public void Study(string filename, bool plotErrorBars, bool plotMarketNeutral, bool plotEvents, string marketSymbol)
{
Console.WriteLine("Starting Event Study");
// compute 0 centered daily returns
foreach (var symbol in symbols)
{
var symList = close.ColumnList(symbol);
dailyReturns[symbol] = symList.Returnize();
}
var marketList = close.ColumnList(marketSymbol);
IList<float> marketDaily = marketList.Returnize();
// make it market neutral
if (plotMarketNeutral)
{
foreach (var symbol in symbols)
{
marketNeutralDm[symbol] = new List<float>();
for (int i = 0; i < marketDays; i++)
marketNeutralDm[symbol].Add(dailyReturns[symbol][i] - marketDaily[i]);
}
// remove the market column from consideration
marketNeutralDm.Remove(marketSymbol);
IList<int> removedEventList;
eventMatrix.TryRemove(marketSymbol, out removedEventList);
symbols.Remove(marketSymbol);
}
else
{
marketNeutralDm = dailyReturns;
}
// Wipe out events which are on the boundary.
Parallel.ForEach(symbols,
symbol =>
{
foreach (int i in Enumerable.Range(0, lookbackDays))
eventMatrix[symbol][i] = 0;
foreach (int i in Enumerable.Range(marketDays - lookforwardDays, lookforwardDays))
eventMatrix[symbol][i] = 0;
});
// prepare to build impact matrix
var returns = marketNeutralDm;
int numCols = eventMatrix.Keys.Count;
int numEvents = eventMatrix.Values.SelectMany(i => i).Sum();
// create a blank impact matrix
var impact = new double[totalGraphDays, numEvents];
int currcol = 0;
// step through each event in event matrix
foreach (int column in Enumerable.Range(0, numCols))
{
string currSym = symbols[column];
// search event days
int eventDays = eventMatrix[currSym].Count;
foreach (int eventIndex in Enumerable.Range(0, eventDays))
{
int eventState = eventMatrix[symbols[column]][eventIndex];
// when we find an event
if (eventState == 1)
{
// copy the daily returns in to the impact matrix
int start = eventIndex - lookbackDays;
start = (start < 0) ? 0 : start;
int end = eventIndex + lookforwardDays + 1;
for (int i = start, j = 0; i < end && j < eventDays; i++, j++)
{
impact[j, currcol] = returns[currSym][i];
}
currcol = currcol + 1;
}
}
}
// now compute cumulative daily returns
Parallel.ForEach(Enumerable.Range(0, impact.GetLength(1)),
j =>
{
foreach (int i in Enumerable.Range(0, impact.GetLength(0)))
impact[i, j] = impact[i, j] + 1.0d;
});
impact = impact.CumulativeProduct();
Parallel.ForEach(ParallelEnumerable.Range(0, impact.GetLength(1)),
j =>
{
var divisor = impact[0, j];
foreach (int i in Enumerable.Range(0, impact.GetLength(0)))
{
impact[i, j] = impact[i, j] / divisor;
}
});
// normalize everything to the time of the event
Parallel.ForEach(ParallelEnumerable.Range(0, impact.GetLength(1)),
j =>
{
var divisor = impact[lookbackDays, j];
foreach (int i in Enumerable.Range(0, impact.GetLength(0)))
impact[i, j] = impact[i, j] / divisor;
});
// prepare data for plot
studyStat = new List<float>(impact.GetLength(0));
studyStd = new List<float>(impact.GetLength(0));
foreach (int i in Enumerable.Range(0, impact.GetLength(0)))
{
var row = new List<float>();
for (int j = 0; j < impact.GetLength(1); j++)
{
row.Add((float)impact[i, j]);
}
studyStat.Add(row.Average());
studyStd.Add(row.StdDev());
}
studyRange = Enumerable.Range(-lookbackDays, (lookbackDays + lookforwardDays + 1));
Plot(filename, impact, numEvents, plotMarketNeutral, plotEvents, plotErrorBars);
}
private ResultsGroup AverageResultsGroup(List<ResultsGroup> combiningRows)
{
List<Int32> NumChargeStates = new List<int>();
List<Double> ScanDensity= new List<Double>() ;
List<Double> NumModiStates= new List<Double>() ;
List<Double> TotalVolume= new List<Double>() ;
List<Double> ExpectedA= new List<Double>() ;
List<Double> Score= new List<Double>() ;
List<Double> CentroidScan= new List<Double>() ;
List<Double> NumScan= new List<Double>() ;
List<Double> AvgSigNoise= new List<Double>() ;
//These are for calculating the features
List<Int32> MaxScanNum= new List<int>() ;
List<Int32> MinScanNum= new List<int>() ;
List<Int32> ScanNums= new List<int>() ;
List<Int32> ChargeStateList = new List<int>();
List<Double> AvgSigNoiseList= new List<Double>() ;
List<Double> CentroidScanLR= new List<Double>() ;
List<Double> AvgAA2List= new List<Double>() ;
List<Double> RelativeTotalVolume = new List<double>();
List<Int32> scan_num = new List<Int32>();
