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Estrategia de Reversión por Autocorrelación

Esta estrategia analiza la autocorrelación de precios a corto plazo para evaluar si los movimientos recientes tienen probabilidad de revertirse. La autocorrelación negativa sugiere que los cambios de precio sucesivos tienden a alternar de dirección, creando condiciones de reversión a la media.

Las pruebas indican un retorno anual promedio de aproximadamente 124%. Funciona mejor en el mercado de divisas.

Cuando la autocorrelación calculada cae por debajo del umbral y el precio está por debajo de una media móvil, el sistema compra anticipando un rebote. Si la autocorrelación es negativa y el precio está por encima del promedio, se abre una posición corta. Las salidas ocurren cuando el precio cruza el promedio o la autocorrelación sube por encima del umbral.

El enfoque es adecuado para traders que buscan ventajas estadísticas en lugar de patrones de gráfico. Se aplica un stop-loss porcentual para protegerse contra tendencias sostenidas que violen la reversión esperada.

Detalles

  • Criterios de entrada:
    • Largo: Autocorrelation < Threshold && Close < MA
    • Corto: Autocorrelation < Threshold && Close > MA
  • Largo/Corto: Ambos lados.
  • Criterios de salida:
    • Largo: Salir cuando Close > MA o autocorrelation > Threshold
    • Corto: Salir cuando Close < MA o autocorrelation > Threshold
  • Stops: Sí, stop-loss porcentual.
  • Valores predeterminados:
    • AutoCorrPeriod = 20
    • AutoCorrThreshold = -0.3m
    • StopLossPercent = 2m
    • CandleType = TimeSpan.FromMinutes(5)
  • Filtros:
    • Categoría: Mean reversion
    • Dirección: Ambos
    • Indicadores: Autocorrelation, MA
    • Stops: Sí
    • Complejidad: Intermedio
    • Marco temporal: Intradía
    • Estacionalidad: No
    • Redes neuronales: No
    • Divergencia: No
    • Nivel de riesgo: Medio
using System;
using System.Linq;
using System.Collections.Generic;

using Ecng.Common;
using Ecng.Collections;
using Ecng.Serialization;

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

namespace StockSharp.Samples.Strategies;

/// <summary>
/// Strategy that trades based on price autocorrelation.
/// Buys when autocorrelation is negative and price is below average.
/// Sells when autocorrelation is negative and price is above average.
/// </summary>
public class AutocorrelationReversionStrategy : Strategy
{
	private readonly StrategyParam<int> _autoCorrPeriod;
	private readonly StrategyParam<decimal> _autoCorrThreshold;
	private readonly StrategyParam<decimal> _stopLossPercent;
	private readonly StrategyParam<DataType> _candleType;

	private SimpleMovingAverage _sma;
	private decimal _currentPrice;
	private readonly Queue<decimal> _priceHistory = [];
	private decimal _latestAutocorrelation;

	/// <summary>
	/// Period for autocorrelation calculation.
	/// </summary>
	public int AutoCorrPeriod
	{
		get => _autoCorrPeriod.Value;
		set => _autoCorrPeriod.Value = value;
	}

	/// <summary>
	/// Autocorrelation threshold for signal generation.
	/// </summary>
	public decimal AutoCorrThreshold
	{
		get => _autoCorrThreshold.Value;
		set => _autoCorrThreshold.Value = value;
	}

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

	/// <summary>
	/// Type of candles to use.
	/// </summary>
	public DataType CandleType
	{
		get => _candleType.Value;
		set => _candleType.Value = value;
	}

	/// <summary>
	/// Constructor.
	/// </summary>
	public AutocorrelationReversionStrategy()
	{
		_autoCorrPeriod = Param(nameof(AutoCorrPeriod), 20)
			.SetDisplay("Autocorrelation period", "Period for autocorrelation calculation", "Strategy parameters")
			
			.SetOptimize(10, 30, 5);

