public void ComputesCorrectly()
{
var fast = new SimpleMovingAverage(3);
var slow = new SimpleMovingAverage(5);
var signal = new SimpleMovingAverage(3);
var macd = new MovingAverageConvergenceDivergence("macd", 3, 5, 3, MovingAverageType.Simple);
foreach (var data in TestHelper.GetDataStream(7))
{
fast.Update(data);
slow.Update(data);
macd.Update(data);
Assert.AreEqual(fast - slow, macd);
if (fast.IsReady && slow.IsReady)
{
signal.Update(new IndicatorDataPoint(data.Time, macd));
Assert.AreEqual(signal.Current.Value, macd.Current.Value);
}
}
}
protected override void Init()
{
InitLogFile();
#region GetIndicatorsAndFrames// Get Indicators' references...
// Event occurs once at the start of the strategy
//XXPrint("Starting TS on account: {0}, comment: {1}, buystop : {2} sellstop : {3}",
// this.Account.Number, "CommentText", listOfBuyStop.Count, listOfSellStop.Count);
Print("Starting TS on account: {0}, comment: {1}", this.Account.Number, CommentText);
_ma1 = GetIndicator <MovingAverage>(Instrument.Id, Timeframe, 85, 0, MethodMA1, ApplyMA1To);
_ma2 = GetIndicator <MovingAverage>(Instrument.Id, Timeframe, 75, 0, MethodMA1, ApplyMA1To);
_ma3 = GetIndicator <MovingAverage>(Instrument.Id, Timeframe, 5, 0, MethodMA2, ApplyMA2To);
_macd = GetIndicator <MovingAverageConvergenceDivergence> (Instrument.Id, Timeframe);
_macd.FastEmaPeriod = 15;
_macd.AppliedPrice = ApplyMA2To;
_macd.SlowEmaPeriod = 26;
_macd.SmaPeriod = 1;
#endregion //GetIndicatorsAndFrames
}
public override void WarmsUpProperly()
{
int fastPeriod = 3,
slowPeriod = 4,
signalPeriod = 2;
var macd = new MovingAverageConvergenceDivergence(fastPeriod: fastPeriod, slowPeriod: slowPeriod, signalPeriod: signalPeriod);
Assert.IsFalse(macd.Signal.IsReady);
Assert.IsFalse(macd.Histogram.IsReady);
Assert.IsFalse(macd.IsReady);
for (var i = 0; i < fastPeriod; i++)
{
Assert.IsFalse(macd.Fast.IsReady);
macd.Update(new IndicatorDataPoint(DateTime.Today.AddSeconds(i), i));
}
Assert.IsTrue(macd.Fast.IsReady);
for (var i = fastPeriod; i < slowPeriod; i++)
{
Assert.IsFalse(macd.Slow.IsReady);
macd.Update(new IndicatorDataPoint(DateTime.Today.AddSeconds(i), i));
}
Assert.IsTrue(macd.Slow.IsReady);
for (var i = slowPeriod; i < macd.WarmUpPeriod; i++)
{
Assert.IsFalse(macd.Signal.IsReady);
Assert.IsFalse(macd.Histogram.IsReady);
Assert.IsFalse(macd.IsReady);
macd.Update(new IndicatorDataPoint(DateTime.Today.AddSeconds(i), i));
}
Assert.IsTrue(macd.Signal.IsReady);
Assert.IsTrue(macd.Histogram.IsReady);
Assert.IsTrue(macd.IsReady);
}
/// <summary>
/// Called at the start of your algorithm to setup your requirements:
/// </summary>
public override void Initialize()
{
SetCash(100000);
symbols.Add("SPY");
SetStartDate(1998, 1, 1);
SetEndDate(2014, 6, 1);
//Present Social Media Stocks:
// symbols.Add("FB");symbols.Add("LNKD");symbols.Add("GRPN");symbols.Add("TWTR");
// SetStartDate(2011, 1, 1);
// SetEndDate(2014, 12, 1);
//2008 Financials:
// symbols.Add("C");symbols.Add("AIG");symbols.Add("BAC");symbols.Add("HBOS");
// SetStartDate(2003, 1, 1);
// SetEndDate(2011, 1, 1);
//2000 Dot.com:
// symbols.Add("IPET");symbols.Add("WBVN");symbols.Add("GCTY");
// SetStartDate(1998, 1, 1);
// SetEndDate(2000, 1, 1);
//CAPE data
AddData <CAPE>("CAPE");
foreach (string stock in symbols)
{
AddSecurity(SecurityType.Equity, stock, Resolution.Minute);
macd = MACD(stock, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily);
macdDic.Add(stock, macd);
rsi = RSI(stock, 14, MovingAverageType.Exponential, Resolution.Daily);
rsiDic.Add(stock, rsi);
Securities[stock].SetLeverage(10);
}
}
public MovingAverageConvergenceDivergenceOscillatorTrend(Equity equity, int emaPeriodCount1, int emaPeriodCount2, int demPeriodCount)
: base(equity, emaPeriodCount1, emaPeriodCount2, demPeriodCount)
{
_macdIndicator = new MovingAverageConvergenceDivergence(equity, emaPeriodCount1, emaPeriodCount2, demPeriodCount);
}
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 Init()
{
// Event occurs once at the start of the strategy
Print("Starting TS on account: {0}, comment: {1}", this.Account.Number, CommentText);
