Jared
141 lines
6.0 KiB
Python
141 lines
6.0 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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Algorithm.Framework")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Indicators")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Algorithm.Framework import QCAlgorithmFrameworkBridge
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from QuantConnect.Algorithm.Framework.Alphas import *
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from QuantConnect.Indicators import *
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from QuantConnect.Orders.Fees import ConstantFeeModel
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from QuantConnect.Data.Consolidators import *
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from datetime import datetime, timedelta
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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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class IntradayReversalCurrencyMarketsFrameworkAlgorithm(QCAlgorithmFramework):
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def Initialize(self):
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self.SetStartDate(2015, 1, 1)
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self.SetCash(100000)
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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 = [
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Symbol.Create("EURUSD", SecurityType.Forex, Market.Oanda)
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]
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# Set requested data resolution
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self.UniverseSettings.Resolution = resolution
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self.SetUniverseSelection(ManualUniverseSelectionModel( symbols ))
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self.SetAlpha(IntradayReversalAlphaModel(5, resolution))
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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self.SetExecution(ImmediateExecutionModel())
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self.SetRiskManagement(NullRiskManagementModel())
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#Set WarmUp for Indicators
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self.SetWarmUp(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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self.timeToClose = datetime(algorithm.Time.year, algorithm.Time.month, algorithm.Time.day, 15, 1, 00, tzinfo = algorithm.Time.tzinfo)
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insights = []
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for security in algorithm.ActiveSecurities.Values:
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if self.ShouldEmitInsight(algorithm, security.Symbol):
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direction = InsightDirection.Down
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if self.cache[security.Symbol].is_uptrend(algorithm.Securities[security.Symbol].Price):
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direction = InsightDirection.Up
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# Ignore signal for same direction as previous signal (when no crossover)
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if direction == self.cache[security.Symbol].PreviousDirection:
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continue
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# Update the predictionInterval so insight goes Flat by timeToClose
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predictionInterval = self.timeToClose - algorithm.Time
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# Generate insight
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insight = Insight.Price(security.Symbol, predictionInterval, direction)
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# Save the current Insight Direction to check when the crossover happens
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self.cache[security.Symbol].PreviousDirection = insight.Direction
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insights.append(insight)
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return insights
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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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def OnSecuritiesChanged(self, algorithm, changes):
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for security in changes.AddedSecurities:
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self.cache[security.Symbol] = SymbolData(algorithm, security.Symbol, self.period_sma, self.resolution)
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# Time to control when to start and finish emitting (10AM to 3PM)
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def ShouldEmitInsight(self, algorithm, symbol):
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current = algorithm.Time
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insightTimeStart = datetime(current.year, current.month, current.day, 10, 00, 00, tzinfo = current.tzinfo).time()
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insightTimeEnd = datetime(current.year, current.month, current.day, 15, 00, 00, tzinfo = current.tzinfo).time()
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currentTime = current.time()
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if not algorithm.Securities[symbol].HasData or currentTime < insightTimeStart or currentTime > insightTimeEnd:
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return False
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else:
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return True
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class SymbolData:
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def __init__(self, algorithm, symbol, period_sma, resolution):
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self.PreviousDirection = None
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self.priceSMA = algorithm.SMA(symbol, period_sma, resolution)
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def is_uptrend(self, price):
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if self.priceSMA.IsReady:
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return price < self.priceSMA.Current.Value * 1.001
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else:
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return False
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