/// <summary>
/// Creates and adds chromosomes to population
/// </summary>
/// <param name="size">Number of cromosomes in initial population</param>
/// <param name="population">Population to fill up</param>
private static void AddChromosomes(int size, ref Population population)
{
// checking for evenness
if (size % 2 != 0)
{
throw new ArgumentException("Population size must be an even number.");
}
// create the chromosomes and add to collection
for (var p = 0; p < size; p++)
{
var chromosome = new Chromosome();
var v = new ConfigVars
{
Vars =
{
["bollinger-period"] = RandomNumberBetweenInt(10, 30),
["bollinger-multiplier"] = RandomNumberBetween(1.8, 2.9)
}
};
chromosome.Genes.Add(new Gene(v));
population.Solutions.Add(chromosome);
}
}
protected bool Equals(ConfigVars other)
{
foreach (KeyValuePair<string,object> kvp in vars) {
if (kvp.Value.ToString () != other.vars [kvp.Key].ToString ())
return false;
}
return true;
}
protected bool Equals(ConfigVars other)
{
foreach (KeyValuePair <string, object> kvp in vars)
{
if (kvp.Value.ToString() != other.vars [kvp.Key].ToString())
{
return(false);
}
}
return(true);
}
protected bool Equals(ConfigVars other)
{
foreach (var kvp in Vars)
{
if (kvp.Value.ToString() != other.Vars [kvp.Key].ToString())
{
return(false);
}
}
return(true);
}
static void ga_OnRunComplete(object sender, GaEventArgs e)
{
var fittest = e.Population.GetTop(1)[0];
foreach (var gene in fittest.Genes)
{
ConfigVars v = (ConfigVars)gene.ObjectValue;
foreach (KeyValuePair <string, object> kvp in v.vars)
{
Console.WriteLine("Variable {0}:, value {1}", kvp.Key, kvp.Value.ToString());
}
}
}
public decimal Run(ConfigVars vars)
{
foreach (KeyValuePair <string, object> kvp in vars.vars)
{
Config.Set(kvp.Key, kvp.Value.ToString());
}
LaunchLean();
ConsoleResultHandler resultshandler = (ConsoleResultHandler)_resultshandler;
var sharpe_ratio = 0.0m;
var ratio = resultshandler.FinalStatistics ["Sharpe Ratio"];
Decimal.TryParse(ratio, out sharpe_ratio);
return(sharpe_ratio);
}
public static void Main(string[] args)
{
_ads = SetupAppDomain();
const double crossoverProbability = 0.65;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create the population
//var population = new Population(100, 44, false, false);
var population = new Population();
//create the chromosomes
for (var p = 0; p < 10; p++)
{
var chromosome = new Chromosome();
for (int i = 0; i < 2; i++)
{
ConfigVars v = new ConfigVars();
v.vars ["EMA_VAR1"] = RandomNumberBetweenInt(0, 20);
v.vars ["EMA_VAR2"] = RandomNumberBetweenInt(0, 100);
//v.vars ["LTD3"] = RandomNumberBetweenInt (0, 100);
//v.vars ["LTD4"] = RandomNumberBetweenInt (2, 200);
chromosome.Genes.Add(new Gene(v));
}
chromosome.Genes.ShuffleFast();
population.Solutions.Add(chromosome);
}
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, CalculateFitness);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
// ga.Operators.Add(elite);
// ga.Operators.Add(crossover);
// ga.Operators.Add(mutation);
var cv_operator = new ConfigVarsOperator();
ga.Operators.Add(cv_operator);
//run the GA
ga.Run(Terminate);
}
public decimal Run(ConfigVars vars)
{
foreach (var kvp in vars.Vars)
{
Config.Set(kvp.Key, kvp.Value.ToString());
}
// settings
Config.Set("environment", "backtesting");
Config.Set("algorithm-type-name", "BitfinexSuperTrend");
Config.Set("algorithm-language", "CSharp");
Config.Set("algorithm-location", "Optimization.exe");
Config.Set("data-folder", "C:/Users/stranger/Google Drive/Data/");
// default value set in QuantConnect.Configuration.Config is invalid. specify explicitely.
