public void InitializeValuesFrom(ParameterDefinitions other)
{
foreach (ParameterDefinition pdNew in this)
{
ParameterDefinition pdOld = other.Find(pdNew);
if (pdOld == null)
{
continue;
}
SetParameterDefinition spdOld = pdOld as SetParameterDefinition;
SetParameterDefinition spdNew = pdNew as SetParameterDefinition;
if (spdOld == null || spdNew == null)
{
continue;
}
spdNew.InitializeFrom(spdOld);
}
}
public override void Optimize()
{
bool fFirstRun = (_form == null);
_rgParameterDefinitions = new ParameterDefinitions(this.Strategy, fFirstRun);
if (fFirstRun)
{
_form = new StochasticsOptionsDialog();
}
_form.InitializeParameters(_rgParameterDefinitions);
DateTime dateStartLast = _dateStartLast;
_dateStartLast = Strategy.BackTestFrom;
if (!_form.WalkForward || Strategy.BackTestFrom <= dateStartLast)
{
if (_form.ShowDialog(Form.ActiveForm) != DialogResult.OK)
{
return;
}
}
#if !NT7
_nPopulationSize = _form.PopulationSize;
_cMaxGenerations = _form.MaxGenerations;
_fitnessGainMax = _form.FitnessGain;
_nPopulationSize = 10; //_form.PercentAnnealing;
_nMinimumPerformce = _form.MinimumPerformance;
#endif
int cParams = _rgParameterDefinitions.Count;
_rgParameterRunCounts = new int [cParams] [];
_rgParameterRunTotals = new double [cParams] [];
_rgParameterRunAverage = new double [cParams] [];
decimal cCombinationsActual = _rgParameterDefinitions.CountCombinations;
long cCombinations = (long)Math.Min(cCombinationsActual, (decimal)long.MaxValue);
for (int ipd = 0; ipd < _rgParameterDefinitions.Count; ipd++)
{
ParameterDefinition pd = _rgParameterDefinitions [ipd];
int cValues = pd.CountValues;
_rgParameterRunCounts [ipd] = new int [cValues];
_rgParameterRunTotals [ipd] = new double [cValues];
_rgParameterRunAverage [ipd] = new double [cValues];
}
Dictionary <ParameterSet, bool> setChildren = new Dictionary <ParameterSet, bool> ();
List <ParameterSet> rgChildren = new List <ParameterSet> ();
int fitnessGainMin = 1;
int cAnnealing = _nPopulationSize * _percentAnnealing / 100;
// create an initial generation of aliens
int cIterations = 0;
while (rgChildren.Count < _nPopulationSize)
{
ParameterSet child = CreateAlien();
if (setChildren.ContainsKey(child))
{
continue;
}
setChildren.Add(child, true);
rgChildren.Add(child);
}
DateTime timeStart = DateTime.Now;
ParameterSet bestChild = null;
int iGeneration = 0;
try
{
Strategy.Print(
string.Format(
"{0}: PH Stochastic Optimizer Start @ {1}, combinations: {2:N0}",
Strategy.Instrument.FullName,
timeStart,
cCombinationsActual
)
);
double diffMax = 0;
double diffCurr = 0;
int fitnessGain = fitnessGainMin;
ParameterSet bestPrev = null;
for (iGeneration = 0; iGeneration < _cMaxGenerations; iGeneration++)
{
// score the new generation
ScoreGeneration(rgChildren);
rgChildren.Sort();
if (bestChild == null)
{
bestChild = rgChildren [0];
}
else if (bestChild.Performance < rgChildren [0].Performance)
{
bestChild = rgChildren [0];
}
if (bestPrev != null)
{
if (double.IsNaN(bestPrev.Performance) || bestChild.Performance > bestPrev.Performance)
{
if (fitnessGain < FitnessGainMax)
{
fitnessGain++;
}
}
else if (fitnessGain > fitnessGainMin)
{
fitnessGain--;
}
}
bestPrev = bestChild;
cIterations += rgChildren.Count;
TimeSpan diff = DateTime.Now - timeStart;
double iterPerSec = cIterations / diff.TotalSeconds;
Strategy.Print(string.Format(
"{0}: gen {1}/{2}, {3:N1} ips, max PERF: {4:N2}, gain={5}\t{6}",
Strategy.Instrument.FullName,
iGeneration + 1,
_cMaxGenerations,
iterPerSec,
bestChild.Performance,
fitnessGain,
bestChild == null ? "" : _rgParameterDefinitions.ToString(bestChild)));
if (bestChild != null && bestChild.Performance > this._nMinimumPerformce)
{
