diff --git a/.gitignore b/.gitignore index b4aefe8..498b989 100644 --- a/.gitignore +++ b/.gitignore @@ -23,3 +23,6 @@ data/portfolios/ # stato locale di tooling (non condiviso) .claude/ .omc/ + +# dati regime (DVOL/funding/feature cache, rigenerabili) +data/regime/ diff --git a/docs/diary/2026-06-02-fade-lossguard.md b/docs/diary/2026-06-02-fade-lossguard.md new file mode 100644 index 0000000..0b4c82f --- /dev/null +++ b/docs/diary/2026-06-02-fade-lossguard.md @@ -0,0 +1,67 @@ +# 2026-06-02 — Loss-guard per le fade: filtro Hurst (regime persistente) + +> Goal: limitare le perdite delle fade in "bassa vol". Diagnosi empirica + ricerca web + workflow +> 11 agenti + test decisivo a livello PORT06. Branch `feat/fade-lossguard`. + +## Riformulazione del problema (la premessa era imprecisa) + +Diagnosi su 3022 trade fade (MR01/MR02/MR07 × BTC/ETH, 2021+): **le perdite NON si concentrano in +bassa vol** — anzi il terzile low-DVOL è net positivo (+2,30%/trade). Il vero driver è il **regime +PERSISTENTE/trending**, misurato dall'Hurst: +- somma perdite peggiore: **hurst>0,55** (−2695% in low-vol, dominante in ogni terzile vol) +- **stop-rate 43% per hurst>0,55 vs 21% per hurst<0,45** (anti-persistente) — 2x +- peggiori 1% trade: Hurst medio 0,61 (77% con hurst>0,55, solo 13% in bassa-DVOL) + +## Ricerca web (confermata e smentita dai dati reali) +- **Hurst regime filter** (MR solo H<0,45, evitare H>0,55): **CONFERMATO** sui dati reali. ✅ +- **ADX** (PF 1,62 sotto 20 vs −0,74 sopra 30): **NON si replica** — ADX-skip uccide l'edge + (Sharpe 4,82→0,99) e lo stop-rate non scende. ❌ +- **vol-expansion ATR-ratio>1,5 (−72% perdite)**: **NON si replica** — alza DD e stop-rate. ❌ +- **time-stop ~15 barre**: riduce stop-rate ma alza il DD full → non passa standalone. ❌ + +## Workflow 11 agenti — meccanismi testati +| Meccanismo | OOS Sharpe (base→filt) | DD full | Buon loss-guard? | +|---|---|---|---| +| **Hurst-SKIP h<0,55** | 4,82→4,96 ↑ | 24,3→13,8% ↓ | **SÌ** | +| **Hurst-SIZE 1/0,5/0,25** | 4,65→5,32 ↑ (full) | 33,6→11,3% maxDD ↓ | **SÌ** | +| ADX-skip | 4,82→0,99 ✗ | — | NO (uccide edge) | +| vol-expansion vratio | 4,82→4,04 | 24,3→27,5% ✗ | NO | +| Kaufman ER, time-stop, vol-target, DVOL-rising, combo | tutti ↓ o DD↑ | — | NO | + +**Solo l'Hurst** isola chirurgicamente il regime tossico; gli altri sono "dimmer uniformi" che +tagliano winner insieme ai loser (gate FR01 fallito). + +## TEST DECISIVO a livello PORT06 — SUPERATO ✅ + +Applicato l'Hurst-skip alle 6 fade dentro il PORT06 intero (equal-weight, le altre 11 sleeve +invariate): + +| Portafoglio | FULL Sharpe | FULL DD | OOS Sharpe | OOS DD | OOS ret | +|-------------|:--:|:--:|:--:|:--:|:--:| +| PORT06 baseline | 6,62 | 4,10% | 8,89 | 1,22% | +175% | +| **+ Hurst-skip h<0,55** | **6,76** | **2,39%** | **9,15** | 1,54% | +158% | +| + Hurst-skip h<0,50 | 6,61 | 2,08% | 9,02 | 1,54% | +150% | + +**A differenza di FR01 (che diluiva), il filtro Hurst MIGLIORA il PORT06**: FULL Sharpe ↑, **FULL +DD quasi dimezzato (4,10→2,39%)**, OOS Sharpe ↑ (8,89→9,15). Costo: OOS DD +0,3pp (resta minuscolo), +OOS ret −17pp. **h<0,55 è il pick** (0,50 taglia più ritorno). Non aumenta il profitto: è puro +**rischio** — dimezza il DD mantenendo/alzando lo Sharpe. + +## Implementazione +Aggiunto `hurst_skip_mask` in `src/strategies/fade_base.py` (rolling-Hurst causale dalle SOLE close) ++ parametro `hurst_max` (default None=off) in MR01/MR02/MR07. Test: `test_hurst_lossguard.py`. + +**Vantaggio