/// <summary>
/// performs adjustment to the original data based on exogenous time series,
/// so that we can use the original likelihood function
/// </summary>
/// <returns></returns>
protected TimeSeries ComputeAdjustedValues()
{
if (values == null)
return null;
if (exogenous == null)
return values;
for (int i=0 ; i<numExogenous ; ++i)
if (exogenous[i] == null || exogenous[i].Count != values.Count)
return values;
var adjustments = new TimeSeries();
adjustments.Add(values.TimeStamp(0), 0, false);
for (int t=1 ; t<values.Count ; ++t)
{
double tx = 0;
for (int i=0 ; i<numExogenous ; ++i)
tx += exogenous[i][t - 1]*GetGamma(i);
for (int i = 0; i < AROrder; ++i)
tx += t - i - 1 >= 0 ? ARCoeff(i)*adjustments[t - i - 1] : 0;
adjustments.Add(values.TimeStamp(t), tx, false);
}
var adjusted = new TimeSeries();
for (int t=0 ; t<values.Count ; ++t)
adjusted.Add(adjustments.TimeStamp(t), values[t] - adjustments[t], false);
return adjusted;
}
public override double Calculate(int index)
{
int i = index - length + 1;
if (i == 0)
{
// 将前一天初始化为50
DateTime dt1 = Input.GetDateTime(index - 1);
K.Add(dt1, 50);
D.Add(dt1, 50);
}
else if (i < 0)
{
return(double.NaN);
}
double KValue = K[i];
double DValue = D[i];
KValue = (2.0 * KValue + rsv[i]) / 3.0;
DValue = (2.0 * DValue + KValue) / 3.0;
DateTime dt = Input.GetDateTime(index);
K.Add(dt, KValue);
D.Add(dt, DValue);
return(3 * KValue - 2 * DValue);
}
protected override TimeSeries _BuildOutput(TimeSeries simulatedData, object userState = null)
{
int predictFuture = 0;
double[] fstar;
double[] Vfsar;
double logpyX;
double[] targets = GetDataArray(simulatedData);
Predict(targets, predictFuture, mSigma0, mLambda, mSigmaN, out fstar, out Vfsar, out logpyX);
TimeSeries ft = new TimeSeries();
for (int i = 0; i < targets.Length; ++i)
{
if (mBuildOutputType == BuildOutputType.Mean)
{
ft.Add(simulatedData.TimeStamp(i), fstar[i], true);
}
else
{
ft.Add(simulatedData.TimeStamp(i), Vfsar[i], true);
}
}
return(ft);
}
//public override Icon GetIcon()
//{
// var x = Images.ResourceManager.GetObject("ThresholdIcon") as Icon;
// return x;
//}
public override void Recompute()
{
var ins = GetInputBundle();
outputs = new List <TimeSeries>();
foreach (var ts in ins)
{
var ots = new TimeSeries()
{
Title = ts.Title
};
for (int t = 0; t < ts.Count; ++t)
{
if (ts[t] > Maximum)
{
ots.Add(ts.TimeStamp(t), Maximum, false);
}
else if (ts[t] < Minimum)
{
ots.Add(ts.TimeStamp(t), Minimum, false);
}
else
{
ots.Add(ts.TimeStamp(t), ts[t], false);
}
}
outputs.Add(ots);
}
IsValid = true;
}
private TimeSeries DoUnivariateSampling(TimeSeries ofTS)
{
var retval = new TimeSeries();
retval.Title = ofTS.Title;
if (SamplingInterval.Ticks == 0)
{
int i0, i1;
bool gotHit;
i0 = ofTS.IndexAtOrBefore(StartTime, out gotHit);
if (!gotHit)
{
++i0;
}
i1 = ofTS.IndexAtOrBefore(EndTime, out gotHit);
++i1;
if (i1 > ofTS.Count)
{
i1 = ofTS.Count;
}
for (int t = i0; t < i1; ++t)
{
retval.Add(ofTS.TimeStamp(t), ofTS[t], false);
}
}
else
{
for (var current = new DateTime(StartTime.Ticks); current < EndTime; current = new DateTime((current + SamplingInterval).Ticks))
{
foreach (var sampleOffset in SamplingBaseOffsets)
{
var adjusted = current + sampleOffset;
bool skipping = false;
if (SkipWeekends)
{
