"""
analytics/execution_layer.py

Tier 5A — Execution Layer (Alpha Release)

Post-model enrichment pass operating exclusively on already-computed outputs
(model probs + book odds). No simulation logic, no probability calculations,
no model changes.

Entry point: enrich_with_execution_layer(df) → df with execution fields added.
"""

from __future__ import annotations

import statistics
from typing import Any

import pandas as pd

from analytics.no_vig_props import american_to_implied_prob

# ---------------------------------------------------------------------------
# Thresholds
# ---------------------------------------------------------------------------
OUTLIER_THRESHOLD = 0.03   # 3pp deviation from median → outlier
STALE_THRESHOLD = 0.025    # 2.5pp worse than median → stale book
AGGRESSIVE_THRESHOLD = 0.02  # 2pp better than median → aggressive/timing flag

_TIMESTAMP_KEYS = ("last_update", "timestamp", "odds_timestamp", "updated_at")


# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------

def _safe_float(val: Any, default: float | None = None) -> float | None:
    if val is None:
        return default
    try:
        return float(val)
    except (TypeError, ValueError):
        return default


def _safe_implied(odds: Any) -> float | None:
    if odds is None:
        return None
    try:
        return american_to_implied_prob(odds)
    except Exception:
        return None


def _make_player_game_key(row: pd.Series) -> str:
    explicit_key = str(row.get("player_event_market_key") or "").strip()
    if explicit_key and explicit_key not in ("nan", "None", ""):
        return explicit_key

    event_id = str(row.get("event_id") or "").strip()
    player_name = str(row.get("player_name") or "").strip()
    market_family = str(row.get("market_family") or row.get("market") or "").strip()
    threshold = str(row.get("threshold") or "").strip()
    if event_id and event_id not in ("nan", "None", ""):
        return f"{event_id}|{player_name}|{market_family}|{threshold}"
    away = str(row.get("away_team") or "").strip()
    home = str(row.get("home_team") or "").strip()
    return f"{away}|{home}|{player_name}|{market_family}|{threshold}"


def _make_game_key(row: pd.Series) -> str:
    event_id = str(row.get("event_id") or "").strip()
    if event_id and event_id not in ("nan", "None", ""):
        return event_id
    away = str(row.get("away_team") or "").strip()
    home = str(row.get("home_team") or "").strip()
    return f"{away}_{home}"


# ---------------------------------------------------------------------------
# Task 1 — Market Disagreement
# ---------------------------------------------------------------------------

def _compute_market_fields(df: pd.DataFrame) -> pd.DataFrame:
    """Add best_price, median_price, market_width, market_outlier_flag, stale_book_flag."""
    df = df.copy()

    # Build scoped player-game keys
    keys = df.apply(_make_player_game_key, axis=1)
    df["_pg_key"] = keys

    # Pre-compute implied probs for each row
    df["_implied"] = df["odds_american"].apply(_safe_implied)

    # Group stats per scoped player-game key
    group_stats: dict[str, dict] = {}
    for key, grp in df.groupby("_pg_key"):
        implied_vals = [v for v in grp["_implied"].tolist() if v is not None]
        if not implied_vals:
            group_stats[key] = {
                "best": None, "worst": None, "median": None, "width": None
            }
            continue
        best = min(implied_vals)   # lowest implied = best for bettor
        worst = max(implied_vals)
        med = statistics.median(implied_vals)
        width = abs(worst - best)
        group_stats[key] = {"best": best, "worst": worst, "median": med, "width": width}

    best_prices: list[float | None] = []
    median_prices: list[float | None] = []
    market_widths: list[float | None] = []
    outlier_flags: list[bool] = []
    stale_flags: list[bool] = []

    for _, row in df.iterrows():
        key = row["_pg_key"]
        stats = group_stats.get(key, {})
        this_implied = row["_implied"]

        best_prices.append(stats.get("best"))
        median_prices.append(stats.get("median"))
        market_widths.append(stats.get("width"))

        med = stats.get("median")
        if this_implied is not None and med is not None:
            outlier_flags.append(abs(this_implied - med) > OUTLIER_THRESHOLD)
            stale_flags.append((this_implied - med) > STALE_THRESHOLD)
        else:
            outlier_flags.append(False)
            stale_flags.append(False)

