//**************************************************************************************
/// <summary>
/// Calculates average precision as seen by implied simulation.
/// </summary>
private static float CalculateAveragePrecision(List <WekaClassifier> iClassifiers)
{
var weight = new List <float>(iClassifiers.Count);
var precisions = new List <float>();
var classfiers = WekaClassifier.SplitByProfitTime(iClassifiers);
foreach (KeyValuePair <int, List <WekaClassifier> > pair in classfiers)
{
var p = WekaClassifier.GetAveragePrecision(pair.Value);
precisions.Add(p);
weight.Add((float)(Math.Pow(p, 6) / Math.Pow(pair.Value[0].GetProfitsStdDev(), 6)));
}
return(Statistics.WeightedArithmeticMean(precisions.ToArray(), Statistics.Normalize(weight.ToArray())));
}
//**************************************************************************************
/// <summary>
/// Starts simulation starting from the end of historical candlesticksm and up to prediction duration.
/// </summary>
private static void Start(CandlestickCollection iHistoricalCandlesticks, CandlestickCollection iCandlesticks, List <WekaClassifier> iClassifiers, int iPredictionDuration)
{
var index = new CandlestickIndexCollection();
var candlesticks = iCandlesticks;
var classfiers = WekaClassifier.SplitByProfitTime(iClassifiers);
var initialCount = candlesticks[WekaClassifier.kTrainingPeriod].Count;
var startIndex = Math.Max(initialCount - classfiers.Keys.Max() - 1, 1);
var endIndex = initialCount + iPredictionDuration - 1;
var priceDiffStdDev = Statistics.StandardDeviation(Statistics.CalculateDifferences(iHistoricalCandlesticks[WekaClassifier.kTrainingPeriod].Select(x => x.MedianPrice).ToArray()));
var wfp = new SortedDictionary <int, List <float> >();
var weight = new List <float>(classfiers.Count);
foreach (KeyValuePair <int, List <WekaClassifier> > pair in classfiers)
{
wfp.Add(pair.Key, new List <float>());
var precision = WekaClassifier.GetAveragePrecision(pair.Value);
weight.Add((float)(Math.Pow(precision, 6) / Math.Pow(pair.Value[0].GetProfitsStdDev(), 6.0f)));
}
weight = Statistics.Normalize(weight.ToArray()).ToList();
for (int i = startIndex; i <= endIndex; i++)
{
// Update indexes
for (int k = (int)WekaClassifier.kTrainingPeriod; k >= 0; k--)
{
index[k] = Math.Max(0, Math.Min(candlesticks[k].Count - 1, Candlestick.FindIndex(candlesticks[k], candlesticks[WekaClassifier.kTrainingPeriod][i].StartTime, index[k]) - 1));
}
// Extract current price
var p = candlesticks[WekaClassifier.kTrainingPeriod][i].MedianPrice;
// Calculate WFP
foreach (KeyValuePair <int, List <WekaClassifier> > pair in classfiers)
{
if (i >= initialCount - pair.Key - 1)
{
var predictionsNow = WekaClassifier.PredictFP(pair.Value, p, candlesticks, index);
var wfpNow = WekaClassifier.FPToWFP(pair.Value, predictionsNow);
wfp[pair.Key].Add(wfpNow);
}
}
// Future
if (i + 1 >= initialCount)
{
var lastCandle = candlesticks[WekaClassifier.kTrainingPeriod][candlesticks[WekaClassifier.kTrainingPeriod].Count - 1];
var estimatedPrices = new List <float>(classfiers.Count);
foreach (KeyValuePair <int, List <WekaClassifier> > pair in classfiers)
{
estimatedPrices.Add(EstimatePrice(pair.Key, wfp[pair.Key], candlesticks[WekaClassifier.kTrainingPeriod]));
}
var targetPrice = Statistics.WeightedArithmeticMean(estimatedPrices.ToArray(), weight.ToArray());
targetPrice = Statistics.Clamp(targetPrice, lastCandle.MedianPrice * (1.0f - 3.0f * priceDiffStdDev), lastCandle.MedianPrice * (1.0f + 3.0f * priceDiffStdDev));
var candle = new Candlestick(lastCandle.EndTime, lastCandle.EndTime + 86400000 / (ulong)Candlestick.PeriodToDaily(Candlestick.Period.H1), lastCandle.ClosePrice, targetPrice, Math.Max(lastCandle.ClosePrice, targetPrice), Math.Min(lastCandle.ClosePrice, targetPrice), targetPrice);
candlesticks.Add(candle, WekaClassifier.kTrainingPeriod);
}
}
}