PYTH Golden Cross 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 14, 16:00 | Jan 16, 12:00 | Long | $0.0717 | $0.0655 | -8.65% |
| Jan 2, 23:00 | Jan 11, 05:00 | Long | $0.0633 | $0.0678 | +7.11% |
| Dec 25, 13:00 | Dec 31, 01:00 | Long | $0.0601 | $0.0591 | -1.66% |
| Dec 21, 22:00 | Dec 24, 13:00 | Long | $0.0590 | $0.0570 | -3.39% |
| Nov 27, 12:00 | Nov 30, 14:00 | Long | $0.0754 | $0.0746 | -1.06% |
| Nov 8, 16:00 | Nov 13, 19:00 | Long | $0.1013 | $0.0940 | -7.21% |
Equity Curve
AI Deep AnalysisPowered by algorithmic insights
With only 16.67% winners, this is an outlier-hunting strategy. The few wins must cover many small losses.
Low profit factor (0.26) indicates potential parameter optimization is needed for PYTH.
At 6 trades, each position carries higher significance. No room for poor execution.
Consider testing Bollinger Band periods of 18-22 candles for potential PYTH optimization.
Volatility-adjusted sizing: reduce position size when PYTH ATR exceeds 150% of average.
The 1h chart captures PYTH's characteristic momentum cycles effectively.
Performance Metrics
See Live Signal
Real-time technical analysis
View the current Golden Cross signal for PYTH with live market data, AI analysis, and trading recommendations.
About The Golden Cross Strategy
Backtest Methodology
Key Takeaways
- Asian session: lower volatility for PYTH.
- US/EU overlap: best liquidity on 1h.
- Weekend signals on PYTH may have higher slippage.
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