"""Trace where V3/V5 maximum drawdowns occur and what holdings they had."""
from __future__ import annotations

import os
import sys
from itertools import product

import numpy as np
import pandas as pd

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from research.trend_rider_robustness import (
    load_price_panel,
    portfolio_returns,
)
from strategies.permanent import TrendRiderV3
from strategies.trend_rider_v5 import TrendRiderV5


def trace(name: str, weights: pd.DataFrame, prices: pd.DataFrame,
          start: str = "2015-01-02") -> None:
    rets = portfolio_returns(weights, prices[weights.columns], 0.001)
    rets = rets[rets.index >= start]
    eq = (1 + rets).cumprod()
    dd = eq / eq.cummax() - 1
    trough = dd.idxmin()
    peak = eq.loc[:trough].idxmax()
    recover = eq.loc[trough:][eq.loc[trough:] >= eq.loc[peak]]
    rec_dt = recover.index[0] if len(recover) else None

    print(f"\n=== {name} ===")
    print(f"  MDD = {dd.min()*100:.2f}%")
    print(f"  Peak  : {peak.date()}  equity={eq.loc[peak]:.3f}")
    print(f"  Trough: {trough.date()}  equity={eq.loc[trough]:.3f}")
    print(f"  Recovered: {rec_dt.date() if rec_dt is not None else 'NOT YET'}")
    print(f"  Days to trough: {(trough - peak).days}")

    # Show holdings around the drawdown
    print(f"\n  Holdings 5 days before peak through 5 days after trough:")
    sl = weights.loc[peak - pd.Timedelta(days=10): trough + pd.Timedelta(days=10)]
    nonzero = (sl != 0).any(axis=0)
    sl = sl.loc[:, nonzero]
    sl_disp = sl.copy()
    # Show only days when holdings change
    changes = (sl_disp != sl_disp.shift(1)).any(axis=1)
    sl_disp = sl_disp.loc[changes]
    print(sl_disp.round(3).head(40).to_string())


def main() -> None:
    prices = load_price_panel()
    print(f"Panel: {prices.index.min().date()} to {prices.index.max().date()}")

    candidates = {
        "V3 default": TrendRiderV3(),
        "V5 default (panic 1.6/4%)": TrendRiderV5(),
        "V5 panic 1.8/5%": TrendRiderV5(panic_vol_ratio=1.8, panic_peak_drop_pct=0.05),
    }
    for name, strat in candidates.items():
        w = strat.generate_signals(prices)
        trace(name, w, prices)


if __name__ == "__main__":
    main()