# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. # Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from AlgorithmImports import * ### ### This algorithm demonstrates the various ways you can call the History function, ### what it returns, and what you can do with the returned values. ### ### ### ### ### class HistoryAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2013,10, 8) #Set Start Date self.SetEndDate(2013,10,11) #Set End Date self.SetCash(100000) #Set Strategy Cash # Find more symbols here: http://quantconnect.com/data self.AddEquity("SPY", Resolution.Daily) self.AddData(CustomDataEquity, "IBM", Resolution.Daily) # specifying the exchange will allow the history methods that accept a number of bars to return to work properly # we can get history in initialize to set up indicators and such self.dailySma = SimpleMovingAverage(14) # get the last calendar year's worth of SPY data at the configured resolution (daily) tradeBarHistory = self.History([self.Securities["SPY"].Symbol], timedelta(365)) self.AssertHistoryCount("History([\"SPY\"], timedelta(365))", tradeBarHistory, 250) # get the last calendar day's worth of SPY data at the specified resolution tradeBarHistory = self.History(["SPY"], timedelta(1), Resolution.Minute) self.AssertHistoryCount("History([\"SPY\"], timedelta(1), Resolution.Minute)", tradeBarHistory, 390) # get the last 14 bars of SPY at the configured resolution (daily) tradeBarHistory = self.History(["SPY"], 14) self.AssertHistoryCount("History([\"SPY\"], 14)", tradeBarHistory, 14) # get the last 14 minute bars of SPY tradeBarHistory = self.History(["SPY"], 14, Resolution.Minute) self.AssertHistoryCount("History([\"SPY\"], 14, Resolution.Minute)", tradeBarHistory, 14) # get the historical data from last current day to this current day in minute resolution # with Fill Forward and Extended Market options intervalBarHistory = self.History(["SPY"], self.Time - timedelta(1), self.Time, Resolution.Minute, True, True) self.AssertHistoryCount("History([\"SPY\"], self.Time - timedelta(1), self.Time, Resolution.Minute, True, True)", intervalBarHistory, 960) # get the historical data from last current day to this current day in minute resolution # with Extended Market option intervalBarHistory = self.History(["SPY"], self.Time - timedelta(1), self.Time, Resolution.Minute, False, True) self.AssertHistoryCount("History([\"SPY\"], self.Time - timedelta(1), self.Time, Resolution.Minute, False, True)", intervalBarHistory, 828) # get the historical data from last current day to this current day in minute resolution # with Fill Forward option intervalBarHistory = self.History(["SPY"], self.Time - timedelta(1), self.Time, Resolution.Minute, True, False) self.AssertHistoryCount("History([\"SPY\"], self.Time - timedelta(1), self.Time, Resolution.Minute, True, False)", intervalBarHistory, 390) # get the historical data from last current day to this current day in minute resolution intervalBarHistory = self.History(["SPY"], self.Time - timedelta(1), self.Time, Resolution.Minute, False, False) self.AssertHistoryCount("History([\"SPY\"], self.Time - timedelta(1), self.Time, Resolution.Minute, False, False)", intervalBarHistory, 390) # we can loop over the return value from these functions and we get TradeBars # we can use these TradeBars to initialize indicators or perform other math for index, tradeBar in tradeBarHistory.loc["SPY"].iterrows(): self.dailySma.Update(index, tradeBar["close"]) # get the last calendar year's worth of customData data at the configured resolution (daily) customDataHistory = self.History(CustomDataEquity, "IBM", timedelta(365)) self.AssertHistoryCount("History(CustomDataEquity, \"IBM\", timedelta(365))", customDataHistory, 10) # get the last 10 bars of IBM at the configured resolution (daily) customDataHistory = self.History(CustomDataEquity, "IBM", 14) self.AssertHistoryCount("History(CustomDataEquity, \"IBM\", 14)", customDataHistory, 10) # we can loop over the return values from these functions and we'll get Custom data # this can be used in much the same way as the tradeBarHistory above self.dailySma.Reset() for index, customData in customDataHistory.loc["IBM"].iterrows(): self.dailySma.Update(index, customData["value"]) # get the last 10 bars worth of Custom data for the specified symbols at the configured resolution (daily) allCustomData = self.History(CustomDataEquity, self.Securities.Keys, 14) self.AssertHistoryCount("History(CustomDataEquity, self.Securities.Keys, 14)", allCustomData, 20) # NOTE: Using different resolutions require that they are properly implemented in your data type. If your # custom data source has different resolutions, it would need to be implemented in the GetSource and # Reader methods properly. #customDataHistory = self.History(CustomDataEquity, "IBM", timedelta(7), Resolution.Minute) #customDataHistory = self.History(CustomDataEquity, "IBM", 14, Resolution.Minute) #allCustomData = self.History(CustomDataEquity, timedelta(365), Resolution.Minute) #allCustomData = self.History(CustomDataEquity, self.Securities.Keys, 14, Resolution.Minute) #allCustomData = self.History(CustomDataEquity, self.Securities.Keys, timedelta(1), Resolution.Minute) #allCustomData = self.History(CustomDataEquity, self.Securities.Keys, 14, Resolution.Minute) # get the last calendar year's worth of all customData data allCustomData = self.History(CustomDataEquity, self.Securities.Keys, timedelta(365)) self.AssertHistoryCount("History(CustomDataEquity, self.Securities.Keys, timedelta(365))", allCustomData, 20) # we can also access the return value from the multiple symbol functions to request a single # symbol and then loop over it singleSymbolCustom = allCustomData.loc["IBM"] self.AssertHistoryCount("allCustomData.loc[\"IBM\"]", singleSymbolCustom, 10) for customData in singleSymbolCustom: # do something with 'IBM.CustomDataEquity' customData data pass customDataSpyValues = allCustomData.loc["IBM"]["value"] self.AssertHistoryCount("allCustomData.loc[\"IBM\"][\"value\"]", customDataSpyValues, 10) for value in customDataSpyValues: # do something with 'IBM.CustomDataEquity' value data pass def OnData(self, data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. Arguments: data: Slice object keyed by symbol containing the stock data ''' if not self.Portfolio.Invested: self.SetHoldings("SPY", 1) def AssertHistoryCount(self, methodCall, tradeBarHistory, expected): count = len(tradeBarHistory.index) if count != expected: raise Exception("{} expected {}, but received {}".format(methodCall, expected, count)) class CustomDataEquity(PythonData): def GetSource(self, config, date, isLive): source = "https://www.dl.dropboxusercontent.com/s/o6ili2svndzn556/custom_data.csv?dl=0" return SubscriptionDataSource(source, SubscriptionTransportMedium.RemoteFile) def Reader(self, config, line, date, isLive): if line == None: return None customData = CustomDataEquity() customData.Symbol = config.Symbol csv = line.split(",") customData.Time = datetime.strptime(csv[0], '%Y%m%d %H:%M') customData.EndTime = customData.Time + timedelta(days=1) customData.Value = float(csv[1]) return customData