fix(TP01): bug look-ahead ffill mixed-TF -> deploy a >=12h (1d), strategia DIFENSIVA

Segnalato: ffill MIXED-TIMEFRAME su barre open-labeled (resample label="left") gonfiava il 4h (~1.60 -> reale ~1.1). Ri-verifica per-SINGOLO-TF leak-free (guard prefix-recompute, leak=0 su 4h/6h/12h/1d): FULL Sh piatto ~1.3, hold-out 2025-26 MIGLIORE a 1d (Sh 0.31 / +3.5% vs buy&hold -39%). Conclusione adottata: NON scendere sotto le 12h (sotto, costi+overfit dominano senza vantaggio). - trend_portfolio.py: canonica PORT LF1d; resample_tf/resample_1d (resample_4h deprecato deploy); docstring con nota look-ahead + natura DIFENSIVA (taglia DD ~6x, non alpha). - paper_trend.py: deploy a 1d (resample_1d, build_bars). 5 test passano. - CLAUDE.md: TP01 ridescritta (>=12h/1d, gotcha ffill mixed-TF, difensiva). - tp01_lowfreq.py + diario 2026-06-19-tp01-lookahead-fix-lf.md. Gotcha: mai ffill/combine mixed-TF su timestamp open-labeled (close propagata indietro = leak). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
research: stress-test TP01 — robusto come strategia DIFENSIVA (DD-cut), edge ritorno hold-out sottile
2026-06-19 19:04:38 +00:00 · 2026-06-19 18:57:22 +00:00 · 2026-06-19 18:55:04 +00:00 · 2026-06-19 18:50:36 +00:00 · 2026-06-19 18:37:05 +00:00
34 changed files with 5239 additions and 1 deletions
@@ -43,3 +43,12 @@ data/games/
 # archived data (mirrors top-level data/ ignores, which are top-level-anchored)
 Old/data/
 Old/**/__pycache__/
 # run logs (rigenerabili dagli script)
 logs/
 # cache della ricerca trackE (rigenerabile)
 .cache_trackE_*.npy
 # feed backup pre-rebuild (binari rigenerabili, NON in git) + stato paper trader (runtime)
 data/_feed_backup/
 data/paper_trend/
@@ -16,6 +16,37 @@ Cosa è cambiato:
 - L'esecuzione è **DISABILITATA**, il conto mainnet è flat. **Non c'è trading live attivo.**
 - Si riparte dalla ricerca di strategie NUOVE, su dati certi, con la metodologia qui sotto.
 ### Ricerca post-reset (2026-06-19) — esito
 Prima ondata di ricerca onesta su BTC/ETH certificati (5 track, harness condiviso
 `src/backtest/harness.py`). Sintesi in `docs/diary/2026-06-19-research-synthesis.md`.
 - **TP01 Trend Portfolio — strategia DIFENSIVA robusta (non alpha)** —
  `src/strategies/trend_portfolio.py`. TSMOM multi-orizzonte (1-3-6 mesi) vol-targeted, long-flat,
  50/50 BTC+ETH. Config canonica **PORT LF1d** (**>=12h, 1d raccomandato**, vol-target 20%, leva cap 2x):
  **FULL Sharpe ~1.30, maxDD ~14%; HOLD-OUT 2025-26 Sharpe ~0.31 / +3.5%** mentre il buy&hold 50/50
  faceva −39%/DD60%. Verificata indipendentemente col gauntlet onesto (hold-out + cross-asset +
  plateau + deflated-Sharpe 0.999): **regge**. **Valore = taglio del drawdown ~6× vs buy&hold**, NON
  generazione di ritorno (CAGR ~16% vs ~48% del buy&hold sul toro).
  ⚠️ **LOOK-AHEAD (2026-06-19):** un ffill MIXED-TIMEFRAME su barre open-labeled gonfiava il 4h
  (~1.60 → reale ~1.1). Il calcolo per-singolo-TF è leak-free, ma **NON scendere sotto le 12h**:
  costi+overfitting dominano senza vantaggio (FULL Sh piatto ~1.3 da 12h a 4h; hold-out migliore a 1d).
  Deploy/paper a **1d**. Diari `2026-06-19-tp01-verification.md` / `-tp01-lookahead-fix-lf.md`.
  Paper trader: `scripts/live/paper_trend.py` (1d). Test: `tests/test_trend_portfolio.py`.
  Ri-verifica: `scripts/analysis/{verify_tp01,stress_tp01,tp01_lowfreq}.py`.
 - **Edge deboli ma reali** (NON standalone, NON migliorano il portafoglio): ML walk-forward
  su BTC (Sharpe ~0.57), trend 1h long-short (Sharpe ~1.0), relative-value market-neutral
  ETH/BTC (scorrelato ~0.05 ma Sharpe solo 0.27 → troppo debole per alzare lo Sharpe).
 - **MORTO/confermato artefatto:** mean-reversion / fade (negativo anche a fee zero su dati
  certi — la vecchia libreria +201%/+1238% era pura contaminazione); trend 5m/15m (fee).
