import sys; sys.path.insert(0,".")
import numpy as np, pandas as pd, importlib
from scripts.analysis.combine_portfolio import IDX, SPLIT, INIT, _norm, metrics, port_returns, build_trades
from src.portfolio.sleeves import all_sleeve_equities
from scripts.analysis.regime_lab import load_features

def load_strat(mod):
    m=importlib.import_module(mod)
    return next(v() for k,v in vars(m).items() if isinstance(v,type) and hasattr(v,'generate_signals') and getattr(v,'__module__','')==m.__name__)
FADES={"MR01":("scripts.strategies.MR01_bollinger_fade",dict(bb_window=50,k=2.5,sl_atr=2.0,max_bars=24,trend_max=3.0)),
       "MR02":("scripts.strategies.MR02_donchian_fade",dict(n=20,sl_atr=2.0,max_bars=24,trend_max=3.0)),
       "MR07":("scripts.strategies.MR07_return_reversal",dict(n=50,k=3.5,tp_atr=2.0,sl_atr=1.5,max_bars=24,trend_max=3.0))}
FEE=0.001; LEV=3; POS=0.15

def fade_equity_filtered(code, asset, hurst_thr=None):
    """equity giornaliera dello sleeve fade, opz. filtrata Hurst<thr (skip hurst>=thr). Convenzione fade_daily_equity."""
    mod,par=FADES[code]; s=load_strat(mod)
    df=load_features(asset,"1h"); ts=pd.to_datetime(df['timestamp'],unit='ms',utc=True)
    h=df['high'].values; l=df['low'].values; c=df['close'].values; hur=df['hurst'].values
    eq=np.full(len(c),INIT,float); cap=INIT; last=-1
    for sg in s.generate_signals(df,ts,**par):
        i=sg.idx
        if i<=last: continue
        if hurst_thr is not None and not np.isnan(hur[i]) and hur[i]>=hurst_thr: continue  # FILTRO
        d=sg.direction; tp=sg.metadata['tp']; sl=sg.metadata['sl']; mb=sg.metadata['max_bars']
        j=min(i+mb,len(c)-1); exit_p=c[j]
        for t in range(i+1,j+1):
            if d==1:
                if l[t]<=sl: exit_p=sl;j=t;break
                if h[t]>=tp: exit_p=tp;j=t;break
            else:
                if h[t]>=sl: exit_p=sl;j=t;break
                if l[t]<=tp: exit_p=tp;j=t;break
        ret=(exit_p-c[i])/c[i]*d*LEV-FEE*LEV
        cap=max(cap+cap*POS*ret,10.0); eq[j:]=cap; last=j
    sser=pd.Series(eq,index=ts).resample("1D").last().reindex(IDX).ffill().bfill()
    return _norm(sser)

base=all_sleeve_equities()
fade_ids=["MR01_BTC","MR02_BTC","MR07_BTC","MR01_ETH","MR02_ETH","MR07_ETH"]

def port(members):
    dr=port_returns(members); return metrics(dr), metrics(dr,lo=SPLIT)

# baseline PORT06
fB,oB=port(base)
print(f"PORT06 baseline (17 sleeve):           FULL Sharpe {fB['sharpe']:.2f} DD {fB['dd']:.2f}%  | OOS Sharpe {oB['sharpe']:.2f} DD {oB['dd']:.2f}% ret {oB['ret']:+.0f}%")

# sostituisci le 6 fade con versione Hurst-skip
for thr in (0.55, 0.50):
    filt=dict(base)
    for fid in fade_ids:
        code,asset=fid.split("_")
        filt[fid]=fade_equity_filtered(code,asset,hurst_thr=thr)
    fF,oF=port(filt)
    print(f"PORT06 + Hurst-skip h<{thr} sulle fade:  FULL Sharpe {fF['sharpe']:.2f} DD {fF['dd']:.2f}%  | OOS Sharpe {oF['sharpe']:.2f} DD {oF['dd']:.2f}% ret {oF['ret']:+.0f}%")