wehub-resource-sync
95 lines
4.1 KiB
Python
95 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.set_start_date(2009, 1, 1) #Set Start Date
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self.set_end_date(2015, 1, 1) #Set End Date
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self._spy = self.add_equity("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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delayed_sma = IndicatorExtensions.of(delay, self._sma)
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# register our new 'delayed_sma' for automatic updates on a daily resolution
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self.register_indicator(self._spy, delayed_sma, Resolution.DAILY)
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# plot indicators each time they update using the plot_indicator function
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self.plot_indicator("Ribbon", delayed_sma)
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self._ribbon.append(delayed_sma)
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self._previous = datetime.min
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# on_data event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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def on_data(self, data):
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if not data[self._spy]: return
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# wait for our entire ribbon to be ready
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if not all(x.is_ready 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.is_ascending(values)):
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self.set_holdings(self._spy, 1.0)
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elif (holding.quantity > 0 and self.is_descending(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 is_ascending(self, values):
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last = None
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for val in values:
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if not last:
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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 is_descending(self, values):
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last = None
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for val in values:
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if not last:
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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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