"""Rule-based entry score model for the backtester.

Computes a composite score in [0, 1] from event, market, and text features
available at entry time (no forward-looking data).  Higher score = more
favorable entry conditions for a long swing trade.

Event/Document Quality (55% weight — primary signal):
  - Event quality (20%) — parser confidence + signal strength
  - Earnings surprise (12%) — SUE/EPS growth (earnings events only)
  - Risk penalty (10%) — oneoff risk flags reduce score
  - Parse confidence (8%) — parse_confidence_overall from parser
  - Direction clarity (5%) — event_direction categorical field

Market Confirmation (35% weight — secondary signal):
  - Reaction quality (12%) — moderate positive return is ideal
  - Close strength (10%) — close near high = buyers won the day
  - Volume conviction (8%) — above-average but not exhaustion
  - Gap quality (5%) — small positive gap = orderly strength

Text (10% weight — filing sentiment):
  - LM sentiment (10%) — Loughran-McDonald filing tone
"""
from __future__ import annotations

from typing import Any

from libs.common.logging import get_logger

logger = get_logger(__name__)


def compute_entry_score(row: dict[str, Any]) -> float:
    """Compute composite entry score from event + market + text features.

    Components and weights:
      Event/Document Quality (55%):
        1. Event quality       (20%) — parser confidence + signal strength
        2. Earnings surprise   (12%) — SUE/EPS growth (earnings events only)
        3. Risk penalty        (10%) — oneoff risk flags reduce score
        4. Parse confidence    (8%)  — parse_confidence_overall from parser
        5. Direction clarity   (5%)  — event_direction categorical field
      Market Confirmation (35%):
        6. Reaction quality    (12%) — moderate positive return is ideal
        7. Close strength      (10%) — close near high = buyers won the day
        8. Volume conviction   (8%)  — above-average but not exhaustion
        9. Gap quality         (5%)  — small positive gap = orderly strength
      Text (10%):
        10. LM sentiment       (10%) — Loughran-McDonald filing tone

    Returns float in [0.0, 1.0].
    """
    # Event/Document Quality components
    event = _event_quality_score(row)
    sue = _earnings_surprise_score(row)
    risk = _risk_penalty_score(row)
    parse_conf = _parse_confidence_score(row)
    direction = _direction_clarity_score(row)

    # Market Confirmation components
    reaction = _reaction_score(row)
    close = _close_strength_score(row)
    volume = _volume_score(row)
    gap = _gap_score(row)

    # Text sentiment component
    text = _text_sentiment_score(row)

    raw = (
        # Event/Document Quality (55%)
        event * 0.20 + sue * 0.12 + risk * 0.10
        + parse_conf * 0.08 + direction * 0.05
        # Market Confirmation (35%)
        + reaction * 0.12 + close * 0.10 + volume * 0.08 + gap * 0.05
        # Text (10%)
        + text * 0.10
    )
    return max(0.0, min(1.0, raw))


# ---------------------------------------------------------------------------
# Market feature scoring
# ---------------------------------------------------------------------------


def _reaction_score(row: dict[str, Any]) -> float:
    """Score based on reaction_day_return.

    Sweet spot: moderate positive return (0.5-3%) suggests post-event
    continuation without being "already priced in".

    Mapping:
      +0.5% to +3%  -> 0.9  (ideal PEAD zone)
      +0%   to +0.5% -> 0.65 (flat, uncertain direction)
      +3%   to +8%  -> 0.45 (getting priced in)
      > +8%         -> 0.2  (extreme — mean reversion risk)
      -2%   to  0%  -> 0.4  (mild negative)
      -5%   to -2%  -> 0.25 (moderate negative)
      < -5%         -> 0.1  (strongly bearish)
    """
    rdr = row.get("reaction_day_return")
    if rdr is None:
        return 0.5

    r = float(rdr)
    if 0.005 <= r <= 0.03:
        return 0.9
    elif 0.0 <= r < 0.005:
        return 0.65
    elif 0.03 < r <= 0.08:
        return 0.45
    elif r > 0.08:
        return 0.2
    elif -0.02 <= r < 0.0:
        return 0.4
    elif -0.05 <= r < -0.02:
        return 0.25
    else:  # r < -0.05
        return 0.1


def _close_strength_score(row: dict[str, Any]) -> float:
    """Score based on close_location [0=low, 1=high].

    Linear mapping: 0.0 -> 0.1, 1.0 -> 1.0.
    Close near session high = buyers controlled the day.
    """
    cl = row.get("close_location")
    if cl is None:
        return 0.5

    c = max(0.0, min(1.0, float(cl)))
    return 0.1 + 0.9 * c


def _volume_score(row: dict[str, Any]) -> float:
    """Score based on volume_ratio_20d.

    Above-average volume confirms conviction, but extreme volume
    (>3x) can signal exhaustion or panic, so it gets discounted.

    Mapping:
      1.2x-2.0x -> 0.8  (healthy conviction)
      1.0x-1.2x -> 0.6  (normal)
      2.0x-3.0x -> 0.55 (high — possible exhaustion)
      > 3.0x    -> 0.4  (extreme — likely exhaustion)
      < 1.0x    -> 0.3  (below average — no conviction)
    """
    vr = row.get("volume_ratio_20d")
    if vr is None:
        return 0.5

    v = float(vr)
    if 1.2 <= v <= 2.0:
        return 0.8
    elif 1.0 <= v < 1.2:
        return 0.6
    elif 2.0 < v <= 3.0:
        return 0.55
    elif v > 3.0:
        return 0.4
    else:  # v < 1.0
        return 0.3


def _gap_score(row: dict[str, Any]) -> float:
    """Score based on gap_size (open vs previous close).

