"""
recommendation_engine.py — Select optimal strike and expiry for a given strategy/horizon.

For each (ticker, strategy, time_horizon) combination:
  1. Determine the target expiration date
  2. Fetch the options chain for that expiry
  3. Filter by delta range (0.20-0.30 for CC/CSP)
  4. Among qualifying strikes, pick highest mid-price premium
  5. Build a Recommendation DB row
"""

import logging
import math
from datetime import date, datetime
from typing import Optional

import pandas as pd

from app.models.db_models import Recommendation
from app.models.schemas import SignalSnapshot
from app.services import market_data as md
from app.services.signal_engine import (
    compute_iv_rank,
    compute_smas,
    compute_swing_levels,
    compute_trend,
    compute_signal_strength,
    compute_signal_hash,
)
from app.utils.date_helpers import (
    next_trading_day,
    next_friday,
    nearest_monthly_expiry,
    within_dte_window_for_0dte,
)

logger = logging.getLogger(__name__)

# Delta target ranges for short premium selling
DELTA_MIN = 0.18
DELTA_MAX = 0.35


def _target_expiry(ticker: str, time_horizon: str) -> Optional[str]:
    """
    Return the best available expiry string for the given time horizon.
    Returns None if no suitable expiry exists.
    """
    today = date.today()

    if time_horizon == "0dte":
        if not within_dte_window_for_0dte():
            return None
        expiry = md.get_same_day_expiry(ticker)
        return expiry  # None if today has no options

    if time_horizon == "1dte":
        target = next_trading_day(today)
        return md.get_nearest_expiry(ticker, target)

    if time_horizon == "weekly":
        target = next_friday(today)
        return md.get_nearest_expiry(ticker, target)

    if time_horizon == "monthly":
        target = nearest_monthly_expiry(today, target_dte=30)
        return md.get_nearest_expiry(ticker, target)

    return None


def _best_strike(
    chain_df: pd.DataFrame,
    strategy: str,
    current_price: float,
    nearest_support: Optional[float],
    nearest_resistance: Optional[float],
) -> Optional[pd.Series]:
    """
    Filter options chain for the best strike.

    Rules:
      covered_call — calls, OTM (strike > current_price), delta 0.20-0.35
      cash_secured_put — puts, OTM (strike < current_price), |delta| 0.20-0.35,
                         strike preferably > nearest_support
    Returns the best row or None.
    """
    df = chain_df.copy()

    # Ensure we have a usable delta column
    has_delta = "delta" in df.columns and df["delta"].notna().any()

    if strategy == "covered_call":
        df = df[df["strike"] > current_price]
        if has_delta:
            df = df[(df["delta"] >= DELTA_MIN) & (df["delta"] <= DELTA_MAX)]
        else:
            # Approximate OTM calls: within 5% above current price
            df = df[df["strike"] <= current_price * 1.07]
    elif strategy == "cash_secured_put":
        df = df[df["strike"] < current_price]
        if has_delta:
            df = df[(df["delta"].abs() >= DELTA_MIN) & (df["delta"].abs() <= DELTA_MAX)]
        else:
            df = df[df["strike"] >= current_price * 0.93]

        # Prefer strikes above nearest support to avoid selling below key level
        if nearest_support:
            above_support = df[df["strike"] >= nearest_support * 0.99]
            if not above_support.empty:
                df = above_support

    if df.empty:
        return None

    # Compute mid-price and pick highest premium
    df = df.copy()
    df["mid"] = (df["bid"] + df["ask"]) / 2
    df = df[df["mid"] > 0]

    if df.empty:
        return None

    return df.loc[df["mid"].idxmax()]


def build_recommendation(
    device_id: int,
    ticker: str,
    strategy: str,
    time_horizon: str,
    snapshot: Optional[SignalSnapshot] = None,
) -> Optional[Recommendation]:
    """
    Build a Recommendation ORM object for the given parameters.
    Returns None if not enough data exists.
    """
    # 1. Get signal snapshot (reuse if provided to avoid duplicate yfinance calls)
    if snapshot is None:
        df = md.get_price_history(ticker)
        if df is None:
            return None
        current_price = md.get_current_price(ticker)
        if current_price is None:
            return None
        iv_rank = compute_iv_rank(df)
        smas = compute_smas(df)
        swing = compute_swing_levels(df)
        trend = compute_trend(current_price, smas["sma_50"], smas["sma_200"])
        earnings_date = md.get_earnings_date(ticker)
        nearest_support = swing["nearest_support"]
        nearest_resistance = swing["nearest_resistance"]
        sma_50 = smas["sma_50"]
        sma_200 = smas["sma_200"]
    else:
        current_price = snapshot.current_price
        iv_rank = snapshot.iv_rank
        sma_50 = snapshot.sma_50
        sma_200 = snapshot.sma_200
        nearest_support = snapshot.nearest_support
        nearest_resistance = snapshot.nearest_resistance
        trend = snapshot.trend
        earnings_date = snapshot.earnings_date

