private void CalculateSumDistribution(double sigma, PointF[] dens,
out double sg1Opt, out double sg2Opt,
out double kOpt)
{
const double mMin = 0.15, mMax = 0.85d, mStep = 0.1;
const double kMin = 0.2, kMax = 0.8, kStep = 0.05;
const double kSigmMin = 0.1, kSigmMax = 5.0, kSigmStep = 0.05;
double?rss = null;
sg1Opt = 0;
sg2Opt = 0;
kOpt = 0;
// посчитать F, вычисляя разность квадратов
for (var kSigm = kSigmMin; kSigm <= kSigmMax; kSigm += kSigmStep)
{
for (var k = kMin; k <= kMax; k += kStep)
{
for (var m = mMin; m < mMax; m += mStep)
{
var sg2 = sigma * kSigm / (1 + m);
var sg1 = sg2 * m;
var curRss = 0d;
float[] testSynthFunc = null;
if (StoreFunction)
{
testSynthFunc = new float[dens.Length];
}
for (var i = 0; i < dens.Length; i++)
{
var x = dens[i].X;
var f = (k * ProbabilityCore.Erf(x / (ProbabilityCore.R2 * sg1))
+ (1 - k) * ProbabilityCore.Erf(x / (ProbabilityCore.R2 * sg2))) * 0.5 + 0.5;
var delta = f - dens[i].Y;
delta = delta * delta;
curRss += delta;
if (StoreFunction)
{
testSynthFunc[i] = (float)f;
}
}
if (!rss.HasValue || rss > curRss)
{
rss = curRss;
kOpt = k;
sg1Opt = sg1;
sg2Opt = sg2;
synthFunc = testSynthFunc;
}
}
}
}
}
private void CalculateSumDistribution(double sigma, PointF[] dens,
out double sg1Opt, out double sg2Opt,
out double kOpt)
{
const double mMin = 0.25, mMax = 0.75d, mStep = 0.1;
const double kMin = 0.5, kMax = 0.8, kStep = 0.05;
const double kSigmMin = 0.8, kSigmMax = 2.0, kSigmStep = 0.05;
double?rss = null;
sg1Opt = 0;
sg2Opt = 0;
kOpt = 0;
var timeStart = DateTime.Now;
// посчитать F, вычисляя разность квадратов
for (var kSigm = kSigmMin; kSigm <= kSigmMax; kSigm += kSigmStep)
{
for (var k = kMin; k <= kMax; k += kStep)
{
for (var m = mMin; m < mMax; m += mStep)
{
var sg2 = sigma * kSigm / (1 + m);
var sg1 = sg2 * m;
var curRss = 0d;
var densResulted = new PointF[dens.Length];
for (var i = 0; i < dens.Length; i++)
{
var x = dens[i].X;
var f = k * ProbabilityCore.Erf(x / (ProbabilityCore.R2 * sg1))
+ (1 - k) * ProbabilityCore.Erf(x / (ProbabilityCore.R2 * sg2));
var delta = f - dens[i].Y;
delta = delta * delta;
curRss += delta;
densResulted[i] = new PointF(x, (float)f);
}
if (!rss.HasValue || rss > curRss)
{
rss = curRss;
kOpt = k;
sg1Opt = sg1;
sg2Opt = sg2;
densSum = densResulted;
}
}
}
}
//var deltaMils = (DateTime.Now - timeStart).TotalMilliseconds;
}
static void Main(string[] args)
{
DinosaurMainDescriptor dino = new DinosaurMainDescriptor();
double dinoOdds = ProbabilityCore.Simulate <DinosaurCard>(dino);
Console.WriteLine($"Dino Simorgh Combo Success Rate: {dinoOdds}%");
DinosaurBackupDescriptor dino2 = new DinosaurBackupDescriptor();
double dinoOdds2 = ProbabilityCore.Simulate <DinosaurCard>(dino2);
Console.WriteLine($"Dino Lost World Combo Success Rate After Failing Simorgh Combo: {dinoOdds2}%");
Console.ReadLine();
}
private double GetTheoreticalProbHighTouchesM(double sigma, int numSteps, double m)
{
return(1 - ProbabilityCore.CalculatePriceDeltaWillBeLessThan(sigma * numSteps, 1, m));
}
private static double CalcProbKSigmaNormal(double sigma, double k)
{
var f = ProbabilityCore.IntegralFunc(k * sigma, sigma, 0);
return(100.0 * (1 - f));
}
private void CalculateQuoteDistribution(int intervals, int minutesDelta)
{
if (candles == null || candles.Count < 1000)
{
return;
}
// перевести свечи в массив длин OC
float[] lengths;
if (minutesDelta == 1)
{
lengths = candles.Select(c => Math.Abs(c.close - c.open)).OrderBy(d => d).ToArray();
}
else
{
var listLen = new List <float>();
