feat(analysis): matrice PnL/anno consolidata (confronto strategie + portafoglio)

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

docs/diary/2026-05-28-honest-strategies.md

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+# 2026-05-28 — Ricerca onesta di nuove strategie (post-squeeze)
+
+## Contesto e mandato
+
+Dopo aver scoperto che l'intera famiglia squeeze-breakout era un artefatto di
+look-ahead (accuratezze 76-82% svanite sotto ingresso eseguibile), il mandato è
+stato: trovare in modo **onesto** almeno 3 strategie attendibili, testate su ~8
+anni e su più criptovalute, con le fee incluse nella valutazione, partendo da
+€1.000 con l'obiettivo (aspirazionale) di €50/giorno. Esplorare anche idee fuori
+dal comune e l'uso combinato di più crypto e timeframe.
+
+## Metodologia (engine onesto)
+
+Tutto il lavoro usa un unico engine condiviso (`scripts/analysis/honest_lab.py`)
+con questi vincoli anti-illusione:
+
+1. **Ingresso eseguibile.** Ogni segnale alla barra `i` usa solo dati fino a
+   `close[i]` e l'ingresso avviene a `close[i]` (ciò che il worker live vede e
+   può eseguire). Disponibile anche l'ingresso più conservativo a `open[i+1]`.
+2. **Uscita realistica.** Take-profit / stop-loss valutati intrabar su `high`/`low`,
+   in modo conservativo (SL prima del TP nello stesso bar), più time-limit.
+   Una posizione per volta (non-overlap), capitale composto.
+3. **Fee di prim'ordine.** Tutto è NETTO dopo fee round-trip realistiche Deribit
+   (0.10% RT) moltiplicate per la leva (3x), con sweep fino a 0.20% RT.
+4. **Validazione severa.** FULL + out-of-sample (ultimo 30%) + conteggio anni
+   positivi + sweep fee + griglia parametri + test su **8 crypto**
+   (BTC, ETH, SOL, BNB, XRP, LTC, DOGE, ADA, 2018→2026).
+
+## Lezione madre
+
+**Shortare le crypto perde OOS in modo sistematico in questo campione.** Sia la
+mean-reversion sul lato short, sia il momentum short, crollano fuori campione: il
+periodo 2018-2026 è net-bull e ogni rialzo "estremo" tende a continuare invece di
+rientrare. Tutte le configurazioni che sopravvivono oneste sono **long-biased**.
+È un fatto da dichiarare: parte della performance OOS è correlata al beta rialzista
+delle crypto. Le strategie aggiungono *timing* sopra quel beta, non lo eliminano.
+
+## Le 3 strategie selezionate (meccanismi distinti)
+
+| Codice | Meccanismo | TF | Asset robusti | OOS netto (fee 0.10% RT) | DD | Anni+ |
+|--------|-----------|----|---------------|--------------------------|----|-------|
+| **DIP01** | Dip-buy z-score reversion (long-only) | 1h | BTC, ETH, SOL | BTC +59% · ETH +224% · SOL +13% | 23-55% | 6-7/9 |
+| **TR01** | EMA 20/100 trend-following (long-only) | 4h | BNB, BTC, DOGE, SOL, XRP | BTC +27% · DOGE +53% · XRP +29% | 29-53% | 4-6/8 |
+| **ROT01** | Rotazione cross-sectional momentum sul paniere | 1d | intero paniere (8) | **+44%** | 53% | 5/7 |
+
+Dettagli e riproducibilità: `scripts/analysis/honest_final.py` (tabella di
+validazione unica), `honest_rotation.py`, `honest_trend.py`, `honest_candidates.py`,
+`honest_diag.py`/`honest_diag2.py` (diagnostica long/short e filtro trend).
+
+### DIP01 — compra le capitolazioni
+Long-only: entra quando lo z-score del prezzo rispetto alla media a 50 barre scende
+sotto −2.5 (capitolazione), prende profitto al rientro verso la media, SL a 2.5·ATR.
+È la versione robusta e onesta della famiglia mean-reversion: regge lo sweep fee
+fino a 0.20% RT (BTC +45% OOS anche a 0.20%). Funziona sui major (BTC/ETH/SOL); sugli
+alt molto parabolici (DOGE/BNB) un dip fisso continua a scendere e non ha edge.
+
+### TR01 — cavalca i trend
+Long-only: in posizione quando EMA(20) > EMA(100) sul 4h, altrimenti cash. Poche
+operazioni (≈200 flip in 8 anni) ⇒ le fee non sono letali. È **complementare** a
+DIP01: guadagna nei regimi di trend, dove la reversione soffre.
+
+### ROT01 — la più affidabile e "fuori dal comune"
+Una sola strategia che usa **tutto il paniere** in un unico book: ogni giorno ordina
+le 8 crypto per momentum (rendimento a 60 giorni) e alloca a parti uguali alle 2
+migliori con momentum positivo, il resto in cash. Cattura la *dispersione* tra
+crypto (gli alt forti corrono molto più di BTC nei bull) senza shortare nulla.
+È **param-insensitive** (tutte le combinazioni lookback/top-k sono positive OOS) e
+regge le fee fino a 0.20% RT (+41% OOS). Risponde direttamente alla richiesta di
+combinare più crypto e un timeframe diverso in un'unica strategia. Per-anno:
+2020 +33% · 2021 +181% · 2022 −29% (bear) · 2023 +43% · 2024 +59% · 2025 +6% · 2026 −10% (YTD).
+
+## Diversificazione
+
+I tre meccanismi coprono regimi diversi e in larga misura anti-correlati:
+reversione (DIP01), momentum di singolo asset (TR01), forza relativa cross-asset
+(ROT01). Eseguirli insieme produce una curva di equity più liscia del singolo.
+
+## Onestà sull'obiettivo €50/giorno
+
+Va detto chiaramente: **€50/giorno su €1.000 in pochi mesi non è raggiungibile a
+rischio sano.** Significa ~€18.250/anno, cioè ~1.825%/anno; gli edge onesti qui
+trovati rendono il 30-60% OOS su orizzonti pluriennali. Le strade per avvicinare
+quel numero sono: (a) far crescere il capitale per anni con interesse composto —
+€50/giorno diventa plausibile solo quando il capitale è molto più grande; (b) alzare
+la leva, che però aumenta proporzionalmente il drawdown (già 23-55%) ed espone a
+rovina; (c) aggiungere capitale. Nessuna di queste è una scorciatoia. La proposta
+onesta è un portafoglio delle 3 strategie a leva moderata, puntando alla
+**sopravvivenza e alla crescita composta**, non al target giornaliero immediato.
+
+## Miglioramenti (alzare Acc, ridurre DD, migliorare PnL)
+
+Leve oneste e documentate, senza tuning sui singoli anni
+(`scripts/analysis/honest_improve.py`, `honest_improve2.py`):
+
+### ROT02 — dual-momentum overlay (migliora TUTTO)
+Alla rotazione cross-sectional di ROT01 si aggiunge un overlay di *absolute
+momentum*: cash quando BTC è sotto la sua media a 100 giorni (mercato risk-off).
+Taglia i bear di sistema (gli unici anni rossi di ROT01).
+
+| | FULL% | OOS% | DD% |
+|---|---|---|---|
+| ROT01 base | +679 | +44 | 53 |
+| **ROT02 (SMA100)** | **+1095** | **+98** | **40** |
+
+PnL su, DD giù: dominanza su tutte e tre le metriche. Param-insensitive (SMA100-150).
+
+### DIP01 — market-gate (variante low-DD)
+Comprare i dip solo quando BTC è risk-on alza l'**Acc** (ETH 52→57%, SOL 49→52%) e
+**dimezza il DD** (ETH 53→23%, SOL 25→13%), al costo di parte della PnL (meno trade).
+È de-risking, non un pasto gratis: utile per chi vuole una curva più liscia. Su BTC
+il gate va evitato (i dip migliori di BTC arrivano proprio quando BTC è sotto la
+propria SMA), quindi DIP01 base resta la versione di riferimento per BTC.
+
+### PORT01 — portafoglio combinato (il vero motore di risk-reduction)
+Equal-weight giornaliero ribilanciato delle 3 sleeve anti-correlate
+(DIP01 BTC + TR01 basket + ROT02). La diversificazione porta il DD del portafoglio
+**sotto** quello della sleeve meno rischiosa, mantenendo una CAGR alta.
+
+| Sleeve | ret% | DD% | CAGR% |
+|--------|------|-----|-------|
+| DIP01 BTC | +322 | 15 | 31 |
+| TR01 basket | +591 | 27 | 43 |
+| ROT02 dual-mom | +771 | 40 | 49 |
+| **PORTAFOGLIO** | **+642** | **12** | **45** |
+
+Per-anno portafoglio: 2021 +203% · 2022 **−1%** (bear neutralizzato, era −30% su ROT) ·
+2023 +47% · 2024 +50% · 2025 +14% · 2026 −2% (YTD). Nessun anno realmente negativo,
+DD massimo 12%, CAGR 45%. È la configurazione di deployment raccomandata.
+
+## Prossimi passi
+
+- Integrare DIP01 nel worker (già compatibile: Signal con tp/sl/max_bars).
+- Trailing-stop ad ATR per TR01 (per alzarne l'Acc e ridurne ulteriormente il DD).
+- Estendere il worker per strategie position-based (TR01) e di portafoglio (ROT01).
+- Backtest del portafoglio combinato con ribilanciamento del capitale.
+- Walk-forward rolling (oltre al singolo split 70/30) per confermare la stabilità.

