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.
93 lines
3.8 KiB
Python
93 lines
3.8 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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from QuantConnect.Data import SubscriptionDataSource
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from QuantConnect.Python import PythonData
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from datetime import datetime, timedelta
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import decimal
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### <summary>
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### Using weather in NYC to rebalance portfolio. Assumption is people are happier when its warm.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="custom data" />
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### <meta name="tag" content="strategy example" />
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class QCUWeatherBasedRebalancing(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2013,1,1) #Set Start Date
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self.SetEndDate(2016,1,1) #Set End Date
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self.SetCash(25000) #Set Strategy Cash
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self.AddEquity("SPY", Resolution.Daily)
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self.symbol = self.Securities["SPY"].Symbol
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# KNYC is NYC Central Park. Find other locations at
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# https://www.wunderground.com/history/
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self.AddData(Weather, "KNYC", Resolution.Minute)
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self.weather = self.Securities["KNYC"].Symbol
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self.tradingDayCount = 0
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self.rebalanceFrequency = 10
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# When we have a new event trigger, buy some stock:
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def OnData(self, data):
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if not data.ContainsKey(self.weather): return
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# Scale from -5C to +25C :: -5C == 100%, +25C = 0% invested
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fraction = -(data[self.weather].MinC + 5) / 30 if self.weather in data else 0
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#self.Debug("Faction {0}".format(faction))
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# Rebalance every 10 days:
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if self.tradingDayCount >= self.rebalanceFrequency:
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self.SetHoldings(self.symbol, fraction)
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self.tradingDayCount = 0
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def OnEndOfDay(self):
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self.tradingDayCount += 1
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class Weather(PythonData):
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''' Weather based rebalancing'''
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def GetSource(self, config, date, isLive):
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source = "https://dl.dropboxusercontent.com/u/44311500/KNYC.csv"
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source = "https://www.wunderground.com/history/airport/{0}/{1}/1/1/CustomHistory.html?dayend=31&monthend=12&yearend={1}&format=1".format(config.Symbol, date.year)
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return SubscriptionDataSource(source, SubscriptionTransportMedium.RemoteFile)
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def Reader(self, config, line, date, isLive):
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# If first character is not digit, pass
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if not (line.strip() and line[0].isdigit()): return None
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data = line.split(',')
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weather = Weather()
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weather.Symbol = config.Symbol
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weather.Time = datetime.strptime(data[0], '%Y-%m-%d') + timedelta(hours=20) # Make sure we only get this data AFTER trading day - don't want forward bias.
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# If the second column is an invalid value (empty string), return None. The algorithm will discard it.
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if not data[2]: return None
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weather.Value = decimal.Decimal(data[2])
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weather["Max.C"] = float(data[1]) # Using a dot in the propety name, it will capitalize the first letter of each word:
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weather["Min.C"] = float(data[3]) # Max.C -> MaxC and Min.C -> MinC
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return weather |