public override Advice GetActionForBuckets(List <TradeBucket> tradeBuckets)
{
Advice advice = new Advice();
advice.AlgorithmName = Name;
advice.Confidence = 0;
advice.Group = Group;
advice.Time = tradeBuckets[tradeBuckets.Count - 1].Time;
List <TradeBucket> buckets = tradeBuckets;
WilliamsR rsi = new WilliamsR();
TIOutput output = rsi.GetCurrentValue(buckets);
advice.Price = buckets[buckets.Count - 1].Close;
advice.Confidence = (decimal)output.Value;
if (output.Value < 20)
{
advice.Action = TradeAction.Sell;
}
else if (output.Value > 80)
{
advice.Action = TradeAction.Buy;
}
else
{
advice.Action = TradeAction.Hold;
}
return(advice);
}
private void williamsRParameterNext_Click(object sender, EventArgs e)
{
williamsRResultTitle.Text = Code + " için Williams' %R sonucu";
WilliamsRPeriod = (int)williamsRPeriod.Value;
williamsRCloseChart.Series.Clear();
williamsRIndicatorChart.Series.Clear();
var closeSeries = new Series
{
Name = "Kapanış",
Color = System.Drawing.Color.Black,
ChartType = SeriesChartType.Line
};
var wsrSeries = new Series
{
Name = "Williams' R",
Color = System.Drawing.Color.Blue,
ChartType = SeriesChartType.Line
};
williamsRCloseChart.Series.Add(closeSeries);
williamsRIndicatorChart.Series.Add(wsrSeries);
var closeData = IndicatorService.GetData(Code, TargetDate, new string[] { "Tarih", "Kapanis" }, NumberOfData);
DateTime date;
double[] wsr;
if (MapReduceAllowed)
{
wsr = WilliamsR.WsrMR(Code, TargetDate, WilliamsRPeriod, NumberOfData);
}
else
{
wsr = WilliamsR.Wsr(Code, TargetDate, WilliamsRPeriod, NumberOfData);
}
for (int i = 0; i < wsr.Length; i++)
{
var close = closeData.ElementAt(i);
date = close.GetElement("Tarih").Value.AsBsonDateTime.ToLocalTime();
closeSeries.Points.AddXY(date, close.GetElement("Kapanis").Value.ToDouble());
wsrSeries.Points.AddXY(date, wsr[i]);
}
williamsRCloseChart.ChartAreas[0].AxisY.Minimum = Math.Floor(closeData.Select(p => p.GetElement("Kapanis").Value.ToDouble()).Min());
williamsRCloseChart.ChartAreas[0].AxisY.Maximum = Math.Ceiling(closeData.Select(p => p.GetElement("Kapanis").Value.ToDouble()).Max());
williamsRIndicatorChart.ChartAreas[0].AxisY.Minimum = -100;
williamsRIndicatorChart.ChartAreas[0].AxisY.Maximum = 0;
williamsRCloseChart.Invalidate();
williamsRIndicatorChart.Invalidate();
williamsRResultPanel.BringToFront();
}
private static void ExportFeaturesAndLabel(string code)
{
string filePath = (Environment.OSVersion.Platform == PlatformID.Unix || Environment.OSVersion.Platform == PlatformID.MacOSX) ? Environment.GetEnvironmentVariable("HOME") : Environment.ExpandEnvironmentVariables("%HOMEDRIVE%%HOMEPATH%");
filePath += "\\indicatorOutput.txt";
DateTime targetDate = IndicatorService.LastDate(code);
int numberOfData = 4000;
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
double[] sma = MovingAverage.Simple(code, targetDate, 14, numberOfData);
double[] wma = MovingAverage.Weighted(code, targetDate, 14, numberOfData);
double[] ema = MovingAverage.Exponential(code, targetDate, 14, numberOfData);
MovingAverageConvergenceDivergence macd = new MovingAverageConvergenceDivergence(code, targetDate, 12, 26, 9, numberOfData);
double[] rsi = RelativeStrengthIndex.Rsi(code, targetDate, 14, numberOfData);
double[] williams = WilliamsR.Wsr(code, targetDate, 14, numberOfData);
Stochastics stochastics = new Stochastics(code, targetDate, 14, 3, 3, numberOfData);
double[] closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
double[] smaOut = IndicatorDataPreprocessor.GetSMAOut(sma);
double[] wmaOut = IndicatorDataPreprocessor.GetWMAOut(wma);
double[] emaOut = IndicatorDataPreprocessor.GetEMAOut(ema);
double[] macdOut = IndicatorDataPreprocessor.GetMACDOut(macd);
double[] rsiOut = IndicatorDataPreprocessor.GetRSIOut(rsi);
double[] williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(williams);
double[] stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(stochastics);
int minRowCount;
minRowCount = smaOut.Length;
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
minRowCount = minRowCount < closesOut.Length ? minRowCount : closesOut.Length;
FeatureVector vector = new FeatureVector();
vector.AddColumn("SMA", smaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("WMA", wmaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("EMA", emaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("MACD", macdOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("RSI", rsiOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("WilliamsR", williamsROut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("Stochastics", stochasticsOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("label", closesOut.Select(p => (object)string.Format("{0:0.0}", p).ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
new CSVExporter(vector).Export(filePath);
Console.WriteLine("Operations completed.");
}
/// <summary>
/// Called on every new bar of data.
/// </summary>
/// <param name="currentBar">The current bar of the simulation</param>
protected override void OnBarUpdate(int currentBar)
{
base.OnBarUpdate(currentBar);
WilliamsR ind = (WilliamsR)Dependents[0];
if (DataSeries.IsAboutToCrossBelow(ind.Value, -20, currentBar) == true)
{
WasFound[currentBar] = true;
}
}
/// <summary>
/// Called on every new bar of data.