if (adjusted.DayOfWeek == DayOfWeek.Saturday || adjusted.DayOfWeek == DayOfWeek.Sunday)
{
skipping = true;
}
}
if (IsWithinSkipIntervals(adjusted))
{
skipping = true;
}
if (!skipping)
{
double sampled = ofTS.ValueAtTime(adjusted);
retval.Add(adjusted, sampled, false);
}
}
}
}
return(retval);
}
//public override Icon GetIcon()
//{
// return null;
//}
public override void Recompute()
{
IsValid = false;
var tsList = GetInputBundle();
if (tsList != null)
{
outputs = new List <TimeSeries>(tsList.Count);
foreach (var ts in tsList)
{
var stripped = new TimeSeries()
{
Title = ts.Title
};
for (int t = 0; t < ts.Count; ++t)
{
if (!(ts[t] < LowerValue || ts[t] > UpperValue))
{
stripped.Add(ts.TimeStamp(t), ts[t], true);
}
}
outputs.Add(stripped);
}
if (outputs.Count > 0)
{
IsValid = true;
}
}
}
private static void Creating()
{
#region creating
TimeSeries <Quarter, decimal> timeSeriesFromConstructor1 = new TimeSeries <Quarter, decimal>(
Quarter.CreateQuarter1(2020),
new [] { 21.3M, 42.4M, 42.5M });
Console.WriteLine(timeSeriesFromConstructor1);
Console.WriteLine();
var timeSeriesFromConstructor2 = new TimeSeries <Day, double>(
new [] { new Day(2019, 9, 17), new Day(2019, 9, 18), new Day(2019, 9, 19) },
new [] { 242.4, 224.42, 262.04 });
Console.WriteLine(timeSeriesFromConstructor2);
Console.WriteLine();
var builder = new TimeSeries <Month, int> .Builder
{
{ Month.CreateJanuary(2019), 1 },
{ Month.CreateFebruary(2019), 2 },
{ Month.CreateMarch(2019), 3 }
};
builder.Add(Month.CreateApril(2019), 4);
TimeSeries <Month, int> timeSeriesFromBuilder = builder.Build();
Console.WriteLine(timeSeriesFromBuilder);
Console.WriteLine();
TimeSeries <Day, string> emptyTimeSeries = TimeSeries <Day, String> .Empty;
Console.WriteLine(emptyTimeSeries);
#endregion
}
public TimeSeries <string> historySeries(DateTime sDate, DateTime eDate)
{
TimeSeries <string> hist = new TimeSeries <string>();
INDEX_DATA_Table_DAOManager dao_m = new INDEX_DATA_Table_DAOManager();
dao_m.INDEX_CODE_ = this.Code_;
dao_m.selectInterval(DataBaseConnectManager.ConnectionFactory("myDB"), sDate, eDate);
foreach (var item in dao_m.DAOList_)
{
string date = item.INDEX_DATE_;
int year = Convert.ToInt32(date.Substring(0, 4));
int month = Convert.ToInt32(date.Substring(4, 2));
int day = Convert.ToInt32(date.Substring(6, 2));
string value = item.INDEX_VALUE_;
hist.Add(new Date(new DateTime(year, month, day)), value);
}
return(hist);
}
public TimeSeries <string> historyCurveSeriesMRO(DateTime sDate, DateTime eDate)
{
TimeSeries <string> hist = new TimeSeries <string>();
IRCurveMarketDataLoader irCurveLoader = IRCurveMarketDataLoader.CreateMarketDataLoader("MRO");
ObservableCollection <Excel_irCurveDataViewModel> e_ircdvmList = new ObservableCollection <Excel_irCurveDataViewModel>();
irCurveLoader.loadInterval(sDate, eDate, this.LinkedCurveCode_, e_ircdvmList);
//TD_MRSTOCKFACTOR_Table_DAOManager dao_m = new TD_MRSTOCKFACTOR_Table_DAOManager();
//dao_m.STOCK_ID_ = this.Code_;
//dao_m.selectInterval(DataBaseConnectManager.ConnectionFactory("MRO"), sDate, eDate);
int tenorIndex = e_ircdvmList[0].getTenorIndex(this.Tenor_);
if (tenorIndex != -1)
{
foreach (var item in e_ircdvmList)
{
string value = item.Excel_rateDataViewModelList_[tenorIndex].Value_;
hist.Add(new Date(item.ReferenceDate_), value);
