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 *
+
+#
+# Academic research suggests that stock market participants generally place their orders at the market open and close.
+# Intraday trading volume is J-Shaped, where the minimum trading volume of the day is during lunch-break. Stocks become
+# more volatile as order flow is reduced and tend to mean-revert during lunch-break.
+#
+# This alpha aims to capture the mean-reversion effect of ETFs during lunch-break by ranking 20 ETFs
+# on their return between the close of the previous day to 12:00 the day after and predicting mean-reversion
+# in price during lunch-break.
+#
+# Source:  Lunina, V. (June 2011). The Intraday Dynamics of Stock Returns and Trading Activity: Evidence from OMXS 30 (Master's Essay, Lund University).
+# Retrieved from http://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=1973850&fileOId=1973852
+#
+# 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 MeanReversionLunchBreakAlpha(QCAlgorithm):
+
+    def initialize(self):
+
+        self.set_start_date(2018, 1, 1)
+
+        self.set_cash(100000)
+
+        # Set zero transaction fees
+        self.set_security_initializer(lambda security: security.set_fee_model(ConstantFeeModel(0)))
+
+        # Use Hourly Data For Simplicity
+        self.universe_settings.resolution = Resolution.HOUR
+        self.set_universe_selection(CoarseFundamentalUniverseSelectionModel(self.coarse_selection_function))
+
+        # Use MeanReversionLunchBreakAlphaModel to establish insights
+        self.set_alpha(MeanReversionLunchBreakAlphaModel())
+
+        # 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())
+
+    # Sort the data by daily dollar volume and take the top '20' ETFs
+    def coarse_selection_function(self, coarse):
+        sorted_by_dollar_volume = sorted(coarse, key=lambda x: x.dollar_volume, reverse=True)
+        filtered = [ x.symbol for x in sorted_by_dollar_volume if not x.has_fundamental_data ]
+        return filtered[:20]
+
+
+class MeanReversionLunchBreakAlphaModel(AlphaModel):
+    '''Uses the price return between the close of previous day to 12:00 the day after to
+    predict mean-reversion of stock price during lunch break and creates direction prediction
+    for insights accordingly.'''
+
+    def __init__(self, *args, **kwargs):
+        lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
+        self.resolution = Resolution.HOUR
+        self.prediction_interval = Time.multiply(Extensions.to_time_span(self.resolution), lookback)
+        self._symbol_data_by_symbol = dict()
+
+    def update(self, algorithm, data):
+
+        for symbol, symbol_data in self._symbol_data_by_symbol.items():
+            if data.bars.contains_key(symbol):
+                bar = data.bars.get(symbol)
+                symbol_data.update(bar.end_time, bar.close)
+
+        return [] if algorithm.time.hour != 12 else \
+               [x.insight for x in self._symbol_data_by_symbol.values()]
+
+    def on_securities_changed(self, algorithm, changes):
+        for security in changes.removed_securities:
+            self._symbol_data_by_symbol.pop(security.symbol, None)
+
+        # Retrieve price history for all securities in the security universe
+        # and update the indicators in the SymbolData object
+        symbols = [x.symbol for x in changes.added_securities]
+        history = algorithm.history(symbols, 1, self.resolution)
+        if history.empty:
+            algorithm.debug(f"No data on {algorithm.time}")
+            return
+        history = history.close.unstack(level = 0)
+
+        for ticker, values in history.items():
+            symbol = next((x for x in symbols if str(x) == ticker ), None)
+            if symbol in self._symbol_data_by_symbol or symbol is None: continue
+            self._symbol_data_by_symbol[symbol] = self.SymbolData(symbol, self.prediction_interval)
+            self._symbol_data_by_symbol[symbol].update(values.index[0], values[0])
+
+
+    class SymbolData:
+        def __init__(self, symbol, period):
+            self._symbol = symbol
+            self.period = period
+            # Mean value of returns for magnitude prediction
+            self.mean_of_price_change = IndicatorExtensions.sma(RateOfChangePercent(1),3)
+            # Price change from close price the previous day
+            self.price_change = RateOfChangePercent(3)
+
+        def update(self, time, value):
+            return self.mean_of_price_change.update(time, value) and \
+                   self.price_change.update(time, value)
+
+        @property
+        def insight(self):
+            direction = InsightDirection.DOWN if self.price_change.current.value > 0 else InsightDirection.UP
+            margnitude = abs(self.mean_of_price_change.current.value)
+            return Insight.price(self._symbol, self.period, direction, margnitude, None)