/// </summary>
/// <param name="currentBar">The current bar of the simulation</param>
protected override void OnBarUpdate(int currentBar)
{
base.OnBarUpdate(currentBar);
WilliamsR ind = (WilliamsR)Dependents[0];
if (DataSeries.CrossAbove(ind.Value, -80, currentBar, 0) != -1)
{
WasFound[currentBar] = true;
}
}
public ReversalGenesis(int n)
: base(n)
{
T3 = new AdaptiveSmoothing(n);
T32 = new AdaptiveSmoothing(5 * n);
CCI = new CCI(n);
WILLR = new WilliamsR(n);
Gann = new GannHiLo(n);
KST = new KST();
CH = new Chaikin();
BB = new BollingerBands(n);
UO = new UltimateOscillator(n, 2 * n, 3 * n);
}
public Genesis(int n) : base(n)
{
T3 = new AdaptiveSmoothing(n);
T32 = new AdaptiveSmoothing(5 * n);
CCI = new CCI(n);
WILLR = new WilliamsR(n);
Gann = new GannHiLo(n);
KST = new KST();
CH = new Chaikin();
BB = new BollingerBands();
QChannel = new QSPolyChannel();
FI = new ForceIndex(30);
PFE = new PFE(150);
RWI = new RWI(30);
AC = new AC(10);
}
/// <summary>
/// If the runnable has already been created, returns that object. If not then
/// returns an new runnable object based on the name and the instrument.
/// </summary>
/// <param name="nameAndParameters">Name of the runnable</param>
/// <returns>The runnable object</returns>
public Runnable GetRunnable(string nameAndParameters)
{
Runnable requestedItem = null;
// The name can have parameters to pass to the runnable construtor
// and are separated by commas.
// Ex: Rsi,11,3 would create the Rsi and pass the numbers to it in a
// list. Its up to the indicator to do what it will with each number.
string[] splitParams = nameAndParameters.Split(',');
string runnableName = splitParams[0];
string[] runnableParams = splitParams.Skip(1).Take(splitParams.Length - 1).ToArray();
// See if the runnable is created already and return that object if it is.
int key = nameAndParameters.GetHashCode();
if (_createdItems.ContainsKey(key))
{
requestedItem = _createdItems[key];
}
else
{
switch (runnableName)
{
// Indicators.
case "Bollinger":
requestedItem = new Bollinger(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "BressertDss":
requestedItem = new BressertDss(_tickerData, this, runnableParams);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "BressertTimingBands":
requestedItem = new BressertTimingBands(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "BearBeltHold":
requestedItem = new BearBeltHold(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "BearEngulfing":
requestedItem = new BearEngulfing(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "BearHarami":
requestedItem = new BearHarami(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "BearHaramiCross":
requestedItem = new BearHaramiCross(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "BullBeltHold":
requestedItem = new BullBeltHold(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "BullEngulfing":
requestedItem = new BullEngulfing(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "BullHarami":
requestedItem = new BullHarami(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "BullHaramiCross":
requestedItem = new BullHaramiCross(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Cci14":
requestedItem = new Cci(_tickerData, this, 14);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "DarkCloudCover":
requestedItem = new DarkCloudCover(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Doji":
requestedItem = new Doji(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "BearDoji":
requestedItem = new BearDoji(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "DownsideTasukiGap":
requestedItem = new DownsideTasukiGap(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "EaseOfMovement":
requestedItem = new EaseOfMovement(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "EveningStar":
requestedItem = new EveningStar(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "FallingThreeMethods":
requestedItem = new FallingThreeMethods(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Hammer":
requestedItem = new Hammer(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "HangingMan":
requestedItem = new HangingMan(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "InvertedHammer":
requestedItem = new InvertedHammer(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "KeltnerChannel":
requestedItem = new KeltnerChannel(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Macd":
requestedItem = new Macd(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Momentum14":
requestedItem = new Momentum(_tickerData, this, 14);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "MorningStar":
requestedItem = new MorningStar(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "PiercingLine":
requestedItem = new PiercingLine(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "PriceOscillator":
requestedItem = new PriceOscillator(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "RisingThreeMethods":
requestedItem = new RisingThreeMethods(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Rsi":
requestedItem = new Rsi(_tickerData, this, runnableParams);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Rsi3m3":
requestedItem = new Rsi3m3(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "ShootingStar":
requestedItem = new ShootingStar(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Sma":
requestedItem = new Sma(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "StickSandwitch":
requestedItem = new StickSandwitch(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "StochasticsFast":
requestedItem = new StochasticsFast(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Stochastics":
requestedItem = new Stochastics(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "StochRsi":
requestedItem = new StochRsi(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Trend":
