dennisthiessen
e355368748
Trade-setup targets now pre-merge near-duplicate S/R levels into zone representatives (same 2% clusterer as chart + alerts) before generate_targets runs. A clustered wall (e.g. 183 + 185) becomes one target carrying the zone's COMBINED strength (capped 100) instead of two near-identical targets that each undervalue the wall — which also feeds a more honest reach-probability via the S/R-strength magnet. Representative price is the zone's near edge; the strongest constituent's id is retained. Singleton levels pass through unchanged, so the downstream band-spreading / probability / primary-selection pipeline and its tests are untouched. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
637 lines
25 KiB
Python
637 lines
25 KiB
Python
from __future__ import annotations
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import json
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import logging
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import math
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from types import SimpleNamespace
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from typing import Any
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.settings import SystemSetting
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from app.models.sr_level import SRLevel
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from app.models.ticker import Ticker
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from app.models.trade_setup import TradeSetup
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from app.services.sr_service import cluster_sr_zones
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logger = logging.getLogger(__name__)
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DEFAULT_RECOMMENDATION_CONFIG: dict[str, float] = {
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"recommendation_high_confidence_threshold": 70.0,
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"recommendation_moderate_confidence_threshold": 50.0,
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"recommendation_confidence_diff_threshold": 20.0,
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"recommendation_signal_alignment_weight": 0.15,
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"recommendation_sr_strength_weight": 0.20,
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"recommendation_momentum_technical_divergence_threshold": 30.0,
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"recommendation_fundamental_technical_divergence_threshold": 40.0,
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}
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# Horizon (trading days) over which a target's reach-probability is estimated.
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# Kept in step with the outcome evaluator's window so probability predicts the
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# metric we actually measure.
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_TARGET_HORIZON_DAYS = 30.0
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# Distance bands (in ATR) used to spread targets across Conservative / Moderate
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# / Aggressive. Aligned with where the touch-probability crosses 60% / 40% over
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# the horizon, so a target from each band tends to land in the matching label.
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_CONSERVATIVE_MAX_ATR = 2.9
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_MODERATE_MAX_ATR = 4.6
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# Merge S/R levels within this fraction into one zone before generating targets —
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# the same tolerance the chart and alerts use, so S/R is one model app-wide.
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_SR_ZONE_TOLERANCE = 0.02
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def _clamp(value: float, low: float, high: float) -> float:
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return max(low, min(high, value))
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def _zone_representative_levels(sr_levels: list[SRLevel], entry_price: float) -> list[Any]:
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"""Collapse near-duplicate S/R levels into one representative per zone.
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Targets are generated from these representatives, so a clustered wall (e.g.
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183 + 185) becomes a single target carrying the zone's COMBINED strength
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(capped at 100) instead of two near-identical targets, each undervaluing the
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wall. Same clusterer as the chart and alerts → one S/R model everywhere.
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The representative price is the zone's near edge (the reachable side of the
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wall) and it keeps the strongest constituent's id for reference. Singleton
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levels pass through unchanged.
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"""
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if not sr_levels or entry_price <= 0:
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return list(sr_levels)
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level_dicts = [
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{"price_level": float(lv.price_level), "strength": int(lv.strength), "type": lv.type}
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for lv in sr_levels
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]
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zones = cluster_sr_zones(level_dicts, entry_price, tolerance=_SR_ZONE_TOLERANCE)
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reps: list[Any] = []
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for zone in zones:
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constituents = [
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lv for lv in sr_levels if zone["low"] <= float(lv.price_level) <= zone["high"]
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]
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if not constituents:
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continue
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strongest = max(constituents, key=lambda lv: lv.strength)
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# Near edge: bottom of a resistance wall (above entry), top of a support
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# wall (below entry) — the first price the move reaches.
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near_edge = zone["low"] if zone["type"] == "resistance" else zone["high"]
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reps.append(
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SimpleNamespace(
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id=int(strongest.id),
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price_level=float(near_edge),
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type=zone["type"],
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strength=int(zone["strength"]),
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)
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)
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return reps
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def _norm_cdf(x: float) -> float:
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"""Standard normal CDF via erf (no SciPy dependency)."""
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return 0.5 * (1.0 + math.erf(x / math.sqrt(2.0)))
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def _classify_by_probability(probability: float) -> str:
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"""Label a target by how likely it is to be reached (derived, not assumed)."""
