"""VaR service — Black-Scholes delta approach with numpy/scipy (no numba dependency)."""

from __future__ import annotations

import numpy as np
import pandas as pd
from scipy.stats import norm
from datetime import datetime, timedelta
from typing import List, Dict

from .database import get_conn


# ─── Option strategy → (type, directional multiplier) ───────────────────────

def _parse_strategy(strategy: str) -> tuple[str, float]:
    """Return (option_type, direction_sign) from strategy name."""
    s = strategy.lower()
    if "straddle" in s or "strangle" in s:
        return "straddle", (1.0 if "long" in s else -1.0)
    if "iron condor" in s or "butterfly" in s or "neutral" in s:
        return "neutral", 0.0
    if "bull" in s:
        return "call", 0.5
    if "bear" in s:
        return "put", -0.5
    if "call" in s:
        return "call", (1.0 if "long" in s else -1.0)
    if "put" in s:
        return "put", (1.0 if "long" in s else -1.0)
    return "call", 0.5  # default


def _bs_delta(S: float, K: float, T_days: float, sigma: float, opt_type: str, direction: float) -> float:
    """Black-Scholes delta, direction-adjusted."""
    r = 0.05
    T = max(T_days, 1) / 252.0
    d1 = (np.log(S / K) + (r + 0.5 * sigma**2) * T) / (sigma * np.sqrt(T))
    if opt_type == "call":
        raw = float(norm.cdf(d1))
    elif opt_type == "put":
        raw = float(norm.cdf(d1) - 1.0)
    elif opt_type == "straddle":
        # Long straddle: net delta ≈ 0 ATM; represent as small residual
        raw = float(norm.cdf(d1) + (norm.cdf(d1) - 1.0))  # ≈ 0
    else:
        raw = 0.0
    return raw * direction


# ─── Market data ─────────────────────────────────────────────────────────────

def _fetch_returns(tickers: List[str], lookback: int) -> pd.DataFrame:
    """Download historical daily returns via yfinance. Returns {} on failure."""
    valid = [t for t in tickers if ":" not in t]
    if not valid:
        return pd.DataFrame()
    try:
        import yfinance as yf
        end = datetime.now()
        start = end - timedelta(days=lookback + 60)
        raw = yf.download(valid, start=start, end=end, progress=False, auto_adjust=True)
        if raw.empty:
            return pd.DataFrame()
        close = raw["Close"] if len(valid) > 1 else raw[["Close"]].rename(columns={"Close": valid[0]})
        return close.pct_change().dropna().tail(lookback)
    except Exception:
        return pd.DataFrame()


def _synthetic_returns(tickers: List[str], lookback: int, seed: int = 42) -> pd.DataFrame:
    """Fallback: simulate realistic returns when market data unavailable."""
    rng = np.random.default_rng(seed)
    idx = pd.date_range(end=datetime.now(), periods=lookback, freq="B")
    data = {t: rng.normal(0.0002, 0.018, lookback) for t in tickers}
    return pd.DataFrame(data, index=idx)


# ─── Core VaR computation ────────────────────────────────────────────────────

def compute_var(
    confidence: float = 0.95,
    horizon_days: int = 1,
    lookback_days: int = 252,
    default_iv: float = 0.20,
) -> Dict:
    conn = get_conn()
    rows = conn.execute(
        "SELECT underlying, strategy, entry_price, capital_invested, "
        "strike_guidance, expiry_days_at_entry, pattern_name "
        "FROM trade_entry_prices WHERE status = 'open'"
    ).fetchall()

    if not rows:
        return {"error": "Aucune position ouverte"}

    positions = [dict(r) for r in rows]

    # Filter positions usable for delta calc
    valid = [
        p for p in positions
        if p.get("underlying") and ":" not in (p["underlying"] or "")
        and p.get("entry_price") and p["entry_price"] > 0
    ]

    if not valid:
        return {"error": "Aucune position avec données de marché disponibles"}

    tickers = list({p["underlying"] for p in valid})

    # Fetch or synthesize returns
    returns_df = _fetch_returns(tickers, lookback_days)
    data_source = "live"
    if returns_df.empty:
        returns_df = _synthetic_returns(tickers, lookback_days)
        data_source = "simulated"

    # Align to available history
    n = len(returns_df)

    # Build delta-weighted portfolio PnL series
    weighted_pnl = pd.Series(0.0, index=returns_df.index)
    total_notional = 0.0
    pos_details = []

    for p in valid:
        ticker = p["underlying"]
        if ticker not in returns_df.columns:
            continue

        S = float(p["entry_price"])
        T = float(p.get("expiry_days_at_entry") or 60)
        capital = float(p.get("capital_invested") or S)
        strategy = p.get("strategy") or "Long Call"
        opt_type, direction = _parse_strategy(strategy)