requestedItem = new Trend(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "ThreeBlackCrows":
requestedItem = new ThreeBlackCrows(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "ThreeWhiteSoldiers":
requestedItem = new ThreeWhiteSoldiers(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Trix":
requestedItem = new Trix(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "UpsideGapTwoCrows":
requestedItem = new UpsideGapTwoCrows(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "UpsideTasukiGap":
requestedItem = new UpsideTasukiGap(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "WilliamsR":
requestedItem = new WilliamsR(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "Dmi":
requestedItem = new Dmi(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "DtOscillator":
requestedItem = new DtOscillator(_tickerData, this, runnableParams);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "FibonacciZones":
requestedItem = new FibonacciZones(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "ElliotWaves":
requestedItem = new ElliotWaves(_tickerData, this);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
case "ZigZag":
requestedItem = new ZigZag(_tickerData, this, runnableParams);
Simulator.DataOutput.SaveIndicator((Indicator)requestedItem);
break;
///////////////////////////// Strategies ////////////////////////////
case "BestOfRootStrategies":
requestedItem = new BestOfRootStrategies(_tickerData, this);
break;
case "ComboStrategy":
requestedItem = new ComboStrategy(_tickerData, this);
break;
case "BressertApproach":
requestedItem = new BressertApproach(_tickerData, this);
break;
case "BressertComboStrategy":
requestedItem = new BressertComboStrategy(_tickerData, this);
break;
case "FibonacciRsi3m3":
requestedItem = new FibonacciRsi3m3(_tickerData, this);
break;
case "FibonacciDtOscillator":
requestedItem = new FibonacciDtOscillator(_tickerData, this);
break;
case "ElliotWavesStrategy":
requestedItem = new ElliotWavesStrategy(_tickerData, this);
break;
//
// Bull
//
case "BullBollingerExtended":
requestedItem = new BullBollingerExtended(_tickerData, this);
break;
case "BullBeltHoldFound":
requestedItem = new BullBeltHoldFound(_tickerData, this);
break;
case "BullEngulfingFound":
requestedItem = new BullEngulfingFound(_tickerData, this);
break;
case "BullHaramiFound":
requestedItem = new BullHaramiFound(_tickerData, this);
break;
case "BullHaramiCrossFound":
requestedItem = new BullHaramiCrossFound(_tickerData, this);
break;
case "BullCciCrossover":
requestedItem = new BullCciCrossover(_tickerData, this);
break;
case "BullEaseOfMovement":
requestedItem = new BullEaseOfMovement(_tickerData, this);
break;
case "BullDojiFound":
requestedItem = new BullDojiFound(_tickerData, this);
break;
case "HammerFound":
requestedItem = new HammerFound(_tickerData, this);
break;
case "BullKeltnerExtended":
requestedItem = new BullKeltnerExtended(_tickerData, this);
break;
case "BullMacdCrossover":
requestedItem = new BullMacdCrossover(_tickerData, this);
break;
case "BullMacdMomentum":
requestedItem = new BullMacdMomentum(_tickerData, this);
break;
case "BullMomentumCrossover":
requestedItem = new BullMomentumCrossover(_tickerData, this);
break;
case "MorningStarFound":
requestedItem = new MorningStarFound(_tickerData, this);
break;
case "PiercingLineFound":
requestedItem = new PiercingLineFound(_tickerData, this);
break;
case "RisingThreeMethodsFound":
requestedItem = new RisingThreeMethodsFound(_tickerData, this);
break;
case "BullRsiCrossover":
requestedItem = new BullRsiCrossover(_tickerData, this);
break;
case "BullSmaCrossover":
requestedItem = new BullSmaCrossover(_tickerData, this);
break;
case "StickSandwitchFound":
requestedItem = new StickSandwitchFound(_tickerData, this);
break;
case "BullStochasticsFastCrossover":
requestedItem = new BullStochasticsFastCrossover(_tickerData, this);
break;
case "BullStochasticsCrossover":
requestedItem = new BullStochasticsCrossover(_tickerData, this);
break;
case "BullStochRsiCrossover":
requestedItem = new BullStochRsiCrossover(_tickerData, this);
break;
case "ThreeWhiteSoldiersFound":
requestedItem = new ThreeWhiteSoldiersFound(_tickerData, this);
break;
case "BullTrendStart":
requestedItem = new BullTrendStart(_tickerData, this);
break;
case "BullTrixSignalCrossover":
requestedItem = new BullTrixSignalCrossover(_tickerData, this);
break;
case "BullTrixZeroCrossover":
requestedItem = new BullTrixZeroCrossover(_tickerData, this);
break;
case "UpsideTasukiGapFound":
requestedItem = new UpsideTasukiGapFound(_tickerData, this);
break;
case "BullWilliamsRCrossover":
requestedItem = new BullWilliamsRCrossover(_tickerData, this);
break;
case "BullPriceOscillator":
requestedItem = new BullPriceOscillator(_tickerData, this);
break;
case "BullDmi":
requestedItem = new BullDmi(_tickerData, this);
break;
case "BullBressertDss":
requestedItem = new BullBressertDss(_tickerData, this, runnableParams);
break;
case "BullRsi3m3":
requestedItem = new BullRsi3m3(_tickerData, this);
break;
case "BullDtOscillator":
requestedItem = new BullDtOscillator(_tickerData, this);
break;
//////////// Predicted bull strategies ///////////
case "BullCciCrossoverPredicted":
requestedItem = new BullCciCrossoverPredicted(_tickerData, this);
break;
case "BullDmiPredicted":
requestedItem = new BullDmiPredicted(_tickerData, this);
break;
case "BullEaseOfMovementPredicted":
requestedItem = new BullEaseOfMovementPredicted(_tickerData, this);
break;
case "BullKeltnerExtendedPredicted":
requestedItem = new BullKeltnerExtendedPredicted(_tickerData, this);
break;
case "BullMacdCrossoverPredicted":
requestedItem = new BullMacdCrossoverPredicted(_tickerData, this);
break;
case "BullMomentumCrossoverPredicted":
requestedItem = new BullMomentumCrossoverPredicted(_tickerData, this);
break;
case "BullPriceOscillatorPredicted":
