wehub-resource-sync
110 lines
5.5 KiB
Python
110 lines
5.5 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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'''
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This Alpha Model uses Wells Fargo 30-year Fixed Rate Mortgage data from Quandl to
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generate Insights about the movement of Real Estate ETFs. Mortgage rates can provide information
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regarding the general price trend of real estate, and ETFs provide good continuous-time instruments
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to measure the impact against. Volatility in mortgage rates tends to put downward pressure on real
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estate prices, whereas stable mortgage rates, regardless of true rate, lead to stable or higher real
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estate prices. This Alpha model seeks to take advantage of this correlation by emitting insights
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based on volatility and rate deviation from its historic mean.
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This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open
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sourced so the community and client funds can see an example of an alpha.
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'''
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from AlgorithmImports import *
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class MortgageRateVolatilityAlpha(QCAlgorithmFramework):
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def initialize(self):
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# Set requested data resolution
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self.set_start_date(2017, 1, 1) #Set Start Date
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self.set_cash(100000) #Set Strategy Cash
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self.universe_settings.resolution = Resolution.DAILY
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## Universe of six liquid real estate ETFs
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etfs = ['VNQ', 'REET', 'TAO', 'FREL', 'SRET', 'HIPS']
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symbols = [ Symbol.create(etf, SecurityType.EQUITY, Market.USA) for etf in etfs ]
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self.set_security_initializer(lambda security: security.set_fee_model(ConstantFeeModel(0)))
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self.set_universe_selection(ManualUniverseSelectionModel(symbols) )
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self.set_alpha(MortgageRateVolatilityAlphaModel(self))
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self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
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self.set_execution(ImmediateExecutionModel())
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self.set_risk_management(NullRiskManagementModel())
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class MortgageRateVolatilityAlphaModel(AlphaModel):
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def __init__(self, algorithm, indicator_period = 15, insight_magnitude = 0.005, deviations = 2):
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## Add Quandl data for a Well's Fargo 30-year Fixed Rate mortgage
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self.mortgage_rate = algorithm.add_data(QuandlMortgagePriceColumns, 'WFC/PR_GOV_30YFIXEDVA_APR').symbol
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self.indicator_period = indicator_period
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self.insight_duration = TimeSpan.from_days(indicator_period)
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self.insight_magnitude = insight_magnitude
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self.deviations = deviations
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## Add indicators for the mortgage rate -- Standard Deviation and Simple Moving Average
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self.mortgage_rate_std = algorithm.std(self.mortgage_rate, indicator_period)
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self.mortgage_rate_sma = algorithm.sma(self.mortgage_rate, indicator_period)
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## Use a history call to warm-up the indicators
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self.warmup_indicators(algorithm)
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def update(self, algorithm, data):
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insights = []
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## Return empty list if data slice doesn't contain monrtgage rate data
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if self.mortgage_rate not in data.keys():
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return []
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## Extract current mortgage rate, the current STD indicator value, and current SMA value
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mortgage_rate = data[self.mortgage_rate].value
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deviation = self.deviations * self.mortgage_rate_std.current.value
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sma = self.mortgage_rate_sma.current.value
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## If volatility in mortgage rates is high, then we emit an Insight to sell
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if (mortgage_rate < sma - deviation) or (mortgage_rate > sma + deviation):
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## Emit insights for all securities that are currently in the Universe,
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## except for the Quandl Symbol
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insights = [Insight(security, self.insight_duration, InsightType.PRICE, InsightDirection.DOWN, self.insight_magnitude, None) \
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for security in algorithm.active_securities.keys if security != self.mortgage_rate]
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## If volatility in mortgage rates is low, then we emit an Insight to buy
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if (mortgage_rate < sma - deviation/2) or (mortgage_rate > sma + deviation/2):
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insights = [Insight(security, self.insight_duration, InsightType.PRICE, InsightDirection.UP, self.insight_magnitude, None) \
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for security in algorithm.active_securities.keys if security != self.mortgage_rate]
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return insights
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def warmup_indicators(self, algorithm):
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## Make a history call and update the indicators
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history = algorithm.history(self.mortgage_rate, self.indicator_period, Resolution.DAILY)
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for index, row in history.iterrows():
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self.mortgage_rate_std.update(index[1], row['value'])
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self.mortgage_rate_sma.update(index[1], row['value'])
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class QuandlMortgagePriceColumns(PythonQuandl):
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def __init__(self):
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## Rename the Quandl object column to the data we want, which is the 'Value' column
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## of the CSV that our API call returns
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self.value_column_name = "Value"
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