English EN
View on GitHub

Burg Extrapolator Strategy

Overview

The Burg Extrapolator Strategy replicates the MetaTrader expert advisor "Burg Extrapolator" using the StockSharp high-level API. The system applies an autoregressive (AR) model solved with the Burg method to forecast future open prices. Trading decisions are driven by the amplitude of the forecast path: when the predicted distance between future highs and lows exceeds configured thresholds, the strategy either opens or closes positions.

Core Logic

  1. Data preparation
    • Collects PastBars open prices on every finished candle.
    • Optionally transforms the series into logarithmic momentum or rate of change values.
    • Normalizes prices by subtracting the rolling average when raw prices are used.
  2. Autoregressive modelling
    • Estimates AR coefficients through the Burg method with an order determined by ModelOrderFraction.
    • Extrapolates several steps ahead (forecast horizon = PastBars - order - 1) and reconstructs price predictions.
  3. Signal generation
    • Tracks the maximum and minimum predicted prices.
    • If the forecast swing exceeds MinProfitPips, generates an entry signal in the respective direction.
    • If the forecast swing exceeds MaxLossPips, issues an exit signal for existing positions.
  4. Order execution
    • Positions are opened with market orders using the calculated risk-based volume.
    • When a stop or opposite signal occurs, the strategy closes positions with market orders.

Parameters

Parameter Description
RiskPercent Equity percentage risked per trade. Used to size orders when a stop-loss distance is available.
MaxPositions Maximum cumulative volume expressed as multiples of the order size allowed per direction.
MinProfitPips Minimum predicted profit swing (in pips) required to open new positions.
MaxLossPips Maximum permitted forecast drawdown (in pips) that will trigger position exits.
TakeProfitPips Static take-profit distance (in pips). Set to zero to disable.
StopLossPips Static stop-loss distance (in pips). Required for risk sizing.
TrailingStopPips Trailing stop distance (in pips). Works only when stop-loss is enabled.
PastBars Number of historical bars used as input to the Burg model.
ModelOrderFraction Fraction of PastBars that defines the AR order (integer truncation).
UseMomentum Enables logarithmic momentum preprocessing (log(p[i]/p[i-1])).
UseRateOfChange Enables rate of change preprocessing (p[i]/p[i-1]-1) when momentum is disabled.
OrderVolume Fallback order size when risk-based sizing cannot be calculated.
CandleType Data type (timeframe) of candles used for calculations.

Trading Rules

  • Entry: When the forecasted path indicates a swing greater than MinProfitPips, open a long position if the highest projected price comes first, or open a short position if the lowest projection appears first.
  • Exit: Close positions when the forecast swing exceeds MaxLossPips or when the opposite entry signal is detected.
  • Protection: Uses StartProtection to configure optional stop-loss, take-profit, and trailing stop in absolute price units derived from pips.
  • Position sizing: If both StopLossPips and RiskPercent are positive, the trade volume is computed as risk_amount / (stop_distance). Otherwise, OrderVolume is used.

Implementation Notes

  • Works exclusively with finished candles to avoid look-ahead bias.
  • Avoids indicator GetValue calls by processing values directly within the Bind callback.
  • Respects StockSharp's high-level API conventions, using SubscribeCandles and StartProtection for risk management.
  • Trailing logic mirrors the original EA by enabling platform-managed trailing stops.

Usage Tips

  • Choose PastBars and ModelOrderFraction carefully; high orders can lead to overfitting or unstable forecasts.
  • The forecast horizon equals PastBars - order - 1; ensure that the horizon is at least a few bars by keeping ModelOrderFraction below 1.
  • Momentum and ROC modes require positive prices. Instruments that can cross zero should stick to raw price mode.
  • For markets with fractional pips, the strategy automatically scales pip size using the security's decimals (×10 for 3 or 5 decimals).

Limitations

  • The AR model assumes stationarity; strong trends or regime shifts can reduce accuracy.
  • Forecast-based signals are sensitive to noise—consider pairing with additional filters if using live trading.
  • Accurate risk sizing requires portfolio valuation and a valid stop-loss distance; otherwise default volumes are used.

Files

  • CS/BurgExtrapolatorStrategy.cs – C# implementation of the strategy.
  • README.md – English documentation (this file).
  • README_ru.md – Russian documentation.
  • README_zh.md – Chinese documentation.

Python version is intentionally omitted as per task requirements.