List<Int32> charge = new List<Int32>();
List<Int32> abundance = new List<Int32>();
List<Double> mz = new List<Double>();
List<Double> fit = new List<Double>();
List<Double> average_mw = new List<Double>();
List<Double> monoisotopic_mw = new List<Double>();
List<Double> mostabundant_mw = new List<Double>();
List<Double> fwhm = new List<Double>();
List<Double> signal_noise = new List<Double>();
List<Int32> mono_abundance = new List<Int32>();
List<Int32> mono_plus2_abundance = new List<Int32>();
List<Int32> flag = new List<Int32>();
List<Double> interference_sore = new List<Double>();
ResultsGroup FinalAns = new ResultsGroup();
FinalAns.ListOfOriginalTotalVolumes = combiningRows[0].ListOfOriginalTotalVolumes;
for (int i = 0; i < combiningRows.Count(); i++)
{
NumChargeStates.Add(combiningRows[i].NumChargeStates);
ScanDensity.Add(combiningRows[i].ScanDensity);
NumModiStates.Add(combiningRows[i].NumModiStates);
TotalVolume.Add(combiningRows[i].TotalVolume);
ExpectedA.Add(combiningRows[i].ExpectedA);
Score.Add(combiningRows[i].Score);
CentroidScan.Add(combiningRows[i].CentroidScan);
NumScan.Add(combiningRows[i].NumOfScan);
AvgSigNoise.Add(combiningRows[i].AvgSigNoise);
//These are for calculating the features
MaxScanNum.Add(combiningRows[i].MaxScanNum);
MinScanNum.Add(combiningRows[i].MinScanNum);
CentroidScanLR.Add(combiningRows[i].CentroidScanLR);
AvgAA2List.AddRange(combiningRows[i].AvgAA2List);
RelativeTotalVolume.Add(combiningRows[i].RelativeTotalVolume);
charge.Add(combiningRows[i].DeconRow.charge);
scan_num.Add(combiningRows[i].DeconRow.ScanNum);
abundance.Add(combiningRows[i].DeconRow.abundance);
mz.Add(combiningRows[i].DeconRow.mz);
fit.Add(combiningRows[i].DeconRow.fit);
average_mw.Add(combiningRows[i].DeconRow.average_mw);
monoisotopic_mw.Add(combiningRows[i].DeconRow.MonoisotopicMassWeight);
mostabundant_mw.Add(combiningRows[i].DeconRow.mostabundant_mw);
fwhm.Add(combiningRows[i].DeconRow.fwhm);
signal_noise.Add(combiningRows[i].DeconRow.SignalNoiseRatio);
mono_abundance.Add(combiningRows[i].DeconRow.MonoisotopicAbundance);
mono_plus2_abundance.Add(combiningRows[i].DeconRow.MonoisotopicPlus2Abundance);
flag.Add(combiningRows[i].DeconRow.flag);
interference_sore.Add(combiningRows[i].DeconRow.interference_sore);
for (int h = 0; h < combiningRows[i].ListOfOriginalTotalVolumes.Count(); h++)
{
if (combiningRows[i].ListOfOriginalTotalVolumes[h] > FinalAns.ListOfOriginalTotalVolumes[h])
{
FinalAns.ListOfOriginalTotalVolumes[h] = combiningRows[i].ListOfOriginalTotalVolumes[h];
}
}
}
FinalAns.DeconRow = new DeconRow();
FinalAns.PredictedComposition = combiningRows[0].PredictedComposition;
FinalAns.NumChargeStates = Convert.ToInt32(NumChargeStates.Average());
FinalAns.ScanDensity = ScanDensity.Average();
FinalAns.NumModiStates = NumModiStates.Average();
FinalAns.TotalVolume = TotalVolume.Average();
FinalAns.ExpectedA = ExpectedA.Average();
FinalAns.Score = Score.Average();
FinalAns.CentroidScan = CentroidScan.Average();
FinalAns.NumOfScan = NumScan.Average();
FinalAns.AvgSigNoise = AvgSigNoise.Average();
FinalAns.MaxScanNum = Convert.ToInt32(MaxScanNum.Average());
FinalAns.MinScanNum = Convert.ToInt32(MinScanNum.Average());
FinalAns.CentroidScanLR = CentroidScanLR.Average();
FinalAns.TotalVolumeSD = TotalVolume.StdDev();
FinalAns.RelativeTotalVolumeSD = RelativeTotalVolume.StdDev();
FinalAns.AvgAA2List = new List<double>();
FinalAns.AvgAA2List.Add(AvgAA2List.Average());
FinalAns.RelativeTotalVolume = RelativeTotalVolume.Average();
FinalAns.DeconRow.ScanNum = Convert.ToInt32(scan_num.Average());
FinalAns.DeconRow.abundance = Convert.ToInt32(abundance.Average());
FinalAns.DeconRow.mz = mz.Average();
FinalAns.DeconRow.fit = fit.Average();
FinalAns.DeconRow.average_mw = average_mw.Average();
FinalAns.DeconRow.MonoisotopicMassWeight = monoisotopic_mw.Average();
FinalAns.DeconRow.mostabundant_mw = mostabundant_mw.Average();
FinalAns.DeconRow.fwhm = fwhm.Average();
FinalAns.DeconRow.SignalNoiseRatio = signal_noise.Average();
FinalAns.DeconRow.MonoisotopicAbundance = Convert.ToInt32(mono_abundance.Average());
FinalAns.DeconRow.MonoisotopicPlus2Abundance = Convert.ToInt32(mono_plus2_abundance.Average());
FinalAns.DeconRow.flag = Convert.ToInt32(flag.Average());
FinalAns.DeconRow.interference_sore = interference_sore.Average();
return FinalAns;
}