    df["best_price"] = best_prices
    df["median_price"] = median_prices
    df["market_width"] = market_widths
    df["market_outlier_flag"] = outlier_flags
    df["stale_book_flag"] = stale_flags

    df.drop(columns=["_pg_key", "_implied"], inplace=True)
    return df


# ---------------------------------------------------------------------------
# Task 2 — Edge Quality Filters
# ---------------------------------------------------------------------------

def _compute_edge_quality(df: pd.DataFrame) -> pd.DataFrame:
    """Add execution_confidence_score, execution_volatility_score, execution_signal_strength_score,
    edge_raw, edge_filtered, edge_filter_flags."""
    df = df.copy()

    conf_scores: list[float] = []
    vol_scores: list[float] = []
    sig_scores: list[float] = []
    edge_raws: list[float | None] = []
    edge_filtered_vals: list[float | None] = []
    edge_flag_strs: list[str] = []

    for _, row in df.iterrows():
        source = str(row.get("model_hr_prob_source") or "unavailable")
        context_applied = bool(row.get("pregame_context_applied") or False)
        edge_raw = _safe_float(row.get("edge"))
        market_width = _safe_float(row.get("market_width"), default=0.0)

        # Context adj magnitude
        pitcher_adj = _safe_float(row.get("pregame_pitcher_context_adj"), default=0.0)
        park_adj = _safe_float(row.get("pregame_park_context_adj"), default=0.0)
        context_mag = abs(pitcher_adj or 0.0) + abs(park_adj or 0.0)

        # Confidence score
        if source == "internal_model_baseline":
            conf = 1.0 if context_applied else 0.7
        elif source == "shared_pregame_engine":
            conf = 0.95 if context_applied else 0.80
        else:
            conf = 0.3

        # Volatility score (weighted blend, range [0, 1])
        width_component = min(1.0, (market_width or 0.0) / 0.10)
        ctx_component = min(1.0, context_mag / 0.02) if context_mag > 0 else 0.0
        vol = 0.7 * width_component + 0.3 * ctx_component

        # Signal strength score
        if source == "internal_model_baseline":
            sig = 0.7 + (0.3 if context_applied else 0.0)
        elif source == "shared_pregame_engine":
            sig = 0.85 + (0.15 if context_applied else 0.0)
        else:
            sig = 0.1
        sig = min(1.0, sig)

        # Edge filtered + flags
        if edge_raw is None:
            edge_filt = None
            flags = "clean"
        else:
            edge_filt = edge_raw
            applied: list[str] = []

            # Confidence penalty
            if conf < 0.5:
                scale = conf / 0.5
                edge_filt = edge_filt * scale
                applied.append("conf_penalty")

            # Volatility penalty
            vol_pen = min(0.02, vol * 0.02)
            if vol_pen > 0:
                edge_filt = edge_filt - vol_pen
                applied.append("vol_penalty")

            # Weak signal suppression
            if sig < 0.3:
                edge_filt = edge_filt * 0.5
                applied.append("weak_signal")

            flags = ",".join(applied) if applied else "clean"

        conf_scores.append(conf)
        vol_scores.append(vol)
        sig_scores.append(sig)
        edge_raws.append(edge_raw)
        edge_filtered_vals.append(edge_filt)
        edge_flag_strs.append(flags)

    df["execution_confidence_score"] = conf_scores
    df["execution_volatility_score"] = vol_scores
    df["execution_signal_strength_score"] = sig_scores
    df["edge_raw"] = edge_raws
    df["edge_filtered"] = edge_filtered_vals
    df["edge_filter_flags"] = edge_flag_strs
    return df


# ---------------------------------------------------------------------------
# Task 3 — Timing Heuristics
# ---------------------------------------------------------------------------

def _compute_timing_fields(df: pd.DataFrame) -> pd.DataFrame:
    """Add timing_flag, timing_reason."""
    df = df.copy()

    timing_flags: list[bool] = []
    timing_reasons: list[str] = []

    for _, row in df.iterrows():
        reasons: list[str] = []