Strategy.Print(string.Format("{0}: Minimum performance reached.", Strategy.Instrument.FullName));
return;
}
if (iGeneration == _cMaxGenerations - 1)
{
break;
}
// initialize a new generation
rgChildren.Clear();
int cCollisions = _nPopulationSize;
while (cCollisions > 0 && rgChildren.Count < _nPopulationSize)
{
int iParam = _rand.Next(cParams);
int cValues = _rgParameterDefinitions [iParam].CountValues;
int [] rgParams = new int [cParams];
for (int iParamFuzz = 0; iParamFuzz < cParams; iParamFuzz++)
{
rgParams [iParamFuzz] = bestChild.GetParam(iParamFuzz);
}
rgParams [iParam] = MutateParam(fitnessGain, cValues, rgParams [iParam]);
ParameterSet child = new ParameterSet(rgParams);
if (setChildren.ContainsKey(child))
{
cCollisions--;
}
else
{
setChildren.Add(child, true);
rgChildren.Add(child);
}
}
double loopFitnessGain = fitnessGain;
if (rgChildren.Count < _nPopulationSize)
{
// pre-calculate the parameter value weights
double [] [] rgrgWeights = new double [cParams] [];
double [] rgTotalWeights = new double [cParams];
for (int iParam = 0; iParam < cParams; iParam++)
{
int cValues = _rgParameterDefinitions [iParam].CountValues;
rgrgWeights [iParam] = new double [cValues];
double [] rgAverages = _rgParameterRunAverage [iParam];
// get the range of PERF values
double minPerformance = double.PositiveInfinity;
double maxPerformance = double.NegativeInfinity;
for (int i = 0; i < cValues; i++)
{
double performance = rgAverages [i];
if (minPerformance > performance)
{
minPerformance = performance;
}
if (maxPerformance < performance)
{
maxPerformance = performance;
}
}
// weight the procreation probabilities
double totalWeight = 0;
//if (maxPerformance != minPerformance)
{
for (int iValue = 0; iValue < cValues; iValue++)
{
double average = rgAverages [iValue];
if (double.IsNaN(average) || double.IsInfinity(average))
{
average = maxPerformance;
}
double weight = Math.Pow((average - minPerformance) / (maxPerformance - minPerformance), loopFitnessGain);
if (double.IsNaN(weight))
{
weight = 0.0;
}
weight += 0.001;
rgrgWeights [iParam] [iValue] = weight;
totalWeight += weight;
}
}
rgTotalWeights [iParam] = totalWeight;
}
cCollisions = _nPopulationSize;
while (cCollisions > 0 && rgChildren.Count < _nPopulationSize && setChildren.Count < cCombinations)
{
// create a new set of genes
int [] rgParams = new int [cParams];
for (int iParam = 0; iParam < cParams; iParam++)
{
int cValues = _rgParameterDefinitions [iParam].CountValues;
double [] rgWeights = rgrgWeights [iParam];
double weightRandom = _rand.NextDouble() * rgTotalWeights [iParam];
int iValueRandom = 0;
while (iValueRandom < cValues && weightRandom > rgWeights [iValueRandom])
{
weightRandom -= rgWeights [iValueRandom];
iValueRandom++;
}
rgParams [iParam] = iValueRandom;
}
// create the new child, and make sure it's unique
ParameterSet child = new ParameterSet(rgParams);
if (setChildren.ContainsKey(child))
{
if (loopFitnessGain > 1)
{
loopFitnessGain--;
}
cCollisions--;
}
else
{
setChildren.Add(child, true);
rgChildren.Add(child);
}
}
}
}
}
catch (AbortOptimizationException)
{
Strategy.Print("Optimization Aborted...");
}
catch (Exception e)
{
System.Diagnostics.Debug.WriteLine(e);
Strategy.Print(e.ToString());
//throw;
}
finally
{
#if NT7
WaitForIterationsCompleted(true);
#endif
DateTime timeEnd = DateTime.Now;
TimeSpan tsDuration = timeEnd - timeStart;
if (iGeneration > 0)
{
Strategy.Print(string.Format(
"{0}: PH Optimizer End @ {1}, duration: {2} ({3} per iteration)",
Strategy.Instrument.FullName,
timeEnd,
tsDuration,
TimeSpan.FromTicks(tsDuration.Ticks / iGeneration)
)
);
}
if (bestChild != null)
{
Dump(bestChild);
}
}
}
internal void InitializeParameters(ParameterDefinitions rgParameterDefinitions)