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if probability >= 60.0:
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return "Conservative"
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if probability >= 40.0:
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return "Moderate"
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return "Aggressive"
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def _sentiment_value(sentiment_classification: str | None) -> str | None:
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if sentiment_classification is None:
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return None
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return sentiment_classification.strip().lower()
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def check_signal_alignment(
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direction: str,
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dimension_scores: dict[str, float],
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sentiment_classification: str | None,
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) -> tuple[bool, str]:
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technical = float(dimension_scores.get("technical", 50.0))
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momentum = float(dimension_scores.get("momentum", 50.0))
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sentiment = _sentiment_value(sentiment_classification)
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if direction == "long":
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aligned_count = sum([
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technical > 60,
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momentum > 60,
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sentiment == "bullish",
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])
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if aligned_count >= 2:
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return True, "Technical, momentum, and/or sentiment align with LONG direction."
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return False, "Signals are mixed for LONG direction."
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aligned_count = sum([
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technical < 40,
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momentum < 40,
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sentiment == "bearish",
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])
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if aligned_count >= 2:
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return True, "Technical, momentum, and/or sentiment align with SHORT direction."
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return False, "Signals are mixed for SHORT direction."
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class SignalConflictDetector:
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def detect_conflicts(
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self,
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dimension_scores: dict[str, float],
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sentiment_classification: str | None,
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config: dict[str, float] | None = None,
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) -> list[str]:
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cfg = config or DEFAULT_RECOMMENDATION_CONFIG
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technical = float(dimension_scores.get("technical", 50.0))
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momentum = float(dimension_scores.get("momentum", 50.0))
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fundamental = float(dimension_scores.get("fundamental", 50.0))
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sentiment = _sentiment_value(sentiment_classification)
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mt_threshold = float(cfg.get("recommendation_momentum_technical_divergence_threshold", 30.0))
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ft_threshold = float(cfg.get("recommendation_fundamental_technical_divergence_threshold", 40.0))
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conflicts: list[str] = []
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if sentiment == "bearish" and technical > 60:
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conflicts.append(
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f"sentiment-technical: Bearish sentiment conflicts with bullish technical ({technical:.0f})"
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)
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if sentiment == "bullish" and technical < 40:
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conflicts.append(
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f"sentiment-technical: Bullish sentiment conflicts with bearish technical ({technical:.0f})"
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)
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mt_diff = abs(momentum - technical)
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if mt_diff > mt_threshold:
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conflicts.append(
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"momentum-technical: "
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f"Momentum ({momentum:.0f}) diverges from technical ({technical:.0f}) by {mt_diff:.0f} points"
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)
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if sentiment == "bearish" and momentum > 60:
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conflicts.append(
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f"sentiment-momentum: Bearish sentiment conflicts with momentum ({momentum:.0f})"
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)
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if sentiment == "bullish" and momentum < 40:
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conflicts.append(
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f"sentiment-momentum: Bullish sentiment conflicts with momentum ({momentum:.0f})"
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)
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ft_diff = abs(fundamental - technical)
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if ft_diff > ft_threshold:
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conflicts.append(
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"fundamental-technical: "
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f"Fundamental ({fundamental:.0f}) diverges significantly from technical ({technical:.0f})"
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)
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return conflicts
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class DirectionAnalyzer:
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def calculate_confidence(
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self,
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direction: str,
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dimension_scores: dict[str, float],
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sentiment_classification: str | None,
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conflicts: list[str] | None = None,
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) -> float:
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"""Directional-agreement confidence around a 50 baseline.
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Each dimension contributes in proportion to how strongly it agrees
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with the proposed direction: a bullish dimension RAISES long confidence
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and LOWERS short confidence (and vice-versa). Signals that oppose the
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direction push confidence below 50 — so a short on a strongly bullish
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stock scores near zero, not 55.
