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Five MA Multi-Timeframe 策略

概述

Five MA Multi-Timeframe Strategy 在 StockSharp 平台中复刻 MT4 的 “5matf” 专家顾问。策略在三个时间框架（信号、确认、趋势）上同时计算 5 条简单移动平均线，并结合 Accelerator Oscillator 加速指标来评估多空力量。当所有时间框架都指向同一方向且强度达到阈值时，策略会开仓或平仓。

指标与数据

  • 简单移动平均线（SMA）：每个时间框架使用周期 5、8、13、21、34。
  • Accelerator Oscillator（AC）：在信号时间框架和趋势时间框架上应用，用于捕捉动量加速。
  • 时间框架：默认设置为 15 分钟（信号）、60 分钟（确认）和 240 分钟（趋势过滤），可通过参数自由调整。

信号逻辑

  1. 对每条 SMA 比较当前收盘值与上一根完成 K 线的值，判断斜率方向。
  2. AC 指标使用最近四个数值识别加速上升或下降的形态。
  3. 将斜率计数与 AC 结果转换成百分比得分，分别对应三个时间框架。
  4. 当三个时间框架的多头得分同时超过 50% 时生成 BUY 信号；超过 75% 时视为强信号。
  5. 空头方向使用相同阈值生成 SELL 信号。
  6. 如果持有仓位，当相反方向的信号超过 CloseLevel 时立即平仓。为了与原版 EA 保持一致，策略只在空仓状态下开新仓位。

参数

名称 默认值 说明
CandleType 15 分钟 K 线 生成交易信号的主要时间框架。
HigherTimeframe1 60 分钟 K 线 第一个较高时间框架，用于确认。
HigherTimeframe2 240 分钟 K 线 最慢时间框架，用于趋势过滤。
FirstPeriod – FifthPeriod 5、8、13、21、34 各时间框架上使用的 SMA 周期。
OpenLevel 0 开仓所需的最小信号等级。
CloseLevel 1 平仓所需的反向信号等级。

使用提示

  • 策略使用市价单，并且不会在同一根 K 线上直接反向开仓，先平仓再等待新信号。
  • 请为所有选定的时间框架提供历史数据，保证指标同步计算。
  • 在不同品种或波动环境下运行时，可以优化 SMA 周期与阈值来匹配市场特性。

转换说明

该实现完整保留了 MT4 “5matf” 的核心逻辑，并利用 StockSharp 的订阅与绑定机制实现高阶封装。与原始版本相同，AC 指标需要至少四根完成的 K 线才会产生有效信号。

using System;
using System.Collections.Generic;
using System.Linq;

using Ecng.Common;

using StockSharp.Algo.Strategies;
using StockSharp.BusinessEntities;
using StockSharp.Messages;

namespace StockSharp.Samples.Strategies;

/// <summary>
/// Five moving averages across three timeframes with Accelerator Oscillator confirmation.
/// </summary>
public class FiveMaMultiTimeframeStrategy : Strategy
{
	private readonly StrategyParam<DataType> _candleType;
	private readonly StrategyParam<DataType> _higherTimeframe1;
	private readonly StrategyParam<DataType> _higherTimeframe2;
	private readonly StrategyParam<int> _firstPeriod;
	private readonly StrategyParam<int> _secondPeriod;
	private readonly StrategyParam<int> _thirdPeriod;
	private readonly StrategyParam<int> _fourthPeriod;
	private readonly StrategyParam<int> _fifthPeriod;
	private readonly StrategyParam<int> _openLevel;
	private readonly StrategyParam<int> _closeLevel;

	private readonly Dictionary<string, FrameState> _frames = new(StringComparer.Ordinal);

	public DataType CandleType { get => _candleType.Value; set => _candleType.Value = value; }
	public DataType HigherTimeframe1 { get => _higherTimeframe1.Value; set => _higherTimeframe1.Value = value; }
	public DataType HigherTimeframe2 { get => _higherTimeframe2.Value; set => _higherTimeframe2.Value = value; }
	public int FirstPeriod { get => _firstPeriod.Value; set => _firstPeriod.Value = value; }
	public int SecondPeriod { get => _secondPeriod.Value; set => _secondPeriod.Value = value; }
	public int ThirdPeriod { get => _thirdPeriod.Value; set => _thirdPeriod.Value = value; }
	public int FourthPeriod { get => _fourthPeriod.Value; set => _fourthPeriod.Value = value; }
	public int FifthPeriod { get => _fifthPeriod.Value; set => _fifthPeriod.Value = value; }
	public int OpenLevel { get => _openLevel.Value; set => _openLevel.Value = value; }
	public int CloseLevel { get => _closeLevel.Value; set => _closeLevel.Value = value; }

	public FiveMaMultiTimeframeStrategy()
	{
		_candleType = Param(nameof(CandleType), TimeSpan.FromMinutes(15).TimeFrame()).SetDisplay("Candle Type", "Primary signal timeframe.", "Timeframes");
		_higherTimeframe1 = Param(nameof(HigherTimeframe1), TimeSpan.FromMinutes(60).TimeFrame()).SetDisplay("Higher Timeframe 1", "First confirmation timeframe.", "Timeframes");
		_higherTimeframe2 = Param(nameof(HigherTimeframe2), TimeSpan.FromMinutes(240).TimeFrame()).SetDisplay("Higher Timeframe 2", "Slow trend timeframe.", "Timeframes");

