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>
+### In this algorithm, we fetch a list of tickers with corresponding dates from a file on Dropbox.
+### We then create a fine fundamental universe which contains those symbols on their respective dates.###
+### </summary>
+### <meta name="tag" content="download" />
+### <meta name="tag" content="universes" />
+### <meta name="tag" content="custom data" />
+class DropboxCoarseFineAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2019, 9, 23) # Set Start Date
+        self.set_end_date(2019, 9, 30) # Set End Date
+        self.set_cash(100000)  # Set Strategy Cash
+        self.add_universe(self.select_coarse, self.select_fine)
+
+        self.universe_data = None
+        self.next_update = datetime(1, 1, 1) # Minimum datetime
+        self.url = "https://www.dropbox.com/s/x2sb9gaiicc6hm3/tickers_with_dates.csv?dl=1"
+
+    def on_end_of_day(self, symbol: Symbol) -> None:
+        self.debug(f"{self.time.date()} {symbol.value} with Market Cap: ${self.securities[symbol].fundamentals.market_cap}")
+
+    def select_coarse(self, coarse):
+        return self.get_symbols()
+
+    def select_fine(self, fine):
+        symbols = self.get_symbols()
+
+        # Return symbols from our list which have a market capitalization of at least 10B
+        return [f.symbol for f in fine if f.market_cap > 1e10 and f.symbol in symbols]
+
+    def get_symbols(self):
+
+        # In live trading update every 12 hours
+        if self.live_mode:
+            if self.time < self.next_update:
+                # Return today's row
+                return self.universe_data[self.time.date()]
+            # When updating set the new reset time.
+            self.next_update = self.time + timedelta(hours=12)
+            self.universe_data = self.parse(self.url)
+
+        # In backtest load once if not set, then just use the dates.
+        if not self.universe_data:
+            self.universe_data = self.parse(self.url)
+
+        # Check if contains the row we need
+        if self.time.date() not in self.universe_data:
+            return Universe.UNCHANGED
+
+        return self.universe_data[self.time.date()]
+
+
+    def parse(self, url):
+        # Download file from url as string
+        file = self.download(url).split("\n")
+
+        # # Remove formatting characters
+        data = [x.replace("\r", "").replace(" ", "") for x in file]
+
+        # # Split data by date and symbol
+        split_data = [x.split(",") for x in data]
+
+        # Dictionary to hold list of active symbols for each date, keyed by date
+        symbols_by_date = {}
+
+        # Parse data into dictionary
+        for arr in split_data:
+            date = datetime.strptime(arr[0], "%Y%m%d").date()
+            symbols = [Symbol.create(ticker, SecurityType.EQUITY, Market.USA) for ticker in arr[1:]]
+            symbols_by_date[date] = symbols
+
+        return symbols_by_date
+
+    def on_securities_changed(self, changes):
+        self.log(f"Added Securities: {[security.symbol.value for security in changes.added_securities]}")