name="tag" content="charting" />
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### <meta name="tag" content="plotting indicators" />
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="indicator classes" />
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class DisplacedMovingAverageRibbon(QCAlgorithm):
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# Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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def Initialize(self):
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self.SetStartDate(2009, 1, 1) #Set Start Date
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self.SetEndDate(2015, 1, 1) #Set End Date
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self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
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count = 6
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offset = 5
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period = 15
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self.ribbon = []
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# define our sma as the base of the ribbon
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self.sma = SimpleMovingAverage(period)
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for x in range(count):
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# define our offset to the zero sma, these various offsets will create our 'displaced' ribbon
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delay = Delay(offset*(x+1))
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# define an indicator that takes the output of the sma and pipes it into our delay indicator
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delayedSma = IndicatorExtensions.Of(delay, self.sma)
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# register our new 'delayedSma' for automaic updates on a daily resolution
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self.RegisterIndicator(self.spy, delayedSma, Resolution.Daily)
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self.ribbon.append(delayedSma)
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self.previous = datetime.min
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# plot indicators each time they update using the PlotIndicator function
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for i in self.ribbon:
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self.PlotIndicator("Ribbon", i)
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# OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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def OnData(self, data):
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if data[self.spy] is None: return
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# wait for our entire ribbon to be ready
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if not all(x.IsReady for x in self.ribbon): return
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# only once per day
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if self.previous.date() == self.Time.date(): return
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self.Plot("Ribbon", "Price", data[self.spy].Price)
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# check for a buy signal
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values = [x.Current.Value for x in self.ribbon]
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holding = self.Portfolio[self.spy]
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if (holding.Quantity <= 0 and self.IsAscending(values)):
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self.SetHoldings(self.spy, 1.0)
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elif (holding.Quantity > 0 and self.IsDescending(values)):
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self.Liquidate(self.spy)
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self.previous = self.Time
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# Returns true if the specified values are in ascending order
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def IsAscending(self, values):
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last = None
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for val in values:
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if last is None:
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last = val
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continue
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if last < val:
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return False
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last = val
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return True
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# Returns true if the specified values are in Descending order
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def IsDescending(self, values):
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last = None
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for val in values:
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if last is None:
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last = val
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continue
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if last > val:
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return False
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last = val
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return True
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