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"""
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technical = float(dimension_scores.get("technical", 50.0))
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momentum = float(dimension_scores.get("momentum", 50.0))
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fundamental = float(dimension_scores.get("fundamental", 50.0))
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sentiment = _sentiment_value(sentiment_classification)
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dir_sign = 1.0 if direction == "long" else -1.0
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def agree(score: float) -> float:
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# -1 (fully against) .. +1 (fully for) the proposed direction
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return ((score - 50.0) / 50.0) * dir_sign
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sentiment_val = {"bullish": 1.0, "bearish": -1.0}.get(sentiment or "", 0.0)
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sentiment_agree = sentiment_val * dir_sign
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confidence = 50.0 + (
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agree(technical) * 25.0
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+ agree(momentum) * 20.0
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+ sentiment_agree * 15.0
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+ agree(fundamental) * 10.0
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)
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# Explicit conflict patterns trim a little more (the agreement terms
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# already capture most disagreement, so penalties are modest).
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for conflict in conflicts or []:
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if "sentiment-technical" in conflict:
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confidence -= 12.0
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elif "momentum-technical" in conflict:
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confidence -= 10.0
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elif "sentiment-momentum" in conflict:
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confidence -= 12.0
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elif "fundamental-technical" in conflict:
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confidence -= 6.0
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return _clamp(confidence, 0.0, 100.0)
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class TargetGenerator:
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def generate_targets(
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self,
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direction: str,
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entry_price: float,
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stop_loss: float,
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sr_levels: list[SRLevel],
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atr_value: float,
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) -> list[dict[str, Any]]:
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if atr_value <= 0:
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return []
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risk = abs(entry_price - stop_loss)
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if risk <= 0:
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return []
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candidates: list[dict[str, Any]] = []
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atr_pct = atr_value / entry_price if entry_price > 0 else 0.0
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max_atr_multiple: float | None = None
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if atr_pct > 0.05:
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max_atr_multiple = 10.0
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elif atr_pct < 0.02:
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max_atr_multiple = 3.0
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for level in sr_levels:
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is_candidate = False
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if direction == "long":
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is_candidate = level.type == "resistance" and level.price_level > entry_price
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else:
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is_candidate = level.type == "support" and level.price_level < entry_price
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if not is_candidate:
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continue
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distance = abs(level.price_level - entry_price)
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distance_atr_multiple = distance / atr_value
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if distance_atr_multiple < 1.0:
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continue
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if max_atr_multiple is not None and distance_atr_multiple > max_atr_multiple:
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continue
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reward = abs(level.price_level - entry_price)
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rr_ratio = reward / risk
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norm_rr = min(rr_ratio / 10.0, 1.0)
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norm_strength = _clamp(level.strength, 0, 100) / 100.0
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norm_proximity = 1.0 - min(distance / entry_price, 1.0)
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quality = 0.35 * norm_rr + 0.35 * norm_strength + 0.30 * norm_proximity
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candidates.append(
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{
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"price": float(level.price_level),
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"distance_from_entry": float(distance),
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"distance_atr_multiple": float(distance_atr_multiple),
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"rr_ratio": float(rr_ratio),
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"classification": "Moderate",
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"sr_level_id": int(level.id),
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"sr_strength": float(level.strength),
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"quality": float(quality),
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}
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)
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if not candidates:
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return []
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# Select up to 5 targets that SPAN the distance range, instead of the
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# top-5 by quality (which biases toward far, high-R:R levels and buries
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# every nearby target). Guarantees the nearest level plus a
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# representative from each distance band when they exist, so the table
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# offers a real mix of Conservative / Moderate / Aggressive.
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conservative = [c for c in candidates if c["distance_atr_multiple"] <= _CONSERVATIVE_MAX_ATR]
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moderate = [c for c in candidates if _CONSERVATIVE_MAX_ATR < c["distance_atr_multiple"] <= _MODERATE_MAX_ATR]
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aggressive = [c for c in candidates if c["distance_atr_multiple"] > _MODERATE_MAX_ATR]
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selected: list[dict[str, Any]] = []
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selected_ids: set[int] = set()
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def _add(candidate: dict[str, Any] | None) -> None:
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if candidate is not None and candidate["sr_level_id"] not in selected_ids:
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selected.append(candidate)
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selected_ids.add(candidate["sr_level_id"])
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# Nearest overall (the most likely / Conservative anchor)
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_add(min(candidates, key=lambda c: c["distance_atr_multiple"]))
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# Best-quality representative from each band → spread across labels
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for bucket in (conservative, moderate, aggressive):
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if bucket:
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_add(max(bucket, key=lambda c: c["quality"]))
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# Fill remaining slots with the next-best by quality
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for candidate in sorted(candidates, key=lambda c: c["quality"], reverse=True):
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if len(selected) >= 5:
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break
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_add(candidate)
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selected.sort(key=lambda row: row["distance_from_entry"])
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for target in selected:
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target.pop("quality", None)
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return selected
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class ProbabilityEstimator:
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def estimate_probability(
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self,
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target: dict[str, Any],
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dimension_scores: dict[str, float],
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sentiment_classification: str | None,
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direction: str,
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config: dict[str, float],
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) -> float:
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"""Probability the target is reached within the outcome horizon.
