The "Oracle" Ensemble
Moving beyond simple thresholds: Using Stacked Machine Learning to filter market noise and predict trade outcomes.
The Stacked Architecture
Traditional bots use a single "Buy/Sell" check. The Oracle uses a Two-Layer Approach. Layer 1 asks specific questions (Will it break even? How long will it take?). Layer 2 (The Meta-Learner) listens to these experts to make the final call.
Raw Input
19 Technical Features
(RSI, MACD, ADX...)
Filtering the Noise
By using the ensemble, we sacrifice raw trade volume for significantly higher precision. The Stacked model filters out "fakeouts" that fool standard indicators.
What Matters to the Oracle?
The Meta-Learner (Layer 2) prioritizes the predictions of Layer 1 over raw indicators. It cares more about "Predicted Probability of Breakeven" than the raw RSI value.
Visualizing the Decision Boundary
Single models create simple linear cutoffs (e.g., "If RSI > 70, Sell"). The Stacked Ensemble creates a complex, non-linear 3D shape. In this plot, teal areas represent high-confidence "Trade" zones, found only where Breakeven Probability is high AND Duration is optimal, regardless of other noise.
From Trading to ICU Triage
The logic of "Auxiliary Tasks" (Layer 1) feeding a "Final Decision" (Layer 2) is perfectly suited for medical environments where resources are scarce and false positives are dangerous.
Financial Context
Predict Breakeven, Duration, ROI
"Is this signal worth the risk?"
Medical Triage Context
Predict Sepsis, Kidney Failure, Length of Stay
"Does this patient need immediate ICU?"