{
if (_rgParameterDefinitions != null)
{
rgParameterDefinitions.InitializeValuesFrom(_rgParameterDefinitions);
}
tableLayoutPanel1.Controls.Clear();
tableLayoutPanel1.RowCount = 1;
_rgParameterDefinitions = rgParameterDefinitions;
bool fShowColumns = false;
int iRow = 0;
foreach (ParameterDefinition pd in rgParameterDefinitions)
{
SetParameterDefinition ed = pd as SetParameterDefinition;
if (ed != null)
{
fShowColumns = true;
CheckBox cbTitle = new CheckBox();
cbTitle.Text = ed.Parameter.Name;
cbTitle.AutoSize = true;
cbTitle.Anchor = AnchorStyles.Top | AnchorStyles.Left | AnchorStyles.Right;
int iRowStart = iRow;
List <CheckBox> rgCheckboxes = new List <CheckBox> ();
bool fUpdating = false;
Action <bool> initTitleCheckbox = delegate(bool f)
{
bool fAllChecked = true;
bool fAllUnchecked = true;
foreach (CheckBox cb in rgCheckboxes)
{
if (cb.Checked)
{
fAllUnchecked = false;
}
else
{
fAllChecked = false;
}
}
if (!fAllChecked && !fAllUnchecked)
{
cbTitle.CheckState = CheckState.Indeterminate;
}
else
{
cbTitle.CheckState = fAllChecked ? CheckState.Checked : CheckState.Unchecked;
}
};
foreach (SetParameterDefinition.ValueInfo pvdEnum in ed.GetPossibleValues())
{
SetParameterDefinition.ValueInfo pvd = pvdEnum; // extract for closure
CheckBox cbValue = new CheckBox();
cbValue.Text = pvd.Name;
cbValue.AutoSize = true;
cbValue.Anchor = AnchorStyles.Top | AnchorStyles.Left | AnchorStyles.Right;
cbValue.Margin = new Padding(0);
cbValue.Checked = pvd.IsActive;
cbValue.CheckedChanged += delegate(object sender, EventArgs args)
{
pvd.Setter(cbValue.Checked);
if (fUpdating)
{
return;
}
try
{
fUpdating = true;
initTitleCheckbox(true);
}
finally
{
fUpdating = false;
}
};
rgCheckboxes.Add(cbValue);
while (iRow >= tableLayoutPanel1.RowCount)
{
tableLayoutPanel1.RowCount++;
}
tableLayoutPanel1.RowStyles.Add(new RowStyle(SizeType.AutoSize));
tableLayoutPanel1.Controls.Add(cbValue, 1, iRow);
iRow++;
}
tableLayoutPanel1.Controls.Add(cbTitle, 0, iRowStart);
tableLayoutPanel1.SetRowSpan(cbTitle, tableLayoutPanel1.RowCount - iRowStart - 1);
initTitleCheckbox(true);
cbTitle.CheckedChanged += delegate(object sender, EventArgs args)
{
if (fUpdating)
{
return;
}
try
{
fUpdating = true;
foreach (CheckBox cb in rgCheckboxes)
{
cb.Checked = cbTitle.Checked;
}
}
finally
{
fUpdating = false;
}
};
}
}
if (fShowColumns != panel1.Enabled)
{
panel1.Visible = panel1.Enabled = fShowColumns;
if (fShowColumns)
{
this.Width += panel1.Width;
}
else
{
this.Width -= panel1.Width;
}
}
}
public override void Optimize()
{
DNA dnaBestEver = null;
try
{
// initialize the parameter definition
bool fFirstRun = (_form == null);
_rgParameterDefinitions = new ParameterDefinitions(this.Strategy, fFirstRun);
if (fFirstRun)
{
_form = new GeneticOptionsDialog();
}
_form.InitializeParameters(_rgParameterDefinitions);
DateTime dateStartLast = _dateStartLast;
_dateStartLast = Strategy.BackTestFrom;
if (!_form.WalkForward || Strategy.BackTestFrom <= dateStartLast)
{
if (_form.ShowDialog(Form.ActiveForm) != DialogResult.OK)
{
#if NT7
WaitForIterationsCompleted(true);
#endif
return;
}
}
#if !NT7
_nPopulationSize = _form.PopulationSize;
_cMaxGenerations = _form.MaxGenerations;
_mutationRate = _form.MutationRate;
_mutationStrength = _form.MutationStrength;
_fitnessGain = _form.FitnessGain;
_percentAliens = _form.Aliens;
_stabilitySize = _form.StabilitySize;
_resetSize = _form.ResetSize;
_nMinimumPerformce = _form.MinimumPerformance;
#endif
int cAliens = (int)Math.Ceiling(_nPopulationSize * _percentAliens);
int nStabilitySize = (int)Math.Ceiling(_nPopulationSize * _stabilitySize);
int nResetSize = _nPopulationSize - (int)Math.Ceiling(_nPopulationSize * _resetSize);