        # Strike: ATM unless guidance specifies otherwise
        K = S

        delta = _bs_delta(S, K, T, default_iv, opt_type, direction)

        weighted_pnl += returns_df[ticker] * delta * capital
        total_notional += capital

        pos_details.append({
            "ticker": ticker,
            "pattern": p.get("pattern_name") or "",
            "strategy": strategy,
            "delta": round(delta, 4),
            "notional": round(capital, 2),
        })

    if total_notional == 0:
        return {"error": "Notionnel total nul"}

    portfolio_pnl = (weighted_pnl / total_notional).dropna()
    pnl = portfolio_pnl.values.astype(float)
    alpha = 1.0 - confidence

    # ── Historical VaR ──
    hist_var_1d = float(np.percentile(pnl, alpha * 100))
    hist_var_nd = hist_var_1d * np.sqrt(horizon_days)
    tail = pnl[pnl <= hist_var_1d]
    hist_cvar = float(np.mean(tail)) if len(tail) > 0 else hist_var_1d

    # ── Parametric VaR (Gaussian) ──
    mu = float(np.mean(pnl))
    sigma = float(np.std(pnl))
    z = float(norm.ppf(alpha))
    param_var_1d = mu + z * sigma
    param_var_nd = param_var_1d * np.sqrt(horizon_days)
    # ES closed-form: μ − σ·φ(z)/α
    param_cvar = mu - sigma * norm.pdf(-z) / alpha

    # ── Monte Carlo (stressed: vol × 1.5) ──
    rng = np.random.default_rng(42)
    stressed_sigma = sigma * 1.5
    mc_draws = rng.normal(mu, stressed_sigma, 10_000)
    mc_var_1d = float(np.percentile(mc_draws, alpha * 100))
    mc_var_nd = mc_var_1d * np.sqrt(horizon_days)
    mc_tail = mc_draws[mc_draws <= mc_var_1d]
    mc_cvar = float(np.mean(mc_tail)) if len(mc_tail) > 0 else mc_var_1d

    # ── Rolling 30-day Historical VaR ──
    rolling_var = []
    for i in range(30, n):
        w = pnl[i - 30:i]
        rolling_var.append({
            "date": portfolio_pnl.index[i].strftime("%Y-%m-%d"),
            "var_95": round(float(np.percentile(w, 5)) * 100, 4),
        })
    rolling_var = rolling_var[-90:]  # last 90 data points max

    # ── Returns histogram ──
    counts, edges = np.histogram(pnl * 100, bins=30)
    histogram = [
        {"x": round(float((edges[i] + edges[i + 1]) / 2), 4), "count": int(counts[i])}
        for i in range(len(counts))
    ]

    # ── Backtest (Kupiec test) ──
    n_breaches = int(np.sum(pnl < hist_var_1d))
    breach_rate = round(n_breaches / n * 100, 2) if n > 0 else 0.0

    # ── VaR in EUR (based on total notional) ──
    def pct_to_eur(pct_val: float) -> float:
        return round(pct_val / 100 * total_notional, 2)

    return {
        "var": {
            "historical": {
                "var_1d_pct": round(hist_var_1d * 100, 3),
                "var_nd_pct": round(hist_var_nd * 100, 3),
                "cvar_pct": round(hist_cvar * 100, 3),
                "var_1d_eur": pct_to_eur(hist_var_1d * 100),
                "var_nd_eur": pct_to_eur(hist_var_nd * 100),
                "cvar_eur": pct_to_eur(hist_cvar * 100),
            },
            "parametric": {
                "var_1d_pct": round(param_var_1d * 100, 3),
                "var_nd_pct": round(param_var_nd * 100, 3),
                "cvar_pct": round(param_cvar * 100, 3),
                "var_1d_eur": pct_to_eur(param_var_1d * 100),
                "var_nd_eur": pct_to_eur(param_var_nd * 100),
                "cvar_eur": pct_to_eur(param_cvar * 100),
            },
            "monte_carlo": {
                "var_1d_pct": round(mc_var_1d * 100, 3),
                "var_nd_pct": round(mc_var_nd * 100, 3),
                "cvar_pct": round(mc_cvar * 100, 3),
                "var_1d_eur": pct_to_eur(mc_var_1d * 100),
                "var_nd_eur": pct_to_eur(mc_var_nd * 100),
                "cvar_eur": pct_to_eur(mc_cvar * 100),
                "stressed": True,
            },
        },
        "portfolio": {
            "total_notional_eur": round(total_notional, 2),
            "n_positions": len(pos_details),
            "horizon_days": horizon_days,
            "confidence_pct": round(confidence * 100, 1),
            "lookback_days": n,
            "data_source": data_source,
        },
        "positions": pos_details,
        "rolling_var": rolling_var,
        "histogram": histogram,
        "backtest": {
            "n_observations": n,
            "n_breaches": n_breaches,
            "breach_rate_pct": breach_rate,
            "expected_breach_rate_pct": round(alpha * 100, 1),
            "kupiec_ok": breach_rate <= alpha * 100 * 2,
        },
    }