PYTH Bollinger Bands 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 25, 20:00 | Jan 27, 16:00 | Long | $0.0559 | $0.0615 | +10.02% |
| Jan 19, 00:00 | Jan 25, 12:00 | Long | $0.0586 | $0.0598 | +2.05% |
| Jan 12, 08:00 | Jan 14, 00:00 | Long | $0.0646 | $0.0716 | +10.84% |
| Dec 31, 16:00 | Jan 4, 16:00 | Long | $0.0547 | $0.0683 | +24.86% |
| Dec 11, 00:00 | Dec 20, 16:00 | Long | $0.0649 | $0.0610 | -6.01% |
| Nov 30, 20:00 | Dec 9, 12:00 | Long | $0.0733 | $0.0711 | -3% |
| Nov 13, 16:00 | Nov 24, 16:00 | Long | $0.0940 | $0.0780 | -17.02% |
| Oct 30, 08:00 | Nov 7, 12:00 | Long | $0.1094 | $0.0994 | -9.14% |
| Oct 10, 12:00 | Oct 21, 12:00 | Long | $0.1471 | $0.1202 | -18.29% |
| Sep 25, 16:00 | Oct 1, 16:00 | Long | $0.1416 | $0.1565 | +10.52% |
Equity Curve
AI Deep AnalysisPowered by algorithmic insights
At 50% accuracy, trade selection becomes important. Consider filtering signals during high-volatility events.
Low profit factor (0.72) indicates potential parameter optimization is needed for PYTH.
At 10 trades, the algorithm filters noise while capturing significant PYTH moves.
Kelly Criterion suggests minimal position sizing for this edge.
Volume filters may improve win rate: require above-average volume for entry confirmation.
PYTH liquidity levels support clean Bollinger Bands execution without significant slippage impact.
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
- Minimum $500 account for micro positions on PYTH.
- 1% risk = $10 per trade on $1,000 account.
- Scale position size with account growth.
Was this analysis helpful?
Your feedback helps us improve our backtest reports and provide better insights.
Have specific suggestions? Contact us