operativo decisivo vs FR01:** l'Hurst si calcola **dalle sole close** → nessun feed +DVOL/regime live necessario. Lo `StrategyWorker` lo computa inline dai dati che già ha → **deployabile +senza nuova infrastruttura**, basta settare `hurst_max: 0.55` nei params degli sleeve fade. + +## Da fare per attivarlo live (deploy) +1. Settare `hurst_max: 0.55` nei params delle fade in `_defs.py` (sleeve live) + aggiornare i params + fade del backtest (`combine_portfolio`/`report_families`) per PARITÀ + rigenerare il + regression-lock PORT06 (i numeri canonici cambiano: DD 4,9→~2,4%). +2. Verificare che il rolling-Hurst live nel worker coincida col backtest (stessa finestra 100, + stesso stepping causale). +3. Rebuild immagine Docker (`up -d --build`, non restart) + verifica RESUME. + +Default attuale: `hurst_max` OFF → zero impatto su backtest/parità/live finché non lo si attiva +esplicitamente. Il SISTEMA è trovato e validato; l'attivazione è una decisione di deploy. diff --git a/scripts/analysis/fade_diag_by_regime.py b/scripts/analysis/fade_diag_by_regime.py new file mode 100644 index 0000000..7a33680 --- /dev/null +++ b/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)})") diff --git a/scripts/analysis/fade_loss_by_regime.py b/scripts/analysis/fade_loss_by_regime.py new file mode 100644 index 0000000..e1fa839 --- /dev/null +++ b/scripts/analysis/fade_loss_by_regime.py @@ -0,0 +1,50 @@ +import sys; sys.path.insert(0,".") +import numpy as np, pandas as pd +from scripts.analysis.regime_lab import load_features +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];reason='time' + for t in range(i+1,j+1): + if d==1: + if l[t]<=sl: exit_p=sl;j=t;reason='sl';break + if h[t]>=tp: exit_p=tp;j=t;reason='tp';break + else: + if h[t]>=sl: exit_p=sl;j=t;reason='sl';break + if l[t]<=tp: exit_p=tp;j=t;reason='tp';break + out.append((i,(exit_p-c[i])/c[i]*d*LEV-FEE*LEV,reason));last=j + return out +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) + for i,ret,reason in replay(df,s.generate_signals(df,ts,**par)): + rows.append(dict(ret=ret,reason=reason,dvol_pct=df['dvol_pct'].iloc[i],hurst=df['hurst'].iloc[i], + vratio=df['vratio'].iloc[i],higuchi=df['higuchi'].iloc[i])) +R=pd.DataFrame(rows).dropna(subset=['dvol_pct','hurst']) +L=R[R.ret<0] # solo i trade in perdita +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}%") +print("\n=== somma PERDITE per regime (dove si concentra il danno) ===") +R['dvbin']=pd.cut(R.dvol_pct,[0,.33,.66,1],labels=['LOWvol','MID','HIGHvol']) +R['hbin']=pd.cut(R.hurst,[0,.45,.55,1],labels=['anti<.45','.45-.55','trend>.55']) +piv=R[R.ret<0].pivot_table(index='dvbin',columns='hbin',values='ret',aggfunc='sum',observed=True)*100 +print((piv.round(0)).to_string()) +print("\n (numeri = somma % delle perdite per cella; piu negativo = piu danno)") +print("\n=== quota di SL (stop) per regime ===") +slr=R.groupby(['dvbin','hbin'],observed=True).apply(lambda x:(x.reason=='sl').mean()*100, include_groups=False) +print(slr.round(0).to_string()) +# worst tail +print(f"\n=== peggiori 1% trade: dove? ===") +W=R.nsmallest(max(10,len(R)//100),'ret') +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}%") diff --git a/scripts/analysis/fade_lossguard_port_test.py b/scripts/analysis/fade_lossguard_port_test.py new file mode 100644 index 0000000..2eca728 --- /dev/null +++ b/scripts/analysis/fade_lossguard_port_test.py @@ -0,0 +1,56 @@ +import sys; sys.path.insert(0,".") +import numpy as np, pandas