wehub-resource-sync
124 lines
6.3 KiB
Python
124 lines
6.3 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 AlgorithmImports import *
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### <summary>
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### Example structure for structuring an algorithm with indicator and consolidator data for many tickers.
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### </summary>
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### <meta name="tag" content="consolidating data" />
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="strategy example" />
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class MultipleSymbolConsolidationAlgorithm(QCAlgorithm):
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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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def initialize(self) -> None:
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# This is the period of bars we'll be creating
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bar_period = TimeSpan.from_minutes(10)
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# This is the period of our sma indicators
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sma_period = 10
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# This is the number of consolidated bars we'll hold in symbol data for reference
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rolling_window_size = 10
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# Holds all of our data keyed by each symbol
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self._data = {}
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# Contains all of our equity symbols
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equity_symbols = ["AAPL","SPY","IBM"]
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# Contains all of our forex symbols
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forex_symbols = ["EURUSD", "USDJPY", "EURGBP", "EURCHF", "USDCAD", "USDCHF", "AUDUSD","NZDUSD"]
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self.set_start_date(2014, 12, 1)
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self.set_end_date(2015, 2, 1)
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# initialize our equity data
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for symbol in equity_symbols:
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equity = self.add_equity(symbol)
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self._data[symbol] = SymbolData(equity.symbol, bar_period, rolling_window_size)
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# initialize our forex data
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for symbol in forex_symbols:
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forex = self.add_forex(symbol)
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self._data[symbol] = SymbolData(forex.symbol, bar_period, rolling_window_size)
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# loop through all our symbols and request data subscriptions and initialize indicator
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for symbol, symbol_data in self._data.items():
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# define the indicator
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symbol_data.sma = SimpleMovingAverage(self.create_indicator_name(symbol, "sma" + str(sma_period), Resolution.MINUTE), sma_period)
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# define a consolidator to consolidate data for this symbol on the requested period
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if symbol_data._symbol.security_type == SecurityType.EQUITY:
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tb_consolidator = TradeBarConsolidator(bar_period)
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tb_consolidator.data_consolidated += self.on_trade_bar_consolidated
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# we need to add this consolidator so it gets auto updates
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self.subscription_manager.add_consolidator(symbol_data._symbol, tb_consolidator)
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else:
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qb_consolidator = QuoteBarConsolidator(bar_period)
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qb_consolidator.data_consolidated += self.on_quote_bar_consolidated
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# we need to add this consolidator so it gets auto updates
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self.subscription_manager.add_consolidator(symbol_data._symbol, qb_consolidator)
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def on_trade_bar_consolidated(self, sender: object, bar: TradeBar) -> None:
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self._on_data_consolidated(sender, bar)
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def on_quote_bar_consolidated(self, sender: object, bar: QuoteBar) -> None:
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self._on_data_consolidated(sender, bar)
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def _on_data_consolidated(self, sender: object, bar: TradeBar | QuoteBar) -> None:
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self._data[bar.symbol.value].sma.update(bar.time, bar.close)
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self._data[bar.symbol.value].bars.add(bar)
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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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# Argument "data": Slice object, dictionary object with your stock data
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def on_data(self, data: Slice) -> None:
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# loop through each symbol in our structure
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for symbol in self._data.keys():
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symbol_data = self._data[symbol]
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# this check proves that this symbol was JUST updated prior to this OnData function being called
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if symbol_data.is_ready() and symbol_data.was_just_updated(self.time):
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if not self.portfolio[symbol].invested:
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self.market_order(symbol, 1)
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# End of a trading day event handler. This method is called at the end of the algorithm day (or multiple times if trading multiple assets).
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# Method is called 10 minutes before closing to allow user to close out position.
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def on_end_of_day(self, symbol: Symbol) -> None:
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i = 0
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for symbol_key in sorted(self._data.keys()):
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symbol_data = self._data[symbol_key]
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# we have too many symbols to plot them all, so plot every other
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i += 1
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if symbol_data.is_ready() and i%2 == 0:
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self.plot(symbol_key, symbol_key, symbol_data.sma.current.value)
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class SymbolData(object):
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def __init__(self, symbol: Symbol, bar_period: timedelta, window_size: int) -> None:
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self._symbol = symbol
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# The period used when population the Bars rolling window
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self.bar_period = bar_period
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# A rolling window of data, data needs to be pumped into Bars by using Bars.update( trade_bar ) and can be accessed like:
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# my_symbol_data.bars[0] - most first recent piece of data
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# my_symbol_data.bars[5] - the sixth most recent piece of data (zero based indexing)
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self.bars = RollingWindow(window_size)
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# The simple moving average indicator for our symbol
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self.sma = None
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# Returns true if all the data in this instance is ready (indicators, rolling windows, ect...)
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def is_ready(self) -> bool:
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return self.bars.is_ready and self.sma.is_ready
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# Returns true if the most recent trade bar time matches the current time minus the bar's period, this
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# indicates that update was just called on this instance
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def was_just_updated(self, current: datetime) -> bool:
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return self.bars.count > 0 and self.bars[0].time == current - self.bar_period
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