signal-platform/app/services/recommendation_service.py

from __future__ import annotations

import json
import logging
from typing import Any

from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession

from app.models.settings import SystemSetting
from app.models.sr_level import SRLevel
from app.models.ticker import Ticker
from app.models.trade_setup import TradeSetup

logger = logging.getLogger(__name__)


DEFAULT_RECOMMENDATION_CONFIG: dict[str, float] = {
    "recommendation_high_confidence_threshold": 70.0,
    "recommendation_moderate_confidence_threshold": 50.0,
    "recommendation_confidence_diff_threshold": 20.0,
    "recommendation_signal_alignment_weight": 0.15,
    "recommendation_sr_strength_weight": 0.20,
    "recommendation_distance_penalty_factor": 0.10,
    "recommendation_momentum_technical_divergence_threshold": 30.0,
    "recommendation_fundamental_technical_divergence_threshold": 40.0,
}


def _clamp(value: float, low: float, high: float) -> float:
    return max(low, min(high, value))


def _sentiment_value(sentiment_classification: str | None) -> str | None:
    if sentiment_classification is None:
        return None
    return sentiment_classification.strip().lower()


def check_signal_alignment(
    direction: str,
    dimension_scores: dict[str, float],
    sentiment_classification: str | None,
) -> tuple[bool, str]:
    technical = float(dimension_scores.get("technical", 50.0))
    momentum = float(dimension_scores.get("momentum", 50.0))
    sentiment = _sentiment_value(sentiment_classification)

    if direction == "long":
        aligned_count = sum([
            technical > 60,
            momentum > 60,
            sentiment == "bullish",
        ])
        if aligned_count >= 2:
            return True, "Technical, momentum, and/or sentiment align with LONG direction."
        return False, "Signals are mixed for LONG direction."

    aligned_count = sum([
        technical < 40,
        momentum < 40,
        sentiment == "bearish",
    ])
    if aligned_count >= 2:
        return True, "Technical, momentum, and/or sentiment align with SHORT direction."
    return False, "Signals are mixed for SHORT direction."


class SignalConflictDetector:
    def detect_conflicts(
        self,
        dimension_scores: dict[str, float],
        sentiment_classification: str | None,
        config: dict[str, float] | None = None,
    ) -> list[str]:
        cfg = config or DEFAULT_RECOMMENDATION_CONFIG
        technical = float(dimension_scores.get("technical", 50.0))
        momentum = float(dimension_scores.get("momentum", 50.0))
        fundamental = float(dimension_scores.get("fundamental", 50.0))
        sentiment = _sentiment_value(sentiment_classification)

        mt_threshold = float(cfg.get("recommendation_momentum_technical_divergence_threshold", 30.0))
        ft_threshold = float(cfg.get("recommendation_fundamental_technical_divergence_threshold", 40.0))

        conflicts: list[str] = []

        if sentiment == "bearish" and technical > 60:
            conflicts.append(
                f"sentiment-technical: Bearish sentiment conflicts with bullish technical ({technical:.0f})"
            )
        if sentiment == "bullish" and technical < 40:
            conflicts.append(
                f"sentiment-technical: Bullish sentiment conflicts with bearish technical ({technical:.0f})"
            )

        mt_diff = abs(momentum - technical)
        if mt_diff > mt_threshold:
            conflicts.append(
                "momentum-technical: "
                f"Momentum ({momentum:.0f}) diverges from technical ({technical:.0f}) by {mt_diff:.0f} points"
            )

        if sentiment == "bearish" and momentum > 60:
            conflicts.append(
                f"sentiment-momentum: Bearish sentiment conflicts with momentum ({momentum:.0f})"
            )
        if sentiment == "bullish" and momentum < 40:
            conflicts.append(
                f"sentiment-momentum: Bullish sentiment conflicts with momentum ({momentum:.0f})"
            )

        ft_diff = abs(fundamental - technical)
        if ft_diff > ft_threshold:
            conflicts.append(
                "fundamental-technical: "
                f"Fundamental ({fundamental:.0f}) diverges significantly from technical ({technical:.0f})"
            )

        return conflicts


class DirectionAnalyzer:
    def calculate_confidence(
        self,
        direction: str,
        dimension_scores: dict[str, float],
        sentiment_classification: str | None,
        conflicts: list[str] | None = None,
    ) -> float:
        """Directional-agreement confidence around a 50 baseline.