    # 2. Determine target expiry
    expiry_str = _target_expiry(ticker, time_horizon)
    if expiry_str is None:
        logger.debug(f"No expiry available for {ticker} {time_horizon}")
        return None

    expiry_date = date.fromisoformat(expiry_str)

    # 3. Earnings warning
    earnings_warning = bool(earnings_date and earnings_date <= expiry_date)

    # 4. Fetch options chain
    chain = md.get_options_chain(ticker, expiry_str)
    if chain is None:
        return None

    chain_df = chain["calls"] if strategy == "covered_call" else chain["puts"]

    # 5. Pick best strike
    best = _best_strike(chain_df, strategy, current_price, nearest_support, nearest_resistance)
    if best is None:
        logger.debug(f"No qualifying strike for {ticker} {strategy} {time_horizon}")
        return None

    strike = float(best["strike"])
    mid_price = float((best["bid"] + best["ask"]) / 2)
    delta = float(best["delta"]) if not math.isnan(best.get("delta", float("nan"))) else _estimate_delta(strike, current_price, strategy)
    theta = float(best["theta"]) if not math.isnan(best.get("theta", float("nan"))) else 0.0

    # 6. Signal strength
    signal_strength = compute_signal_strength(
        iv_rank=iv_rank,
        trend=trend,
        strategy=strategy,
        nearest_support=nearest_support,
        nearest_resistance=nearest_resistance,
        recommended_strike=strike,
        earnings_warning=earnings_warning,
    )

    # 7. Signal hash
    sig_hash = compute_signal_hash(
        iv_rank=iv_rank,
        sma_50=sma_50,
        sma_200=sma_200,
        nearest_support=nearest_support,
        nearest_resistance=nearest_resistance,
        recommended_strike=strike,
        recommended_expiration=expiry_date,
        earnings_warning=earnings_warning,
    )

    # 8. Build human-readable rationale
    rationale = _build_rationale(
        strategy=strategy,
        time_horizon=time_horizon,
        current_price=current_price,
        strike=strike,
        iv_rank=iv_rank,
        trend=trend,
        earnings_warning=earnings_warning,
        earnings_date=earnings_date,
        signal_strength=signal_strength,
    )

    return Recommendation(
        device_id=device_id,
        ticker=ticker,
        strategy=strategy,
        time_horizon=time_horizon,
        current_price=current_price,
        recommended_strike=strike,
        recommended_expiration=expiry_date,
        estimated_premium=mid_price,
        delta=delta,
        theta=theta,
        iv_rank=iv_rank,
        signal_strength=signal_strength,
        earnings_warning=earnings_warning,
        earnings_date=earnings_date,
        rationale=rationale,
        signal_hash=sig_hash,
        created_at=datetime.utcnow(),
    )


def _estimate_delta(strike: float, current_price: float, strategy: str) -> float:
    """Rough delta estimate when yfinance doesn't provide it."""
    moneyness = (strike - current_price) / current_price
    if strategy == "covered_call":
        return max(0.05, 0.50 - moneyness * 3)
    else:
        return max(0.05, 0.50 - abs(moneyness) * 3)


def _build_rationale(
    strategy: str,
    time_horizon: str,
    current_price: float,
    strike: float,
    iv_rank: float,
    trend: str,
    earnings_warning: bool,
    earnings_date: Optional[date],
    signal_strength: str,
) -> str:
    parts = []

    strategy_label = "Covered Call" if strategy == "covered_call" else "Cash-Secured Put"
    direction = "above" if strategy == "covered_call" else "below"

    parts.append(
        f"{signal_strength.capitalize()} {strategy_label} setup. "
        f"Strike ${strike:.2f} is {direction} current price ${current_price:.2f}."
    )

    if iv_rank >= 50:
        parts.append(f"IV rank is elevated at {iv_rank:.0f}% — favorable premium-selling environment.")
    elif iv_rank >= 30:
        parts.append(f"IV rank is moderate at {iv_rank:.0f}%.")
    else:
        parts.append(f"IV rank is low at {iv_rank:.0f}% — premiums may be thin.")

    trend_map = {"uptrend": "bullish uptrend", "downtrend": "bearish downtrend", "sideways": "sideways range"}
    parts.append(f"Price is in a {trend_map.get(trend, trend)}.")

    if earnings_warning and earnings_date:
        parts.append(
            f"⚠️ Earnings on {earnings_date} fall within this expiry — elevated risk. Consider a shorter expiry."
        )

    horizon_map = {"0dte": "0DTE", "1dte": "1DTE", "weekly": "weekly", "monthly": "monthly"}
    parts.append(f"Horizon: {horizon_map.get(time_horizon, time_horizon)}.")

    return " ".join(parts)