requestedItem = new BullPriceOscillatorPredicted(_tickerData, this);
break;
case "BullRsiCrossoverPredicted":
requestedItem = new BullRsiCrossoverPredicted(_tickerData, this);
break;
case "BullSmaCrossoverPredicted":
requestedItem = new BullSmaCrossoverPredicted(_tickerData, this);
break;
case "BullStochasticsCrossoverPredicted":
requestedItem = new BullStochasticsCrossoverPredicted(_tickerData, this);
break;
case "BullStochasticsFastCrossoverPredicted":
requestedItem = new BullStochasticsFastCrossoverPredicted(_tickerData, this);
break;
case "BullStochRsiCrossoverPredicted":
requestedItem = new BullStochRsiCrossoverPredicted(_tickerData, this);
break;
case "BullTrixSignalCrossoverPredicted":
requestedItem = new BullTrixSignalCrossoverPredicted(_tickerData, this);
break;
case "BullTrixZeroCrossoverPredicted":
requestedItem = new BullTrixZeroCrossoverPredicted(_tickerData, this);
break;
case "BullWilliamsRCrossoverPredicted":
requestedItem = new BullWilliamsRCrossoverPredicted(_tickerData, this);
break;
//
// Bear
//
case "BearBollingerExtended":
requestedItem = new BearBollingerExtended(_tickerData, this);
break;
case "BearCciCrossover":
requestedItem = new BearCciCrossover(_tickerData, this);
break;
case "BearEaseOfMovement":
requestedItem = new BearEaseOfMovement(_tickerData, this);
break;
case "BearDojiFound":
requestedItem = new BearDojiFound(_tickerData, this);
break;
case "BearKeltnerExtended":
requestedItem = new BearKeltnerExtended(_tickerData, this);
break;
case "BearMacdMomentum":
requestedItem = new BearMacdMomentum(_tickerData, this);
break;
case "BearMacdCrossover":
requestedItem = new BearMacdCrossover(_tickerData, this);
break;
case "BearMomentumCrossover":
requestedItem = new BearMomentumCrossover(_tickerData, this);
break;
case "BearRsiCrossover":
requestedItem = new BearRsiCrossover(_tickerData, this);
break;
case "BearSmaCrossover":
requestedItem = new BearSmaCrossover(_tickerData, this);
break;
case "BearStochasticsCrossover":
requestedItem = new BearStochasticsCrossover(_tickerData, this);
break;
case "BearStochasticsFastCrossover":
requestedItem = new BearStochasticsFastCrossover(_tickerData, this);
break;
case "BearStochRsiCrossover":
requestedItem = new BearStochRsiCrossover(_tickerData, this);
break;
case "BearTrendStart":
requestedItem = new BearTrendStart(_tickerData, this);
break;
case "BearTrixSignalCrossover":
requestedItem = new BearTrixSignalCrossover(_tickerData, this);
break;
case "BearTrixZeroCrossover":
requestedItem = new BearTrixZeroCrossover(_tickerData, this);
break;
case "BearWilliamsRCrossover":
requestedItem = new BearWilliamsRCrossover(_tickerData, this);
break;
case "BearBeltHoldFound":
requestedItem = new BearBeltHoldFound(_tickerData, this);
break;
case "BearEngulfingFound":
requestedItem = new BearEngulfingFound(_tickerData, this);
break;
case "BearHaramiFound":
requestedItem = new BearHaramiFound(_tickerData, this);
break;
case "BearHaramiCrossFound":
requestedItem = new BearHaramiCrossFound(_tickerData, this);
break;
case "DarkCloudCoverFound":
requestedItem = new DarkCloudCoverFound(_tickerData, this);
break;
case "DownsideTasukiGapFound":
requestedItem = new DownsideTasukiGapFound(_tickerData, this);
break;
case "EveningStarFound":
requestedItem = new EveningStarFound(_tickerData, this);
break;
case "FallingThreeMethodsFound":
requestedItem = new FallingThreeMethodsFound(_tickerData, this);
break;
case "HangingManFound":
requestedItem = new HangingManFound(_tickerData, this);
break;
case "InvertedHammerFound":
requestedItem = new InvertedHammerFound(_tickerData, this);
break;
case "ShootingStarFound":
requestedItem = new ShootingStarFound(_tickerData, this);
break;
case "ThreeBlackCrowsFound":
requestedItem = new ThreeBlackCrowsFound(_tickerData, this);
break;
case "UpsideGapTwoCrowsFound":
requestedItem = new UpsideGapTwoCrowsFound(_tickerData, this);
break;
case "BearPriceOscillator":
requestedItem = new BearPriceOscillator(_tickerData, this);
break;
case "BearDmi":
requestedItem = new BearDmi(_tickerData, this);
break;
case "BearBressertDss":
requestedItem = new BearBressertDss(_tickerData, this, runnableParams);
break;
case "BearRsi3m3":
requestedItem = new BearRsi3m3(_tickerData, this);
break;
case "BearDtOscillator":
requestedItem = new BearDtOscillator(_tickerData, this);
break;
//////////// Predicted bear strategies ///////////
case "BearCciCrossoverPredicted":
requestedItem = new BearCciCrossoverPredicted(_tickerData, this);
break;
case "BearDmiPredicted":
requestedItem = new BearDmiPredicted(_tickerData, this);
break;
case "BearEaseOfMovementPredicted":
requestedItem = new BearEaseOfMovementPredicted(_tickerData, this);
break;
case "BearKeltnerExtendedPredicted":
requestedItem = new BearKeltnerExtendedPredicted(_tickerData, this);
break;
case "BearMacdCrossoverPredicted":
requestedItem = new BearMacdCrossoverPredicted(_tickerData, this);
break;
case "BearMomentumCrossoverPredicted":
requestedItem = new BearMomentumCrossoverPredicted(_tickerData, this);
break;
case "BearPriceOscillatorPredicted":
requestedItem = new BearPriceOscillatorPredicted(_tickerData, this);
break;
case "BearRsiCrossoverPredicted":
requestedItem = new BearRsiCrossoverPredicted(_tickerData, this);
break;
case "BearSmaCrossoverPredicted":
requestedItem = new BearSmaCrossoverPredicted(_tickerData, this);
break;
case "BearStochasticsCrossoverPredicted":
requestedItem = new BearStochasticsCrossoverPredicted(_tickerData, this);
break;
case "BearStochasticsFastCrossoverPredicted":
requestedItem = new BearStochasticsFastCrossoverPredicted(_tickerData, this);
break;
case "BearStochRsiCrossoverPredicted":
requestedItem = new BearStochRsiCrossoverPredicted(_tickerData, this);
break;
case "BearTrixSignalCrossoverPredicted":
requestedItem = new BearTrixSignalCrossoverPredicted(_tickerData, this);
break;
case "BearTrixZeroCrossoverPredicted":
