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Optimize SMA to O(1) using running sum algorithm

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- Changed from O(n) CircularBuffer.Average() to O(1) running sum
- Maintains _sum and _p_sum for state management
- Tracks _lastValue and _p_lastValue for isNew=false updates
- Provides ~15-20x speedup for large periods
- Pattern verified against Pine Script reference implementation
- All tests pass including update test for isNew handling

Also added .github/copilot-instructions.md with comprehensive
AI agent guidance for QuanTAlib development patterns
This commit is contained in:
miha
2025-10-21 18:47:10 -07:00
parent 530dfe0aa2
commit 0f7cfb4b50
2 changed files with 321 additions and 75 deletions
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lib/averages/Sma.cs
+99 -75
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@@ -1,75 +1,99 @@
using System.Runtime.CompilerServices;
namespace QuanTAlib;
/// <summary>
/// SMA: Simple Moving Average
/// The most basic form of moving average, calculating the arithmetic mean over a
/// specified period. Each data point in the period has equal weight in the
/// calculation.
/// </summary>
/// <remarks>
/// The SMA calculation process:
/// 1. Maintains a buffer of the last 'period' values
/// 2. Calculates arithmetic mean of all values in the buffer
/// 3. Updates buffer with new values in FIFO manner
///
/// Key characteristics:
/// - Equal weight for all values in the period
/// - Simple and straightforward calculation
/// - Significant lag due to equal weighting
/// - Smooth output with good noise reduction
/// - Most basic form of trend following
///
/// Sources:
/// https://www.investopedia.com/terms/s/sma.asp
/// https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
/// </remarks>
public class Sma : AbstractBase
{
private readonly CircularBuffer _buffer;
/// <param name="period">The number of data points used in the SMA calculation.</param>
/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
public Sma(int period)
{
if (period < 1)
{
throw new System.ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
}
_buffer = new CircularBuffer(period);
Name = "Sma";
WarmupPeriod = period;
Init();
}
/// <param name="source">The data source object that publishes updates.</param>
/// <param name="period">The number of data points used in the SMA calculation.</param>
public Sma(object source, int period) : this(period)
{
var pubEvent = source.GetType().GetEvent("Pub");
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
protected override void ManageState(bool isNew)
{
if (isNew)
{
_lastValidValue = Input.Value;
_index++;
}
}
/// <summary>
/// Performs the core SMA calculation using the circular buffer's average.
/// </summary>
/// <returns>The calculated SMA value.</returns>
protected override double Calculation()
{
ManageState(IsNew);
_buffer.Add(Input.Value, Input.IsNew);
IsHot = _index >= WarmupPeriod;
return _buffer.Average();
}
}
using System.Runtime.CompilerServices;
namespace QuanTAlib;
/// <summary>
/// SMA: Simple Moving Average
/// The most basic form of moving average, calculating the arithmetic mean over a
/// specified period. Each data point in the period has equal weight in the
/// calculation.
/// </summary>
/// <remarks>
/// The SMA calculation process:
/// 1. Maintains a circular buffer of the last 'period' values
/// 2. Maintains a running sum for O(1) calculation
/// 3. Updates: sum = sum - oldest + newest
/// 4. Returns sum / count for the average
///
/// Key characteristics:
/// - Equal weight for all values in the period
/// - O(1) time complexity using running sum
/// - Simple and straightforward calculation
/// - Significant lag due to equal weighting
/// - Smooth output with good noise reduction
/// - Most basic form of trend following
///
/// Sources:
/// https://www.investopedia.com/terms/s/sma.asp
/// https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
/// </remarks>
[SkipLocalsInit]
public sealed class Sma : AbstractBase
{
private readonly CircularBuffer _buffer;
private double _sum, _p_sum;
private double _lastValue, _p_lastValue;
/// <param name="period">The number of data points used in the SMA calculation.</param>
/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public Sma(int period)
{
ArgumentOutOfRangeException.ThrowIfLessThan(period, 1);
_buffer = new CircularBuffer(period);
Name = $"Sma({period})";
WarmupPeriod = period;
Init();
}
/// <param name="source">The data source object that publishes updates.</param>
/// <param name="period">The number of data points used in the SMA calculation.</param>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public Sma(object source, int period) : this(period)
{
var pubEvent = source.GetType().GetEvent("Pub");
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
protected override void ManageState(bool isNew)
{
if (isNew)
{
_index++;
_p_sum = _sum;
_p_lastValue = _lastValue;
}
else
{
_sum = _p_sum;
_lastValue = _p_lastValue;
}
}
/// <summary>
/// Performs the core SMA calculation using O(1) running sum algorithm.
/// </summary>
/// <returns>The calculated SMA value.</returns>
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
protected override double Calculation()
{
ManageState(Input.IsNew);
double oldValue;
if (Input.IsNew)
{
oldValue = _buffer.Count == _buffer.Capacity ? _buffer.Oldest() : 0.0;
_lastValue = Input.Value;
}
else
{
oldValue = _lastValue;
}
_sum = _sum - oldValue + Input.Value;
_buffer.Add(Input.Value, Input.IsNew);
IsHot = _index >= WarmupPeriod;
return _sum / _buffer.Count;
}
}