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.
88 lines
3.7 KiB
Python
88 lines
3.7 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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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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import numpy as np
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import decimal as d
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from datetime import timedelta, datetime
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### <summary>
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### Algorithm demonstrating custom charting support in QuantConnect.
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### The entire charting system of quantconnect is adaptable. You can adjust it to draw whatever you'd like.
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### Charts can be stacked, or overlayed on each other. Series can be candles, lines or scatter plots.
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### Even the default behaviours of QuantConnect can be overridden.
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### </summary>
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### <meta name="tag" content="charting" />
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### <meta name="tag" content="adding charts" />
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### <meta name="tag" content="series types" />
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### <meta name="tag" content="plotting indicators" />
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class CustomChartingAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2016,1,1)
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self.SetEndDate(2017,1,1)
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self.SetCash(100000)
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self.AddEquity("SPY", Resolution.Daily)
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# In your initialize method:
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# Chart - Master Container for the Chart:
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stockPlot = Chart("Trade Plot")
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# On the Trade Plotter Chart we want 3 series: trades and price:
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stockPlot.AddSeries(Series("Buy", SeriesType.Scatter, 0))
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stockPlot.AddSeries(Series("Sell", SeriesType.Scatter, 0))
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stockPlot.AddSeries(Series("Price", SeriesType.Line, 0))
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self.AddChart(stockPlot)
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avgCross = Chart("Average Cross")
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avgCross.AddSeries(Series("FastMA", SeriesType.Line, 1))
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avgCross.AddSeries(Series("SlowMA", SeriesType.Line, 1))
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self.AddChart(avgCross)
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self.fastMA = 0
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self.slowMA = 0
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self.lastPrice = 0
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self.resample = datetime.min
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self.resamplePeriod = (self.EndDate - self.StartDate) / 2000
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def OnData(self, slice):
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if slice["SPY"] is None: return
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self.lastPrice = slice["SPY"].Close
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if self.fastMA == 0: self.fastMA = self.lastPrice
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if self.slowMA == 0: self.slowMA = self.lastPrice
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self.fastMA = (d.Decimal(0.01) * self.lastPrice) + (d.Decimal(0.99) * self.fastMA)
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self.slowMA = (d.Decimal(0.001) * self.lastPrice) + (d.Decimal(0.999) * self.slowMA)
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if self.Time > self.resample:
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self.resample = self.Time + self.resamplePeriod
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self.Plot("Average Cross", "FastMA", self.fastMA)
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self.Plot("Average Cross", "SlowMA", self.slowMA)
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# On the 5th days when not invested buy:
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if not self.Portfolio.Invested and self.Time.day % 13 == 0:
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self.Order("SPY", (int)(self.Portfolio.MarginRemaining / self.lastPrice))
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self.Plot("Trade Plot", "Buy", self.lastPrice)
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elif self.Time.day % 21 == 0 and self.Portfolio.Invested:
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self.Plot("Trade Plot", "Sell", self.lastPrice)
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self.Liquidate()
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def OnEndOfDay(self):
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#Log the end of day prices:
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self.Plot("Trade Plot", "Price", self.lastPrice) |