AlexCatarino
145 lines
7.7 KiB
Python
145 lines
7.7 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.Indicators")
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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.Indicators import *
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from QuantConnect.Python import PythonQuandl
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from QuantConnect.Securities.Equity import EquityExchange
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from datetime import datetime, timedelta
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### <summary>
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### This algorithm demonstrates the various ways you can call the History function,
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### what it returns, and what you can do with the returned values.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="history and warm up" />
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### <meta name="tag" content="history" />
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### <meta name="tag" content="warm up" />
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class HistoryAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2013,10, 8) #Set Start Date
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self.SetEndDate(2013,10,11) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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self.AddEquity("SPY", Resolution.Daily)
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self.AddData(QuandlFuture,"CHRIS/CME_SP1", Resolution.Daily)
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# specifying the exchange will allow the history methods that accept a number of bars to return to work properly
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self.Securities["CHRIS/CME_SP1"].Exchange = EquityExchange()
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# we can get history in initialize to set up indicators and such
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self.spyDailySma = SimpleMovingAverage(14)
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# get the last calendar year's worth of SPY data at the configured resolution (daily)
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tradeBarHistory = self.History([self.Securities["SPY"].Symbol], timedelta(365))
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self.AssertHistoryCount("History<TradeBar>([\"SPY\"], timedelta(365))", tradeBarHistory, 250)
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# get the last calendar day's worth of SPY data at the specified resolution
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tradeBarHistory = self.History(["SPY"], timedelta(1), Resolution.Minute)
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self.AssertHistoryCount("History([\"SPY\"], timedelta(1), Resolution.Minute)", tradeBarHistory, 390)
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# get the last 14 bars of SPY at the configured resolution (daily)
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tradeBarHistory = self.History(["SPY"], 14)
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self.AssertHistoryCount("History([\"SPY\"], 14)", tradeBarHistory, 14)
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# get the last 14 minute bars of SPY
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tradeBarHistory = self.History(["SPY"], 14, Resolution.Minute)
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self.AssertHistoryCount("History([\"SPY\"], 14, Resolution.Minute)", tradeBarHistory, 14)
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# we can loop over the return value from these functions and we get TradeBars
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# we can use these TradeBars to initialize indicators or perform other math
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for index, tradeBar in tradeBarHistory.loc["SPY"].iterrows():
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self.spyDailySma.Update(index, tradeBar["close"])
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# get the last calendar year's worth of quandl data at the configured resolution (daily)
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quandlHistory = self.History(QuandlFuture, "CHRIS/CME_SP1", timedelta(365))
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self.AssertHistoryCount("History(QuandlFuture, \"CHRIS/CME_SP1\", timedelta(365))", quandlHistory, 250)
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# get the last 14 bars of SPY at the configured resolution (daily)
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quandlHistory = self.History(QuandlFuture, "CHRIS/CME_SP1", 14)
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self.AssertHistoryCount("History(QuandlFuture, \"CHRIS/CME_SP1\", 14)", quandlHistory, 14)
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# we can loop over the return values from these functions and we'll get Quandl data
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# this can be used in much the same way as the tradeBarHistory above
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self.spyDailySma.Reset()
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for index, quandl in quandlHistory.loc["CHRIS/CME_SP1"].iterrows():
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self.spyDailySma.Update(index, quandl["settle"])
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# get the last year's worth of all configured Quandl data at the configured resolution (daily)
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#allQuandlData = self.History(QuandlFuture, timedelta(365))
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#self.AssertHistoryCount("History(QuandlFuture, timedelta(365))", allQuandlData, 250)
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# get the last 14 bars worth of Quandl data for the specified symbols at the configured resolution (daily)
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allQuandlData = self.History(QuandlFuture, self.Securities.Keys, 14)
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self.AssertHistoryCount("History(QuandlFuture, self.Securities.Keys, 14)", allQuandlData, 14)
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# NOTE: using different resolutions require that they are properly implemented in your data type, since
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# Quandl doesn't support minute data, this won't actually work, but if your custom data source has
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# different resolutions, it would need to be implemented in the GetSource and Reader methods properly
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#quandlHistory = self.History(QuandlFuture, "CHRIS/CME_SP1", timedelta(7), Resolution.Minute)
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#quandlHistory = self.History(QuandlFuture, "CHRIS/CME_SP1", 14, Resolution.Minute)
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#allQuandlData = self.History(QuandlFuture, timedelta(365), Resolution.Minute)
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#allQuandlData = self.History(QuandlFuture, self.Securities.Keys, 14, Resolution.Minute)
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#allQuandlData = self.History(QuandlFuture, self.Securities.Keys, timedelta(1), Resolution.Minute)
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#allQuandlData = self.History(QuandlFuture, self.Securities.Keys, 14, Resolution.Minute)
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# get the last calendar year's worth of all quandl data
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allQuandlData = self.History(QuandlFuture, self.Securities.Keys, timedelta(365))
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self.AssertHistoryCount("History(QuandlFuture, self.Securities.Keys, timedelta(365))", allQuandlData, 250)
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# we can also access the return value from the multiple symbol functions to request a single
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# symbol and then loop over it
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singleSymbolQuandl = allQuandlData.loc["CHRIS/CME_SP1"]
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self.AssertHistoryCount("allQuandlData.loc[\"CHRIS/CME_SP1\"]", singleSymbolQuandl, 250);
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for quandl in singleSymbolQuandl:
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# do something with 'CHRIS/CME_SP1' quandl data
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pass
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quandlSpyLows = allQuandlData.loc["CHRIS/CME_SP1"]["low"]
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self.AssertHistoryCount("allQuandlData.loc[\"CHRIS/CME_SP1\"][\"low\"]", quandlSpyLows, 250);
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for low in quandlSpyLows:
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# do something with 'CHRIS/CME_SP1' quandl data
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pass
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def OnData(self, data):
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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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Arguments:
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data: Slice object keyed by symbol containing the stock data
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'''
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if not self.Portfolio.Invested:
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self.SetHoldings("SPY", 1)
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def AssertHistoryCount(self, methodCall, tradeBarHistory, expected):
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count = len(tradeBarHistory.index)
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if count != expected:
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raise Exception("{} expected {}, but received {}".format(methodCall, expected, count))
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class QuandlFuture(PythonQuandl):
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'''Custom quandl data type for setting customized value column name. Value column is used for the primary trading calculations and charting.'''
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def __init__(self):
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# Define ValueColumnName: cannot be None, Empty or non-existant column name
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# If ValueColumnName is "Close", do not use PythonQuandl, use Quandl:
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# self.AddData[QuandlFuture](self.crude, Resolution.Daily)
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self.ValueColumnName = "Settle" |