chore(reset): v2.0.0 — storico certificato Deribit mainnet, ripartenza pulita

Reset del progetto su fondamenta verificate dopo la scoperta che l'intera
libreria "validata OOS" era artefatto di feed contaminato (print fantasma del
feed Cerbero TESTNET + storico Binance/USDT).

- Storico ricostruito da Deribit MAINNET (ccxt pubblico, tokenless) e
  CERTIFICATO (certify_feed.py): BTC/ETH puliti su TUTTA la storia
  (mediana 2-6 bps vs Coinbase USD), integrita' OHLC + coerenza resample
  (maxΔ 0.00) + cross-venue OK. Alt esclusi (illiquidi/divergenti: LTC/DOGE
  50-82% barre flat; XRP/BNB non certificabili).
- Verdetto sul feed pulito: FADE / PAIRS / XS01 / TSM01 morti (ogni
  portafoglio Sharpe -2.3..-3.0, DD ~40%); solo SH01 e frammenti HONEST
  con segnale residuo, da ri-validare in isolamento.
- Cleanup "restart pulito": strategie, stack live (src/live, src/portfolio,
  runner/executor, yml, docker), ~100 script ricerca/gate, waste/games/
  portfolios, dati non certificati + cache e 60+ diari -> archiviati in Old/
  (preservati, non cancellati). Diario consolidato in un unico documento.
- Skeleton ricerca tenuto: Strategy ABC + indicatori + src/fractal +
  src/backtest/engine + load_data; tool dati certificati (rebuild_history,
  certify_feed, audit_feed, multi_source_check).
- Universo dati ATTIVO: solo BTC/ETH (5m/15m/1h); guardrail fisico
  (load_data su alt -> FileNotFoundError). Esecuzione DISABILITATA, conto flat.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Old/scripts/analysis/fade_diag_by_regime.py

@@ -0,0 +1,66 @@
+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
+import importlib
+FEE=0.001; LEV=3
+
+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__)
+
+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)),
+     "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))}
+
+def replay(df, sigs):
+    h=df['high'].values; l=df['low'].values; c=df['close'].values
+    out=[]; last=-1
+    for s in sigs:
+        i=s.idx
+        if i<=last: continue
+        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]
+        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
+        out.append((i,ret)); last=j
+    return out
+
+# raccogli tutti i trade con il loro dvol_pct e hurst all'ingresso
+rows=[]
+for asset in ("BTC","ETH"):
+    df=load_features(asset,"1h"); ts=pd.to_datetime(df['timestamp'],unit='ms',utc=True)
+    for code,(mod,par) in STR.items():
+        s=load_strat(mod); sigs=s.generate_signals(df,ts,**par)
+        for i,ret in replay(df,sigs):
+            rows.append(dict(asset=asset,code=code,year=ts.iloc[i].year,ret=ret,
+                             dvol_pct=df['dvol_pct'].iloc[i], hurst=df['hurst'].iloc[i],
+                             dvol=df['dvol'].iloc[i]))
+R=pd.DataFrame(rows).dropna(subset=['dvol_pct'])
+print(f"trade totali (con DVOL, 2021+): {len(R)}")
+
+print("\n=== PnL medio per trade per TERZILE DVOL (bassa/media/alta vol) ===")
+R['dvbin']=pd.cut(R['dvol_pct'],[0,.33,.66,1.0],labels=['LOW-vol','MID','HIGH-vol'])
+g=R.groupby('dvbin',observed=True)['ret']
+print(f"  {'regime':<10}{'n':>6}{'ret_medio%':>12}{'win%':>8}{'somma%':>10}")
+for b in ['LOW-vol','MID','HIGH-vol']:
+    x=R[R.dvbin==b]['ret']
+    print(f"  {b:<10}{len(x):>6}{x.mean()*100:>12.3f}{(x>0).mean()*100:>8.1f}{x.sum()*100:>10.0f}")
+
+print("\n=== dentro LOW-vol: split per HURST (anti-persistente vs trending) ===")
+LV=R[R.dvbin=='LOW-vol'].copy()
+LV['hbin']=pd.cut(LV['hurst'],[0,.45,.55,1.0],labels=['hurst<.45 (anti-pers)','.45-.55','>.55 (trend)'])
+for b in ['hurst<.45 (anti-pers)','.45-.55','>.55 (trend)']:
+    x=LV[LV.hbin==b]['ret']
+    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}%")
+
+print("\n=== per anno: PnL fade in LOW-vol vs resto ===")
+for y in range(2021,2027):
+    lo=R[(R.year==y)&(R.dvbin=='LOW-vol')]['ret']; hi=R[(R.year==y)&(R.dvbin!='LOW-vol')]['ret']
+    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)})")