from typing import List

from . import calc
from .payoffs import PayoffContext
from .report import generate_report


def clone_context(original: PayoffContext) -> PayoffContext:
    return PayoffContext(
        days_to_maturity=original.days_to_maturity,
        r=original.r,
        capital_value=original.capital_value,
        prot_min_val=original.prot_min_val,
        pdi_barrier=original.pdi_barrier,
        pdi_style=original.pdi_style,
        coupon_in_memory=original.coupon_in_memory,
        days_to_obs_y_fract=list(original.days_to_obs_y_fract),
        coupon_triggers=list(original.coupon_triggers),
        coupon_values=list(original.coupon_values),
        autocall_triggers=list(original.autocall_triggers),
        autocall_values=list(original.autocall_values),
        memory_flags=list(original.memory_flags),
        caso=original.caso,
        pdi_strike=original.pdi_strike,
        fattore_airbag=original.fattore_airbag,
        trigger_one_star=original.trigger_one_star,
        cap=original.cap,
        days_to_obs=list(original.days_to_obs),
        airbag=original.airbag,
        sigma=original.sigma,
        relief=original.relief,
        twin_win=original.twin_win,
        one_star=original.one_star,
        leva=original.leva,
        volatility=list(original.volatility),
        prices_ul=list(original.prices_ul),
        nominal_amount=original.nominal_amount,
    )


def compare_fair_values(
    num_simulations: int,
    prices_ul: List[float],
    corr_matrix: List[List[float]],
    num_assets: int,
    days_to_maturity: int,
    rate: float,
    dividends: List[float],
    context: PayoffContext,
    nominal_amount: float,
    isin: str,
    ask: float,
    bid: float,
    last_date,
    category: str,
) -> None:
    print(f"Motore GBM con payoff engine '{context.caso}'")

    results = calc.simulate_payoffs(
        num_simulations,
        prices_ul,
        corr_matrix,
        days_to_maturity,
        rate,
        dividends,
        context.volatility,
        context.days_to_obs,
        context.days_to_obs_y_fract,
        context.coupon_values,
        context.coupon_triggers,
        context.autocall_values,
        context.autocall_triggers,
        context.memory_flags,
        context.coupon_in_memory,
        context.pdi_style,
        context.pdi_strike,
        context.pdi_barrier,
        context.capital_value,
        context.prot_min_val,
        context.airbag,
        context.sigma,
        context.twin_win,
        context.relief,
        context.fattore_airbag,
        context.one_star,
        context.trigger_one_star,
        context.cap,
    )
    results = [float(val) * 100.0 for val in results]

    mean = sum(results) / num_simulations
    variance = sum((val - mean) ** 2 for val in results) / num_simulations
    stddev = variance ** 0.5

    fair_value_scaled = mean * nominal_amount / 100.0
    fair_values = [int(val * nominal_amount / 100) for val in results]

    generate_report(
        isin=isin,
        category=category,
        fair_value=fair_value_scaled,
        fair_values=fair_values,
        ask=ask,
        bid=bid,
        num_sims=num_simulations,
        last_date_price=last_date,
    )