Config.Set("job-queue-handler", "QuantConnect.Queues.JobQueue");
// log handler
Log.LogHandler = Composer.Instance.GetExportedValueByTypeName <ILogHandler>(Config.Get("log-handler", "CompositeLogHandler"));
// == LeanEngineSystemHandlers ==
LeanEngineSystemHandlers leanEngineSystemHandlers;
try
{
leanEngineSystemHandlers = LeanEngineSystemHandlers.FromConfiguration(Composer.Instance);
}
catch (CompositionException compositionException)
{
Log.Error("Engine.Main(): Failed to load library: " + compositionException);
throw;
}
// can this be omitted?
leanEngineSystemHandlers.Initialize();
string assemblyPath;
var job = leanEngineSystemHandlers.JobQueue.NextJob(out assemblyPath);
if (job == null)
{
throw new Exception("Engine.Main(): Job was null.");
}
// == LeanEngineAlgorithmHandlers ==
LeanEngineAlgorithmHandlers leanEngineAlgorithmHandlers;
try
{
leanEngineAlgorithmHandlers = LeanEngineAlgorithmHandlers.FromConfiguration(Composer.Instance);
}
catch (CompositionException compositionException)
{
Log.Error("Engine.Main(): Failed to load library: " + compositionException);
throw;
}
// == Engine ==
try
{
var liveMode = Config.GetBool("live-mode");
var algorithmManager = new AlgorithmManager(liveMode);
// can this be omitted?
leanEngineSystemHandlers.LeanManager.Initialize(leanEngineSystemHandlers, leanEngineAlgorithmHandlers, job, algorithmManager);
var engine = new Engine(leanEngineSystemHandlers, leanEngineAlgorithmHandlers, liveMode);
engine.Run(job, algorithmManager, assemblyPath);
}
finally
{
// no Acknowledge Job, clean up resources
Log.Trace("Engine.Main(): Packet removed from queue: " + job.AlgorithmId);
leanEngineSystemHandlers.Dispose();
leanEngineAlgorithmHandlers.Dispose();
Log.LogHandler.Dispose();
}
// obtain results
var sharpeRatio = 0.0m;
var resultshandler = leanEngineAlgorithmHandlers.Results as BacktestingResultHandler;
if (resultshandler != null)
{
var ratio = resultshandler.FinalStatistics["Sharpe Ratio"];
Decimal.TryParse(ratio, out sharpeRatio);
}
else
{
Log.Error("Unable to cast: BacktestingResultHandler");
}
return(sharpeRatio);
}
public static void Main(string[] args)
{
_ads = SetupAppDomain ();
const double crossoverProbability = 0.65;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create the population
//var population = new Population(100, 44, false, false);
var population = new Population();
//create the chromosomes
for (var p = 0; p < 10; p++)
{
var chromosome = new Chromosome();
for (int i = 0; i < 2; i++) {
ConfigVars v = new ConfigVars ();
v.vars ["EMA_VAR1"] = RandomNumberBetweenInt (0, 20);
v.vars ["EMA_VAR2"] = RandomNumberBetweenInt (0, 100);
//v.vars ["LTD3"] = RandomNumberBetweenInt (0, 100);
//v.vars ["LTD4"] = RandomNumberBetweenInt (2, 200);
chromosome.Genes.Add (new Gene (v));
}
chromosome.Genes.ShuffleFast();
population.Solutions.Add(chromosome);
}
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, CalculateFitness);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
// ga.Operators.Add(elite);
// ga.Operators.Add(crossover);
// ga.Operators.Add(mutation);
var cv_operator = new ConfigVarsOperator ();
ga.Operators.Add (cv_operator);
//run the GA
ga.Run(Terminate);
}
public decimal Run(ConfigVars vars)
{
foreach(KeyValuePair<string,object> kvp in vars.vars)
Config.Set (kvp.Key, kvp.Value.ToString ());
LaunchLean ();
ConsoleResultHandler resultshandler = (ConsoleResultHandler)_resultshandler;
var sharpe_ratio = 0.0m;
var ratio = resultshandler.FinalStatistics ["Sharpe Ratio"];
Decimal.TryParse(ratio,out sharpe_ratio);
return sharpe_ratio;
}