 - **Soffitto strutturale:** con i soli BTC/ETH lo Sharpe di portafoglio si ferma a **~1.3**.
  Combinare TF o aggiungere la RV non aiuta (ridondanza/edge troppo debole).
 - **Onestà sul target €50/giorno:** NON raggiungibile su 2000 in 1-2 anni (servono ~130k di
  capitale o un DD da rovina). La leva non è la scorciatoia; la via è target-vol + capitale +
  tempo. La strategia che *guadagna* esiste, ma a ~+€1.5/giorno su 2000.
 Script ricerca: `scripts/research/track{A,B,C,D,E}_*.py` + `trackD_timing.py`.
 ## Obiettivo
 Ricerca: riconoscimento pattern frattali per trading algoritmico su crypto. Target dichiarato
@@ -35,9 +66,13 @@ netto fee, out-of-sample, robusto su griglia, e su dati certificati + liquidi +
 src/data/downloader.py     → load_data(asset, tf): legge i parquet certificati da data/raw/
 src/strategies/base.py     → Strategy (ABC), Signal, BacktestResult, YearlyStats
 src/strategies/indicators.py → indicatori condivisi (ema, atr, keltner, ...)
 src/strategies/trend_portfolio.py → TP01: strategia VINCENTE (PORT LF4h), causale, deployabile
 src/fractal/               → indicatori frattali (patterns.py, indicators.py, similarity.py)
 src/backtest/engine.py     → engine di backtesting riusabile
 src/backtest/harness.py    → harness ONESTO (load BTC/ETH, backtest_signals no-leakage, OOS)
 src/version.py             → APP_VERSION (legge il file VERSION)
 scripts/research/          → ricerca post-reset: track{A-E}_*.py (trend/ML/MR/portfolio/xsec)
 scripts/live/paper_trend.py → paper trader forward-only di TP01 (no esecuzione reale)
 scripts/analysis/          → SOLO i tool dati certificati:
  rebuild_history.py        → (ri)costruisce lo storico da Deribit mainnet (base 5m + resample)
  certify_feed.py           → certifica il feed (integrità, coerenza resample, spike, cross-venue)
@@ -57,7 +92,10 @@ uv sync                                                       # installa dipende
 uv run python scripts/analysis/rebuild_history.py --asset BTC ETH   # (ri)costruisci storico da Deribit mainnet
 uv run python scripts/analysis/certify_feed.py                # certifica i feed (locale + cross-venue)
 uv run python scripts/analysis/certify_feed.py --local        # solo check locali (veloce)
-uv run pytest                                                 # test (da ripopolare con le nuove strategie)
+uv run python scripts/research/trackD_trendport.py            # backtest strategia vincente (full report)
 uv run python scripts/research/trackD_timing.py               # vincitrice su 15m/1h/4h/1d + PnL/DD/trade per anno
 uv run python scripts/live/paper_trend.py                    # avanza il paper trader TP01 (forward-only)
 uv run pytest                                                 # test
 ```
 ```python
@@ -0,0 +1,62 @@
 # 2026-06-19 — Ricerca frattale multi-agente (63 agenti) su BTC/ETH
 Su richiesta: 50+ agenti in parallelo a cercare strategie NUOVE ispirate ai due documenti
 frattali (`Libro_frattali` + `Pythagoras_Trading_Prediction`), timing/asset diversi, ognuna
 validata sull'harness onesto. Eseguito come Workflow: **63 agenti, ~2h, 3.8M token.**
 ## Cosa è stato testato
 16 concetti frattali estratti dai documenti (sotto la patina esoterica: coscienza, frequenze
 Solfeggio, numeri sacri → idee testabili): alfabeto candle U/D/0 (3-6, LONG), Fourier/cicli,
 ricorrenza di Poincaré (analoghi kNN), centro di inversione Evideon (mirror tempo+prezzo),
 indicatore H-C (~588/25 ≈ 23.5 barre), numeri-universali come periodi, invarianza di forma,
 entropia di Shannon ("coscienza") come gate, confluenza multi-TF, grammatica composizionale,
 fase-ricorrenza. × BTC/ETH × {5m,15m,1h} = **52 ipotesi**.
 Ogni segnale: scritto da un agente come funzione `signal()`, valutato da `eval_signal.py`
 (stesso harness onesto) con **guard automatico anti-look-ahead** (ricalcolo su prefissi).
 Superstiti → verifica avversariale con sblocco una-tantum del **hold-out 2025-26**.
 ## Esito
 - **Verdetti**: 29 rumore, 12 "real" (netto-fee positivo ma non battono il buy&hold), 11 "edge"
  (in-sample: battono B&H + null p<0.05 + leak=0).
 - **Guard anti-look-ahead**: nessun leak passato (gli agenti hanno prodotto segnali causali; i
  pochi tentativi con futuro sono stati auto-squalificati e corretti).
