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 *
+from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
+
+class SykesShortMicroCapAlpha(QCAlgorithm):
+    ''' Alpha Streams: Benchmark Alpha: Identify "pumped" penny stocks and predict that the price of a "pumped" penny stock reverts to mean
+
+    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.'''
+
+    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)))
+
+        # select stocks using PennyStockUniverseSelectionModel
+        self.universe_settings.resolution = Resolution.DAILY
+        self.universe_settings.schedule.on(self.date_rules.month_start())
+        self.set_universe_selection(PennyStockUniverseSelectionModel())
+
+        # Use SykesShortMicroCapAlphaModel to establish insights
+        self.set_alpha(SykesShortMicroCapAlphaModel())
+
+        # 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 SykesShortMicroCapAlphaModel(AlphaModel):
+    '''Uses ranking of intraday percentage difference between open price and close price to create magnitude and direction prediction for insights'''
+
+    def __init__(self, *args, **kwargs):
+        lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
+        resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.DAILY
+        self.prediction_interval = Time.multiply(Extensions.to_time_span(resolution), lookback)
+        self.number_of_stocks = kwargs['number_of_stocks'] if 'number_of_stocks' in kwargs else 10
+
+    def update(self, algorithm, data):
+        insights = []
+        symbols_ret = dict()
+
+        for security in algorithm.active_securities.values:
+            if security.has_data:
+                open_ = security.open
+                if open_ != 0:
+                    # Intraday price change for penny stocks
+                    symbols_ret[security.symbol] = security.close / open_ - 1
+
+        # Rank penny stocks on one day price change and retrieve list of ten "pumped" penny stocks
+        pumped_stocks = dict(sorted(symbols_ret.items(),
+                                   key = lambda kv: (-round(kv[1], 6), kv[0]))[:self.number_of_stocks])
+
+        # Emit "down" insight for "pumped" penny stocks
+        for symbol, value in pumped_stocks.items():
+            insights.append(Insight.price(symbol, self.prediction_interval, InsightDirection.DOWN, abs(value), None))
+
+        return insights
+
+
+class PennyStockUniverseSelectionModel(FundamentalUniverseSelectionModel):
+    '''Defines a universe of penny stocks, as a universe selection model for the framework algorithm:
+    The stocks must have fundamental data
+    The stock must have positive previous-day close price
+    The stock must have volume between $1000000 and $10000 on the previous trading day
+    The stock must cost less than $5'''
+    def __init__(self):
+        super().__init__()
+
+        # Number of stocks in Coarse Universe
+        self.number_of_symbols_coarse = 500
+
+    def select(self, algorithm, fundamental):
+        # sort the stocks by dollar volume and take the top 500
+        top = sorted([x for x in fundamental if x.has_fundamental_data
+                                       and 5 > x.price > 0
+                                       and 1000000 > x.volume > 10000],
+                    key=lambda x: x.dollar_volume, reverse=True)[:self.number_of_symbols_coarse]
+
+        return [x.symbol for x in top]