        Each dimension contributes in proportion to how strongly it agrees
        with the proposed direction: a bullish dimension RAISES long confidence
        and LOWERS short confidence (and vice-versa). Signals that oppose the
        direction push confidence below 50 — so a short on a strongly bullish
        stock scores near zero, not 55.
        """
        technical = float(dimension_scores.get("technical", 50.0))
        momentum = float(dimension_scores.get("momentum", 50.0))
        fundamental = float(dimension_scores.get("fundamental", 50.0))
        sentiment = _sentiment_value(sentiment_classification)
        dir_sign = 1.0 if direction == "long" else -1.0

        def agree(score: float) -> float:
            # -1 (fully against) .. +1 (fully for) the proposed direction
            return ((score - 50.0) / 50.0) * dir_sign

        sentiment_val = {"bullish": 1.0, "bearish": -1.0}.get(sentiment or "", 0.0)
        sentiment_agree = sentiment_val * dir_sign

        confidence = 50.0 + (
            agree(technical) * 25.0
            + agree(momentum) * 20.0
            + sentiment_agree * 15.0
            + agree(fundamental) * 10.0
        )

        # Explicit conflict patterns trim a little more (the agreement terms
        # already capture most disagreement, so penalties are modest).
        for conflict in conflicts or []:
            if "sentiment-technical" in conflict:
                confidence -= 12.0
            elif "momentum-technical" in conflict:
                confidence -= 10.0
            elif "sentiment-momentum" in conflict:
                confidence -= 12.0
            elif "fundamental-technical" in conflict:
                confidence -= 6.0

        return _clamp(confidence, 0.0, 100.0)


class TargetGenerator:
    def generate_targets(
        self,
        direction: str,
        entry_price: float,
        stop_loss: float,
        sr_levels: list[SRLevel],
        atr_value: float,
    ) -> list[dict[str, Any]]:
        if atr_value <= 0:
            return []

        risk = abs(entry_price - stop_loss)
        if risk <= 0:
            return []

        candidates: list[dict[str, Any]] = []
        atr_pct = atr_value / entry_price if entry_price > 0 else 0.0

        max_atr_multiple: float | None = None
        if atr_pct > 0.05:
            max_atr_multiple = 10.0
        elif atr_pct < 0.02:
            max_atr_multiple = 3.0

        for level in sr_levels:
            is_candidate = False
            if direction == "long":
                is_candidate = level.type == "resistance" and level.price_level > entry_price
            else:
                is_candidate = level.type == "support" and level.price_level < entry_price

            if not is_candidate:
                continue

            distance = abs(level.price_level - entry_price)
            distance_atr_multiple = distance / atr_value

            if distance_atr_multiple < 1.0:
                continue
            if max_atr_multiple is not None and distance_atr_multiple > max_atr_multiple:
                continue

            reward = abs(level.price_level - entry_price)
            rr_ratio = reward / risk

            norm_rr = min(rr_ratio / 10.0, 1.0)
            norm_strength = _clamp(level.strength, 0, 100) / 100.0
            norm_proximity = 1.0 - min(distance / entry_price, 1.0)
            quality = 0.35 * norm_rr + 0.35 * norm_strength + 0.30 * norm_proximity

            candidates.append(
                {
                    "price": float(level.price_level),
                    "distance_from_entry": float(distance),
                    "distance_atr_multiple": float(distance_atr_multiple),
                    "rr_ratio": float(rr_ratio),
                    "classification": "Moderate",
                    "sr_level_id": int(level.id),
                    "sr_strength": float(level.strength),
                    "quality": float(quality),
                }
            )

        candidates.sort(key=lambda row: row["quality"], reverse=True)
        selected = candidates[:5]
        selected.sort(key=lambda row: row["distance_from_entry"])

        if not selected:
            return []

        n = len(selected)
        for idx, target in enumerate(selected):
            if n <= 2:
                target["classification"] = "Conservative" if idx == 0 else "Aggressive"
            elif idx <= 1:
                target["classification"] = "Conservative"
            elif idx >= n - 2:
                target["classification"] = "Aggressive"
            else:
                target["classification"] = "Moderate"

            target.pop("quality", None)

        return selected


class ProbabilityEstimator:
    def estimate_probability(
        self,
        target: dict[str, Any],
        dimension_scores: dict[str, float],
        sentiment_classification: str | None,
        direction: str,
        config: dict[str, float],
    ) -> float:
        classification = str(target.get("classification", "Moderate"))
        strength = float(target.get("sr_strength", 50.0))
        atr_multiple = float(target.get("distance_atr_multiple", 1.0))

        if classification == "Conservative":
            base_prob = 70.0
        elif classification == "Aggressive":
            base_prob = 40.0
        else:
            base_prob = 55.0

        if strength >= 80:
            strength_adj = 15.0
        elif strength >= 60:
            strength_adj = 10.0
        elif strength >= 40:
            strength_adj = 5.0
        else:
            strength_adj = -10.0