}
}
return(hist);
}
public TimeSeries <string> historySeriesMRO(DateTime sDate, DateTime eDate)
{
TimeSeries <string> hist = new TimeSeries <string>();
TD_MRSTOCKFACTOR_Table_DAOManager dao_m = new TD_MRSTOCKFACTOR_Table_DAOManager();
dao_m.STOCK_ID_ = this.Code_;
dao_m.selectInterval(DataBaseConnectManager.ConnectionFactory("MRO"), sDate, eDate);
foreach (var item in dao_m.DAOList_)
{
string date = item.STD_DATE_;
int year = Convert.ToInt32(date.Substring(0, 4));
int month = Convert.ToInt32(date.Substring(4, 2));
int day = Convert.ToInt32(date.Substring(6, 2));
string value = item.CURR_PRICE_;
hist.Add(new Date(new DateTime(year, month, day)), value);
}
return(hist);
}
public double SharpeRatio(double RiskFreeRate)
{
int Days = Solution.Performance.PnLSeries.Count;
TimeSeries ReturnSeries = new TimeSeries();
double Portfolio0 = Solution.Cash;
double Portfolio1;
for (int t = 0; t < Days; ++t)
{
DateTime date = Solution.Performance.PnLSeries.GetDateTime(t);
Portfolio1 = Solution.Performance.PnLSeries[t] + Portfolio0;
double DailyReturn = Portfolio1 / Portfolio0 - 1;
Portfolio0 = Portfolio1;
ReturnSeries.Add(date, DailyReturn);
}
double Mean = ReturnSeries.GetMean(0, Days - 1);
double Std = ReturnSeries.GetStdDev(0, Days - 1);
//年化收益均值、波动率
Mean = Mean * 250;
Std = Std * Math.Sqrt(250);
double SR = (Mean - RiskFreeRate) / Std;
return(SR);
}
//public override Icon GetIcon()
//{
// return null;
//}
public override void Recompute()
{
IsValid = false;
if (GetInputType(-1) != InputType.UnivariateTS)
{
return;
}
var series = GetInputBundle();
if (series.Count != 1)
{
return; // something wrong!
}
var input = series[0];
var agg = new TimeSeries();
double accumulated = 0;
for (int t = 0; t < input.Count; ++t)
{
accumulated += input[t];
if (t % Period == Period - 1)
{
agg.Add(input.TimeStamp(t), accumulated, false);
accumulated = 0;
}
}
outputs = new List <TimeSeries>();
outputs.Add(agg);
IsValid = true;
}
public override void OnBarSlice(long size)
{
if (Instrument1 == Instrument)
{
return;
}
if (BarSeries1.Count < 1 || BarSeries2.Count < 1)
{
return;
}
DateTime dt = BarSeries1.Last.DateTime;
if (dt.CompareTo(BarSeries2.Last.DateTime) < 0)
{
dt = BarSeries2.Last.DateTime;
}
spreadSeries.Add(dt, Calculate(BarSeries1.Last, BarSeries2.Last));
if (sma.Count < 1)
{
return;
}
//操作
//Sell(Instrument2,Qty2,"O|卖开");
//Buy(Instrument1,Qty1,"O|买开");
}
public void TestEMA()
{
long ticks = 1000;
var data = new double[] { 20, 40, 22, 35, 33, 78, 21, 45, 33, 5, 67, 22, 98, 22, 34, 54 };
var input = new TimeSeries();
var ema = new EMA(input, 10);
foreach (var d in data)
{
input.Add(new DateTime().AddTicks(ticks++), d);
}
for (var i = 0; i < ema.Count; i++)
{
output.WriteLine(ema[i].ToString());
}
Assert.Equal(ema[0], 20, precision);
Assert.Equal(ema[1], 23.6363636363636, precision);
Assert.Equal(ema[2], 23.3388429752066, precision);
Assert.Equal(ema[3], 25.4590533433509, precision);
Assert.Equal(ema[4], 26.8301345536507, precision);
Assert.Equal(ema[5], 36.1337464529869, precision);
Assert.Equal(ema[6], 33.3821561888075, precision);
Assert.Equal(ema[7], 35.4944914272061, precision);
Assert.Equal(ema[8], 35.0409475313505, precision);
Assert.Equal(ema[9], 29.5789570711049, precision);
Assert.Equal(ema[10], 36.3827830581768, precision);
