"""SKH_R_EXPAND_study — final study() on the two best volatility-expansion configs.

We use the project's HONEST study() harness. Because the expansion regime is a STRUCTURAL
change (not expressible via SkyhookParams bands), we monkeypatch htf_features INSIDE
skyhooklib's namespace to our expansion-features for the duration of each study, so study()
runs the exact same leak-free FULL+HOLDOUT+fee-sweep+per-year machinery on our entries.

This is safe: we only swap the feature builder (regime def); pattern/composer/entry/exit and
all the eval code are unchanged. We restore the original after each study.
"""
from __future__ import annotations

import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/skyhook")

import numpy as np
import pandas as pd

import skyhooklib as sk
from src.strategies import skyhook as S
from src.strategies.skyhook import SkyhookParams

# import our feature builder
from SKH_R_EXPAND import expand_htf_features

ORIG_FEAT = S.htf_features


def make_patched_features(cfg):
    def _feat(htf, p):
        return expand_htf_features(htf, p, **cfg)
    return _feat


def study_expand(name, p, cfg):
    """Run sk.study with htf_features patched to the expansion regime defined by cfg."""
    patched = make_patched_features(cfg)
    # skyhook_entries calls skyhook.htf_features via the module-level name S.htf_features.
    S.htf_features = patched
    try:
        rep = sk.study(name, p)
        caus = sk.causality(p, "BTC")
        caus_eth = sk.causality(p, "ETH")
        marg = sk.marginal(p)
    finally:
        S.htf_features = ORIG_FEAT
    return rep, (caus, caus_eth), marg


if __name__ == "__main__":
    p = SkyhookParams(ptn_n=55, sl_atr=2.5, tp_atr=6.0)

    configs = {
        # best DD-cut + above-V1 full, balanced volume gate (maxDD 34%)
        "EXP_atr20vol20": dict(w_atr=20, k_atr=1.0, w_vol=20, k_vol=1.0),
        # best hold-out / no volume gate (minFull 0.81, DD 40%)
        "EXP_atr20volOFF": dict(w_atr=20, k_atr=1.0, w_vol=20, k_vol=0.0),
    }

    for name, cfg in configs.items():
        print(f"\n########## {name}  cfg={cfg} ##########")
        rep, (caus, caus_eth), marg = study_expand(name, p, cfg)
        print(sk.fmt(rep))
        print(f"causality BTC={caus} ETH={caus_eth}")
        print(f"marginal: verdict={marg.get('marginal_verdict')} corr_full={marg.get('corr_full')}"
              f" blend_w25_uplift_hold={marg.get('blends',{}).get('w25',{}).get('uplift_hold')}")
        print(f"          has_insample_edge={marg.get('has_insample_edge')} is_hedge={marg.get('is_hedge')}"
              f" robust_oos={marg.get('robust_oos')} clean_year_uplift={marg.get('clean_year_uplift')}"
              f" multicut_persistent={marg.get('multicut_persistent')}")
        v = rep["verdict"]
        print(f"VERDICT[{name}]: grade={v['grade']} minFull={v['min_asset_full_sharpe']}"
              f" minHold={v['min_asset_holdout_sharpe']} minTrades={v['min_trades']} feeOK={v['fee_survives']}")