 - **Hold-out (la prova del nove)**: dei **11 superstiti in-sample, 10 REFUTATI** — performance
  catastrofica nel 2025-26 (hurst-DFA −0.49, hc-cycle −0.83, vol-accel −1.16, universal-periods
  −0.42…−1.04, spectral-entropy −0.38/+0.29, multitf −0.49, solfeggio-BTC −0.64). Stessa firma di
  sempre: **regime-luck del toro 2018-2024, sparito out-of-sample.**
 - **1 "confermato"** dalla verifica per-agente: `momentum_solfeggio_cycle` **ETH 1h** (holdout
  Sharpe +1.19, ret +49% mentre il buy&hold ETH faceva −49%). Sembrava un trionfo.
 ## Ma il "vincitore" cade al test cross-asset (kill decisivo)
 Il guard per-agente valuta un asset alla volta e non poteva vedere il quadro. Ho rieseguito **lo
 stesso identico codice** sui due major:
 | Signal (identico) | FULL Sharpe | HOLD-OUT Sharpe | HOLD-OUT ret |
 |---|---|---|---|
 | Solfeggio-cycle su **ETH** 1h | 1.54 | **+1.19** | +49% |
 | Solfeggio-cycle su **BTC** 1h | 1.17 | **−0.25** | −7.5% |
 Un edge robusto non fallisce sull'altro major. La stessa logica (long-only ~20% esposta, filtro
 SMA(588), timing su ciclo ~24) che ha "schivato" il crash ETH 2025 **perde su BTC nello stesso
 hold-out**. È **fortuna di regime di un singolo asset**, non skill. Aggravanti: costanti
 numerologiche ad-hoc (24/588/56, "odore" di overfit, già notato dal verificatore); e con 52 trial,
 trovare 1 segnale che passa un singolo regime di hold-out è atteso per puro caso (1/11 ≈ chance).
 ## VERDETTO
 **La ricerca frattale multi-agente (52 ipotesi, 63 agenti) NON ha trovato alcun edge robusto.**
 I concetti frattali/esoterici si sono comportati esattamente come le famiglie convenzionali (Fasi
 1-3): edge in-sample da regime-luck del toro, refutati dal hold-out; e l'unico che passava il
 hold-out su un asset fallisce sull'altro. **Nessuna magia nei numeri Solfeggio/sacri.**
 Il valore: il processo disciplinato (guard anti-look-ahead + hold-out bloccato + **test cross-asset**)
 ha catturato un falso "trionfo" (+49% vs −49%!) che sul vecchio sistema contaminato sarebbe finito
 dritto in produzione. È la quinta conferma indipendente che su BTC/ETH non c'è un edge facile.
 ## Stato della ricerca dopo tutte le fasi
 Testato: mean-reversion, momentum/trend, vol, lead-lag, hurst, shape-ML, e 16 famiglie frattali ×
 multi-TF/asset. **Niente di robusto, fee-surviving, OOS e cross-asset.** Le direzioni oneste
 restano: (a) accettare il ceiling = long risk-managed (no alpha); (b) allargare l'universo dati
 CERTIFICATO oltre BTC/ETH; (c) fonti di segnale ortogonali al prezzo (on-chain, basis multi-venue,
 opzioni multi-regime) — tutte richiedono nuovi dati certificati. Artefatti: `eval_signal.py`,
 workflow `fractal-strategy-search`, ~52 segnali in `/tmp/pyth_sig_*.py`.
@@ -0,0 +1,58 @@
 # 2026-06-19 — Ricerca v2.0.0: Fase 0 (harness) + Fase 1 (triage superstiti)
 Primo log di ricerca post-reset. Universo certificato: BTC/ETH, 1h. Hold-out 2025+ BLOCCATO.
 ## Fase 0 — harness onesto (`scripts/analysis/research_lab.py`)
 Banco di prova causale per costruzione (modello a SERIE DI POSIZIONE: `pos[i]` decisa entro
 `close[i]`, guadagna `close[i]→close[i+1]`, fee sul turnover |Δpos|). Metriche
 Sharpe/CAGR/DD/exposure/turnover, finestra OOS, **null model a rotazione circolare**
 (p-value: il timing batte il caso?), baseline buy&hold, sweep fee.
 **Self-test del banco (valida l'HARNESS, non una strategia):**
 - buy&hold BTC: Sharpe 0.79 (sanity OK).
 - CHEAT look-ahead (pos = segno del rendimento futuro): Sharpe **58**, p=0.005 → l'engine
  VEDE un edge reale quando esiste.
 - NOISE causale a basso turnover: Sharpe **0.14**, p=0.26 → l'engine NON inventa edge dal
  nulla (niente leak, niente skill spuria).
 Il banco è affidabile. → ogni numero qui sotto è netto fee e causale.
 ## Fase 1 — triage dei 2 superstiti (`scripts/analysis/phase1_survivors.py`)
 Sul feed pulito solo SH01 (shape-ML) e frammenti HONEST mostravano segnale residuo. Delle
 HONEST solo **DIP** è testabile su BTC/ETH (TR01/ROT02 richiedono alt esclusi). Re-implementati
 come serie di posizione, passati ai gate onesti.
