mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-24 21:48:03 +00:00
319 lines
9.2 KiB
C#
319 lines
9.2 KiB
C#
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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/// FISHER: Fisher Transform
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/// </summary>
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/// <remarks>
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/// Converts price into a Gaussian normal distribution via the inverse
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/// hyperbolic tangent, producing sharp turning points for reversal detection:
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/// <c>Fisher = 0.5 × ln((1 + v) / (1 − v))</c>
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/// where <c>v</c> is the EMA-smoothed normalized price clamped to (−0.999, 0.999).
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///
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/// Normalization maps price to [−1, 1] using highest/lowest over <c>period</c> bars.
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/// Signal line is an EMA of <c>Fisher</c> with the same smoothing factor (α = 0.33).
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///
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/// References:
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/// John Ehlers, "Using The Fisher Transform", 2002
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/// PineScript reference: fisher.pine
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Fisher : AbstractBase
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{
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private readonly int _period;
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private readonly double _alpha;
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private readonly double _decay;
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private readonly RingBuffer _buffer;
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[StructLayout(LayoutKind.Auto)]
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private record struct State(
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double Value,
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double FisherValue,
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double Signal,
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double LastValid,
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int Count);
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private State _state;
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private State _p_state;
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/// <summary>
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/// Creates Fisher Transform with specified period.
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/// </summary>
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/// <param name="period">Lookback period for min/max normalization (must be > 0)</param>
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/// <param name="alpha">EMA smoothing factor (0 < alpha <= 1, default 0.33)</param>
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public Fisher(int period = 10, double alpha = 0.33)
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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 (alpha is <= 0 or > 1)
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{
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throw new ArgumentException("Alpha must be in (0, 1]", nameof(alpha));
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}
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_period = period;
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_alpha = alpha;
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_decay = 1.0 - alpha;
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_buffer = new RingBuffer(period);
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Name = $"Fisher({period})";
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WarmupPeriod = period;
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}
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/// <summary>
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/// Creates Fisher Transform with specified source and period.
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/// </summary>
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public Fisher(ITValuePublisher source, int period = 10, double alpha = 0.33) : this(period, alpha)
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{
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source.Pub += Handle;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private void Handle(object? sender, in TValueEventArgs e) => Update(e.Value, e.IsNew);
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/// <summary>
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/// True if the indicator has enough data for valid results.
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/// </summary>
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public override bool IsHot => _buffer.IsFull;
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/// <summary>
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/// Period of the indicator.
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/// </summary>
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public int Period => _period;
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/// <summary>
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/// Current Fisher Transform value.
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/// </summary>
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public double FisherValue => _state.FisherValue;
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/// <summary>
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/// Current Signal line value.
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/// </summary>
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public double Signal => _state.Signal;
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/// <inheritdoc/>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public override TValue Update(TValue input, bool isNew = true)
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{
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double value = input.Value;
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// Sanitize input
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if (!double.IsFinite(value))
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{
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value = double.IsFinite(_state.LastValid) ? _state.LastValid : 0.0;
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}
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else
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{
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_state.LastValid = value;
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}
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if (isNew)
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{
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_p_state = _state;
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_buffer.Add(value);
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_state.Count++;
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}
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else
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{
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_state = _p_state;
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_buffer.UpdateNewest(value);
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}
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// Find min/max over the buffer
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double highest = double.MinValue;
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double lowest = double.MaxValue;
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int count = _buffer.Count;
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for (int i = 0; i < count; i++)
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{
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double v = _buffer[i];
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if (v > highest)
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{
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highest = v;
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}
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if (v < lowest)
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{
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lowest = v;
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}
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}
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// Normalize to [-1, 1]
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double range = highest - lowest;
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double normalized = range > 0.0
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? 2.0 * ((value - lowest) / range) - 1.0
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: 0.0;
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// EMA smooth the normalized value
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_state.Value = Math.FusedMultiplyAdd(_state.Value, _decay, _alpha * normalized);
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// Clamp to (-0.999, 0.999) — domain protection for arctanh
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double clamped = Math.Clamp(_state.Value, -0.999, 0.999);
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// Fisher Transform: arctanh(x) = 0.5 * ln((1+x)/(1-x))
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double fisher = 0.5 * Math.Log((1.0 + clamped) / (1.0 - clamped));
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_state.FisherValue = fisher;
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// Signal line: EMA of Fisher
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_state.Signal = Math.FusedMultiplyAdd(_state.Signal, _decay, _alpha * fisher);