DateTime timeStart = DateTime.Now;
int cParams = _rgParameterDefinitions.Count;
decimal cCombinationsActual = _rgParameterDefinitions.CountCombinations;
long cCombinations = (long)Math.Min(cCombinationsActual, (decimal)long.MaxValue);
Strategy.Print(
string.Format(
"{0}: PH Genetic Optimizer Start @ {1}, combinations: {2:N0}",
Strategy.Instrument.FullName,
timeStart,
cCombinationsActual
)
);
List <DNA> rgChildren = new List <DNA> ();
Dictionary <DNA, bool> setChildren = new Dictionary <DNA, bool> ();
// create an initial generation of aliens
int cIterations = 0;
double stabilityScorePrev = double.NaN;
List <DNA> rgParents = new List <DNA> ();
int iGeneration;
for (iGeneration = 1; iGeneration <= _cMaxGenerations; iGeneration++)
{
// fill the rest of this generation with aliens
while (rgChildren.Count < _nPopulationSize && setChildren.Count < cCombinations)
{
DNA child = CreateAlien(0);
if (setChildren.ContainsKey(child))
{
continue;
}
setChildren.Add(child, true);
rgChildren.Add(child);
}
DNA dnaBestSinceReset = null;
try
{
// score the new generation
ScoreGeneration(rgChildren);
//if (rgParents.Count > nPopulationSize)
// rgParents.RemoveRange (nPopulationSize, rgParents.Count - nPopulationSize);
rgParents.AddRange(rgChildren);
rgParents.Sort();
dnaBestSinceReset = rgParents [0];
}
finally
{
if (rgParents.Count > 0 &&
dnaBestSinceReset != null &&
(dnaBestEver == null || dnaBestEver.Performance < dnaBestSinceReset.Performance))
{
dnaBestEver = dnaBestSinceReset;
}
}
double stabilityScore = 0.0;
int cStabilityIndividuals = (int)Math.Min(rgParents.Count, nStabilitySize);
if (cStabilityIndividuals < 1)
{
cStabilityIndividuals = 1;
}
for (int i = 0; i < cStabilityIndividuals; i++)
{
double scoreParent = rgParents [i].Performance;
if (scoreParent > double.NegativeInfinity)
{
stabilityScore += rgParents [i].Performance;
}
}
cIterations += rgChildren.Count;
TimeSpan diff = DateTime.Now - timeStart;
double iterPerSec = cIterations / diff.TotalSeconds;
Strategy.Print(string.Format(
"{0}: gen {1}/{2}, {3:N1} ips, max PERF: {4:N2}, stability PERF: {5:N2}\t{6}",
Strategy.Instrument.FullName,
iGeneration,
_cMaxGenerations,
iterPerSec,
dnaBestSinceReset.Performance,
stabilityScore / cStabilityIndividuals,
rgParents.Count == 0 ? "" : _rgParameterDefinitions.ToString(dnaBestSinceReset)));
if (dnaBestEver != null)
{
Strategy.Print(dnaBestEver.Performance + ", " + _nMinimumPerformce);
}
if (dnaBestEver != null && dnaBestEver.Performance > _nMinimumPerformce)
{
Strategy.Print(string.Format("{0}: Minimum performance reached.", Strategy.Instrument.FullName));
return;
}
if (iGeneration == _cMaxGenerations)
{
break;
}
if (setChildren.Count >= cCombinations)
{
break;
}
if (stabilityScore == stabilityScorePrev)
{
//rgParents.RemoveRange (1, rgParents.Count - 1);
rgParents.Clear();
rgChildren.Clear();
Strategy.Print(string.Format(
"{0}: reset",
Strategy.Instrument.FullName
)
);
//stabilityScore = double.NaN;
continue;
}
// get the range of PERF values
double minPerformance = double.MaxValue;
double maxPerformance = double.MinValue;
for (int i = 0; i < rgParents.Count; i++)
{
double performance = rgParents [i].Performance;
if (performance == double.NegativeInfinity)
{
continue;
}
if (minPerformance > performance)
{
minPerformance = performance;
}
if (maxPerformance < performance)
{
maxPerformance = performance;
}
}
// weight the procreation probabilities
if (maxPerformance != minPerformance)
{
for (int i = 0; i < rgParents.Count; i++)
{
DNA parent = rgParents [i];
double performance = parent.Performance;
if (performance == double.NegativeInfinity)
{
parent.Weight = 0;
}
else
