| Time | Type | BTC Price | Score | Signal | Confidence | Price After 24h | Correct? |
|---|---|---|---|---|---|---|---|
| Mar 7, 04:45 AM | DCA | $68,120 | 54 | HOLD | 41% | Pending | — |
| Mar 7, 04:15 AM | DCA | $67,847 | 55 | HOLD | 30% | Pending | — |
| Mar 7, 02:43 AM | DCA | $68,095 | 58 | BUY | 50% | Pending | — |
| Mar 7, 12:51 AM | DCA | $68,312 | 59 | BUY | 58% | Pending | — |
| Mar 6, 11:57 PM | DCA | $68,294 | 55 | HOLD | 30% | Pending | — |
| Mar 6, 09:39 PM | DCA | $68,294 | 55 | HOLD | 30% | Pending | — |
| Mar 6, 08:35 PM | DCA | $68,294 | 54 | HOLD | 31% | Pending | — |
| Mar 6, 08:11 PM | DCA | $0 | 50 | HOLD | 20% | Pending | — |
| Mar 6, 08:01 PM | DCA | $68,294 | 56 | HOLD | 54% | Pending | — |
| Mar 6, 08:01 PM | DCA | $68,294 | 57 | HOLD | 55% | Pending | — |
| Mar 6, 08:01 PM | DCA | $68,294 | 58 | HOLD | 54% | Pending | — |
| Mar 6, 08:01 PM | DCA | $68,294 | 58 | BUY | 54% | Pending | — |
| Mar 6, 08:01 PM | DCA | $68,294 | 58 | HOLD | 54% | Pending | — |
| Mar 6, 08:01 PM | DCA | $68,294 | 57 | HOLD | 55% | Pending | — |
| Mar 6, 08:01 PM | DCA | $68,294 | 57 | HOLD | 54% | Pending | — |
| Mar 6, 08:01 PM | DCA | $68,294 | 59 | BUY | 55% | Pending | — |
| Mar 6, 08:01 PM | DCA | $68,294 | 59 | BUY | 55% | Pending | — |
| Mar 6, 08:00 PM | DCA | $68,294 | 58 | HOLD | 54% | Pending | — |
| Mar 6, 08:00 PM | DCA | $68,294 | 58 | BUY | 54% | Pending | — |
| Mar 6, 08:00 PM | DCA | $68,294 | 57 | HOLD | 54% | Pending | — |
Factor Signal Consistency
How Factor Backtesting Works
Every time the ML prediction engine runs, it saves a factor snapshot — a record of all 34 factor scores, signals, and the BTC price at that moment. Over time, the system fills in price outcomes (1h, 4h, 24h, 7d after each prediction).
The backtest engine then analyzes each factor's directional accuracy: when a factor said BULLISH, did the price actually go up? It calculates Pearson correlation between factor scores and price changes, and uses a Bayesian-inspired algorithm to suggest optimal weight adjustments.
Factors with high accuracy and reliability get weight boosts; factors with low accuracy get reductions. The system needs at least 10+ evaluated snapshots per factor for meaningful suggestions. Weight changes are suggestions only — they are not applied automatically.