        # Aggressive price: this book > 2pp better than median (lower implied)
        this_implied = _safe_implied(row.get("odds_american"))
        median_price = _safe_float(row.get("median_price"))
        if (
            this_implied is not None
            and median_price is not None
            and (median_price - this_implied) > AGGRESSIVE_THRESHOLD
        ):
            reasons.append("aggressive_price")

        # Timestamp presence
        has_ts = any(
            row.get(k) is not None and str(row.get(k)).strip() not in ("", "nan", "None")
            for k in _TIMESTAMP_KEYS
        )
        if has_ts:
            reasons.append("has_timestamp")

        if not reasons:
            reasons.append("none")

        timing_flags.append(len(reasons) > 1 or (len(reasons) == 1 and reasons[0] != "none"))
        timing_reasons.append(",".join(reasons))

    df["timing_flag"] = timing_flags
    df["timing_reason"] = timing_reasons
    return df


# ---------------------------------------------------------------------------
# Task 4 — Correlation Awareness
# ---------------------------------------------------------------------------

def _compute_correlation_fields(df: pd.DataFrame) -> pd.DataFrame:
    """Add correlation_flag, correlation_direction."""
    df = df.copy()

    # Count distinct players per game
    game_keys = df.apply(_make_game_key, axis=1)
    df["_game_key"] = game_keys

    player_counts: dict[str, int] = {}
    for key, grp in df.groupby("_game_key"):
        player_counts[key] = grp["player_name"].nunique()

    corr_directions: list[str] = []
    for _, row in df.iterrows():
        key = row["_game_key"]
        count = player_counts.get(key, 1)
        corr_directions.append("positive_stacked" if count > 2 else "positive")

    df["correlation_flag"] = True  # always True for HR props
    df["correlation_direction"] = corr_directions

    df.drop(columns=["_game_key"], inplace=True)
    return df


# ---------------------------------------------------------------------------
# Task 5 — Final Execution Score
# ---------------------------------------------------------------------------

def _compute_execution_score(df: pd.DataFrame) -> pd.DataFrame:
    """Add final_recommendation_score."""
    df = df.copy()

    scores: list[float | None] = []

    for _, row in df.iterrows():
        edge_filtered = _safe_float(row.get("edge_filtered"))
        if edge_filtered is None:
            scores.append(None)
            continue

        confidence_score = _safe_float(row.get("execution_confidence_score"), default=0.3)
        volatility_score = _safe_float(row.get("execution_volatility_score"), default=0.0)
        market_width = _safe_float(row.get("market_width"), default=0.0)
        timing_flag = bool(row.get("timing_flag") or False)

        base = edge_filtered * (0.4 + (confidence_score or 0.0) * 0.6)
        vol_penalty = min(0.015, (volatility_score or 0.0) * 0.015)
        market_bonus = min(0.01, max(0.0, 0.01 - (market_width or 0.0) * 0.5))
        timing_bonus = 0.005 if timing_flag else 0.0

        score = base - vol_penalty + market_bonus + timing_bonus
        score = max(-0.30, min(0.30, score))
        scores.append(score)

    df["final_recommendation_score"] = scores
    return df


# ---------------------------------------------------------------------------
# Public entry point
# ---------------------------------------------------------------------------

def enrich_with_execution_layer(df: pd.DataFrame) -> pd.DataFrame:
    """
    Run all five execution-layer passes on the mapped props DataFrame.

    Passes (in order):
      1. Market Disagreement  — best_price, median_price, market_width, flags
      2. Edge Quality         — execution confidence, volatility, signal, edge_filtered
      3. Timing Heuristics    — timing_flag, timing_reason
      4. Correlation          — correlation_flag, correlation_direction
      5. Execution Score      — final_recommendation_score

    Returns the enriched DataFrame. Does not modify simulation logic or
    model probabilities.
    """
    if df.empty:
        return df

    df = _compute_market_fields(df)
    df = _compute_edge_quality(df)
    df = _compute_timing_fields(df)
    df = _compute_correlation_fields(df)
    df = _compute_execution_score(df)
    return df