Assert.Equal(ema[11], 33.7677315930537, precision);
Assert.Equal(ema[12], 45.4463258488621, precision);
Assert.Equal(ema[13], 41.1833575127054, precision);
Assert.Equal(ema[14], 39.8772925103953, precision);
Assert.Equal(ema[15], 42.4450575085053, precision);
}
public void TestEMA()
{
long ticks = 1000;
var data = new double[] { 20, 40, 22, 35, 33, 78, 21, 45, 33, 5, 67, 22, 98, 22, 34, 54 };
var input = new TimeSeries();
var ema = new EMA(input, 10);
foreach (var d in data)
input.Add(new DateTime().AddTicks(ticks++), d);
for (var i = 0; i < ema.Count; i++)
output.WriteLine(ema[i].ToString());
Assert.Equal(ema[0], 20, precision);
Assert.Equal(ema[1], 23.6363636363636, precision);
Assert.Equal(ema[2], 23.3388429752066, precision);
Assert.Equal(ema[3], 25.4590533433509, precision);
Assert.Equal(ema[4], 26.8301345536507, precision);
Assert.Equal(ema[5], 36.1337464529869, precision);
Assert.Equal(ema[6], 33.3821561888075, precision);
Assert.Equal(ema[7], 35.4944914272061, precision);
Assert.Equal(ema[8], 35.0409475313505, precision);
Assert.Equal(ema[9], 29.5789570711049, precision);
Assert.Equal(ema[10], 36.3827830581768, precision);
Assert.Equal(ema[11], 33.7677315930537, precision);
Assert.Equal(ema[12], 45.4463258488621, precision);
Assert.Equal(ema[13], 41.1833575127054, precision);
Assert.Equal(ema[14], 39.8772925103953, precision);
Assert.Equal(ema[15], 42.4450575085053, precision);
}
public override void Recompute()
{
IsValid = false;
var inputs = GetInputBundle();
outputs = new List <TimeSeries>(inputs.Count);
bool failure = false;
for (int i = 0; i < inputs.Count; ++i)
{
var lrs = new TimeSeries();
for (int t = 1; t < inputs[i].Count; ++t)
{
double x1 = inputs[i][t - 1];
double x2 = inputs[i][t];
if (x1 > 0 && x2 > 0)
{
lrs.Add(inputs[i].TimeStamp(t), Math.Log(x2) - Math.Log(x1), false);
}
else
{
failure = true;
}
}
lrs.Title = inputs[i].Title;
outputs.Add(lrs);
}
multivariateOutputPrefix = "LR";
IsValid = true;
if (failure)
{
MessageBox.Show("One or more values was non-positive, corresponding log-returns were left out.", "Warning");
}
}
public static TimeSeries <T> Hourly(Func <T> empty, T initialValue)
{
var ts = new TimeSeries <T>(60, empty);
ts.Add(initialValue);
return(ts);
}
public TimeSeries Predict(SupportVectorMachine network, NormalizeArray norm, TimeSeries simulatedData)
{
double[] data = GenerateData(simulatedData);
int data_count = simulatedData.Count;
TimeSeries ts = new TimeSeries();
double input_val = 0;
for (int idx = 0; idx < data_count; ++idx)
{
var input = new BasicMLData(WindowSize);
for (var i = 0; i < WindowSize; i++)
{
int idx2 = (idx - WindowSize) + i;
if (idx2 < 0)
{
input_val = 0;
}
else
{
input_val = norm.Stats.Normalize(data[idx2]);
}
input[i] = input_val;
}
IMLData output = network.Compute(input);
double prediction = norm.Stats.DeNormalize(output[0]);
ts.Add(simulatedData.TimeStamp(idx), prediction, false);
}
return(ts);
}
public override object SimulateData(object inputs, int simSeed)
{
var times = inputs as List <DateTime>;
if (times == null)
{
return(null); // inputs should be a list of DateTimes
}
int n = times.Count;
var simulated = new TimeSeries();
var randomSource = new Palf(simSeed);
var stdnormal = new Normal();
