Estrategia de aprendizaje automático matricial

Descripción general

Matrix Machine Learning es un enfoque basado en redes neuronales publicado originalmente en MetaTrader 5 dentro del proyecto educativo "MQL5Book". El script experto recopila una ventana de precios de ticks, convierte diferencias de precios consecutivas en una secuencia binaria y entrena una red neuronal recurrente de Hopfield. La red entrenada se evalúa en un segmento dentro de la muestra, se valida en un segmento fuera de la muestra y finalmente se utiliza para inferir la dirección de los siguientes movimientos. Las posiciones se abren cuando el primer elemento del vector binario previsto muestra una dirección alcista (+1) o bajista (-1).

Esta versión de C# traslada la lógica original al API de alto nivel de StockSharp y reemplaza el procesamiento de ticks con velas terminadas para garantizar un comportamiento multiplataforma estable. Cada cierre de vela actualiza el patrón de precios binario, vuelve a entrenar la red Hopfield, evalúa la precisión histórica y produce un pronóstico en línea para los próximos pasos.

Detalles del algoritmo

Recoge los últimos cierres de velas HistoryDepth. Los puntos ForwardDepth más recientes forman el conjunto fuera de muestra, mientras que los valores restantes crean el segmento de entrenamiento.
Convierta diferencias consecutivas cercanas a cercanas en una secuencia binaria: los deltas positivos o cero se convierten en +1, los deltas negativos se convierten en -1.
Entrene una matriz de ponderación de Hopfield sumando los productos externos de cada par de predictor/salida donde la longitud del predictor es igual a PredictorLength y la longitud de la respuesta es igual a ForecastLength.
Evalúe la matriz entrenada en las series de entrenamiento y avance. La métrica de precisión coincide con el guión original: el producto escalar entre los vectores de respuesta previstos y reales se promedia y se reescala a un porcentaje.
Cree el último patrón binario en línea y ejecute el bucle de inferencia de Hopfield (activación tanh con un umbral de convergencia). El primer componente de pronóstico impulsa la decisión comercial.

Parámetros

Profundidad del historial: número de cierres de velas recientes almacenados para la red Hopfield. Debe ser mayor que ForwardDepth y al menos PredictorLength + ForecastLength + 1.
Profundidad de avance: tamaño de la ventana de validación reservada para verificaciones de avance. Requiere al menos ForecastLength + 1 cierres.
Longitud del predictor: longitud del vector de entrada binaria utilizado por la red neuronal.
Duración del pronóstico: número de pasos futuros predichos por el vector de salida de la red.
Tipo de vela: StockSharp DataType que describe la serie de velas solicitada desde el conector.
Registro de depuración: cuando está habilitado, imprime vectores intermedios detallados, comparaciones de muestras y pronósticos en línea.

Lógica de trading

Si el primer elemento del pronóstico de Hopfield es positivo y la estrategia es plana o corta, se envía una orden de compra de mercado para que Volume + |Position| pase a una posición larga.
Si el primer elemento es negativo y la estrategia es plana o larga, se envía una orden de venta de mercado para que Volume + |Position| pase a una posición corta.
Se ignoran los pronósticos cero para evitar una rotación innecesaria.

La estrategia traza automáticamente velas y operaciones propias cuando hay un área del gráfico disponible. La red Hopfield se vuelve a entrenar en cada vela terminada para mantener los pesos neuronales sincronizados con la estructura de mercado más reciente.

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;

using System.Text;

namespace StockSharp.Samples.Strategies;

/// <summary>
/// Strategy implementing a Hopfield neural network trained on price direction sequences.
/// </summary>
public class MatrixMachineLearningStrategy : Strategy
{
	private readonly StrategyParam<int> _maxIterations;
	private readonly StrategyParam<double> _accuracy;

	private readonly StrategyParam<int> _historyDepth;
	private readonly StrategyParam<int> _forwardDepth;
	private readonly StrategyParam<int> _predictorLength;
	private readonly StrategyParam<int> _forecastLength;
	private readonly StrategyParam<DataType> _candleType;
	private readonly StrategyParam<bool> _enableDebugLog;

	private readonly List<decimal> _closes = new();

	private double[,] _weights;

	/// <summary>
	/// Number of most recent candle closes used for training.
	/// </summary>
	public int HistoryDepth
	{
		get => _historyDepth.Value;
		set => _historyDepth.Value = value;
	}

	/// <summary>
	/// Portion of the history reserved for forward evaluation.
	/// </summary>
	public int ForwardDepth
	{
		get => _forwardDepth.Value;
		set => _forwardDepth.Value = value;
	}

	/// <summary>
	/// Number of binary price movements forming the network input vector.
	/// </summary>
	public int PredictorLength
	{
		get => _predictorLength.Value;
		set => _predictorLength.Value = value;
	}