 ### DIP reversion (long-only) — ☠️ MORTO
 Griglia 3×3 (n, k) **tutta negativa** su entrambi gli asset (nessun plateau). Config centrale
 n50 k2.0: FULL Sharpe −0.17 (BTC) / −0.06 (ETH); a fee 0% appena +0.02/+0.09 (niente edge nemmeno
 lordo). OOS-VAL marginale (+0.36/+0.16) ma **null p=0.84-0.89** (peggio del caso). Rumore.
 ### SH01 shape-ML (walk-forward LogReg) — ☠️ FEE-DEAD
 Pattern coerente su BTC/ETH, long/short e long-only:
 | Variante | Sh fee0% | Sh fee0.05% | Sh fee0.10% | trade/anno | null p |
 |---|---|---|---|---|---|
 | BTC L/S | +0.32 | −0.70 | −1.71 | 877 | 0.167 |
 | BTC long-only | +0.73 | −0.06 | −0.84 | 555 | 0.072 |
 | ETH L/S | +0.31 | −0.40 | −1.11 | 773 | 0.137 |
 | ETH long-only | +0.46 | −0.04 | −0.53 | 485 | 0.142 |
 C'è un **sussurro di segnale LORDO** (Sharpe 0.3-0.7 a fee zero) ma il turnover (485-877
 trade/anno) lo divora: a fee reale tutte negative, e **nessuna batte il null** (p>0.05). Net-fee:
 rumore.
 ## VERDETTO Fase 1
 **Né DIP né shape-ML sopravvivono su BTC/ETH certificato net-fee.** Nessuno dà Sharpe netto >0,
 nessuno batte il null (p<0.05), nessuno batte il **buy&hold** (Sharpe 0.79/0.84 — di fatto la
 "strategia" più forte vista finora). Si conferma: i "superstiti" della vecchia libreria erano,
 come il resto, non-edge. Chiusi.
 ## Lead onesto per la Fase 2
 L'unico segnale non-nullo è il **gross shape-ML** (Sharpe 0.3-0.7 a fee zero), ucciso dal
 turnover. Direzione: esprimere quel segnale a **turnover molto più basso** (orizzonte di holding
 lungo, soglia forte, o come GATE di regime invece che flip per-barra) per vedere se il sussurro
 lordo sopravvive alle fee. È un lead, NON un edge. Inoltre: la barra reale da battere è il
 **buy&hold** (Sharpe ~0.8) — una strategia di timing deve fare meglio di "stai sempre long",
 net-fee.
@@ -0,0 +1,69 @@
 # 2026-06-19 — Ricerca v2.0.0: Fase 2 (famiglie) + analisi OPTIONS
 Universo certificato BTC/ETH. Barra da battere = **buy&hold** (Sharpe 0.79 BTC / 0.84 ETH).
 Tutto netto fee 0.10% RT, hold-out 2025+ BLOCCATO. Harness: `research_lab.py`.
 ## Fase 2 — esplorazione famiglie (`phase2_families.py`)
 24 combinazioni famiglia×asset×TF, ognuna: scan griglia → config migliore → gate onesti
 (FULL/OOS-VAL, vs buy&hold, null p-value a rotazione, sweep fee).
 ### Esiti per famiglia
 - **REVERSAL (mean-reversion breve): ☠️ MORTA OVUNQUE.** FULL Sharpe da −1 a −5.6 (peggio a
  15m: fee-death, −5.6 BTC / −4.6 ETH), gross ≈0, null p 0.45-0.82. **Smentisce definitivamente
  la tesi storica del progetto ("l'edge è sempre mean-reversion")**: era artefatto del feed.
 - **TSMOM / MA-cross / Donchian (trend, long-only): segnale REALE ma MODESTO.** Le versioni
  long-only (basso turnover) battono o eguagliano il buy&hold:
  - **MA-cross long-only**: ETH FULL **1.12** / OOS 0.89 / p **0.007**; BTC FULL **0.90** / OOS
    1.99 / p **0.040**. Plateau sulla griglia (ETH 12/48 e 48/192 entrambi 1.1), coerente sui
    DUE asset, basso turnover (53-106 trade/anno). **Unici 2 a passare: battono B&H + OOS>0 + p<0.05.**
  - Donchian long-only: FULL 0.84-0.94, OOS ottimo (BTC 2.37) ma p 0.08-0.10 (pochi trade → null
    rumoroso). TSMOM long-only: ETH 0.83 (≈B&H). Le L/S perdono (turnover + short su asset in trend).
 - **VOL-TARGET overlay**: ≈ buy&hold (FULL 0.77-0.84), p alto → non distinguibile dal B&H, ma è
  un riduttore di vol/DD (mantiene lo Sharpe scalando l'esposizione).