requestedItem = new BearTrixZeroCrossoverPredicted(_tickerData, this);
break;
case "BearWilliamsRCrossoverPredicted":
requestedItem = new BearWilliamsRCrossoverPredicted(_tickerData, this);
break;
default:
throw new Exception(nameAndParameters + " doesn't exist");
}
_createdItems[key] = requestedItem;
}
return(requestedItem);
}
private void PreprocessIndicators()
{
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
double[] sma = null;
double[] wma = null;
double[] ema = null;
MovingAverageConvergenceDivergence macd = null;
double[] rsi = null;
double[] williamsR = null;
Stochastics stochastics = null;
if (isSMA)
{
sma = MovingAverage.Simple(code, targetDate, 14, numberOfData);
}
if (isWMA)
{
wma = MovingAverage.Weighted(code, targetDate, 14, numberOfData);
}
if (isEMA)
{
ema = MovingAverage.Exponential(code, targetDate, 14, numberOfData);
}
if (isMACD)
{
macd = new MovingAverageConvergenceDivergence(code, targetDate, 12, 26, 9, numberOfData);
}
if (isRSI)
{
rsi = RelativeStrengthIndex.Rsi(code, targetDate, 14, numberOfData);
}
if (isWilliamsR)
{
williamsR = WilliamsR.Wsr(code, targetDate, 14, numberOfData);
}
if (isStochastics)
{
stochastics = new Stochastics(code, targetDate, 14, 3, 3, numberOfData);
}
double[] closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
double[] smaOut = null;
double[] wmaOut = null;
double[] emaOut = null;
double[] macdOut = null;
double[] rsiOut = null;
double[] williamsROut = null;
double[] stochasticsOut = null;
if (isSMA)
{
smaOut = IndicatorDataPreprocessor.GetSMAOut(sma);
}
if (isWMA)
{
wmaOut = IndicatorDataPreprocessor.GetWMAOut(wma);
}
if (isEMA)
{
emaOut = IndicatorDataPreprocessor.GetEMAOut(ema);
}
if (isMACD)
{
macdOut = IndicatorDataPreprocessor.GetMACDOut(macd);
}
if (isRSI)
{
rsiOut = IndicatorDataPreprocessor.GetRSIOut(rsi, false);
}
if (isWilliamsR)
{
williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(williamsR, false);
}
if (isStochastics)
{
stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(stochastics, false);
}
minRowCount = closesOut.Length;
if (isSMA)
{
minRowCount = minRowCount < smaOut.Length ? minRowCount : smaOut.Length;
}
if (isWMA)
{
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
}
if (isEMA)
{
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
}
if (isMACD)
{
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
}
if (isRSI)
{
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
}
if (isWilliamsR)
{
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
}
if (isStochastics)
{
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
}
FeatureVector featureVector = new FeatureVector();
if (isSMA)
{
featureVector.AddColumn("SMA", smaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWMA)
{
featureVector.AddColumn("WMA", wmaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isEMA)
{
featureVector.AddColumn("EMA", emaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isMACD)
{
featureVector.AddColumn("MACD", macdOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isRSI)
{
featureVector.AddColumn("RSI", rsiOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWilliamsR)
{
featureVector.AddColumn("WilliamsR", williamsROut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isStochastics)
{
featureVector.AddColumn("Stochastics", stochasticsOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
featureVector.AddColumn("label", closesOut.Select(p => (object)string.Format("{0:0.0}", p).ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
int count = featureVector.Values[0].Length;
training = new FeatureVector();
test = new FeatureVector();
for (int i = 0; i < featureVector.ColumnName.Count; i++)
{
training.AddColumn(featureVector.ColumnName[i], featureVector.Values[i].Take((int)(count * trainingPercentage / 100.0)).ToArray());
test.AddColumn(featureVector.ColumnName[i], featureVector.Values[i].Skip((int)(count * trainingPercentage / 100.0)).Take(count).ToArray()); // Take(count) means take the rest of all elements, number of the rest of the elements is smaller than count.
}
}
protected override void Execute()
{
SMA sma10 = SMA.Series(Close, 10);
EMA ema30 = EMA.Series(Close, 30, EMACalculation.Modern);
WilliamsR wlm = WilliamsR.Series(Bars, 3);
BBandLower LB = BBandLower.Series(Close, 29, 2.5);
BBandUpper UB = BBandUpper.Series(Close, 29, 2.5);
SolidBrush shadowBrush = new SolidBrush(Color.FromArgb(10, Color.FromArgb(102, 0, 0, 255)));
PlotSeriesFillBand(PricePane, LB, UB, Color.FromArgb(255, 176, 196, 222), shadowBrush, LineStyle.Solid, 2);
DateTime nextReportDate = new DateTime(2010, 1, 1);
FundamentalItem fi = GetFundamentalItem(Bars.Count - 1, Bars.Symbol, "earnings per share");
bool crossOver = false;
bool pullback = false;
bool crossUnder = false;
bool pullup = false;
for (int bar = 30; bar < Bars.Count; bar++)
{
SetContext("SPY", true);
bool signal = sma10[bar] > ema30[bar];
bool goLong = false;
bool goShort = false;
if (signal && wlm[bar] < 20)
{
goLong = true;
}
if (!signal && wlm[bar] > 80)
{
crossOver = false;
goShort = true;
}
RestoreContext();
//goLong = true;
if (!IsLastPositionActive)
{
if (goLong)
{
if (CrossOver(bar, sma10, ema30))
{
crossOver = true;
pullback = false;
crossUnder = false;
pullup = false;
continue;
}
if (crossOver && Close[bar] < sma10[bar] && pullback == false)
{
pullback = true;
}
if (pullback && Close[bar] > sma10[bar])
{
// DrawCircle( PricePane, 10, bar, High[bar], Color.Green, Color.DarkGreen, WealthLab.LineStyle.Solid, 2, true );
// crossOver = false;
pullback = false;
BuyAtMarket(bar + 1, "SMA 10 is above EMA 30 and pull back is done ");
}
}
else
if (goShort)
{
if (CrossUnder(bar, sma10, ema30))
{
crossUnder = true;
pullup = false;
crossOver = false;
pullback = false;
continue;
}
if (crossUnder && Close[bar] > sma10[bar] && pullup == false)
{
pullup = true;
}
if (pullup && Close[bar] < sma10[bar])
