Chart & Technical · JPM
Strategy Cup Tier for JPM
AI-generated plain-English read of strategy cup tier for JPM. Updated daily.
Strong tier: no statistical validation
lowCup-3 performance (an unknown margin) without MC validation. The outperformance is real on the val window but statistically indistinguishable from a lucky run. Proceed with caution.
What will probably happen
- High cup tier without MC significance: in-sample results look good but the forward edge is unconfirmed; reduce size and watch the next val window closely.
- Walk-forward stability data is not available; robustness cannot be assessed.
- 10-29 trades: the sample is thin. Percentile statistics are noisy and overfit risk is elevated.
What to do
- If you HOLD this stock and follow this strategy
- Strong tier but incomplete validation: hold with reduced position size. Set a drawdown trigger to re-evaluate if performance decays.
- If you're EVALUATING whether to use this strategy
- Strong tier without validation: paper-trade. Strong cup without MC significance often reflects lucky optimisation on the training window.
- If you trade OPTIONS on this strategy's signals
- Don't increase options size on this strategy's signals: the edge may not persist out-of-sample. Treat signals as weak directional hints only.
Weak credibility profile: no MC significance or DSR support.
Technical details
Technical analysis
- Cup tier
- cup_3 (strong)
- Robustness
- Unknown Stability
- Tier grade
- Strong
- Trade count
- 22
- MC significant
- No
- Sample quality
- Small Sample
- Validation window
- --
- Statistical support
- No Validation
- Deflated Sharpe Ratio (DSR)
- 0.000
- Delta return vs buy-and-hold
- --
- Walk-forward stability score
- --
Key levels
| Level | Value |
|---|---|
| dsr | 0 |
| cup tier | 3 |
| num trades | 22 |
Caveats
- Cup tiers reflect val-window performance. Live forward returns may differ from backtest; even validated strategies have drawdown risk.
- High cup tier without MC significance is a classic overfit warning sign. The in-sample optimiser can find parameter sets that look exceptional on the training window without generalisable edge.