Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
108 lines
4.5 KiB
Python
108 lines
4.5 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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Indicators")
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from System import *
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from QuantConnect import *
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from QuantConnect.Indicators import *
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from QuantConnect.Data import *
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from QuantConnect.Data.Market import *
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from QuantConnect.Algorithm import *
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import numpy as np
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from datetime import datetime
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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 |