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 QuantConnect.Data.Consolidators;
+using QuantConnect.Data.Market;
+using System;
+using System.Collections.Generic;
+using QuantConnect.Brokerages;
+using QuantConnect.Securities;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm for fractional forex pair
+    /// </summary>
+    public class FractionalQuantityRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        /// <summary>
+        /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(2015, 11, 12);
+            SetEndDate(2016, 04, 01);
+
+            //Set the cash for the strategy:
+            SetCash(100000);
+            SetBrokerageModel(BrokerageName.GDAX, AccountType.Cash);
+
+            SetTimeZone(NodaTime.DateTimeZone.Utc);
+            var security = AddSecurity(SecurityType.Crypto, "BTCUSD", Resolution.Daily, Market.GDAX, false, 1, true);
+
+            // The default buying power model for the Crypto security type is now CashBuyingPowerModel.
+            // Since this test algorithm uses leverage we need to set a buying power model with margin.
+            security.SetBuyingPowerModel(new SecurityMarginModel(3.3m));
+
+            var con = new TradeBarConsolidator(1);
+            SubscriptionManager.AddConsolidator("BTCUSD", con);
+            con.DataConsolidated += DataConsolidated;
+            SetBenchmark(security.Symbol);
+        }
+
+        private void DataConsolidated(object sender, TradeBar e)
+        {
+            var quantity = Math.Truncate((Portfolio.Cash + Portfolio.TotalFees) / Math.Abs(e.Value + 1));
+            if (!Portfolio.Invested)
+            {
+                Order("BTCUSD", quantity);
+            }
+            else if (Portfolio["BTCUSD"].Quantity == quantity)
+            {
+                Order("BTCUSD", 0.1);
+            }
+            else if (Portfolio["BTCUSD"].Quantity == quantity + 0.1m)
+            {
+                Order("BTCUSD", 0.01);
+            }
+            else if (Portfolio["BTCUSD"].Quantity == quantity + 0.11m)
+            {
+                Order("BTCUSD", -0.02);
+            }
+            else if (Portfolio["BTCUSD"].Quantity == quantity + 0.09m)
+            {
+                //should fail (below minimum order quantity)
+                Order("BTCUSD", 0.00001);
+
+                SetHoldings("BTCUSD", -2.0m);
+                SetHoldings("BTCUSD", 2.0m);
+                Quit();
+            }
+        }
+
+        /// <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; } = true;
+
+        /// <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, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 37;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 10;
+
+        /// <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", "7"},
+            {"Average Win", "6.02%"},
+            {"Average Loss", "-2.40%"},
+            {"Compounding Annual Return", "1497.266%"},
+            {"Drawdown", "5.500%"},
+            {"Expectancy", "1.339"},
+            {"Start Equity", "100000.00"},
+            {"End Equity", "113775.23"},
+            {"Net Profit", "13.775%"},
+            {"Sharpe Ratio", "4.906"},
+            {"Sortino Ratio", "11.482"},
+            {"Probabilistic Sharpe Ratio", "63.289%"},
+            {"Loss Rate", "33%"},
+            {"Win Rate", "67%"},
+            {"Profit-Loss Ratio", "2.51"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0.456"},
+            {"Annual Variance", "0.208"},
+            {"Information Ratio", "4.922"},
+            {"Tracking Error", "0.456"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$2650.41"},
+            {"Estimated Strategy Capacity", "$29000.00"},
+            {"Lowest Capacity Asset", "BTCUSD 2XR"},
+            {"Portfolio Turnover", "46.79%"},
+            {"Drawdown Recovery", "14"},
+            {"OrderListHash", "70610cb67cc63d197e22ca71180b2df2"}
+        };
+    }
+}