/// <summary>
/// Drawing plot of a given regression
/// </summary>
/// <param name="container"></param>
private void DrawPlot(RegressionContainer container)
{
AbstractRegression regression = container.Regression;
Brush brush = colorValuePairs[container.BrushKeyValue];
int predictDays = container.PredictDays;
double min = regression.yVals.Min();
double max = regression.yVals.Max();
TopYLabel.Content = $"{max:f2}";
BottomYLabel.Content = $"{min:f2}";
double height = max - min;
MiddleYLabel.Content = $"{(min + max) / 2.0:f2}";
double predictCoefficient = 1 + (double)predictDays / regression.xVals.Length;
double width = regression.xVals.Length;
double heightCoefficient = PlotCanvas.ActualHeight / (height * predictCoefficient);
double widthCoefficient = PlotCanvas.ActualWidth / (width * predictCoefficient);
Line line = new Line
{
Y2 = Math.Min(PlotCanvas.ActualHeight,
PlotCanvas.ActualHeight - heightCoefficient * (regression.PredictY(0.1) - min)),
X2 = 0.1 * widthCoefficient,
HorizontalAlignment = HorizontalAlignment.Left,
VerticalAlignment = VerticalAlignment.Center,
StrokeThickness = 1.25,
Stroke = brush
};
for (double i = 0.1; i < regression.xVals.Length * predictCoefficient; i += 0.5)
{
double lastY = line.Y2;
double lastX = line.X2;
line = new Line
{
Y1 = lastY,
X1 = lastX,
Y2 = Math.Min(PlotCanvas.ActualHeight,
PlotCanvas.ActualHeight - heightCoefficient * (regression.PredictY(i) - min)),
X2 = i * widthCoefficient,
HorizontalAlignment = HorizontalAlignment.Left,
VerticalAlignment = VerticalAlignment.Center,
StrokeThickness = 1.25,
Stroke = brush
};
PlotCanvas.Children.Add(line);
}
}
/// <summary>
/// Drawing source values
/// </summary>
/// <param name="container"></param>
private void DrawPoints(RegressionContainer container)
{
AbstractRegression regression = container.Regression;
int predictDays = container.PredictDays;
double min = regression.yVals.Min();
double max = regression.yVals.Max();
TopYLabel.Content = $"{max:f2}";
BottomYLabel.Content = $"{min:f2}";
double height = max - min;
MiddleYLabel.Content = $"{(min + max) / 2.0:f2}";
double predictCoefficient = 1 + (double)predictDays / regression.xVals.Length;
double width = regression.xVals.Length;
double heightCoefficient = PlotCanvas.ActualHeight / (height * predictCoefficient);
double widthCoefficient = PlotCanvas.ActualWidth / (width * predictCoefficient);
for (int i = 0; i < regression.xVals.Length; i++)
{
Rectangle point = new Rectangle
{
HorizontalAlignment = HorizontalAlignment.Left,
VerticalAlignment = VerticalAlignment.Center,
Stroke = Brushes.Red,
Width = 2,
Height = 2,
Margin = new Thickness(regression.xVals[i] * widthCoefficient,
PlotCanvas.ActualHeight - heightCoefficient * (regression.yVals[i] - min),
regression.xVals[i] * widthCoefficient,
PlotCanvas.ActualHeight - heightCoefficient * (regression.yVals[i] - min))
};
PlotCanvas.Children.Add(point);
}
companyChanged = false;
}
/// <summary>
/// Downloading data, making regression list, drawing
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void DrawButton_Click(object sender, RoutedEventArgs e)
{
double[] xVals;
double[] yVals;
CsvFile file;
string name;
try
{
name = CompanyListBox.SelectedItem.ToString();
GetVals(out xVals, out yVals, out file, name);
}
catch (Exception)
{
return;
}
int baseDays = 0, predictDays = 0;
if (!TryParseTextBoxes(ref baseDays, ref predictDays))
{
MessageBox.Show("Неверный ввод данных для предсказания");
return;
}
baseDays = Math.Min(baseDays, file.Fields[0].Count);
Array.Resize(ref xVals, baseDays);
double[] yValsConv = new double[baseDays];
for (int i = Math.Max(yVals.Length - baseDays, 0); i < yVals.Length; i++)
{
yValsConv[i - yVals.Length + baseDays] = yVals[i];
}
try
{
GetRegressionsList(xVals, yValsConv, file, predictDays, name);
}
catch (ArgumentException)
{
MessageBox.Show("Не удалось предсказать котировки по скачанным данным, попробуйте другую компанию");
ClearButton_Click(this, new RoutedEventArgs());
return;
}
if (OutputCheck.IsChecked == true)
{
SaveFileDialog fileDialog = new SaveFileDialog
{
Filter = "Comma separated file (*.csv)|*.csv"
};
try
{
if (fileDialog.ShowDialog() == true)
{
file.Save(fileDialog.FileName, regressions);
}
}
catch (IOException)
{
MessageBox.Show("Не удалось получить доступ к файлу по указанному пути");
}
}
if (regressions.Count != 0)
{
TopXLabel.Content = file.Fields[0][0];
BottomXLabel.Content = file.Fields[0][baseDays - 1];
MiddleXLabel.Content = file.Fields[0][baseDays / 2];
DrawPoints(regressions[regressions.Count - 1]);
double bestCorreliation = regressions.Max(rc => rc.Regression.correlationCoefficient);
AbstractRegression bestRegression =
regressions.Where(rc => Math.Abs(rc.Regression.correlationCoefficient - bestCorreliation)
< double.Epsilon).ToList()[0].Regression;
MessageBox.Show($"Основываясь на лучшей модели предсказания: {bestRegression.ToString()} с коэффициентом корреляции {bestCorreliation:f5}, " +
$"вы можете заработать ${-bestRegression.yVals[bestRegression.yVals.Length - 1] + bestRegression.PredictY(xVals.Length + 2):f5} за каждую акцию, если вложитесь сейчас и продадите акции через" +
$" {predictDays} дней");
}
}