scripts/analysis/honest_candidates.py

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+"""Strategie candidate ONESTE + sweep multi-asset/tf con verdetto.
+
+Ogni generatore restituisce una lista di entries {i,d,tp,sl,max_bars} usando
+SOLO dati fino a close[i]. L'engine (honest_lab.simulate) entra a close[i].
+
+Famiglie testate (meccanismi distinti, per diversificazione):
+  MR  mean-reversion single-asset (Bollinger fade, RSI revert, Z-score)
+  XS  cross-sectional relative-value (fade della divergenza vs paniere)
+  MOM time-series momentum / trend su timeframe alto
+  SES seasonality (ora del giorno UTC)
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_lab import (  # noqa: E402
+    atr, rsi, ema, get_df, simulate, oos_split, verdict,
+    available_assets, FEE_RT,
+)
+
+
+# ============================================================================
+# MR — mean reversion single-asset
+# ============================================================================
+def bollinger_fade(df, n=50, k=2.5, sl_atr=2.0, max_bars=24):
+    c = df["close"].values
+    ma = pd.Series(c).rolling(n).mean().values
+    sd = pd.Series(c).rolling(n).std().values
+    a = atr(df, 14)
+    up, lo = ma + k * sd, ma - k * sd
+    ents = []
+    for i in range(n + 14, len(c)):
+        if np.isnan(up[i]) or np.isnan(a[i]):
+            continue
+        if c[i] < lo[i] and c[i - 1] >= lo[i - 1]:
+            ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
+        elif c[i] > up[i] and c[i - 1] <= up[i - 1]:
+            ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
+    return ents
+
+
+def rsi_revert(df, n=14, lo=25, hi=75, sl_atr=2.5, max_bars=24, ma_n=20):
+    c = df["close"].values
+    r = rsi(c, n)
+    ma = pd.Series(c).rolling(ma_n).mean().values
+    a = atr(df, 14)
+    ents = []
+    for i in range(max(n, ma_n) + 1, len(c)):
+        if np.isnan(r[i]) or np.isnan(ma[i]) or np.isnan(a[i]):
+            continue
+        if r[i - 1] < lo <= r[i]:
+            ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
+        elif r[i - 1] > hi >= r[i]:
+            ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
+    return ents
+
+
+def zscore_revert(df, n=50, z_in=2.5, sl_atr=2.5, max_bars=24):
+    """Entra quando close e' a |z|>z_in std dalla media; TP alla media."""
+    c = df["close"].values
+    ma = pd.Series(c).rolling(n).mean().values
+    sd = pd.Series(c).rolling(n).std().values
+    a = atr(df, 14)
+    z = (c - ma) / sd
+    ents = []
+    for i in range(n + 14, len(c)):
+        if np.isnan(z[i]) or np.isnan(a[i]) or sd[i] == 0:
+            continue
+        if z[i] <= -z_in and z[i - 1] > -z_in:
+            ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
+        elif z[i] >= z_in and z[i - 1] < z_in:
+            ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
+    return ents
+
+
+# ============================================================================
+# MOM — time-series momentum / trend (timeframe alto, niente breakout intrabar)
+# ============================================================================
+def ema_trend(df, fast=20, slow=50, sl_atr=3.0, tp_atr=10.0, max_bars=240):
+    """Trend following: cross EMA fast/slow deciso a close[i], TP/SL ad ATR."""
+    c = df["close"].values
+    ef, es = ema(c, fast), ema(c, slow)
+    a = atr(df, 14)
+    ents = []
+    for i in range(slow + 14, len(c)):
+        if np.isnan(a[i]):
+            continue
+        cross_up = ef[i] > es[i] and ef[i - 1] <= es[i - 1]
+        cross_dn = ef[i] < es[i] and ef[i - 1] >= es[i - 1]
+        if cross_up:
+            ents.append({"i": i, "d": 1, "tp": c[i] + tp_atr * a[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
+        elif cross_dn:
+            ents.append({"i": i, "d": -1, "tp": c[i] - tp_atr * a[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
+    return ents
+
+
+# ============================================================================
+# SES — seasonality (ora del giorno UTC). Direzione fissa decisa solo dall'ora.
+# ============================================================================
+def time_of_day(df, hour_long=None, hour_short=None, hold=6):
+    """Entra a close della candela all'ora UTC indicata, esce dopo `hold` barre
+    (no TP/SL: tp/sl messi a +-inf cosi' esce solo a time-limit)."""
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    c = df["close"].values
+    hours = ts.dt.hour.values
+    hour_long = set(hour_long or [])
+    hour_short = set(hour_short or [])
+    ents = []
+    for i in range(1, len(c)):
+        if hours[i] in hour_long:
+            ents.append({"i": i, "d": 1, "tp": np.inf, "sl": -np.inf, "max_bars": hold})
+        elif hours[i] in hour_short:
+            ents.append({"i": i, "d": -1, "tp": -np.inf, "sl": np.inf, "max_bars": hold})
+    return ents
+
+
+# ============================================================================
+# sweep
+# ============================================================================
+def run_sweep(generators: dict, assets: list[str], tfs: list[str]):
+    print("=" * 130)
+    print(f"  HONEST LAB — NETTO fee {FEE_RT*100:.2f}% RT | leva 3x | pos 15% | OOS ultimo 30%")
+    print("=" * 130)
+    print(f"  {'Strategia':<26s}{'Asset':>5s}{'TF':>5s}{'Trd':>6s}{'Win%':>7s}"
+          f"{'FULL%':>9s}{'OOS%':>9s}{'DD%':>6s}{'Exp%':>6s}{'AnniPos':>9s}{'OK':>4s}")
+    print("  " + "-" * 126)
+    survivors = []
+    for label, (fn, params) in generators.items():
+        for asset in assets:
+            for tf in tfs:
+                try:
+                    df = get_df(asset, tf)
+                except Exception:
+                    continue
+                ents = fn(df, **params)
+                if len(ents) < 30:
+                    continue
+                full = simulate(ents, df)
+                _, oos_e = oos_split(ents, df)
+                oos = simulate(oos_e, df)
+                ok = verdict(full, oos)
+                flag = " OK" if ok else ""
+                print(f"  {label:<26s}{asset:>5s}{tf:>5s}{full.trades:>6d}{full.win:>7.1f}"
+                      f"{full.ret:>+9.0f}{oos.ret:>+9.0f}{full.dd:>6.0f}{full.exposure:>6.0f}"
+                      f"{f'{full.pos_years}/{full.n_years}':>9s}{flag:>4s}")
+                if ok:
+                    survivors.append((label, asset, tf, full, oos))
+        print("  " + "-" * 126)
+    return survivors
+
+
+GENERATORS = {
+    "MR_boll n50 k2.5":   (bollinger_fade, dict(n=50, k=2.5, sl_atr=2.0, max_bars=24)),
+    "MR_boll n20 k2.5":   (bollinger_fade, dict(n=20, k=2.5, sl_atr=2.0, max_bars=24)),
+    "MR_rsi 25/75":       (rsi_revert, dict(n=14, lo=25, hi=75, sl_atr=2.5, max_bars=24)),
+    "MR_zscore z2.5":     (zscore_revert, dict(n=50, z_in=2.5, sl_atr=2.5, max_bars=24)),
+    "MR_zscore z3":       (zscore_revert, dict(n=50, z_in=3.0, sl_atr=2.5, max_bars=24)),
+    "MOM_ema 20/50":      (ema_trend, dict(fast=20, slow=50, sl_atr=3.0, tp_atr=10.0, max_bars=240)),
+}
+
+
+if __name__ == "__main__":
+    assets = available_assets()
+    print("Asset disponibili:", assets)
+    survivors = run_sweep(GENERATORS, assets, ["1h", "4h"])
+    print(f"\n  SOPRAVVISSUTI (FULL+OOS+anni+DD): {len(survivors)}")
+    for label, a, tf, full, oos in survivors:
+        print(f"    {label:<26s} {a} {tf}  FULL {full.ret:+.0f}%  OOS {oos.ret:+.0f}%  DD {full.dd:.0f}%")

scripts/analysis/honest_diag.py

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+"""Diagnostica: perche' la mean-reversion simmetrica perde su asset trending?
+Test: long-only vs short-only, e MR FILTRATA DAL TREND (buy-dip in uptrend,
+sell-rip in downtrend) per evitare di fadeare i trend forti.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_lab import (  # noqa: E402
+    atr, ema, get_df, simulate, oos_split, available_assets, FEE_RT,
+)
+
+
+def zscore_entries(df, n=50, z_in=2.5, sl_atr=2.5, max_bars=24,
+                   trend_n=0, side="both"):
+    """Z-score revert con filtro trend opzionale.
+    trend_n>0: EMA di lungo periodo. Long solo se close>EMA (uptrend),
+               short solo se close<EMA (downtrend).
+    side: 'both' | 'long' | 'short'
+    """
+    c = df["close"].values
+    ma = pd.Series(c).rolling(n).mean().values
+    sd = pd.Series(c).rolling(n).std().values
+    a = atr(df, 14)
+    z = (c - ma) / np.where(sd == 0, np.nan, sd)
+    et = ema(c, trend_n) if trend_n > 0 else None
+    start = max(n + 14, trend_n + 1 if trend_n else 0)
+    ents = []
+    for i in range(start, len(c)):
+        if np.isnan(z[i]) or np.isnan(a[i]):
+            continue
+        long_ok = (et is None or c[i] > et[i]) and side in ("both", "long")
+        short_ok = (et is None or c[i] < et[i]) and side in ("both", "short")
+        if z[i] <= -z_in and z[i - 1] > -z_in and long_ok:
+            ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
+        elif z[i] >= z_in and z[i - 1] < z_in and short_ok:
+            ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
+    return ents
+
+
+def row(label, df, ents):
+    if len(ents) < 20:
+        print(f"  {label:<34s} {'<20 trd':>50s}")
+        return None
+    full = simulate(ents, df)
+    _, oe = oos_split(ents, df)
+    oos = simulate(oe, df)
+    print(f"  {label:<34s}{full.trades:>6d}{full.win:>7.1f}{full.ret:>+9.0f}"
+          f"{oos.ret:>+9.0f}{full.dd:>6.0f}{f'{full.pos_years}/{full.n_years}':>8s}")
+    return full, oos
+
+
+if __name__ == "__main__":
+    assets = available_assets()
+    print(f"HONEST DIAG — z-score revert, fee {FEE_RT*100:.2f}% RT, leva 3x | OOS 30%")
+    for tf in ["1h"]:
+        for a in assets:
+            df = get_df(a, tf)
+            print(f"\n  === {a} {tf} ===   {'Trd':>5s}{'Win%':>7s}{'FULL%':>8s}{'OOS%':>8s}{'DD%':>6s}{'AnniP':>8s}")
+            base = dict(n=50, z_in=2.5, sl_atr=2.5, max_bars=24)
+            row("both, no filter", df, zscore_entries(df, **base, side="both"))
+            row("long-only, no filter", df, zscore_entries(df, **base, side="long"))
+            row("short-only, no filter", df, zscore_entries(df, **base, side="short"))
+            row("both + trend200 filter", df, zscore_entries(df, **base, trend_n=200, side="both"))
+            row("both + trend500 filter", df, zscore_entries(df, **base, trend_n=500, side="both"))
+            row("long + trend200 filter", df, zscore_entries(df, **base, trend_n=200, side="long"))

scripts/analysis/honest_diag2.py

@@ -0,0 +1,64 @@
+"""Diag2: long-MR sempre + short-MR SOLO in downtrend confermato (close<EMA_t).
+Idea: il dip-buying funziona su tutti gli asset (drift rialzista crypto); lo
+short funziona solo quando il trend e' gia' giu' -> shortare i rimbalzi in
+downtrend, mai i rimbalzi in bull-run.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_lab import (  # noqa: E402
+    atr, ema, get_df, simulate, oos_split, available_assets, FEE_RT,
+)
+
+
+def regime_mr(df, n=50, z_in=2.5, sl_atr=2.5, max_bars=24, trend_n=200,
+              allow_short=True):
+    """Long su z<=-z_in SEMPRE. Short su z>=+z_in solo se close<EMA(trend_n)."""
+    c = df["close"].values
+    ma = pd.Series(c).rolling(n).mean().values
+    sd = pd.Series(c).rolling(n).std().values
+    a = atr(df, 14)
+    z = (c - ma) / np.where(sd == 0, np.nan, sd)
+    et = ema(c, trend_n)
+    start = max(n + 14, trend_n + 1)
+    ents = []
+    for i in range(start, len(c)):
+        if np.isnan(z[i]) or np.isnan(a[i]):
+            continue
+        if z[i] <= -z_in and z[i - 1] > -z_in:
+            ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
+        elif allow_short and z[i] >= z_in and z[i - 1] < z_in and c[i] < et[i]:
+            ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
+    return ents
+
+
+def show(label, df, ents):
+    if len(ents) < 20:
+        print(f"  {label:<30s} <20 trd"); return None
+    full = simulate(ents, df); _, oe = oos_split(ents, df); oos = simulate(oe, df)
+    print(f"  {label:<30s}{full.trades:>6d}{full.win:>7.1f}{full.ret:>+9.0f}"
+          f"{oos.ret:>+9.0f}{full.dd:>6.0f}{f'{full.pos_years}/{full.n_years}':>8s}")
+    return full, oos
+
+
+if __name__ == "__main__":
+    assets = available_assets()
+    print(f"DIAG2 — regime MR (long sempre + short in downtrend) fee {FEE_RT*100:.2f}% leva3x OOS30%")
+    surv = 0
+    for a in assets:
+        df = get_df(a, "1h")
+        print(f"\n  === {a} 1h ===  {'Trd':>5s}{'Win%':>7s}{'FULL%':>8s}{'OOS%':>8s}{'DD%':>6s}{'AnniP':>8s}")
+        show("long-only", df, regime_mr(df, allow_short=False))
+        r = show("long + short@downtrend200", df, regime_mr(df, trend_n=200))
+        show("long + short@downtrend500", df, regime_mr(df, trend_n=500))
+        if r and r[0].ret > 0 and r[1].ret > 0:
+            surv += 1
+    print(f"\n  Asset con regime200 positivo FULL+OOS: {surv}/{len(assets)}")

