chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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+# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from AlgorithmImports import *
+
+#
+# In a perfect market, you could buy 100 EUR worth of USD, sell 100 EUR worth of GBP,
+# and then use the GBP to buy USD and wind up with the same amount in USD as you received when
+# you bought them with EUR. This relationship is expressed by the Triangle Exchange Rate, which is
+#
+#     Triangle Exchange Rate = (A/B) * (B/C) * (C/A)
+#
+# where (A/B) is the exchange rate of A-to-B. In a perfect market, TER = 1, and so when
+# there is a mispricing in the market, then TER will not be 1 and there exists an arbitrage opportunity.
+#
+# 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.
+#
+
+class TriangleExchangeRateArbitrageAlpha(QCAlgorithm):
+
+    def initialize(self):
+
+        self.set_start_date(2019, 2, 1)   #Set Start Date
+        self.set_cash(100000)           #Set Strategy Cash
+
+        # Set zero transaction fees
+        self.set_security_initializer(lambda security: security.set_fee_model(ConstantFeeModel(0)))
+
+        ## Select trio of currencies to trade where
+        ## Currency A = USD
+        ## Currency B = EUR
+        ## Currency C = GBP
+        currencies = ['EURUSD','EURGBP','GBPUSD']
+        symbols = [ Symbol.create(currency, SecurityType.FOREX, Market.OANDA) for currency in currencies]
+
+        ## Manual universe selection with tick-resolution data
+        self.universe_settings.resolution = Resolution.MINUTE
+        self.set_universe_selection( ManualUniverseSelectionModel(symbols) )
+
+        self.set_alpha(ForexTriangleArbitrageAlphaModel(Resolution.MINUTE, symbols))
+
+        ## Set Equal Weighting Portfolio Construction Model
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+
+        ## Set Immediate Execution Model
+        self.set_execution(ImmediateExecutionModel())
+
+        ## Set Null Risk Management Model
+        self.set_risk_management(NullRiskManagementModel())
+
+
+class ForexTriangleArbitrageAlphaModel(AlphaModel):
+
+    def __init__(self, insight_resolution, symbols):
+        self.insight_period = Time.multiply(Extensions.to_time_span(insight_resolution), 5)
+        self._symbols = symbols
+
+    def update(self, algorithm, data):
+        ## Check to make sure all currency symbols are present
+        if len(data.keys()) < 3:
+            return []
+
+        ## Extract QuoteBars for all three Forex securities
+        bar_a = data[self._symbols[0]]
+        bar_b = data[self._symbols[1]]
+        bar_c = data[self._symbols[2]]
+
+        ## Calculate the triangle exchange rate
+        ## Bid(Currency A -> Currency B) * Bid(Currency B -> Currency C) * Bid(Currency C -> Currency A)
+        ## If exchange rates are priced perfectly, then this yield 1. If it is different than 1, then an arbitrage opportunity exists
+        triangle_rate = bar_a.ask.close / bar_b.bid.close / bar_c.ask.close
+
+        ## If the triangle rate is significantly different than 1, then emit insights
+        if triangle_rate > 1.0005:
+            return Insight.group(
+                [
+                    Insight.price(self._symbols[0], self.insight_period, InsightDirection.UP, 0.0001, None),
+                    Insight.price(self._symbols[1], self.insight_period, InsightDirection.DOWN, 0.0001, None),
+                    Insight.price(self._symbols[2], self.insight_period, InsightDirection.UP, 0.0001, None)
+                ] )
+
+        return []