import sys; sys.path.insert(0,".")
import numpy as np, pandas as pd
from scripts.analysis.regime_lab import load_features
from scripts.analysis.explore_lab import atr

FEE=0.001; LEV=3

def build(df, gate, k=2.5, sl_atr=2.0, mb=24, bb=50):
    c=df['close'].values; a=atr(df,14)
    ma=pd.Series(c).rolling(bb).mean().values; sd=pd.Series(c).rolling(bb).std().values
    ent=[]
    for i in range(bb+14,len(c)-1):
        if np.isnan(sd[i]) or sd[i]==0 or np.isnan(a[i]): continue
        if not gate(df,i): continue
        if c[i]<ma[i]-k*sd[i]: d,sl=1,c[i]-sl_atr*a[i]
        elif c[i]>ma[i]+k*sd[i]: d,sl=-1,c[i]+sl_atr*a[i]
        else: continue
        ent.append({'i':i,'d':d,'tp':ma[i],'sl':sl,'mb':mb})
    return ent

def per_year(df, ent):
    """replay intrabar fedele (sl-first, tp, poi max_bars@close) -> per anno {n,ret%,win%}."""
    h=df['high'].values; l=df['low'].values; c=df['close'].values
    ts=pd.to_datetime(df['timestamp'],unit='ms',utc=True)
    Y={}
    last=-1
    for e in ent:
        i=e['i']
        if i<=last: continue
        d=e['d']; tp=e['tp']; sl=e['sl']; j=min(i+e['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
        last=j; yr=ts.iloc[i].year
        if yr not in Y: Y[yr]=[0,0.0,0]
        Y[yr][0]+=1; Y[yr][1]+=ret*100; Y[yr][2]+= (ret>0)
    return Y

# gate functions
def g_hurst_calm(df,i): return df['hurst'].iloc[i]<0.55 and not np.isnan(df['dvol_pct'].iloc[i]) and df['dvol_pct'].iloc[i]<0.40
def g_vrp_neg(df,i):    return not np.isnan(df['vrp'].iloc[i]) and df['vrp'].iloc[i]<0
def g_hig_vrp(df,i):
    hi=df['higuchi'].iloc[i]; return (not np.isnan(hi)) and hi>1.5 and (not np.isnan(df['vrp'].iloc[i])) and df['vrp'].iloc[i]<0
def g_none(df,i): return True

STRATS=[("HurstCalmFade (hurst<.55 & DVOL<p40)",g_hurst_calm),
        ("VRP<0 Fade (core driver)",g_vrp_neg),
        ("HigVRP Fade (Higuchi>1.5 & VRP<0)",g_hig_vrp),
        ("Fade NUDA (no gate, baseline)",g_none)]

# regime mercato BTC per anno (da BTC close annuale)
btc=load_features("BTC","1d")
bts=pd.to_datetime(btc['timestamp'],unit='ms',utc=True); bc=btc['close'].values
mkt={}
for yr in range(2021,2027):
    m=(bts.dt.year==yr).values
    if m.sum()>5:
        r=(bc[m][-1]/bc[m][0]-1)*100
        mkt[yr]=("BULL" if r>40 else "BEAR" if r<-30 else "RANGE", r)

for asset in ("BTC","ETH"):
    df=load_features(asset,"1h")
    print(f"\n{'='*78}\n  {asset} 1h — performance per anno (somma ret% per-trade, netto leva3x+fee0.10%)\n{'='*78}")
    print(f"  {'Strategia':<40} " + " ".join(f"{y}" for y in range(2021,2027)))
    for name,g in STRATS:
        ent=build(df,g); Y=per_year(df,ent)
        cells=[]
        for y in range(2021,2027):
            if y in Y and Y[y][0]>0:
                cells.append(f"{Y[y][1]:+5.0f}")
            else: cells.append("   . ")
        print(f"  {name:<40} " + " ".join(cells))
    # riga trades/anno per la strategia principale
    ent=build(df,g_hurst_calm); Y=per_year(df,ent)
    tr=" ".join(f"{Y.get(y,[0])[0]:>5}" for y in range(2021,2027))
    print(f"  {'(HurstCalmFade trades/anno)':<40} {tr}")

print(f"\n  REGIME MERCATO BTC per anno (ret% annuale prezzo):")
for y in range(2021,2027):
    if y in mkt: print(f"    {y}: {mkt[y][0]:6} ({mkt[y][1]:+.0f}%)")