protected override IList <AnnDataPoint> GetRawAnnDataPoints(Stock stock, HistoricalData historicalData)
{
var result = new List <AnnDataPoint>();
lock (Locker)
{
if (!data.ContainsKey(historicalData))
{
data.Add(historicalData, Data.GetData(stock, DownloaderService, historicalData));
}
}
var movingAverageFast = DataProcessorService.CalculateMovingAverage(data[historicalData].Quotes, Settings.MovingAverageCloseFast);
var cci = DataProcessorService.CalculateCci(data[historicalData].Quotes, Settings.CCI);
var rsi = DataProcessorService.CalculateRsi(data[historicalData].Quotes, Settings.RSI);
var macD = DataProcessorService.CalculateMacd(data[historicalData].Quotes, Settings.MacdFast, Settings.MacdSlow, Settings.MacdSignal);
var atr = DataProcessorService.CalculateAtr(data[historicalData].Quotes, Settings.Atr);
var ema = DataProcessorService.CalculateEMA(data[historicalData].Quotes, Settings.Ema);
var obv = DataProcessorService.CalculateObv(data[historicalData].Quotes, Settings.Obv);
var crsi = DataProcessorService.CalculateConnorsRsi(data[historicalData].Quotes, Settings.ConnorsRsi);
var pmo = DataProcessorService.CalculatePmo(data[historicalData].Quotes, Settings.Pmo);
var roc = DataProcessorService.CalculateRoc(data[historicalData].Quotes, Settings.Roc);
int fwdDays = Settings.FwdDays;
int yesterdayStep = 1;
for (int i = 0; i < historicalData.Quotes.Values.Count; i++)
{
var fwdDate = i + fwdDays >= historicalData.Quotes.Values.Count
? historicalData.Quotes.Values.Count - 1
: i + fwdDays;
var yesterdayIndex = i - yesterdayStep >= 0 ? i - yesterdayStep : 0;
var yesterday = historicalData.Quotes.Values[yesterdayIndex];
var today = historicalData.Quotes.Values[i];
var future = historicalData.Quotes.Values[fwdDate];
var percentageChange = (future.Close - today.Close) / today.Close * 100d;
var annDataPoint = new AnnDataPoint
{
Date = today.Date,
Inputs = new[]
{
today.Close,
today.High,
today.Volume,
Compare(today.Close, yesterday.Close),
Compare(today.High, yesterday.High),
Compare(today.Volume, yesterday.Volume),
(double)movingAverageFast.Single(x => x.Date.Date == today.Date.Date).Sma.GetValueOrDefault(),
(double)ema.Single(x => x.Date.Date == today.Date.Date).Ema.GetValueOrDefault(),
(double)cci.Single(x => x.Date.Date == today.Date.Date).Cci.GetValueOrDefault(),
(double)rsi.Single(x => x.Date.Date == today.Date.Date).Rsi.GetValueOrDefault(),
(double)atr.Single(x => x.Date.Date == today.Date.Date).Atrp.GetValueOrDefault(),
(double)macD.Single(x => x.Date.Date == today.Date.Date).Macd.GetValueOrDefault(),
(double)obv.Single(x => x.Date.Date == today.Date.Date).Obv,
(double)crsi.Single(x => x.Date.Date == today.Date.Date).ConnorsRsi.GetValueOrDefault(),
(double)pmo.Single(x => x.Date.Date == today.Date.Date).Pmo.GetValueOrDefault(),
(double)roc.Single(x => x.Date.Date == today.Date.Date).Roc.GetValueOrDefault()
},
Outputs = new[]
{
percentageChange > Settings.PercentageChangeHigh ? 1d :
percentageChange < Settings.PercentageChangeLow ? -1d : 0d
}
};
result.Add(annDataPoint);
}
return(result);
}
protected override IList <AnnDataPoint> GetRawAnnDataPoints(HistoricalData historicalData)
{
var result = new List <AnnDataPoint>();
var historicalQuotes = historicalData.Quotes.Values;
var close = historicalQuotes.Select(x => x.Close).ToArray();
var volume = historicalQuotes.Select(x => x.Volume).ToArray();
var movingAverageCloseFast = this.DataProcessorService.CalculateMovingAverage(close, this.Settings.MovingAverageCloseFast);
var movingAverageCloseSlow = this.DataProcessorService.CalculateMovingAverage(close, this.Settings.MovingAverageCloseSlow);
var cci = this.DataProcessorService.CalculateCCI(historicalQuotes, this.Settings.CCI);
var rsi = this.DataProcessorService.CalculateRSI(close, this.Settings.RSI);
var rsi2 = this.DataProcessorService.CalculateRSI(close, this.Settings.RSI2);
var macD = this.DataProcessorService.CalculateMacD(close, this.Settings.MacdFast, this.Settings.MacdSlow, this.Settings.MacdSignal);
var hv = this.DataProcessorService.CalculateHV(close, this.Settings.Hv1);
var fitClose = this.DataProcessorService.CalculateMovingLinearFit(close, this.Settings.FitClose);
var fitOfFit = this.DataProcessorService.CalculateMovingLinearFit(fitClose.Item2, this.Settings.FitOfFit);
var fitRSI = this.DataProcessorService.CalculateMovingLinearFit(this.DataProcessorService.CalculateRSI(close, this.Settings.RSI1Fit), this.Settings.RSI2Fit);
int fwdDays = this.Settings.FwdDays;
int yesterdayStep = 1;
for (int i = 0; i < historicalQuotes.Count; i++)
{
var fwdDate = i + fwdDays >= historicalQuotes.Count
? historicalQuotes.Count - 1
: i + fwdDays;
var yesterdayIndex = i - yesterdayStep >= 0 ? i - yesterdayStep : 0;
var yesterday = historicalQuotes[yesterdayIndex];
var today = historicalQuotes[i];
var future = historicalQuotes[fwdDate];
var percentageChange = ((future.Close - today.Close) / today.Close) * 100d;
var annDataPoint = new AnnDataPoint
{
Date = today.Date,
Inputs = new[]
{
Math.Sinh(rsi[i]) + Math.Sinh(rsi2[i]),
today.Close - today.High,
yesterday.Close - yesterday.High,
Math.Abs(volume[i] - volume[yesterdayIndex]),
movingAverageCloseFast[i] / (movingAverageCloseSlow[yesterdayIndex] + 1E-6),
movingAverageCloseSlow[i] / (movingAverageCloseFast[yesterdayIndex] + 1E-6),
cci[i],
today.Date.Month,
macD.Item1[i],
macD.Item2[i],
rsi[i],
hv[i],
fitClose.Item2[i],
fitOfFit.Item2[i],
fitRSI.Item2[i],
MathNet.Numerics.Distance.Pearson(
new[]
{
today.Close, today.High, today.Open, today.Low, today.Volume,
rsi[i], cci[i], macD.Item2[i]
},
new[]
{
yesterday.Close, yesterday.High, yesterday.Open, yesterday.Low, yesterday.Volume,
rsi[yesterdayIndex], cci[yesterdayIndex], macD.Item2[yesterdayIndex]
})
},
Outputs = new[]
{
percentageChange > this.Settings.PercentageChangeHigh ? 1d : percentageChange < this.Settings.PercentageChangeLow ? -1d : 0d
}
};
result.Add(annDataPoint);
}
return(result);
}
