chore: import upstream snapshot with attribution
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wehub-resource-sync
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# 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 AlgorithmImports import *
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### <summary>
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### Basic template algorithm simply initializes the date range and cash. This is a skeleton
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### framework you can use for designing an algorithm.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="trading and orders" />
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class IndicatorSuiteAlgorithm(QCAlgorithm):
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'''Demonstration algorithm of popular indicators and plotting them.'''
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def initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self._symbol = "SPY"
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self._symbol2 = "GOOG"
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self.custom_symbol = "IBM"
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self.price = 0.0
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self.set_start_date(2013, 1, 1) #Set Start Date
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self.set_end_date(2014, 12, 31) #Set End Date
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self.set_cash(25000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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self.add_equity(self._symbol, Resolution.DAILY)
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self.add_equity(self._symbol2, Resolution.DAILY)
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self.add_data(CustomData, self.custom_symbol, Resolution.DAILY)
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# Set up default Indicators, these indicators are defined on the Value property of incoming data (except ATR and AROON which use the full TradeBar object)
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self.indicators = {
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'BB' : self.bb(self._symbol, 20, 1, MovingAverageType.SIMPLE, Resolution.DAILY),
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'RSI' : self.rsi(self._symbol, 14, MovingAverageType.SIMPLE, Resolution.DAILY),
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'EMA' : self.ema(self._symbol, 14, Resolution.DAILY),
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'SMA' : self.sma(self._symbol, 14, Resolution.DAILY),
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'MACD' : self.macd(self._symbol, 12, 26, 9, MovingAverageType.SIMPLE, Resolution.DAILY),
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'MOM' : self.mom(self._symbol, 20, Resolution.DAILY),
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'MOMP' : self.momp(self._symbol, 20, Resolution.DAILY),
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'STD' : self.std(self._symbol, 20, Resolution.DAILY),
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# by default if the symbol is a tradebar type then it will be the min of the low property
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'MIN' : self.min(self._symbol, 14, Resolution.DAILY),
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# by default if the symbol is a tradebar type then it will be the max of the high property
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'MAX' : self.max(self._symbol, 14, Resolution.DAILY),
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'ATR' : self.atr(self._symbol, 14, MovingAverageType.SIMPLE, Resolution.DAILY),
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'AROON' : self.aroon(self._symbol, 20, Resolution.DAILY),
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'B' : self.b(self._symbol, self._symbol2, 14)
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}
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# Here we're going to define indicators using 'selector' functions. These 'selector' functions will define what data gets sent into the indicator
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# These functions have a signature like the following: decimal Selector(BaseData base_data), and can be defined like: base_data => base_data.value
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# We'll define these 'selector' functions to select the Low value
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#
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# For more information on 'anonymous functions' see: http:#en.wikipedia.org/wiki/Anonymous_function
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# https:#msdn.microsoft.com/en-us/library/bb397687.aspx
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#
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self.selector_indicators = {
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'BB' : self.bb(self._symbol, 20, 1, MovingAverageType.SIMPLE, Resolution.DAILY, Field.LOW),
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'RSI' :self.rsi(self._symbol, 14, MovingAverageType.SIMPLE, Resolution.DAILY, Field.LOW),
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'EMA' :self.ema(self._symbol, 14, Resolution.DAILY, Field.LOW),
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'SMA' :self.sma(self._symbol, 14, Resolution.DAILY, Field.LOW),
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'MACD' : self.macd(self._symbol, 12, 26, 9, MovingAverageType.SIMPLE, Resolution.DAILY, Field.LOW),
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'MOM' : self.mom(self._symbol, 20, Resolution.DAILY, Field.LOW),
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'MOMP' : self.momp(self._symbol, 20, Resolution.DAILY, Field.LOW),
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'STD' : self.std(self._symbol, 20, Resolution.DAILY, Field.LOW),
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'MIN' : self.min(self._symbol, 14, Resolution.DAILY, Field.HIGH),
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'MAX' : self.max(self._symbol, 14, Resolution.DAILY, Field.LOW),
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# ATR and AROON are special in that they accept a TradeBar instance instead of a decimal, we could easily project and/or transform the input TradeBar
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# before it gets sent to the ATR/AROON indicator, here we use a function that will multiply the input trade bar by a factor of two
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'ATR' : self.atr(self._symbol, 14, MovingAverageType.SIMPLE, Resolution.DAILY, self.selector_double__trade_bar),
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'AROON' : self.aroon(self._symbol, 20, Resolution.DAILY, self.selector_double__trade_bar)
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}
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# Custom Data Indicator:
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self.rsi_custom = self.rsi(self.custom_symbol, 14, MovingAverageType.SIMPLE, Resolution.DAILY)
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self.min_custom = self.min(self.custom_symbol, 14, Resolution.DAILY)
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self.max_custom = self.max(self.custom_symbol, 14, Resolution.DAILY)
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# in addition to defining indicators on a single security, you can all define 'composite' indicators.
