Files
QuanTAlib/lib/momentum/adxr/Adxr.cs
T

236 lines
6.9 KiB
C#

using System.Runtime.CompilerServices;
using QuanTAlib;
namespace QuanTAlib;
/// <summary>
/// ADXR: Average Directional Movement Rating
/// </summary>
/// <remarks>
/// ADXR quantifies the change in momentum of the ADX. It is calculated by averaging
/// the current ADX value and the ADX value from 'Period' bars ago.
///
/// Calculation:
/// ADXR = (ADX + ADX[Period]) / 2
///
/// Sources:
/// https://www.investopedia.com/terms/a/adxr.asp
/// "New Concepts in Technical Trading Systems" by J. Welles Wilder
/// </remarks>
[SkipLocalsInit]
public sealed class Adxr : ITValuePublisher
{
private readonly int _period;
private readonly Adx _adx;
private readonly RingBuffer _adxHistory;
private readonly RingBuffer _p_adxHistory;
/// <summary>
/// Display name for the indicator.
/// </summary>
public string Name { get; }
public event Action<TValue>? Pub;
/// <summary>
/// Current ADXR value.
/// </summary>
public TValue Last { get; private set; }
/// <summary>
/// True if the ADXR has warmed up and is providing valid results.
/// </summary>
public bool IsHot => _adx.IsHot && _adxHistory.IsFull;
/// <summary>
/// The number of bars required for the indicator to warm up.
/// </summary>
public int WarmupPeriod { get; }
/// <summary>
/// Creates ADXR with specified period.
/// </summary>
/// <param name="period">Period for ADXR calculation (must be > 0)</param>
public Adxr(int period)
{
if (period <= 0)
throw new ArgumentException("Period must be greater than 0", nameof(period));
_period = period;
Name = $"Adxr({period})";
_adx = new Adx(period);
// We need the ADX value from 'period' bars ago.
// TA-Lib uses (Period-1) lag for ADXR.
_adxHistory = new RingBuffer(period - 1);
_p_adxHistory = new RingBuffer(period - 1);
// ADXR needs valid ADX from 'period' bars ago.
// ADX takes 2*period to warm up.
// So ADXR takes 2*period + period - 1 to warm up.
WarmupPeriod = _adx.WarmupPeriod + period - 1;
}
/// <summary>
/// Resets the ADXR state.
/// </summary>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public void Reset()
{
_adx.Reset();
_adxHistory.Clear();
_p_adxHistory.Clear();
Last = default;
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public TValue Update(TBar input, bool isNew = true)
{
// Update ADX first
TValue adxResult = _adx.Update(input, isNew);
double currentAdx = adxResult.Value;
if (isNew)
{
_p_adxHistory.CopyFrom(_adxHistory);
}
else
{
_adxHistory.CopyFrom(_p_adxHistory);
}
double prevAdx = double.NaN;
if (_adxHistory.IsFull)
{
prevAdx = _adxHistory.Oldest;
}
_adxHistory.Add(currentAdx);
double adxr = 0;
// We calculate ADXR even if not fully hot, as long as we have history
if (!double.IsNaN(prevAdx))
{
adxr = (currentAdx + prevAdx) / 2.0;
}
else
{
// Fallback if we don't have enough history yet?
// Usually ADXR is just ADX or 0 until we have history.
// TA-Lib returns 0 until valid.
adxr = (currentAdx + (double.IsNaN(prevAdx) ? currentAdx : prevAdx)) / 2.0;
// Actually if prevAdx is NaN, we can't really calculate ADXR properly.
// But to avoid returning 0 when ADX is valid but history isn't full (which is rare given ADX warmup is longer),
// we might just return 0 or currentAdx.
// Given ADX warmup is 2*Period, and buffer fills in Period,
// _adxHistory will be full long before ADX is valid.
// So prevAdx will be 0 (from cold ADX) rather than NaN, once we pass Period bars.
// So this branch is only for the very first 'Period' bars.
// In that case ADX is 0, so ADXR is 0.
}
Last = new TValue(input.Time, adxr);
Pub?.Invoke(Last);
return Last;
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public TValue Update(TValue input, bool isNew = true)
{
return Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew);
}
public TSeries Update(TBarSeries source)
{
if (source.Count == 0) return new TSeries([], []);
int len = source.Count;
var v = new double[len];
Calculate(source.Open.Values, source.High.Values, source.Low.Values, source.Close.Values, _period, v);
var tList = new List<long>(len);
var vList = new List<double>(v);
var times = source.Open.Times;
for (int i = 0; i < len; i++)
{
tList.Add(times[i]);
}
Reset();
for (int i = 0; i < len; i++)
{
Update(source[i], true);
}
return new TSeries(tList, vList);
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public static void Calculate(ReadOnlySpan<double> open, ReadOnlySpan<double> high, ReadOnlySpan<double> low, ReadOnlySpan<double> close, int period, Span<double> destination)
{
int len = high.Length;
if (len == 0 || len != low.Length || len != close.Length || len != open.Length || len != destination.Length)
{
if (destination.Length > 0)
{
destination.Clear();
}
return;
}
const int StackallocThreshold = 256;
Span<double> adxSpan = len <= StackallocThreshold
? stackalloc double[len]
: new double[len];
Adx.Calculate(open, high, low, close, period, adxSpan);
destination.Clear();
int lag = period - 1;
if (lag <= 0)
{
adxSpan.CopyTo(destination);
return;
}
if (lag >= len)
{
return;
}
ReadOnlySpan<double> current = adxSpan[lag..];
ReadOnlySpan<double> previous = adxSpan[..(len - lag)];
Span<double> destTail = destination[lag..];
SimdExtensions.Add(current, previous, destTail);
for (int i = 0; i < destTail.Length; i++)
{
destTail[i] *= 0.5;
}
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public static TSeries Batch(TBarSeries source, int period)
{
if (source.Count == 0) return new TSeries([], []);
int len = source.Count;
var v = new double[len];
Calculate(source.Open.Values, source.High.Values, source.Low.Values, source.Close.Values, period, v);
var tList = new List<long>(len);
var times = source.Open.Times;
for (int i = 0; i < len; i++)
{
tList.Add(times[i]);
}
return new TSeries(tList, [.. v]);
}
}