wehub-resource-sync
77 lines
3.2 KiB
Python
77 lines
3.2 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 of custom volatility model
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### </summary>
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="reality modelling" />
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class CustomVolatilityModelAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2013,10,7) #Set Start Date
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self.set_end_date(2015,7,15) #Set End Date
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self.set_cash(100000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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self.equity = self.add_equity("SPY", Resolution.DAILY)
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self.equity.set_volatility_model(CustomVolatilityModel(10))
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def on_data(self, data):
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if not self.portfolio.invested and self.equity.volatility_model.volatility > 0:
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self.set_holdings("SPY", 1)
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# Python implementation of StandardDeviationOfReturnsVolatilityModel
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# Computes the annualized sample standard deviation of daily returns as the volatility of the security
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# https://github.com/QuantConnect/Lean/blob/master/Common/Securities/Volatility/StandardDeviationOfReturnsVolatilityModel.cs
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class CustomVolatilityModel():
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def __init__(self, periods):
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self.last_update = datetime.min
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self.last_price = 0
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self.needs_update = False
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self.period_span = timedelta(1)
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self.window = RollingWindow(periods)
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# Volatility is a mandatory attribute
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self.volatility = 0
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# Updates this model using the new price information in the specified security instance
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# Update is a mandatory method
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def update(self, security, data):
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time_since_last_update = data.end_time - self.last_update
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if time_since_last_update >= self.period_span and data.price > 0:
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if self.last_price > 0:
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self.window.add(float(data.price / self.last_price) - 1.0)
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self.needs_update = self.window.is_ready
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self.last_update = data.end_time
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self.last_price = data.price
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if self.window.count < 2:
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self.volatility = 0
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return
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if self.needs_update:
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self.needs_update = False
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std = np.std([ x for x in self.window ])
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self.volatility = std * np.sqrt(252.0)
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# Returns history requirements for the volatility model expressed in the form of history request
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# GetHistoryRequirements is a mandatory method
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def get_history_requirements(self, security, utc_time):
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# For simplicity's sake, we will not set a history requirement
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return None
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