mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-07-31 02:47:44 +00:00
codefactor-io
640 lines
23 KiB
C#
640 lines
23 KiB
C#
using System.Buffers;
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using System.Runtime.CompilerServices;
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using System.Runtime.InteropServices;
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namespace QuanTAlib;
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/// <summary>
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/// SQUEEZE: Squeeze Momentum Oscillator
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/// Detects low-volatility compressions (Bollinger Bands inside Keltner Channel)
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/// and measures directional momentum via linear regression of detrended price.
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/// Outputs: Momentum (histogram value) and SqueezeOn (true = BB inside KC).
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/// Algorithm: BB(SMA+StdDev) vs KC(EMA+ATR/RMA), then LinReg of delta from Donchian midline.
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/// </summary>
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[SkipLocalsInit]
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public sealed class Squeeze : ITValuePublisher
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{
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private readonly int _period;
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private readonly double _bbMult;
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private readonly double _kcMult;
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// Circular buffers — managed separately for snapshot/rollback
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private readonly double[] _smaBuf; // close values for SMA + variance
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private readonly double[] _hiBuf; // high values for Donchian
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private readonly double[] _loBuf; // low values for Donchian
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private readonly double[] _lrBuf; // delta values for LinReg
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// Snapshots for bar-correction rollback (circular-buffer-snapshot-rollback pattern)
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private readonly double[] _smaBufSnap;
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private readonly double[] _hiBufSnap;
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private readonly double[] _loBufSnap;
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private readonly double[] _lrBufSnap;
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[StructLayout(LayoutKind.Auto)]
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private record struct State(
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// SMA + variance (Bollinger Bands, §3 count-based warmup)
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double SmaSum, double SmaSumSq, int SmaHead, int SmaCount,
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// EMA for KC midline (§2 exponential warmup)
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double RawEma, double EEma,
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// ATR via Wilder RMA (§2 exponential warmup)
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double RawRma, double ERma, double PrevClose,
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// Donchian high/low buffers (O(period) scan for max/min)
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int DonHead, int DonCount,
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// LinReg incremental state (O(1))
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double SumY, double SumXY, int LrHead, int LrCount,
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// NaN substitution tracking
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double LastValidHigh, double LastValidLow, double LastValidClose);
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private State _s;
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private State _ps;
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private readonly TBarPublishedHandler _barHandler;
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public string Name { get; }
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public int WarmupPeriod { get; }
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public TValue Last { get; private set; }
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public double Momentum { get; private set; }
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public bool SqueezeOn { get; private set; }
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public bool IsHot => _s.LrCount >= _period;
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public event TValuePublishedHandler? Pub;
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public Squeeze(int period = 20, double bbMult = 2.0, double kcMult = 1.5)
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{
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if (period <= 0)
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{
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throw new ArgumentException("Period must be greater than 0", nameof(period));
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}
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if (bbMult <= 0.0)
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{
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throw new ArgumentException("BB multiplier must be greater than 0", nameof(bbMult));
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}
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if (kcMult <= 0.0)
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{
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throw new ArgumentException("KC multiplier must be greater than 0", nameof(kcMult));
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}
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_period = period;
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_bbMult = bbMult;
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_kcMult = kcMult;
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_smaBuf = new double[period];
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_hiBuf = new double[period];
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_loBuf = new double[period];
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_lrBuf = new double[period];
