wehub-resource-sync
123 lines
5.0 KiB
Python
123 lines
5.0 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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#
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# Reversal strategy that goes long when price crosses below SMA and Short when price crosses above SMA.
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# The trading strategy is implemented only between 10AM - 3PM (NY time). Research suggests this is due to
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# institutional trades during market hours which need hedging with the USD. Source paper:
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# LeBaron, Zhao: Intraday Foreign Exchange Reversals
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# http://people.brandeis.edu/~blebaron/wps/fxnyc.pdf
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# http://www.fma.org/Reno/Papers/ForeignExchangeReversalsinNewYorkTime.PDF
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#
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# This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open sourced so the community and client funds can see an example of an alpha.
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#
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class IntradayReversalCurrencyMarketsAlpha(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2015, 1, 1)
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self.set_cash(100000)
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# Set zero transaction fees
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self.set_security_initializer(lambda security: security.set_fee_model(ConstantFeeModel(0)))
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# Select resolution
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resolution = Resolution.HOUR
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# Reversion on the USD.
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symbols = [Symbol.create("EURUSD", SecurityType.FOREX, Market.OANDA)]
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# Set requested data resolution
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self.universe_settings.resolution = resolution
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self.set_universe_selection(ManualUniverseSelectionModel(symbols))
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self.set_alpha(IntradayReversalAlphaModel(5, resolution))
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# Equally weigh securities in portfolio, based on insights
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self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
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# Set Immediate Execution Model
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self.set_execution(ImmediateExecutionModel())
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# Set Null Risk Management Model
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self.set_risk_management(NullRiskManagementModel())
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#Set WarmUp for Indicators
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self.set_warm_up(20)
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class IntradayReversalAlphaModel(AlphaModel):
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'''Alpha model that uses a Price/SMA Crossover to create insights on Hourly Frequency.
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Frequency: Hourly data with 5-hour simple moving average.
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Strategy:
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Reversal strategy that goes Long when price crosses below SMA and Short when price crosses above SMA.
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The trading strategy is implemented only between 10AM - 3PM (NY time)'''
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# Initialize variables
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def __init__(self, period_sma = 5, resolution = Resolution.HOUR):
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self.period_sma = period_sma
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self.resolution = resolution
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self.cache = {} # Cache for SymbolData
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self.name = 'IntradayReversalAlphaModel'
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def update(self, algorithm, data):
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# Set the time to close all positions at 3PM
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time_to_close = algorithm.time.replace(hour=15, minute=1, second=0)
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insights = []
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for kvp in algorithm.active_securities:
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symbol = kvp.key
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if self.should_emit_insight(algorithm, symbol) and symbol in self.cache:
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price = kvp.value.price
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symbol_data = self.cache[symbol]
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direction = InsightDirection.UP if symbol_data.is_uptrend(price) else InsightDirection.DOWN
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# Ignore signal for same direction as previous signal (when no crossover)
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if direction == symbol_data.previous_direction:
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continue
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# Save the current Insight Direction to check when the crossover happens
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symbol_data.previous_direction = direction
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# Generate insight
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insights.append(Insight.price(symbol, time_to_close, direction))
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return insights
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def on_securities_changed(self, algorithm, changes):
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'''Handle creation of the new security and its cache class.
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Simplified in this example as there is 1 asset.'''
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for security in changes.added_securities:
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self.cache[security.symbol] = SymbolData(algorithm, security.symbol, self.period_sma, self.resolution)
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def should_emit_insight(self, algorithm, symbol):
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'''Time to control when to start and finish emitting (10AM to 3PM)'''
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time_of_day = algorithm.time.time()
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return algorithm.securities[symbol].has_data and time_of_day >= time(10) and time_of_day <= time(15)
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class SymbolData:
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def __init__(self, algorithm, symbol, period_sma, resolution):
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self.previous_direction = InsightDirection.FLAT
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self.price_sma = algorithm.sma(symbol, period_sma, resolution)
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def is_uptrend(self, price):
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return self.price_sma.is_ready and price < round(self.price_sma.current.value * 1.001, 6)
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