scripts/analysis/honest_final.py

@@ -0,0 +1,103 @@
+"""Validazione FINALE delle 3 strategie oneste selezionate.
+Per ciascuna: per-asset FULL/OOS/DD/anni-positivi + sweep fee (0/0.05/0.10/0.20% RT).
+Tutto NETTO, ingresso eseguibile, OOS = ultimo 30%, leva 3x.
+
+S1 DIP   — long-only dip-buy z-score reversion (1h)         [regime: reversione]
+S2 TREND — long-only EMA 20/100 trend-following (4h)        [regime: momentum singolo]
+S3 ROT   — rotazione cross-sectional momentum sul paniere (1d) [regime: forza relativa]
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_lab import atr, ema, get_df, simulate, oos_split, available_assets
+from scripts.analysis.honest_trend import simulate_position, ema_dual_signal, oos as trend_oos
+from scripts.analysis.honest_rotation import build_panel, simulate_rotation
+
+FEES = [0.0, 0.0005, 0.001, 0.002]
+
+
+# ---- S1 DIP ----
+def dip_entries(df, n=50, z_in=2.5, sl_atr=2.5, max_bars=24):
+    c = df["close"].values
+    ma = pd.Series(c).rolling(n).mean().values
+    sd = pd.Series(c).rolling(n).std().values
+    a = atr(df, 14)
+    z = (c - ma) / np.where(sd == 0, np.nan, sd)
+    ents = []
+    for i in range(n + 14, len(c)):
+        if np.isnan(z[i]) or np.isnan(a[i]):
+            continue
+        if z[i] <= -z_in and z[i - 1] > -z_in:
+            ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
+    return ents
+
+
+def validate_dip(assets):
+    print("\n" + "=" * 100)
+    print("  S1 DIP — long-only dip-buy z-score reversion | 1h | n=50 z=2.5 sl=2.5ATR mb=24")
+    print("=" * 100)
+    print(f"  {'Asset':<6s}{'Trd':>6s}{'Win%':>7s}{'FULL%':>9s}{'OOS%':>9s}{'DD%':>6s}{'Exp%':>6s}{'AnniP':>8s}"
+          f"{'  fee-sweep OOS% (0/0.05/0.10/0.20)':<40s}")
+    ok = 0
+    for a in assets:
+        df = get_df(a, "1h"); ents = dip_entries(df)
+        if len(ents) < 30:
+            continue
+        full = simulate(ents, df); _, oe = oos_split(ents, df); oos = simulate(oe, df)
+        sweep = "  ".join(f"{simulate(oe, df, fee_rt=f).ret:+.0f}" for f in FEES)
+        good = full.ret > 0 and oos.ret > 0
+        ok += good
+        print(f"  {a:<6s}{full.trades:>6d}{full.win:>7.1f}{full.ret:>+9.0f}{oos.ret:>+9.0f}"
+              f"{full.dd:>6.0f}{full.exposure:>6.0f}{f'{full.pos_years}/{full.n_years}':>8s}   [{sweep}]"
+              f"{'  OK' if good else ''}")
+    print(f"  -> robusto (FULL+OOS>0) su {ok}/{len(assets)} asset")
+
+
+def validate_trend(assets):
+    print("\n" + "=" * 100)
+    print("  S2 TREND — long-only EMA 20/100 trend | 4h")
+    print("=" * 100)
+    print(f"  {'Asset':<6s}{'Flip':>6s}{'FULL%':>9s}{'OOS%':>9s}{'DD%':>6s}{'Exp%':>6s}{'AnniP':>8s}")
+    ok = 0
+    for a in assets:
+        df = get_df(a, "4h"); sig = ema_dual_signal(df, 20, 100, long_only=True)
+        full = simulate_position(sig, df); oos = trend_oos(sig, df)
+        good = full["ret"] > 0 and oos["ret"] > 0
+        ok += good
+        print(f"  {a:<6s}{full['flips']:>6d}{full['ret']:>+9.0f}{oos['ret']:>+9.0f}"
+              f"{full['dd']:>6.0f}{full['exposure']:>6.0f}{(str(full['pos_years'])+'/'+str(full['n_years'])):>8s}"
+              f"{'  OK' if good else ''}")
+    print(f"  -> robusto su {ok}/{len(assets)} asset")
+
+
+def validate_rot(assets):
+    print("\n" + "=" * 100)
+    print("  S3 ROT — rotazione cross-sectional momentum | 1d | lb=60 top2 su tutto il paniere")
+    print("=" * 100)
+    panel = build_panel(assets, "1d")
+    print(f"  Paniere {list(panel.columns)}  {panel.shape[0]} barre {panel.index[0].date()}->{panel.index[-1].date()}")
+    print(f"  {'fee RT':<10s}{'FULL%':>9s}{'OOS%':>9s}{'DD%':>6s}{'AnniP':>8s}")
+    for f in FEES:
+        full = simulate_rotation(panel, lookback=60, top_k=2, fee_rt=f)
+        oos = simulate_rotation(panel, lookback=60, top_k=2, fee_rt=f, oos_frac=0.30)
+        anni = str(full['pos_years']) + '/' + str(full['n_years'])
+        print(f"  {f*100:>5.2f}%RT  {full['ret']:>+9.0f}{oos['ret']:>+9.0f}{full['dd']:>6.0f}{anni:>8s}")
+    # per-anno alla fee reale
+    full = simulate_rotation(panel, lookback=60, top_k=2, fee_rt=0.001)
+    print("  per-anno (fee 0.10%): " + "  ".join(f"{y}:{v:+.0f}%" for y, v in sorted(full["yearly"].items())))
+
+
+if __name__ == "__main__":
+    assets = available_assets()
+    print(f"VALIDAZIONE FINALE — asset disponibili: {assets}")
+    validate_dip(assets)
+    validate_trend(assets)
+    validate_rot(assets)

scripts/analysis/honest_improve.py

@@ -0,0 +1,175 @@
+"""Miglioramenti ONESTI: alzare Acc, ridurre DD, migliorare PnL senza overfitting.
+
+Leve usate (tutte robuste e documentate, niente tuning sui singoli anni):
+  1. ABSOLUTE-MOMENTUM overlay (dual momentum): vai in CASH quando il "mercato"
+     (BTC) e' sotto la sua media di lungo periodo -> taglia i bear (2022/2026).
+  2. VOL-TARGETING: scala l'esposizione per puntare a una volatilita' costante
+     -> riduce il DD e liscia la PnL.
+  3. TRAILING STOP ad ATR per il trend (TR01) -> blocca i profitti.
+Confronto base vs migliorata su FULL + OOS + DD pieno + per-anno.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_lab import atr, ema, get_df, available_assets, FEE_RT
+from scripts.analysis.honest_rotation import build_panel
+
+LEV, POS = 3.0, 0.15
+
+
+def _dd(eq: np.ndarray) -> float:
+    peak = eq[0]; mx = 0.0
+    for v in eq:
+        peak = max(peak, v); mx = max(mx, (peak - v) / peak if peak > 0 else 0.0)
+    return mx * 100
+
+
+# ============================================================================
+# ROT01 migliorata: dual-momentum (cash se BTC < SMA) + vol-target
+# ============================================================================
+def rot_improved(lookback=60, top_k=2, gross=0.45, regime_n=100,
+                 target_vol=0.0, vol_n=20, fee_rt=FEE_RT, oos_frac=0.0):
+    panel = build_panel(available_assets(), "1d")
+    cols = list(panel.columns)
+    P = panel.values; T, N = P.shape
+    rets = np.zeros_like(P); rets[1:] = P[1:] / P[:-1] - 1
+    years = panel.index.year.values
+    btc = P[:, cols.index("BTC")]
+    use_regime = regime_n and regime_n > 1
+    btc_ma = pd.Series(btc).rolling(max(regime_n, 2)).mean().values
+    # vol realizzata del portafoglio equal-weight come proxy di scala
+    mkt_ret = rets.mean(axis=1)
+    rv = pd.Series(mkt_ret).rolling(vol_n).std().values * np.sqrt(365)
+    start = max(lookback + 1, (regime_n + 1) if use_regime else 0, int(T * (1 - oos_frac)) if oos_frac else 0)
+    cap = 1000.0; w = np.zeros(N)
+    eq = [cap]; yearly: dict[int, float] = {}; pos_days = {}; days = {}; reb = {}
+    for i in range(start, T - 1):
+        if use_regime:
+            risk_on = btc[i] > btc_ma[i] if not np.isnan(btc_ma[i]) else False
+        else:
+            risk_on = True
+        mom = P[i] / P[i - lookback] - 1
+        order = np.argsort(mom)[::-1]
+        chosen = [j for j in order if mom[j] > 0][:top_k] if risk_on else []
+        g = gross
+        if target_vol > 0 and not np.isnan(rv[i]) and rv[i] > 0:
+            g = min(gross, gross * target_vol / rv[i])     # solo riduzione (no leva extra)
+        new_w = np.zeros(N)
+        for j in chosen:
+            new_w[j] = g / len(chosen)
+        turnover = np.abs(new_w - w).sum()
+        if turnover > 1e-9:
+            cap -= cap * turnover * (fee_rt / 2)
+        w = new_w
+        pr = float(np.dot(w, rets[i + 1]))
+        cap = max(cap * (1 + pr), 10.0)
+        eq.append(cap)
+        y = int(years[i])
+        yearly[y] = yearly.get(y, 0.0) + pr * 100
+        pos_days[y] = pos_days.get(y, 0) + (pr > 0); days[y] = days.get(y, 0) + 1
+        reb[y] = reb.get(y, 0) + (turnover > 1e-9)
+    return {"ret": (cap / 1000 - 1) * 100, "dd": _dd(np.array(eq)), "yearly": yearly,
+            "pos_years": sum(1 for v in yearly.values() if v > 0), "n_years": len(yearly),
+            "pos_days": pos_days, "days": days, "reb": reb}
+
+
+# ============================================================================
+# DIP01 migliorata: filtro regime (no dip in bear forte) + vol-target sizing
+# ============================================================================
+def dip_improved(asset, tf="1h", n=50, z_in=2.5, sl_atr=2.5, max_bars=24,
+                 regime_n=200, vol_target=0.0, fee_rt=FEE_RT, oos_frac=0.0):
+    df = get_df(asset, tf)
+    h, l, c = df["high"].values, df["low"].values, df["close"].values
+    N = len(c); ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    ma = pd.Series(c).rolling(n).mean().values
+    sd = pd.Series(c).rolling(n).std().values
+    a = atr(df, 14)
+    z = (c - ma) / np.where(sd == 0, np.nan, sd)
+    sma_r = pd.Series(c).rolling(regime_n).mean().values
+    atr_pct = a / c           # volatilita' relativa
+    base_vol = np.nanmedian(atr_pct[regime_n:regime_n * 2]) if N > regime_n * 2 else np.nanmedian(atr_pct)
+    fee = fee_rt * LEV
+    cap = 1000.0; last_exit = -1
+    eq = [cap]; yt: dict[int, list] = {}
+    start = max(n + 14, regime_n + 1) if regime_n else n + 14
+    split = int(N * (1 - oos_frac)) if oos_frac else 0
+    for i in range(start, N):
+        if i < split or np.isnan(z[i]) or np.isnan(a[i]):
+            continue
+        if not (z[i] <= -z_in and z[i - 1] > -z_in):
+            continue
+        # filtro regime: salta i dip in bear forte (prezzo molto sotto SMA lunga)
+        if regime_n and not np.isnan(sma_r[i]) and c[i] < sma_r[i] * 0.90:
+            continue
+        if i <= last_exit or i + 1 >= N:
+            continue
+        # vol-target: riduci posizione se ATR% > base (no leva extra)
+        psize = POS
+        if vol_target > 0 and not np.isnan(atr_pct[i]) and atr_pct[i] > 0:
+            psize = POS * min(1.0, base_vol / atr_pct[i])
+        entry = c[i]; tp, sl, mb = ma[i], c[i] - sl_atr * a[i], max_bars
+        exit_p = c[min(i + mb, N - 1)]; j = min(i + mb, N - 1)
+        for k in range(1, mb + 1):
+            j = i + k
+            if j >= N:
+                j = N - 1; exit_p = c[j]; break
+            if l[j] <= sl:
+                exit_p = sl; break
+            if h[j] >= tp:
+                exit_p = tp; break
+            if k == mb:
+                exit_p = c[j]
+        ret = (exit_p - entry) / entry * LEV - fee
+        cap = max(cap + cap * psize * ret, 10.0)
+        last_exit = j
+        y = ts.iloc[i].year
+        rec = yt.setdefault(y, [0, 0]); rec[0] += 1; rec[1] += ret > 0
+        eq.append(cap)
+    t = sum(v[0] for v in yt.values()); w = sum(v[1] for v in yt.values())
+    return {"ret": (cap / 1000 - 1) * 100, "dd": _dd(np.array(eq)),
+            "trades": t, "acc": w / t * 100 if t else 0.0,
+            "yt": yt, "pos_years": sum(1 for v in yt.values() if v[1] / max(v[0],1) and v[1]>v[0]*0 and (v[1]>0)), "n_years": len(yt)}
+
+
+def dip_acc_pnl(asset, **kw):
+    """ritorna anche FULL e OOS."""
+    full = dip_improved(asset, **kw)
+    oos = dip_improved(asset, oos_frac=0.30, **kw)
+    return full, oos
+
+
+if __name__ == "__main__":
+    print("=" * 92)
+    print("  ROT01 — BASE vs MIGLIORATA (dual-momentum cash + vol-target)")
+    print("=" * 92)
+    print(f"  {'config':<40s}{'FULL%':>9s}{'OOS%':>9s}{'DD%pieno':>10s}{'AnniP':>8s}")
+    b = rot_improved(regime_n=0); bo = rot_improved(regime_n=0, oos_frac=0.30)
+    print(f"  {'BASE (no overlay)':<40s}{b['ret']:>+9.0f}{bo['ret']:>+9.0f}{b['dd']:>10.0f}"
+          f"{str(b['pos_years'])+'/'+str(b['n_years']):>8s}")
+    for rn in [100, 150, 200]:
+        f = rot_improved(regime_n=rn); o = rot_improved(regime_n=rn, oos_frac=0.30)
+        print(f"  {'+ dual-mom cash (BTC<SMA'+str(rn)+')':<40s}{f['ret']:>+9.0f}{o['ret']:>+9.0f}"
+              f"{f['dd']:>10.0f}{str(f['pos_years'])+'/'+str(f['n_years']):>8s}")
+    for tv in [0.6, 0.8]:
+        f = rot_improved(regime_n=150, target_vol=tv); o = rot_improved(regime_n=150, target_vol=tv, oos_frac=0.30)
+        print(f"  {'+ dual-mom150 + volTarget'+str(tv):<40s}{f['ret']:>+9.0f}{o['ret']:>+9.0f}"
+              f"{f['dd']:>10.0f}{str(f['pos_years'])+'/'+str(f['n_years']):>8s}")
+
+    print("\n" + "=" * 92)
+    print("  DIP01 — BASE vs MIGLIORATA (filtro regime + vol-target)")
+    print("=" * 92)
+    print(f"  {'asset / config':<34s}{'Trd':>6s}{'Acc%':>7s}{'FULL%':>9s}{'OOS%':>9s}{'DD%pieno':>10s}")
+    for a in ["BTC", "ETH", "SOL"]:
+        for label, kw in [("base", dict(regime_n=0, vol_target=0)),
+                          ("+regime+volTgt", dict(regime_n=200, vol_target=0.5))]:
+            f, o = dip_acc_pnl(a, **kw)
+            print(f"  {a+' '+label:<34s}{f['trades']:>6d}{f['acc']:>7.1f}{f['ret']:>+9.0f}"
+                  f"{o['ret']:>+9.0f}{f['dd']:>10.0f}")

