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>
67 lines
3.2 KiB
Python
67 lines
3.2 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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from scripts.analysis.explore_lab import atr
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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
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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']
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j=min(i+mb,len(c)-1); exit_p=c[j]
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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;break
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if h[t]>=tp: exit_p=tp;j=t;break
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else:
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if h[t]>=sl: exit_p=sl;j=t;break
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if l[t]<=tp: exit_p=tp;j=t;break
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ret=(exit_p-c[i])/c[i]*d*LEV-FEE*LEV
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out.append((i,ret)); last=j
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return out
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# raccogli tutti i trade con il loro dvol_pct e hurst all'ingresso
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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); sigs=s.generate_signals(df,ts,**par)
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for i,ret in replay(df,sigs):
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rows.append(dict(asset=asset,code=code,year=ts.iloc[i].year,ret=ret,
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dvol_pct=df['dvol_pct'].iloc[i], hurst=df['hurst'].iloc[i],
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dvol=df['dvol'].iloc[i]))
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R=pd.DataFrame(rows).dropna(subset=['dvol_pct'])
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print(f"trade totali (con DVOL, 2021+): {len(R)}")
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print("\n=== PnL medio per trade per TERZILE DVOL (bassa/media/alta vol) ===")
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R['dvbin']=pd.cut(R['dvol_pct'],[0,.33,.66,1.0],labels=['LOW-vol','MID','HIGH-vol'])
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g=R.groupby('dvbin',observed=True)['ret']
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print(f" {'regime':<10}{'n':>6}{'ret_medio%':>12}{'win%':>8}{'somma%':>10}")
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for b in ['LOW-vol','MID','HIGH-vol']:
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x=R[R.dvbin==b]['ret']
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print(f" {b:<10}{len(x):>6}{x.mean()*100:>12.3f}{(x>0).mean()*100:>8.1f}{x.sum()*100:>10.0f}")
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print("\n=== dentro LOW-vol: split per HURST (anti-persistente vs trending) ===")
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LV=R[R.dvbin=='LOW-vol'].copy()
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LV['hbin']=pd.cut(LV['hurst'],[0,.45,.55,1.0],labels=['hurst<.45 (anti-pers)','.45-.55','>.55 (trend)'])
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for b in ['hurst<.45 (anti-pers)','.45-.55','>.55 (trend)']:
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x=LV[LV.hbin==b]['ret']
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if len(x): print(f" {b:<24}{len(x):>6} ret_medio {x.mean()*100:>+7.3f}% win {(x>0).mean()*100:>5.1f}% somma {x.sum()*100:>+6.0f}%")
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print("\n=== per anno: PnL fade in LOW-vol vs resto ===")
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for y in range(2021,2027):
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lo=R[(R.year==y)&(R.dvbin=='LOW-vol')]['ret']; hi=R[(R.year==y)&(R.dvbin!='LOW-vol')]['ret']
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print(f" {y}: LOW-vol somma {lo.sum()*100:>+6.0f}% (n{len(lo)}) | MID/HIGH somma {hi.sum()*100:>+6.0f}% (n{len(hi)})")
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