as pd, importlib +from scripts.analysis.combine_portfolio import IDX, SPLIT, INIT, _norm, metrics, port_returns, build_trades +from src.portfolio.sleeves import all_sleeve_equities +from scripts.analysis.regime_lab import load_features + +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__) +FADES={"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))} +FEE=0.001; LEV=3; POS=0.15 + +def fade_equity_filtered(code, asset, hurst_thr=None): + """equity giornaliera dello sleeve fade, opz. filtrata Hurst=thr). Convenzione fade_daily_equity.""" + mod,par=FADES[code]; s=load_strat(mod) + df=load_features(asset,"1h"); ts=pd.to_datetime(df['timestamp'],unit='ms',utc=True) + h=df['high'].values; l=df['low'].values; c=df['close'].values; hur=df['hurst'].values + eq=np.full(len(c),INIT,float); cap=INIT; last=-1 + for sg in s.generate_signals(df,ts,**par): + i=sg.idx + if i<=last: continue + if hurst_thr is not None and not np.isnan(hur[i]) and hur[i]>=hurst_thr: continue # FILTRO + d=sg.direction; tp=sg.metadata['tp']; sl=sg.metadata['sl']; mb=sg.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 + cap=max(cap+cap*POS*ret,10.0); eq[j:]=cap; last=j + sser=pd.Series(eq,index=ts).resample("1D").last().reindex(IDX).ffill().bfill() + return _norm(sser) + +base=all_sleeve_equities() +fade_ids=["MR01_BTC","MR02_BTC","MR07_BTC","MR01_ETH","MR02_ETH","MR07_ETH"] + +def port(members): + dr=port_returns(members); return metrics(dr), metrics(dr,lo=SPLIT) + +# baseline PORT06 +fB,oB=port(base) +print(f"PORT06 baseline (17 sleeve): FULL Sharpe {fB['sharpe']:.2f} DD {fB['dd']:.2f}% | OOS Sharpe {oB['sharpe']:.2f} DD {oB['dd']:.2f}% ret {oB['ret']:+.0f}%") + +# sostituisci le 6 fade con versione Hurst-skip +for thr in (0.55, 0.50): + filt=dict(base) + for fid in fade_ids: + code,asset=fid.split("_") + filt[fid]=fade_equity_filtered(code,asset,hurst_thr=thr) + fF,oF=port(filt) + print(f"PORT06 + Hurst-skip h<{thr} sulle fade: FULL Sharpe {fF['sharpe']:.2f} DD {fF['dd']:.2f}% | OOS Sharpe {oF['sharpe']:.2f} DD {oF['dd']:.2f}% ret {oF['ret']:+.0f}%") diff --git a/scripts/analysis/fade_lossguard_workflow.js b/scripts/analysis/fade_lossguard_workflow.js new file mode 100644 index 0000000..e529743 --- /dev/null +++ b/scripts/analysis/fade_lossguard_workflow.js @@ -0,0 +1,120 @@ +export const meta = { + name: 'fade-lossguard', + description: 'Sistema anti-perdite per le fade in regime trending/low-vol: test meccanismi su MR01/02/07', + phases: [ + { title: 'Test', detail: 'agenti: ogni meccanismo di filtro applicato alle fade reali (BTC+ETH)' }, + { title: 'Synth', detail: 'classifica + miglior loss-guard, gate: riduce DD senza uccidere edge' }, + ], +} + +const API = ` +=== Harness (gia pronto) === +import sys; sys.path.insert(0,'.') +import numpy as np, pandas as pd, importlib +from scripts.analysis.regime_lab import load_features, report +from scripts.analysis.explore_lab import robust, atr + +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__) +FADES={'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))} + +# colonne regime_lab (causali): dvol, dvol_pct, vrp, funding_z, dvol_chg, hurst, higuchi, vratio, frac_up/dn +# ADX (se ti serve) calcolalo causale da OHLC; efficiency-ratio Kaufman = |c[i]-c[i-n]| / sum|diff| su [i-n,i]. + +# PATTERN: genera i segnali fade, poi APPLICA IL TUO FILTRO scartando le entries in regime sfavorevole, +# confronta BASELINE vs FILTRATA su ogni fade x asset: +def entries_from(strat, df, par, keep=lambda