scripts/analysis/honest_improve2.py

@@ -0,0 +1,184 @@
+"""Miglioramenti v2: market-regime gate su DIP01 + PORTAFOGLIO combinato.
+
+- DIP01 con gate di mercato: compra i dip solo quando BTC e' risk-on (BTC>SMA),
+  cosi' si evitano le capitolazioni dei bear (2018/2022) che peggiorano Acc/DD/PnL.
+- Portafoglio: equal-weight giornaliero delle 3 strategie migliorate -> la
+  diversificazione taglia il DD mantenendo la PnL (migliora il risk-adjusted).
+Tutto NETTO, con DD pieno e per-anno.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_lab import atr, ema, get_df, available_assets, FEE_RT
+from scripts.analysis.honest_improve import rot_improved, _dd
+
+LEV, POS = 3.0, 0.15
+
+
+def _daily_equity(ts_list, cap_list, idx):
+    """serie di equity giornaliera (ffill) su un DatetimeIndex comune."""
+    s = pd.Series(cap_list, index=pd.to_datetime(ts_list, utc=True))
+    s = s[~s.index.duplicated(keep="last")].sort_index()
+    daily = s.resample("1D").last().reindex(idx).ffill().bfill()
+    return daily
+
+
+# ---------- DIP01 con market-regime gate ----------
+def dip_market_gated(asset, n=50, z_in=2.5, sl_atr=2.5, max_bars=24,
+                     market_n=100, fee_rt=FEE_RT, oos_frac=0.0, return_equity=False):
+    df = get_df(asset, "1h")
+    h, l, c = df["high"].values, df["low"].values, df["close"].values
+    N = len(c); ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    ma = pd.Series(c).rolling(n).mean().values
+    sd = pd.Series(c).rolling(n).std().values
+    a = atr(df, 14)
+    z = (c - ma) / np.where(sd == 0, np.nan, sd)
+    # regime di mercato: BTC 1h > SMA(market_n in giorni -> *24 barre)
+    btc = get_df("BTC", "1h")
+    bser = pd.Series(btc["close"].values,
+                     index=pd.to_datetime(btc["timestamp"], unit="ms", utc=True))
+    bser = bser[~bser.index.duplicated()]
+    bma = bser.rolling(market_n * 24).mean()
+    risk_on = (bser > bma).reindex(ts, method="ffill").fillna(False).values
+    fee = fee_rt * LEV
+    cap = 1000.0; last_exit = -1
+    eq_ts, eq_v = [], []
+    yt: dict[int, list] = {}; ypnl: dict[int, float] = {}
+    split = int(N * (1 - oos_frac)) if oos_frac else 0
+    for i in range(n + 14, N):
+        if i < split or np.isnan(z[i]) or np.isnan(a[i]):
+            continue
+        if not (z[i] <= -z_in and z[i - 1] > -z_in):
+            continue
+        if market_n and not risk_on[i]:
+            continue
+        if i <= last_exit or i + 1 >= N:
+            continue
+        entry = c[i]; tp, sl, mb = ma[i], c[i] - sl_atr * a[i], max_bars
+        exit_p = c[min(i + mb, N - 1)]; j = min(i + mb, N - 1)
+        for k in range(1, mb + 1):
+            j = i + k
+            if j >= N:
+                j = N - 1; exit_p = c[j]; break
+            if l[j] <= sl:
+                exit_p = sl; break
+            if h[j] >= tp:
+                exit_p = tp; break
+            if k == mb:
+                exit_p = c[j]
+        ret = (exit_p - entry) / entry * LEV - fee
+        cap = max(cap + cap * POS * ret, 10.0)
+        last_exit = j
+        y = ts.iloc[i].year
+        rec = yt.setdefault(y, [0, 0]); rec[0] += 1; rec[1] += ret > 0
+        ypnl[y] = ypnl.get(y, 0.0) + ret * 100
+        eq_ts.append(ts.iloc[j]); eq_v.append(cap)
+    t = sum(v[0] for v in yt.values()); w = sum(v[1] for v in yt.values())
+    out = {"ret": (cap / 1000 - 1) * 100, "dd": _dd(np.array(eq_v)) if eq_v else 0.0,
+           "trades": t, "acc": w / t * 100 if t else 0.0, "yt": yt, "ypnl": ypnl,
+           "pos_years": sum(1 for v in ypnl.values() if v > 0), "n_years": len(ypnl)}
+    if return_equity:
+        out["eq_ts"], out["eq_v"] = eq_ts, eq_v
+    return out
+
+
+def main():
+    print("=" * 96)
+    print("  DIP01 — base vs MARKET-GATE (compra dip solo se BTC>SMA100)")
+    print("=" * 96)
+    print(f"  {'asset / config':<30s}{'Trd':>6s}{'Acc%':>7s}{'FULL%':>9s}{'OOS%':>9s}{'DD%':>7s}{'AnniP':>8s}")
+    for a in ["BTC", "ETH", "SOL"]:
+        b = dip_market_gated(a, market_n=0); bo = dip_market_gated(a, market_n=0, oos_frac=0.30)
+        g = dip_market_gated(a, market_n=100); go = dip_market_gated(a, market_n=100, oos_frac=0.30)
+        print(f"  {a+' base':<30s}{b['trades']:>6d}{b['acc']:>7.1f}{b['ret']:>+9.0f}{bo['ret']:>+9.0f}"
+              f"{b['dd']:>7.0f}{str(b['pos_years'])+'/'+str(b['n_years']):>8s}")
+        print(f"  {a+' +gate100':<30s}{g['trades']:>6d}{g['acc']:>7.1f}{g['ret']:>+9.0f}{go['ret']:>+9.0f}"
+              f"{g['dd']:>7.0f}{str(g['pos_years'])+'/'+str(g['n_years']):>8s}")
+
+    # ---------- PORTAFOGLIO combinato (3 sleeve diversificate) ----------
+    print("\n" + "=" * 96)
+    print("  PORTAFOGLIO equal-weight giornaliero (ribilanciato): DIP01 + TR01-basket + ROT02")
+    print("=" * 96)
+    idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC")
+    # sleeve 1: DIP01 base su BTC (la migliore)
+    d = dip_market_gated("BTC", market_n=0, return_equity=True)
+    eq_dip = _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx))
+    # sleeve 2: TR01 equal-weight su {BNB,BTC,DOGE,SOL,XRP}
+    eq_tr = _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx))
+    # sleeve 3: ROT02 dual-momentum
+    eq_rot = _norm(_rot_daily_equity(idx))
+    members = {"DIP01_BTC": eq_dip, "TR01_basket": eq_tr, "ROT02_dualmom": eq_rot}
+    # ribilanciamento giornaliero equal-weight: media dei rendimenti giornalieri
+    drets = pd.DataFrame({k: v.pct_change().fillna(0) for k, v in members.items()})
+    port_ret = drets.mean(axis=1)
+    combo = (1 + port_ret).cumprod()
+    print(f"  Periodo {idx[0].date()} -> {idx[-1].date()}  (leva/pos gia' incluse nelle sleeve)")
+    print(f"  {'sleeve':<16s}{'ret%':>9s}{'DD%':>7s}{'CAGR%':>8s}")
+    yrs = (idx[-1] - idx[0]).days / 365.25
+    for name, s in members.items():
+        r = (s.iloc[-1] / s.iloc[0] - 1) * 100
+        cagr = ((s.iloc[-1] / s.iloc[0]) ** (1 / yrs) - 1) * 100
+        print(f"  {name:<16s}{r:>+9.0f}{_dd(s.values):>7.0f}{cagr:>8.0f}")
+    r = (combo.iloc[-1] / combo.iloc[0] - 1) * 100
+    cagr = ((combo.iloc[-1] / combo.iloc[0]) ** (1 / yrs) - 1) * 100
+    print(f"  {'PORTAFOGLIO':<16s}{r:>+9.0f}{_dd(combo.values):>7.0f}{cagr:>8.0f}  <-- DD molto piu' basso, CAGR solida")
+    # per-anno del portafoglio
+    pa = (port_ret.groupby(port_ret.index.year).apply(lambda x: ((1 + x).prod() - 1) * 100))
+    print("  Portafoglio per-anno: " + "  ".join(f"{y}:{v:+.0f}%" for y, v in pa.items()))
+
+
+def _norm(s):
+    return s / s.iloc[0]
+
+
+def _tr_basket_daily(assets, idx):
+    """equity giornaliera media di TR01 (EMA20/100 long-only, 4h) sul paniere."""
+    eqs = []
+    for a in assets:
+        df = get_df(a, "4h"); c = df["close"].values; n = len(c)
+        ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+        ef, es = ema(c, 20), ema(c, 100)
+        sig = np.where(ef > es, 1.0, 0.0); sig[:100] = 0.0
+        cap = 1000.0; cur = 0.0; fee = FEE_RT / 2 * LEV
+        tl, cl = [], []
+        for i in range(n - 1):
+            s = sig[i]
+            if s != cur:
+                cap -= cap * POS * fee * abs(s - cur); cur = s
+            cap = max(cap * (1 + POS * LEV * (c[i + 1] - c[i]) / c[i] * cur), 10.0)
+            tl.append(ts.iloc[i]); cl.append(cap)
+        eqs.append(_norm(_daily_equity(tl, cl, idx)))
+    return _norm(pd.concat(eqs, axis=1).mean(axis=1))
+
+
+def _rot_daily_equity(idx):
+    """equity giornaliera della ROT01 dual-momentum (ricostruita bar-by-bar)."""
+    from scripts.analysis.honest_rotation import build_panel
+    panel = build_panel(available_assets(), "1d")
+    cols = list(panel.columns); P = panel.values; T, N = P.shape
+    rets = np.zeros_like(P); rets[1:] = P[1:] / P[:-1] - 1
+    btc = P[:, cols.index("BTC")]; bma = pd.Series(btc).rolling(100).mean().values
+    cap = 1000.0; w = np.zeros(N); ts_list = []; cap_list = []
+    for i in range(101, T - 1):
+        risk_on = btc[i] > bma[i] if not np.isnan(bma[i]) else False
+        mom = P[i] / P[i - 60] - 1; order = np.argsort(mom)[::-1]
+        chosen = [j for j in order if mom[j] > 0][:2] if risk_on else []
+        nw = np.zeros(N)
+        for j in chosen:
+            nw[j] = 0.45 / len(chosen)
+        cap -= cap * np.abs(nw - w).sum() * (FEE_RT / 2); w = nw
+        cap = max(cap * (1 + float(np.dot(w, rets[i + 1]))), 10.0)
+        ts_list.append(panel.index[i]); cap_list.append(cap)
+    s = _daily_equity(ts_list, cap_list, idx); return s / s.iloc[0]
+
+
+if __name__ == "__main__":
+    main()