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Last = new TValue(input.Time, fisher);
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PubEvent(Last, isNew);
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return Last;
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}
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/// <inheritdoc/>
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public override TSeries Update(TSeries source)
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{
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int len = source.Count;
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var t = new List<long>(len);
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var v = new List<double>(len);
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CollectionsMarshal.SetCount(t, len);
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CollectionsMarshal.SetCount(v, len);
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var tSpan = CollectionsMarshal.AsSpan(t);
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var vSpan = CollectionsMarshal.AsSpan(v);
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Batch(source.Values, vSpan, _period, _alpha);
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source.Times.CopyTo(tSpan);
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for (int i = 0; i < len; i++)
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{
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Update(new TValue(source.Times[i], source.Values[i]), isNew: true);
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}
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return new TSeries(t, v);
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}
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/// <inheritdoc/>
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public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
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{
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TimeSpan interval = step ?? TimeSpan.FromTicks(1);
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DateTime baseTime = DateTime.UtcNow - (interval * (source.Length - 1));
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for (int i = 0; i < source.Length; i++)
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{
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Update(new TValue(baseTime + (interval * i), source[i]), isNew: true);
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}
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}
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/// <inheritdoc/>
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public override void Reset()
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{
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_buffer.Clear();
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_state = default;
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_p_state = default;
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Last = default;
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}
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/// <summary>
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/// Calculates Fisher Transform for entire series.
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/// </summary>
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public static TSeries Batch(TSeries source, int period = 10, double alpha = 0.33)
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{
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int len = source.Count;
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var t = new List<long>(len);
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var v = new List<double>(len);
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CollectionsMarshal.SetCount(t, len);
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CollectionsMarshal.SetCount(v, len);
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var tSpan = CollectionsMarshal.AsSpan(t);
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var vSpan = CollectionsMarshal.AsSpan(v);
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Batch(source.Values, vSpan, period, alpha);
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source.Times.CopyTo(tSpan);
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return new TSeries(t, v);
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}
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/// <summary>
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/// Batch Fisher Transform with O(period) streaming min/max.
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/// </summary>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public static void Batch(ReadOnlySpan<double> source, Span<double> output, int period = 10, double alpha = 0.33)
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{
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if (source.Length != output.Length)
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{
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throw new ArgumentException("Source and output must have the same length", nameof(output));
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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 (alpha <= 0 || alpha > 1)
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{
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throw new ArgumentOutOfRangeException(nameof(alpha), "Alpha must be in the range (0, 1].");
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}
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int len = source.Length;
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if (len == 0)
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{
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return;
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}
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double decay = 1.0 - alpha;
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var buffer = new RingBuffer(period);
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double emaValue = 0.0;
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double fisherValue = 0.0;
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double lastValid = 0.0;
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for (int i = 0; i < len; i++)
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{
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double val = source[i];
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if (!double.IsFinite(val))
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{
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val = lastValid;
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}
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else
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{
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lastValid = val;
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}
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buffer.Add(val);
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// Find min/max
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double highest = double.MinValue;
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double lowest = double.MaxValue;
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int count = buffer.Count;
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for (int j = 0; j < count; j++)
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{
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double v = buffer[j];
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if (v > highest)
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{
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highest = v;
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}
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if (v < lowest)
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{
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lowest = v;
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}
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}
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// Normalize
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double range = highest - lowest;
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double normalized = range > 0.0
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? 2.0 * ((val - lowest) / range) - 1.0
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: 0.0;
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// EMA smooth
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emaValue = Math.FusedMultiplyAdd(emaValue, decay, alpha * normalized);
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// Clamp and transform
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double clamped = Math.Clamp(emaValue, -0.999, 0.999);
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fisherValue = 0.5 * Math.Log((1.0 + clamped) / (1.0 - clamped));
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output[i] = fisherValue;
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}
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}
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/// <summary>
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/// Creates a Fisher Transform indicator, processes the source, and returns results with the indicator.
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/// </summary>
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public static (TSeries Results, Fisher Indicator) Calculate(TSeries source, int period = 10, double alpha = 0.33)
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{
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var indicator = new Fisher(period, alpha);
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return (indicator.Update(source), indicator);
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}
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}
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