#!/usr/bin/env python3
"""Sizing-Simulation: was bringt 96%-All-in vs. Bruchteil-Margin vs. risiko-basiert
für das LANGFRIST-Wachstum (Endkapital) und den Max-Drawdown?

Modell (skalenfrei, fixe Bruchteil-Sizing → geometrisches Wachstum):
  - Signale: echte _build-Logik (≥55% Konf, M30-Filter, Winkel) — wie live.
  - Exit: SL fix 2,0×ATR + Trailing-TP (= aktuelles Live-Modell), pessimistisch.
  - Pro Trade R (=Profit/ATR) → EUR-Rendite je nach Lots/Margin (echte MT5-Params).
  - Endkapital = Π(1+ret_i) (bei fixer Bruchteil-Sizing ordnungs-UNABHÄNGIG).
  - Max-Drawdown: historische Reihenfolge + Monte-Carlo (Shuffle) für die Verteilung.

Kernfrage: 96%-All-in liegt vermutlich WEIT über dem Kelly-Optimum → höhere
Bruchteil-Sizing gibt mehr Endkapital UND weniger Drawdown (Vola-Drag).
"""
import sys, random, math
import MetaTrader5 as mt5
from core.analysis import calc_trend_angle
from core.wave_rec import (WaveRecommender, _atr, _ema_last, _EMA_FAST, _EMA_SLOW,
                           _N_BARS, _HTF_DEADBAND, _ANGLE_LR)

_MAXH=240; _TRAILON=0.3; _TPTRAIL=0.5; _ATRMIN=0.12
_SL_ATR=2.0           # fixer Initial-SL (Live-Mitte des Bands)
_MARGIN_CAP=0.96      # nie mehr als 96% Margin (auch risiko-basiert gedeckelt)


class _TU:
    def snapshot(self): return {"intervals": {}}
def _ema_series(v,p):
    k=2.0/(p+1); o=[]; e=v[0]
    for i,x in enumerate(v): e=x if i==0 else x*k+e*(1-k); o.append(e)
    return o
def _atr_series(H,L,C,p=14):
    t=[0.0]
    for i in range(1,len(C)): t.append(max(H[i]-L[i],abs(H[i]-C[i-1]),abs(L[i]-C[i-1])))
    return [(sum(t[max(1,i-p+1):i+1])/max(1,len(t[max(1,i-p+1):i+1]))) if i else None for i in range(len(C))]

def simulate(entry,d,atr,sl,H,L,C,j0):
    eff=sl; hw=entry; trail=False
    end=min(j0+_MAXH,len(C)-1); exit_px=C[end]
    for j in range(j0,end+1):
        hi,lo=H[j],L[j]
        if (lo<=eff) if d>0 else (hi>=eff):
            return (eff-entry)*d/atr
        hw=max(hw,hi) if d>0 else min(hw,lo)
        if (C[j]-entry)*d>=_TRAILON*atr: trail=True
        if trail:
            lock=hw-d*_TPTRAIL*atr
            eff=max(eff,lock) if d>0 else min(eff,lock)
    return (exit_px-entry)*d/atr

def curve_stats(rets):
    """Endkapital-Faktor, Wachstum/Trade (mean ln), Max-DD (hist. Reihenfolge)."""
    eq=1.0; peak=1.0; mdd=0.0; gsum=0.0; ruin=False
    for r in rets:
        r=max(r,-0.99)
        eq*=(1+r); gsum+=math.log(max(1+r,1e-9))
        peak=max(peak,eq); dd=1-eq/peak
        if dd>mdd: mdd=dd
        if eq<0.10*1.0: ruin=True
    return eq, gsum/len(rets), mdd, ruin

def mc_maxdd(rets, runs=400):
    """Monte-Carlo: Verteilung des Max-DD über zufällige Trade-Reihenfolgen."""
    dds=[]
    base=list(rets)
    for _ in range(runs):
        random.shuffle(base)
        eq=1.0; peak=1.0; mdd=0.0
        for r in base:
            eq*=(1+max(r,-0.99)); peak=max(peak,eq); mdd=max(mdd,1-eq/peak)
        dds.append(mdd)
    dds.sort()
    p=lambda q: dds[min(len(dds)-1,int(q*len(dds)))]
    over80=sum(1 for x in dds if x>0.80)/len(dds)
    return p(0.50), p(0.95), over80

def main():
    n=int(sys.argv[1]) if len(sys.argv)>1 else 12000
    mt5.initialize()
    sym=None
    for c in ("SpotCrude","USOIL","WTI","XTIUSD"):
        if mt5.symbol_info(c): sym=c; break
    si=mt5.symbol_info(sym); acc=mt5.account_info()
    tick=mt5.symbol_info_tick(sym)
    price_now=(tick.bid+tick.ask)/2
    margin1=mt5.order_calc_margin(mt5.ORDER_TYPE_BUY, sym, 1.0, price_now)
    vpp=(si.trade_tick_value or 1.0)/(si.trade_tick_size or si.point)   # € je 1.0 Preis je Lot
    lev=vpp*price_now/margin1 if margin1 else 0
    bars=mt5.copy_rates_from_pos(sym,mt5.TIMEFRAME_M5,0,n+_N_BARS+_MAXH+5)
    m30=mt5.copy_rates_from_pos(sym,mt5.TIMEFRAME_M30,0,n//6+400)
    mt5.shutdown()
    T=[int(b["time"]) for b in bars]; H=[float(b["high"]) for b in bars]
    L=[float(b["low"]) for b in bars]; C=[float(b["close"]) for b in bars]
    mT=[int(b["time"]) for b in m30]; mc=[float(b["close"]) for b in m30]
    mh=[float(b["high"]) for b in m30]; ml=[float(b["low"]) for b in m30]
    mEf=_ema_series(mc,_EMA_FAST); mEs=_ema_series(mc,_EMA_SLOW); mA=_atr_series(mh,ml,mc)
    def m30s(ts):
        lo,hi,idx=0,len(mT)-1,-1
        while lo<=hi:
            md=(lo+hi)//2
            if mT[md]<=ts: idx=md; lo=md+1
            else: hi=md-1
        if idx<_EMA_SLOW or mA[idx] is None or mA[idx]<=0: return 0
        dd=mEf[idx]-mEs[idx]
        return 0 if abs(dd)<_HTF_DEADBAND*mA[idx] else (1 if dd>0 else -1)

