"""
Generic N-leg (1-4) option strategy pricer: entry cost with real broker spread,
scenario repricing at a future horizon on a shocked vol surface, payoff curves,
greeks, and bounded-risk / non-directional checks.

A "leg" dict: {expiry_date, days_to_expiry, strike, option_type ("call"/"put"),
               position ("long"/"short"), quantity}
"""
import math
from typing import Any, Dict, List, Optional

import numpy as np

from services.options_pricer import black_scholes
from services.option_chain import find_quote
from services.vol_surface import Surface, ScenarioSurface

DEFAULT_SPREAD_PCT = 0.05  # fallback relative bid/ask spread when no live quote is found


def _sign(leg: Dict[str, Any]) -> int:
    return 1 if leg.get("position", "long") == "long" else -1


def _intrinsic(S: float, K: float, option_type: str) -> float:
    return max(0.0, S - K) if option_type == "call" else max(0.0, K - S)


def _quote_spread_pct(quote: Optional[Dict[str, Any]]) -> float:
    if not quote or quote["bid"] <= 0 or quote["ask"] <= 0:
        return DEFAULT_SPREAD_PCT
    mid = (quote["bid"] + quote["ask"]) / 2
    if mid <= 0:
        return DEFAULT_SPREAD_PCT
    return (quote["ask"] - quote["bid"]) / mid


def entry_price(leg: Dict[str, Any], chain_slice: Dict[str, Any], surface_now: Surface, r: float) -> Dict[str, float]:
    """Real execution price (crossing the spread) + theoretical mid, for one leg today."""
    quote = find_quote(chain_slice, leg["expiry_date"], leg["strike"], leg["option_type"])
    T = max(leg["days_to_expiry"], 0.001) / 365
    sigma = surface_now.iv_at(leg["strike"], leg["days_to_expiry"])
    theo_mid = black_scholes(chain_slice["spot"], leg["strike"], T, r, sigma, leg["option_type"])["price"]

    if quote and quote["bid"] > 0 and quote["ask"] > 0:
        exec_price = quote["ask"] if leg.get("position", "long") == "long" else quote["bid"]
        mid = quote["mid"] or theo_mid
    else:
        spread = theo_mid * DEFAULT_SPREAD_PCT
        exec_price = theo_mid + spread / 2 if leg.get("position", "long") == "long" else max(0.0, theo_mid - spread / 2)
        mid = theo_mid

    return {"exec_price": exec_price, "mid": mid, "spread_pct": _quote_spread_pct(quote)}


def value_at(
    legs: List[Dict[str, Any]],
    S: float,
    eval_days_from_now: float,
    surface: Any,
    r: float,
) -> float:
    """Signed portfolio value (BS reprice for unexpired legs, intrinsic for expired ones)."""
    total = 0.0
    for leg in legs:
        remaining = leg["days_to_expiry"] - eval_days_from_now
        qty = leg.get("quantity", 1)
        sign = _sign(leg)
        if remaining <= 0:
            price = _intrinsic(S, leg["strike"], leg["option_type"])
        else:
            sigma = surface.iv_at(leg["strike"], remaining)
            price = black_scholes(S, leg["strike"], remaining / 365, r, sigma, leg["option_type"])["price"]
        total += sign * price * qty * 100
    return total


def greeks_at(legs: List[Dict[str, Any]], S: float, eval_days_from_now: float, surface: Any, r: float) -> Dict[str, float]:
    net = {"delta": 0.0, "gamma": 0.0, "theta": 0.0, "vega": 0.0}
    for leg in legs:
        remaining = max(leg["days_to_expiry"] - eval_days_from_now, 0.001)
        qty = leg.get("quantity", 1)
        sign = _sign(leg)
        sigma = surface.iv_at(leg["strike"], remaining)
        g = black_scholes(S, leg["strike"], remaining / 365, r, sigma, leg["option_type"])
        for k in net:
            net[k] += g[k] * qty * sign
    return {k: round(v, 4) for k, v in net.items()}


def price_combo(
    legs: List[Dict[str, Any]],
    chain_slice: Dict[str, Any],
    surface_now: Surface,
    surface_scenario: ScenarioSurface,
    horizon_days: int,
    r: float = 0.05,
) -> Dict[str, Any]:
    spot_now = chain_slice["spot"]
    spot_scenario = surface_scenario.spot

    entry_ref = 0.0
    entry_ref_mid = 0.0
    for leg in legs:
        ep = entry_price(leg, chain_slice, surface_now, r)
        sign = _sign(leg)
        qty = leg.get("quantity", 1)
        entry_ref += sign * ep["exec_price"] * qty * 100
        entry_ref_mid += sign * ep["mid"] * qty * 100

    # Scenario exit: apply each leg's own bid/ask spread (est. from entry quote) to the
    # theoretical scenario value, since we don't have a live quote for the future date.
    scenario_mid = 0.0
    scenario_exec = 0.0
    for leg in legs:
        remaining = max(leg["days_to_expiry"] - horizon_days, 0.001)
        qty = leg.get("quantity", 1)
        sign = _sign(leg)
        sigma = surface_scenario.iv_at(leg["strike"], remaining)
        theo = black_scholes(spot_scenario, leg["strike"], remaining / 365, r, sigma, leg["option_type"])["price"]
        quote = find_quote(chain_slice, leg["expiry_date"], leg["strike"], leg["option_type"])
        spread_pct = _quote_spread_pct(quote)
        exec_price = theo * (1 - spread_pct / 2) if leg.get("position", "long") == "long" else theo * (1 + spread_pct / 2)
        scenario_mid += sign * theo * qty * 100
        scenario_exec += sign * exec_price * qty * 100

    net_pnl = scenario_exec - entry_ref
    broker_cost = (entry_ref - entry_ref_mid) + (scenario_mid - scenario_exec)

    bounded = check_bounded_risk(legs, entry_ref, surface_now, spot_now)
    delta_now = greeks_at(legs, spot_now, 0, surface_now, r)["delta"]
    delta_scenario = greeks_at(legs, spot_scenario, horizon_days, surface_scenario, r)["delta"]

    return {
        "entry_cost": round(entry_ref, 2),
        "entry_cost_mid": round(entry_ref_mid, 2),
        "scenario_value": round(scenario_exec, 2),
        "scenario_value_mid": round(scenario_mid, 2),
        "net_pnl": round(net_pnl, 2),
        "broker_spread_cost": round(broker_cost, 2),
        "max_gain": bounded["max_gain"],
        "max_loss": bounded["max_loss"],
        "bounded_risk": bounded["bounded"],
        "greeks_now": greeks_at(legs, spot_now, 0, surface_now, r),
        "greeks_scenario": greeks_at(legs, spot_scenario, horizon_days, surface_scenario, r),
        "net_delta_now": delta_now,
        "net_delta_scenario": delta_scenario,
    }


def check_bounded_risk(legs: List[Dict[str, Any]], entry_ref: float, surface: Any, spot: float) -> Dict[str, Any]:
    """
    Scan a wide log-spaced spot range at expiry and inspect both tails independently for LOSS
    vs GAIN direction. "Bounded risk" only requires the loss side to be capped — a long
    straddle (loss capped at the premium, gain uncapped) is a textbook risk-bounded,
    non-directional trade and must not be excluded just because its gain is open-ended.

    A tail is loss-bounded if moving further to that extreme does not make the P&L any worse
    than a point already well into that tail; symmetrically for gain-bounded.
    """
    eval_days = min(l["days_to_expiry"] for l in legs)
    grid = np.geomspace(spot * 0.05, spot * 20, 300)
    values = [value_at(legs, float(p), eval_days, surface, 0.05) - entry_ref for p in grid]

    tail_n = max(3, len(values) // 30)
    tol = max(abs(entry_ref), 1.0) * 0.01
    lo_edge, lo_in = values[0], values[tail_n]
    hi_edge, hi_in = values[-1], values[-1 - tail_n]

    loss_bounded = (lo_edge >= lo_in - tol) and (hi_edge >= hi_in - tol)
    gain_bounded = (lo_edge <= lo_in + tol) and (hi_edge <= hi_in + tol)

    return {
        "bounded": loss_bounded,
        "max_loss": round(min(values), 2) if loss_bounded else None,
        "max_gain": round(max(values), 2) if gain_bounded else None,
    }


def payoff_curve_expiry(legs: List[Dict[str, Any]], surface_now: Surface, spot: float, n: int = 100) -> List[Dict[str, float]]:
    eval_days = min(l["days_to_expiry"] for l in legs)
    prices = np.linspace(spot * 0.5, spot * 1.5, n)
    return [
        {"underlying": round(float(p), 2), "pnl": round(float(value_at(legs, float(p), eval_days, surface_now, 0.05)), 2)}
        for p in prices
    ]


def expected_pnl_scenario(
    legs: List[Dict[str, Any]],
    surface_scenario: ScenarioSurface,
    horizon_days: int,
    r: float,
    entry_ref: float,
    n: int = 200,
) -> float:
    """
    Probability-weighted expected P&L at the scenario date: integrates the payoff over a
    risk-neutral lognormal density for the underlying, centered on the scenario spot with a
    variance driven by the scenario surface's ATM IV over the residual time to the nearest
    leg's expiry (the remaining uncertainty once the scenario date is reached).
    """
    spot_scenario = surface_scenario.spot
    remaining = max(min(l["days_to_expiry"] for l in legs) - horizon_days, 1)
    sigma = surface_scenario.iv_at(spot_scenario, remaining)
    T = remaining / 365
    sd = sigma * math.sqrt(T)
    if sd <= 1e-6:
        return value_at(legs, spot_scenario, horizon_days, surface_scenario, r) - entry_ref

    mu = math.log(spot_scenario) + (r - 0.5 * sigma ** 2) * T
    grid = np.geomspace(spot_scenario * 0.15, spot_scenario * 4, n)
    log_grid = np.log(grid)
    density = np.exp(-0.5 * ((log_grid - mu) / sd) ** 2) / (grid * sd * math.sqrt(2 * math.pi))
    pnl = np.array([value_at(legs, float(s), horizon_days, surface_scenario, r) - entry_ref for s in grid])

    numerator = np.trapz(pnl * density, grid)
    denominator = np.trapz(density, grid)
    return float(numerator / denominator) if denominator > 1e-12 else float(pnl.mean())


def payoff_curves(
    legs: List[Dict[str, Any]],
    chain_slice: Dict[str, Any],
    surface_now: Surface,
    surface_scenario: ScenarioSurface,
    horizon_days: int,
    r: float = 0.05,
    n: int = 100,
) -> Dict[str, Any]:
    spot = chain_slice["spot"]
    priced = price_combo(legs, chain_slice, surface_now, surface_scenario, horizon_days, r)
    entry_ref = priced["entry_cost"]

    lo, hi = spot * 0.6, spot * 1.4
    prices = np.linspace(lo, hi, n)
    eval_days_expiry = min(l["days_to_expiry"] for l in legs)

    at_expiry = [
        {"underlying": round(float(p), 2), "pnl": round(float(value_at(legs, float(p), eval_days_expiry, surface_now, r) - entry_ref), 2)}
        for p in prices
    ]
    at_scenario = [
        {"underlying": round(float(p), 2), "pnl": round(float(value_at(legs, float(p), horizon_days, surface_scenario, r) - entry_ref), 2)}
        for p in prices
    ]
    return {"at_expiry": at_expiry, "at_scenario": at_scenario, **priced}