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Stochastic 均值回归策略

该策略将随机指标 %K 与其平均值比较，寻找过度波动的情形。当 %K 偏离均值数个标准差时，预期其回落。

测试表明年均收益约为 64%，该策略在外汇市场表现最佳。

当 %K 低于平均值减 Multiplier 倍标准差时买入；当 %K 高于平均值加同倍数时卖出。%K 回到均值线附近即平仓。

此方法适合短线交易者捕捉超买或超卖的极端，止损可避免持续的动量造成亏损。

详细信息

  • 入场条件:
    • 做多: %K < Avg - Multiplier * StdDev
    • 做空: %K > Avg + Multiplier * StdDev
  • 多空方向: 双向
  • 退出条件:
    • 做多: Exit when %K > Avg
    • 做空: Exit when %K < Avg
  • 止损: 是
  • 默认值:
    • StochPeriod = 14
    • KPeriod = 3
    • AveragePeriod = 20
    • Multiplier = 2
    • StopLossPercent = 2
    • CandleType = TimeSpan.FromMinutes(5) Avg和StdDev为最近AveragePeriod个%K值（含当前值）的平均值和标准差。止损为入场价的固定StopLossPercent百分比，在K线之间同样监控；设为0则关闭。%K为第一个周期的随机指标再经KPeriod根K线平滑；%D不参与规则，因此去掉了DPeriod。与持仓方向相反的入场信号会反转持仓。
  • 筛选条件:
    • 类别: 均值回归
    • 方向: 双向
    • 指标: Stochastic Oscillator
    • 止损: 是
    • 复杂度: 中等
    • 时间框架: 日内
    • 季节性: 否
    • 神经网络: 否
    • 背离: 否
    • 风险等级: 中等
using System;
using System.Collections.Generic;
using System.Linq;

using Ecng.Common;

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

namespace StockSharp.Samples.Strategies;

/// <summary>
/// Stochastic Mean Reversion strategy.
/// The bands lie Multiplier standard deviations around the average of the last AveragePeriod %K values, the current one included.
/// %K below the lower band goes long and %K above the upper band goes short,
/// reversing an opposite position. A long closes once %K is back above its average and a short once it is back below it, and a percent stop limits the loss.
/// </summary>
public class StochasticMeanReversionStrategy : Strategy
{
	private readonly StrategyParam<int> _stochPeriod;
	private readonly StrategyParam<int> _kPeriod;
	private readonly StrategyParam<int> _averagePeriod;
	private readonly StrategyParam<decimal> _multiplier;
	private readonly StrategyParam<decimal> _stopLossPercent;
	private readonly StrategyParam<DataType> _candleType;

	private readonly Queue<decimal> _values = [];

	/// <summary>
	/// Lookback period of the raw stochastic.
	/// </summary>
	public int StochPeriod
	{
		get => _stochPeriod.Value;
		set => _stochPeriod.Value = value;
	}

	/// <summary>
	/// Smoothing period of %K.
	/// </summary>
	public int KPeriod
	{
		get => _kPeriod.Value;
		set => _kPeriod.Value = value;
	}

	/// <summary>
	/// Values of %K the average and the standard deviation span.
	/// </summary>
	public int AveragePeriod
	{
		get => _averagePeriod.Value;
		set => _averagePeriod.Value = value;
	}

	/// <summary>
	/// Standard deviations between the average and a band.
	/// </summary>
	public decimal Multiplier
	{
		get => _multiplier.Value;
		set => _multiplier.Value = value;
	}

	/// <summary>
	/// Stop loss percentage from entry price.
	/// </summary>
	public decimal StopLossPercent
	{
		get => _stopLossPercent.Value;
		set => _stopLossPercent.Value = value;
	}

	/// <summary>
	/// Candle type.
	/// </summary>
	public DataType CandleType
	{
		get => _candleType.Value;
		set => _candleType.Value = value;
	}