    w=WaveRecommender(_TU(), mt5.TIMEFRAME_M5)
    trades=[]  # (price_i, atr_i, R_i)
    for i in range(_N_BARS, len(C)-_MAXH-1):
        wc=C[i-_N_BARS:i]; wh=H[i-_N_BARS:i]; wl=L[i-_N_BARS:i]
        atr=_atr(wh,wl,wc)
        if not atr or atr<=0: continue
        ef=_ema_last(wc,_EMA_FAST); es=_ema_last(wc,_EMA_SLOW)
        ang=calc_trend_angle(C[i-_ANGLE_LR-2:i],_ANGLE_LR)
        rec,_=w._build(ef,es,C[i-1],atr,"M5",5,htf_trend=m30s(T[i]),angle=ang)
        if rec["signal"]=="WARTEN": continue
        d=1 if rec["signal"]=="LONG" else -1
        atr=max(atr,_ATRMIN); e=C[i]
        R=simulate(e,d,atr,e-d*_SL_ATR*atr,H,L,C,i+1)
        trades.append((e,atr,R))

    def rets_for(rule, val):
        out=[]
        for (e,atr,R) in trades:
            margin_i=margin1*e/price_now
            if rule=="margin":
                lots=val/margin_i                       # je 1 € Equity
            else:  # risk: val=Risiko-Anteil bei _SL_ATR-Stop
                lots=val/(_SL_ATR*atr*vpp)
                if lots*margin_i>_MARGIN_CAP: lots=_MARGIN_CAP/margin_i
            out.append(lots*(R*atr)*vpp)               # frakt. Rendite/Trade
        return out

    print("="*88)
    print(f"  Sizing-Simulation — {sym}  Trades={len(trades)}  SL fix {_SL_ATR}×ATR + Trailing-TP")
    print(f"  Hebel≈{lev:.0f}× · Margin/Lot≈{margin1:.2f} · Wert/1.0Preis/Lot≈{vpp:.2f}{acc.currency} · Kurs {price_now:.2f}")
    print("="*88)
    hdr=f"  {'Sizing':<22}{'Endkapital':>12}{'Wachstum/Tr':>13}{'MaxDD-hist':>11}{'MaxDD-MC95':>11}{'P(DD>80%)':>11}"
    print(hdr); print("  "+"-"*84)
    rules=[("Margin 96% (AKTUELL)","margin",0.96),("Margin 50%","margin",0.50),
           ("Margin 30%","margin",0.30),("Margin 20%","margin",0.20),
           ("Margin 10%","margin",0.10),
           ("Risiko 1%/Trade","risk",0.01),("Risiko 2%/Trade","risk",0.02),
           ("Risiko 3%/Trade","risk",0.03)]
    for name,rule,val in rules:
        rets=rets_for(rule,val)
        eq,g,mdd,ruin=curve_stats(rets)
        mdd50,mdd95,over80=mc_maxdd(rets)
        eqs=(f"{eq:.2e}×" if (eq>=1e4 or eq<1e-2) else f"{eq:7.2f}×")
        print(f"  {name:<22}{eqs:>12}{g:>+13.4f}{100*mdd:>10.0f}%{100*mdd95:>10.0f}%{100*over80:>10.0f}%"
              + ("  ⚠RUIN" if ruin else ""))

    # Kelly-Scan: welcher Margin-Bruchteil maximiert das Wachstum/Trade?
    print("\n  Kelly-Scan (Margin-Bruchteil → Wachstum pro Trade, ln):")
    best=(-9,0)
    for f in [x/100 for x in range(2,99,2)]:
        g=curve_stats(rets_for("margin",f))[1]
        if g>best[0]: best=(g,f)
    print(f"    Wachstum-Optimum bei Margin ≈ {best[1]*100:.0f}%  (g={best[0]:+.4f}/Trade)")
    g96=curve_stats(rets_for("margin",0.96))[1]
    print(f"    Zum Vergleich 96%: g={g96:+.4f}/Trade  →  "
          + ("96% liegt ÜBER dem Optimum (Vola-Drag)" if g96<best[0] else "96% ist ~optimal"))
    print("\n  Endkapital = Faktor auf Startkapital über alle Trades (≈"
          f"{len(trades)} Trades ~{len(trades)//50} Handelstage). MaxDD-MC95 = 95%-Quantil"
          " des Drawdowns über zufällige Reihenfolgen. P(DD>80%) = Quasi-Ruin-Risiko.")

if __name__=="__main__":
    main()