95 lines
4.3 KiB
Python
95 lines
4.3 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. The core selection is
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residual cross-sectional momentum: a setup's ticker must rank in the top
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``min_momentum_percentile`` of the universe by beta-adjusted 12-1 month momentum.
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R:R and confidence remain as floors, and conviction/conflict survive as optional
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tighteners (off by default). The activation percentile is computed across the
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universe and attached to each setup upstream; when it's absent the gate falls
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back to the floors.
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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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Gate order: R:R floor → freshness (live R:R) → confidence floor → momentum
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percentile (the core selection) → optional conviction / conflict tighteners.
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``min_momentum_percentile`` defaults to 0 (off) for callers that pass a legacy
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config without the key.
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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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# Residual cross-sectional momentum: the core selection. A setup's ticker
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# must rank in the top ``min_momentum_percentile`` of the universe by
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# beta-adjusted 12-1 momentum. The validated edge is long-only, so while the
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# gate is active shorts (which fight the trend) never qualify. The percentile
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# floor is only enforced when a percentile is attached (live setups /
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# backtest); callers that don't attach it defer to the floors above.
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min_pct = float(config.get("min_momentum_percentile", 0.0))
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if min_pct > 0:
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if (getattr(setup, "direction", "long") or "long") == "short":
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return False
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momentum_percentile = getattr(setup, "momentum_percentile", None)
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if momentum_percentile is not None and momentum_percentile < min_pct:
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return False
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# A NEUTRAL recommendation means the engine found no clear directional setup —
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# not an actionable signal, so by default it doesn't qualify (and can't be a
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# top pick). ``exclude_neutral`` defaults on; turn it off to also count
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# no-clear-direction residual momentum leaders.
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if config.get("exclude_neutral"):
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if (setup.recommended_action or "NEUTRAL") == "NEUTRAL":
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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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return True
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