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Base = probability of price *touching* a level at the target's distance
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within the evaluation window, under a driftless random walk (reflection
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principle): 2·(1 − Φ(d / (ATR·√T))). Distance is in ATR multiples and T
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is the horizon in trading days, so a far target is inherently unlikely —
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no more 90% on a +39% move. Strength and signal alignment (drift toward
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the target) then modulate it modestly.
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"""
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strength = float(target.get("sr_strength", 50.0))
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atr_multiple = float(target.get("distance_atr_multiple", 1.0))
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expected_move_atr = math.sqrt(_TARGET_HORIZON_DAYS) # ≈ 5.48 ATR over 30d
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z = atr_multiple / expected_move_atr if expected_move_atr > 0 else 99.0
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touch_prob = 2.0 * (1.0 - _norm_cdf(z)) # 0..1
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probability = touch_prob * 100.0
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technical = float(dimension_scores.get("technical", 50.0))
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momentum = float(dimension_scores.get("momentum", 50.0))
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sentiment = _sentiment_value(sentiment_classification)
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# Drift toward the target raises touch probability; against it lowers.
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if direction == "long":
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aligned = technical > 60 and (sentiment == "bullish" or momentum > 60)
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opposed = technical < 40 or (sentiment == "bearish" and momentum < 40)
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else:
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aligned = technical < 40 and (sentiment == "bearish" or momentum < 40)
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opposed = technical > 60 or (sentiment == "bullish" and momentum > 60)
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sr_weight = float(config.get("recommendation_sr_strength_weight", 0.20))
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signal_weight = float(config.get("recommendation_signal_alignment_weight", 0.15))
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# Strength magnet: ±(sr_weight·50) at the extremes (±10 by default)
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probability += ((strength - 50.0) / 50.0) * (sr_weight * 50.0)
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# Alignment: ±(signal_weight·100) (±15 by default)
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if aligned:
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probability += signal_weight * 100.0
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elif opposed:
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probability -= signal_weight * 100.0
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return round(_clamp(probability, 3.0, 95.0), 2)
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signal_conflict_detector = SignalConflictDetector()
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direction_analyzer = DirectionAnalyzer()
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target_generator = TargetGenerator()
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probability_estimator = ProbabilityEstimator()
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async def get_recommendation_config(db: AsyncSession) -> dict[str, float]:
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result = await db.execute(
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select(SystemSetting).where(SystemSetting.key.like("recommendation_%"))
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)
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rows = result.scalars().all()
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config: dict[str, float] = dict(DEFAULT_RECOMMENDATION_CONFIG)
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for setting in rows:
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try:
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config[setting.key] = float(setting.value)
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except (TypeError, ValueError):
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logger.warning("Invalid recommendation setting value for %s: %s", setting.key, setting.value)
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return config
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def _risk_level_from_conflicts(conflicts: list[str]) -> str:
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if not conflicts:
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return "Low"
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severe = [c for c in conflicts if "sentiment-technical" in c or "sentiment-momentum" in c]
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if len(severe) >= 2 or len(conflicts) >= 3:
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return "High"
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return "Medium"
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def _choose_recommended_action(
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long_confidence: float,
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short_confidence: float,
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config: dict[str, float],
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available_directions: set[str] | None = None,
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) -> str:
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"""Pick the ticker action — but only recommend a direction you can trade.
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A direction is recommendable only if a tradeable setup exists for it
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(``available_directions``). So a strong LONG bias on a stock at all-time
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highs — where the scanner can build no long target — does NOT yield
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LONG_HIGH; it falls through to NEUTRAL, and the reasoning explains why.
