Burg Extrapolator Strategie

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;
	}
}

import clr
import math

clr.AddReference("StockSharp.Messages")
clr.AddReference("StockSharp.Algo")
clr.AddReference("StockSharp.Algo.Indicators")
clr.AddReference("StockSharp.Algo.Strategies")

from System import TimeSpan, Math, Array
from StockSharp.Messages import DataType, CandleStates, UnitTypes, Unit
from StockSharp.Algo.Strategies import Strategy

class burg_extrapolator_strategy(Strategy):
    """
    Strategy that extrapolates future prices using the Burg autoregressive model
    and opens trades when forecasted swings exceed thresholds.
    """

    def __init__(self):
        super(burg_extrapolator_strategy, self).__init__()

        self._risk_percent = self.Param("RiskPercent", 5.0) \
            .SetDisplay("Risk %", "Risk percent per trade", "Money")
        self._max_positions = self.Param("MaxPositions", 1) \
            .SetDisplay("Max Positions", "Maximum simultaneous trades", "Risk")
        self._min_profit_pips = self.Param("MinProfitPips", 2.0) \
            .SetDisplay("Min Profit", "Minimum predicted profit (pips)", "Signals")
        self._max_loss_pips = self.Param("MaxLossPips", 5.0) \
            .SetDisplay("Max Loss", "Maximum tolerated loss (pips)", "Risk")
        self._take_profit_pips = self.Param("TakeProfitPips", 0.0) \
            .SetDisplay("Take Profit", "Take profit distance (pips)", "Risk")
        self._stop_loss_pips = self.Param("StopLossPips", 5.0) \
            .SetDisplay("Stop Loss", "Stop loss distance (pips)", "Risk")
        self._trailing_stop_pips = self.Param("TrailingStopPips", 10.0) \
            .SetDisplay("Trailing Stop", "Trailing stop distance (pips)", "Risk")
        self._past_bars = self.Param("PastBars", 50) \
            .SetDisplay("Past Bars", "Bars used for Burg model", "Model")
        self._model_order_fraction = self.Param("ModelOrderFraction", 0.37) \
            .SetDisplay("Model Order", "Fraction of bars used for AR order", "Model")
        self._use_momentum = self.Param("UseMomentum", True) \
            .SetDisplay("Use Momentum", "Use logarithmic momentum input", "Model")
        self._use_rate_of_change = self.Param("UseRateOfChange", False) \
            .SetDisplay("Use ROC", "Use rate of change input when momentum is off", "Model")
        self._order_volume = self.Param("OrderVolume", 1.0) \
            .SetDisplay("Order Volume", "Fallback order volume", "Money")
        self._candle_type = self.Param("CandleType", DataType.TimeFrame(TimeSpan.FromHours(4))) \
            .SetDisplay("Candle Type", "Type of candles to use", "General")

        self._open_history = []
        self._pip_size = 0.0
        self._effective_past_bars = 0
        self._model_order = 1
        self._forecast_steps = 1
        self._history_capacity = 0

    @property
    def candle_type(self):
        return self._candle_type.Value

    @candle_type.setter
    def candle_type(self, value):
        self._candle_type.Value = value

    def OnReseted(self):
        super(burg_extrapolator_strategy, self).OnReseted()
        self._open_history = []
        self._pip_size = 0.0
        self._effective_past_bars = 0
        self._model_order = 1
        self._forecast_steps = 1
        self._history_capacity = 0

    def OnStarted2(self, time):
        super(burg_extrapolator_strategy, self).OnStarted2(time)

        self._pip_size = self.Security.PriceStep if self.Security.PriceStep is not None else 1.0
        decimals = self.Security.Decimals if self.Security.Decimals is not None else 0
        if decimals in (3, 5):
            self._pip_size *= 10.0

        self._ensure_capacity()

        subscription = self.SubscribeCandles(self.candle_type)
        subscription.Bind(self.on_process).Start()

        tp = None
        sl = None
        tp_pips = self._take_profit_pips.Value
        sl_pips = self._stop_loss_pips.Value
        trailing_pips = self._trailing_stop_pips.Value

        if tp_pips > 0:
            tp = Unit(tp_pips * self._pip_size, UnitTypes.Absolute)
        if sl_pips > 0:
            sl = Unit(sl_pips * self._pip_size, UnitTypes.Absolute)

        self.StartProtection(tp, sl, trailing_pips > 0)

    def _ensure_capacity(self):
        bars = max(self._past_bars.Value, 3)
        momentum_enabled = self._use_momentum.Value
        roc_enabled = not momentum_enabled and self._use_rate_of_change.Value
        required = bars + 1 if (momentum_enabled or roc_enabled) else bars

        if self._effective_past_bars != bars:
            self._effective_past_bars = bars
            self._open_history = []
            self._history_capacity = required

        order = int(math.floor(self._model_order_fraction.Value * bars))
        if order < 1:
            order = 1
        if order >= bars:
            order = bars - 1
        if order < 1:
            order = 1

        nf = bars - order - 1
        if nf < 1:
            nf = 1

        self._model_order = order
        self._forecast_steps = nf

    def on_process(self, candle):
        if candle.State != CandleStates.Finished:
            return

        self._ensure_capacity()
        self._open_history.append(float(candle.OpenPrice))

        if len(self._open_history) > self._history_capacity:
            self._open_history = self._open_history[-self._history_capacity:]

        if len(self._open_history) < self._history_capacity:
            return

        bars = self._effective_past_bars
        momentum_enabled = self._use_momentum.Value
        roc_enabled = not momentum_enabled and self._use_rate_of_change.Value

