Jack Simonson
92 lines
3.7 KiB
Python
92 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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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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### <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 = 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 |