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 extrapolates future prices using the Burg autoregressive model and opens trades when forecasted swings exceed thresholds.
/// Converted from the MetaTrader Burg Extrapolator expert.
/// </summary>
public class BurgExtrapolatorStrategy : Strategy
{
	private readonly StrategyParam<decimal> _riskPercent;
	private readonly StrategyParam<int> _maxPositions;
	private readonly StrategyParam<decimal> _minProfitPips;
	private readonly StrategyParam<decimal> _maxLossPips;
	private readonly StrategyParam<decimal> _takeProfitPips;
	private readonly StrategyParam<decimal> _stopLossPips;
	private readonly StrategyParam<decimal> _trailingStopPips;
	private readonly StrategyParam<int> _pastBars;
	private readonly StrategyParam<decimal> _modelOrderFraction;
	private readonly StrategyParam<bool> _useMomentum;
	private readonly StrategyParam<bool> _useRateOfChange;
	private readonly StrategyParam<decimal> _orderVolume;
	private readonly StrategyParam<DataType> _candleType;

	private decimal[] _openHistory = Array.Empty<decimal>();
	private decimal[] _inputSeries = Array.Empty<decimal>();
	private double[] _inputBuffer = Array.Empty<double>();
	private double[] _coefficients = Array.Empty<double>();
	private double[] _predictions = Array.Empty<double>();
	private double[] _forwardErrors = Array.Empty<double>();
	private double[] _backwardErrors = Array.Empty<double>();
	private decimal[] _priceForecast = Array.Empty<decimal>();

	private int _historyCapacity;
	private int _openCount;
	private int _modelOrder;
	private int _forecastSteps;
	private int _effectivePastBars;
	private decimal _pipSize;

	/// <summary>
	/// Risk percent of equity per trade.
	/// </summary>
	public decimal RiskPercent
	{
		get => _riskPercent.Value;
		set => _riskPercent.Value = value;
	}

	/// <summary>
	/// Maximum simultaneous positions in the same direction.
	/// </summary>
	public int MaxPositions
	{
		get => _maxPositions.Value;
		set => _maxPositions.Value = value;
	}

	/// <summary>
	/// Minimum predicted profit in pips required to open a position.
	/// </summary>
	public decimal MinProfitPips
	{
		get => _minProfitPips.Value;
		set => _minProfitPips.Value = value;
	}

	/// <summary>
	/// Maximum tolerated loss in pips that triggers position close.
	/// </summary>
	public decimal MaxLossPips
	{
		get => _maxLossPips.Value;
		set => _maxLossPips.Value = value;
	}

	/// <summary>
	/// Take profit distance in pips.
	/// </summary>
	public decimal TakeProfitPips
	{
		get => _takeProfitPips.Value;
		set => _takeProfitPips.Value = value;
	}

	/// <summary>
	/// Stop loss distance in pips.
	/// </summary>
	public decimal StopLossPips
	{
		get => _stopLossPips.Value;
		set => _stopLossPips.Value = value;
	}

	/// <summary>
	/// Trailing stop distance in pips.
	/// </summary>
	public decimal TrailingStopPips
	{
		get => _trailingStopPips.Value;
		set => _trailingStopPips.Value = value;
	}

	/// <summary>
	/// Number of past bars used for the Burg model input.
	/// </summary>
	public int PastBars
	{
		get => _pastBars.Value;
		set => _pastBars.Value = value;
	}

	/// <summary>
	/// Fraction of past bars that determines the autoregressive order.
	/// </summary>
	public decimal ModelOrderFraction
	{
		get => _modelOrderFraction.Value;
		set => _modelOrderFraction.Value = value;
	}

	/// <summary>
	/// Enables logarithmic momentum input instead of raw prices.
	/// </summary>
	public bool UseMomentum
	{
		get => _useMomentum.Value;
		set => _useMomentum.Value = value;
	}

	/// <summary>
	/// Enables rate of change input when momentum is disabled.
	/// </summary>
	public bool UseRateOfChange
	{
		get => _useRateOfChange.Value;
		set => _useRateOfChange.Value = value;
	}

	/// <summary>
	/// Base order volume when risk calculation is not available.
	/// </summary>
	public decimal OrderVolume
	{
		get => _orderVolume.Value;
		set => _orderVolume.Value = value;
	}

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

	/// <summary>
	/// Initializes <see cref="BurgExtrapolatorStrategy"/>.
	/// </summary>
	public BurgExtrapolatorStrategy()
	{
		_riskPercent = Param(nameof(RiskPercent), 5m)
		.SetDisplay("Risk %", "Risk percent per trade", "Money")
		.SetNotNegative();