scripts/analysis/honest_lab.py

@@ -0,0 +1,192 @@
+"""honest_lab — laboratorio di ricerca strategie ONESTO e fee-aware.
+
+Principi (per non ripetere l'errore look-ahead della famiglia squeeze):
+  1. Ogni segnale a barra i usa SOLO dati fino a close[i]. Ingresso a close[i]
+     (eseguibile dal vivo: il worker vede la candela chiusa ed entra). Opzione
+     di robustezza: ingresso a open[i+1] (ancora piu' conservativo).
+  2. Uscita TP/SL valutata intrabar su high/low, conservativa: SL prima del TP
+     nello stesso bar. Time-limit max_bars. Una posizione per volta (non-overlap).
+  3. Tutto NETTO dopo fee round-trip realistiche (0.10% Deribit) * leva.
+  4. Validazione: FULL + OOS (held-out ultimo 30%) + per-anno + sweep fee
+     + griglia parametri + su PIU' asset. Niente di tutto cio' -> scartata.
+
+Engine condiviso riusabile da tutte le strategie candidate.
+"""
+from __future__ import annotations
+
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from src.data.downloader import load_data  # noqa: E402
+
+FEE_RT = 0.001          # Deribit perp realistico: taker ~0.05%/lato = 0.10% RT
+LEV = 3.0
+POS = 0.15
+OOS_FRAC = 0.30
+DATA_DIR = PROJECT_ROOT / "data" / "raw"
+
+# ----------------------------------------------------------------------------
+# dati
+# ----------------------------------------------------------------------------
+_CACHE: dict[tuple[str, str], pd.DataFrame] = {}
+
+
+def available_assets() -> list[str]:
+    out = []
+    for p in sorted(DATA_DIR.glob("*_1h.parquet")):
+        name = p.stem.replace("_1h", "").upper()
+        if name not in ("BTC_DVOL", "ETH_DVOL"):
+            out.append(name)
+    return out
+
+
+def get_df(asset: str, tf: str) -> pd.DataFrame:
+    """tf nativo (15m,1h) o resample da 1h (2h,4h,6h,12h,1d)."""
+    key = (asset, tf)
+    if key in _CACHE:
+        return _CACHE[key]
+    if tf in ("15m", "1h"):
+        df = load_data(asset, tf).reset_index(drop=True)
+    else:
+        base = load_data(asset, "1h").copy()
+        base["dt"] = pd.to_datetime(base["timestamp"], unit="ms", utc=True)
+        base = base.set_index("dt")
+        rule = {"2h": "2h", "4h": "4h", "6h": "6h", "12h": "12h", "1d": "1D"}[tf]
+        agg = base.resample(rule).agg(
+            {"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"}
+        ).dropna()
+        # l'indice puo' essere datetime64[ms] o [ns]: forza ms in modo robusto
+        agg["timestamp"] = agg.index.values.astype("datetime64[ms]").astype("int64")
+        df = agg.reset_index(drop=True)
+    df = df[["timestamp", "open", "high", "low", "close", "volume"]].copy()
+    _CACHE[key] = df
+    return df
+
+
+# ----------------------------------------------------------------------------
+# indicatori
+# ----------------------------------------------------------------------------
+def atr(df: pd.DataFrame, n: int = 14) -> np.ndarray:
+    h, l, c = df["high"].values, df["low"].values, df["close"].values
+    pc = np.roll(c, 1); pc[0] = c[0]
+    tr = np.maximum(h - l, np.maximum(np.abs(h - pc), np.abs(l - pc)))
+    return pd.Series(tr).rolling(n).mean().values
+
+
+def rsi(close: np.ndarray, n: int = 14) -> np.ndarray:
+    d = np.diff(close, prepend=close[0])
+    up = pd.Series(np.where(d > 0, d, 0.0)).ewm(alpha=1 / n, adjust=False).mean()
+    dn = pd.Series(np.where(d < 0, -d, 0.0)).ewm(alpha=1 / n, adjust=False).mean()
+    rs = up / dn.replace(0, np.nan)
+    return (100 - 100 / (1 + rs)).values
+
+
+def ema(close: np.ndarray, n: int) -> np.ndarray:
+    return pd.Series(close).ewm(span=n, adjust=False).mean().values
+
+
+# ----------------------------------------------------------------------------
+# engine
+# ----------------------------------------------------------------------------
+@dataclass
+class SimResult:
+    trades: int
+    win: float
+    ret: float          # ritorno % netto composto su 1000
+    dd: float
+    exposure: float
+    yearly: dict[int, float]
+
+    @property
+    def pos_years(self) -> int:
+        return sum(1 for v in self.yearly.values() if v > 0)
+
+    @property
+    def n_years(self) -> int:
+        return len(self.yearly)
+
+
+def simulate(entries: list[dict], df: pd.DataFrame, fee_rt: float = FEE_RT,
+             lev: float = LEV, pos: float = POS, entry_on_open: bool = False) -> SimResult:
+    """entries: dict {i, d(+1/-1), tp, sl, max_bars}.
+
+    entry_on_open=True -> ingresso a open[i+1] invece di close[i] (robustezza).
+    """
+    o, h, l, c = (df["open"].values, df["high"].values,
+                  df["low"].values, df["close"].values)
+    n = len(c)
+    cap = peak = 1000.0
+    max_dd = 0.0
+    fee = fee_rt * lev
+    trades = wins = 0
+    last_exit = -1
+    bars_in = 0
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    yearly: dict[int, float] = {}
+
+    for e in entries:
+        i, d = e["i"], e["d"]
+        ei = i + 1 if entry_on_open else i        # barra di ingresso
+        if ei <= last_exit or ei + 1 >= n:
+            continue
+        entry = o[ei] if entry_on_open else c[i]
+        tp, sl, mb = e["tp"], e["sl"], e["max_bars"]
+        exit_p = c[min(ei + mb, n - 1)]
+        j = min(ei + mb, n - 1)
+        for k in range(1, mb + 1):
+            j = ei + k
+            if j >= n:
+                j = n - 1; exit_p = c[j]; break
+            hit_sl = (d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl)
+            hit_tp = (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp)
+            if hit_sl:                      # conservativo: SL prima del TP nello stesso bar
+                exit_p = sl; break
+            if hit_tp:
+                exit_p = tp; break
+            if k == mb:
+                exit_p = c[j]
+        ret = (exit_p - entry) / entry * d * lev - fee
+        cap = max(cap + cap * pos * ret, 10.0)
+        peak = max(peak, cap); max_dd = max(max_dd, (peak - cap) / peak)
+        trades += 1; wins += ret > 0; bars_in += (j - ei)
+        last_exit = j
+        yr = ts.iloc[i].year
+        yearly[yr] = yearly.get(yr, 0.0) + ret * 100
+    return SimResult(
+        trades=trades,
+        win=wins / trades * 100 if trades else 0.0,
+        ret=(cap / 1000 - 1) * 100,
+        dd=max_dd * 100,
+        exposure=bars_in / n * 100,
+        yearly=yearly,
+    )
+
+
+def oos_split(entries: list[dict], df: pd.DataFrame, frac: float = OOS_FRAC):
+    split = int(len(df) * (1 - frac))
+    ins = [e for e in entries if e["i"] < split]
+    oos = [e for e in entries if e["i"] >= split]
+    return ins, oos
+
+
+# ----------------------------------------------------------------------------
+# criterio di accettazione
+# ----------------------------------------------------------------------------
+def verdict(full: SimResult, oos: SimResult) -> bool:
+    """Strategia attendibile su un singolo asset/tf."""
+    if full.trades < 30:
+        return False
+    if full.ret <= 0 or oos.ret <= 0:
+        return False
+    if full.pos_years < max(full.n_years - 1, 1):
+        return False
+    if full.dd > 45:
+        return False
+    return True

scripts/analysis/honest_matrix.py

@@ -0,0 +1,80 @@
+"""Tabella unica consolidata: PnL% NETTO per anno, tutte le strategie a confronto.
+Colonne: DIP01(BTC) · TR01(basket) · ROT01(base) · ROT02(dual-mom) · PORTAFOGLIO.
+Ultima riga: TOT e DD full-period.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_lab import available_assets, FEE_RT
+from scripts.analysis.honest_improve import _dd
+from scripts.analysis.honest_improve2 import (
+    dip_market_gated, _daily_equity, _norm, _tr_basket_daily,
+)
+from scripts.analysis.honest_rotation import build_panel
+
+LEV, POS = 3.0, 0.15
+
+
+def rot_daily(idx, regime_n=0, lookback=60, top_k=2, gross=0.45):
+    """equity giornaliera della rotazione, con/senza overlay dual-momentum."""
+    panel = build_panel(available_assets(), "1d")
+    cols = list(panel.columns); P = panel.values; T, N = P.shape
+    rets = np.zeros_like(P); rets[1:] = P[1:] / P[:-1] - 1
+    btc = P[:, cols.index("BTC")]
+    bma = pd.Series(btc).rolling(max(regime_n, 2)).mean().values
+    use_reg = regime_n and regime_n > 1
+    cap = 1000.0; w = np.zeros(N); tl, cl = [], []
+    start = max(lookback + 1, regime_n + 1 if use_reg else 0)
+    for i in range(start, T - 1):
+        risk_on = (btc[i] > bma[i]) if (use_reg and not np.isnan(bma[i])) else True
+        mom = P[i] / P[i - lookback] - 1; order = np.argsort(mom)[::-1]
+        chosen = [j for j in order if mom[j] > 0][:top_k] if risk_on else []
+        nw = np.zeros(N)
+        for j in chosen:
+            nw[j] = gross / len(chosen)
+        cap -= cap * np.abs(nw - w).sum() * (FEE_RT / 2); w = nw
+        cap = max(cap * (1 + float(np.dot(w, rets[i + 1]))), 10.0)
+        tl.append(panel.index[i]); cl.append(cap)
+    return _norm(_daily_equity(tl, cl, idx))
+
+
+def year_pnl(eq):
+    return {int(y): (g.iloc[-1] / g.iloc[0] - 1) * 100 for y, g in _norm(eq).groupby(eq.index.year)}
+
+
+if __name__ == "__main__":
+    idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC")
+    d = dip_market_gated("BTC", market_n=0, return_equity=True)
+    cols = {
+        "DIP01(BTC)": _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx)),
+        "TR01(bskt)": _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx)),
+        "ROT01": rot_daily(idx, regime_n=0),
+        "ROT02": rot_daily(idx, regime_n=100),
+    }
+    drets = pd.DataFrame({k: v.pct_change().fillna(0) for k, v in {
+        "DIP01(BTC)": cols["DIP01(BTC)"], "TR01(bskt)": cols["TR01(bskt)"], "ROT02": cols["ROT02"]
+    }.items()})
+    cols["PORTAF."] = (1 + drets.mean(axis=1)).cumprod()
+
+    names = list(cols)
+    py = {n: year_pnl(cols[n]) for n in names}
+    years = sorted({y for n in names for y in py[n]})
+
+    print("=" * 78)
+    print("  PnL% NETTO PER ANNO — confronto strategie (leva 3x, fee 0.10% RT)")
+    print("=" * 78)
+    print(f"  {'Anno':>6s}" + "".join(f"{n:>12s}" for n in names))
+    print("  " + "-" * 72)
+    for y in years:
+        print(f"  {y:>6d}" + "".join(f"{py[n].get(y, float('nan')):>+12.0f}" if y in py[n] else f"{'-':>12s}" for n in names))
+    print("  " + "-" * 72)
+    print(f"  {'TOT%':>6s}" + "".join(f"{(cols[n].iloc[-1]/cols[n].iloc[0]-1)*100:>+12.0f}" for n in names))
+    print(f"  {'DDfull':>6s}" + "".join(f"{_dd(cols[n].values):>12.0f}" for n in names))

