wehub-resource-sync
73 lines
3.8 KiB
Python
73 lines
3.8 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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### Basic Continuous Futures Template Algorithm
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### </summary>
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class BasicTemplateContinuousFutureAlgorithm(QCAlgorithm):
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'''Basic template algorithm simply initializes the date range and cash'''
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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.set_start_date(2013, 7, 1)
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self.set_end_date(2014, 1, 1)
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self._continuous_contract = self.add_future(Futures.Indices.SP_500_E_MINI,
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data_normalization_mode = DataNormalizationMode.BACKWARDS_RATIO,
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data_mapping_mode = DataMappingMode.LAST_TRADING_DAY,
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contract_depth_offset = 0)
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self._fast = self.sma(self._continuous_contract.symbol, 4, Resolution.DAILY)
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self._slow = self.sma(self._continuous_contract.symbol, 10, Resolution.DAILY)
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self._current_contract = None
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# Minimum SMA gap required before acting on a cross; see the workaround note in on_data.
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self._cross_threshold = 0.001
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def on_data(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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for changed_event in data.symbol_changed_events.values():
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if changed_event.symbol == self._continuous_contract.symbol:
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self.log(f"SymbolChanged event: {changed_event}")
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# Workaround so the C# and Python versions take the exact same trades on the limited
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# sample data in the repository (decimal vs double rounding can disagree at a cross).
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if not self.portfolio.invested:
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if self._fast.current.value - self._slow.current.value > self._cross_threshold:
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self._current_contract = self.securities[self._continuous_contract.mapped]
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self.buy(self._current_contract.symbol, 1)
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elif self._slow.current.value - self._fast.current.value > self._cross_threshold:
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self.liquidate()
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# We check exchange hours because the contract mapping can call OnData outside of regular hours.
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if self._current_contract is not None and self._current_contract.symbol != self._continuous_contract.mapped and self._continuous_contract.exchange.exchange_open:
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self.log(f"{self.time} - rolling position from {self._current_contract.symbol} to {self._continuous_contract.mapped}")
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current_position_size = self._current_contract.holdings.quantity
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self.liquidate(self._current_contract.symbol)
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self.buy(self._continuous_contract.mapped, current_position_size)
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self._current_contract = self.securities[self._continuous_contract.mapped]
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def on_order_event(self, order_event):
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self.debug("Purchased Stock: {0}".format(order_event.symbol))
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def on_securities_changed(self, changes):
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self.debug(f"{self.time}-{changes}")
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