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_smaBufSnap = new double[period];
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_hiBufSnap = new double[period];
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_loBufSnap = new double[period];
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_lrBufSnap = new double[period];
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// NaN sentinels — unfilled slots are distinguishable from real values
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Array.Fill(_smaBuf, double.NaN);
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Array.Fill(_hiBuf, double.NaN);
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Array.Fill(_loBuf, double.NaN);
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Array.Fill(_lrBuf, double.NaN);
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_s = MakeInitialState();
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_ps = _s;
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Name = $"Squeeze({period},{bbMult},{kcMult})";
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WarmupPeriod = period;
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_barHandler = HandleBar;
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}
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public Squeeze(TBarSeries source, int period = 20, double bbMult = 2.0, double kcMult = 1.5)
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: this(period, bbMult, kcMult)
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{
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Prime(source);
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source.Pub += _barHandler;
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}
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private static State MakeInitialState() =>
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new(SmaSum: 0.0, SmaSumSq: 0.0, SmaHead: 0, SmaCount: 0,
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RawEma: 0.0, EEma: 1.0,
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RawRma: 0.0, ERma: 1.0, PrevClose: double.NaN,
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DonHead: 0, DonCount: 0,
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SumY: 0.0, SumXY: 0.0, LrHead: 0, LrCount: 0,
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LastValidHigh: double.NaN, LastValidLow: double.NaN, LastValidClose: double.NaN);
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private void HandleBar(object? sender, in TBarEventArgs e) => Update(e.Value, e.IsNew);
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private void PubEvent(TValue value, bool isNew = true) =>
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Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew });
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// Extracted from Update() — SMA circular buffer step (satisfies S1199)
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private void UpdateSmaBuf(ref State s, double close)
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{
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double oldVal = _smaBuf[s.SmaHead];
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if (double.IsNaN(oldVal))
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{
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s.SmaCount++;
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}
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else
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{
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s.SmaSum -= oldVal;
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s.SmaSumSq -= oldVal * oldVal;
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}
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s.SmaSum += close;
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s.SmaSumSq += close * close;
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_smaBuf[s.SmaHead] = close;
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s.SmaHead = (s.SmaHead + 1) % _period;
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}
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// Extracted from Update() — LinReg circular buffer step (satisfies S1199)
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private void UpdateLrBuf(ref State s, double delta)
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{
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double oldLr = _lrBuf[s.LrHead];
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if (!double.IsNaN(oldLr))
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{
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int oldIdx = s.LrCount - _period;
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s.SumY -= oldLr;
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s.SumXY -= (double)oldIdx * oldLr;
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}
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s.SumY += delta;
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s.SumXY += (double)s.LrCount * delta;
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_lrBuf[s.LrHead] = delta;
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s.LrHead = (s.LrHead + 1) % _period;
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s.LrCount++;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public TValue Update(TBar input, bool isNew = true)
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{
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if (isNew)
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{
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// Snapshot state + circular buffers before advancing
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_ps = _s;
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Array.Copy(_smaBuf, _smaBufSnap, _period);
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Array.Copy(_hiBuf, _hiBufSnap, _period);
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Array.Copy(_loBuf, _loBufSnap, _period);
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Array.Copy(_lrBuf, _lrBufSnap, _period);
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}
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else
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{
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// Rollback to previous snapshot
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_s = _ps;
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Array.Copy(_smaBufSnap, _smaBuf, _period);