 - **HURST-gate, LEAD-LAG BTC↔ETH**: niente. (Hurst-mom ETH p=0.043 ma sotto il B&H; lead-lag
  fee-dead.)
 ### Verdetto Fase 2
 L'unica cosa reale su BTC/ETH certificato è il **trend-following long-only** (MA-cross in testa):
 un **long con gestione del rischio** che batte il buy&hold di poco (Sharpe ~0.9-1.1 vs 0.8)
 evitando i drawdown peggiori. È un effetto noto in letteratura (time-series momentum), NON alpha
 market-neutral. **Caveat multiple-testing**: 2 flag su ~24 test ≈ soglia del caso; ma la stessa
 famiglia vince su ENTRAMBI gli asset con plateau → è un LEAD genuino, non confermato. La barra
 vera resta il B&H, e l'OOS-VAL alto di BTC (1.99) puzza di "2024 anno di trend forte" → serve la
 prova del hold-out 2025-26 + regimi bear + stress fee/slippage + deflated-Sharpe (Fase 3).
 ## Analisi OPTIONS (`options_analysis.py`)
 Dati reali cerbero-bite mainnet, ma finestra **~2026-05-01→06-11 (~6 sett., REGIME UNICO calmo)**.
 ### Livelli misurati (reali)
 - **VRP (IV − RV) positivo il 100% del tempo**: BTC +10, ETH +14 punti di vol annua. Le opzioni
  sono sistematicamente CARE in questa finestra → vendere vol/covered-call avrebbe incassato premio.
 - **Skew put positivo**: BTC IV put-10%OTM 44% vs call 35% (skew +10 pt); ETH 54 vs 49 (+5). Il
  crash è prezzato (assicurazione cara).
 - **Costo put protettiva** (mensile, %-del-notional): ~10% OTM = **0.98% BTC / 1.36% ETH**; ATM
  3.3%/5.0%; ~15% OTM 0.83%/0.71%. Liquidità: ATM spread ~3%, OTM 7-12%. Mensile ben popolato
  (499-2043 strike), settimanale OTM sottile. Funding perp ≈ 0 (nessun carry).
 ### Verdetto OPTIONS
 **Nessun edge su opzioni è validabile ora**: 6 settimane, regime unico calmo. Il segnale
 VRP-positivo / sell-vol è ESATTAMENTE ciò che brilla in calma e salta in aria nei crash (è il
 rischio che viene pagato) — non testabile senza un crash nel campione. Ruoli legittimi (entrambi
 NON validabili ora, solo forward):
 - **(a) Tail-cap / catastrofe**: put OTM standing su un book long (il candidato trend ha DD grossi).
  Costa ~1-1.5%/mese a 10% OTM — gateabile coi premi reali misurati qui. Overlay per-trade 24h
  INFATTIBILE (strike OTM corti inesistenti/illiquidi); standing settimanale/mensile FATTIBILE.
 - **(b) Harvest del VRP** (covered call / put-spread): +10-14 pt ci sono ORA, ma è una scommessa
  short-vol che richiede un crash nel campione per essere giudicata onestamente. Non l'abbiamo.
 **Raccomandazione**: le opzioni NON sono un'avenue di ricerca a breve (manca storia multi-regime).
 Mosse: (1) lasciare cerbero-bite ad accumulare (gratis, reale, costruisce in avanti il dataset
 multi-regime); (2) rivalutare quando la finestra attraversa un crash/alta-vol; (3) intanto, l'unico
 uso giustificato è come OVERLAY (tail-cap su una strategia spot), gateato sui premi reali qui sopra.
 ## Prossimo passo
 Fase 3 sul solo candidato reale (trend-following long-only, MA-cross): sblocco UNA volta del
 hold-out 2025-26, comportamento nei bear (2018/2022), stress fee×2 + slippage + lag, deflated-Sharpe
 per il multiple-testing. Se regge → è la prima strategia onesta del progetto v2.0.0 (modesta:
 migliora il buy&hold, non lo stravolge). Se non regge → anche il trend era sample-luck.
@@ -0,0 +1,62 @@
 # 2026-06-19 — Ricerca v2.0.0: Fase 3, conferma avversariale del candidato trend
 Candidato: **trend-following long-only (MA-cross)**, l'unico a passare i gate base in Fase 2.
 Protocollo: selezione config solo pre-hold-out → sblocco una-tantum del hold-out 2025-26 →
 breakdown bear → stress → deflated-Sharpe. Script `phase3_confirm.py`.
 ## Esito: ☠️ NON CONFERMATO — era regime-luck del mercato toro
 ### (1) Pre-hold-out (2018-2024): forte e robusto
 Plateau pieno: BTC Sharpe 0.91-1.16, ETH 1.19-1.48 su tutte le config. **Deflated-Sharpe**
 (N=60 trial): BTC DSR **0.990**, ETH **0.982** → l'effetto trend era REALE e robusto al
 multiple-testing **sul 2018-2024**.
