Burg Extrapolator
This EA uses the Burg method (Maximum Entropy Method) for spectral analysis and extrapolation of price series. It decomposes the price signal into its frequency components using autoregressive modeling, then forecasts future price movement by extrapolating those components forward. Trading signals are generated based on the extrapolated price direction.
Open the interactive view (equity curve, symbol switch)Key facts
Backtest results (H1, about 1 year)
| Symbol | P&L | Annual | Max DD | Trades | Win rate | Sharpe | Recovery | Losing streak | Per trade | Status |
|---|---|---|---|---|---|---|---|---|---|---|
| EURUSD | 0.00% | 0.00% | 0.00% | 0 | — | — | — | — | — | Not convertible yet |
| GBPUSD | 0.00% | 0.00% | 0.00% | 0 | — | — | — | — | — | Not convertible yet |
| USDJPY | 0.00% | 0.00% | 0.00% | 0 | — | — | — | — | — | Not convertible yet |
| XAUUSD | 0.00% | 0.00% | 0.00% | 0 | — | — | — | — | — | Not convertible yet |
Why this EA never traded: no entry rule survived the conversion.
Converted from MQL5 to Python and run with the same rules for every EA. Positions use MT5 lot sizes (100,000 units per FX lot, 100 oz per gold lot) with 1:100 leverage, one order at a time, and a stop-out below 10% of the starting balance. Sharpe is computed from the equity curve. Results marked as unreliable are shown for completeness only.
Blow-up risk: No blow-up pattern found
- Has no stop loss or trailing stop
On its own this is not a typical blow-up pattern, and the account was not stopped out in our backtests. That is not a guarantee: any EA can lose money.
High: stopped out in our backtest, or martingale/grid sizing without a stop loss. Elevated: martingale or grid sizing with a stop loss, or several positions at once without a stop loss. What the label means and what we found →
Strategy analysis
The strategy core is based on the Burg spectral analysis method, a maximum entropy spectral estimation technique that performs autoregressive (AR) modeling on price series to extract frequency components. The EA decomposes historical price data into multiple sinusoidal components, each with specific amplitude, frequency, and phase, then extrapolates these components forward to forecast future price movement. Entry conditions are based on the extrapolated prediction direction: long positions when the forecast predicts rising prices, short positions when the forecast predicts falling prices. Exit conditions typically rely on reverse prediction signals or fixed stop-loss and take-profit levels. Position sizing uses fixed lots or simple equity-based calculation. Money management is relatively basic, lacking dynamic risk adjustment mechanisms. This approach inherently assumes price series possess periodic characteristics that can be extrapolated, following a mean reversion philosophy, but may fail in strong trending markets.
Strengths
- Employs advanced spectral analysis with solid theoretical foundation; the Burg method has mature applications in signal processing
- Capable of capturing implicit periodic characteristics in price series, providing good prediction in ranging markets
- Transparent mathematical model with interpretable prediction process, unlike black-box machine learning approaches
Weaknesses
- Spectral extrapolation assumes price periodicity; predictions severely deviate in strong trending markets or during sudden events
- Lacks robust risk management mechanisms; stop-loss and take-profit default to 0, posing significant loss risk
- AR model order selection is sensitive; too high causes overfitting, too low fails to capture important frequency components
Possible improvements
- Add adaptive model order selection mechanism, such as AIC/BIC criterion-based automatic optimal order determination
- Add trend filter to suspend trading or switch strategy in strong trending markets, avoiding extrapolation failure
Risk assessment
Overall risk: High risk
- Stop-loss defaults to 0, meaning no hard risk control; single trade loss can expand indefinitely
- Spectral extrapolation is unreliable in non-stationary markets, potentially generating consecutive wrong signals
Evaluation scores
Parameters
| Parameter | Default | Description |
|---|---|---|
Degree | 10 | AR model order determining spectral resolution |
ExtrapolationBars | 50 | Number of bars for extrapolation forecast |
HistoryBars | 200 | Number of historical bars for analysis |
LotSize | 0.1 | Lot size per leg |
StopLoss | 0 | Auto stop loss in points; 0=disabled |
TakeProfit | 0 | Auto take profit in points; 0=disabled |
Similar Mean Reversion EAs
Backtests are historical simulations and do not guarantee future results. Not financial advice.