	/// <summary>
	/// Constructor.
	/// </summary>
	public StochasticMeanReversionStrategy()
	{
		_stochPeriod = Param(nameof(StochPeriod), 14)
			.SetGreaterThanZero()
			.SetDisplay("Stochastic Period", "Lookback period of the raw stochastic", "Indicators");

		_kPeriod = Param(nameof(KPeriod), 3)
			.SetGreaterThanZero()
			.SetDisplay("%K Period", "Smoothing period of %K", "Indicators");

		_averagePeriod = Param(nameof(AveragePeriod), 20)
			.SetGreaterThanZero()
			.SetDisplay("Average Period", "Values of %K the average and the standard deviation span", "Indicators");

		_multiplier = Param(nameof(Multiplier), 2m)
			.SetGreaterThanZero()
			.SetDisplay("Multiplier", "Standard deviations between the average and a band", "Indicators");

		_stopLossPercent = Param(nameof(StopLossPercent), 2m)
			.SetNotNegative()
			.SetDisplay("Stop Loss %", "Stop loss percentage from entry price", "Risk");

		_candleType = Param(nameof(CandleType), TimeSpan.FromMinutes(5).TimeFrame())
			.SetDisplay("Candle Type", "Type of candles to use", "General");
	}

	/// <inheritdoc />
	public override IEnumerable<(Security sec, DataType dt)> GetWorkingSecurities()
	{
		return [(Security, CandleType)];
	}

	/// <inheritdoc />
	protected override void OnReseted()
	{
		base.OnReseted();
		_values.Clear();
	}

	/// <inheritdoc />
	protected override void OnStarted2(DateTime time)
	{
		base.OnStarted2(time);

		_values.Clear();

		var stochastic = new StochasticOscillator
		{
			K = { Length = StochPeriod },
			D = { Length = KPeriod },
		};

		var subscription = SubscribeCandles(CandleType);
		subscription
			.BindEx(stochastic, ProcessCandle)
			.Start();

		StartProtection(new Unit(), new Unit(StopLossPercent, UnitTypes.Percent), useMarketOrders: true, isLocalStop: true);

		// The stop has to see prices between candles, not only at their close.
		foreach (var field in new[] { Level1Fields.BestBidPrice, Level1Fields.BestAskPrice })
		{
			var quotes = new Subscription(DataType.Level1, Security);
			quotes.MarketData.BuildField = field;
			SubscribeLevel1(quotes).Bind(ObserveProtectionQuote).Start();
		}

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

			var oscillators = CreateChartArea();
			if (oscillators != null)
			{
				DrawIndicator(oscillators, stochastic);
			}
		}
	}

	private void ObserveProtectionQuote(Level1ChangeMessage quote)
	{
		// The high-level handler activates native protection before this callback, also between signal bars.
	}

	private void ProcessCandle(ICandleMessage candle, IIndicatorValue stochasticValue)
	{
		if (candle.State != CandleStates.Finished)
			return;

		// The smoothed %K is the moving average the core oscillator exposes as D.
		if (!stochasticValue.IsFormed || stochasticValue is not IStochasticOscillatorValue { D: decimal value })
			return;

		_values.Enqueue(value);

		if (_values.Count > AveragePeriod)
			_values.Dequeue();

		if (_values.Count < AveragePeriod)
			return;

		if (!IsFormedAndOnlineAndAllowTrading())
			return;

		var mean = _values.Average();
		var deviation = (decimal)Math.Sqrt((double)_values.Average(v => (v - mean) * (v - mean)));
		var upper = mean + Multiplier * deviation;
		var lower = mean - Multiplier * deviation;

		if (value < lower && Position <= 0)
			BuyMarket(Volume + Math.Abs(Position));
		else if (value > upper && Position >= 0)
			SellMarket(Volume + Math.Abs(Position));
		else if (Position > 0 && value > mean)
			SellMarket(Position);
		else if (Position < 0 && value < mean)
			BuyMarket(-Position);
	}
}