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"""
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high = float(config.get("recommendation_high_confidence_threshold", 70.0))
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moderate = float(config.get("recommendation_moderate_confidence_threshold", 50.0))
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diff = float(config.get("recommendation_confidence_diff_threshold", 20.0))
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long_ok = available_directions is None or "long" in available_directions
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short_ok = available_directions is None or "short" in available_directions
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if long_ok and long_confidence >= high and (long_confidence - short_confidence) >= diff:
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return "LONG_HIGH"
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if short_ok and short_confidence >= high and (short_confidence - long_confidence) >= diff:
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return "SHORT_HIGH"
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if long_ok and long_confidence >= moderate and (long_confidence - short_confidence) >= diff:
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return "LONG_MODERATE"
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if short_ok and short_confidence >= moderate and (short_confidence - long_confidence) >= diff:
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return "SHORT_MODERATE"
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return "NEUTRAL"
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def _build_reasoning(
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action: str,
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long_confidence: float,
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short_confidence: float,
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conflicts: list[str],
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dimension_scores: dict[str, float],
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sentiment_classification: str | None,
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||
config: dict[str, float],
|
||
available_directions: set[str] | None = None,
|
||
) -> str:
|
||
"""Ticker-level reasoning that always matches the recommended action.
|
||
|
||
Stored identically on both setups so the displayed summary can never mix a
|
||
SHORT setup's reasoning under a LONG action.
|
||
"""
|
||
sentiment = _sentiment_value(sentiment_classification) or "unknown"
|
||
technical = float(dimension_scores.get("technical", 50.0))
|
||
momentum = float(dimension_scores.get("momentum", 50.0))
|
||
signals = f"technical={technical:.0f}, momentum={momentum:.0f}, sentiment={sentiment}"
|
||
conflict_note = f" {len(conflicts)} conflict(s) detected, risk-adjusted." if conflicts else ""
|
||
|
||
if action != "NEUTRAL":
|
||
direction = "long" if action.startswith("LONG") else "short"
|
||
tier = "high" if action.endswith("HIGH") else "moderate"
|
||
confidence = long_confidence if direction == "long" else short_confidence
|
||
aligned, _ = check_signal_alignment(direction, dimension_scores, sentiment_classification)
|
||
return (
|
||
f"{direction.upper()} ({tier} confidence): {confidence:.0f}% with "
|
||
f"{'aligned' if aligned else 'mixed'} signals ({signals}).{conflict_note}"
|
||
)
|
||
|
||
# NEUTRAL — explain whether it's a missing setup or genuinely mixed signals.
|
||
moderate = float(config.get("recommendation_moderate_confidence_threshold", 50.0))
|
||
avail = available_directions if available_directions is not None else {"long", "short"}
|
||
bias_dir = "long" if long_confidence >= short_confidence else "short"
|
||
bias_conf = max(long_confidence, short_confidence)
|
||
|
||
if bias_conf >= moderate and bias_dir not in avail:
|
||
other = "short" if bias_dir == "long" else "long"
|
||
extreme = "highs (no resistance target above)" if bias_dir == "long" else "lows (no support target below)"
|
||
return (
|
||
f"Ticker bias is {bias_dir.upper()} (confidence {bias_conf:.0f}%, {signals}) but price is "
|
||
f"extended near {extreme}, so no high-conviction {bias_dir} setup is available. "
|
||
f"The available {other.upper()} setup is counter-trend.{conflict_note}"
|
||
)
|
||
|
||
return (
|
||
f"No high-conviction setup: LONG {long_confidence:.0f}%, SHORT {short_confidence:.0f}% "
|
||
f"({signals}).{conflict_note}"
|
||
)
|
||
|
||
|
||
PRIMARY_TARGET_MIN_RR = 1.5
|
||
|
||
|
||
def _select_primary_target(targets: list[dict], min_rr: float = PRIMARY_TARGET_MIN_RR) -> dict | None:
|
||
"""Primary = the most LIKELY target that still offers real asymmetry.
|
||
|
||
Among targets clearing a minimal R:R floor, pick the highest probability
|
||
(tie-break by R:R). This fixes the old pick, which ignored probability and
|
||
could land on the furthest, least-likely 'lottery' level. Stronger-reward
|
||
levels remain in the table as stretch targets. Falls back to the highest-R:R
|
||
target if nothing clears the floor.