		_maxPositions = Param(nameof(MaxPositions), 1)
		.SetDisplay("Max Positions", "Maximum simultaneous trades", "Risk")
		.SetGreaterThanZero();

		_minProfitPips = Param(nameof(MinProfitPips), 2m)
		.SetDisplay("Min Profit", "Minimum predicted profit (pips)", "Signals")
		.SetNotNegative();

		_maxLossPips = Param(nameof(MaxLossPips), 5m)
		.SetDisplay("Max Loss", "Maximum tolerated loss (pips)", "Risk")
		.SetNotNegative();

		_takeProfitPips = Param(nameof(TakeProfitPips), 0m)
		.SetDisplay("Take Profit", "Take profit distance (pips)", "Risk")
		.SetNotNegative();

		_stopLossPips = Param(nameof(StopLossPips), 5m)
		.SetDisplay("Stop Loss", "Stop loss distance (pips)", "Risk")
		.SetNotNegative();

		_trailingStopPips = Param(nameof(TrailingStopPips), 10m)
		.SetDisplay("Trailing Stop", "Trailing stop distance (pips)", "Risk")
		.SetNotNegative();

		_pastBars = Param(nameof(PastBars), 50)
		.SetDisplay("Past Bars", "Bars used for Burg model", "Model")
		.SetGreaterThanZero();

		_modelOrderFraction = Param(nameof(ModelOrderFraction), 0.37m)
		.SetDisplay("Model Order", "Fraction of bars used for AR order", "Model")
		.SetRange(0.1m, 0.9m);

		_useMomentum = Param(nameof(UseMomentum), true)
		.SetDisplay("Use Momentum", "Use logarithmic momentum input", "Model");

		_useRateOfChange = Param(nameof(UseRateOfChange), false)
		.SetDisplay("Use ROC", "Use rate of change input when momentum is off", "Model");

		_orderVolume = Param(nameof(OrderVolume), 1m)
		.SetDisplay("Order Volume", "Fallback order volume", "Money")
		.SetGreaterThanZero();

		_candleType = Param(nameof(CandleType), TimeSpan.FromHours(4).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();

		ResetBuffers();
	}

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

		_pipSize = Security?.PriceStep ?? 1m;
		var decimals = Security?.Decimals ?? 0;
		if (decimals is 3 or 5)
		_pipSize *= 10m;

		EnsureCapacity();

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

		StartProtection(
		takeProfit: TakeProfitPips > 0m ? new Unit(TakeProfitPips * _pipSize, UnitTypes.Absolute) : null,
		stopLoss: StopLossPips > 0m ? new Unit(StopLossPips * _pipSize, UnitTypes.Absolute) : null,
		isStopTrailing: TrailingStopPips > 0m);
	}

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

		EnsureCapacity();

		PushOpen(candle.OpenPrice);

		if (_openCount < _historyCapacity)
		return;

		var currentOpen = _openHistory[_openCount - 1];
		if (!TryBuildInputSeries(out var average))
		return;

		// no bound indicators; skip only until enough bars collected

		if (!TryCalculateSignals(average, currentOpen, out var openSignal, out var closeSignal))
		return;

		var hasPosition = Position != 0m;
		if (hasPosition)
		{
			if (Position > 0m && (closeSignal == -1 || openSignal == -1))
			{
				SellMarket();
				return;
			}

			if (Position < 0m && (closeSignal == 1 || openSignal == 1))
			{
				BuyMarket();
				return;
			}
		}

		if (openSignal == 0)
		return;

		var volume = CalculateOrderVolume();
		if (volume <= 0m)
		return;

		var maxExposure = MaxPositions * volume;

		if (openSignal > 0)
		{
			if (Position < maxExposure)
			{
				var remaining = maxExposure - Math.Max(Position, 0m);
				var tradeVolume = Math.Min(volume, remaining);
				if (tradeVolume > 0m)
				BuyMarket();
			}
		}
		else if (openSignal < 0)
		{
			var shortExposure = Math.Abs(Math.Min(Position, 0m));
			if (shortExposure < maxExposure)
			{
				var remaining = maxExposure - shortExposure;
				var tradeVolume = Math.Min(volume, remaining);
				if (tradeVolume > 0m)
				SellMarket();
			}
		}
	}

