dennisthiessen
da83f027e1
Answers "why does a too-far-progressed setup still show": setups are only recalculated by the scheduled R:R scan and manual fetch; at creation entry == current price (0% progress), so over-progression is a between-scans drift effect and must be judged at read time. - /trades now attaches current_price (latest close per ticker). - Qualification drops setups whose R:R recomputed from the current price falls below min_rr — i.e. price already ran toward target (reward consumed) or through the stop. Reuses the existing min_rr threshold instead of a separate progress %; far cleaner (a 3:1 is already ~1:1 by 33% progress). Skipped for historical setups (no current_price). - Fix: useFetchSymbolData now invalidates the trades queries, so a fetch/ recompute actually refreshes confidence/setups in the UI (was the cause of the stale 100% confidence lingering after recompute). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
69 lines
2.7 KiB
Python
69 lines
2.7 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 live_risk_reward(setup: Any, current_price: float) -> float | None:
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"""R:R recomputed from the CURRENT price, not the (possibly stale) entry.
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Returns None / a low value when the setup is no longer actionable: price
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already at/past the target (no reward left) or through the stop. This is how
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over-progressed setups get filtered without a separate 'max progress' knob.
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"""
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if setup.direction == "long":
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reward = setup.target - current_price
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risk = current_price - setup.stop_loss
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else:
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reward = current_price - setup.target
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risk = setup.stop_loss - current_price
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if reward <= 0 or risk <= 0:
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return 0.0
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return reward / risk
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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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# Live R:R from the current price: drops setups whose price has already run
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# toward the target (reward consumed) or through the stop. Only applied when
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# a current price is attached (live list); skipped for historical setups.
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current_price = getattr(setup, "current_price", None)
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if current_price is not None:
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live_rr = live_risk_reward(setup, float(current_price))
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if live_rr is not None and live_rr < 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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