|
||
"""
|
||
if not targets:
|
||
return None
|
||
|
||
worthwhile = [t for t in targets if float(t.get("rr_ratio", 0.0)) >= min_rr]
|
||
pool = worthwhile or targets
|
||
return max(
|
||
pool,
|
||
key=lambda t: (float(t.get("probability", 0.0)), float(t.get("rr_ratio", 0.0))),
|
||
)
|
||
|
||
|
||
async def enhance_trade_setup(
|
||
db: AsyncSession,
|
||
ticker: Ticker,
|
||
setup: TradeSetup,
|
||
dimension_scores: dict[str, float],
|
||
sr_levels: list[SRLevel],
|
||
sentiment_classification: str | None,
|
||
atr_value: float,
|
||
available_directions: set[str] | None = None,
|
||
) -> TradeSetup:
|
||
config = await get_recommendation_config(db)
|
||
|
||
conflicts = signal_conflict_detector.detect_conflicts(
|
||
dimension_scores=dimension_scores,
|
||
sentiment_classification=sentiment_classification,
|
||
config=config,
|
||
)
|
||
|
||
long_confidence = direction_analyzer.calculate_confidence(
|
||
direction="long",
|
||
dimension_scores=dimension_scores,
|
||
sentiment_classification=sentiment_classification,
|
||
conflicts=conflicts,
|
||
)
|
||
short_confidence = direction_analyzer.calculate_confidence(
|
||
direction="short",
|
||
dimension_scores=dimension_scores,
|
||
sentiment_classification=sentiment_classification,
|
||
conflicts=conflicts,
|
||
)
|
||
|
||
direction = setup.direction.lower()
|
||
confidence = long_confidence if direction == "long" else short_confidence
|
||
|
||
# Merge near-duplicate levels into zone representatives first, so a clustered
|
||
# wall yields one strength-combined target instead of several near-identical
|
||
# ones — consistent with the chart and alerts.
|
||
zone_levels = _zone_representative_levels(sr_levels, setup.entry_price)
|
||
targets = target_generator.generate_targets(
|
||
direction=direction,
|
||
entry_price=setup.entry_price,
|
||
stop_loss=setup.stop_loss,
|
||
sr_levels=zone_levels,
|
||
atr_value=atr_value,
|
||
)
|
||
|
||
for target in targets:
|
||
target["probability"] = probability_estimator.estimate_probability(
|
||
target=target,
|
||
dimension_scores=dimension_scores,
|
||
sentiment_classification=sentiment_classification,
|
||
direction=direction,
|
||
config=config,
|
||
)
|
||
# Label follows from the reach-probability: high prob = Conservative.
|
||
target["classification"] = _classify_by_probability(target["probability"])
|
||
|
||
# Primary target = most-likely target with real asymmetry (see
|
||
# _select_primary_target), not the old quality-score pick that ignored
|
||
# probability. Sync the setup's headline target/rr_ratio so the chart, gate
|
||
# and outcome eval all agree with the table's starred row.
|
||
primary = _select_primary_target(targets)
|
||
if primary is not None:
|
||
for target in targets:
|
||
target["is_primary"] = target is primary
|
||
setup.target = round(float(primary["price"]), 4)
|
||
setup.rr_ratio = round(float(primary["rr_ratio"]), 4)
|
||
|
||
# Per-setup conflicts (target availability is specific to this setup)
|
||
setup_conflicts = list(conflicts)
|
||
if len(targets) < 3:
|
||
setup_conflicts.append("target-availability: Fewer than 3 valid S/R targets available")
|
||
|
||
# Action and reasoning are ticker-level: they consider both directions and
|
||
# which directions are actually tradeable, and are identical on every setup.
|
||
action = _choose_recommended_action(
|
||
long_confidence, short_confidence, config, available_directions
|
||
)
|
||
reasoning = _build_reasoning(
|
||
action=action,
|
||
long_confidence=long_confidence,
|
||
short_confidence=short_confidence,
|
||
conflicts=conflicts,
|
||
dimension_scores=dimension_scores,
|
||
sentiment_classification=sentiment_classification,
|
||
config=config,
|
||
available_directions=available_directions,
|
||
)
|
||
|
||
setup.confidence_score = round(confidence, 2)
|
||
setup.targets_json = json.dumps(targets)
|
||
setup.conflict_flags_json = json.dumps(setup_conflicts)
|
||
setup.recommended_action = action
|
||
setup.reasoning = reasoning
|
||
setup.risk_level = _risk_level_from_conflicts(setup_conflicts)
|
||
|
||
return setup
|