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.
+*/
+
+using Accord.MachineLearning.VectorMachines.Learning;
+using QuantConnect.Indicators;
+using System;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Machine Learning example using Accord VectorMachines Learning
+    /// In this example, the algorithm forecasts the direction based on the last 5 days of rate of return
+    /// </summary>
+    public class AccordVectorMachinesAlgorithm : QCAlgorithm
+    {
+        // Define the size of the data used to train the model
+        // It will use _lookback sets with _inputSize members
+        // Those members are rate of return
+        private const int _lookback = 30;
+        private const int _inputSize = 5;
+        private RollingWindow<double> _window = new RollingWindow<double>(_inputSize * _lookback + 2);
+
+        public override void Initialize()
+        {
+            SetStartDate(2013, 10, 07);
+            SetEndDate(2013, 10, 11);
+            SetCash(100000);
+
+            var symbol = AddEquity("SPY").Symbol;
+
+            ROC(symbol, 1, Resolution.Daily).Updated += (s, e) => _window.Add((double)e.Value);
+
+            Schedule.On(DateRules.Every(DayOfWeek.Monday),
+                TimeRules.AfterMarketOpen(symbol, 10),
+                TrainAndTrade);
+
+            SetWarmUp(_window.Size, Resolution.Daily);
+        }
+
+        private void TrainAndTrade()
+        {
+            if (!_window.IsReady) return;
+
+            // Convert the rolling window of rate of change into the Learn method
+            var returns = new double[_inputSize];
+            var targets = new double[_lookback];
+            var inputs = new double[_lookback][];
+
+            // Use the sign of the returns to predict the direction
+            for (var i = 0; i < _lookback; i++)
+            {
+                for (var j = 0; j < _inputSize; j++)
+                {
+                    returns[j] = Math.Sign(_window[i + j + 1]);
+                }
+
+                targets[i] = Math.Sign(_window[i]);
+                inputs[i] = returns;
+            }
+
+            // Train SupportVectorMachine using SetHoldings("SPY", percentage);
+            var teacher = new LinearCoordinateDescent();
+            teacher.Learn(inputs, targets);
+
+            var svm = teacher.Model;
+
+            // Compute the value for the last rate of change
+            var last = (double) Math.Sign(_window[0]);
+            var value = svm.Compute(new[] {last});
+            if (value.IsNaNOrZero()) return;
+
+            SetHoldings("SPY", Math.Sign(value));
+        }
+    }
+}