Fix Train method during warmup (#6416)

- During warmup period the algorithms initial time might not be a rounded date
  value, so it's important to take into account hours/minutes. Adding
  regression algorithm reproducing and asserting issue

Algorithm.CSharp/TrainingExampleAlgorithm.cs

@@ -1,4 +1,4 @@
-/*
+/*
  * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
  * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
  *
@@ -14,6 +14,8 @@
 */
 
 using System;
+using QuantConnect.Interfaces;
+using System.Collections.Generic;
 
 namespace QuantConnect.Algorithm.CSharp
 {
@@ -22,8 +24,9 @@ namespace QuantConnect.Algorithm.CSharp
     /// <meta name="tag" content="using quantconnect" />
     /// <meta name="tag" content="training" />
     /// </summary>
-    public class TrainingExampleAlgorithm : QCAlgorithm
+    public class TrainingExampleAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
     {
+        private Queue<DateTime> _trainTimes = new();
         public override void Initialize()
         {
             SetStartDate(2013, 10, 7);
@@ -46,6 +49,94 @@ namespace QuantConnect.Algorithm.CSharp
 
             // ML code:
 
+
+            // let's keep this to assert in the end of the algorithm
+            _trainTimes.Enqueue(Time);
         }
+
+        /// <summary>
+        /// Let's assert the behavior of our traning schedule
+        /// </summary>
+        public override void OnEndOfAlgorithm()
+        {
+            if (_trainTimes.Count != 2)
+            {
+                throw new Exception($"Unexpected train count: {_trainTimes.Count}");
+            }
+            if (_trainTimes.Dequeue() != StartDate
+                || _trainTimes.Dequeue() != new DateTime(2013, 10, 13, 8, 0, 0))
+            {
+                throw new Exception($"Unexpected train times!");
+            }
+        }
+
+        /// <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 Language[] Languages { get; } = { Language.CSharp };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 56;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 0;
+
+        /// <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 Trades", "0"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0%"},
+            {"Sharpe Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-7.357"},
+            {"Tracking Error", "0.161"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Lowest Capacity Asset", ""},
+            {"Fitness Score", "0"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "79228162514264337593543950335"},
+            {"Portfolio Turnover", "0"},
+            {"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", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
     }
-}
+}