        # Build input series
        input_buffer = [0.0] * bars
        average = 0.0

        if momentum_enabled:
            for i in range(bars):
                prev_val = self._open_history[i]
                next_val = self._open_history[i + 1]
                if prev_val <= 0 or next_val <= 0:
                    input_buffer[i] = 0.0
                else:
                    input_buffer[i] = math.log(next_val / prev_val)
        elif roc_enabled:
            for i in range(bars):
                prev_val = self._open_history[i]
                next_val = self._open_history[i + 1]
                if prev_val == 0:
                    input_buffer[i] = 0.0
                else:
                    input_buffer[i] = next_val / prev_val - 1.0
        else:
            for i in range(bars):
                average += self._open_history[i]
            average /= bars
            for i in range(bars):
                input_buffer[i] = self._open_history[i] - average

        # Burg coefficients
        order = self._model_order
        nf = self._forecast_steps
        coefficients = [0.0] * (order + 1)
        forward_errors = list(input_buffer)
        backward_errors = list(input_buffer)

        den = sum(v * v for v in input_buffer) * 2.0
        reflection = 0.0

        for k in range(1, order + 1):
            num = sum(forward_errors[i] * backward_errors[i - 1] for i in range(k, bars))
            left = forward_errors[k - 1]
            right = backward_errors[bars - 1]
            denom = (1.0 - reflection * reflection) * den - left * left - right * right
            reflection = -2.0 * num / denom if abs(denom) > 1e-15 else 0.0

            coefficients[k] = reflection
            half = k // 2
            for i in range(1, half + 1):
                ki = k - i
                temp = coefficients[i]
                coefficients[i] += reflection * coefficients[ki]
                if i != ki:
                    coefficients[ki] += reflection * temp

            if k < order:
                for i in range(bars - 1, k - 1, -1):
                    temp = forward_errors[i]
                    forward_errors[i] += reflection * backward_errors[i - 1]
                    backward_errors[i] = backward_errors[i - 1] + reflection * temp

        # Forecast
        predictions = [0.0] * (nf + 1)
        for n in range(bars - 1, bars + nf):
            s = 0.0
            for i in range(1, order + 1):
                idx = n - i
                if idx < bars:
                    s -= coefficients[i] * input_buffer[idx]
                else:
                    pf_idx = idx - bars + 1
                    if 0 <= pf_idx < len(predictions):
                        s -= coefficients[i] * predictions[pf_idx]
            target = n - bars + 1
            if 0 <= target < len(predictions):
                predictions[target] = s

        # Convert to price forecast
        current_open = self._open_history[-1]
        price_forecast = [0.0] * (nf + 1)

        if momentum_enabled:
            price_forecast[0] = current_open
            for i in range(1, nf + 1):
                price_forecast[i] = price_forecast[i - 1] * math.exp(predictions[i])
        elif roc_enabled:
            price_forecast[0] = current_open
            for i in range(1, nf + 1):
                price_forecast[i] = price_forecast[i - 1] * (1.0 + predictions[i])
        else:
            for i in range(nf + 1):
                price_forecast[i] = predictions[i] + average

        # Evaluate signals
        min_profit = self._min_profit_pips.Value * self._pip_size
        max_loss = self._max_loss_pips.Value * self._pip_size
        ymax = price_forecast[0]
        ymin = price_forecast[0]
        imax = 0
        imin = 0
        open_signal = 0
        close_signal = 0

        for i in range(1, nf):
            value = price_forecast[i]
            if value > ymax and open_signal == 0:
                ymax = value
                imax = i
                if imin == 0 and ymax - ymin >= max_loss:
                    close_signal = 1
                if imin == 0 and ymax - ymin >= min_profit:
                    open_signal = 1
            if value < ymin and open_signal == 0:
                ymin = value
                imin = i
                if imax == 0 and ymax - ymin >= max_loss:
                    close_signal = -1
                if imax == 0 and ymax - ymin >= min_profit:
                    open_signal = -1

        # Trade
        has_position = self.Position != 0
        if has_position:
            if self.Position > 0 and (close_signal == -1 or open_signal == -1):
                self.SellMarket()
                return
            if self.Position < 0 and (close_signal == 1 or open_signal == 1):
                self.BuyMarket()
                return

        if open_signal == 0:
            return

        if open_signal > 0 and self.Position < self._max_positions.Value * self._order_volume.Value:
            self.BuyMarket()
        elif open_signal < 0:
            short_exposure = abs(min(self.Position, 0))
            if short_exposure < self._max_positions.Value * self._order_volume.Value:
                self.SellMarket()

    def CreateClone(self):
        return burg_extrapolator_strategy()