        technical = float(dimension_scores.get("technical", 50.0))
        momentum = float(dimension_scores.get("momentum", 50.0))
        sentiment = _sentiment_value(sentiment_classification)

        alignment_adj = 0.0
        if direction == "long":
            if technical > 60 and (sentiment == "bullish" or momentum > 60):
                alignment_adj = 15.0
            elif technical < 40 or (sentiment == "bearish" and momentum < 40):
                alignment_adj = -15.0
        else:
            if technical < 40 and (sentiment == "bearish" or momentum < 40):
                alignment_adj = 15.0
            elif technical > 60 or (sentiment == "bullish" and momentum > 60):
                alignment_adj = -15.0

        volatility_adj = 0.0
        if atr_multiple > 5:
            volatility_adj = 5.0
        elif atr_multiple < 2:
            volatility_adj = 5.0

        signal_weight = float(config.get("recommendation_signal_alignment_weight", 0.15))
        sr_weight = float(config.get("recommendation_sr_strength_weight", 0.20))
        distance_penalty = float(config.get("recommendation_distance_penalty_factor", 0.10))

        scaled_alignment_adj = alignment_adj * (signal_weight / 0.15)
        scaled_strength_adj = strength_adj * (sr_weight / 0.20)
        distance_adj = -distance_penalty * max(atr_multiple - 1.0, 0.0) * 2.0

        probability = base_prob + scaled_strength_adj + scaled_alignment_adj + volatility_adj + distance_adj
        probability = _clamp(probability, 10.0, 90.0)

        if classification == "Conservative":
            probability = max(probability, 61.0)
        elif classification == "Moderate":
            probability = _clamp(probability, 40.0, 70.0)
        elif classification == "Aggressive":
            probability = min(probability, 49.0)

        return round(probability, 2)


signal_conflict_detector = SignalConflictDetector()
direction_analyzer = DirectionAnalyzer()
target_generator = TargetGenerator()
probability_estimator = ProbabilityEstimator()


async def get_recommendation_config(db: AsyncSession) -> dict[str, float]:
    result = await db.execute(
        select(SystemSetting).where(SystemSetting.key.like("recommendation_%"))
    )
    rows = result.scalars().all()

    config: dict[str, float] = dict(DEFAULT_RECOMMENDATION_CONFIG)
    for setting in rows:
        try:
            config[setting.key] = float(setting.value)
        except (TypeError, ValueError):
            logger.warning("Invalid recommendation setting value for %s: %s", setting.key, setting.value)

    return config


def _risk_level_from_conflicts(conflicts: list[str]) -> str:
    if not conflicts:
        return "Low"
    severe = [c for c in conflicts if "sentiment-technical" in c or "sentiment-momentum" in c]
    if len(severe) >= 2 or len(conflicts) >= 3:
        return "High"
    return "Medium"


def _choose_recommended_action(
    long_confidence: float,
    short_confidence: float,
    config: dict[str, float],
    available_directions: set[str] | None = None,
) -> str:
    """Pick the ticker action — but only recommend a direction you can trade.

    A direction is recommendable only if a tradeable setup exists for it
    (``available_directions``). So a strong LONG bias on a stock at all-time
    highs — where the scanner can build no long target — does NOT yield
    LONG_HIGH; it falls through to NEUTRAL, and the reasoning explains why.
    """
    high = float(config.get("recommendation_high_confidence_threshold", 70.0))
    moderate = float(config.get("recommendation_moderate_confidence_threshold", 50.0))
    diff = float(config.get("recommendation_confidence_diff_threshold", 20.0))

    long_ok = available_directions is None or "long" in available_directions
    short_ok = available_directions is None or "short" in available_directions

    if long_ok and long_confidence >= high and (long_confidence - short_confidence) >= diff:
        return "LONG_HIGH"
    if short_ok and short_confidence >= high and (short_confidence - long_confidence) >= diff:
        return "SHORT_HIGH"
    if long_ok and long_confidence >= moderate and (long_confidence - short_confidence) >= diff:
        return "LONG_MODERATE"
    if short_ok and short_confidence >= moderate and (short_confidence - long_confidence) >= diff:
        return "SHORT_MODERATE"
    return "NEUTRAL"


def _build_reasoning(
    action: str,
    long_confidence: float,
    short_confidence: float,
    conflicts: list[str],
    dimension_scores: dict[str, float],
    sentiment_classification: str | None,
    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}"
    )


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

    targets = target_generator.generate_targets(
        direction=direction,
        entry_price=setup.entry_price,
        stop_loss=setup.stop_loss,
        sr_levels=sr_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,
        )

    # 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