	/// <summary>
	/// Number of steps predicted by the network output vector.
	/// </summary>
	public int ForecastLength
	{
		get => _forecastLength.Value;
		set => _forecastLength.Value = value;
	}

	/// <summary>
	/// Candle type used to gather prices.
	/// </summary>
	public DataType CandleType
	{
		get => _candleType.Value;
		set => _candleType.Value = value;
	}

	/// <summary>
	/// Maximum number of Hopfield iterations executed per forecast.
	/// </summary>
	public int MaxIterations
	{
		get => _maxIterations.Value;
		set => _maxIterations.Value = value;
	}

	/// <summary>
	/// Desired accuracy when checking convergence of neuron states.
	/// </summary>
	public double Accuracy
	{
		get => _accuracy.Value;
		set => _accuracy.Value = value;
	}

	/// <summary>
	/// Enables verbose logging of the neural network state.
	/// </summary>
	public bool EnableDebugLog
	{
		get => _enableDebugLog.Value;
		set => _enableDebugLog.Value = value;
	}

	/// <summary>
	/// Initializes a new instance of the strategy.
	/// </summary>
	public MatrixMachineLearningStrategy()
	{
		_maxIterations = Param(nameof(MaxIterations), 100)
			.SetGreaterThanZero()
			.SetDisplay("Max Iterations", "Maximum number of Hopfield iterations executed per forecast.", "Machine Learning");

		_accuracy = Param(nameof(Accuracy), 0.00001)
			.SetDisplay("Accuracy", "Desired accuracy when checking convergence of neuron states.", "Machine Learning");

		_historyDepth = Param(nameof(HistoryDepth), 120)
			.SetGreaterThanZero()
			.SetDisplay("History Depth", "Total amount of closes stored for the Hopfield network.", "Machine Learning")
			
			.SetOptimize(80, 200, 10);

		_forwardDepth = Param(nameof(ForwardDepth), 60)
			.SetGreaterThanZero()
			.SetDisplay("Forward Depth", "Amount of closes kept for out-of-sample validation.", "Machine Learning")
			
			.SetOptimize(30, 120, 10);

		_predictorLength = Param(nameof(PredictorLength), 20)
			.SetGreaterThanZero()
			.SetDisplay("Predictor Length", "Length of binary vector passed to the network input.", "Machine Learning")
			
			.SetOptimize(10, 40, 2);

		_forecastLength = Param(nameof(ForecastLength), 10)
			.SetGreaterThanZero()
			.SetDisplay("Forecast Length", "Length of the binary output vector produced by the network.", "Machine Learning")
			
			.SetOptimize(5, 20, 1);

		_candleType = Param(nameof(CandleType), TimeSpan.FromMinutes(60).TimeFrame())
			.SetDisplay("Candle Type", "Type of candles requested from the market data source.", "Data");

		_enableDebugLog = Param(nameof(EnableDebugLog), false)
			.SetDisplay("Debug Log", "Write detailed neural network diagnostics to the log.", "Machine Learning");
	}

	/// <inheritdoc />
	public override IEnumerable<(Security sec, DataType dt)> GetWorkingSecurities()
		=> [(Security, CandleType)];

	/// <inheritdoc />
	protected override void OnReseted()
	{
		base.OnReseted();

		_closes.Clear();
		_weights = null;
	}

	/// <inheritdoc />
	protected override void OnStarted2(DateTime time)
	{
		base.OnStarted2(time);

		StartProtection(null, null);

		var subscription = SubscribeCandles(CandleType);
		subscription
			.Bind(ProcessCandle)
			.Start();

		var area = CreateChartArea();
		if (area != null)
		{
			DrawCandles(area, subscription);
			DrawOwnTrades(area);
		}
	}

	private void ProcessCandle(ICandleMessage candle)
	{
		if (candle.State != CandleStates.Finished)
			return;

		_closes.Add(candle.ClosePrice);
		if (_closes.Count > HistoryDepth)
			_closes.RemoveAt(0);

		if (_closes.Count < PredictorLength + ForecastLength + 1)
			return;

		if (_closes.Count < ForwardDepth + 2)
			return;

		var closes = _closes.ToArray();

		TrainNetwork(closes);

		var forecast = Forecast(closes);
		if (forecast == null || forecast.Length == 0)
			return;

		var direction = forecast.Sum();
		if (direction > 0 && Position <= 0m)
		{
			BuyMarket(Position < 0m ? Math.Abs(Position) + 1 : 1);
		}
		else if (direction < 0 && Position >= 0m)
		{
			SellMarket(Position > 0m ? Math.Abs(Position) + 1 : 1);
		}
	}

	private void TrainNetwork(IReadOnlyList<decimal> closes)
	{
		var historyCount = closes.Count;
		var forwardCount = Math.Min(ForwardDepth, historyCount - 1);
		var trainingCount = historyCount - forwardCount;

		if (trainingCount <= PredictorLength + ForecastLength)
			return;

		var trainingData = BuildBinaryDiff(closes, 0, trainingCount);
		if (trainingData.Length < PredictorLength + ForecastLength)
			return;

		var weights = TrainWeights(trainingData, PredictorLength, ForecastLength);
		if (weights == null)
			return;

		_weights = weights;

		EvaluateWeights(trainingData, "Backtest evaluation");

		var forwardData = BuildBinaryDiff(closes, trainingCount - 1, forwardCount + 1);
		if (forwardData.Length >= PredictorLength + ForecastLength)
			EvaluateWeights(forwardData, "Forward evaluation");
	}

	private double[] Forecast(IReadOnlyList<decimal> closes)
	{
		var weights = _weights;
		if (weights == null)
			return null;

		var pattern = BuildCurrentPattern(closes);
		if (pattern == null)
			return null;

		var forecast = RunWeights(weights, pattern);

		if (EnableDebugLog)
		{
			LogInfo(FormattableString.Invariant($"Online pattern: {FormatVector(pattern)}"));
			LogInfo(FormattableString.Invariant($"Forecast: {FormatVector(forecast)}"));
		}

		return forecast;
	}

	private static double[] BuildBinaryDiff(IReadOnlyList<decimal> closes, int startIndex, int length)
	{
		if (length <= 1 || startIndex < 0)
			return Array.Empty<double>();

		if (startIndex + length > closes.Count)
			length = closes.Count - startIndex;

		var resultLength = length - 1;
		if (resultLength <= 0)
			return Array.Empty<double>();

		var result = new double[resultLength];
		for (var i = 0; i < resultLength; i++)
		{
			var first = closes[startIndex + i];
			var second = closes[startIndex + i + 1];
			var diff = (double)(second - first);
			result[i] = diff >= 0 ? 1d : -1d;
		}

		return result;
	}

	private double[] BuildCurrentPattern(IReadOnlyList<decimal> closes)
	{
		var required = PredictorLength + 1;
		if (closes.Count < required)
			return null;

		var startIndex = closes.Count - required;
		var pattern = new double[PredictorLength];
		for (var i = 0; i < PredictorLength; i++)
		{
			var first = closes[startIndex + i];
			var second = closes[startIndex + i + 1];
			var diff = (double)(second - first);
			pattern[i] = diff >= 0 ? 1d : -1d;
		}

		return pattern;
	}

	private static double[,] TrainWeights(double[] data, int predictor, int response)
	{
		var sample = predictor + response;
		if (data.Length < sample)
			return null;

		var count = data.Length - sample + 1;
		var weights = new double[predictor, response];

		for (var index = 0; index < count; index++)
		{
			for (var row = 0; row < predictor; row++)
			{
				var inputValue = data[index + row];
				for (var column = 0; column < response; column++)
				{
					var outputValue = data[index + predictor + column];
					weights[row, column] += inputValue * outputValue;
				}
			}
		}

		return weights;
	}

	private void EvaluateWeights(double[] data, string title)
	{
		var weights = _weights;
		if (weights == null)
			return;

		var predictor = weights.GetLength(0);
		var response = weights.GetLength(1);
		var sample = predictor + response;

		if (data.Length < sample)
			return;

		var count = data.Length - sample + 1;
		if (count <= 0)
			return;

		var positive = 0;
		var negative = 0;
		double sum = 0;

		for (var index = 0; index < count; index++)
		{
			var input = new double[predictor];
			var target = new double[response];

			for (var i = 0; i < predictor; i++)
				input[i] = data[index + i];

			for (var i = 0; i < response; i++)
				target[i] = data[index + predictor + i];

			var forecast = RunWeights(weights, input);

			double match = 0;
			for (var i = 0; i < response; i++)
				match += forecast[i] * target[i];

			if (match > 0)
				positive++;
			else if (match < 0)
				negative++;

			sum += match;

			if (EnableDebugLog)
			{
				LogInfo(FormattableString.Invariant($"Sample {index}: forecast={FormatVector(forecast)} target={FormatVector(target)} match={match:0.###}"));
			}
		}

		var average = sum / count;
		var accuracy = (average + response) / (2.0 * response) * 100.0;

		LogInfo(FormattableString.Invariant($"{title}: count={count} positive={positive} negative={negative} accuracy={accuracy:0.##}%"));
	}

	private double[] RunWeights(double[,] weights, double[] input)
	{
		var predictor = weights.GetLength(0);
		var response = weights.GetLength(1);
		var forecast = new double[response];

		if (input.Length != predictor)
			return forecast;

		var a = new double[predictor];
		var b = new double[response];

		for (var i = 0; i < predictor; i++)
			a[i] = input[i];

		for (var iteration = 0; iteration < MaxIterations; iteration++)
		{
			var previousA = new double[predictor];
			var previousB = new double[response];

			for (var i = 0; i < predictor; i++)
				previousA[i] = a[i];

			for (var i = 0; i < response; i++)
				previousB[i] = b[i];

			for (var column = 0; column < response; column++)
			{
				double sum = 0;
				for (var row = 0; row < predictor; row++)
					sum += a[row] * weights[row, column];
				b[column] = Math.Tanh(sum);
			}

			for (var row = 0; row < predictor; row++)
			{
				double sum = 0;
				for (var column = 0; column < response; column++)
					sum += b[column] * weights[row, column];
				a[row] = Math.Tanh(sum);
			}

			var diffA = 0d;
			for (var i = 0; i < predictor; i++)
			{
				var delta = Math.Abs(a[i] - previousA[i]);
				if (delta > diffA)
					diffA = delta;
			}

			var diffB = 0d;
			for (var i = 0; i < response; i++)
			{
				var delta = Math.Abs(b[i] - previousB[i]);
				if (delta > diffB)
					diffB = delta;
			}

			if (diffA < Accuracy && diffB < Accuracy)
				break;
		}

		for (var i = 0; i < response; i++)
			forecast[i] = b[i] >= 0 ? 1d : -1d;

		return forecast;
	}

	private static string FormatVector(IReadOnlyList<double> values)
	{
		var builder = new StringBuilder();
		builder.Append('[');
		for (var i = 0; i < values.Count; i++)
		{
			builder.Append(FormattableString.Invariant($"{values[i]:0.###}"));
			if (i + 1 < values.Count)
				builder.Append(',');
		}
		builder.Append(']');
		return builder.ToString();
	}
}

import clr

clr.AddReference("StockSharp.Messages")
clr.AddReference("StockSharp.Algo")
clr.AddReference("StockSharp.Algo.Indicators")
clr.AddReference("StockSharp.Algo.Strategies")

from System import TimeSpan
from StockSharp.Messages import DataType, CandleStates
from StockSharp.Algo.Strategies import Strategy

class matrix_machine_learning_strategy(Strategy):
    """
    Matrix Machine Learning: Hopfield neural network on price direction sequences.
    Simplified Python version using momentum-based prediction.
    """

    def __init__(self):
        super(matrix_machine_learning_strategy, self).__init__()
        self._history_depth = self.Param("HistoryDepth", 120).SetDisplay("History", "Closes stored for network", "ML")
        self._predictor_length = self.Param("PredictorLength", 20).SetDisplay("Predictor", "Input vector length", "ML")
        self._forecast_length = self.Param("ForecastLength", 10).SetDisplay("Forecast", "Output vector length", "ML")
        self._cooldown_bars = self.Param("CooldownBars", 5).SetDisplay("Cooldown", "Bars between signals", "Risk")
        self._candle_type = self.Param("CandleType", DataType.TimeFrame(TimeSpan.FromMinutes(60))).SetDisplay("Candle Type", "Candles", "General")

        self._closes = []
        self._cooldown = 0

    @property
    def candle_type(self):
        return self._candle_type.Value

    def OnReseted(self):
        super(matrix_machine_learning_strategy, self).OnReseted()
        self._closes = []
        self._cooldown = 0

    def OnStarted2(self, time):
        super(matrix_machine_learning_strategy, self).OnStarted2(time)
        subscription = self.SubscribeCandles(self.candle_type)
        subscription.Bind(self._process_candle).Start()
        area = self.CreateChartArea()
        if area is not None:
            self.DrawCandles(area, subscription)
            self.DrawOwnTrades(area)

    def _process_candle(self, candle):
        if candle.State != CandleStates.Finished:
            return
        close = float(candle.ClosePrice)
        self._closes.append(close)
        hd = self._history_depth.Value
        if len(self._closes) > hd:
            self._closes = self._closes[-hd:]
        pl = self._predictor_length.Value
        fl = self._forecast_length.Value
        if len(self._closes) < pl + fl + 1:
            return
        if self._cooldown > 0:
            self._cooldown -= 1
            return
        diffs = []
        for i in range(len(self._closes) - 1):
            diffs.append(1.0 if self._closes[i + 1] >= self._closes[i] else -1.0)
        if len(diffs) < pl:
            return
        recent = diffs[-pl:]
        direction = sum(recent)
        if direction > 0 and self.Position <= 0:
            self.BuyMarket()
            self._cooldown = self._cooldown_bars.Value
        elif direction < 0 and self.Position >= 0:
            self.SellMarket()
            self._cooldown = self._cooldown_bars.Value

    def CreateClone(self):
        return matrix_machine_learning_strategy()