dennisthiessen
d53ed972d1
Make "qualified" mean an edge candidate, not just R:R + confidence. The gate now also requires (all admin-configurable, defaults on): - high conviction: recommended_action LONG_HIGH / SHORT_HIGH only - clean read: risk_level Low (no contradicting signals) - probable primary target: best target probability >= min (default 60) - Shared predicate: app/services/qualification.py + frontend/src/lib/qualification.ts (mirrored) - Activation config extended (min_target_probability, require_high_conviction, exclude_conflicts) with bool-aware get/update + validation - /trades/performance switched to ?qualified_only=true, applying the full gate server-side; confidence breakdown stays unfiltered - Dashboard "Qualified", Signals "Qualified only" toggle, and Track Record all use the one gate; Admin gains the new controls Sentiment provider runtime config (prior change) included. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
"""Shared definition of a 'qualified' (actionable) trade setup.
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A single predicate, driven by the admin activation config, used by the
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performance stats (server) and mirrored on the frontend. Beyond raw R:R and
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confidence, an actionable setup must show genuine conviction: a high-conviction
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recommended action, a clean (conflict-free) read, and a probable primary target.
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"""
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from __future__ import annotations
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from typing import Any
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HIGH_CONVICTION_ACTIONS = {"LONG_HIGH", "SHORT_HIGH"}
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def best_target_probability(setup: Any) -> float:
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"""Highest probability among a setup's targets, 0 if none."""
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targets = getattr(setup, "targets", None) or []
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probs = [float(t.get("probability", 0.0)) for t in targets if isinstance(t, dict)]
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return max(probs, default=0.0)
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def setup_qualifies(setup: Any, config: dict) -> bool:
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"""Whether a setup clears the activation gate.
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``setup`` is duck-typed: any object exposing rr_ratio, confidence_score,
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recommended_action, risk_level and a ``targets`` list of dicts.
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"""
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if setup.rr_ratio < config["min_rr"]:
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return False
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if (setup.confidence_score or 0.0) < config["min_confidence"]:
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return False
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if config.get("require_high_conviction"):
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if (setup.recommended_action or "") not in HIGH_CONVICTION_ACTIONS:
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return False
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if config.get("exclude_conflicts"):
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if (setup.risk_level or "") != "Low":
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return False
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min_tp = float(config.get("min_target_probability", 0.0))
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if min_tp > 0 and best_target_probability(setup) < min_tp:
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return False
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return True
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