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Array.Copy(_hiBufSnap, _hiBuf, _period);
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Array.Copy(_loBufSnap, _loBuf, _period);
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Array.Copy(_lrBufSnap, _lrBuf, _period);
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}
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var s = _s;
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// === NaN/Infinity substitution (last-valid-value) ===
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double high = input.High;
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double low = input.Low;
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double close = input.Close;
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if (double.IsFinite(high)) { s.LastValidHigh = high; }
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else { high = s.LastValidHigh; }
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if (double.IsFinite(low)) { s.LastValidLow = low; }
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else { low = s.LastValidLow; }
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if (double.IsFinite(close)) { s.LastValidClose = close; }
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else { close = s.LastValidClose; }
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if (double.IsNaN(high) || double.IsNaN(low) || double.IsNaN(close))
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{
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_s = s;
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Last = new TValue(input.Time, double.NaN);
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Momentum = double.NaN;
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SqueezeOn = false;
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PubEvent(Last, isNew);
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return Last;
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}
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// ===== STAGE 1: SMA + Variance (Bollinger Bands, §3 count-based warmup) =====
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UpdateSmaBuf(ref s, close);
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int n = Math.Max(1, s.SmaCount);
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double smaVal = s.SmaSum / n;
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double variance = Math.Max(0.0, (s.SmaSumSq / n) - (smaVal * smaVal));
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double stddev = Math.Sqrt(variance);
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double bbUpper = Math.FusedMultiplyAdd(_bbMult, stddev, smaVal);
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double bbLower = Math.FusedMultiplyAdd(-_bbMult, stddev, smaVal);
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// ===== STAGE 2: EMA for KC midline + ATR via RMA (§2 exponential warmup) =====
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const double EPSILON = 1e-10;
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double emaAlpha = 2.0 / (_period + 1.0);
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double emaBeta = 1.0 - emaAlpha;
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double rmaAlpha = 1.0 / _period;
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double rmaBeta = 1.0 - rmaAlpha;
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s.RawEma = Math.FusedMultiplyAdd(s.RawEma, emaBeta, emaAlpha * close);
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s.EEma *= emaBeta;
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double cEma = s.EEma > EPSILON ? 1.0 / (1.0 - s.EEma) : 1.0;
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double emaVal = s.RawEma * cEma;
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// True Range
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double tr = high - low;
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if (double.IsFinite(s.PrevClose))
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{
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double hiPrev = Math.Abs(high - s.PrevClose);
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double loPrev = Math.Abs(low - s.PrevClose);
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if (hiPrev > tr) { tr = hiPrev; }
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if (loPrev > tr) { tr = loPrev; }
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}
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s.PrevClose = close;
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s.RawRma = Math.FusedMultiplyAdd(s.RawRma, rmaBeta, rmaAlpha * tr);
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s.ERma *= rmaBeta;
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double cRma = s.ERma > EPSILON ? 1.0 / (1.0 - s.ERma) : 1.0;
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double atr = s.RawRma * cRma;
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double kcUpper = Math.FusedMultiplyAdd(_kcMult, atr, emaVal);
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double kcLower = Math.FusedMultiplyAdd(-_kcMult, atr, emaVal);
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// ===== STAGE 3: Squeeze detection =====
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bool squeezeOn = bbUpper < kcUpper && bbLower > kcLower;
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// ===== STAGE 4: Donchian midline (O(period) scan) =====
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_hiBuf[s.DonHead] = high;
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_loBuf[s.DonHead] = low;
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if (s.DonCount < _period) { s.DonCount++; }
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s.DonHead = (s.DonHead + 1) % _period;
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double highest = high;
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double lowest = low;
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int donFilled = s.DonCount;
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for (int i = 0; i < donFilled; i++)
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{
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double dh = _hiBuf[i];
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double dl = _loBuf[i];
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if (!double.IsNaN(dh) && dh > highest) { highest = dh; }
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if (!double.IsNaN(dl) && dl < lowest) { lowest = dl; }
