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
@@ -0,0 +1,133 @@
+/*
+ * 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 System.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Scalps TRYJPY using an EMA cross strategy at minute resolution.
+    /// This tests FOREX strategies that trade at a higher frequency, which
+    /// should have a reduced capacity estimate as a result. This tests that
+    /// currency conversions are applied properly to the capacity estimate calculation.
+    /// </summary>
+    public class IntradayMinuteScalpingTRYJPY : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _tryjpy;
+        private ExponentialMovingAverage _fast;
+        private ExponentialMovingAverage _slow;
+
+
+        public override void Initialize()
+        {
+            SetStartDate(2021, 1, 1);
+            SetEndDate(2021, 1, 30);
+            SetCash(100000);
+            SetWarmup(100);
+
+            _tryjpy = AddForex("TRYJPY", Resolution.Minute, Market.Oanda).Symbol;
+            _fast = EMA(_tryjpy, 20);
+            _slow = EMA(_tryjpy, 40);
+        }
+
+        public override void OnData(Slice slice)
+        {
+            if (Portfolio[_tryjpy].Quantity <= 0 && _fast > _slow)
+            {
+                SetHoldings(_tryjpy, 1);
+            }
+            else if (Portfolio[_tryjpy].Quantity >= 0 && _fast < _slow)
+            {
+                SetHoldings(_tryjpy, -1);
+            }
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = false;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public List<Language> Languages { get; } = new() { Language.CSharp };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 0;
+
+        /// </summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 0;
+
+        /// <summary>
+        /// Final status of the algorithm
+        /// </summary>
+        public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Orders", "603"},
+            {"Average Win", "0.20%"},
+            {"Average Loss", "-0.26%"},
+            {"Compounding Annual Return", "-100.000%"},
+            {"Drawdown", "73.200%"},
+            {"Expectancy", "-0.849"},
+            {"Net Profit", "-73.118%"},
+            {"Sharpe Ratio", "-2.046"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "91%"},
+            {"Win Rate", "9%"},
+            {"Profit-Loss Ratio", "0.75"},
+            {"Alpha", "-0.95"},
+            {"Beta", "0.541"},
+            {"Annual Standard Deviation", "0.489"},
+            {"Annual Variance", "0.239"},
+            {"Information Ratio", "-1.863"},
+            {"Tracking Error", "0.487"},
+            {"Treynor Ratio", "-1.849"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$4400000.00"},
+            {"Fitness Score", "0.259"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-2.135"},
+            {"Return Over Maximum Drawdown", "-1.389"},
+            {"Portfolio Turnover", "49.501"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "4eb4d703a9f200b6bb3d8b0ebbc9db7f"}
+        };
+    }
+}