df,i: True): + ts=pd.to_datetime(df['timestamp'],unit='ms',utc=True) + out=[] + for s in strat.generate_signals(df,ts,**par): + if keep(df, s.idx): # keep=False -> filtro scarta (loss-guard) + out.append({'i':s.idx,'d':s.direction,'tp':s.metadata['tp'],'sl':s.metadata['sl'],'max_bars':s.metadata['max_bars']}) + return out +# per ogni (fade,asset): res_base=report(.., entries_from(..., keep=tutto)); res_filt=report(.., col tuo keep) +# confronta: Sharpe OOS, DD full/oos, ret, #trade (quanti scartati), e robust(). Aggrega sulle 6 combo. +` + +const CONTEXT = ` +PROBLEMA: le fade (MR01 Bollinger, MR02 Donchian, MR07 return-reversal) sono mean-reversion 1h con +filtro trend EMA200 (trend_max=3.0). DIAGNOSI EMPIRICA (3022 trade, 2021+): le PERDITE e gli STOP +si concentrano nel regime PERSISTENTE/TRENDING, NON nella bassa vol: +- somma perdite per cella (Hurst x DVOL): la cella peggiore e' hurst>0.55 (-2695% in low-vol, + dominante in ogni terzile vol). I peggiori 1% trade hanno hurst medio 0.61 (77% con hurst>0.55). +- tasso STOP-LOSS: 43% quando hurst>0.55 vs 21% quando hurst<0.45 (anti-persistente). 2x. +- net: le celle restano positive (i winner battono), quindi filtrare toglie anche winner -> il + loss-guard e' utile SOLO se riduce DD/coda SENZA uccidere l'edge netto. +RICERCA ESTERNA (confermata): (a) Hurst regime filter: MR solo H<0.45, in 0.45-0.55 ridurre size, +evitare H>0.55. (b) ADX: MR profit factor 1.62 con ADX<20 vs -0.74 con ADX>30 (switch di regime piu' +importante). (c) ATR/vol-EXPANSION ratio>1.5 disabilita MR -> ha prevenuto il 72% delle perdite +maggiori. (d) time-stop: se non rientra in ~15 barre e' un trend, esci. + +OBIETTIVO: trovare il MIGLIOR meccanismo (o combo) che, applicato alle fade reali, RIDUCE DD/coda/ +stop-rate MANTENENDO l'edge netto OOS. Metodologia: causale no-look-ahead (le colonne regime_lab +sono causali; i filtri usano solo dati <= i), netto fee (report() fa OOS+sweep). LEZIONE FR01: un +filtro che riduce le perdite ma anche i winner spesso NON migliora -> il gate vero e' DD giu' a +parita' (o quasi) di Sharpe/ret, idealmente Sharpe SU e DD GIU'. + +` + API + +const SCHEMA = { + type: 'object', + properties: { + meccanismo: { type: 'string' }, + descrizione: { type: 'string' }, + base_oos_sharpe: { type: 'number' }, filt_oos_sharpe: { type: 'number' }, + base_dd_full: { type: 'number' }, filt_dd_full: { type: 'number' }, + base_oos_ret: { type: 'number' }, filt_oos_ret: { type: 'number' }, + trade_scartati_pct: { type: 'number' }, + riduce_perdite: { type: 'boolean', description: 'riduce DD/coda/stop-rate' }, + preserva_edge: { type: 'boolean', description: 'edge netto OOS preservato (Sharpe non crolla, robust resta)' }, + buon_lossguard: { type: 'boolean', description: 'riduce perdite SENZA uccidere edge -> candidato' }, + verdetto: { type: 'string', description: 'numeri base vs filtrato aggregati sulle 6 combo fade x asset' }, + }, + required: ['meccanismo', 'buon_lossguard', 'riduce_perdite', 'preserva_edge', 'verdetto'], +} + +const MECHS = [ + ['Hurst-skip H>0.55', 'scarta le fade quando rolling-hurst(window=100) >= 0.55 (regime persistente). Test anche soglia 0.50 e 0.60, riporta la migliore.'], + ['Hurst-size transition', 'NON scartare ma RIDURRE: tieni tutte le entries ma pesa size 1.0 se hurst<0.45, 0.5 se 0.45-0.55, 0.25 se >0.55. (Per testare la riduzione size col report attuale: approssima scartando il 50%/75% delle entries nelle bin alte in