	private void ResetBuffers()
	{
		_openHistory = Array.Empty<decimal>();
		_inputSeries = Array.Empty<decimal>();
		_inputBuffer = Array.Empty<double>();
		_coefficients = Array.Empty<double>();
		_predictions = Array.Empty<double>();
		_forwardErrors = Array.Empty<double>();
		_backwardErrors = Array.Empty<double>();
		_priceForecast = Array.Empty<decimal>();
		_pipSize = 0m;
		_historyCapacity = 0;
		_openCount = 0;
		_modelOrder = 1;
		_forecastSteps = 1;
		_effectivePastBars = 0;
	}

	private void EnsureCapacity()
	{
		var bars = Math.Max(PastBars, 3);
		var momentumEnabled = UseMomentum;
		var rocEnabled = !momentumEnabled && UseRateOfChange;
		var requiredHistory = momentumEnabled || rocEnabled ? bars + 1 : bars;

		if (_effectivePastBars != bars)
		{
			_effectivePastBars = bars;
			_inputSeries = new decimal[bars];
			_inputBuffer = new double[bars];
			_forwardErrors = new double[bars];
			_backwardErrors = new double[bars];
			_openHistory = new decimal[requiredHistory];
			_historyCapacity = requiredHistory;
			_openCount = 0;
		}
		else if (_historyCapacity != requiredHistory)
		{
			_openHistory = new decimal[requiredHistory];
			_historyCapacity = requiredHistory;
			_openCount = 0;
		}

		var order = (int)Math.Floor((double)(ModelOrderFraction * bars));
		if (order < 1)
		order = 1;
		if (order >= bars)
		order = bars - 1;
		if (order < 1)
		order = 1;

		var nf = bars - order - 1;
		if (nf < 1)
		nf = 1;

		if (_coefficients.Length != order + 1)
		_coefficients = new double[order + 1];

		if (_predictions.Length != nf + 1)
		_predictions = new double[nf + 1];

		if (_priceForecast.Length != nf + 1)
		_priceForecast = new decimal[nf + 1];

		_modelOrder = order;
		_forecastSteps = nf;
	}

	private void PushOpen(decimal open)
	{
		if (_historyCapacity == 0)
		return;

		if (_openCount < _historyCapacity)
		{
			_openHistory[_openCount++] = open;
		}
		else
		{
			Array.Copy(_openHistory, 1, _openHistory, 0, _historyCapacity - 1);
			_openHistory[_historyCapacity - 1] = open;
		}
	}

	private bool TryBuildInputSeries(out decimal average)
	{
		average = 0m;
		var bars = _effectivePastBars;
		if (bars == 0 || _openCount < _historyCapacity)
		return false;

		var momentumEnabled = UseMomentum;
		var rocEnabled = !momentumEnabled && UseRateOfChange;

		if (momentumEnabled)
		{
			for (var i = 0; i < bars; i++)
			{
				var prev = _openHistory[i];
				var next = _openHistory[i + 1];
				if (prev <= 0m || next <= 0m)
				{
					_inputSeries[i] = 0m;
				}
				else
				{
					var ratio = next / prev;
					_inputSeries[i] = (decimal)Math.Log((double)ratio);
				}
			}
		}
		else if (rocEnabled)
		{
			for (var i = 0; i < bars; i++)
			{
				var prev = _openHistory[i];
				var next = _openHistory[i + 1];
				if (prev == 0m)
				{
					_inputSeries[i] = 0m;
				}
				else
				{
					_inputSeries[i] = next / prev - 1m;
				}
			}
		}
		else
		{
			for (var i = 0; i < bars; i++)
			average += _openHistory[i];
			average /= bars;

			for (var i = 0; i < bars; i++)
			_inputSeries[i] = _openHistory[i] - average;
		}

		for (var i = 0; i < bars; i++)
		_inputBuffer[i] = (double)_inputSeries[i];

		return true;
	}

	private bool TryCalculateSignals(decimal average, decimal currentOpen, out int openSignal, out int closeSignal)
	{
		openSignal = 0;
		closeSignal = 0;

		var bars = _effectivePastBars;
		if (bars == 0 || _modelOrder < 1 || _forecastSteps < 1)
		return false;

		Array.Clear(_coefficients, 0, _coefficients.Length);
		Array.Clear(_predictions, 0, _predictions.Length);
		Array.Copy(_inputBuffer, _forwardErrors, bars);
		Array.Copy(_inputBuffer, _backwardErrors, bars);

