using System.Runtime.CompilerServices;
namespace QuanTAlib;
///
/// RMA: Running Moving Average (also known as Wilder's Moving Average or SMMA)
///
///
/// RMA is an Exponential Moving Average (EMA) with a different smoothing factor.
/// While EMA uses alpha = 2 / (period + 1), RMA uses alpha = 1 / period.
///
/// Calculation:
/// alpha = 1 / period
/// RMA_new = RMA_old + alpha * (newest - RMA_old)
///
/// This implementation wraps the EMA implementation to ensure identical behavior and performance,
/// utilizing the same O(1) update complexity and zero-allocation architecture.
///
[SkipLocalsInit]
public sealed class Rma : AbstractBase
{
private readonly Ema _ema;
///
/// Creates RMA with specified period.
/// Alpha = 1 / period
///
/// Period for RMA calculation (must be > 0)
public Rma(int period)
{
if (period <= 0)
throw new ArgumentException("Period must be greater than 0", nameof(period));
_ema = new Ema(1.0 / period);
Name = $"Rma({period})";
WarmupPeriod = _ema.WarmupPeriod;
}
///
/// Creates RMA with specified source and period.
/// Subscribes to source.Pub event.
///
/// Source to subscribe to
/// Period for RMA calculation
public Rma(ITValuePublisher source, int period) : this(period)
{
source.Pub += (item) => Update(item);
}
///
/// Creates RMA with specified source and period.
///
/// Source series
/// Period for RMA calculation
public Rma(TSeries source, int period) : this(period)
{
Prime(source.Values);
if (source.Count > 0)
{
Last = new TValue(source.LastTime, Last.Value);
}
source.Pub += (item) => Update(item);
}
///
/// True if the RMA has warmed up and is providing valid results.
///
public override bool IsHot => _ema.IsHot;
///
/// Initializes the indicator state using the provided history.
///
/// Historical data
public override void Prime(ReadOnlySpan source)
{
_ema.Prime(source);
Last = _ema.Last;
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public override TValue Update(TValue input, bool isNew = true)
{
TValue result = _ema.Update(input, isNew);
Last = result;
PubEvent(Last);
return result;
}
public override TSeries Update(TSeries source)
{
TSeries result = _ema.Update(source);
Last = _ema.Last;
return result;
}
///
/// Calculates RMA for the entire series using a new instance.
///
/// Input series
/// RMA period
/// RMA series
public static TSeries Batch(TSeries source, int period)
{
var rma = new Rma(period);
return rma.Update(source);
}
///
/// Calculates RMA in-place using period, writing results to pre-allocated output span.
/// Zero-allocation method for maximum performance.
/// Alpha = 1 / period
///
/// Input values
/// Output span (must be same length as source)
/// RMA period (must be > 0)
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public static void Batch(ReadOnlySpan source, Span output, int period)
{
if (period <= 0)
throw new ArgumentException("Period must be greater than 0", nameof(period));
double alpha = 1.0 / period;
Ema.Batch(source, output, alpha);
}
///
/// Runs a high-performance batch calculation on history and returns
/// a "Hot" Rma instance ready to process the next tick immediately.
///
/// Historical time series
/// RMA Period
/// A tuple containing the full calculation results and the hot indicator instance
public static (TSeries Results, Rma Indicator) Calculate(TSeries source, int period)
{
var rma = new Rma(period);
TSeries results = rma.Update(source);
return (results, rma);
}
///
/// Resets the RMA state.
///
public override void Reset()
{
_ema.Reset();
Last = default;
}
}