stdnormal.RandomSource = randomSource;
double mVar = GetVariance(Parameters);
Vector <double> ss = Vector <double> .Build.Dense(n);
for (int i = 0; i < n; ++i)
{
double variance = GetConditionalSig2(i, simulated, ss, Parameters, mVar);
double simLR = stdnormal.RandomSource.NextDouble() * Math.Sqrt(variance);
simulated.Add(times[i], simLR, false);
}
simulated.Title = "Simulation";
simulated.Description = "Simulation from " + Description;
return(simulated);
}
public TimeSeries BuildForecasts(List <DateTime> x_values, List <double> y_values, int day_interval, int future_count)
{
TimeSeries simulatedData = new TimeSeries();
int used_data_count = x_values.Count;
//Create the data
for (int t = 0; t < used_data_count; ++t)
{
double s1 = y_values[t];
DateTime current = x_values[t];
simulatedData.Add(current, s1, false);
}
var nextTime = simulatedData.GetLastTime();
var futureTimes = new List <DateTime>();
for (int t = 0; t < future_count; ++t) // go eight days into the future beyond the end of the data we have
{
nextTime = nextTime.AddDays(day_interval);
futureTimes.Add(nextTime);
}
return(BuildForecasts(simulatedData, futureTimes));
}
public List <double> BuildForecasts(List <double> data, int future_count)
{
TimeSeries simulatedData = new TimeSeries();
DateTime current = DateTime.UtcNow;
//Create the data
for (int t = 0; t < data.Count; ++t)
{
double s1 = data[t];
simulatedData.Add(current, s1, false);
current = current.AddDays(1);
}
var nextTime = simulatedData.GetLastTime();
var futureTimes = new List <DateTime>();
for (int t = 0; t < future_count; ++t) // go eight days into the future beyond the end of the data we have
{
nextTime = nextTime.AddDays(1);
futureTimes.Add(nextTime);
}
TimeSeries preds1 = BuildForecasts(simulatedData, futureTimes);
List <double> preds2 = new List <double>();
for (int i = 0; i < future_count; ++i)
{
preds2.Add(preds1[i]);
}
return(preds2);
}
public void TimeSeriesBasics()
{
TimeSeries series = new TimeSeries();
Assert.IsTrue(series.Count == 0);
series.Add(3.0, 2.0, 1.0);
Assert.IsTrue(series[0] == 3.0);
Assert.IsTrue(series[1] == 2.0);
Assert.IsTrue(series[2] == 1.0);
Assert.IsTrue(series.Count == 3);
Assert.IsFalse(series.Contains(0.0));
Assert.IsTrue(series.Contains(1.0));
Assert.IsTrue(series.IndexOf(0.0) == -1);
Assert.IsTrue(series.IndexOf(1.0) == 2);
Assert.IsTrue(TestUtilities.IsNearlyEqual(series.Mean, 2.0));
series[2] = 0.0;
Assert.IsTrue(series.Count == 3);
Assert.IsTrue(series.Contains(0.0));
Assert.IsFalse(series.Contains(1.0));
Assert.IsTrue(series.IndexOf(0.0) == 2);
Assert.IsTrue(series.IndexOf(1.0) == -1);
Assert.IsTrue(TestUtilities.IsNearlyEqual(series.Mean, 5.0 / 3.0));
series.Clear();
Assert.IsTrue(series.Count == 0);
Assert.IsFalse(series.Contains(0.0));
Assert.IsTrue(series.IndexOf(0.0) == -1);
}
protected override TimeSeries _BuildOutput(TimeSeries simulatedData, object userState = null)
{
if (mModel == null)
{
mModel = BuildModel(simulatedData, out mScaleFactor);
}
TimeSeries preds = new TimeSeries();
for (int i = 0; i < simulatedData.Count; ++i)
{
double[] input = new double[mWindowSize];
for (int j = 0; j < mWindowSize; ++j)
{
int index = i - (mWindowSize - j);
if (index >= 0)
{
input[j] = simulatedData[index] / mScaleFactor;
}
}
double[] output = new double[1];
