public static string InvestmentSimulations(string stockDataFileName, string predictionsFileName, double investorStartMoney = 100000.0)
{
var spfr = new StockPredictionsFileReader(predictionsFileName);
var rsdr = new RawStockDataReader(stockDataFileName);
int diffrence = 0;
List<InvestorRow> mergedData = new List<InvestorRow>();
for (int i = 0; i < rsdr.GetCount(); i++)
{
if (spfr.GetDate(0) == rsdr.GetDates()[i])
{
diffrence = i;
break;
}
}
for (int i = diffrence; i < rsdr.GetCount() && i - diffrence < spfr.GetCount(); i++)
{
var row = new InvestorRow();
row.Closing = rsdr.GetClosing()[i];
row.Date = rsdr.GetDates()[i];
row.Highest = rsdr.GetHighest()[i];
row.Lowest = rsdr.GetLowest()[i];
row.Opening = rsdr.GetOpening()[i];
row.Volume = rsdr.GetVolume()[i];
row.NetworkFitness = spfr.GetFitness(i - diffrence);
row.PredictionForTommorow = spfr.GetPredicted(i - diffrence);
row.AccualClosingForTommorow = spfr.GetAccual(i - diffrence);
mergedData.Add(row);
}
Investor.Investor investor = new Investor.Investor(mergedData);
investor.StartMoney = investorStartMoney;
investor.SimulateAll();
//save the output
string f = Utilities.OutputFileName("investor.csv");
System.IO.StreamWriter file = new System.IO.StreamWriter(f);
file.Write(investor.GetCSVOutput());
file.Close();
return f;
}
public string PreprocesData()
{
var rsdr = new RawStockDataReader(_filename);
int cnt = rsdr.GetCount();
if (rsdr.GetCount() < _period + 1)
throw new IndexOutOfRangeException("Dataset is to small");
var closing = rsdr.GetClosing();
var highest = rsdr.GetHighest();
var opening = rsdr.GetOpening();
var lowest = rsdr.GetLowest();
var volume = rsdr.GetVolume();
var dates = rsdr.GetDates();
var sma = TechnicalAnalisysMethods.SimpleMovingAverage(closing, _period, 0, cnt-1);
sma = NormalizeColumnRescaling(sma).ToArray();
var ema = TechnicalAnalisysMethods.ExponentialMovingAverage(closing, _period, 0, cnt - 1);
ema = NormalizeColumnRescaling(ema).ToArray();
var dema = TechnicalAnalisysMethods.DoubleExponentialMovingAverage(closing, _period, 0, cnt - 1);
dema = NormalizeColumnRescaling(dema).ToArray();
var roc = TechnicalAnalisysMethods.ROC(closing, _period, 0, cnt - 1);
var rsi = TechnicalAnalisysMethods.RSI(closing, _period, 0, cnt - 1);
rsi = NormalizeColumnRescaling(rsi).ToArray();
var natr = TechnicalAnalisysMethods.NormalizedAverageTrueRange(closing, highest, lowest, _period, 0, cnt - 1);
natr = NormalizeColumnRescaling(natr).ToArray();
var obv = TechnicalAnalisysMethods.OnBalanceVolume(closing, volume, 0, cnt - 1);
obv = NormalizeColumnRescaling(obv).ToArray();
var chosc = TechnicalAnalisysMethods.ChaikinOscilator(closing, highest, lowest, volume, _period, _period*2, 0, cnt - 1);
chosc = NormalizeColumnRescaling(chosc).ToArray();
var adx = TechnicalAnalisysMethods.AverageDirectionalMovementIndex(closing, highest, lowest, _period, 0, cnt - 1);
adx = NormalizeColumnRescaling(adx).ToArray();
closing = NormalizeColumnRescaling(closing).ToArray();
highest = NormalizeColumnRescaling(highest).ToArray();
opening = NormalizeColumnRescaling(opening).ToArray();
lowest = NormalizeColumnRescaling(lowest).ToArray();
volume = NormalizeColumnRescaling(volume).ToArray();
var tsf = TechnicalAnalisysMethods.TimeSeriesForecast(closing, _period, 0, cnt - 1);
for (int i = _period; i < cnt -1; i++)
{
_outputDates.Add(dates[i]);
List<double> learingExample = new List<double>();
for (int j = _period-1; j >= 0; j--)
{
//Opening,Highest,Lowest,Closing,Volume
List<double> day = new List<double>();
int i_day = i - j;
day.Add(opening[i_day]); //0
day.Add(highest[i_day]); //1
day.Add(lowest[i_day]); //2
day.Add(closing[i_day]); //3
day.Add(volume[i_day]); //4
day.AddRange(rsdr.Records[i_day].CalculatePercentChange(rsdr.Records[i_day-1])); //+5 kolumn
day.Add(sma[i_day]); //9
day.Add(ema[i_day]); //10
day.Add(dema[i_day]); //11
day.Add(roc[i_day]); //12
day.Add(rsi[i_day]); //13
day.Add(natr[i_day]); //14
day.Add(obv[i_day]); //15
day.Add(chosc[i_day]); //16
day.Add(tsf[i_day]); //17 nowosc
day.Add(adx[i_day]); //18 nowosc
learingExample.AddRange(day);
}
learingExample.Add(GetNextDayOfWeek(rsdr.Records[i].Date));
double desiredOutput = (rsdr.Records[i+1].Closing - rsdr.Records[i].Closing) / rsdr.Records[i].Closing;
learingExample.Add(desiredOutput);
_outputNumbers.Add(learingExample);
}
SaveResults();
return _outputFilename;
}