CandleData prev = null;
foreach (var candle in candles)
{
if (prev != null)
{
if ((candle.timeOpen - prev.timeOpen).TotalMinutes < minutesDelta)
{
continue;
}
var delta = Math.Abs(candle.close - prev.close);
listLen.Add(delta);
prev = candle;
}
else
{
prev = candle;
}
}
lengths = listLen.OrderBy(d => d).ToArray();
}
// посчитать интегральную функцию распределения вероятности длина тела свечи / N свечей
dens = MakeDistributionCurve(lengths, intervals);
// получить оценку согласно "нормальному" закону
densMath = new PointF[dens.Length];
var sigma = Math.Sqrt(lengths.Sum(l => l * l) / lengths.Length);
for (var i = 0; i < dens.Length; i++)
{
var x = dens[i].X;
var y = 1 * ProbabilityCore.Erf(x / (ProbabilityCore.R2 * sigma)); // P за вычетом 0.5
densMath[i] = new PointF(x, (float)y);
}
// получить параметры 2-х норм. распределений, дающих в сумме исходное
double sg1, sg2, k;
CalculateSumDistribution(sigma, dens, out sg1, out sg2, out k);
DrawChart();
// сохранить данные в Excel
var dlg = new SaveFileDialog
{
Title = "Сохранить файл",
DefaultExt = "csv",
Filter = "All Files (*.*)|*.*|CSV (*.csv)|*.csv"
};
if (dlg.ShowDialog() != DialogResult.OK)
{
return;
}
using (var fs = new StreamWriter(dlg.FileName, false, Encoding.UTF8))
{
fs.WriteLine("Sigma;{0}", sigma.ToStringUniform());
for (var i = 0; i < dens.Length; i++)
{
fs.WriteLine("{0},{1},{2},{3}",
i, dens[i].X.ToStringUniform(5), dens[i].Y.ToStringUniform(5), densMath[i].Y.ToStringUniform(5));
}
}
}
private void BtnCalculateClick(object sender, EventArgs e)
{
var candles = chart.chart.StockSeries.Data.Candles;
if (candles.Count == 0)
{
return;
}
var curPrice = candles[candles.Count - 1].close;
// посчитать дельта цену и дельта время
var targetPrice = tbPrice.Text.ToDoubleUniform();
var side = cbSide.SelectedIndex == 0 ? -1 : 1;
var logPrice = Math.Log(targetPrice / curPrice) * side;
var deltaTime = (int)Math.Round((dtEnd.Value - DateTime.Now).TotalMinutes);
// получить эмпирическую функцию распределения
const int funcIntervalsCount = 100;
var nSteps = 1d;
const int longestInterval = 1440;
if (deltaTime > longestInterval)
{
var delta = longestInterval / 2;
nSteps = (double)deltaTime / delta;
deltaTime = delta;
}
var lengths = GetTargetCandlesLengths(deltaTime);
lengths.Sort();
var func = MakeDistributionCurve(lengths, funcIntervalsCount);
// получить параметры 2-х СВ (нормально распределенных),
// в сумме дающих приближение к эмпирической F
var avgLen = lengths.Average();
var sigma = Math.Sqrt(lengths.Sum(l => (l - avgLen) * (l - avgLen)) / lengths.Count);
double sigm1, sigm2, k;
CalculateSumDistribution(sigma, func, out sigm1, out sigm2, out k);
// посчитать вреоятность
var x = logPrice;
var scale = nSteps == 1 ? 1 : Math.Sqrt(nSteps);
var f = k * ProbabilityCore.Erf(x / (ProbabilityCore.R2 * sigm1 * scale))
+ (1 - k) * ProbabilityCore.Erf(x / (ProbabilityCore.R2 * sigm2 * scale));
f = f * 0.5 + 0.5;
var p = 1 - f;
tbResult.Text = (p * 100).ToStringUniform(2) + "%";
if (!StoreFunction)
{
return;
}
// вывести в Excel synthFunc
var dlg = new SaveFileDialog
{
Title = "Сохранить таблицы",
DefaultExt = "csv",
Filter = "*.csv|*.csv|*.*|*.*",
FilterIndex = 0
};
if (dlg.ShowDialog() != DialogResult.OK)
{
return;
}
using (var sw = new StreamWriter(dlg.FileName, false, Encoding.ASCII))
{
sw.WriteLine("delta(X);F;F synth");
for (var i = 0; i < func.Count(); i++)
{
sw.WriteLine("{0};{1};{2}",
func[i].X.ToStringUniform(5), func[i].Y.ToStringUniform(4), synthFunc[i].ToStringUniform(4));
}
}
}