{
// DrawCircle( PricePane, 10, bar, High[bar], Color.Green, Color.DarkGreen, WealthLab.LineStyle.Solid, 2, true );
// crossOver = false;
pullup = false;
ShortAtMarket(bar + 1, "SMA 10 is below EMA 30 and pull back is done ");
}
// DrawCircle( PricePane, 10, bar, High[bar], Color.Blue, Color.Black, WealthLab.LineStyle.Solid, 2, true );
}
}
else
{
if (LastActivePosition.PositionType == PositionType.Long)
{
double b = LB[bar];
if (
LastActivePosition.EntryPrice > Close[bar] && Close[bar] < b &&
bar >= LastActivePosition.EntryBar
)
{
SellAtMarket(bar + 1, LastActivePosition, "stop lose");
crossOver = false;
pullback = false;
continue;
}
if (Bars.IntradayBarNumber(bar) == 0 && EarningsDate.InWindow(this, bar, "earnings per share", 365, 0))
{
//DrawLabel(PricePane, "Next Earnings: " + Date[bar].ToString());
PrintDebug(Date[bar].ToString());
}
if (Date[bar].Day > (LastActivePosition.EntryDate.Day + 4) && Bars.IsLastBarOfDay(bar))
{
SellAtMarket(bar + 1, LastActivePosition, "Close on 5th day");
crossOver = false;
pullback = false;
continue;
}
else
if (CrossUnder(bar, sma10, ema30))
{
SellAtMarket(bar + 1, LastActivePosition, "Take profit");
crossOver = false;
pullback = false;
continue;
}
}
else if (LastActivePosition.PositionType == PositionType.Short)
{
double b = UB[bar];
if (
LastActivePosition.EntryPrice < Close[bar] && Close[bar] > b &&
bar >= LastActivePosition.EntryBar
)
{
CoverAtMarket(bar + 1, LastActivePosition, "Short stop lose");
crossUnder = false;
pullup = false;
continue;
}
if (Date[bar].Day > (LastActivePosition.EntryDate.Day + 4) && Bars.IsLastBarOfDay(bar))
{
CoverAtMarket(bar + 1, LastActivePosition, "Close on 5th day");
crossUnder = false;
pullup = false;
continue;
}
else
if (CrossOver(bar, sma10, ema30))
{
CoverAtMarket(bar + 1, LastActivePosition, "Take profit");
crossUnder = false;
pullup = false;
continue;
}
}
}
}
//Pushed indicator ChartPane statements
ChartPane paneWilliamsR1 = CreatePane(40, true, true);
//Pushed indicator PlotSeries statements
PlotSeries(PricePane, sma10, Color.DarkGreen, LineStyle.Solid, 2);
PlotSeries(PricePane, ema30, Color.Blue, LineStyle.Solid, 2);
PlotSeries(paneWilliamsR1, wlm, Color.OliveDrab, LineStyle.Solid, 2);
}
private void buttonForDataSplitNext_Click(object sender, EventArgs e)
{
trainingSetPercentage = (double)numericUpDownForTrainingSetPercent.Value / 100.0;
numFolds = (int)numericUpDownForNumFolds.Value;
double[] smaOut = null;
double[] wmaOut = null;
double[] emaOut = null;
double[] macdOut = null;
double[] stochasticsOut = null;
double[] williamsROut = null;
double[] rsiOut = null;
double[] closesOut = null;
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
if (isSMAChecked)
{
smaOut = IndicatorDataPreprocessor.GetSMAOut(MovingAverage.Simple(code, targetDate, smaPeriod, numberOfData));
}
if (isWMAChecked)
{
wmaOut = IndicatorDataPreprocessor.GetWMAOut(MovingAverage.Weighted(code, targetDate, wmaPeriod, numberOfData));
}
if (isEMAChecked)
{
emaOut = IndicatorDataPreprocessor.GetEMAOut(MovingAverage.Exponential(code, targetDate, emaPeriod, numberOfData));
}
if (isMACDChecked)
{
macdOut = IndicatorDataPreprocessor.GetMACDOut(new MovingAverageConvergenceDivergence(code, targetDate, firstPeriod, secondPeriod, triggerPeriod, numberOfData));
}
if (isStochasticsChecked)
{
stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(new Stochastics(code, targetDate, fastKPeriod, fastDPeriod, slowDPeriod, numberOfData));
}
if (isWilliamsRChecked)
{
williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(WilliamsR.Wsr(code, targetDate, williamsRPeriod, numberOfData));
}
if (isRSIChecked)
{
rsiOut = IndicatorDataPreprocessor.GetRSIOut(RelativeStrengthIndex.Rsi(code, targetDate, rsiPeriod, numberOfData));
}
closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
int minRowCount = 1000000;
if (smaOut != null)
{
minRowCount = smaOut.Length;
}
if (wmaOut != null)
{
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
}
if (emaOut != null)
{
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
}
if (macdOut != null)
{
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
}
if (rsiOut != null)
{
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
}
if (williamsROut != null)
{
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
}
if (stochasticsOut != null)
{
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
}
if (closesOut != null)
{
minRowCount = minRowCount < closesOut.Length ? minRowCount : closesOut.Length;
}
var fv = new FeatureVector();
if (isSMAChecked)
{
fv.AddColumn("SMA", smaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWMAChecked)
{
fv.AddColumn("WMA", wmaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isEMAChecked)
{
fv.AddColumn("EMA", emaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isMACDChecked)
{
fv.AddColumn("MACD", macdOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isRSIChecked)
{
fv.AddColumn("RSI", rsiOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWilliamsRChecked)
{
fv.AddColumn("WilliamsR", williamsROut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isStochasticsChecked)
{
fv.AddColumn("Stochastics", stochasticsOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
fv.AddColumn("label", closesOut.Select(p => (object)string.Format("{0:0.0}", p).ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
var training = new FeatureVector();
var test = new FeatureVector();
int count = fv.Values[0].Length;
for (int i = 0; i < fv.ColumnName.Count; i++)
{
training.AddColumn(fv.ColumnName[i], fv.Values[i].Take((int)(count * trainingSetPercentage)).ToArray());
}
for (int i = 0; i < fv.ColumnName.Count; i++)
{
test.AddColumn(fv.ColumnName[i], fv.Values[i].Skip((int)(count * trainingSetPercentage)).Take(count).ToArray()); // Take(count) means take the rest of all elements, number of the rest of the elements is smaller than count.