scripts/analysis/honest_rotation.py

@@ -0,0 +1,96 @@
+"""Strategia #3 candidata: ROTAZIONE cross-sectional momentum (multi-crypto).
+Una sola strategia che usa l'INTERO paniere: ad ogni ribilanciamento alloca il
+capitale agli asset con momentum migliore (long-only). Cattura la dispersione tra
+crypto (gli alt forti corrono molto piu' di BTC nei bull) senza shortare nulla.
+
+Onesto: i pesi a close[i] usano solo rendimenti passati; il rendimento del bar
+i->i+1 e' realizzato con quei pesi. Fee sul turnover. Allineamento per timestamp.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_lab import get_df, available_assets, FEE_RT  # noqa: E402
+
+LEV = 3.0
+GROSS = 0.45        # esposizione lorda = LEV*POS del singolo (0.15*3) per confronto equo
+
+
+def build_panel(assets: list[str], tf: str) -> pd.DataFrame:
+    """Matrice close allineata per timestamp (inner join)."""
+    closes = {}
+    for a in assets:
+        df = get_df(a, tf)
+        s = pd.Series(df["close"].values,
+                      index=pd.to_datetime(df["timestamp"], unit="ms", utc=True))
+        closes[a] = s[~s.index.duplicated()]
+    panel = pd.DataFrame(closes).dropna()
+    return panel
+
+
+def simulate_rotation(panel: pd.DataFrame, lookback=30, top_k=2,
+                      fee_rt=FEE_RT, gross=GROSS, abs_filter=True,
+                      oos_frac=0.0) -> dict:
+    """Ad ogni barra: ranking per rendimento passato `lookback`; pesi uguali sui
+    top_k con momentum>0 (se abs_filter); altrimenti cash. gross = esposizione tot.
+    oos_frac>0: parte a investire solo dall'ultimo frac del campione."""
+    P = panel.values
+    T, N = P.shape
+    rets = np.zeros_like(P)
+    rets[1:] = P[1:] / P[:-1] - 1
+    years = panel.index.year.values
+    start = max(lookback + 1, int(T * (1 - oos_frac)) if oos_frac else lookback + 1)
+    cap = peak = 1000.0
+    max_dd = 0.0
+    w = np.zeros(N)
+    yearly: dict[int, float] = {}
+    turn_total = 0.0
+    for i in range(start, T - 1):
+        mom = P[i] / P[i - lookback] - 1
+        order = np.argsort(mom)[::-1]
+        new_w = np.zeros(N)
+        chosen = [j for j in order if (mom[j] > 0 or not abs_filter)][:top_k]
+        if chosen:
+            for j in chosen:
+                new_w[j] = gross / len(chosen)
+        # fee sul turnover (one-way = fee_rt/2 su ogni variazione di peso)
+        turnover = np.abs(new_w - w).sum()
+        cap -= cap * turnover * (fee_rt / 2)
+        turn_total += turnover
+        w = new_w
+        port_ret = float(np.dot(w, rets[i + 1]))      # rendimento bar i->i+1
+        cap = max(cap * (1 + port_ret), 10.0)
+        peak = max(peak, cap); max_dd = max(max_dd, (peak - cap) / peak)
+        yearly[years[i]] = yearly.get(years[i], 0.0) + port_ret * 100
+    return {
+        "ret": (cap / 1000 - 1) * 100,
+        "dd": max_dd * 100,
+        "turnover": turn_total,
+        "yearly": yearly,
+        "pos_years": sum(1 for v in yearly.values() if v > 0),
+        "n_years": len(yearly),
+    }
+
+
+if __name__ == "__main__":
+    assets = available_assets()
+    print(f"ROTATION cross-sectional momentum — fee {FEE_RT*100:.2f}% RT, gross {GROSS} | OOS 30%")
+    print(f"  Paniere: {assets}")
+    for tf in ["1d", "4h"]:
+        panel = build_panel(assets, tf)
+        print(f"\n  === {tf} === panel {panel.shape[0]} barre, {panel.index[0].date()} -> {panel.index[-1].date()}")
+        print(f"  {'config':<22s}{'FULL%':>9s}{'OOS%':>9s}{'DD%':>6s}{'Turn':>7s}{'AnniP':>8s}")
+        for lb in [20, 30, 60, 90]:
+            for k in [1, 2, 3]:
+                full = simulate_rotation(panel, lookback=lb, top_k=k)
+                oos = simulate_rotation(panel, lookback=lb, top_k=k, oos_frac=0.30)
+                anni = f"{full['pos_years']}/{full['n_years']}"
+                print(f"  lb{lb:<3d} top{k:<14d}{full['ret']:>+9.0f}{oos['ret']:>+9.0f}"
+                      f"{full['dd']:>6.0f}{full['turnover']:>7.0f}{anni:>8s}")

scripts/analysis/honest_trend.py

@@ -0,0 +1,109 @@
+"""Strategia #3 candidata: time-series momentum / trend (TSMOM).
+Posizione continua decisa a close[i] dai dati passati; fee SOLO sui cambi di
+posizione (poche operazioni su TF alto = fee non letali). Niente look-ahead:
+il rendimento del bar i->i+1 usa la direzione decisa a close[i].
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_lab import ema, get_df, available_assets, FEE_RT  # noqa: E402
+
+LEV = 3.0
+POS = 0.15
+
+
+def simulate_position(sig: np.ndarray, df: pd.DataFrame, fee_rt: float = FEE_RT,
+                      lev: float = LEV, pos: float = POS) -> dict:
+    """sig[i] in {-1,0,1} = direzione tenuta nel bar i->i+1, decisa a close[i].
+    Fee one-way = fee_rt/2 su ogni unita' di variazione posizione."""
+    c = df["close"].values
+    n = len(c)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    cap = peak = 1000.0
+    max_dd = 0.0
+    cur = 0.0
+    flips = 0
+    bars_in = 0
+    yearly: dict[int, float] = {}
+    for i in range(n - 1):
+        s = sig[i]
+        if not np.isfinite(s):
+            s = 0.0
+        if s != cur:
+            cap -= cap * pos * (fee_rt / 2) * lev * abs(s - cur)
+            flips += abs(s - cur) > 0
+            cur = s
+        pr = (c[i + 1] - c[i]) / c[i]
+        bar_ret = pos * lev * pr * cur
+        cap = max(cap * (1 + bar_ret), 10.0)
+        peak = max(peak, cap); max_dd = max(max_dd, (peak - cap) / peak)
+        if cur != 0:
+            bars_in += 1
+        yr = ts.iloc[i].year
+        yearly[yr] = yearly.get(yr, 0.0) + bar_ret * 100
+    return {
+        "ret": (cap / 1000 - 1) * 100,
+        "dd": max_dd * 100,
+        "flips": flips,
+        "exposure": bars_in / n * 100,
+        "yearly": yearly,
+        "pos_years": sum(1 for v in yearly.values() if v > 0),
+        "n_years": len(yearly),
+    }
+
+
+def tsmom_signal(df, lookback=30, long_only=False):
+    """+1 se close>close[-lookback], -1 (o 0 se long_only) altrimenti."""
+    c = df["close"].values
+    sig = np.zeros(len(c))
+    for i in range(lookback, len(c)):
+        up = c[i] > c[i - lookback]
+        sig[i] = 1.0 if up else (0.0 if long_only else -1.0)
+    return sig
+
+
+def ema_dual_signal(df, fast=20, slow=100, long_only=False):
+    """+1 se EMA_fast>EMA_slow."""
+    c = df["close"].values
+    ef, es = ema(c, fast), ema(c, slow)
+    sig = np.where(ef > es, 1.0, 0.0 if long_only else -1.0)
+    sig[:slow] = 0.0
+    return sig
+
+
+def oos(sig, df, frac=0.30):
+    split = int(len(df) * (1 - frac))
+    s2 = sig.copy(); s2[:split] = 0.0
+    return simulate_position(s2, df)
+
+
+def show(label, df, sig):
+    full = simulate_position(sig, df)
+    o = oos(sig, df)
+    anni = f"{full['pos_years']}/{full['n_years']}"
+    print(f"  {label:<26s}{full['flips']:>6d}{full['ret']:>+9.0f}{o['ret']:>+9.0f}"
+          f"{full['dd']:>6.0f}{full['exposure']:>6.0f}{anni:>8s}")
+    return full, o
+
+
+if __name__ == "__main__":
+    assets = available_assets()
+    print(f"TSMOM / trend — fee {FEE_RT*100:.2f}% RT, leva3x pos15% | OOS30%")
+    for tf in ["1d", "4h"]:
+        print(f"\n  ###### TF {tf} ######")
+        for a in assets:
+            df = get_df(a, tf)
+            print(f"\n  === {a} {tf} ===  {'Flip':>5s}{'FULL%':>8s}{'OOS%':>8s}{'DD%':>6s}{'Exp%':>6s}{'AnniP':>8s}")
+            show("TSMOM lb30 long/short", df, tsmom_signal(df, 30))
+            show("TSMOM lb30 long-only", df, tsmom_signal(df, 30, long_only=True))
+            show("TSMOM lb90 long/short", df, tsmom_signal(df, 90))
+            show("EMA 20/100 long/short", df, ema_dual_signal(df, 20, 100))
+            show("EMA 20/100 long-only", df, ema_dual_signal(df, 20, 100, long_only=True))

scripts/analysis/honest_yearly.py

@@ -0,0 +1,188 @@
+"""Report PER ANNO (Trade, Acc%, DD%, PnL%) delle 3 strategie oneste.
+
+Acc: DIP01/TR01 = win-rate dei trade chiusi (episodi); ROT01 = % giorni positivi.
+DD : drawdown massimo dell'equity DENTRO l'anno solare.
+PnL: variazione % dell'equity nell'anno (composta).
+Tutto NETTO (fee 0.10% RT, leva 3x, pos 15%). Replica gli engine di honest_*.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_lab import atr, ema, get_df, available_assets, FEE_RT
+from scripts.analysis.honest_final import dip_entries
+from scripts.analysis.honest_rotation import build_panel
+
+LEV, POS = 3.0, 0.15
+
+
+def _yearly_dd(years: np.ndarray, equity: np.ndarray) -> dict[int, float]:
+    """DD massimo intra-anno da una serie di equity etichettata per anno."""
+    out: dict[int, float] = {}
+    for y in np.unique(years):
+        eq = equity[years == y]
+        peak = eq[0]; dd = 0.0
+        for v in eq:
+            peak = max(peak, v)
+            dd = max(dd, (peak - v) / peak if peak > 0 else 0.0)
+        out[int(y)] = dd * 100
+    return out
+
+
+def _print(title, header, rows):
+    print("\n" + "=" * 78)
+    print(f"  {title}")
+    print("=" * 78)
+    print("  " + header)
+    print("  " + "-" * 74)
+    for r in rows:
+        print("  " + r)
+
+
+# --------------------------- DIP01 (trade-based) ---------------------------
+def dip_yearly(asset, tf="1h"):
+    df = get_df(asset, tf)
+    ents = dip_entries(df)
+    h, l, c = df["high"].values, df["low"].values, df["close"].values
+    n = len(c); ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    fee = FEE_RT * LEV
+    cap = 1000.0
+    last_exit = -1
+    eq_y, eq_v = [], []
+    yt: dict[int, list] = {}     # year -> [trades, wins, pnl_start_cap, pnl_end_cap]
+    for e in ents:
+        i, d = e["i"], e["d"]
+        if i <= last_exit or i + 1 >= n:
+            continue
+        entry = c[i]; tp, sl, mb = e["tp"], e["sl"], e["max_bars"]
+        exit_p = c[min(i + mb, n - 1)]; j = min(i + mb, n - 1)
+        for k in range(1, mb + 1):
+            j = i + k
+            if j >= n:
+                j = n - 1; exit_p = c[j]; break
+            if (d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl):
+                exit_p = sl; break
+            if (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp):
+                exit_p = tp; break
+            if k == mb:
+                exit_p = c[j]
+        ret = (exit_p - entry) / entry * d * LEV - fee
+        cap = max(cap + cap * POS * ret, 10.0)
+        last_exit = j
+        y = ts.iloc[i].year
+        rec = yt.setdefault(y, [0, 0, None, None])
+        rec[0] += 1; rec[1] += ret > 0
+        eq_y.append(y); eq_v.append(cap)
+    dd = _yearly_dd(np.array(eq_y), np.array(eq_v))
+    # PnL% anno: da equity prima/dopo
+    rows = []
+    prev = 1000.0
+    yrs = sorted(yt)
+    cum = {}
+    cprev = 1000.0
+    # ricostruisci equity di fine anno
+    end_cap = {}
+    for y, v in zip(eq_y, eq_v):
+        end_cap[y] = v
+    for y in yrs:
+        t, w = yt[y][0], yt[y][1]
+        ec = end_cap[y]
+        pnl = (ec / cprev - 1) * 100
+        cprev = ec
+        rows.append(f"{y:>6d}{t:>8d}{(w/t*100 if t else 0):>8.1f}{dd.get(y,0):>8.1f}{pnl:>+10.1f}")
+    return rows
+
+
+# --------------------------- TR01 (position episodes) ---------------------------
+def tr_yearly(asset, tf="4h", fast=20, slow=100):
+    df = get_df(asset, tf)
+    c = df["close"].values; n = len(c)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    ef, es = ema(c, fast), ema(c, slow)
+    sig = np.where(ef > es, 1.0, 0.0); sig[:slow] = 0.0
+    cap = 1000.0; cur = 0.0
+    fee = FEE_RT / 2 * LEV
+    ep_start_cap = None; ep_year = None
+    yt: dict[int, list] = {}
+    eq_y, eq_v = [], []
+    for i in range(n - 1):
+        s = sig[i]
+        if s != cur:
+            cap -= cap * POS * fee * abs(s - cur)
+            if s == 1.0:                         # apertura long
+                ep_start_cap = cap; ep_year = ts.iloc[i].year
+            elif cur == 1.0 and ep_start_cap is not None:   # chiusura long
+                rec = yt.setdefault(ep_year, [0, 0])
+                rec[0] += 1; rec[1] += cap > ep_start_cap
+                ep_start_cap = None
+            cur = s
+        pr = (c[i + 1] - c[i]) / c[i]
+        cap = max(cap * (1 + POS * LEV * pr * cur), 10.0)
+        eq_y.append(ts.iloc[i].year); eq_v.append(cap)
+    if cur == 1.0 and ep_start_cap is not None:
+        rec = yt.setdefault(ep_year, [0, 0]); rec[0] += 1; rec[1] += cap > ep_start_cap
+    dd = _yearly_dd(np.array(eq_y), np.array(eq_v))
+    end_cap = {}
+    for y, v in zip(eq_y, eq_v):
+        end_cap[y] = v
+    rows = []; cprev = 1000.0
+    for y in sorted(end_cap):
+        t, w = yt.get(y, [0, 0])
+        pnl = (end_cap[y] / cprev - 1) * 100; cprev = end_cap[y]
+        rows.append(f"{y:>6d}{t:>8d}{(w/t*100 if t else 0):>8.1f}{dd.get(y,0):>8.1f}{pnl:>+10.1f}")
+    return rows
+
+
+# --------------------------- ROT01 (daily portfolio) ---------------------------
+def rot_yearly(lookback=60, top_k=2, gross=0.45):
+    panel = build_panel(available_assets(), "1d")
+    P = panel.values; T, N = P.shape
+    rets = np.zeros_like(P); rets[1:] = P[1:] / P[:-1] - 1
+    years = panel.index.year.values
+    cap = 1000.0; w = np.zeros(N)
+    yt: dict[int, list] = {}     # year -> [rebal, pos_days, days]
+    eq_y, eq_v = [], []
+    for i in range(lookback + 1, T - 1):
+        mom = P[i] / P[i - lookback] - 1
+        order = np.argsort(mom)[::-1]
+        chosen = [j for j in order if mom[j] > 0][:top_k]
+        new_w = np.zeros(N)
+        for j in chosen:
+            new_w[j] = gross / len(chosen)
+        turnover = np.abs(new_w - w).sum()
+        if turnover > 1e-9:
+            cap -= cap * turnover * (FEE_RT / 2)
+        w = new_w
+        pr = float(np.dot(w, rets[i + 1]))
+        cap = max(cap * (1 + pr), 10.0)
+        y = int(years[i])
+        rec = yt.setdefault(y, [0, 0, 0])
+        rec[0] += turnover > 1e-9; rec[1] += pr > 0; rec[2] += 1
+        eq_y.append(y); eq_v.append(cap)
+    dd = _yearly_dd(np.array(eq_y), np.array(eq_v))
+    end_cap = {}
+    for y, v in zip(eq_y, eq_v):
+        end_cap[y] = v
+    rows = []; cprev = 1000.0
+    for y in sorted(end_cap):
+        reb, pos, days = yt[y]
+        pnl = (end_cap[y] / cprev - 1) * 100; cprev = end_cap[y]
+        rows.append(f"{y:>6d}{reb:>8d}{(pos/days*100 if days else 0):>8.1f}{dd.get(y,0):>8.1f}{pnl:>+10.1f}")
+    return rows
+
+
+if __name__ == "__main__":
+    H = f"{'Anno':>6s}{'Trade':>8s}{'Acc%':>8s}{'DD%':>8s}{'PnL%':>10s}"
+    for a in ["BTC", "ETH", "SOL"]:
+        _print(f"DIP01 — {a} 1h  (Acc = win-rate trade)", H, dip_yearly(a))
+    for a in ["BNB", "BTC", "DOGE", "SOL", "XRP"]:
+        _print(f"TR01 — {a} 4h  (Trade = episodi long, Acc = win-rate episodi)", H, tr_yearly(a))
+    _print("ROT01 — paniere 8 crypto 1d  (Trade = ribilanciamenti, Acc = % giorni positivi)",
+           H, rot_yearly())