    Small positive gap (0-2%) = orderly bullish opening.
    Large gap (>5%) = potential exhaustion gap.
    Negative gap = bearish opening pressure.

    Mapping:
      +0.5% to +2%  -> 0.8  (orderly strength)
      0%    to +0.5% -> 0.6  (neutral-to-mild)
      +2%   to +5%  -> 0.5  (getting extended)
      > +5%         -> 0.3  (exhaustion gap risk)
      -2%   to  0%  -> 0.4  (mild weakness)
      < -2%         -> 0.2  (bearish gap)
    """
    gs = row.get("gap_size")
    if gs is None:
        return 0.5

    g = float(gs)
    if 0.005 <= g <= 0.02:
        return 0.8
    elif 0.0 <= g < 0.005:
        return 0.6
    elif 0.02 < g <= 0.05:
        return 0.5
    elif g > 0.05:
        return 0.3
    elif -0.02 <= g < 0.0:
        return 0.4
    else:  # g < -0.02
        return 0.2


# ---------------------------------------------------------------------------
# Earnings surprise (SUE) scoring
# ---------------------------------------------------------------------------


def _earnings_surprise_score(row: dict[str, Any]) -> float:
    """Score based on earnings surprise (eps_growth_qoq as naive SUE proxy).

    Only active for earnings_release events; returns neutral 0.5 for others.
    Bernard & Thomas (1989): drift magnitude is proportional to surprise.

    Mapping:
      > +20% EPS growth  -> 0.9  (strong beat)
      > +5%              -> 0.75 (moderate beat)
      > -5%              -> 0.5  (in-line)
      > -20%             -> 0.25 (moderate miss)
      <= -20%            -> 0.1  (severe miss)
    """
    event_type = row.get("event_type", "")
    if event_type != "earnings_release":
        return 0.5

    sue = row.get("eps_growth_qoq")
    if sue is None:
        return 0.5

    s = float(sue)
    if s > 0.20:
        return 0.9
    if s > 0.05:
        return 0.75
    if s > -0.05:
        return 0.5
    if s > -0.20:
        return 0.25
    return 0.1


# ---------------------------------------------------------------------------
# Event feature scoring
# ---------------------------------------------------------------------------


def _event_quality_score(row: dict[str, Any]) -> float:
    """Score based on event parsing quality and signal strength.

    Combines:
      - document_quality_score [0-1]: parser confidence in the extraction
      - signal_strength_score [0-1]: strength of business fundamentals signals
      - guidance_direction_score: 1.0=raised, 0.5=inline, 0.25=unclear, 0.0=lowered

    When event features are absent (market_v1 only Parquet), returns 0.5.
    """
    doc_q = row.get("document_quality_score")
    sig_s = row.get("signal_strength_score")
    guid = row.get("guidance_direction_score")

    # If no event features at all, return neutral
    if doc_q is None and sig_s is None and guid is None:
        return 0.5

    # Weight: signal_strength 40%, guidance 35%, document_quality 25%
    scores = []
    weights = []

    if sig_s is not None:
        scores.append(float(sig_s))
        weights.append(0.40)

    if guid is not None:
        scores.append(float(guid))
        weights.append(0.35)

    if doc_q is not None:
        scores.append(float(doc_q))
        weights.append(0.25)

    if not scores:
        return 0.5

    total_weight = sum(weights)
    return sum(s * w for s, w in zip(scores, weights)) / total_weight


def _risk_penalty_score(row: dict[str, Any]) -> float:
    """Score based on oneoff_penalty (risk flags).

    oneoff_penalty [0-1]: fraction of active risk flags.
    Higher penalty = lower score (more risk = less favorable entry).

    Inverted: 0.0 penalty -> 0.9 score, 1.0 penalty -> 0.2 score.
    When absent, returns neutral 0.5.
    """
    penalty = row.get("oneoff_penalty")
    if penalty is None:
        return 0.5

    p = max(0.0, min(1.0, float(penalty)))
    # Linear inversion: 0 -> 0.9, 1.0 -> 0.2
    return 0.9 - 0.7 * p


def _parse_confidence_score(row: dict[str, Any]) -> float:
    """Score based on parse_confidence_overall [0-1]."""
    conf = row.get("parse_confidence_overall")
    if conf is None:
        return 0.5
    c = float(conf)
    if c > 0.8:
        return 0.9
    if c > 0.6:
        return 0.7
    if c > 0.5:
        return 0.5
    if c > 0.4:
        return 0.3
    return 0.2


def _direction_clarity_score(row: dict[str, Any]) -> float:
    """Score based on event_direction categorical field."""
    direction = row.get("event_direction")
    if direction is None:
        return 0.5
    return {"bullish": 0.9, "mixed": 0.4, "neutral": 0.3,
            "bearish": 0.1, "unknown": 0.2}.get(str(direction).lower(), 0.5)


# ---------------------------------------------------------------------------
# Text sentiment scoring
# ---------------------------------------------------------------------------


def _text_sentiment_score(row: dict[str, Any]) -> float:
    """Score based on Loughran-McDonald text sentiment features.

    Uses lm_net_sentiment (positive - negative word fraction).
    Typical range is [-0.02, +0.02] for SEC filings.

    Mapping:
      > +0.005  -> 0.8  (noticeably positive tone)
      > +0.001  -> 0.65 (mildly positive)
      > -0.001  -> 0.5  (neutral)
      > -0.005  -> 0.35 (mildly negative)
      <= -0.005 -> 0.2  (noticeably negative tone)

    When absent, returns neutral 0.5.
    """
    net = row.get("lm_net_sentiment")
    if net is None:
        return 0.5

    n = float(net)
    if n > 0.005:
        return 0.8
    if n > 0.001:
        return 0.65
    if n > -0.001:
        return 0.5
    if n > -0.005:
        return 0.35
    return 0.2