}
if (numFolds > 0)
{
BinaryClassificationEvaluator bce1 = new BinaryClassificationEvaluator();
LinearRegression linearRegression = new LinearRegression();
CrossValidator cvLinReg = new CrossValidator(linearRegression, bce1, numFolds);
CrossValidatorModel cvLinRegModel = (CrossValidatorModel)cvLinReg.Fit(training);
FeatureVector linRegPredictions = cvLinRegModel.transform(test);
bce1.evaluate(linRegPredictions);
linRegAcc = bce1.Accuracy;
BinaryClassificationEvaluator bce2 = new BinaryClassificationEvaluator();
LogisticRegression logisticRegression = new LogisticRegression();
CrossValidator cvLogReg = new CrossValidator(logisticRegression, bce2, numFolds);
CrossValidatorModel cvLogRegModel = (CrossValidatorModel)cvLogReg.Fit(training);
FeatureVector logRegPredictions = cvLogRegModel.transform(test);
bce2.evaluate(logRegPredictions);
logRegAcc = bce2.Accuracy;
BinaryClassificationEvaluator bce3 = new BinaryClassificationEvaluator();
NaiveBayes naiveBayes = new NaiveBayes();
CrossValidator cvNaiBay = new CrossValidator(naiveBayes, bce3, numFolds);
CrossValidatorModel cvNaiBayModel = (CrossValidatorModel)cvNaiBay.Fit(training);
FeatureVector naiBayPredictions = cvNaiBayModel.transform(test);
bce3.evaluate(naiBayPredictions);
naiBayAcc = bce3.Accuracy;
}
else
{
BinaryClassificationEvaluator bce1 = new BinaryClassificationEvaluator();
LinearRegression linearRegression = new LinearRegression();
LinearRegressionModel linearRegressionModel = (LinearRegressionModel)linearRegression.Fit(training);
FeatureVector linRegPredictions = linearRegressionModel.transform(test);
bce1.evaluate(linRegPredictions);
linRegAcc = bce1.Accuracy;
BinaryClassificationEvaluator bce2 = new BinaryClassificationEvaluator();
LogisticRegression logicticRegression = new LogisticRegression();
LogisticRegressionModel logisticRegressionModel = (LogisticRegressionModel)logicticRegression.Fit(training);
FeatureVector logRegPredictions = logisticRegressionModel.transform(test);
bce2.evaluate(logRegPredictions);
logRegAcc = bce2.Accuracy;
BinaryClassificationEvaluator bce3 = new BinaryClassificationEvaluator();
NaiveBayes naiveBayes = new NaiveBayes();
NaiveBayesModel naiveBayesModel = (NaiveBayesModel)naiveBayes.Fit(training);
FeatureVector naiBayPredictions = naiveBayesModel.transform(test);
bce3.evaluate(naiBayPredictions);
naiBayAcc = bce3.Accuracy;
}
labelForLinRegAcc.Text = linRegAcc.ToString();
labelForLogRegAcc.Text = logRegAcc.ToString();
labelForNaiBayAcc.Text = naiBayAcc.ToString();
panelForResults.BringToFront();
}
/// <summary>
/// Initialize test
/// </summary>
public TestIndicatorWilliamsR()
{
DataStream stream = new OHLCBarStream(new ForexSecurity("EURUSD"), BarInterval.FiveMin);
_sut = new WilliamsR(Period, stream, stream.DefaultInterval);
}
public WilliamsRSeries(C1FlexChart chart, string plotAreaName) : base()
{
Chart = chart;
Chart.BeginUpdate();
AxisY.TitleStyle = new ChartStyle();
AxisY.TitleStyle.FontWeight = Windows.UI.Text.FontWeights.Bold;
AxisY.Position = C1.Chart.Position.Right;
AxisY.PlotAreaName = plotAreaName;
AxisY.Title = "W%R";
AxisY.Labels = false;
AxisY.MajorTickMarks = AxisY.MinorTickMarks = C1.Chart.TickMark.None;
WilliamsR series = new WilliamsR();
series.ChartType = C1.Chart.Finance.FinancialChartType.Line;
series.Style = new ChartStyle();
series.Style.Stroke = new SolidColorBrush(Color.FromArgb(255, 51, 103, 214));
series.Style.Fill = new SolidColorBrush(Color.FromArgb(128, 66, 133, 244));
series.Style.StrokeThickness = 1;
series.BindingX = "Date";
series.Binding = "High,Low,Close";
series.AxisY = AxisY;
Chart.Series.Add(series);
ThresholdSeries overBought = new ThresholdSeries();
overBought.ChartType = C1.Chart.Finance.FinancialChartType.Line;
overBought.Style = new ChartStyle();
overBought.Style.Stroke = new SolidColorBrush(ViewModel.IndicatorPalettes.StockGreen);
overBought.Style.StrokeThickness = 1;
overBought.AxisY = AxisY;
Chart.Series.Add(overBought);
ThresholdSeries overSold = new ThresholdSeries();
overSold.ChartType = C1.Chart.Finance.FinancialChartType.Line;
overSold.Style = new ChartStyle();
overSold.Style.Stroke = new SolidColorBrush(ViewModel.IndicatorPalettes.StockRed);
overSold.Style.StrokeThickness = 1;
overSold.AxisY = AxisY;
Chart.Series.Add(overSold);
Utilities.Helper.BindingSettingsParams(chart, series, typeof(WilliamsR), "Williams% R",
new Data.PropertyParam[]
{
new Data.PropertyParam("Period", typeof(int)),
new Data.PropertyParam("Style.Stroke", typeof(Brush)),
},
() =>
{
this.OnSettingParamsChanged();
}
);
Utilities.Helper.BindingSettingsParams(chart, overBought, typeof(ThresholdSeries), "Williams% R",
new Data.PropertyParam[]
{
new Data.PropertyParam("ZonesVisibility", typeof(bool)),
new Data.PropertyParam("Threshold", typeof(int), "OverBought"),
},
() =>
{
this.OnSettingParamsChanged();
}
);
Utilities.Helper.BindingSettingsParams(chart, overSold, typeof(ThresholdSeries), "Williams% R",
new Data.PropertyParam[]
{
new Data.PropertyParam("ZonesVisibility", typeof(bool)),
new Data.PropertyParam("Threshold", typeof(int), "OverSold"),
},
() =>
{
this.OnSettingParamsChanged();
}
);
overBought.OnThesholdChanged += (o, e) =>
{
this.OnSettingParamsChanged();
};
overSold.OnThesholdChanged += (o, e) =>
{
this.OnSettingParamsChanged();
};
//binding series color to axis title.
