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
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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>
+### Strategy example using a portfolio of ETF Global Rotation
+### </summary>
+### <meta name="tag" content="strategy example" />
+### <meta name="tag" content="momentum" />
+### <meta name="tag" content="using data" />
+
+### <summary>
+### Strategy example using a portfolio of ETF Global Rotation
+### </summary>
+### <meta name="tag" content="strategy example" />
+### <meta name="tag" content="momentum" />
+### <meta name="tag" content="using data" />
+class ETFGlobalRotationAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_cash(25000)
+        self.set_start_date(2007,1,1)
+        self.last_rotation_time = datetime.min
+        self.rotation_interval = timedelta(days=30)
+        self.first = True
+
+        # these are the growth symbols we'll rotate through
+        growth_symbols =["MDY", # US S&P mid cap 400
+                        "IEV", # i_shares S&P europe 350
+                        "EEM", # i_shared MSCI emerging markets
+                        "ILF", # i_shares S&P latin america
+                        "EPP" ] # i_shared MSCI Pacific ex-Japan
+
+        # these are the safety symbols we go to when things are looking bad for growth
+        safety_symbols = ["EDV", "SHY"] # "EDV" Vangaurd TSY 25yr, "SHY" Barclays Low Duration TSY
+        # we'll hold some computed data in these guys
+        self.symbol_data = []
+        for symbol in list(set(growth_symbols) | set(safety_symbols)):
+            self.add_security(SecurityType.EQUITY, symbol, Resolution.MINUTE)
+            self.one_month_performance = self.mom(symbol, 30, Resolution.DAILY)
+            self.three_month_performance = self.mom(symbol, 90, Resolution.DAILY)
+            self.symbol_data.append((symbol, self.one_month_performance, self.three_month_performance))
+
+    def on_data(self, data):
+        # the first time we come through here we'll need to do some things such as allocation
+        # and initializing our symbol data
+
+        if self.first:
+            self.first = False
+            self.last_rotation_time = self.time
+            return
+        delta = self.time - self.last_rotation_time
+        if delta > self.rotation_interval:
+            self.last_rotation_time = self.time
+
+            ordered_obj_scores = sorted(self.symbol_data, key=lambda x: Score(x[1].current.value,x[2].current.value).objective_score(), reverse=True)
+            for x in ordered_obj_scores:
+                self.log(">>SCORE>>" + x[0] + ">>" + str(Score(x[1].current.value,x[2].current.value).objective_score()))
+            # pick which one is best from growth and safety symbols
+            best_growth = ordered_obj_scores[0]
+            if Score(best_growth[1].current.value,best_growth[2].current.value).objective_score() > 0:
+                if (self.portfolio[best_growth[0]].quantity == 0):
+                    self.log("PREBUY>>LIQUIDATE>>")
+                    self.liquidate()
+                self.log(">>BUY>>" + str(best_growth[0]) + "@" + str(100 * best_growth[1].current.value))
+                qty = self.portfolio.margin_remaining / self.securities[best_growth[0]].close
+                self.market_order(best_growth[0], int(qty))
+            else:
+            # if no one has a good objective score then let's hold cash this month to be safe
+                self.log(">>LIQUIDATE>>CASH")
+                self.liquidate()
+
+class Score(object):
+
+    def __init__(self,one_month_performance_value,three_month_performance_value):
+        self.one_month_performance = one_month_performance_value
+        self.three_month_performance = three_month_performance_value
+
+    def objective_score(self):
+        weight1 = 100
+        weight2 = 75
+        return (weight1 * self.one_month_performance + weight2 * self.three_month_performance) / (weight1 + weight2)