wehub-resource-sync
93 lines
4.1 KiB
Python
93 lines
4.1 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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# 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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#
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# Triangle Exchange Rate = (A/B) * (B/C) * (C/A)
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#
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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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#
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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 TriangleExchangeRateArbitrageAlpha(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2019, 2, 1) #Set Start Date
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self.set_cash(100000) #Set Strategy Cash
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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 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_settings.resolution = Resolution.MINUTE
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self.set_universe_selection( ManualUniverseSelectionModel(symbols) )
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self.set_alpha(ForexTriangleArbitrageAlphaModel(Resolution.MINUTE, symbols))
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## Set Equal Weighting Portfolio Construction Model
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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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class ForexTriangleArbitrageAlphaModel(AlphaModel):
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def __init__(self, insight_resolution, symbols):
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self.insight_period = Time.multiply(Extensions.to_time_span(insight_resolution), 5)
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self._symbols = symbols
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def update(self, algorithm, data):
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## Check to make sure all currency symbols are present
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if len(data.keys()) < 3:
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return []
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## Extract QuoteBars for all three Forex securities
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bar_a = data[self._symbols[0]]
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bar_b = data[self._symbols[1]]
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bar_c = data[self._symbols[2]]
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## Calculate the triangle exchange rate
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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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triangle_rate = bar_a.ask.close / bar_b.bid.close / bar_c.ask.close
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## If the triangle rate is significantly different than 1, then emit insights
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if triangle_rate > 1.0005:
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return Insight.group(
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[
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Insight.price(self._symbols[0], self.insight_period, InsightDirection.UP, 0.0001, None),
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Insight.price(self._symbols[1], self.insight_period, InsightDirection.DOWN, 0.0001, None),
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Insight.price(self._symbols[2], self.insight_period, InsightDirection.UP, 0.0001, None)
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] )
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return []
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