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}
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double donMid = (highest + lowest) * 0.5;
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double delta = close - ((donMid + smaVal) * 0.5);
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// ===== STAGE 5: Linear regression of delta over period (O(1) incremental) =====
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UpdateLrBuf(ref s, delta);
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int pn = Math.Min(s.LrCount, _period);
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int startIdx = s.LrCount - pn;
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// Closed-form sums: ΣX and ΣX²
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double sumX = (double)pn * ((2.0 * startIdx) + pn - 1) * 0.5;
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double sumX2 = Math.FusedMultiplyAdd(
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pn, (double)startIdx * startIdx,
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Math.FusedMultiplyAdd(
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(double)startIdx * (pn - 1), pn,
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(double)(pn - 1) * pn * ((2 * pn) - 1) / 6.0));
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double denomX = Math.FusedMultiplyAdd(pn, sumX2, -(sumX * sumX));
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double slope = denomX == 0.0 ? 0.0
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: Math.FusedMultiplyAdd(pn, s.SumXY, -(sumX * s.SumY)) / denomX;
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double intercept = (s.SumY - (slope * sumX)) / pn;
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double momentum = Math.FusedMultiplyAdd(slope, s.LrCount - 1, intercept);
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_s = s;
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Momentum = momentum;
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SqueezeOn = squeezeOn;
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Last = new TValue(input.Time, momentum);
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PubEvent(Last, isNew);
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return Last;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public TValue Update(TValue input, bool isNew = true) =>
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Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew);
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public (TSeries Momentum, TSeries SqueezeOn) Update(TBarSeries source)
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{
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if (source.Count == 0)
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{
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return (new TSeries([], []), new TSeries([], []));
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}
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int len = source.Count;
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var tMom = new List<long>(len);
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var vMom = new List<double>(len);
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var tSq = new List<long>(len);
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var vSq = new List<double>(len);
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CollectionsMarshal.SetCount(tMom, len);
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CollectionsMarshal.SetCount(vMom, len);
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CollectionsMarshal.SetCount(tSq, len);
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CollectionsMarshal.SetCount(vSq, len);
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var vMomSpan = CollectionsMarshal.AsSpan(vMom);
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var vSqSpan = CollectionsMarshal.AsSpan(vSq);
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Batch(source.HighValues, source.LowValues, source.CloseValues,
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vMomSpan, vSqSpan, _period, _bbMult, _kcMult);
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var tSpan = CollectionsMarshal.AsSpan(tMom);
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source.Times.CopyTo(tSpan);
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tSpan.CopyTo(CollectionsMarshal.AsSpan(tSq));
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Prime(source); // restore streaming state to end of series
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if (len > 0)
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{
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var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc);
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Momentum = vMomSpan[^1];
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SqueezeOn = vSqSpan[^1] >= 0.5;
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Last = new TValue(lastTime, Momentum);
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}
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return (new TSeries(tMom, vMom), new TSeries(tSq, vSq));
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}
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public void Prime(TBarSeries source)
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{
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Reset();
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for (int i = 0; i < source.Count; i++)
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{
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Update(source[i], isNew: true);
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}
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}
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public void Reset()
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{
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Array.Fill(_smaBuf, double.NaN);
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Array.Fill(_hiBuf, double.NaN);
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Array.Fill(_loBuf, double.NaN);
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Array.Fill(_lrBuf, double.NaN);
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Array.Fill(_smaBufSnap, double.NaN);
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Array.Fill(_hiBufSnap, double.NaN);
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Array.Fill(_loBufSnap, double.NaN);
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Array.Fill(_lrBufSnap, double.NaN);
