private void outcome_sampling_button_Click(object sender, EventArgs e)
{
DataminingInputDialog id = new DataminingInputDialog(new string[] { "instrument", "steps", "indicatorid", "outcometimeframe" }, dataminingDb.getInfo());
id.ShowDialog();
if (id.isValidResult())
{
new Thread(delegate()
{
Dictionary <string, string> parameters = id.getResult();
if (Directory.Exists(Application.StartupPath + @"\Analysis\") == false)
{
Directory.CreateDirectory(Application.StartupPath + @"\Analysis\");
}
SampleOutcomeExcelGenerator excel = new SampleOutcomeExcelGenerator(Application.StartupPath + @"\Analysis\" + DateTime.Now.ToString("yyyy_dd_mm") + "_" + parameters["instrument"] + ".xls");
setState("Outcomesampling");
dataminingDb.getOutcomeIndicatorSampling(excel, parameters["indicatorid"], Convert.ToInt32(parameters["outcometimeframe"]), Convert.ToInt32(parameters["steps"]), dataminingDb.getInfo(parameters["indicatorid"]).getDecentRange(), parameters["instrument"]);
excel.FinishDoc();
excel.ShowDocument();
}).Start();
}
}
void IDataminingDatabase.getOutcomeIndicatorSampling(SampleOutcomeExcelGenerator excel, string indicatorId, int outcomeTimeframe, string instument)
{
throw new NotImplementedException();
}
void IDataminingDatabase.getOutcomeIndicatorSampling(SampleOutcomeExcelGenerator excel, double min, double max, int steps, string indicatorId, int outcomeTimeframe, string instument)
{
List <Thread> threads = new List <Thread>();
string sheetName = indicatorId + "_" + outcomeTimeframe + "_" + instument;
if (sheetName.Length >= 30)
{
sheetName = sheetName.Substring(0, 29);
}
excel.CreateSheet(sheetName);
var collection = mongodb.getDB().GetCollection("prices");
double stepsize = (Convert.ToDouble(max - min) / Convert.ToDouble(steps));
int startedThreads = 0;
double current = min;
while (current <= max)
{
startedThreads++;
double valueMin = current;
double valueMax = current + stepsize;
Thread thread = new Thread(delegate()
{
instument = null;
IMongoQuery query = Query.And(Query.Exists(indicatorId), Query.Exists("last"), Query.Exists("outcome_actual_" + outcomeTimeframe), Query.Exists("outcome_max_" + outcomeTimeframe), Query.Exists("outcome_min_" + outcomeTimeframe), Query.LT(indicatorId, valueMax), Query.GTE(indicatorId, valueMin));
if (instument != null)
{
query = Query.And(query, Query.EQ("instrument", instument));
}
var docs = collection.FindAs <BsonDocument>(query);
double sumMax = 0d, sumMin = 0d, sumActual = 0d;
double count = docs.Count();
foreach (BsonDocument doc in docs)
{
double onePercent = doc["last"].AsDouble / 100d;
double maxDiff = doc["outcome_max_" + outcomeTimeframe].AsDouble / onePercent - 100d; //calculate percent difference
double minDiff = doc["outcome_min_" + outcomeTimeframe].AsDouble / onePercent - 100d;
double actualDiff = doc["outcome_actual_" + outcomeTimeframe].AsDouble / onePercent - 100d;
sumMax += maxDiff;
sumMin += minDiff;
sumActual += actualDiff;
}
if (count == 0)
{
excel.addRow(sheetName, 0, 0, 0, 0, 0, 0, 0, 0);
}
else
{
double maxAvg = sumMax / count;
double minAvg = sumMin / count;
double actualAvg = sumActual / count;
double minVsMax = sumMin + sumMax / count;
excel.addRow(sheetName, valueMin, valueMax, Convert.ToInt32(count), maxAvg, minAvg, minVsMax, actualAvg, (maxAvg + minAvg));
}
});
thread.Start();
threads.Add(thread);
current += stepsize;
}
//Darstellungsthread
waitForThreads(threads);
excel.FinishSheet(sheetName);
}