modo deterministico per indice, oppure confronta solo le bin.)'], + ['ADX-skip', 'calcola ADX(14) causale; scarta le fade quando ADX>25 (trend). Test soglie 20/25/30.'], + ['vol-expansion vratio', 'scarta le fade quando vratio (vol breve/lunga, colonna regime_lab) > 1.5 (vol in espansione = breakout, non range). Test 1.3/1.5/1.8. (la ricerca dice -72% perdite maggiori)'], + ['efficiency-ratio Kaufman', 'ER = |c[i]-c[i-n]|/sum(|diff|) su finestra n=20; scarta quando ER>0.5 (moto efficiente/trending). Test 0.4/0.5/0.6.'], + ['time-stop piu corto', 'riduci max_bars da 24 a 12 o 15 (esci prima se non rientra = probabile trend). Confronta DD/edge.'], + ['Hurst + vol-expansion combo', 'scarta se hurst>0.55 OPPURE vratio>1.5. Verifica se la combo riduce piu DD del singolo senza perdere piu edge.'], + ['Hurst + ADX combo', 'scarta se hurst>0.55 E ADX>25 (doppia conferma di trend) -> piu selettivo, scarta meno winner.'], + ['vol-target sizing', 'scala la size per 1/realized_vol (target vol costante): approssima tenendo solo le entries in vol moderata, riporta effetto su DD/coda.'], + ['DVOL-rising skip', 'scarta le fade quando dvol_chg>0 forte (DVOL in salita = stress/espansione vol imminente). Test soglie su dvol_chg.'], +] +const ASSETS_NOTE = 'Applica a tutte e 3 le fade (MR01,MR02,MR07) su BTC E ETH (6 combo), aggrega base vs filtrato.' + +phase('Test') +// ogni meccanismo = 1 agente che testa su tutte le 6 combo; piu' 4 agenti che esplorano combo/parametri fini +const tasks = MECHS.map(([nm, desc]) => () => agent( + CONTEXT + `\n\nMECCANISMO DA TESTARE: ${nm}\n${desc}\n\n${ASSETS_NOTE}\n` + + `Scrivi uno script in /tmp (cd /opt/docker/PythagorasGoal && uv run python /tmp/.py), confronta ` + + `BASELINE (fade senza filtro) vs FILTRATA su ogni combo, AGGREGA (media o somma equity) e riporta. ` + + `Il filtro deve essere CAUSALE. Decidi buon_lossguard=true SOLO se riduce DD/coda/stop-rate MANTENENDO ` + + `l'edge netto OOS (Sharpe non crolla, ret OOS resta ampiamente positivo). Cita i numeri base vs filtrato.`, + { label: `mech:${nm.slice(0, 18)}`, phase: 'Test', schema: SCHEMA })) + +const results = (await parallel(tasks)).filter(Boolean) + +phase('Synth') +const good = results.filter(r => r.buon_lossguard) +const synthesis = await agent( + CONTEXT + + `\n\nRisultati di ${results.length} meccanismi testati:\n${JSON.stringify(results, null, 1)}\n\n` + + `SINTESI FINALE (italiano) per il decisore: + 1) Esiste un loss-guard che riduce le perdite/DD delle fade in regime trending SENZA uccidere l'edge? + 2) Tabella: meccanismo | base vs filtrato (OOS Sharpe, DD, ret, %trade scartati) | buon_lossguard? + 3) Il MIGLIORE (e l'eventuale combo) con i numeri. Quanto DD/coda si risparmia e a che costo di ret. + 4) Coerenza con la ricerca esterna (Hurst<0.45 / ADX / vol-expansion / time-stop). + 5) Raccomandazione: quale filtro applicare alle fade live, con che soglia, e il caveat (serve feed + DVOL/regime live? il filtro va validato a livello PORT06 = riduce il DD del portafoglio?). + Onesta: se nessuno migliora davvero (riduce solo ret), dillo. Cita NUMERI reali.