mModel.Compute(input, output);
preds.Add(simulatedData.TimeStamp(i), output[0] * mScaleFactor, false);
}
return(preds);
}
public override void OnBar(Bar bar)
{
if (bar.DateTime.Day != day)
{
day = bar.DateTime.Day;
ts.Add(bar.DateTime, bar.High + 1);
}
}
public override void OnBar(Bar bar)
{
DateTime time = Clock.Now;
int dateTime = time.Hour * 10000 + time.Minute * 100 + time.Second;
if (dateTime > closeTime)
{
ClosePosition("T|尾盘平仓");
return;
}
if (86400 == bar.Size)
{
return;
}
if (double.IsNaN(UpLine))
{
return;
}
UpSeries.Add(bar.DateTime, UpLine);
DownSeries.Add(bar.DateTime, DownLine);
RangeSeries.Add(bar.DateTime, Range);
if (Mode == StrategyMode.Simulation)
{
// 从模拟切换实盘时用
//return;
}
if (HasPosition)
{
if (Position.Amount < 0 &&
bar.Close > UpLine)
{
ClosePosition("T|反手");
Buy(Qty, "O|");
}
if (Position.Amount > 0 &&
bar.Close < DownLine)
{
ClosePosition("T|反手");
Sell(Qty, "O|");
}
}
else
{
if (bar.Close > UpLine)
{
Buy(Qty, "O|");
}
else if (bar.Close < DownLine)
{
Sell(Qty, "O|");
}
}
}
public void TestAdd()
{
object obj1 = new Object();
DateTime dt = new DateTime(2014, 1, 1, 8, 8, 8);
ts.Add(dt, obj1);
Assert.AreSame(obj1, ts[dt]);
Assert.AreSame(obj1, ts[0]);
}
public void TimeSeriesAutocovariance()
{
TimeSeries series1 = new TimeSeries();
series1.Add(0.0, -1.0, 2.0, -3.0);
double[] acv1 = series1.Autocovariance();
TestAutocovariance(acv1);
}
public static void Load(this TimeSeries s, Stream stream)
{
using (var reader = new BinaryReader(stream)) {
while (reader.PeekChar() != -1)
{
s.Add(new DateTime(reader.ReadInt64()), reader.ReadDouble());
}
}
}
//public override Icon GetIcon()
//{
// return null;
//}
private TimeSeries ApplyFilterTo(TimeSeries ts)
{
var retval = new TimeSeries();
int maOrder = maCoeffs.Length;
int arOrder = arCoeffs.Length;
double arSum = 0;
for (int i = 1; i < arOrder; ++i)
{
arSum += arCoeffs[i];
}
double maSum = 0;
for (int i = 0; i < maOrder; ++i)
{
maSum += maCoeffs[i];
}
// now go through and apply filter
for (int t = 0; t < ts.Count; ++t)
{
double tx = 0.0;
if (TimeInterval.Ticks == 0) // apply the filter to successive data points
{
// get MA part
for (int i = 0; i < maOrder; ++i)
{
tx += t >= i ? maCoeffs[i] * ts[t - i] : ts[0];
}
// get AR part
for (int i = 1; i < arOrder; ++i)
{
tx += t >= i ? arCoeffs[i] * retval[t - i] : ts[0];
}
tx /= (arSum + maSum);
}
else // apply the filter to data points at specified sampling interval
{
// get MA part
for (int i = 0; i < maOrder; ++i)
{
tx += maCoeffs[i] * ts.ValueAtTime(ts.TimeStamp(t) - new TimeSpan(i * TimeInterval.Ticks));
}
// get AR part
for (int i = 1; i < arOrder; ++i)
{
tx += arCoeffs[i] * retval.ValueAtTime(ts.TimeStamp(t) - new TimeSpan(i * TimeInterval.Ticks));
}
tx /= (arSum + maSum);
}
retval.Add(ts.TimeStamp(t), tx, false);
}
return(retval);
}
public void addFixings(List<Date> d, List<double> v, bool forceOverwrite) {
if ((d.Count != v.Count) || d.Count == 0)
throw new ArgumentException("Wrong collection dimensions when creating index fixings");
TimeSeries<double> t = new TimeSeries<double>();
for(int i=0; i<d.Count; i++)
t.Add(d[i], v[i]);
addFixings(t, forceOverwrite);
}
void UpdateAtBar_RBreaker(Bar bar)
{
ssetup.Add(bar.DateTime, _ssetup);
bsetup.Add(bar.DateTime, _bsetup);
