Fix scoring/recommendation correctness and calibration
Triggered by CNC showing "LONG (High Confidence)" with SHORT reasoning and no long setup. - A: recommendation action + reasoning are ticker-level and identical on both setups; reasoning always matches the shown action - B: recommended_action only picks a direction with a tradeable setup; strong bias with no setup (e.g. price at ATH) → NEUTRAL with an explanatory reason instead of a fake LONG_HIGH - C: confidence is a directional-agreement model — opposing signals push it below 50 (SHORT on a 92-technical/99-momentum stock ~0%, not 55%) - D: fundamental score requires >=2 real metrics (market-cap-only no longer yields a high score) - E: RSI score peaks at healthy momentum (~60) and penalizes overbought/oversold extremes instead of treating RSI 90 as maximal - F: fundamentals chain merges fields across providers (FMP market cap + Finnhub P/E) instead of stopping at the first with any field - NEUTRAL label: "No Clear Setup" (covers untradeable-bias case) Scores recompute on next scan/scoring run; C and E shift score distributions intentionally. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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@@ -127,58 +127,45 @@ class DirectionAnalyzer:
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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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confidence = 50.0
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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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if direction == "long":
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if technical > 70:
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confidence += 25.0
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elif technical > 60:
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confidence += 15.0
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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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if momentum > 70:
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confidence += 20.0
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elif momentum > 60:
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confidence += 15.0
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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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if sentiment == "bullish":
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confidence += 15.0
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elif sentiment == "neutral":
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confidence += 5.0
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if fundamental > 60:
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confidence += 10.0
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else:
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if technical < 30:
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confidence += 25.0
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elif technical < 40:
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confidence += 15.0
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if momentum < 30:
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confidence += 20.0
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elif momentum < 40:
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confidence += 15.0
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if sentiment == "bearish":
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confidence += 15.0
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elif sentiment == "neutral":
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confidence += 5.0
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if fundamental < 40:
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confidence += 10.0
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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 -= 20.0
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confidence -= 12.0
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elif "momentum-technical" in conflict:
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confidence -= 15.0
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elif "sentiment-momentum" in conflict:
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confidence -= 20.0
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elif "fundamental-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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@@ -377,53 +364,83 @@ 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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if long_confidence >= high and (long_confidence - short_confidence) >= diff:
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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_confidence >= high and (short_confidence - long_confidence) >= diff:
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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_confidence >= moderate and (long_confidence - short_confidence) >= diff:
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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_confidence >= moderate and (short_confidence - long_confidence) >= diff:
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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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direction: str,
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confidence: float,
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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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action: str,
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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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aligned, alignment_text = check_signal_alignment(
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direction,
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dimension_scores,
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sentiment_classification,
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)
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"""Ticker-level reasoning that always matches the recommended action.
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Stored identically on both setups so the displayed summary can never mix a
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SHORT setup's reasoning under a LONG action.
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"""
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sentiment = _sentiment_value(sentiment_classification) or "unknown"
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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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signals = f"technical={technical:.0f}, momentum={momentum:.0f}, sentiment={sentiment}"
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conflict_note = f" {len(conflicts)} conflict(s) detected, risk-adjusted." if conflicts else ""
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direction_text = direction.upper()
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alignment_summary = "aligned" if aligned else "mixed"
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base = (
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f"{direction_text} confidence {confidence:.1f}% with {alignment_summary} signals "
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f"(technical={technical:.0f}, momentum={momentum:.0f}, sentiment={sentiment})."
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)
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if conflicts:
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if action != "NEUTRAL":
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direction = "long" if action.startswith("LONG") else "short"
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tier = "high" if action.endswith("HIGH") else "moderate"
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confidence = long_confidence if direction == "long" else short_confidence
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aligned, _ = check_signal_alignment(direction, dimension_scores, sentiment_classification)
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return (
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f"{base} {alignment_text} Detected {len(conflicts)} conflict(s), "
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f"so recommendation is risk-adjusted. Action={action}."
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f"{direction.upper()} ({tier} confidence): {confidence:.0f}% with "
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f"{'aligned' if aligned else 'mixed'} signals ({signals}).{conflict_note}"
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)
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return f"{base} {alignment_text} No major conflicts detected. Action={action}."
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# NEUTRAL — explain whether it's a missing setup or genuinely mixed signals.
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moderate = float(config.get("recommendation_moderate_confidence_threshold", 50.0))
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avail = available_directions if available_directions is not None else {"long", "short"}
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bias_dir = "long" if long_confidence >= short_confidence else "short"
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bias_conf = max(long_confidence, short_confidence)
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if bias_conf >= moderate and bias_dir not in avail:
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other = "short" if bias_dir == "long" else "long"
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extreme = "highs (no resistance target above)" if bias_dir == "long" else "lows (no support target below)"
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return (
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f"Ticker bias is {bias_dir.upper()} (confidence {bias_conf:.0f}%, {signals}) but price is "
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f"extended near {extreme}, so no high-conviction {bias_dir} setup is available. "
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f"The available {other.upper()} setup is counter-trend.{conflict_note}"
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)
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return (
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f"No high-conviction setup: LONG {long_confidence:.0f}%, SHORT {short_confidence:.0f}% "
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f"({signals}).{conflict_note}"
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)
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async def enhance_trade_setup(
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@@ -434,6 +451,7 @@ async def enhance_trade_setup(
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sr_levels: list[SRLevel],
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sentiment_classification: str | None,
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atr_value: float,
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available_directions: set[str] | None = None,
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) -> TradeSetup:
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config = await get_recommendation_config(db)
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@@ -476,24 +494,32 @@ async def enhance_trade_setup(
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config=config,
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)
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# Per-setup conflicts (target availability is specific to this setup)
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setup_conflicts = list(conflicts)
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if len(targets) < 3:
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conflicts = [*conflicts, "target-availability: Fewer than 3 valid S/R targets available"]
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setup_conflicts.append("target-availability: Fewer than 3 valid S/R targets available")
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action = _choose_recommended_action(long_confidence, short_confidence, config)
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risk_level = _risk_level_from_conflicts(conflicts)
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setup.confidence_score = round(confidence, 2)
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setup.targets_json = json.dumps(targets)
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setup.conflict_flags_json = json.dumps(conflicts)
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setup.recommended_action = action
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setup.reasoning = _build_reasoning(
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direction=direction,
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confidence=confidence,
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# Action and reasoning are ticker-level: they consider both directions and
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# which directions are actually tradeable, and are identical on every setup.
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action = _choose_recommended_action(
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long_confidence, short_confidence, config, available_directions
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)
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reasoning = _build_reasoning(
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action=action,
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long_confidence=long_confidence,
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short_confidence=short_confidence,
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conflicts=conflicts,
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dimension_scores=dimension_scores,
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sentiment_classification=sentiment_classification,
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action=action,
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config=config,
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available_directions=available_directions,
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)
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setup.risk_level = risk_level
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setup.confidence_score = round(confidence, 2)
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setup.targets_json = json.dumps(targets)
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setup.conflict_flags_json = json.dumps(setup_conflicts)
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setup.recommended_action = action
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setup.reasoning = reasoning
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setup.risk_level = _risk_level_from_conflicts(setup_conflicts)
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return setup
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