# 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 *### <summary>### This algorithm demonstrates the various ways you can call the History function,### what it returns, and what you can do with the returned values.### </summary>### <meta name="tag" content="using data" />### <meta name="tag" content="history and warm up" />### <meta name="tag" content="history" />### <meta name="tag" content="warm up" />class HistoryAlgorithm(QCAlgorithm):def initialize(self):self.set_start_date(2013,10, 8) #Set Start Dateself.set_end_date(2013,10,11) #Set End Dateself.set_cash(100000) #Set Strategy Cash# Find more symbols here: http://quantconnect.com/dataself.add_equity("SPY", Resolution.DAILY)IBM = self.add_data(CustomDataEquity, "IBM", Resolution.DAILY)# specifying the exchange will allow the history methods that accept a number of bars to return to work properlyIBM.exchange = EquityExchange()# we can get history in initialize to set up indicators and suchself.daily_sma = SimpleMovingAverage(14)# get the last calendar year's worth of SPY data at the configured resolution (daily)trade_bar_history = self.history([self.securities["SPY"].symbol], timedelta(365))self.assert_history_count("History<TradeBar>([\"SPY\"], timedelta(365))", trade_bar_history, 250)# get the last calendar day's worth of SPY data at the specified resolutiontrade_bar_history = self.history(["SPY"], timedelta(1), Resolution.MINUTE)self.assert_history_count("History([\"SPY\"], timedelta(1), Resolution.MINUTE)", trade_bar_history, 390)# get the last 14 bars of SPY at the configured resolution (daily)trade_bar_history = self.history(["SPY"], 14)self.assert_history_count("History([\"SPY\"], 14)", trade_bar_history, 14)# get the last 14 minute bars of SPYtrade_bar_history = self.history(["SPY"], 14, Resolution.MINUTE)self.assert_history_count("History([\"SPY\"], 14, Resolution.MINUTE)", trade_bar_history, 14)# get the historical data from last current day to this current day in minute resolution# with Fill Forward and Extended Market optionsinterval_bar_history = self.history(["SPY"], self.time - timedelta(1), self.time, Resolution.MINUTE, True, True)self.assert_history_count("History([\"SPY\"], self.time - timedelta(1), self.time, Resolution.MINUTE, True, True)", interval_bar_history, 960)# get the historical data from last current day to this current day in minute resolution# with Extended Market optioninterval_bar_history = self.history(["SPY"], self.time - timedelta(1), self.time, Resolution.MINUTE, False, True)self.assert_history_count("History([\"SPY\"], self.time - timedelta(1), self.time, Resolution.MINUTE, False, True)", interval_bar_history, 919)# get the historical data from last current day to this current day in minute resolution# with Fill Forward optioninterval_bar_history = self.history(["SPY"], self.time - timedelta(1), self.time, Resolution.MINUTE, True, False)self.assert_history_count("History([\"SPY\"], self.time - timedelta(1), self.time, Resolution.MINUTE, True, False)", interval_bar_history, 390)# get the historical data from last current day to this current day in minute resolutioninterval_bar_history = self.history(["SPY"], self.time - timedelta(1), self.time, Resolution.MINUTE, False, False)self.assert_history_count("History([\"SPY\"], self.time - timedelta(1), self.time, Resolution.MINUTE, False, False)", interval_bar_history, 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 mathfor index, trade_bar in trade_bar_history.loc["SPY"].iterrows():self.daily_sma.update(index, trade_bar["close"])# get the last calendar year's worth of custom_data data at the configured resolution (daily)custom_data_history = self.history(CustomDataEquity, "IBM", timedelta(365))self.assert_history_count("History(CustomDataEquity, \"IBM\", timedelta(365))", custom_data_history, 250)# get the last 10 bars of IBM at the configured resolution (daily)custom_data_history = self.history(CustomDataEquity, "IBM", 14)self.assert_history_count("History(CustomDataEquity, \"IBM\", 14)", custom_data_history, 14)# 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 trade_bar_history aboveself.daily_sma.reset()for index, custom_data in custom_data_history.loc["IBM"].iterrows():self.daily_sma.update(index, custom_data["value"])# get the last 10 bars worth of Custom data for the specified symbols at the configured resolution (daily)all_custom_data = self.history(CustomDataEquity, self.securities.keys(), 14)self.assert_history_count("History(CustomDataEquity, self.securities.keys(), 14)", all_custom_data, 14 * 2)# 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.#custom_data_history = self.history(CustomDataEquity, "IBM", timedelta(7), Resolution.MINUTE)#custom_data_history = self.history(CustomDataEquity, "IBM", 14, Resolution.MINUTE)#all_custom_data = self.history(CustomDataEquity, timedelta(365), Resolution.MINUTE)#all_custom_data = self.history(CustomDataEquity, self.securities.keys(), 14, Resolution.MINUTE)#all_custom_data = self.history(CustomDataEquity, self.securities.keys(), timedelta(1), Resolution.MINUTE)#all_custom_data = self.history(CustomDataEquity, self.securities.keys(), 14, Resolution.MINUTE)# get the last calendar year's worth of all custom_data dataall_custom_data = self.history(CustomDataEquity, self.securities.keys(), timedelta(365))self.assert_history_count("History(CustomDataEquity, self.securities.keys(), timedelta(365))", all_custom_data, 250 * 2)# we can also access the return value from the multiple symbol functions to request a single# symbol and then loop over itsingle_symbol_custom = all_custom_data.loc["IBM"]self.assert_history_count("all_custom_data.loc[\"IBM\"]", single_symbol_custom, 250)for custom_data in single_symbol_custom:# do something with 'IBM.custom_data_equity' custom_data datapasscustom_data_spyvalues = all_custom_data.loc["IBM"]["value"]self.assert_history_count("all_custom_data.loc[\"IBM\"][\"value\"]", custom_data_spyvalues, 250)for value in custom_data_spyvalues:# do something with 'IBM.custom_data_equity' value datapassdef on_data(self, data):'''on_data 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.set_holdings("SPY", 1)def assert_history_count(self, method_call, trade_bar_history, expected):count = len(trade_bar_history.index)if count != expected:raise AssertionError("{} expected {}, but received {}".format(method_call, expected, count))class CustomDataEquity(PythonData):def get_source(self, config, date, is_live):zip_file_name = LeanData.generate_zip_file_name(config.Symbol, date, config.Resolution, config.TickType)source = Globals.data_folder + "/equity/usa/daily/" + zip_file_namereturn SubscriptionDataSource(source)def reader(self, config, line, date, is_live):if line == None:return Nonecustom_data = CustomDataEquity()custom_data.symbol = config.symbolcsv = line.split(",")custom_data.time = datetime.strptime(csv[0], '%Y%m%d %H:%M')custom_data.end_time = custom_data.time + timedelta(days=1)custom_data.value = float(csv[1])return custom_data
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