		ComputeBurgCoefficients(bars);
		ForecastSeries(bars);

		var momentumEnabled = UseMomentum;
		var rocEnabled = !momentumEnabled && UseRateOfChange;

		if (momentumEnabled)
		{
			_priceForecast[0] = currentOpen;
			for (var i = 1; i <= _forecastSteps; i++)
			{
				var prev = _priceForecast[i - 1];
				var next = prev * (decimal)Math.Exp(_predictions[i]);
				_priceForecast[i] = next;
			}
		}
		else if (rocEnabled)
		{
			_priceForecast[0] = currentOpen;
			for (var i = 1; i <= _forecastSteps; i++)
			{
				var prev = _priceForecast[i - 1];
				_priceForecast[i] = prev * (1m + (decimal)_predictions[i]);
			}
		}
		else
		{
			for (var i = 0; i <= _forecastSteps; i++)
			_priceForecast[i] = (decimal)_predictions[i] + average;
		}

		var minProfit = MinProfitPips * _pipSize;
		var maxLoss = MaxLossPips * _pipSize;
		var ymax = _priceForecast[0];
		var ymin = _priceForecast[0];
		var imax = 0;
		var imin = 0;

		for (var i = 1; i < _forecastSteps; i++)
		{
			var value = _priceForecast[i];

			if (value > ymax && openSignal == 0)
			{
				ymax = value;
				imax = i;

				if (imin == 0 && ymax - ymin >= maxLoss)
				closeSignal = 1;

				if (imin == 0 && ymax - ymin >= minProfit)
				openSignal = 1;
			}

			if (value < ymin && openSignal == 0)
			{
				ymin = value;
				imin = i;

				if (imax == 0 && ymax - ymin >= maxLoss)
				closeSignal = -1;

				if (imax == 0 && ymax - ymin >= minProfit)
				openSignal = -1;
			}
		}

		return true;
	}

	private void ComputeBurgCoefficients(int bars)
	{
		var den = 0.0;
		for (var i = 0; i < bars; i++)
		{
			den += _inputBuffer[i] * _inputBuffer[i];
		}
		den *= 2.0;

		var reflection = 0.0;

		for (var k = 1; k <= _modelOrder; k++)
		{
			double num = 0.0;
			for (var i = k; i < bars; i++)
			{
				num += _forwardErrors[i] * _backwardErrors[i - 1];
			}

			var left = _forwardErrors[k - 1];
			var right = _backwardErrors[bars - 1];
			var denom = (1.0 - reflection * reflection) * den - left * left - right * right;
			reflection = Math.Abs(denom) > double.Epsilon ? -2.0 * num / denom : 0.0;

			_coefficients[k] = reflection;
			var half = k / 2;
			for (var i = 1; i <= half; i++)
			{
				var ki = k - i;
				var temp = _coefficients[i];
				_coefficients[i] += reflection * _coefficients[ki];
				if (i != ki)
				{
					_coefficients[ki] += reflection * temp;
				}
			}

			if (k < _modelOrder)
			{
				for (var i = bars - 1; i >= k; i--)
				{
					var temp = _forwardErrors[i];
					_forwardErrors[i] += reflection * _backwardErrors[i - 1];
					_backwardErrors[i] = _backwardErrors[i - 1] + reflection * temp;
				}
			}
		}
	}

	private void ForecastSeries(int bars)
	{
		for (var n = bars - 1; n < bars + _forecastSteps; n++)
		{
			double sum = 0.0;
			for (var i = 1; i <= _modelOrder; i++)
			{
				var index = n - i;
				if (index < bars)
				{
					sum -= _coefficients[i] * _inputBuffer[index];
				}
				else
				{
					var pfIndex = index - bars + 1;
					if (pfIndex >= 0 && pfIndex < _predictions.Length)
					{
						sum -= _coefficients[i] * _predictions[pfIndex];
					}
				}
			}

			var targetIndex = n - bars + 1;
			if (targetIndex >= 0 && targetIndex < _predictions.Length)
			{
				_predictions[targetIndex] = sum;
			}
		}
	}

	private decimal CalculateOrderVolume()
	{
		if (StopLossPips <= 0m || RiskPercent <= 0m)
		{
			return OrderVolume;
		}

		var equity = Portfolio?.CurrentValue ?? 0m;
		if (equity <= 0m)
		{
			return OrderVolume;
		}

		var riskAmount = equity * RiskPercent / 100m;
		var stopDistance = StopLossPips * _pipSize;
		if (stopDistance <= 0m)
		{
			return OrderVolume;
		}

		var volume = riskAmount / stopDistance;
		return volume > 0m ? volume : OrderVolume;
	}
}