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_s = MakeInitialState();
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_ps = _s;
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Last = default;
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Momentum = 0.0;
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SqueezeOn = false;
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}
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/// <summary>
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/// Span-based batch Squeeze calculation. Populates both the momentum and squeeze-state output spans.
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/// </summary>
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/// <param name="momOut">Output span for Squeeze momentum values (linear regression of detrended price).</param>
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/// <param name="sqOut">Output span for squeeze-state values (positive when Bollinger Bands are inside Keltner Channel).</param>
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public static void Batch(
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ReadOnlySpan<double> high,
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ReadOnlySpan<double> low,
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ReadOnlySpan<double> close,
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Span<double> momOut,
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Span<double> sqOut,
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int period = 20,
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double bbMult = 2.0,
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double kcMult = 1.5)
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{
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if (period <= 0)
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{
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throw new ArgumentException("Period must be greater than 0", nameof(period));
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}
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if (bbMult <= 0.0)
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{
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throw new ArgumentException("BB multiplier must be greater than 0", nameof(bbMult));
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}
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if (kcMult <= 0.0)
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{
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throw new ArgumentException("KC multiplier must be greater than 0", nameof(kcMult));
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}
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if (high.Length != low.Length || high.Length != close.Length)
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{
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throw new ArgumentException("Input spans must have the same length", nameof(high));
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}
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if (momOut.Length < high.Length)
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{
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throw new ArgumentException("Momentum output span must be at least as long as input", nameof(momOut));
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}
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if (sqOut.Length < high.Length)
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{
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throw new ArgumentException("SqueezeOn output span must be at least as long as input", nameof(sqOut));
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}
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int len = high.Length;
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if (len == 0) { return; }
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const int StackallocThreshold = 256;
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double[]? rentedSma = null;
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double[]? rentedHi = null;
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double[]? rentedLo = null;
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double[]? rentedLr = null;
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scoped Span<double> smaBuf;
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scoped Span<double> hiBuf;
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scoped Span<double> loBuf;
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scoped Span<double> lrBuf;
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if (period <= StackallocThreshold)
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{
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smaBuf = stackalloc double[period];
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hiBuf = stackalloc double[period];
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loBuf = stackalloc double[period];
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lrBuf = stackalloc double[period];
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}
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else
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{
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rentedSma = ArrayPool<double>.Shared.Rent(period);
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rentedHi = ArrayPool<double>.Shared.Rent(period);
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rentedLo = ArrayPool<double>.Shared.Rent(period);
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rentedLr = ArrayPool<double>.Shared.Rent(period);
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smaBuf = rentedSma.AsSpan(0, period);
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hiBuf = rentedHi.AsSpan(0, period);
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loBuf = rentedLo.AsSpan(0, period);
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lrBuf = rentedLr.AsSpan(0, period);
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}
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// NaN sentinels for unfilled slots
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smaBuf.Fill(double.NaN);
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hiBuf.Fill(double.NaN);
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loBuf.Fill(double.NaN);
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lrBuf.Fill(double.NaN);
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try
