Jack Simonson
124 lines
5.2 KiB
Python
124 lines
5.2 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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'''
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In a perfect market, you could buy 100 EUR worth of USD, sell 100 EUR worth of GBP,
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and then use the GBP to buy USD and wind up with the same amount in USD as you received when
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you bought them with EUR. This relationship is expressed by the Triangle Exchange Rate, which is
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Triangle Exchange Rate = (A/B) * (B/C) * (C/A)
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where (A/B) is the exchange rate of A-to-B. In a perfect market, TER = 1, and so when
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there is a mispricing in the market, then TER will not be 1 and there exists an arbitrage opportunity.
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This Alpha Model is an implementation of this theory.
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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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Common")
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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 *
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from QuantConnect.Algorithm.Framework.Risk import *
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from QuantConnect.Orders.Fees import ConstantFeeModel
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from QuantConnect.Algorithm.Framework.Alphas import *
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from QuantConnect.Algorithm.Framework.Selection import *
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from QuantConnect.Algorithm.Framework.Execution import *
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from QuantConnect.Algorithm.Framework.Portfolio import PortfolioTarget, EqualWeightingPortfolioConstructionModel
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from datetime import datetime, timedelta
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class TriangleArbitrageAlgorithm(QCAlgorithmFramework):
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def Initialize(self):
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self.SetStartDate(2019, 1, 1) #Set Start Date
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self.SetCash(100000) #Set Strategy Cash
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## Select trio of currencies to trade where
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## Currency A = USD
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## Currency B = EUR
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## Currency C = GBP
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currencies = ['EURUSD','EURGBP','GBPUSD']
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symbols = [ Symbol.Create(currency, SecurityType.Forex, Market.Oanda) for currency in currencies]
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## Manual universe selection with tick-resolution data
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self.Universe.Resolution = Resolution.Tick
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self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
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## Set $0 fees
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self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0)))
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## Set custom Alpha Model
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self.SetAlpha(ForexTriangleArbitrageAlphaModel(currencies, Resolution.Second))
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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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class ForexTriangleArbitrageAlphaModel:
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def __init__(self, currencies, insight_resolution):
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self.TriangleRate = 0
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self.currency_a = currencies[0]
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self.currency_b = currencies[1]
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self.currency_c = currencies[2]
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self.insight_resolution = insight_resolution
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self.insight_period = Time.Multiply(Extensions.ToTimeSpan(self.insight_resolution), 5)
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def Update(self, algorithm, data):
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insights = []
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## Extract QuoteBars for all three Forex securities
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bar_a = data[self.currency_a]
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bar_b = data[self.currency_b]
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bar_c = data[self.currency_c]
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## Calculate the triangle exchange rate
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self.TriangleRate = self.CalculateTriangleRate(bar_a, bar_b, bar_c)
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algorithm.Log(str(self.TriangleRate))
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## If the triangle rate is significantly different than 1, then emit insights
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if self.TriangleRate > 1.00015:
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insights.append(Insight(self.currency_a, self.insight_period, InsightType.Price, InsightDirection.Up, 0.0001, None))
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insights.append(Insight(self.currency_b, self.insight_period, InsightType.Price, InsightDirection.Down, 0.0001, None))
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insights.append(Insight(self.currency_c, self.insight_period, InsightType.Price, InsightDirection.Up, 0.0001, None))
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return insights
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def CalculateTriangleRate(self, bar_a, bar_b, bar_c):
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## Bid(Currency A -> Currency B) * Bid(Currency B -> Currency C) * Bid(Currency C -> Currency A)
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## If exchange rates are priced perfectly, then this yield 1. If it is different than 1, then an arbitrage opportunity exists
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return bar_a.Bid.Close * (1/bar_b.Bid.Close) * (1/bar_c.Bid.Close)
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def OnSecuritiesChanged(self, algorithm, changes):
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## Set fees = 0 tom better mimic HFT
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for security in changes.AddedSecurities:
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security.FeeModel = ConstantFeeModel(0) |