Examples with MeanVariance suite.math.numeric.Statistic.MeanVariance used on opensource projects

Example 1 with MeanVariance

use of suite.math.numeric.Statistic.MeanVariance in project suite by stupidsing.

the class AnalyzeTimeSeriesTest method analyze.

private void analyze(float[] prices) {
    int length = prices.length;
    int log2 = Quant.log2trunc(length);
    double nYears = length * Trade_.invTradeDaysPerYear;
    float[] fds = dct.dct(Arrays.copyOfRange(prices, length - log2, length));
    float[] returns = ts.returns(prices);
    float[] logPrices = To.vector(prices, Math::log);
    float[] logReturns = ts.differences(1, logPrices);
    MeanVariance rmv = stat.meanVariance(returns);
    double variance = rmv.variance;
    double kelly = rmv.mean / variance;
    IntFltPair max = IntFltPair.of(Integer.MIN_VALUE, Float.MIN_VALUE);
    for (int i = 4; i < fds.length; i++) {
        float f = Math.abs(fds[i]);
        if (max.t1 < f)
            max.update(i, f);
    }
    IntFunction<BuySell> momFun = n -> {
        int d0 = 1 + n;
        int d1 = 1;
        return buySell(d -> Quant.sign(prices[d - d0], prices[d - d1])).start(d0);
    };
    IntFunction<BuySell> revert = d -> momFun.apply(d).scale(0f, -1f);
    IntFunction<BuySell> trend_ = d -> momFun.apply(d).scale(0f, +1f);
    BuySell[] reverts = To.array(8, BuySell.class, revert);
    BuySell[] trends_ = To.array(8, BuySell.class, trend_);
    BuySell tanh = buySell(d -> Tanh.tanh(3.2d * reverts[1].apply(d)));
    float[] holds = mt.hold(prices, 1f, 1f, 1f);
    float[] ma200 = ma.movingAvg(prices, 200);
    BuySell mat = buySell(d -> {
        int last = d - 1;
        return Quant.sign(ma200[last], prices[last]);
    }).start(1).longOnly();
    BuySell mt_ = buySell(d -> holds[d]);
    Pair<float[], float[]> bbmv = bb.meanVariances(VirtualVector.of(logReturns), 9, 0);
    float[] bbmean = bbmv.t0;
    float[] bbvariances = bbmv.t1;
    BuySell ms2 = buySell(d -> {
        int last = d - 1;
        int ref = last - 250;
        float mean = bbmean[last];
        return Quant.sign(logPrices[last], logPrices[ref] - bbvariances[last] / (2d * mean * mean));
    }).start(1 + 250);
    LogUtil.info(// 
    "" + "\nsymbol = " + // 
    symbol + "\nlength = " + // 
    length + "\nnYears = " + // 
    nYears + "\nups = " + // 
    Floats_.of(returns).filter(return_ -> 0f <= return_).size() + "\ndct period = " + // 
    max.t0 + // 
    Ints_.range(// 
    10).map(// 
    d -> "\ndct component [" + d + "d] = " + fds[d]).collect(// 
    As::joined) + "\nreturn yearly sharpe = " + // 
    rmv.mean / Math.sqrt(variance / nYears) + "\nreturn kelly = " + // 
    kelly + "\nreturn skew = " + // 
    stat.skewness(returns) + "\nreturn kurt = " + // 
    stat.kurtosis(returns) + // 
    Ints_.of(1, 2, 4, 8, 16, // 
    32).map(// 
    d -> "\nmean reversion ols [" + d + "d] = " + ts.meanReversion(prices, d).coefficients[0]).collect(// 
    As::joined) + // 
    Ints_.of(4, // 
    16).map(// 
    d -> "\nvariance ratio [" + d + "d over 1d] = " + ts.varianceRatio(prices, d)).collect(// 
    As::joined) + "\nreturn hurst = " + // 
    ts.hurst(prices, prices.length / 2) + "\nhold " + // 
    buySell(d -> 1d).invest(prices) + "\nkelly " + // 
    buySell(d -> kelly).invest(prices) + "\nma200 trend " + // 
    mat.invest(prices) + // 
    Ints_.range(1, // 
    8).map(// 
    d -> "\nrevert [" + d + "d] " + reverts[d].invest(prices)).collect(// 
    As::joined) + // 
    Ints_.range(1, // 
    8).map(// 
    d -> "\ntrend_ [" + d + "d] " + trends_[d].invest(prices)).collect(// 
    As::joined) + // 
    Ints_.range(1, // 
    8).map(// 
    d -> "\nrevert [" + d + "d] long-only " + reverts[d].longOnly().invest(prices)).collect(// 
    As::joined) + // 
    Ints_.range(1, // 
    8).map(// 
    d -> "\ntrend_ [" + d + "d] long-only " + trends_[d].longOnly().invest(prices)).collect(// 
    As::joined) + "\nms2 " + // 
    ms2.invest(prices) + "\nms2 long-only " + // 
    ms2.longOnly().invest(prices) + "\ntanh " + // 
    tanh.invest(prices) + "\ntimed " + // 
    mt_.invest(prices) + "\ntimed long-only " + mt_.longOnly().invest(prices));
}

Example 2 with MeanVariance

use of suite.math.numeric.Statistic.MeanVariance in project suite by stupidsing.

the class TimeSeries method hurstFwf.

// http://www.financialwisdomforum.org/gummy-stuff/hurst.htm
public double hurstFwf(float[] ys, int tor) {
    float[] logys = To.vector(ys, Math::log);
    float[] returns0 = dropDiff_(1, logys);
    int length = returns0.length;
    List<FltObjPair<float[]>> pairs = new ArrayList<>();
    for (int n = 0; n < length * 3 / 4; n++) {
        float[] returns = Arrays.copyOfRange(returns0, n, length);
        MeanVariance mv = stat.meanVariance(returns);
        double mean = mv.mean;
        float[] devs = To.vector(returns, r -> r - mean);
        double min = Double.MAX_VALUE;
        double max = Double.MIN_VALUE;
        double sum = 0d;
        for (float dev : devs) {
            sum += dev;
            min = min(sum, min);
            max = max(sum, max);
        }
        double x = Math.log(returns.length);
        double y = (max - min) / mv.standardDeviation();
        pairs.add(FltObjPair.of((float) y, new float[] { (float) x, 1f }));
    }
    return stat.linearRegression(Read.from(pairs)).coefficients[0];
}