PYTH Parabolic SAR Strategy (4h) - Backtest Results
Price Action & Trades
Recent Trade History (Live Proof)
| Entry Date | Exit Date | Type | Entry Price | Exit Price | Profit/Loss Ratio |
|---|---|---|---|---|---|
| Jan 17, 08:00 | Jan 19, 00:00 | Long | $0.0663 | $0.0586 | -11.61% |
| Jan 13, 12:00 | Jan 15, 04:00 | Long | $0.0682 | $0.0682 | 0% |
| Jan 9, 16:00 | Jan 11, 20:00 | Long | $0.0683 | $0.0668 | -2.2% |
| Jan 5, 12:00 | Jan 7, 16:00 | Long | $0.0688 | $0.0685 | -0.44% |
| Jan 1, 12:00 | Jan 5, 04:00 | Long | $0.0587 | $0.0667 | +13.63% |
| Dec 28, 00:00 | Dec 29, 12:00 | Long | $0.0614 | $0.0593 | -3.42% |
| Dec 25, 00:00 | Dec 26, 16:00 | Long | $0.0589 | $0.0592 | +0.51% |
| Dec 19, 12:00 | Dec 21, 20:00 | Long | $0.0572 | $0.0583 | +1.92% |
| Dec 17, 12:00 | Dec 17, 16:00 | Long | $0.0585 | $0.0564 | -3.59% |
| Dec 12, 12:00 | Dec 12, 16:00 | Long | $0.0656 | $0.0629 | -4.12% |
Equity Curve
AI Deep AnalysisPowered by algorithmic insights
Low precision (28.12%) on 4h indicates signal noise. Higher timeframes may improve accuracy.
At 0.41x, consider wider stop-losses to improve average win size on PYTH.
The 32 trade count provides excellent statistical significance for performance evaluation.
During strong PYTH trends, this Parabolic SAR captures continuation moves effectively.
Walk-forward optimization suggests these parameters remained stable over previous quarters.
Anti-martingale approach recommended: increase size after wins, reduce after losses.
Performance Metrics
See Live Signal
Real-time technical analysis
View the current Parabolic SAR signal for PYTH with live market data, AI analysis, and trading recommendations.
About The Parabolic SAR Strategy
Backtest Methodology
Key Takeaways
- Trailing stops capture extended PYTH moves.
- Take partial profits at 1.5R for Parabolic SAR trades.
- Exit at 4h candle close for clean fills.
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