chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
5008 changed files with 910633 additions and 0 deletions
@@ -0,0 +1,177 @@
+# 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 *
+
+#
+# This is a demonstration algorithm. It trades UVXY.
+# Dual Thrust alpha model is used to produce insights.
+# Those input parameters have been chosen that gave acceptable results on a series
+# of random backtests run for the period from Oct, 2016 till Feb, 2019.
+#
+
+class VIXDualThrustAlpha(QCAlgorithm):
+
+    def initialize(self):
+
+        # -- STRATEGY INPUT PARAMETERS --
+        self.k1 = 0.63
+        self.k2 = 0.63
+        self.range_period = 20
+        self.consolidator_bars = 30
+
+        # Settings
+        self.set_start_date(2018, 10, 1)
+        self.set_security_initializer(lambda security: security.set_fee_model(ConstantFeeModel(0)))
+        self.set_brokerage_model(BrokerageName.INTERACTIVE_BROKERS_BROKERAGE, AccountType.MARGIN)
+
+        # Universe Selection
+        self.universe_settings.resolution = Resolution.MINUTE   # it's minute by default, but lets leave this param here
+        symbols = [Symbol.create("SPY", SecurityType.EQUITY, Market.USA)]
+        self.set_universe_selection(ManualUniverseSelectionModel(symbols))
+
+        # Warming up
+        resolution_in_time_span =  Extensions.to_time_span(self.universe_settings.resolution)
+        warm_up_time_span = Time.multiply(resolution_in_time_span, self.consolidator_bars)
+        self.set_warm_up(warm_up_time_span)
+
+        # Alpha Model
+        self.set_alpha(DualThrustAlphaModel(self.k1, self.k2, self.range_period, self.universe_settings.resolution, self.consolidator_bars))
+
+        ## Portfolio Construction
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+
+        ## Execution
+        self.set_execution(ImmediateExecutionModel())
+
+        ## Risk Management
+        self.set_risk_management(MaximumDrawdownPercentPerSecurity(0.03))
+
+
+class DualThrustAlphaModel(AlphaModel):
+    '''Alpha model that uses dual-thrust strategy to create insights
+    https://medium.com/@FMZ_Quant/dual-thrust-trading-strategy-2cc74101a626
+    or here:
+    https://www.quantconnect.com/tutorials/strategy-library/dual-thrust-trading-algorithm'''
+
+    def __init__(self,
+                 k1,
+                 k2,
+                 range_period,
+                 resolution = Resolution.DAILY,
+                 bars_to_consolidate = 1):
+        '''Initializes a new instance of the class
+        Args:
+            k1: Coefficient for upper band
+            k2: Coefficient for lower band
+            range_period: Amount of last bars to calculate the range
+            resolution: The resolution of data sent into the EMA indicators
+            bars_to_consolidate: If we want alpha to work on trade bars whose length is different
+                from the standard resolution - 1m 1h etc. - we need to pass this parameters along
+                with proper data resolution'''
+
+        # coefficient that used to determine upper and lower borders of a breakout channel
+        self.k1 = k1
+        self.k2 = k2
+
+        # period the range is calculated over
+        self.range_period = range_period
+
+        # initialize with empty dict.
+        self._symbol_data_by_symbol = dict()
+
+        # time for bars we make the calculations on
+        resolution_in_time_span =  Extensions.to_time_span(resolution)
+        self.consolidator_time_span = Time.multiply(resolution_in_time_span, bars_to_consolidate)
+
+        # in 5 days after emission an insight is to be considered expired
+        self.period = timedelta(5)
+
+    def update(self, algorithm, data):
+        insights = []
+
+        for symbol, symbol_data in self._symbol_data_by_symbol.items():
+            if not symbol_data.is_ready:
+                continue
+
+            holding = algorithm.portfolio[symbol]
+            price = algorithm.securities[symbol].price
+
+            # buying condition
+            # - (1) price is above upper line
+            # - (2) and we are not long. this is a first time we crossed the line lately
+            if price > symbol_data.upper_line and not holding.is_long:
+                insight_close_time_utc = algorithm.utc_time + self.period
+                insights.append(Insight.price(symbol, insight_close_time_utc, InsightDirection.UP))
+
+            # selling condition
+            # - (1) price is lower that lower line
+            # - (2) and we are not short. this is a first time we crossed the line lately
+            if price < symbol_data.lower_line and not holding.is_short:
+                insight_close_time_utc = algorithm.utc_time + self.period
+                insights.append(Insight.price(symbol, insight_close_time_utc, InsightDirection.DOWN))
+
+        return insights
+
+    def on_securities_changed(self, algorithm, changes):
+        # added
+        for symbol in [x.symbol for x in changes.added_securities]:
+            if symbol not in self._symbol_data_by_symbol:
+                # add symbol/symbol_data pair to collection
+                symbol_data = self.SymbolData(symbol, self.k1, self.k2, self.range_period, self.consolidator_time_span)
+                self._symbol_data_by_symbol[symbol] = symbol_data
+                # register consolidator
+                algorithm.subscription_manager.add_consolidator(symbol, symbol_data.get_consolidator())
+
+        # removed
+        for symbol in [x.symbol for x in changes.removed_securities]:
+            symbol_data = self._symbol_data_by_symbol.pop(symbol, None)
+            if symbol_data is None:
+                algorithm.error("Unable to remove data from collection: DualThrustAlphaModel")
+            else:
+                # unsubscribe consolidator from data updates
+                algorithm.subscription_manager.remove_consolidator(symbol, symbol_data.get_consolidator())
+
+
+    class SymbolData:
+        '''Contains data specific to a symbol required by this model'''
+        def __init__(self, symbol, k1, k2, range_period, consolidator_resolution):
+
+            self.symbol = symbol
+            self.range_window = RollingWindow(range_period)
+            self.consolidator = TradeBarConsolidator(consolidator_resolution)
+
+            def on_data_consolidated(sender, consolidated):
+                # add new tradebar to
+                self.range_window.add(consolidated)
+
+                if self.range_window.is_ready:
+                    hh = max([x.high for x in self.range_window])
+                    hc = max([x.close for x in self.range_window])
+                    lc = min([x.close for x in self.range_window])
+                    ll = min([x.low for x in self.range_window])
+
+                    range = max([hh - lc, hc - ll])
+                    self.upper_line = consolidated.close + k1 * range
+                    self.lower_line = consolidated.close - k2 * range
+
+            # event fired at new consolidated trade bar
+            self.consolidator.data_consolidated += on_data_consolidated
+
+        # Returns the interior consolidator
+        def get_consolidator(self):
+            return self.consolidator
+
+        @property
+        def is_ready(self):
+            return self.range_window.is_ready