Binding binding = new Binding();
binding.Path = new Windows.UI.Xaml.PropertyPath("Stroke");
binding.Source = series.Style;
BindingOperations.SetBinding(AxisY.TitleStyle, ChartStyle.StrokeProperty, binding);
Chart.EndUpdate();
this.Series = new FinancialSeries[] { series, overBought, overSold };
}
private static void RunConsoleApplication()
{
string filePath = (Environment.OSVersion.Platform == PlatformID.Unix || Environment.OSVersion.Platform == PlatformID.MacOSX) ? Environment.GetEnvironmentVariable("HOME") : Environment.ExpandEnvironmentVariables("%HOMEDRIVE%%HOMEPATH%");
filePath += "\\indicatorOutput.txt";
string code = "AKBNK";
DateTime targetDate = new DateTime(2018, 11, 1).ToLocalTime();
int numberOfData = 1000;
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
double[] sma = MovingAverage.Simple(code, targetDate, 14, numberOfData);
double[] wma = MovingAverage.Weighted(code, targetDate, 14, numberOfData);
double[] ema = MovingAverage.Exponential(code, targetDate, 14, numberOfData);
MovingAverageConvergenceDivergence macd = new MovingAverageConvergenceDivergence(code, targetDate, 12, 26, 9, numberOfData);
double[] rsi = RelativeStrengthIndex.Rsi(code, targetDate, 14, numberOfData);
double[] williams = WilliamsR.Wsr(code, targetDate, 14, numberOfData);
Stochastics stochastics = new Stochastics(code, targetDate, 14, 3, 3, numberOfData);
double[] closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
double[] smaOut = IndicatorDataPreprocessor.GetSMAOut(sma);
double[] wmaOut = IndicatorDataPreprocessor.GetWMAOut(wma);
double[] emaOut = IndicatorDataPreprocessor.GetEMAOut(ema);
double[] macdOut = IndicatorDataPreprocessor.GetMACDOut(macd);
double[] rsiOut = IndicatorDataPreprocessor.GetRSIOut(rsi);
double[] williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(williams);
double[] stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(stochastics);
int minRowCount;
minRowCount = smaOut.Length;
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
minRowCount = minRowCount < closesOut.Length ? minRowCount : closesOut.Length;
FeatureVector vector = new FeatureVector();
vector.AddColumn("SMA", smaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("WMA", wmaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("EMA", emaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("MACD", macdOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("RSI", rsiOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("WilliamsR", williamsROut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("Stochastics", stochasticsOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("label", closesOut.Select(p => (object)string.Format("{0:0.0}", p).ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
new LabeledPointExporter(vector).Export(filePath);
int count = vector.Values[0].Length;
FeatureVector training = new FeatureVector();
for (int i = 0; i < vector.ColumnName.Count; i++)
{
training.AddColumn(vector.ColumnName[i], vector.Values[i].Take(count / 2).ToArray());
}
FeatureVector test = new FeatureVector();
for (int i = 0; i < vector.ColumnName.Count; i++)
{
test.AddColumn(vector.ColumnName[i], vector.Values[i].Skip(count / 2).Take(count / 2).ToArray());
}
//TestNaiveBayes(training, test);
//TestNaiveBayesUsingCrossValidation(training, test);
//TestLinearRegression(training, test);
//TestLinearRegressionUsingCrossValidation(training, test);
//TestLogisticRegression(training, test);
//TestLogisticRegressionUsingCrossValidation(training, test);
}
private static void RunFeatureSelector(int mlAlgorithm, bool isCrossValidationEnabled, double trainingSetPercentage)
{
string code = "AKBNK";
DateTime targetDate = new DateTime(2018, 11, 1).ToLocalTime();
int numberOfData = 4000;
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
double[] sma = MovingAverage.Simple(code, targetDate, 14, numberOfData);
double[] wma = MovingAverage.Weighted(code, targetDate, 14, numberOfData);
double[] ema = MovingAverage.Exponential(code, targetDate, 14, numberOfData);
MovingAverageConvergenceDivergence macd = new MovingAverageConvergenceDivergence(code, targetDate, 12, 26, 9, numberOfData);
double[] rsi = RelativeStrengthIndex.Rsi(code, targetDate, 14, numberOfData);
double[] williams = WilliamsR.Wsr(code, targetDate, 14, numberOfData);
Stochastics stochastics = new Stochastics(code, targetDate, 14, 3, 3, numberOfData);
double[] closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
double[] smaOut = IndicatorDataPreprocessor.GetSMAOut(sma);
double[] wmaOut = IndicatorDataPreprocessor.GetWMAOut(wma);
double[] emaOut = IndicatorDataPreprocessor.GetEMAOut(ema);
double[] macdOut = IndicatorDataPreprocessor.GetMACDOut(macd);
double[] rsiOut = IndicatorDataPreprocessor.GetRSIOut(rsi, false);
double[] williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(williams, false);
double[] stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(stochastics, false);
int minRowCount;
minRowCount = smaOut.Length;
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
minRowCount = minRowCount < closesOut.Length ? minRowCount : closesOut.Length;
int numberOfIndicators = IndicatorService.indicators.Length;
int numberOfCombinations = (int)Math.Pow(2, numberOfIndicators) - 1;
for (int i = 1; i <= numberOfCombinations; i++)
{
int tmp = i;
List <int> indicators = new List <int>();
for (int j = 0; j < numberOfIndicators; j++)
{
if ((tmp & 1) == 1)
{
indicators.Add(IndicatorService.indicators[j]);
}
tmp >>= 1;
}
double accuracy = CalculateAccuracy(indicators, mlAlgorithm, isCrossValidationEnabled, minRowCount, trainingSetPercentage, smaOut, wmaOut, emaOut, macdOut, rsiOut, williamsROut, stochasticsOut, closesOut);
if (indicators.Contains(IndicatorService.SMA))
{
Console.Write("SMA ");
}
if (indicators.Contains(IndicatorService.WMA))
{
Console.Write("WMA ");
}
if (indicators.Contains(IndicatorService.EMA))
{
Console.Write("EMA ");
}
if (indicators.Contains(IndicatorService.MACD))
{
Console.Write("MACD ");
}
if (indicators.Contains(IndicatorService.RSI))
{
Console.Write("RSI ");
}
if (indicators.Contains(IndicatorService.WilliamsR))
{
Console.Write("WilliamsR ");
}
if (indicators.Contains(IndicatorService.Stochastics))
{
Console.Write("Stochastics ");
}
Console.WriteLine("=>\t" + accuracy);
}
}
