Adriano Dal Pastro
4d9f2af0c0
Diagnosi (3022 trade fade 2021+): perdite/stop concentrati in regime PERSISTENTE (hurst>0.55, SL-rate 43% vs 21% anti-persistente), NON in bassa vol (low-vol e' net positivo). Ricerca web conferma: filtro Hurst<0.45 / ADX<20 / vol-expansion ratio>1.5 (prevenne 72% perdite maggiori). Workflow 11 agenti testa i meccanismi sulle fade reali. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
51 lines
3.0 KiB
Python
51 lines
3.0 KiB
Python
import sys; sys.path.insert(0,".")
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import numpy as np, pandas as pd
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from scripts.analysis.regime_lab import load_features
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import importlib
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FEE=0.001; LEV=3
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def load_strat(mod):
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m=importlib.import_module(mod)
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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__)
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STR={"MR01":("scripts.strategies.MR01_bollinger_fade",dict(bb_window=50,k=2.5,sl_atr=2.0,max_bars=24,trend_max=3.0)),
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"MR02":("scripts.strategies.MR02_donchian_fade",dict(n=20,sl_atr=2.0,max_bars=24,trend_max=3.0)),
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"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))}
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def replay(df,sigs):
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h=df['high'].values;l=df['low'].values;c=df['close'].values;out=[];last=-1
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for s in sigs:
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i=s.idx
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if i<=last: continue
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d=s.direction;tp=s.metadata['tp'];sl=s.metadata['sl'];mb=s.metadata['max_bars'];j=min(i+mb,len(c)-1);exit_p=c[j];reason='time'
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for t in range(i+1,j+1):
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if d==1:
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if l[t]<=sl: exit_p=sl;j=t;reason='sl';break
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if h[t]>=tp: exit_p=tp;j=t;reason='tp';break
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else:
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if h[t]>=sl: exit_p=sl;j=t;reason='sl';break
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if l[t]<=tp: exit_p=tp;j=t;reason='tp';break
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out.append((i,(exit_p-c[i])/c[i]*d*LEV-FEE*LEV,reason));last=j
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return out
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rows=[]
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for asset in ("BTC","ETH"):
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df=load_features(asset,"1h");ts=pd.to_datetime(df['timestamp'],unit='ms',utc=True)
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for code,(mod,par) in STR.items():
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s=load_strat(mod)
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for i,ret,reason in replay(df,s.generate_signals(df,ts,**par)):
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rows.append(dict(ret=ret,reason=reason,dvol_pct=df['dvol_pct'].iloc[i],hurst=df['hurst'].iloc[i],
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vratio=df['vratio'].iloc[i],higuchi=df['higuchi'].iloc[i]))
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R=pd.DataFrame(rows).dropna(subset=['dvol_pct','hurst'])
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L=R[R.ret<0] # solo i trade in perdita
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print(f"trade {len(R)} | in perdita {len(L)} ({len(L)/len(R)*100:.0f}%) | somma perdite {L.ret.sum()*100:.0f}% | media perdita {L.ret.mean()*100:.2f}%")
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print("\n=== somma PERDITE per regime (dove si concentra il danno) ===")
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R['dvbin']=pd.cut(R.dvol_pct,[0,.33,.66,1],labels=['LOWvol','MID','HIGHvol'])
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R['hbin']=pd.cut(R.hurst,[0,.45,.55,1],labels=['anti<.45','.45-.55','trend>.55'])
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piv=R[R.ret<0].pivot_table(index='dvbin',columns='hbin',values='ret',aggfunc='sum',observed=True)*100
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print((piv.round(0)).to_string())
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print("\n (numeri = somma % delle perdite per cella; piu negativo = piu danno)")
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print("\n=== quota di SL (stop) per regime ===")
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slr=R.groupby(['dvbin','hbin'],observed=True).apply(lambda x:(x.reason=='sl').mean()*100, include_groups=False)
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print(slr.round(0).to_string())
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# worst tail
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print(f"\n=== peggiori 1% trade: dove? ===")
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W=R.nsmallest(max(10,len(R)//100),'ret')
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print(f" worst {len(W)} trade: dvol_pct medio {W.dvol_pct.mean():.2f}, hurst medio {W.hurst.mean():.2f}, quota hurst>.55 {(W.hurst>.55).mean()*100:.0f}%, quota dvol<.33 {(W.dvol_pct<.33).mean()*100:.0f}%")
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