senter.Add(bar.DateTime, _senter);
benter.Add(bar.DateTime, _benter);
bbreak.Add(bar.DateTime, _bbreak);
sbreak.Add(bar.DateTime, _sbreak);
}
public override void OnBar(Bar bar)
{
// 这个地方是关键
lengths.Add(bar.DateTime, 5);
// 别的就自己写吧
}
public static double Value(ISeries input, int index, int length1, int length2)
{
if (index >= length1 - 1 && index >= length2 - 1)
{
var ts = new TimeSeries();
for (var i = index - Math.Max(length1, length2) + 1; i <= index; i++)
ts.Add(input.GetDateTime(i), input[i, BarData.Volume]);
return SMA.Value(ts, length1 - 1, length1) - SMA.Value(ts, length2 - 1, length2);
}
return double.NaN;
}
public EstimationResult Estimate(IEnumerable<IDateValue> dateValues)
{
var series = new TimeSeries();
dateValues.ForEach(x => series.Add(x.Date, x.Value, true));
armaModel.SetInput(0, series, null);
armaModel.FitByMLE(200, 100, 0, null);
armaModel.ComputeResidualsAndOutputs();
var result = armaModel.GetOutput(3) as TimeSeries;
return EstimationResult.Create(result[0], this);
}
public static double Value(ISeries input, int index, int length1, int length2)
{
if (index >= length2 - 1)
{
var ts = new TimeSeries();
for (var i = 0; i <= index; i++)
ts.Add(input.GetDateTime(i), input[i, BarData.High] - input[i, BarData.Low]);
var ema = new EMA(ts, length1);
return (ema[index] - ema[index - length2 + 1])/ema[index - length2 + 1]*100;
}
return double.NaN;
}
public static double Value(ISeries input, int index, int length, int order)
{
if (index >= length - 1)
{
var ts = new TimeSeries();
for (int i = 0; i <= index; i++)
ts.Add(input.GetDateTime(i), input[i, BarData.High] - input[i, BarData.Low]);
var ema = new EMA(ts, order);
var ema_ema = new EMA(ema, order);
return Enumerable.Range(index - length + 1, length).Reverse().Sum(i => ema[i]/ema_ema[i]);
}
return double.NaN;
}
public static double Value(ISeries input, int index, int length1, int length2)
{
if (index >= length1 - 1 && index >= length2 - 1)
{
TimeSeries timeSeries = new TimeSeries();
for (int i = index - Math.Max(length1, length2) + 1; i <= index; i++)
{
timeSeries.Add(input.GetDateTime(i), input[i, BarData.Volume]);
}
double num = SMA.Value(timeSeries, length1 - 1, length1, BarData.Close);
double num2 = SMA.Value(timeSeries, length2 - 1, length2, BarData.Close);
return num - num2;
}
return double.NaN;
}
public void TestWMA()
{
long ticks = 1000;
var data = new double[] { 20, 40, 22, 35, 33, 78, 21, 45, 33, 5, 67, 22, 98, 22, 34, 54 };
var input = new TimeSeries();
var wma = new WMA(input, 10);
foreach (var d in data)
input.Add(new DateTime().AddTicks(ticks++), d);
Assert.Equal(wma[0], 32.6, precision);
Assert.Equal(wma[1], 38.7454545454545, precision);
Assert.Equal(wma[2], 35.8545454545455, precision);
Assert.Equal(wma[3], 47.1090909090909, precision);
Assert.Equal(wma[4], 43.1636363636364, precision);
Assert.Equal(wma[5], 41.6363636363636, precision);
Assert.Equal(wma[6], 43.7272727272727, precision);
}
public static double Value(ISeries input, int index, int length, BarData barData = BarData.Close)
{
if (index >= 1)
{
TimeSeries timeSeries = new TimeSeries();
for (int i = 0; i <= index; i++)
{
timeSeries.Add(input.GetDateTime(i), input[i, barData]);
}
EMA input2 = new EMA(timeSeries, length, barData);
EMA input3 = new EMA(input2, length, barData);