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# these are indicators that require multiple inputs. the most common of which is a ratio.
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# suppose we seek the ratio of BTC to SPY, we could write the following:
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spy_close = Identity(self._symbol)
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ibm_close = Identity(self.custom_symbol)
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# this will create a new indicator whose value is IBM/SPY
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self.ratio = IndicatorExtensions.over(ibm_close, spy_close)
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# we can also easily plot our indicators each time they update using th PlotIndicator function
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self.plot_indicator("Ratio", self.ratio)
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# The following methods will add multiple charts to the algorithm output.
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# Those chatrs names will be used later to plot different series in a particular chart.
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# For more information on Lean Charting see: https://www.quantconnect.com/docs#Charting
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Chart('BB')
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Chart('STD')
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Chart('ATR')
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Chart('AROON')
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Chart('MACD')
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Chart('Averages')
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# Here we make use of the Schelude method to update the plots once per day at market close.
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self.schedule.on(self.date_rules.every_day(), self.time_rules.before_market_close(self._symbol), self.update_plots)
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def on_data(self, data: Slice):
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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 data.bars.contains_key(self._symbol) or
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not self.indicators['BB'].is_ready or
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not self.indicators['RSI'].is_ready):
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return
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if not data.bars.contains_key(self._symbol):
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return
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self.price = data[self._symbol].close
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if not self.portfolio.hold_stock:
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quantity = int(self.portfolio.cash / self.price)
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self.order(self._symbol, quantity)
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self.debug('Purchased SPY on ' + self.time.strftime('%Y-%m-%d'))
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def update_plots(self):
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if not self.indicators['BB'].is_ready or not self.indicators['STD'].is_ready:
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return
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# Plots can also be created just with this one line command.
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self.plot('RSI', self.indicators['RSI'])
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# Custom data indicator
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self.plot('RSI-FB', self.rsi_custom)
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# Here we make use of the chats decalred in the Initialize method, plotting multiple series
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# in each chart.
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self.plot('STD', 'STD', self.indicators['STD'].current.value)
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self.plot('BB', 'Price', self.price)
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self.plot('BB', 'BollingerUpperBand', self.indicators['BB'].upper_band.current.value)
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self.plot('BB', 'BollingerMiddleBand', self.indicators['BB'].middle_band.current.value)
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self.plot('BB', 'BollingerLowerBand', self.indicators['BB'].lower_band.current.value)
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self.plot('AROON', 'Aroon', self.indicators['AROON'].current.value)
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self.plot('AROON', 'AroonUp', self.indicators['AROON'].aroon_up.current.value)
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self.plot('AROON', 'AroonDown', self.indicators['AROON'].aroon_down.current.value)
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# The following Plot method calls are commented out because of the 10 series limit for backtests
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#self.plot('ATR', 'ATR', self.indicators['ATR'].current.value)
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#self.plot('ATR', 'ATRDoubleBar', self.selector_indicators['ATR'].current.value)
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#self.plot('Averages', 'SMA', self.indicators['SMA'].current.value)
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#self.plot('Averages', 'EMA', self.indicators['EMA'].current.value)
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#self.plot('MOM', self.indicators['MOM'].current.value)
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#self.plot('MOMP', self.indicators['MOMP'].current.value)
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#self.plot('MACD', 'MACD', self.indicators['MACD'].current.value)
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#self.plot('MACD', 'MACDSignal', self.indicators['MACD'].signal.current.value)
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def selector_double__trade_bar(self, bar):
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trade_bar = TradeBar()
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trade_bar.close = 2 * bar.close
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trade_bar.data_type = bar.data_type
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trade_bar.high = 2 * bar.high
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trade_bar.low = 2 * bar.low
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trade_bar.open = 2 * bar.open
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trade_bar.symbol = bar.symbol
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trade_bar.time = bar.time
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trade_bar.value = 2 * bar.value
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trade_bar.period = bar.period
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return trade_bar
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class CustomData(PythonData):
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def get_source(self, config, date, is_live):
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zip_file_name = LeanData.generate_zip_file_name(config.Symbol, date, config.Resolution, config.TickType)
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source = Globals.data_folder + "/equity/usa/daily/" + zip_file_name
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return SubscriptionDataSource(source)
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def reader(self, config, line, date, is_live):
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if line == None:
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return None
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custom_data = CustomData()
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custom_data.symbol = config.symbol
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csv = line.split(",")
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custom_data.time = datetime.strptime(csv[0], '%Y%m%d %H:%M')
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custom_data.end_time = custom_data.time + timedelta(days=1)
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custom_data.value = float(csv[1]) / 10000.0
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return custom_data
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