_macd = GetIndicator <MovingAverageConvergenceDivergence>(Instrument.Id, Timeframe, 15, 30, 4, PriceMode.Close);
}
private void macdParameterNext_Click(object sender, EventArgs e)
{
macdResultTitle.Text = Code + " için Moving Average Convergence Divergence sonucu";
FirstPeriod = (int)firstPeriod.Value;
SecondPeriod = (int)secondPeriod.Value;
TriggerPeriod = (int)triggerPeriod.Value;
closeEma1Ema2Chart.Series.Clear();
macdLineChart.Series.Clear();
triggerLineChart.Series.Clear();
var closeSeries = new Series
{
Name = "Kapanış",
Color = System.Drawing.Color.Black,
ChartType = SeriesChartType.Line
};
var emaFirstSeries = new Series
{
Name = "EMA Kısa Period",
Color = System.Drawing.Color.Red,
ChartType = SeriesChartType.Line
};
var emaSecondSeries = new Series
{
Name = "EMA Uzun Period",
Color = System.Drawing.Color.Green,
ChartType = SeriesChartType.Line
};
var macdLineSeries = new Series
{
Name = "MACD",
Color = System.Drawing.Color.Blue,
ChartType = SeriesChartType.Line
};
var triggerLineSeries = new Series
{
Name = "Trigger",
Color = System.Drawing.Color.Blue,
ChartType = SeriesChartType.Line
};
closeEma1Ema2Chart.Series.Add(closeSeries);
closeEma1Ema2Chart.Series.Add(emaFirstSeries);
closeEma1Ema2Chart.Series.Add(emaSecondSeries);
macdLineChart.Series.Add(macdLineSeries);
triggerLineChart.Series.Add(triggerLineSeries);
var closeData = IndicatorService.GetData(Code, TargetDate, new string[] { "Tarih", "Kapanis" }, NumberOfData);
MovingAverageConvergenceDivergence macd = new MovingAverageConvergenceDivergence(Code, TargetDate, FirstPeriod, SecondPeriod, TriggerPeriod, NumberOfData, MapReduceAllowed);
double[] emaFirst = macd.EmaFirst;
double[] emaSecond = macd.EmaSecond;
double[] macdLine = macd.MacdLine;
double[] triggerLine = macd.TriggerLine;
DateTime date;
int i;
for (i = 0; i < triggerLine.Length; i++)
{
var close = closeData.ElementAt(i);
date = close.GetElement("Tarih").Value.AsBsonDateTime.ToLocalTime();
closeSeries.Points.AddXY(date, close.GetElement("Kapanis").Value.ToDouble());
emaFirstSeries.Points.AddXY(date, emaFirst[i]);
emaSecondSeries.Points.AddXY(date, emaSecond[i]);
macdLineSeries.Points.AddXY(date, macdLine[i]);
triggerLineSeries.Points.AddXY(date, triggerLine[i]);
}
for (; i < emaFirst.Length; i++)
{
var close = closeData.ElementAt(i);
date = close.GetElement("Tarih").Value.AsBsonDateTime.ToLocalTime();
closeSeries.Points.AddXY(date, close.GetElement("Kapanis").Value.ToDouble());
emaFirstSeries.Points.AddXY(date, emaFirst[i]);
emaSecondSeries.Points.AddXY(date, emaSecond[i]);
macdLineSeries.Points.AddXY(date, macdLine[i]);
}
closeEma1Ema2Chart.ChartAreas[0].AxisY.Minimum = Math.Floor(new double[] { closeData.Select(p => p.GetElement("Kapanis").Value.ToDouble()).Min(), emaFirst.Min(), emaSecond.Min() }.Min());
closeEma1Ema2Chart.ChartAreas[0].AxisY.Maximum = Math.Ceiling(new double[] { closeData.Select(p => p.GetElement("Kapanis").Value.ToDouble()).Max(), emaFirst.Max(), emaSecond.Max() }.Max());
macdLineChart.ChartAreas[0].AxisY.Minimum = Math.Floor(macdLine.Min());
macdLineChart.ChartAreas[0].AxisY.Maximum = Math.Ceiling(macdLine.Max());
triggerLineChart.ChartAreas[0].AxisY.Minimum = Math.Floor(triggerLine.Min());
triggerLineChart.ChartAreas[0].AxisY.Maximum = Math.Floor(triggerLine.Max());
closeEma1Ema2Chart.Invalidate();
macdLineChart.Invalidate();
triggerLineChart.Invalidate();
macdResultPanel.BringToFront();
}
public MovingAverageConvergenceDivergenceCrossover(IList <decimal> closes, int emaPeriodCount1, int emaPeriodCount2, int demPeriodCount)
: base(closes)
{
_macd = new MovingAverageConvergenceDivergence(closes, emaPeriodCount1, emaPeriodCount2, demPeriodCount);
}
protected override void Init()
{
_macdInd = GetIndicator <MovingAverageConvergenceDivergence>(Instrument.Id, Timeframe);
_bbInd = GetIndicator <BollingerBands>(Instrument.Id, Timeframe);
}
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);
}
}
public override void Initialize()
{
_symbol = AddForex(_ticker, Resolution.Minute).Symbol;
_macd = Algo.MACD(_symbol, 12, 26, 9, MovingAverageType.Exponential, _resolution);
}