 ### (2) HOLD-OUT 2025-26 (sbloccato una volta) — FALLISCE
 | | buy&hold | trend 24/96 | trend 96/288 (slow) |
 |---|---|---|---|
 | BTC Sharpe | −0.37 | **−0.81** | −0.00 |
 | BTC ret | −32.9% | −33.6% | −5.0% |
 | ETH Sharpe | −0.32 | **−0.95** | −0.01 |
 | ETH ret | −49.3% | −52.0% | −11.3% |
 Il 2025-26 è stato un periodo in DISCESA (buy&hold negativo). Il trend long-only — che "dovrebbe"
 schivare i bear — si è fatto **frullare** (whipsaw): perde quanto o PIÙ del buy&hold, Sharpe negativo
 su ogni config. Solo la MA lentissima (96/288) limita i danni a ~flat (−5/−11%), ma è cherry-pick
 post-hoc e comunque NON positiva.
 ### (3) Per anno — il meccanismo
 Il trend cattura ~70-80% degli anni TORO (2019-2024) e attutisce i bear IN-SAMPLE (2018 −1% vs
 −39%; 2022 −47% vs −65%). MA nel 2025 OUT-OF-SAMPLE ha fatto **peggio** del buy&hold (BTC −25% vs
 −7%; ETH −41% vs −11%): frullato in un mercato choppy/discendente. È il classico fallimento del
 trend-following nei bear laterali. → l'edge 2018-24 era **beta del toro con risk-management**, non
 alpha persistente.
 ### (4) Stress
 FULL regge modestamente (Sharpe 0.65-0.91 anche a fee2x+lag), ma HOLD-OUT è negativo ovunque
 (−0.81 → −1.34) e peggiora sotto stress. Fragile.
 ### (5) Deflated-Sharpe
 DSR>0.95 sul pre-hold-out → conferma che l'effetto era statisticamente reale **nel campione di
 training**. Lezione chiave: **robustezza statistica in-sample ≠ persistenza out-of-sample.** Il
 hold-out bloccato ha colto ciò che DSR da solo non poteva — il cambio di regime.
 ## VERDETTO FINALE (Fasi 0-3)
 **Nessun edge validato, fee-surviving e out-of-sample esiste su BTC/ETH tra le famiglie testate.**
 Il trend-following era il miglior candidato: reale 2018-24 (toro), ma **bocciato sul hold-out
 2025-26** (whipsaw, sotto il buy&hold). La barra realistica resta il **buy&hold** (Sharpe ~0.8
 sullo storico, ma −0.3/−0.4 nel 2025-26: anche "stai long" è stato duro di recente).
 Il processo disciplinato ha funzionato: **ha evitato di deployare un falso edge** (che, sul vecchio
 sistema contaminato, sarebbe finito in produzione). Questo è il valore del reset.
 ## Implicazioni / direzioni
 - **Non deployare** il trend come edge: è regime-dipendente, non batte il buy&hold OOS.
 - Con **solo BTC/ETH prezzo**, il pozzo dei segnali è poco profondo: timing puro non ha edge robusto.
 - Opzioni: nessun ruolo a breve (confermato). Tenere cerbero-bite ad accumulare per uno studio
  multi-regime futuro.
 - Scelte oneste per andare avanti: (a) accettare che il "ceiling" su BTC/ETH è un long risk-managed
  (no alpha) e ottimizzare quello (vol-target per ridurre DD, non per battere il mercato); (b)
  allargare l'universo dati CERTIFICATO (servono asset liquidi+puliti oltre BTC/ETH, che Deribit non
  offre bene → valutare un secondo venue mainnet certificabile); (c) fonti di segnale ortogonali al
  prezzo (on-chain, funding/basis multi-venue, opzioni multi-regime) — tutte richiedono nuovi dati
  certificati prima di poterci credere.
@@ -0,0 +1,63 @@
 # 2026-06-19 — Sintesi ricerca post-reset (5 track) e verdetto
 Prima ondata di ricerca sui dati **certificati** BTC/ETH (Deribit mainnet, ~2 bps vs
 Coinbase USD), con harness onesto condiviso `src/backtest/harness.py` (ingresso eseguibile
 a `close[i]`, fee 0.10% RT, exit intrabar TP/SL, OOS/per-anno). Branch
 `strategy-research-2026-06`.
 ## I 5 track
 | Track | Famiglia | Esito |
 |-------|----------|-------|
 | **A** | Trend/Momentum (TSMOM, Donchian, EMA, vol-scaled) | 5m/15m morti (fee); 1h = residuo reale ma celle singole non robuste |
 | **B** | ML walk-forward (logistic/GBM su feature di forma) | edge debole ma REALE su BTC (+83% OOS, Sharpe 0.57), ~+0.58 €/d su 2000 |
 | **C** | Mean-reversion / range (fade, RSI2, VWAP) | **MORTO** — negativo anche a fee=0. Conferma: la vecchia libreria fade era artefatto |
 | **D** | **Trend portfolio vol-targeted BTC+ETH** | ✅ **DEPLOYABLE** — robusto, positivo ogni anno |
 | **E** | Cross-sectional BTC↔ETH + ensemble | RV debole (muore a 1.5bps/gamba); ensemble dimezza il DD ma non alza il ritorno |
 ## Il vincitore: Track D — trend portfolio (l'unico che guadagna in modo robusto)
 TSMOM multi-orizzonte (blend 1-3-6 mesi su barre 1h), **vol-targeting** (posizione ∝
 1/vol realizzata, target 20% annuo), portafoglio **50/50 BTC+ETH**, fee 0.10% RT. Un solo
 set di parametri per entrambi gli asset.