		_autoCorrThreshold = Param(nameof(AutoCorrThreshold), -0.3m)
			.SetDisplay("Autocorr threshold", "Threshold for autocorrelation signals", "Strategy parameters")
			
			.SetOptimize(-0.5m, -0.1m, 0.1m);

		_stopLossPercent = Param(nameof(StopLossPercent), 2m)
			.SetDisplay("Stop-loss %", "Stop-loss as percentage from entry price", "Risk management")
			
			.SetOptimize(1m, 3m, 0.5m);

		_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();

		_priceHistory.Clear();
		_latestAutocorrelation = default;
		_currentPrice = default;
	}

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

		// Initialize the SMA indicator (using same period as autocorrelation for simplicity)
		_sma = new SMA { Length = AutoCorrPeriod };

		// Create a subscription to candlesticks
		var subscription = SubscribeCandles(CandleType);

		// Subscribe to candle processing
		subscription
			.Bind(_sma, ProcessCandle)
			.Start();

		// Start position protection
		StartProtection(
			new Unit(StopLossPercent, UnitTypes.Percent),
			new Unit(StopLossPercent * 1.5m, UnitTypes.Percent));

		// Setup chart if available
		var area = CreateChartArea();
		if (area != null)
		{
			DrawCandles(area, subscription);
			DrawIndicator(area, _sma);
			DrawOwnTrades(area);
		}
	}

	private void ProcessCandle(ICandleMessage candle, decimal smaValue)
	{
		// Skip unfinished candles
		if (candle.State != CandleStates.Finished)
			return;

		// Update current price and price history
		_currentPrice = candle.ClosePrice;
		
		// Update price history queue
		_priceHistory.Enqueue(_currentPrice);
		if (_priceHistory.Count > AutoCorrPeriod)
			_priceHistory.Dequeue();

		// Wait until we have enough data
		if (_priceHistory.Count < AutoCorrPeriod)
			return;

		// Check if strategy is ready to trade
		if (!IsFormedAndOnlineAndAllowTrading())
			return;

		// Calculate autocorrelation
		_latestAutocorrelation = CalculateAutocorrelation();

		// Log the autocorrelation value
		LogInfo($"Autocorrelation: {_latestAutocorrelation}, Current price: {_currentPrice}, SMA: {smaValue}");

		// Trading logic: Look for negative autocorrelation below threshold
		if (_latestAutocorrelation < AutoCorrThreshold)
		{
			// Price below average - buy signal
			if (_currentPrice < smaValue && Position <= 0)
			{
				BuyMarket(Volume);
				LogInfo($"Buy signal: Autocorr={_latestAutocorrelation}, Price={_currentPrice}, SMA={smaValue}");
			}
			// Price above average - sell signal
			else if (_currentPrice > smaValue && Position >= 0)
			{
				SellMarket(Volume + Math.Abs(Position));
				LogInfo($"Sell signal: Autocorr={_latestAutocorrelation}, Price={_currentPrice}, SMA={smaValue}");
			}
		}
	}

	private decimal CalculateAutocorrelation()
	{
		// Convert queue to array for easier calculation
		decimal[] prices = [.. _priceHistory];
		
		// Calculate price changes
		decimal[] priceChanges = new decimal[prices.Length - 1];
		for (int i = 0; i < prices.Length - 1; i++)
		{
			priceChanges[i] = prices[i + 1] - prices[i];
		}

		// Calculate autocorrelation of lag 1
		decimal meanChange = priceChanges.Average();
		
		decimal numerator = 0;
		decimal denominator = 0;
		
		for (int i = 0; i < priceChanges.Length - 1; i++)
		{
			decimal deviation1 = priceChanges[i] - meanChange;
			decimal deviation2 = priceChanges[i + 1] - meanChange;
			
			numerator += deviation1 * deviation2;
			denominator += deviation1 * deviation1;
		}
		
		// Guard against division by zero
		if (denominator == 0)
			return 0;
			
		return numerator / denominator;
	}
}