		_firstPeriod = Param(nameof(FirstPeriod), 5).SetGreaterThanZero();
		_secondPeriod = Param(nameof(SecondPeriod), 8).SetGreaterThanZero();
		_thirdPeriod = Param(nameof(ThirdPeriod), 13).SetGreaterThanZero();
		_fourthPeriod = Param(nameof(FourthPeriod), 21).SetGreaterThanZero();
		_fifthPeriod = Param(nameof(FifthPeriod), 34).SetGreaterThanZero();

		_openLevel = Param(nameof(OpenLevel), 0).SetNotNegative().SetDisplay("Open Level", "Minimum signal grade required to open.", "Signal");
		_closeLevel = Param(nameof(CloseLevel), 1).SetNotNegative().SetDisplay("Close Level", "Opposite signal grade required to close.", "Signal");
	}

	public override IEnumerable<(Security sec, DataType dt)> GetWorkingSecurities()
		=> [(Security, CandleType), (Security, HigherTimeframe1), (Security, HigherTimeframe2)];

	protected override void OnReseted()
	{
		base.OnReseted();
		_frames.Clear();
	}

	protected override void OnStarted2(DateTime time)
	{
		base.OnStarted2(time);
		StartFrame("primary", CandleType, true);
		StartFrame("higher1", HigherTimeframe1, false);
		StartFrame("higher2", HigherTimeframe2, true);
	}

	private void StartFrame(string key, DataType candleType, bool useAc)
	{
		var frame = new FrameState([FirstPeriod, SecondPeriod, ThirdPeriod, FourthPeriod, FifthPeriod], useAc);
		_frames[key] = frame;

		var subscription = SubscribeCandles(candleType);
		subscription.Bind(candle =>
		{
			if (candle.State != CandleStates.Finished)
				return;

			frame.Process(candle);
			Evaluate();
		}).Start();

		var area = CreateChartArea();
		if (area != null)
			DrawCandles(area, subscription);
	}

	private void Evaluate()
	{
		if (_frames.Count != 3 || _frames.Values.Any(f => !f.IsReady))
			return;

		var bullishGrade = _frames.Values.Min(f => Grade(f.BullishScore));
		var bearishGrade = _frames.Values.Min(f => Grade(f.BearishScore));

		if (Position > 0)
		{
			if (bearishGrade > CloseLevel)
				SellMarket(Math.Abs(Position));
			return;
		}

		if (Position < 0)
		{
			if (bullishGrade > CloseLevel)
				BuyMarket(Math.Abs(Position));
			return;
		}

		if (bullishGrade > OpenLevel && bullishGrade > bearishGrade)
			BuyMarket();
		else if (bearishGrade > OpenLevel && bearishGrade > bullishGrade)
			SellMarket();
	}

	private static int Grade(decimal score)
		=> score > 75m ? 2 : score > 50m ? 1 : 0;

	private sealed class FrameState(int[] periods, bool useAc)
	{
		private readonly int[] _periods = periods;
		private readonly bool _useAc = useAc;
		private readonly List<decimal> _closes = [];
		private readonly List<decimal> _medians = [];
		private readonly List<decimal> _ao = [];
		private readonly List<decimal> _ac = [];
		private decimal[] _previousMas;

		public decimal BullishScore { get; private set; }
		public decimal BearishScore { get; private set; }
		public bool IsReady { get; private set; }

		public void Process(ICandleMessage candle)
		{
			_closes.Add(candle.ClosePrice);

			if (_useAc)
			{
				_medians.Add((candle.HighPrice + candle.LowPrice) / 2m);

				if (_medians.Count >= 34)
				{
					var ao = AverageTail(_medians, 5) - AverageTail(_medians, 34);
					_ao.Add(ao);

					if (_ao.Count >= 5)
						_ac.Add(ao - AverageTail(_ao, 5));
				}
			}

			if (_closes.Count < _periods.Max())
				return;

			var mas = _periods.Select(p => AverageTail(_closes, p)).ToArray();

			if (_previousMas is null)
			{
				_previousMas = mas;
				return;
			}

			var bullishVotes = 0;
			var bearishVotes = 0;

			for (var i = 0; i < mas.Length; i++)
			{
				if (mas[i] > _previousMas[i])
					bullishVotes++;
				else if (mas[i] < _previousMas[i])
					bearishVotes++;
			}

			if (_useAc && _ac.Count >= 4)
			{
				var last = _ac.Count - 1;
				var bullishAc = _ac[last - 3] < _ac[last - 2] && _ac[last - 2] < _ac[last - 1] && _ac[last - 1] < _ac[last];
				var bearishAc = _ac[last - 3] > _ac[last - 2] && _ac[last - 2] > _ac[last - 1] && _ac[last - 1] > _ac[last];

				if (bullishAc)
					bullishVotes++;
				else if (bearishAc)
					bearishVotes++;
			}

			var total = _useAc ? 6m : 5m;
			BullishScore = bullishVotes / total * 100m;
			BearishScore = bearishVotes / total * 100m;
			IsReady = true;
			_previousMas = mas;

			var keep = Math.Max(_periods.Max(), 40);
			if (_closes.Count > keep)
				_closes.RemoveRange(0, _closes.Count - keep);
			if (_medians.Count > 40)
				_medians.RemoveRange(0, _medians.Count - 40);
			if (_ao.Count > 10)
				_ao.RemoveRange(0, _ao.Count - 10);
			if (_ac.Count > 10)
				_ac.RemoveRange(0, _ac.Count - 10);
		}

		private static decimal AverageTail(List<decimal> values, int count)
		{
			var start = values.Count - count;
			var sum = 0m;
			for (var i = start; i < values.Count; i++)
				sum += values[i];
			return sum / count;
		}
	}
}