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 *
+
+### <summary>
+### Alpha Benchmark Strategy capitalizing on ETF rebalancing causing momentum during trending markets.
+### </summary>
+### <meta name="tag" content="alphastream" />
+### <meta name="tag" content="etf" />
+### <meta name="tag" content="algorithm framework" />
+class RebalancingLeveragedETFAlpha(QCAlgorithm):
+    ''' Alpha Streams: Benchmark Alpha: Leveraged ETF Rebalancing
+        Strategy by Prof. Shum, reposted by Ernie Chan.
+        Source: http://epchan.blogspot.com/2012/10/a-leveraged-etfs-strategy.html'''
+
+    def initialize(self):
+
+        self.set_start_date(2017, 6, 1)
+        self.set_end_date(2018, 8, 1)
+        self.set_cash(100000)
+
+        underlying = ["SPY","QLD","DIA","IJR","MDY","IWM","QQQ","IYE","EEM","IYW","EFA","GAZB","SLV","IEF","IYM","IYF","IYH","IYR","IYC","IBB","FEZ","USO","TLT"]
+        ultra_long =  ["SSO","UGL","DDM","SAA","MZZ","UWM","QLD","DIG","EET","ROM","EFO","BOIL","AGQ","UST","UYM","UYG","RXL","URE","UCC","BIB","ULE","UCO","UBT"]
+        ultra_short = ["SDS","GLL","DXD","SDD","MVV","TWM","QID","DUG","EEV","REW","EFU","KOLD","ZSL","PST","SMN","SKF","RXD","SRS","SCC","BIS","EPV","SCO","TBT"]
+
+        groups = []
+        for i in range(len(underlying)):
+            group = ETFGroup(self.add_equity(underlying[i], Resolution.MINUTE).symbol,
+                              self.add_equity(ultra_long[i], Resolution.MINUTE).symbol,
+                              self.add_equity(ultra_short[i], Resolution.MINUTE).symbol)
+            groups.append(group)
+
+        # Manually curated universe
+        self.set_universe_selection(ManualUniverseSelectionModel())
+        # Select the demonstration alpha model
+        self.set_alpha(RebalancingLeveragedETFAlphaModel(groups))
+
+        # Equally weigh securities in portfolio, based on insights
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+
+        # Set Immediate Execution Model
+        self.set_execution(ImmediateExecutionModel())
+
+        # Set Null Risk Management Model
+        self.set_risk_management(NullRiskManagementModel())
+
+
+class RebalancingLeveragedETFAlphaModel(AlphaModel):
+
+    '''
+        If the underlying ETF has experienced a return >= 1% since the previous day's close up to the current time at 14:15,
+        then buy it's ultra ETF right away, and exit at the close. If the return is <= -1%, sell it's ultra-short ETF.
+    '''
+
+    def __init__(self, ETFgroups):
+
+        self.etfgroups = ETFgroups
+        self.date = datetime.min.date
+        self.name = "RebalancingLeveragedETFAlphaModel"
+
+    def update(self, algorithm, data):
+        '''Scan to see if the returns are greater than 1% at 2.15pm to emit an insight.'''
+
+        insights = []
+        magnitude = 0.0005
+        # Paper suggests leveraged ETF's rebalance from 2.15pm - to close
+        # giving an insight period of 105 minutes.
+        period = timedelta(minutes=105)
+
+        # Get yesterday's close price at the market open
+        if algorithm.time.date() != self.date:
+            self.date = algorithm.time.date()
+            # Save yesterday's price and reset the signal
+            for group in self.etfgroups:
+                history = algorithm.history([group.underlying], 1, Resolution.DAILY)
+                group.yesterday_close = None if history.empty else history.loc[str(group.underlying)]['close'][0]
+
+        # Check if the returns are > 1% at 14.15
+        if algorithm.time.hour == 14 and algorithm.time.minute == 15:
+            for group in self.etfgroups:
+                if group.yesterday_close == 0 or group.yesterday_close is None: continue
+                returns = round((algorithm.portfolio[group.underlying].price - group.yesterday_close) / group.yesterday_close, 10)
+                if returns > 0.01:
+                    insights.append(Insight.price(group.ultra_long, period, InsightDirection.UP, magnitude))
+                elif returns < -0.01:
+                    insights.append(Insight.price(group.ultra_short, period, InsightDirection.DOWN, magnitude))
+
+        return insights
+
+class ETFGroup:
+    '''
+    Group the underlying ETF and it's ultra ETFs
+    Args:
+        underlying: The underlying index ETF
+        ultra_long: The long-leveraged version of underlying ETF
+        ultra_short: The short-leveraged version of the underlying ETF
+    '''
+    def __init__(self,underlying, ultra_long, ultra_short):
+        self.underlying = underlying
+        self.ultra_long = ultra_long
+        self.ultra_short = ultra_short
+        self.yesterday_close = 0