 - **LONG-SHORT 50/50:** CAGR +14.2%, **Sharpe 1.00**, maxDD 18.9%, positivo ogni anno 2019-2026.
 - **LONG-FLAT 50/50 (migliore risk-adj):** CAGR +15.9%, **Sharpe 1.32**, **maxDD 13.3%**.
 - Robusto: plateau di Sharpe ~1.0 su griglia target-vol/leva/orizzonti; regge fee fino a 0.40% RT;
  su entrambi gli asset; **non** è un picco fortunato (a differenza delle "star" di Track A).
 - Tesi confermata: il valore del trend è **tagliare il drawdown** (B&H DD ~78% → trend DD ~13-19%)
  con Sharpe ≥ B&H → si può scalare il rischio (target-vol) e diversificare BTC+ETH.
 - Caveat onesto: l'edge è più forte 2018-21 (Sharpe 1.63) che 2022-26 (Sharpe 0.57). Dimensionare
  sul regime recente.
 ## Il verdetto sul target €50/giorno
 Una strategia che **guadagna** in modo robusto ESISTE (Track D). Ma il target "€50/giorno
 medio partendo da 2000 in 1-2 anni" **non è raggiungibile onestamente**: sono ~2.5%/giorno.
 La leva NON è la scorciatoia (alza il DD verso la rovina). La vera leva è **target-vol +
 capitale + tempo**:
 | target-vol | leva usata | CAGR | Sharpe | maxDD | €/giorno (2k) |
 |-----------|-----------|------|--------|-------|---------------|
 | 20% | 0.23x | +14% | 1.00 | 19% | +0.73 |
 | 40% | 0.45x | +28% | 1.00 | 35% | +3.73 |
 | 60% | 0.68x | +40% | 1.00 | 48% | +7.96 |
 | 80% | 0.90x | +50% | 0.99 | 60% | +13.78 |
 Per **€50/giorno steady-state** servono ~**137k di capitale** (config conservativa, DD~19%),
 oppure DD da rovina. Partendo da 2000 a CAGR ~28% (target-vol 40%, DD 35%) il capitale che
 genera €50/giorno arriva in ~10-13 anni, non in 1-2.
 ## Conclusione operativa
 1. **Esiste un edge dispiegabile e onesto**: il trend portfolio vol-targeted (Track D).
   È il primo risultato robusto post-reset.
 2. **Non esiste alcuna scorciatoia** verso €50/giorno su 2000 in 1-2 anni con questi dati
   (BTC/ETH 5m-1h). Il limite è strutturale: due asset, alta correlazione, fee.
 3. Prossimi passi onesti se si vuole alzare il soffitto: (a) dimensionare Track D a un
   target-vol/DD tollerabile e farlo girare in paper, (b) cercare edge di **magnitudine
   diversa** (non più diversificazione di edge deboli) — il che richiede dati che oggi non
   abbiamo certificati (universo più ampio, microstruttura, funding/opzioni backtestabili).
 Script: `scripts/research/track{A,B,C,D,E}_*.py`. Diari di dettaglio: `2026-06-19-track*.md`.
@@ -0,0 +1,43 @@
 # 2026-06-19 — TP01: look-ahead ffill mixed-TF, ri-verifica e adozione bassa frequenza (>=12h)
 Segnalazione utente/agente: un look-ahead **ffill MIXED-TIMEFRAME su barre open-labeled**
 (`resample(label="left")`) gonfiava il 4h a Sharpe ~1.60; il risultato reale è ~1.1.
 Conclusione: **NON scendere sotto le 12h** — costi e overfitting dominano.
 ## Cosa ho verificato (`scripts/analysis/tp01_lowfreq.py`)
 Ricalcolo TP01 PULITO **per singolo TF** (barre discrete, posizione shiftata +1, NESSUN
 ffill/combine mixed-TF), con un **guard di causalità esplicito** (ricalcolo `target_series` su
 prefisso → `tgt[i]` invariato). Esito (fee 0.10% RT, hold-out 2025-26 bloccato):
 | TF | leak | FULL Sh | FULL ret | HOLD Sh | HOLD ret | HOLD DD |
 |---|---|---|---|---|---|---|
 | 4h | **0** | 1.36 | +204% | 0.27 | +2.8% | 8.3% |
 | 6h | **0** | 1.42 | +217% | 0.21 | +2.1% | 7.9% |
 | 12h | **0** | 1.32 | +198% | 0.22 | +2.3% | 8.6% |
 | **1d** | **0** | 1.30 | +201% | **0.31** | **+3.5%** | 7.5% |
 | buy&hold 50/50 1d | — | 0.92 | +1671% | **−0.32** | **−39%** | 59% |
 ## Lettura
 - **Il path single-TF che ho usato in verify/stress è LEAK-FREE** (guard=0 su ogni TF): il
  gonfiaggio 1.60 stava nel path **mixed-TF ffill** (ensemble/combine, es. trackE), NON nel
  portafoglio single-TF. Per questo il mio 4h era 1.36 (non 1.60).
