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>
+### This algorithm shows how to grab symbols from an external api each day
+### and load data using the universe selection feature. In this example we
+### define a custom data type for the NYSE top gainers and then short the
+### top 2 gainers each day
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="universes" />
+### <meta name="tag" content="custom universes" />
+class CustomDataUniverseAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+
+        # Data ADDED via universe selection is added with Daily resolution.
+        self.universe_settings.resolution = Resolution.DAILY
+
+        self.set_start_date(2015,1,5)
+        self.set_end_date(2015,7,1)
+        self.set_cash(100000)
+
+        self.add_equity("SPY", Resolution.DAILY)
+        self.set_benchmark("SPY")
+
+        # add a custom universe data source (defaults to usa-equity)
+        self.add_universe(NyseTopGainers, "universe-nyse-top-gainers", Resolution.DAILY, self.nyse_top_gainers)
+    
+    def nyse_top_gainers(self, data):
+        return [ x.symbol for x in data if x["TopGainersRank"] <= 2 ]
+
+
+    def on_data(self, slice):
+        pass
+    
+    def on_securities_changed(self, changes):
+        self._changes = changes
+
+        for security in changes.removed_securities:
+            #  liquidate securities that have been removed
+            if security.invested:
+                self.liquidate(security.symbol)
+                self.log("Exit {0} at {1}".format(security.symbol, security.close))
+
+        for security in changes.added_securities:
+            # enter short positions on new securities
+            if not security.invested and security.close != 0:
+                qty = self.calculate_order_quantity(security.symbol, -0.25)
+                self.market_on_open_order(security.symbol, qty)
+                self.log("Enter {0} at {1}".format(security.symbol, security.close))
+
+        
+class NyseTopGainers(PythonData):
+    def __init__(self):
+        self.count = 0
+        self.last_date = datetime.min
+
+    def get_source(self, config, date, is_live_mode):
+        url = "http://www.wsj.com/mdc/public/page/2_3021-gainnyse-gainer.html" if is_live_mode else \
+            "https://www.dropbox.com/s/vrn3p38qberw3df/nyse-gainers.csv?dl=1"
+
+        return SubscriptionDataSource(url, SubscriptionTransportMedium.REMOTE_FILE)
+    
+    def reader(self, config, line, date, is_live_mode):
+        
+        if not is_live_mode:
+            # backtest gets data from csv file in dropbox
+            if not (line.strip() and line[0].isdigit()): return None
+            csv = line.split(',')
+            nyse = NyseTopGainers()
+            nyse.time = datetime.strptime(csv[0], "%Y%m%d")
+            nyse.end_time = nyse.time + timedelta(1)
+            nyse.symbol = Symbol.create(csv[1], SecurityType.EQUITY, Market.USA)
+            nyse["TopGainersRank"] = int(csv[2])
+            return nyse
+
+        if self.last_date != date:
+            # reset our counter for the new day
+            self.last_date = date
+            self.count = 0
+        
+        # parse the html into a symbol
+        if not line.startswith('<a href=\"/public/quotes/main.html?symbol='):
+            # we're only looking for lines that contain the symbols
+            return None
+        
+        last_close_paren = line.rfind(')')
+        last_open_paren = line.rfind('(')
+        if last_open_paren == -1 or last_close_paren == -1:
+            return None
+
+        symbol_string = line[last_open_paren + 1:last_close_paren]
+        nyse = NyseTopGainers()
+        nyse.time = date
+        nyse.end_time = nyse.time + timedelta(1)
+        nyse.symbol = Symbol.create(symbol_string, SecurityType.EQUITY, Market.USA)
+        nyse["TopGainersRank"] = self.count
+        self.count = self.count + 1
+        return nyse