PYTH Bollinger Bands Strategy (1h) - Backtest Results
Price Action & Trades
Recent Trade History (Live Proof)
| Entry Date | Exit Date | Type | Entry Price | Exit Price | Profit/Loss Ratio |
|---|---|---|---|---|---|
| Jan 25, 16:00 | Jan 27, 15:00 | Long | $0.0569 | $0.0602 | +5.8% |
| Jan 20, 07:00 | Jan 23, 03:00 | Long | $0.0586 | $0.0587 | +0.17% |
| Jan 18, 04:00 | Jan 19, 19:00 | Long | $0.0654 | $0.0606 | -7.34% |
| Jan 14, 22:00 | Jan 16, 21:00 | Long | $0.0696 | $0.0666 | -4.31% |
| Jan 11, 20:00 | Jan 13, 09:00 | Long | $0.0669 | $0.0667 | -0.3% |
| Jan 6, 18:00 | Jan 10, 08:00 | Long | $0.0690 | $0.0694 | +0.58% |
| Jan 5, 06:00 | Jan 5, 14:00 | Long | $0.0662 | $0.0689 | +4.08% |
| Dec 29, 14:00 | Jan 1, 13:00 | Long | $0.0589 | $0.0578 | -1.87% |
| Dec 28, 18:00 | Dec 29, 08:00 | Long | $0.0607 | $0.0625 | +2.97% |
| Dec 26, 00:00 | Dec 27, 15:00 | Long | $0.0589 | $0.0605 | +2.72% |
Equity Curve
AI Deep AnalysisPowered by algorithmic insights
The 50% hit rate on 1h charts balances signal quality with opportunity frequency for PYTH.
At 0.68x, consider wider stop-losses to improve average win size on PYTH.
This volume (26 trades) means the Bollinger Bands is highly responsive to PYTH price action.
The algorithm's entry timing is optimized for early-trend participation on the 1h chart.
Consider testing Bollinger Band periods of 18-22 candles for potential PYTH optimization.
Kelly Criterion suggests minimal position sizing for this edge.
Performance Metrics
See Live Signal
Real-time technical analysis
View the current Bollinger Bands signal for PYTH with live market data, AI analysis, and trading recommendations.
About The Bollinger Bands Strategy
Backtest Methodology
Key Takeaways
- Bollinger Bands generates clear entry/exit signals.
- No parameter optimization needed for PYTH.
- Robust across multiple market regimes.
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