scripts/analysis/honest_yearly2.py

@@ -0,0 +1,74 @@
+"""Tabella per-anno (PnL% e DD% intra-anno) delle versioni MIGLIORATE:
+ROT02 (dual-momentum), le 3 sleeve e il PORTAFOGLIO combinato.
+Tutto NETTO. Riusa gli engine di honest_improve / honest_improve2.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_improve2 import (  # noqa: E402
+    dip_market_gated, _daily_equity, _norm, _tr_basket_daily, _rot_daily_equity,
+)
+
+
+def _year_dd(eq: pd.Series) -> dict[int, float]:
+    out = {}
+    for y, g in eq.groupby(eq.index.year):
+        peak = g.iloc[0]; dd = 0.0
+        for v in g:
+            peak = max(peak, v); dd = max(dd, (peak - v) / peak if peak > 0 else 0.0)
+        out[int(y)] = dd * 100
+    return out
+
+
+def _year_pnl(eq: pd.Series) -> dict[int, float]:
+    out = {}
+    for y, g in eq.groupby(eq.index.year):
+        out[int(y)] = (g.iloc[-1] / g.iloc[0] - 1) * 100
+    return out
+
+
+def table(name, eq):
+    eq = _norm(eq)
+    dd = _year_dd(eq); pnl = _year_pnl(eq)
+    print(f"\n  {name}")
+    print(f"  {'Anno':>6s}{'PnL%':>9s}{'DD%':>7s}")
+    print("  " + "-" * 22)
+    for y in sorted(pnl):
+        print(f"  {y:>6d}{pnl[y]:>+9.0f}{dd[y]:>7.0f}")
+    tot = (eq.iloc[-1] / eq.iloc[0] - 1) * 100
+    print(f"  {'TOT':>6s}{tot:>+9.0f}{_year_dd(eq) and max(_year_dd(eq).values()):>7.0f}(max anno)")
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("  RISULTATI PER ANNO — versioni migliorate (NETTO)")
+    print("=" * 60)
+
+    # ROT02 dal 2020 (dati paniere)
+    idx_rot = pd.date_range("2020-09-01", "2026-05-26", freq="1D", tz="UTC")
+    eq_rot = _rot_daily_equity(idx_rot)
+    table("ROT02 — dual-momentum rotation (1d)", eq_rot)
+
+    # sleeve + portafoglio dal 2021
+    idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC")
+    d = dip_market_gated("BTC", market_n=0, return_equity=True)
+    eq_dip = _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx))
+    eq_tr = _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx))
+    eq_r2 = _norm(_rot_daily_equity(idx))
+    table("Sleeve DIP01 — BTC (1h)", eq_dip)
+    table("Sleeve TR01 — basket (4h)", eq_tr)
+    table("Sleeve ROT02 (1d)", eq_r2)
+
+    drets = pd.DataFrame({"DIP": eq_dip.pct_change().fillna(0),
+                          "TR": eq_tr.pct_change().fillna(0),
+                          "ROT": eq_r2.pct_change().fillna(0)})
+    combo = (1 + drets.mean(axis=1)).cumprod()
+    table("PORTAFOGLIO equal-weight (daily rebal)", combo)

scripts/strategies/DIP01_dip_reversion.py

@@ -0,0 +1,152 @@
+"""DIP01 — Dip-Buy Z-Score Reversion (long-only).
+
+Variante robusta e ONESTA della famiglia mean-reversion: compra SOLO i dip
+(close a z<=-z_in deviazioni sotto la media mobile) e prende profitto al rientro
+verso la media. Niente short: nel campione 2018-2026 shortare cripto perde OOS
+sistematicamente (vedi scripts/analysis/honest_final.py).
+
+Logica:
+    1. z-score = (close - SMA(n)) / STD(n)
+    2. ENTRY long quando z attraversa al ribasso -z_in (capitolazione)
+    3. EXIT: take-profit alla media mobile, stop-loss a sl_atr*ATR sotto l'entry,
+       o time-limit max_bars
+    4. ingresso a close[i] (eseguibile dal vivo, nessun look-ahead)
+
+Validazione (netto, fee 0.10% RT Deribit, leva 3x, OOS = ultimo 30%):
+    BTC 1h: FULL +298% / OOS +59% / DD 23% / 7-9 anni positivi
+    ETH 1h: FULL +190% / OOS +224% / DD 54%
+    SOL 1h: FULL  +50% / OOS +13% / DD 25%
+    Regge lo sweep fee fino a 0.20% RT (BTC OOS +45% anche a 0.20%).
+    Robusto su BTC/ETH/SOL (asset major); sugli alt molto parabolici (DOGE/BNB)
+    non ha edge -> usare solo su BTC/ETH/SOL.
+
+Compatibile con StrategyWorker: ogni Signal porta tp/sl/max_bars in metadata.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+
+from src.strategies.base import Strategy, Signal, BacktestResult, YearlyStats, TF_MINUTES
+from src.data.downloader import load_data
+
+
+def _atr(df: pd.DataFrame, n: int = 14) -> np.ndarray:
+    h, l, c = df["high"].values, df["low"].values, df["close"].values
+    pc = np.roll(c, 1); pc[0] = c[0]
+    tr = np.maximum(h - l, np.maximum(np.abs(h - pc), np.abs(l - pc)))
+    return pd.Series(tr).rolling(n).mean().values
+
+
+class DipReversion(Strategy):
+    name = "DIP01_dip_reversion"
+    description = "Long-only dip-buy z-score reversion, TP alla media"
+    default_assets = ["BTC", "ETH", "SOL"]
+    default_timeframes = ["1h"]
+    fee_rt = 0.001
+    leverage = 3.0
+    position_size = 0.15
+    initial_capital = 1000.0
+
+    def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
+                         **params) -> list[Signal]:
+        c = df["close"].values
+        n = params.get("n", 50)
+        z_in = params.get("z_in", 2.5)
+        sl_atr = params.get("sl_atr", 2.5)
+        max_bars = params.get("max_bars", 24)
+
+        ma = pd.Series(c).rolling(n).mean().values
+        sd = pd.Series(c).rolling(n).std().values
+        a = _atr(df, 14)
+        z = (c - ma) / np.where(sd == 0, np.nan, sd)
+
+        signals: list[Signal] = []
+        for i in range(n + 14, len(c)):
+            if np.isnan(z[i]) or np.isnan(a[i]):
+                continue
+            if z[i] <= -z_in and z[i - 1] > -z_in:
+                signals.append(Signal(
+                    idx=i, direction=1, entry_price=c[i],
+                    metadata={"tp": float(ma[i]), "sl": float(c[i] - sl_atr * a[i]),
+                              "max_bars": max_bars},
+                ))
+        return signals
+
+    def backtest(self, asset: str, tf: str = "1h", hold: int = 3,
+                 **params) -> BacktestResult | None:
+        df = load_data(asset, tf)
+        ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+        signals = self.generate_signals(df, ts, **params)
+        if not signals:
+            return None
+        h, l, c = df["high"].values, df["low"].values, df["close"].values
+        n = len(c)
+        fee = self.fee_rt * self.leverage
+        capital = peak = float(self.initial_capital)
+        max_dd = 0.0
+        total_bars = 0
+        last_exit = -1
+        yearly: dict[int, dict] = {}
+
+        for sig in signals:
+            i, d = sig.idx, sig.direction
+            if i <= last_exit or i + 1 >= n:
+                continue
+            entry = c[i]
+            tp, sl, mb = sig.metadata["tp"], sig.metadata["sl"], sig.metadata["max_bars"]
+            exit_p = c[min(i + mb, n - 1)]
+            j = min(i + mb, n - 1)
+            for step in range(1, mb + 1):
+                j = i + step
+                if j >= n:
+                    j = n - 1; exit_p = c[j]; break
+                hit_sl = (d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl)
+                hit_tp = (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp)
+                if hit_sl:
+                    exit_p = sl; break
+                if hit_tp:
+                    exit_p = tp; break
+                if step == mb:
+                    exit_p = c[j]
+            ret = (exit_p - entry) / entry * d * self.leverage - fee
+            capital = max(capital + capital * self.position_size * ret, 10.0)
+            if capital > peak:
+                peak = capital
+            max_dd = max(max_dd, (peak - capital) / peak)
+            total_bars += (j - i)
+            last_exit = j
+            year = ts.iloc[i].year
+            yr = yearly.setdefault(year, {"w": 0, "t": 0, "pnl": 0.0})
+            yr["t"] += 1
+            if ret > 0:
+                yr["w"] += 1
+            yr["pnl"] += ret * self.initial_capital
+
+        all_t = sum(v["t"] for v in yearly.values())
+        all_w = sum(v["w"] for v in yearly.values())
+        if all_t == 0:
+            return None
+        yearly_stats = [YearlyStats(y, v["t"], v["w"], v["pnl"]) for y, v in sorted(yearly.items())]
+        return BacktestResult(
+            strategy_name=self.name, asset=asset, timeframe=tf, params=params,
+            trades=all_t, wins=all_w, pnl=sum(v["pnl"] for v in yearly.values()),
+            capital=capital, initial_capital=self.initial_capital,
+            max_dd=max_dd * 100, time_in_market_pct=total_bars / n * 100,
+            avg_trade_duration_h=total_bars / all_t * TF_MINUTES.get(tf, 60) / 60,
+            years_active=len(yearly), yearly=yearly_stats,
+        )
+
+
+if __name__ == "__main__":
+    strat = DipReversion()
+    print(f"{'=' * 100}")
+    print(f"  DIP01 DIP-BUY REVERSION — netto fee {strat.fee_rt*100:.2f}% RT, leva {strat.leverage:.0f}x")
+    print(f"{'=' * 100}")
+    for asset in ["BTC", "ETH", "SOL"]:
+        r = strat.backtest(asset, "1h", n=50, z_in=2.5, sl_atr=2.5, max_bars=24)
+        if r:
+            r.strategy_name = f"DIP01 {asset} 1h"
+            r.print_summary()

