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 *
+import talib
+
+class CalibratedResistanceAtmosphericScrubbers(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2020, 1, 2)
+        self.set_end_date(2020, 1, 6) 
+        self.set_cash(100000) 
+        self.add_equity("SPY", Resolution.HOUR)
+        
+        self.rolling_window = pd.DataFrame()
+        self.dema_period = 3
+        self.sma_period = 3
+        self.wma_period = 3
+        self.window_size = self.dema_period * 2
+        self.set_warm_up(self.window_size)
+        
+    def on_data(self, data):
+        if "SPY" not in data.bars:
+            return
+        
+        close = data["SPY"].close
+        
+        if self.is_warming_up:
+            # Add latest close to rolling window
+            row = pd.DataFrame({"close": [close]}, index=[data.time])
+            self.rolling_window = pd.concat([self.rolling_window, row]).iloc[-self.window_size:]
+            
+            # If we have enough closing data to start calculating indicators...
+            if self.rolling_window.shape[0] == self.window_size:
+                closes = self.rolling_window['close'].values
+                
+                # Add indicator columns to DataFrame
+                self.rolling_window['DEMA'] = talib.DEMA(closes, self.dema_period)
+                self.rolling_window['EMA'] = talib.EMA(closes, self.sma_period)
+                self.rolling_window['WMA'] = talib.WMA(closes, self.wma_period)
+            return
+        
+        closes = np.append(self.rolling_window['close'].values, close)[-self.window_size:]
+        
+        # Update talib indicators time series with the latest close
+        row = pd.DataFrame({"close": close,
+                            "DEMA" : talib.DEMA(closes, self.dema_period)[-1],
+                            "EMA"  : talib.EMA(closes, self.sma_period)[-1],
+                            "WMA"  : talib.WMA(closes, self.wma_period)[-1]},
+                            index=[data.time])
+        
+        self.rolling_window = pd.concat([self.rolling_window, row]).iloc[-self.window_size:]
+
+        
+    def on_end_of_algorithm(self):
+        self.log(f"\nRolling Window:\n{self.rolling_window.to_string()}\n")
+        self.log(f"\nLatest Values:\n{self.rolling_window.iloc[-1].to_string()}\n")