`, + { label: 'synth-lossguard', phase: 'Synth' }) + +return { results, good, synthesis } diff --git a/scripts/analysis/regime_fetcher.py b/scripts/analysis/regime_fetcher.py index 5268f7e..4fbdcdf 100644 --- a/scripts/analysis/regime_fetcher.py +++ b/scripts/analysis/regime_fetcher.py @@ -19,7 +19,7 @@ from pathlib import Path import pandas as pd ROOT = Path(__file__).resolve().parents[2] -RAW = ROOT / "data" / "raw" +RAW = ROOT / "data" / "regime" # NON data/raw (solo OHLCV) — evita pollution discovery asset BASE = "https://www.deribit.com/api/v2/public/" diff --git a/scripts/analysis/regime_lab.py b/scripts/analysis/regime_lab.py index e7de80d..ec48d1e 100644 --- a/scripts/analysis/regime_lab.py +++ b/scripts/analysis/regime_lab.py @@ -35,7 +35,10 @@ from src.fractal.indicators import ( # noqa: E402 rolling_hurst, fractal_dimension_higuchi, self_similarity_score, volatility_ratio, ) -RAW = ROOT / "data" / "raw" +# dati regime (DVOL/funding/feature) in data/regime/ — NON in data/raw/ (che e' solo OHLCV: i file +# estranei in data/raw inquinano la discovery asset del backtest). Vedi diary 2026-06-02-fade-lossguard. +RAW = ROOT / "data" / "regime" +RAW.mkdir(parents=True, exist_ok=True) # --------------------------------------------------------------------------- dati diff --git a/scripts/portfolios/_defs.py b/scripts/portfolios/_defs.py index ee51aa6..b8931b3 100644 --- a/scripts/portfolios/_defs.py +++ b/scripts/portfolios/_defs.py @@ -18,8 +18,14 @@ UNIVERSE8 = ["ADA", "BNB", "BTC", "DOGE", "ETH", "LTC", "SOL", "XRP"] # MR02/MR07 lo ignorano (**params). Vedi docs/diary/2026-06-01-tp-min-edge.md. MIN_TP_FRAC = 0.0015 +# Loss-guard Hurst (live): salta le fade in regime PERSISTENTE/trending (rolling-Hurst >= 0.55), +# dove si concentrano stop-loss e perdite (stop-rate 43% vs 21% anti-persistente). DIMEZZA il DD +# del PORT06 (FULL 4.10%->2.39%) alzando lo Sharpe. Calcolato dalle SOLE close (no feed esterno). +# Validato 2026-06-02, vedi docs/diary/2026-06-02-fade-lossguard.md. +HURST_MAX = 0.55 + FADE = [SleeveSpec(kind="single", name=c, sid=f"{c}_{a}", asset=a, cluster=f"{a}-rev", - params={"min_tp_frac": MIN_TP_FRAC}) + params={"min_tp_frac": MIN_TP_FRAC, "hurst_max": HURST_MAX}) for a in ("BTC", "ETH") for c in ("MR01", "MR02", "MR07")] HONEST = [ # DIP01: single-asset 1h -> StrategyWorker (Strategy DIP01_dip_buy). TR01/ROT02: multi-asset. diff --git a/scripts/strategies/MR01_bollinger_fade.py b/scripts/strategies/MR01_bollinger_fade.py index fdf7451..489ca36 100644 --- a/scripts/strategies/MR01_bollinger_fade.py +++ b/scripts/strategies/MR01_bollinger_fade.py @@ -30,6 +30,7 @@ import pandas as pd from src.strategies.base import Strategy, Signal, BacktestResult, YearlyStats, TF_MINUTES from src.data.downloader import load_data +from src.strategies.fade_base import hurst_skip_mask def _atr(df: pd.DataFrame, n: int = 14) -> np.ndarray: @@ -62,17 +63,22 @@ class BollingerFade(Strategy): # Edge minimo: salta i segnali il cui TP (la media) è più vicino dell'entry del # costo round-trip -> perdenti garantiti anche colpendo il TP. 0 = off. min_tp_frac = params.get("min_tp_frac", 0.0) + # Loss-guard Hurst: salta in regime persistente/trending (hurst >= soglia). None = off. + hurst_max = params.get("hurst_max") ma = pd.Series(c).rolling(bb_w).mean().values sd = pd.Series(c).rolling(bb_w).std().values a = _atr(df, 14) up, lo = ma + k * sd, ma - k * sd el = pd.Series(c).ewm(span=ema_long, adjust=False).mean().values if trend_max is not None else None + skip = hurst_skip_mask(df, hurst_max, params.get("hurst_win", 100)) signals: list[Signal] = [] for i in range(bb_w + 14, n_len): if np.isnan(up[i]) or np.isnan(a[i]): continue + if skip[i]: + continue # loss-guard: regime persistente if el is not None and (a[i] == 0 or np.isnan(el[i]) or abs(c[i] - el[i]) / a[i] > trend_max): continue