Refactor GetFilePath() (#6164)

* Refactor `GetFilePath()`
Add also useful methods to use with this one

* Nit changes

* Requested changes

* Requested changes

* Restore SaveString()

* Nit changes

* Address self review

* Test improvements

* Adjust example KerasNeuralNetworkAlgorithm

* Minor tweak for KerasNeuralNetworkAlgorithm.py

Co-authored-by: Martin-Molinero <martin@quantconnect.com>

Algorithm.CSharp/ObjectStoreExampleAlgorithm.cs

@@ -1,4 +1,4 @@
-/*
+/*
  * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
  * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
  *
@@ -18,6 +18,7 @@ using QuantConnect.Indicators;
 using QuantConnect.Interfaces;
 using QuantConnect.Storage;
 using System;
+using System.Collections.Generic;
 using System.Linq;
 
 namespace QuantConnect.Algorithm.CSharp
@@ -29,7 +30,7 @@ namespace QuantConnect.Algorithm.CSharp
     /// history call. This pattern can be equally applied to a machine learning model being
     /// trained and then saving the model weights in the object store.
     /// </summary>
-    public class ObjectStoreExampleAlgorithm : QCAlgorithm
+    public class ObjectStoreExampleAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
     {
         private const string SPY_Close_ObjectStore_Key = "spy_close";
         private Symbol SPY;
@@ -127,5 +128,64 @@ namespace QuantConnect.Algorithm.CSharp
                 }
             }
         }
+
+        /// <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, Language.Python };
+
+        /// <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", "1"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "271.453%"},
+            {"Drawdown", "2.200%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "1.692%"},
+            {"Sharpe Ratio", "8.888"},
+            {"Probabilistic Sharpe Ratio", "67.609%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "-0.005"},
+            {"Beta", "0.996"},
+            {"Annual Standard Deviation", "0.222"},
+            {"Annual Variance", "0.049"},
+            {"Information Ratio", "-14.565"},
+            {"Tracking Error", "0.001"},
+            {"Treynor Ratio", "1.978"},
+            {"Total Fees", "$3.44"},
+            {"Estimated Strategy Capacity", "$56000000.00"},
+            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
+            {"Fitness Score", "0.248"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "93.728"},
+            {"Portfolio Turnover", "0.248"},
+            {"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", "9e4bfd2eb0b81ee5bc1b197a87ccedbe"}
+        };
     }
 }