import pandas as pd
import matplotlib.pyplot as plt
import math


RED = "\033[91m"
GREEN = "\033[92m"
RESET = "\033[0m"

def colorize(value):
    if isinstance(value, (int, float)):
        color = GREEN if value >= 0 else RED
        return f"{color}{value}{RESET}"
    return value
# ========================= CONFIGURACIÓN GENERAL =========================

PATH_UBUNTU = "/var/www/html/flask_project/"

desde = "2025-04-01"
hasta = "2025-12-29"
symbol = "SPX"
timeHour = "1245"
desplazamiento = -20
estrategia = "Vertical"

# ===== NUEVA VARIABLE CLAVE =====
CREDITO_TARGET = 180   # en dólares (ej: 50 = 0.50 de crédito)

elArchivo = f"/var/www/html/backtestingmarket/predictor_data/makekos/{symbol}/{symbol}_{estrategia}_strikes_{timeHour}.csv"

# ========================= FUNCIONES =========================

def buscar_entrada_por_credito(
    archivo_option_chain,
    strike1,
    strike2,
    option_type,
    entry_time,
    credito_target
):
    """
    Busca el PRIMER snapshot >= entry_time
    donde el crédito del spread >= credito_target.
    Devuelve:
        credit_entry (float),
        entry_timestamp (datetime)
    o (None, None) si nunca se alcanza.
    """
    try:
        df = pd.read_csv(archivo_option_chain)
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        df = df[df['timestamp'] >= entry_time]
    except:
        return None, None

    for ts, snap in df.groupby('timestamp'):
        s1 = snap[snap['strike'] == strike1]
        s2 = snap[snap['strike'] == strike2]

        if s1.empty or s2.empty:
            continue

        if option_type == "CALL":
            p1 = (s1['bid_call'].iloc[0] + s1['ask_call'].iloc[0]) / 2
            p2 = (s2['bid_call'].iloc[0] + s2['ask_call'].iloc[0]) / 2
        else:
            p1 = (s1['bid_put'].iloc[0] + s1['ask_put'].iloc[0]) / 2
            p2 = (s2['bid_put'].iloc[0] + s2['ask_put'].iloc[0]) / 2

        credit_dollars = (p1 - p2) * 100

        if credit_dollars >= credito_target:
            return p1 - p2, ts

    return None, None


def obtener_ultimo_underlying_price(archivo_option_chain):
    try:
        df = pd.read_csv(archivo_option_chain)
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        return df.sort_values('timestamp')['underlying_price'].iloc[-1]
    except:
        return None


# ========================= BACKTEST PRINCIPAL =========================

def backtest_credito_target(df_strikes):

    resultados = []
    symbol = df_strikes['Symbol'].iloc[0]

    df_strikes['Day'] = pd.to_datetime(df_strikes['Day'])
    df_strikes = df_strikes[(df_strikes['Day'] >= desde) & (df_strikes['Day'] <= hasta)].copy()

    df_strikes['Strike1_Desplazado'] = df_strikes.apply(
        lambda r: r['Strike1'] + desplazamiento if r['Option_Type'] == 'CALL'
        else r['Strike1'] - desplazamiento, axis=1
    )
    df_strikes['Strike2_Desplazado'] = df_strikes.apply(
        lambda r: r['Strike2'] + desplazamiento if r['Option_Type'] == 'CALL'
        else r['Strike2'] - desplazamiento, axis=1
    )

    for _, r in df_strikes.iterrows():

        fecha = r['Day'].strftime("%Y-%m-%d")
        hora = r['Hour']
        strike1 = r['Strike1_Desplazado']
        strike2 = r['Strike2_Desplazado']
        option_type = r['Option_Type']
        entry_time = pd.to_datetime(f"{r['Day']} {hora}")

        if symbol in ["SPX", "RUT", "XSP"]:
            archivo = f"{PATH_UBUNTU}chains/optionChain_${symbol}_{fecha}.csv"
        else:
            archivo = f"{PATH_UBUNTU}chains/optionChain_{symbol}_{fecha}.csv"

        credit_entry, entry_ts = buscar_entrada_por_credito(
            archivo,
            strike1,
            strike2,
            option_type,
            entry_time,
            CREDITO_TARGET
        )

        close_price = obtener_ultimo_underlying_price(archivo)

        # ===== SI NO HUBO ENTRADA =====
        if credit_entry is None:
            resultados.append({
                "Day": fecha,
                "Time": hora,
                "Strike1": strike1,
                "Strike2": strike2,
                "Option": option_type,
                "EntryTime": None,
                "EntryCredit": None,
                "Exit": "NO_ENTRY",
                "P/L": 0.0
            })
            continue

        # ===== SI HUBO ENTRADA =====
        spread = abs(strike1 - strike2)

        if option_type == "CALL":
            strike_venta = min(strike1, strike2)
            strike_compra = max(strike1, strike2)

            if close_price > strike_compra:
                pnl = (credit_entry - spread) * 100
            elif close_price < strike_venta:
                pnl = credit_entry * 100
            else:
                pnl = credit_entry * 100 - (close_price - strike_venta) * 100

        else:  # PUT
            strike_venta = max(strike1, strike2)
            strike_compra = min(strike1, strike2)

            if close_price < strike_compra:
                pnl = (credit_entry - spread) * 100
            elif close_price > strike_venta:
                pnl = credit_entry * 100
            else:
                pnl = credit_entry * 100 - (strike_venta - close_price) * 100

        resultados.append({
            "Day": fecha,
            "Time": hora,
            "Strike1": strike1,
            "Strike2": strike2,
            "Option": option_type,
            "EntryTime": entry_ts,
            "EntryCredit": round(credit_entry * 100, 2),
            "Exit": "CLOSE",
            "P/L": round(pnl, 2)
        })

    df = pd.DataFrame(resultados)
    return df


# ========================= EJECUCIÓN =========================

df_strikes = pd.read_csv(elArchivo)

df_resultados = backtest_credito_target(df_strikes)

profit_total = df_resultados['P/L'].sum()
wins = (df_resultados['P/L'] > 0).sum()
losses = (df_resultados['P/L'] < 0).sum()
no_trades = (df_resultados['Exit'] == "NO_ENTRY").sum()

df_console = df_resultados.copy()
df_console['P/L'] = df_console['P/L'].apply(colorize)

print(df_console.to_string(index=False))
print("\n================ RESUMEN =================")
print(f"Profit Total: ${profit_total:.2f}")
print(f"Wins: {wins} | Losses: {losses}")
print(f"No Trade Days: {no_trades}")
print(f"Credito Target: ${CREDITO_TARGET}")

# ========================= GUARDAR CSV =========================

df_resultados.to_csv(
    f"resultados/P&L_{estrategia}_{symbol}_{timeHour}_{desde.replace('-','')}_{hasta.replace('-','')}_D{desplazamiento}_CT_{CREDITO_TARGET}.csv",
    index=False
)