{
double weight = (parent.Performance - minPerformance) / (maxPerformance - minPerformance);
#if true
parent.Weight = Math.Pow(weight, _fitnessGain) + 0.001;
#else
parent.Weight = (Math.Pow(weight, fitnessGain / (iGeneration - parent.Generation)) + 0.001);
#endif
}
}
}
double totalWeight = 0;
for (int i = 0; i < rgParents.Count; i++)
{
totalWeight += rgParents [i].Weight;
}
// initialize a new generation
rgChildren.Clear();
// a certain percentage of the new population are aliens:
int cAliensThisGeneration;
if (stabilityScore == stabilityScorePrev)
{
cAliensThisGeneration = nResetSize;
}
else
{
cAliensThisGeneration = cAliens;
}
while (rgChildren.Count < cAliensThisGeneration && setChildren.Count < cCombinations)
{
DNA child = CreateAlien(iGeneration);
if (setChildren.ContainsKey(child))
{
continue;
}
setChildren.Add(child, true);
rgChildren.Add(child);
}
// fuzz each param individually
for (int iParam = 0; iParam < cParams; iParam++)
{
int cValues = _rgParameterDefinitions [iParam].CountValues;
for (int iFuzz = 0; iFuzz < 5; iFuzz++)
{
int [] rgParams = new int [cParams];
for (int iParamFuzz = 0; iParamFuzz < cParams; iParamFuzz++)
{
rgParams [iParamFuzz] = dnaBestSinceReset.GetParam(iParamFuzz);
}
rgParams [iParam] = MutateParam(_mutationStrength, cValues, rgParams [iParam]);
DNA child = new DNA(rgParams, dnaBestSinceReset.Generation + 1);
if (!setChildren.ContainsKey(child))
{
setChildren.Add(child, true);
rgChildren.Add(child);
}
}
}
while (rgChildren.Count < _nPopulationSize - cAliensThisGeneration && setChildren.Count < cCombinations)
{
if (UserAbort)
{
throw new AbortOptimizationException();
}
// find parents rendomly according to weighted fitness
double weightA = _rand.NextDouble() * totalWeight;
int iParentA = 0;
while (iParentA < rgParents.Count && weightA > rgParents [iParentA].Weight)
{
weightA -= rgParents [iParentA].Weight;
iParentA++;
}
double weightB = _rand.NextDouble() * totalWeight;
int iParentB = 0;
while (iParentB < rgParents.Count && weightB > rgParents [iParentB].Weight)
{
weightB -= rgParents [iParentB].Weight;
iParentB++;
}
// these are the parents:
DNA parentA = rgParents [iParentA];
DNA parentB = rgParents [iParentB];
// create a new set of genes
int [] rgParams = new int [cParams];
for (int iParam = 0; iParam < cParams; iParam++)
{
// Take an attribute from parents
DNA parent = (_rand.Next(100) < 50) ? parentA : parentB;
rgParams [iParam] = parent.GetParam(iParam);
// Should we mutate?
int cValues = _rgParameterDefinitions [iParam].CountValues;
if (cValues > 1 && _rand.NextDouble() < _mutationRate)
{
rgParams [iParam] = MutateParam(_mutationStrength, cValues, rgParams [iParam]);
}
}
// create the new child, and make sure it's unique
//int iGenerationChild = iGeneration;
//int iGenerationChild = 1 + Math.Min (parentA.Generation, parentB.Generation);
int iGenerationChild = (parentA.Generation + parentB.Generation) / 2;
DNA child = new DNA(rgParams, iGenerationChild);
if (!setChildren.ContainsKey(child))
{
setChildren.Add(child, true);
rgChildren.Add(child);
}
}
stabilityScorePrev = stabilityScore;
}
DateTime timeEnd = DateTime.Now;
TimeSpan tsDuration = timeEnd - timeStart;
Strategy.Print(string.Format(
"{0}: PH Optimizer End @ {1}, duration: {2} ({3} per iteration)",
Strategy.Instrument.FullName,
timeEnd,
tsDuration,
TimeSpan.FromTicks(tsDuration.Ticks / iGeneration)
)
);
}
catch (AbortOptimizationException)
{
Strategy.Print("Optimization Aborted...");
}
catch (Exception e)
{
System.Diagnostics.Debug.WriteLine(e);
Strategy.Print(e.ToString());
//throw;
}
finally
{
if (dnaBestEver != null)
{
Dump(dnaBestEver);
}
}
#if NT7
WaitForIterationsCompleted(true);
#endif
}
