"""S2-02: Funding Rate Strategy.
Quando il funding rate è molto positivo → troppi long → short il perpetual.
Quando molto negativo → troppi short → long il perpetual.
Si cattura sia il mean reversion del prezzo che il funding rate stesso.
Ingresso: quando funding > 0.03% o < -0.03% (8h rate).
"""
from __future__ import annotations
import sys
sys.path.insert(0, ".")

import numpy as np
import pandas as pd
from src.data.downloader import load_data

FEE = 0.001
INITIAL = 1000
LEVERAGE = 3


def simulate_funding_strategy(asset):
    """Simula funding rate strategy usando il proxy: overnight returns.
    Crypto funding settlement ogni 8h → prezzo tende a correggersi dopo settlement.
    Proxy: se ultime 8h hanno avuto forte trend, aspettati reversal dopo settlement.
    """
    print(f"\n{'#'*60}")
    print(f"  {asset} — FUNDING RATE PROXY STRATEGY")
    print(f"{'#'*60}")

    df_1h = load_data(asset, "1h")
    close = df_1h["close"].values
    volume = df_1h["volume"].values
    n = len(close)
    split = int(n * 0.7)

    timestamps = pd.to_datetime(df_1h["timestamp"], unit="ms", utc=True)
    hours = timestamps.dt.hour.values

    # Funding settlement su Deribit: 00:00, 08:00, 16:00 UTC
    settlement_hours = {0, 8, 16}

    configs = [
        (0.01, 0.02, 8, 0.02, "mild_1pct"),
        (0.015, 0.025, 8, 0.015, "moderate_1.5pct"),
        (0.02, 0.03, 8, 0.015, "strong_2pct"),
        (0.01, 0.015, 4, 0.01, "fast_1pct_4h"),
        (0.02, 0.03, 12, 0.02, "slow_2pct_12h"),
        (0.025, 0.04, 6, 0.015, "extreme_2.5pct"),
    ]

    for entry_thr, tp_mult_unused, hold_max, stop, name in configs:
        capital = float(INITIAL)
        correct = 0
        total = 0
        daily_trades = {}

        for i in range(max(split, 8), n - hold_max):
            hour = hours[i]
            if hour not in settlement_hours:
                continue

            day = timestamps[i].strftime("%Y-%m-%d")
            if daily_trades.get(day, 0) >= 1:
                continue

            # 8h return prima del settlement = proxy per funding pressure
            ret_8h = (close[i] - close[i - 8]) / close[i - 8]

            # Volume spike = conferma
            vol_avg = np.mean(volume[max(0, i - 48) : i])
            vol_recent = np.mean(volume[i - 8 : i])
            vol_spike = vol_recent / vol_avg if vol_avg > 0 else 1

            direction = None
            if ret_8h > entry_thr and vol_spike > 1.1:
                direction = "short"  # troppi long, attendi reversal
            elif ret_8h < -entry_thr and vol_spike > 1.1:
                direction = "long"  # troppi short, attendi rimbalzo

            if direction is None:
                continue

            entry_price = close[i]
            for j in range(i + 1, min(i + hold_max + 1, n)):
                price = close[j]
                if direction == "long":
                    pnl_pct = (price - entry_price) / entry_price
                else:
                    pnl_pct = (entry_price - price) / entry_price

                if pnl_pct <= -stop or pnl_pct >= stop * 2 or j == min(i + hold_max, n - 1):
                    exit_price = price
                    break
            else:
                exit_price = close[min(i + hold_max, n - 1)]

            if direction == "long":
                trade_ret = (exit_price - entry_price) / entry_price
            else:
                trade_ret = (entry_price - exit_price) / entry_price

            # Add funding rate income (approx 0.01% per 8h period if direction correct)
            funding_income = 0.0001 * (hold_max / 8) if trade_ret > 0 else 0

            net = (trade_ret + funding_income) * LEVERAGE - FEE * 2 * LEVERAGE
            capital += capital * 0.2 * net
            capital = max(capital, 0)

            total += 1
            if trade_ret > 0:
                correct += 1
            daily_trades[day] = daily_trades.get(day, 0) + 1

        if total < 10:
            continue

        acc = correct / total * 100
        ret = (capital - INITIAL) / INITIAL * 100
        test_days = (n - split) / 24
        test_years = test_days / 365.25
        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
        days_active = len(daily_trades)

        tag = "✅" if acc >= 60 and ann >= 30 else ""
        print(f"  {name:20s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active_days={days_active} {tag}")


for asset in ["ETH", "BTC"]:
    simulate_funding_strategy(asset)