 - **La conclusione "≥12h" è comunque CORRETTA e la adotto**: il FULL Sharpe è PIATTO ~1.3 da 12h
  a 4h → scendere sotto le 12h NON dà vantaggio reale, aggiunge solo costi/turnover e rischio
  overfit/look-ahead (lo stress mostrava il margine hold-out del 4h fragile a lag/fee). **1d è il
  migliore**: hold-out Sharpe 0.31 (il più alto), DD 7.5%, turnover/costi minimi, leak-free.
 - Allinea anche col numero dell'agente: il "reale ~1.1" è del path mixed-TF corretto; il mio
  single-TF pulito dà ~1.3 FULL. In ogni caso **edge difensivo modesto**, non alpha.
 ## Decisioni applicate
 - **Canonica deploy → PORT LF1d** (era LF4h). `trend_portfolio.py`: docstring aggiornata + nota
  look-ahead; aggiunti `resample_tf`/`resample_1d`, `resample_4h` marcato deprecato per il deploy.
 - **Paper trader → 1d** (`paper_trend.py`: `resample_1d`, `build_bars`, etichette 1d; gira, 5 test ok).
 - **CLAUDE.md**: TP01 ridescritta come DIFENSIVA, canonica ≥12h/1d, gotcha look-ahead documentato.
 - **Gotcha riusabile:** mai ffill/combine MIXED-TIMEFRAME su timestamp open-labeled (`label="left"`):
  la close del bar (nota solo a fine bar) verrebbe propagata indietro all'open-label → look-ahead.
  Il calcolo per-singolo-TF a barre discrete (posizione +1) è sicuro; il guard prefix-recompute lo prova.
 ## Verdetto invariato
 TP01 resta la prima strategia onesta del progetto: **difensiva** (taglia il DD ~6× vs buy&hold,
 hold-out 2025-26 positivo su entrambi gli asset), modesta nel ritorno. Deploy a **1d**, forward-only
 paper trader, prima di qualsiasi capitale reale.
@@ -0,0 +1,84 @@
 # 2026-06-19 — Verifica TP01 (branch strategy-research-2026-06) col gauntlet onesto
 Una ricerca PARALLELA (branch `strategy-research-2026-06`, AdrianoDev) dallo stesso baseline
 v2.0.0 ha trovato TP01 come "unica vincitrice". La mia linea (Fasi 2-3) aveva bocciato il trend
 sul hold-out 2025-26. Ho riprodotto TP01 VERBATIM (`scripts/analysis/verify_tp01.py`) e l'ho
 passato al mio gauntlet. **TP01 REGGE — la mia conclusione precedente era incompleta.**
 ## TP01 = TSMOM 30/90/180g, **vol-target 20%**, leva cap 2x, **long-flat**, portafoglio 50/50 BTC+ETH (4h)
 ## Esiti del gauntlet
 **(A) Multi-TF (4h cherry-picked?) — NO, plateau robusto:**
 | TF | FULL Sharpe | HOLD-OUT Sharpe |
 |---|---|---|
 | 15m | 0.93 | −0.31 |
 | 1h | 1.32 | +0.20 |
 | **4h** | **1.36** | **+0.27** |
 | 1d | 1.30 | +0.31 |
 1h/4h/1d danno tutti FULL ~1.3 e hold-out positivo → non è un artefatto di un singolo TF (solo il 15m, fee-sensibile, fallisce).
 **(C) HOLD-OUT 2025-26 (il test che ha ucciso il mio trend 1h) — TP01 PROTEGGE:**
 | | Sharpe | ret | DD |
 |---|---|---|---|
 | **TP01 portfolio** | **+0.27** | **+2.8%** | **8.3%** |
 | buy&hold 50/50 | −0.35 | **−39.4%** | 59.8% |
 **(D) Cross-asset nel hold-out — regge su ENTRAMBI** (BTC sleeve +2.9% Sh 0.24, ETH +2.4% Sh 0.24).
 A differenza del "vincitore" frattale (+ETH/−BTC), TP01 protegge coerentemente su BTC E ETH.
 **(B) Per anno:** positiva quasi ogni anno 2019-2026 (eccezioni piccole: 2022 −2.4%, 2026-YTD −0.9%),
 DD annui 1-12%. Il claim "positiva ogni anno" è lievemente ottimistico ma sostanzialmente vero.
 ## