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Monte Carlo Simulation - Random Walk Strategy

This sample strategy performs a Monte Carlo simulation of future price paths using historical log returns. It does not place trades but demonstrates how to generate random walks and estimate future max and min price levels.

Details

  • Entry Criteria: none, this strategy does not trade.
  • Long/Short: none.
  • Exit Criteria: not applicable.
  • Stops: none.
  • Default Values:
    • NumberOfBarsToPredict = 50.
    • NumberOfSimulations = 500.
    • DataLength = 2000.
    • KeepPastMinMaxLevels = false.
  • Filters: not applicable.
  • Complexity: medium.
  • Timeframe: configurable.
using System;
using System.Linq;
using System.Collections.Generic;

using Ecng.Common;

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

namespace StockSharp.Samples.Strategies;

/// <summary>
/// Monte Carlo simulation random walk strategy.
/// Uses MC simulation to estimate expected price range and trades based on mean forecast.
/// </summary>
public class MonteCarloSimulationRandomWalkStrategy : Strategy
{
	private readonly StrategyParam<int> _forecastBars;
	private readonly StrategyParam<int> _simulations;
	private readonly StrategyParam<int> _dataLength;
	private readonly StrategyParam<decimal> _minForecastEdgePercent;
	private readonly StrategyParam<int> _signalCooldownBars;
	private readonly StrategyParam<DataType> _candleType;

	private readonly List<decimal> _returns = new();
	private decimal? _prevClose;
	private int _barsFromSignal;

	public int ForecastBars { get => _forecastBars.Value; set => _forecastBars.Value = value; }
	public int Simulations { get => _simulations.Value; set => _simulations.Value = value; }
	public int DataLength { get => _dataLength.Value; set => _dataLength.Value = value; }
	public decimal MinForecastEdgePercent { get => _minForecastEdgePercent.Value; set => _minForecastEdgePercent.Value = value; }
	public int SignalCooldownBars { get => _signalCooldownBars.Value; set => _signalCooldownBars.Value = value; }
	public DataType CandleType { get => _candleType.Value; set => _candleType.Value = value; }

	public MonteCarloSimulationRandomWalkStrategy()
	{
		_forecastBars = Param(nameof(ForecastBars), 10).SetGreaterThanZero();
		_simulations = Param(nameof(Simulations), 100).SetGreaterThanZero();
		_dataLength = Param(nameof(DataLength), 100).SetGreaterThanZero();
		_minForecastEdgePercent = Param(nameof(MinForecastEdgePercent), 0.5m).SetGreaterThanZero();
		_signalCooldownBars = Param(nameof(SignalCooldownBars), 12).SetGreaterThanZero();
		_candleType = Param(nameof(CandleType), TimeSpan.FromMinutes(15).TimeFrame());
	}

	/// <inheritdoc />
	protected override void OnReseted()
	{
		base.OnReseted();
		_returns.Clear();
		_prevClose = null;
		_barsFromSignal = 0;
	}

	protected override void OnStarted2(DateTime time)
	{
		base.OnStarted2(time);
		StartProtection(null, null);

		_returns.Clear();
		_prevClose = null;
		_barsFromSignal = SignalCooldownBars;

		var subscription = SubscribeCandles(CandleType);
		subscription.Bind(ProcessCandle).Start();
	}

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

		if (_prevClose is decimal prevClose && prevClose > 0)
		{
			var ret = (decimal)Math.Log((double)(candle.ClosePrice / prevClose));
			_returns.Add(ret);
			if (_returns.Count > DataLength)
				_returns.RemoveAt(0);
		}

		_prevClose = candle.ClosePrice;

		if (_returns.Count < 20)
			return;

		if (!IsFormedAndOnlineAndAllowTrading())
			return;

		_barsFromSignal++;

		var history = _returns.ToArray();
		var avg = history.Average();
		var variance = history.Select(r => (r - avg) * (r - avg)).Average();
		var drift = avg - variance / 2m;
		var random = new Random(unchecked((int)candle.OpenTime.Ticks));

		double sum = 0;
		for (var sim = 0; sim < Simulations; sim++)
		{
			var price = (double)candle.ClosePrice;
			for (var step = 0; step < ForecastBars; step++)
			{
				var idx = random.Next(history.Length);
				price *= Math.Exp((double)(history[idx] + drift));
			}
			sum += price;
		}

		var meanForecast = (decimal)(sum / Simulations);
		var current = candle.ClosePrice;
		var edgePercent = (meanForecast - current) / current * 100m;

		if (_barsFromSignal >= SignalCooldownBars && edgePercent >= MinForecastEdgePercent && Position <= 0)
		{
			BuyMarket();
			_barsFromSignal = 0;
		}
		else if (_barsFromSignal >= SignalCooldownBars && edgePercent <= -MinForecastEdgePercent && Position >= 0)
		{
			SellMarket();
			_barsFromSignal = 0;
		}
	}
}