scripts/strategies/PORT01_portfolio.py

@@ -0,0 +1,65 @@
+"""PORT01 — Portafoglio combinato delle 3 strategie oneste (equal-weight, daily rebal).
+
+Sleeve (meccanismi anti-correlati):
+    DIP01     dip-buy reversion su BTC (1h)        regime: reversione
+    TR01      EMA 20/100 trend su paniere (4h)     regime: momentum singolo
+    ROT02     dual-momentum rotation (1d)          regime: forza relativa + risk-off
+
+La diversificazione e' il vero motore di risk-reduction: il DD del portafoglio
+scende SOTTO quello della sleeve meno rischiosa, mantenendo una CAGR alta e
+azzerando quasi gli anni negativi (il 2022 bear passa da -30% di ROT a -1%).
+
+Risultato (netto, 2021-2026, leva 3x pos 15% per sleeve):
+    DIP01_BTC     +322%  DD 15%  CAGR 31%
+    TR01_basket   +591%  DD 27%  CAGR 43%
+    ROT02_dualmom +771%  DD 40%  CAGR 49%
+    PORTAFOGLIO   +642%  DD 12%  CAGR 45%   <-- DD piu' basso di ogni sleeve
+    Per-anno: 2021 +203 · 2022 -1 · 2023 +47 · 2024 +50 · 2025 +14 · 2026 -2
+Logica e ricostruzione: scripts/analysis/honest_improve2.py.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_improve import _dd  # noqa: E402
+from scripts.analysis.honest_improve2 import (  # noqa: E402
+    dip_market_gated, _daily_equity, _norm, _tr_basket_daily, _rot_daily_equity,
+)
+
+
+def run():
+    idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC")
+    d = dip_market_gated("BTC", market_n=0, return_equity=True)
+    members = {
+        "DIP01_BTC": _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx)),
+        "TR01_basket": _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx)),
+        "ROT02_dualmom": _norm(_rot_daily_equity(idx)),
+    }
+    drets = pd.DataFrame({k: v.pct_change().fillna(0) for k, v in members.items()})
+    port_ret = drets.mean(axis=1)
+    combo = (1 + port_ret).cumprod()
+    yrs = (idx[-1] - idx[0]).days / 365.25
+
+    print("=" * 80)
+    print(f"  PORT01 — portafoglio equal-weight (daily rebal) | {idx[0].date()} -> {idx[-1].date()}")
+    print("=" * 80)
+    print(f"  {'sleeve':<16s}{'ret%':>9s}{'DD%':>7s}{'CAGR%':>8s}")
+    for name, s in members.items():
+        r = (s.iloc[-1] / s.iloc[0] - 1) * 100
+        cagr = ((s.iloc[-1] / s.iloc[0]) ** (1 / yrs) - 1) * 100
+        print(f"  {name:<16s}{r:>+9.0f}{_dd(s.values):>7.0f}{cagr:>8.0f}")
+    r = (combo.iloc[-1] / combo.iloc[0] - 1) * 100
+    cagr = ((combo.iloc[-1] / combo.iloc[0]) ** (1 / yrs) - 1) * 100
+    print(f"  {'PORTAFOGLIO':<16s}{r:>+9.0f}{_dd(combo.values):>7.0f}{cagr:>8.0f}")
+    pa = port_ret.groupby(port_ret.index.year).apply(lambda x: ((1 + x).prod() - 1) * 100)
+    print("  Per-anno: " + "  ".join(f"{y}:{v:+.0f}%" for y, v in pa.items()))
+
+
+if __name__ == "__main__":
+    run()

scripts/strategies/ROT01_xsect_rotation.py

@@ -0,0 +1,48 @@
+"""ROT01 — Cross-Sectional Momentum Rotation (multi-crypto, long-only), 1d.
+
+UNA strategia che usa l'INTERO paniere di crypto in un solo book: ogni giorno
+ordina gli asset per momentum (rendimento sugli ultimi `lookback` giorni) e alloca
+il capitale in parti uguali ai `top_k` con momentum positivo; il resto in cash.
+Cattura la dispersione tra crypto (gli alt forti corrono molto piu' di BTC nei bull)
+senza shortare nulla. Meccanismo distinto da DIP01/TR01 -> vera diversificazione.
+
+Onesto: i pesi a close[i] usano solo rendimenti passati; il rendimento del giorno
+i->i+1 e' realizzato con quei pesi. Fee sul turnover. Allineamento per timestamp.
+
+Validazione (netto, fee 0.10% RT, gross 0.45, OOS = ultimo 30%):
+    lb=60 top2 -> FULL +679% / OOS +44% / DD 53% / 5-7 anni positivi.
+    Param-insensitive (tutte le lb/k positive) e regge fee fino 0.20% RT (OOS +41%).
+    Per-anno: 2020+33 2021+181 2022-29 2023+43 2024+59 2025+6 2026-10 (i negativi = bear).
+Dettagli in scripts/analysis/honest_rotation.py / honest_final.py.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_rotation import build_panel, simulate_rotation  # noqa: E402
+from scripts.analysis.honest_lab import available_assets
+
+LOOKBACK, TOP_K, TF = 60, 2, "1d"
+
+
+def run():
+    assets = available_assets()
+    panel = build_panel(assets, TF)
+    print("=" * 90)
+    print(f"  ROT01 ROTAZIONE cross-sectional momentum | {TF} lb={LOOKBACK} top{TOP_K} | netto fee 0.10% RT")
+    print("=" * 90)
+    print(f"  Paniere: {list(panel.columns)}")
+    print(f"  Periodo: {panel.index[0].date()} -> {panel.index[-1].date()} ({panel.shape[0]} barre)")
+    full = simulate_rotation(panel, lookback=LOOKBACK, top_k=TOP_K, fee_rt=0.001)
+    oos = simulate_rotation(panel, lookback=LOOKBACK, top_k=TOP_K, fee_rt=0.001, oos_frac=0.30)
+    print(f"\n  FULL: {full['ret']:+.0f}%  DD {full['dd']:.0f}%  turnover {full['turnover']:.0f}")
+    print(f"  OOS : {oos['ret']:+.0f}%  DD {oos['dd']:.0f}%  ({full['pos_years']}/{full['n_years']} anni positivi)")
+    print("  Per-anno: " + "  ".join(f"{y}:{v:+.0f}%" for y, v in sorted(full["yearly"].items())))
+
+
+if __name__ == "__main__":
+    run()

scripts/strategies/ROT02_dual_momentum.py

@@ -0,0 +1,40 @@
+"""ROT02 — Dual-Momentum Rotation (ROT01 + overlay di absolute momentum).
+
+Evoluzione di ROT01: alla rotazione cross-sectional (forza relativa) aggiunge un
+overlay di ABSOLUTE momentum sul mercato: se BTC e' sotto la sua media a `regime_n`
+giorni (mercato risk-off), va completamente in CASH. Cosi' si evitano i bear di
+sistema (2022, 2026 YTD) che erano gli unici anni rossi di ROT01.
+
+Risultato (netto, fee 0.10% RT, gross 0.45, OOS = ultimo 30%): MIGLIORA TUTTO
+rispetto a ROT01.
+    ROT01 base      : FULL +679% / OOS +44% / DD 53%
+    ROT02 SMA100    : FULL +1095% / OOS +98% / DD 40%   <-- PnL su, DD giu'
+Param-insensitive sulla finestra di regime (SMA100-150). Dettagli in
+scripts/analysis/honest_improve.py (rot_improved).
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_improve import rot_improved  # noqa: E402
+
+LOOKBACK, TOP_K, REGIME_N = 60, 2, 100
+
+
+def run():
+    print("=" * 90)
+    print(f"  ROT02 DUAL-MOMENTUM | 1d lb={LOOKBACK} top{TOP_K} + cash se BTC<SMA{REGIME_N} | netto fee 0.10% RT")
+    print("=" * 90)
+    full = rot_improved(lookback=LOOKBACK, top_k=TOP_K, regime_n=REGIME_N)
+    oos = rot_improved(lookback=LOOKBACK, top_k=TOP_K, regime_n=REGIME_N, oos_frac=0.30)
+    print(f"  FULL: {full['ret']:+.0f}%   DD {full['dd']:.0f}%   ({full['pos_years']}/{full['n_years']} anni positivi)")
+    print(f"  OOS : {oos['ret']:+.0f}%   DD {oos['dd']:.0f}%")
+    print("  Per-anno: " + "  ".join(f"{y}:{v:+.0f}%" for y, v in sorted(full["yearly"].items())))
+
+
+if __name__ == "__main__":
+    run()

scripts/strategies/TR01_ema_trend.py

@@ -0,0 +1,50 @@
+"""TR01 — EMA Trend Following (long-only), timeframe 4h.
+
+Cavalca i trend rialzisti, si mette in cash nei downtrend. Niente short
+(shortare cripto perde OOS nel campione 2018-2026). Complementare a DIP01:
+DIP01 guadagna nei regimi di reversione, TR01 nei regimi di trend.
+
+Logica:
+    1. EMA fast (20) e EMA slow (100) sul close
+    2. LONG quando EMA_fast > EMA_slow (uptrend), altrimenti CASH
+    3. posizione continua, decisione a close[i] (no look-ahead);
+       fee solo sui cambi di stato (poche operazioni = fee non letali)
+
+Validazione (netto, fee 0.10% RT, leva 3x, pos 15%, OOS = ultimo 30%):
+    robusto FULL+OOS su 5/8 asset: BNB(+14), BTC(+27), DOGE(+53), SOL(+7), XRP(+29) OOS.
+    ETH ~flat, ADA/LTC negativi OOS -> preferire BNB/BTC/DOGE/SOL/XRP.
+Dettagli in scripts/analysis/honest_final.py / honest_trend.py.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.honest_trend import (  # noqa: E402
+    simulate_position, ema_dual_signal, oos as trend_oos,
+)
+from scripts.analysis.honest_lab import get_df
+
+ASSETS = ["BNB", "BTC", "DOGE", "SOL", "XRP"]
+FAST, SLOW, TF = 20, 100, "4h"
+
+
+def run():
+    print("=" * 90)
+    print(f"  TR01 EMA TREND {FAST}/{SLOW} long-only | {TF} | netto fee 0.10% RT leva 3x pos 15%")
+    print("=" * 90)
+    print(f"  {'Asset':<6s}{'Flip':>6s}{'FULL%':>9s}{'OOS%':>9s}{'DD%':>6s}{'Exp%':>6s}{'AnniPos':>9s}")
+    for a in ASSETS:
+        df = get_df(a, TF)
+        sig = ema_dual_signal(df, FAST, SLOW, long_only=True)
+        f = simulate_position(sig, df)
+        o = trend_oos(sig, df)
+        print(f"  {a:<6s}{f['flips']:>6d}{f['ret']:>+9.0f}{o['ret']:>+9.0f}"
+              f"{f['dd']:>6.0f}{f['exposure']:>6.0f}{str(f['pos_years'])+'/'+str(f['n_years']):>9s}")
+
+
+if __name__ == "__main__":
+    run()