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{
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BatchCore(high, low, close, momOut, sqOut, period, bbMult, kcMult,
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smaBuf, hiBuf, loBuf, lrBuf);
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}
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finally
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{
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if (rentedSma != null) { ArrayPool<double>.Shared.Return(rentedSma); }
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if (rentedHi != null) { ArrayPool<double>.Shared.Return(rentedHi); }
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if (rentedLo != null) { ArrayPool<double>.Shared.Return(rentedLo); }
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if (rentedLr != null) { ArrayPool<double>.Shared.Return(rentedLr); }
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}
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}
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public static (TSeries Momentum, TSeries SqueezeOn) Batch(
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TBarSeries source, int period = 20, double bbMult = 2.0, double kcMult = 1.5)
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{
|
|
if (source == null || source.Count == 0)
|
|
{
|
|
return (new TSeries([], []), new TSeries([], []));
|
|
}
|
|
|
|
int len = source.Count;
|
|
var tMom = new List<long>(len);
|
|
var vMom = new List<double>(len);
|
|
var tSq = new List<long>(len);
|
|
var vSq = new List<double>(len);
|
|
|
|
CollectionsMarshal.SetCount(tMom, len);
|
|
CollectionsMarshal.SetCount(vMom, len);
|
|
CollectionsMarshal.SetCount(tSq, len);
|
|
CollectionsMarshal.SetCount(vSq, len);
|
|
|
|
Batch(source.HighValues, source.LowValues, source.CloseValues,
|
|
CollectionsMarshal.AsSpan(vMom),
|
|
CollectionsMarshal.AsSpan(vSq),
|
|
period, bbMult, kcMult);
|
|
|
|
var tSpan = CollectionsMarshal.AsSpan(tMom);
|
|
source.Times.CopyTo(tSpan);
|
|
tSpan.CopyTo(CollectionsMarshal.AsSpan(tSq));
|
|
|
|
return (new TSeries(tMom, vMom), new TSeries(tSq, vSq));
|
|
}
|
|
|
|
public static ((TSeries Momentum, TSeries SqueezeOn) Results, Squeeze Indicator) Calculate(
|
|
TBarSeries source, int period = 20, double bbMult = 2.0, double kcMult = 1.5)
|
|
{
|
|
var indicator = new Squeeze(period, bbMult, kcMult);
|
|
var results = indicator.Update(source);
|
|
return (results, indicator);
|
|
}
|
|
|
|
private static void BatchCore(
|
|
ReadOnlySpan<double> high, ReadOnlySpan<double> low, ReadOnlySpan<double> close,
|
|
Span<double> momOut, Span<double> sqOut,
|
|
int period, double bbMult, double kcMult,
|
|
Span<double> smaBuf, Span<double> hiBuf, Span<double> loBuf, Span<double> lrBuf)
|
|
{
|
|
int len = high.Length;
|
|
int smaHead = 0, smaCount = 0;
|
|
double smaSum = 0.0, smaSumSq = 0.0;
|
|
|
|
double rawEma = 0.0, eEma = 1.0;
|
|
double rawRma = 0.0, eRma = 1.0;
|
|
double prevClose = double.NaN;
|
|
|
|
int donHead = 0, donCount = 0;
|
|
|
|
double sumY = 0.0, sumXY = 0.0;
|
|
int lrHead = 0, lrCount = 0;
|
|
|
|
double emaAlpha = 2.0 / (period + 1.0);
|
|
double emaBeta = 1.0 - emaAlpha;
|
|
double rmaAlpha = 1.0 / period;
|
|
double rmaBeta = 1.0 - rmaAlpha;
|
|
const double EPSILON = 1e-10;
|
|
|
|
for (int i = 0; i < len; i++)
|
|
{
|
|
double h = high[i];
|
|
double l = low[i];
|
|
double c = close[i];
|
|
if (!double.IsFinite(h)) { h = 0.0; }
|
|
if (!double.IsFinite(l)) { l = 0.0; }
|
|
if (!double.IsFinite(c)) { c = 0.0; }
|
|
|
|
// Stage 1: SMA + StdDev for BB
|
|
double oldSma = smaBuf[smaHead];
|
|
if (double.IsNaN(oldSma))
|
|
{
|
|
smaCount++;
|
|
}
|
|
else
|
|
{
|
|
smaSum -= oldSma;
|
|
smaSumSq -= oldSma * oldSma;
|
|
}
|
|
smaSum += c;
|
|
smaSumSq += c * c;
|
|
smaBuf[smaHead] = c;
|
|
smaHead = (smaHead + 1) % period;
|
|
|
|
int n = Math.Max(1, smaCount);
|
|
double smaVal = smaSum / n;
|
|
double vari = Math.Max(0.0, (smaSumSq / n) - (smaVal * smaVal));
|
|
double sd = Math.Sqrt(vari);
|
|
double bbUpper = Math.FusedMultiplyAdd(bbMult, sd, smaVal);
|
|
double bbLower = Math.FusedMultiplyAdd(-bbMult, sd, smaVal);
|
|
|
|
// Stage 2: EMA + ATR for KC
|
|
rawEma = Math.FusedMultiplyAdd(rawEma, emaBeta, emaAlpha * c);
|
|
eEma *= emaBeta;
|
|
double cEma = eEma > EPSILON ? 1.0 / (1.0 - eEma) : 1.0;
|
|
double emaVal = rawEma * cEma;
|
|
|
|
double tr = h - l;
|
|
if (double.IsFinite(prevClose))
|
|
{
|
|
double hp = Math.Abs(h - prevClose);
|
|
double lp = Math.Abs(l - prevClose);
|
|
if (hp > tr) { tr = hp; }
|
|
if (lp > tr) { tr = lp; }
|
|
}
|
|
prevClose = c;
|
|
|
|
rawRma = Math.FusedMultiplyAdd(rawRma, rmaBeta, rmaAlpha * tr);
|
|
eRma *= rmaBeta;
|
|
double cRma = eRma > EPSILON ? 1.0 / (1.0 - eRma) : 1.0;
|
|
double atr = rawRma * cRma;
|
|
|
|
double kcUpper = Math.FusedMultiplyAdd(kcMult, atr, emaVal);
|
|
double kcLower = Math.FusedMultiplyAdd(-kcMult, atr, emaVal);
|
|
|
|
// Stage 3: Squeeze detection
|
|
double sqVal = bbUpper < kcUpper && bbLower > kcLower ? 1.0 : 0.0;
|
|
|
|
// Stage 4: Donchian midline
|
|
hiBuf[donHead] = h;
|
|
loBuf[donHead] = l;
|
|
if (donCount < period) { donCount++; }
|
|
donHead = (donHead + 1) % period;
|
|
|
|
double highest = h;
|
|
double lowest = l;
|
|
for (int j = 0; j < donCount; j++)
|
|
{
|
|
double dh = hiBuf[j];
|
|
double dl = loBuf[j];
|
|
if (!double.IsNaN(dh) && dh > highest) { highest = dh; }
|
|
if (!double.IsNaN(dl) && dl < lowest) { lowest = dl; }
|
|
}
|
|
double donMid = (highest + lowest) * 0.5;
|
|
double delta = c - ((donMid + smaVal) * 0.5);
|
|
|
|
// Stage 5: LinReg incremental
|
|
double oldLr = lrBuf[lrHead];
|
|
if (!double.IsNaN(oldLr))
|
|
{
|
|
int oldIdx = lrCount - period;
|
|
sumY -= oldLr;
|
|
sumXY -= (double)oldIdx * oldLr;
|
|
}
|
|
sumY += delta;
|
|
sumXY += (double)lrCount * delta;
|
|
lrBuf[lrHead] = delta;
|
|
lrHead = (lrHead + 1) % period;
|
|
lrCount++;
|
|
|
|
int pn = Math.Min(lrCount, period);
|
|
int startI = lrCount - pn;
|
|
double sx = (double)pn * ((2.0 * startI) + pn - 1) * 0.5;
|
|
double sx2 = Math.FusedMultiplyAdd(
|
|
pn, (double)startI * startI,
|
|
Math.FusedMultiplyAdd(
|
|
(double)startI * (pn - 1), pn,
|
|
(double)(pn - 1) * pn * ((2 * pn) - 1) / 6.0));
|
|
double denomX = Math.FusedMultiplyAdd(pn, sx2, -(sx * sx));
|
|
double slope = denomX == 0.0 ? 0.0
|
|
: Math.FusedMultiplyAdd(pn, sumXY, -(sx * sumY)) / denomX;
|
|
double intc = (sumY - (slope * sx)) / pn;
|
|
double momentum = Math.FusedMultiplyAdd(slope, lrCount - 1, intc);
|
|
|
|
momOut[i] = momentum;
|
|
sqOut[i] = sqVal;
|
|
}
|
|
}
|
|
}
|