dennisthiessen
f48d8705de
min_target_probability is gone: it filtered on the probability model the calibration has repeatedly shown to be weak and overconfident, it was redundant with the momentum gate, and as an off-by-default knob it just invited bad tuning. Removed from the backend gate, activation config/schema, the frontend mirror (qualifiesSetup / activationSummary), and ActivationSettings. The probability model stays where it does real work (primary-target selection + display). Charts: with multi-year history the all-bars default was unreadable. Added time-range presets (1M / 3M / 6M / YTD / 1Y / 3Y / 5Y / All), defaulting to 1Y; clicking a preset always re-applies (snaps back after a manual zoom). Y-axis autoscale and wheel-zoom / drag-pan were already there. 339 backend tests pass; frontend build clean. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
88 lines
3.9 KiB
Python
88 lines
3.9 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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cross-sectional momentum: a setup's ticker must rank in the top
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``min_momentum_percentile`` of the universe by 12-1 month momentum — the one
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signal the backtest showed actually sorts forward returns. R:R and confidence
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remain as floors, and conviction/conflict survive as optional tighteners (off by
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default). The momentum percentile is computed across the universe and attached to
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each setup upstream; when it's absent the gate falls 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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# Cross-sectional momentum: the core selection. A setup's ticker must rank in
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# the top ``min_momentum_percentile`` of the universe by 12-1 momentum. The
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# validated edge is long-only, so while the gate is active shorts (which fight
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# the trend) never qualify. The percentile floor is only enforced when a
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# percentile is attached (live setups / backtest); callers that don't attach
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# 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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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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