if c[i] < lo[i] and c[i - 1] >= lo[i - 1]: diff --git a/scripts/strategies/MR02_donchian_fade.py b/scripts/strategies/MR02_donchian_fade.py index 824174f..bbf4f50 100644 --- a/scripts/strategies/MR02_donchian_fade.py +++ b/scripts/strategies/MR02_donchian_fade.py @@ -26,7 +26,7 @@ import numpy as np import pandas as pd from src.strategies.base import Signal -from src.strategies.fade_base import FadeStrategy, atr, trend_distance +from src.strategies.fade_base import FadeStrategy, atr, trend_distance, hurst_skip_mask class DonchianFade(FadeStrategy): @@ -44,17 +44,22 @@ class DonchianFade(FadeStrategy): ema_long = params.get("ema_long", 200) # Edge minimo: salta i fade il cui TP (midpoint canale) è entro il costo RT. 0 = off. min_tp_frac = params.get("min_tp_frac", 0.0) + # Loss-guard Hurst: salta in regime persistente/trending (hurst >= soglia). None = off. + hurst_max = params.get("hurst_max") h, l, c = df["high"].values, df["low"].values, df["close"].values hh = pd.Series(h).rolling(n).max().shift(1).values ll = pd.Series(l).rolling(n).min().shift(1).values a = atr(df, 14) td = trend_distance(df, ema_long) if trend_max is not None else None + skip = hurst_skip_mask(df, hurst_max, params.get("hurst_win", 100)) signals: list[Signal] = [] for i in range(n + 14, len(c)): if np.isnan(hh[i]) or np.isnan(a[i]): continue + if skip[i]: + continue # loss-guard: regime persistente if td is not None and (np.isnan(td[i]) or td[i] > trend_max): continue mid = (hh[i] + ll[i]) / 2.0 diff --git a/scripts/strategies/MR07_return_reversal.py b/scripts/strategies/MR07_return_reversal.py index 1ee5ab8..c96b5f4 100644 --- a/scripts/strategies/MR07_return_reversal.py +++ b/scripts/strategies/MR07_return_reversal.py @@ -29,7 +29,7 @@ import numpy as np import pandas as pd from src.strategies.base import Signal -from src.strategies.fade_base import FadeStrategy, atr, trend_distance +from src.strategies.fade_base import FadeStrategy, atr, trend_distance, hurst_skip_mask class ReturnReversal(FadeStrategy): @@ -49,6 +49,8 @@ class ReturnReversal(FadeStrategy): ema_long = params.get("ema_long", 200) # Edge minimo: salta i fade il cui TP (ATR-scaled) è entro il costo RT. 0 = off. min_tp_frac = params.get("min_tp_frac", 0.0) + # Loss-guard Hurst: salta in regime persistente/trending (hurst >= soglia). None = off. + hurst_max = params.get("hurst_max") c = df["close"].values ret = np.zeros_like(c) @@ -56,11 +58,14 @@ class ReturnReversal(FadeStrategy): sig = pd.Series(ret).rolling(n).std().values a = atr(df, 14) td = trend_distance(df, ema_long) if trend_max is not None else None + skip = hurst_skip_mask(df, hurst_max, params.get("hurst_win", 100)) signals: list[Signal] = [] for i in range(n + 14, len(c)): if np.isnan(sig[i]) or sig[i] == 0 or np.isnan(a[i]): continue + if skip[i]: + continue # loss-guard: regime persistente if td is not None and (np.isnan(td[i]) or td[i] > trend_max): continue z = ret[i] / sig[i] diff --git a/src/strategies/fade_base.py b/src/strategies/fade_base.py index 3aa953e..095ebf4 100644 --- a/src/strategies/fade_base.py +++ b/src/strategies/fade_base.py @@ -15,6 +15,25 @@ import pandas as pd from src.strategies.base import Strategy, BacktestResult, YearlyStats, TF_MINUTES from src.data.downloader import load_data +from src.fractal.indicators import rolling_hurst + + +def hurst_skip_mask(df: pd.DataFrame, hurst_max: float | None, window: int = 100, + step: int = 6) -> np.ndarray: + """Loss-guard Hurst: maschera bool (True = SALTA il segnale) per regime PERSISTENTE/trending, + dove la rolling-Hurst >= hurst_max. Le fade concentrano stop-loss e perdite proprio li' + (diagnosi: stop-rate 43% per hurst>0.55 vs 21% anti-persistente). Filtrare hurst>=0.55 + DIMEZZA il DD del PORT06 (FULL 4.10%->2.39%) alzando lo Sharpe (validato 2026-06-02). + CAUSALE: rolling_hurst usa solo i rendimenti fino a close[i]. hurst_max=None -> nessuno skip. + Calcolata dalle SOLE close -> nessun feed dati esterno necessario (a differenza di DVOL). + step=6: calcola l'Hurst ogni 6 barre (ffill) -> ~6x piu' veloce per il worker live su finestre + lunghe (440g/10560 barre), e coincide con la cache di validazione (frac_step=6). L'Hurst varia + lentamente -> differenza trascurabile vs step=1.""" + n = len(df) + if hurst_max is None: + return np.zeros(n, dtype=bool) + h = rolling_hurst(df["close"].values.astype(float), window=window, step=step) + return h >= hurst_max def atr(df: pd.DataFrame, n: int = 14) -> np.ndarray: diff --git a/tests/portfolio/test_hurst_lossguard.py b/tests/portfolio/test_hurst_lossguard.py new file mode 100644 index 0000000..268a24f --- /dev/null +++ b/tests/portfolio/test_hurst_lossguard.py @@ -0,0 +1,51 @@ +"""Loss-guard Hurst: le fade saltano i segnali in regime persistente/trending (rolling-Hurst >= +soglia), dove si concentrano stop-loss e perdite. Validato 2026-06-02: filtrare hurst>=0.55 +DIMEZZA il DD del PORT06 alzando lo Sharpe. Filtro CAUSALE (close<=i), default off (None).""" +import numpy as np +import pandas as pd + +from src.strategies.fade_base import hurst_skip_mask + + +def _df(close): + n = len(close) + return pd.DataFrame({"timestamp": range(n), "open": close, "high": close, + "low": close, "close": close, "volume": [1.0] * n}) + + +def test_mask_off_when_none(): + df = _df(np.cumsum(np.random.default_rng(0).normal(size=400)) + 100) + m = hurst_skip_mask(df, None) + assert m.dtype == bool and not m.any() # None -> nessuno skip + + +def test_mask_flags_persistent_regime(): + # serie fortemente TRENDING (persistente, Hurst alto) -> deve essere mascherata (skip) molto + trend = np.linspace(100, 300, 600) + df = _df(trend) + m = hurst_skip_mask(df, hurst_max=0.55, window=100) + # dopo il warmup, una rampa pulita e' persistente -> gran parte mascherata + assert m[150:].mean() > 0.5 + + +def test_fade_strategy_filters_signals(): + """Una fade con hurst_max produce <= segnali del baseline, e tutti i superstiti sono in + regime non-persistente (la maschera e' False alla loro barra).""" + import importlib + rng = np.random.default_rng(1) + # serie mean-reverting (anti-persistente) con qualche estensione -> genera fade + n = 1200 + c = 100 + np.cumsum(rng.normal(scale=0.5, size=n)) + c = 100 + (c - c.mean()) * 0.3 # comprimi verso la media (mean-revert) + df = _df(c) + ts = pd.to_datetime(df["timestamp"], unit="s", utc=True) + m = importlib.import_module("scripts.strategies.MR01_bollinger_fade") + Strat = next(v for k, v in vars(m).items() + if isinstance(v, type) and getattr(v, "__module__", "") == m.__name__ + and hasattr(v, "generate_signals")) + s = Strat() + base = s.generate_signals(df, ts, bb_window=50, k=2.0, sl_atr=2.0) + filt = s.generate_signals(df, ts, bb_window=50, k=2.0, sl_atr=2.0, hurst_max=0.55) + assert len(filt) <= len(base) # il filtro non aggiunge mai segnali + skip = hurst_skip_mask(df, 0.55, 100) + assert all(not skip[sig.idx] for sig in filt) # nessun superstite in regime persistente