wehub-resource-sync
130 lines
5.2 KiB
Python
130 lines
5.2 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 algorithm for trading continuous future
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### </summary>
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class BasicTemplateFutureRolloverAlgorithm(QCAlgorithm):
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### <summary>
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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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### </summary>
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def initialize(self):
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self.set_start_date(2013, 10, 8)
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self.set_end_date(2013, 12, 10)
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self.set_cash(1000000)
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self._symbol_data_by_symbol = {}
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futures = [
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Futures.Indices.SP_500_E_MINI
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]
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for future in futures:
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# Requesting data
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continuous_contract = self.add_future(future,
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resolution = Resolution.DAILY,
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extended_market_hours = True,
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data_normalization_mode = DataNormalizationMode.BACKWARDS_RATIO,
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data_mapping_mode = DataMappingMode.OPEN_INTEREST,
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contract_depth_offset = 0
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)
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symbol_data = SymbolData(self, continuous_contract)
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self._symbol_data_by_symbol[continuous_contract.symbol] = symbol_data
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### <summary>
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### on_data event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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### </summary>
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### <param name="slice">Slice object keyed by symbol containing the stock data</param>
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def on_data(self, slice):
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for symbol, symbol_data in self._symbol_data_by_symbol.items():
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# Call SymbolData.update() method to handle new data slice received
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symbol_data.update(slice)
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# Check if information in SymbolData class and new slice data are ready for trading
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if not symbol_data.is_ready or not slice.bars.contains_key(symbol):
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return
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ema_current_value = symbol_data.EMA.current.value
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if ema_current_value < symbol_data.price and not symbol_data.is_long:
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self.market_order(symbol_data.mapped, 1)
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elif ema_current_value > symbol_data.price and not symbol_data.is_short:
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self.market_order(symbol_data.mapped, -1)
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### <summary>
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### Abstracted class object to hold information (state, indicators, methods, etc.) from a Symbol/Security in a multi-security algorithm
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### </summary>
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class SymbolData:
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### <summary>
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### Constructor to instantiate the information needed to be hold
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### </summary>
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def __init__(self, algorithm, future):
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self._algorithm = algorithm
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self._future = future
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self.EMA = algorithm.ema(future.symbol, 20, Resolution.DAILY)
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self.price = 0
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self.is_long = False
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self.is_short = False
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self.reset()
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@property
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def symbol(self):
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return self._future.symbol
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@property
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def mapped(self):
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return self._future.mapped
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@property
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def is_ready(self):
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return self.mapped is not None and self.EMA.is_ready
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### <summary>
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### Handler of new slice of data received
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### </summary>
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def update(self, slice):
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if slice.symbol_changed_events.contains_key(self.symbol):
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changed_event = slice.symbol_changed_events[self.symbol]
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old_symbol = changed_event.old_symbol
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new_symbol = changed_event.new_symbol
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tag = f"Rollover - Symbol changed at {self._algorithm.time}: {old_symbol} -> {new_symbol}"
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quantity = self._algorithm.portfolio[old_symbol].quantity
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# Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
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self._algorithm.liquidate(old_symbol, tag = tag)
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self._algorithm.market_order(new_symbol, quantity, tag = tag)
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self.reset()
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self.price = slice.bars[self.symbol].price if slice.bars.contains_key(self.symbol) else self.price
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self.is_long = self._algorithm.portfolio[self.mapped].is_long
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self.is_short = self._algorithm.portfolio[self.mapped].is_short
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### <summary>
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### reset RollingWindow/indicator to adapt to newly mapped contract, then warm up the RollingWindow/indicator
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### </summary>
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def reset(self):
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self.EMA.reset()
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self._algorithm.warm_up_indicator(self.symbol, self.EMA, Resolution.DAILY)
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### <summary>
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### disposal method to remove consolidator/update method handler, and reset RollingWindow/indicator to free up memory and speed
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### </summary>
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def dispose(self):
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self.EMA.reset()
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