EMA eMA = new EMA(input3, length, barData);
return (eMA[index] - eMA[index - 1]) / eMA[index - 1] * 100.0;
}
return double.NaN;
}
public TimeSeries<string> historyCurveSeriesMRO(DateTime sDate, DateTime eDate)
{
TimeSeries<string> hist = new TimeSeries<string>();
IRCurveMarketDataLoader irCurveLoader = IRCurveMarketDataLoader.CreateMarketDataLoader("MRO");
ObservableCollection<Excel_irCurveDataViewModel> e_ircdvmList = new ObservableCollection<Excel_irCurveDataViewModel>();
irCurveLoader.loadInterval(sDate, eDate, this.LinkedCurveCode_, e_ircdvmList);
//TD_MRSTOCKFACTOR_Table_DAOManager dao_m = new TD_MRSTOCKFACTOR_Table_DAOManager();
//dao_m.STOCK_ID_ = this.Code_;
//dao_m.selectInterval(DataBaseConnectManager.ConnectionFactory("MRO"), sDate, eDate);
int tenorIndex = e_ircdvmList[0].getTenorIndex(this.Tenor_);
if (tenorIndex != -1)
{
foreach (var item in e_ircdvmList)
{
string value = item.Excel_rateDataViewModelList_[tenorIndex].Value_;
hist.Add(new Date(item.ReferenceDate_), value);
}
}
return hist;
}
public TimeSeries<string> historySeries(DateTime sDate, DateTime eDate )
{
TimeSeries<string> hist = new TimeSeries<string>();
INDEX_DATA_Table_DAOManager dao_m = new INDEX_DATA_Table_DAOManager();
dao_m.INDEX_CODE_ = this.Code_;
dao_m.selectInterval(DataBaseConnectManager.ConnectionFactory("myDB"),sDate,eDate);
foreach (var item in dao_m.DAOList_)
{
string date = item.INDEX_DATE_;
int year = Convert.ToInt32(date.Substring(0, 4));
int month = Convert.ToInt32(date.Substring(4, 2));
int day = Convert.ToInt32(date.Substring(6, 2));
string value = item.INDEX_VALUE_;
hist.Add(new Date(new DateTime(year,month, day)), value);
}
return hist;
}
public TimeSeries<string> historySeriesMRO(DateTime sDate, DateTime eDate)
{
TimeSeries<string> hist = new TimeSeries<string>();
TD_MRSTOCKFACTOR_Table_DAOManager dao_m = new TD_MRSTOCKFACTOR_Table_DAOManager();
dao_m.STOCK_ID_ = this.Code_;
dao_m.selectInterval(DataBaseConnectManager.ConnectionFactory("MRO"), sDate, eDate);
foreach (var item in dao_m.DAOList_)
{
string date = item.STD_DATE_;
int year = Convert.ToInt32(date.Substring(0, 4));
int month = Convert.ToInt32(date.Substring(4, 2));
int day = Convert.ToInt32(date.Substring(6, 2));
string value = item.CURR_PRICE_;
hist.Add(new Date(new DateTime(year, month, day)), value);
}
return hist;
}
public static double Value(ISeries input, int index, int length1, int length2)
{
if (index >= length2 - 1)
{
TimeSeries timeSeries = new TimeSeries();
for (int i = 0; i <= index; i++)
{
timeSeries.Add(input.GetDateTime(i), input[i, BarData.High] - input[i, BarData.Low]);
}
EMA eMA = new EMA(timeSeries, length1, BarData.Close);
return (eMA[index] - eMA[index - length2 + 1]) / eMA[index - length2 + 1] * 100.0;
}
return double.NaN;
}
public static double Value(ISeries input, int index, int length, int order)
{
if (index >= length - 1)
{
TimeSeries timeSeries = new TimeSeries();
for (int i = 0; i <= index; i++)
{
timeSeries.Add(input.GetDateTime(i), input[i, BarData.High] - input[i, BarData.Low]);
}
EMA eMA = new EMA(timeSeries, order, BarData.Close);
EMA eMA2 = new EMA(eMA, order, BarData.Close);
double num = 0.0;
for (int j = index; j > index - length; j--)
{
num += eMA[j] / eMA2[j];
}
return num;
}
return double.NaN;
}
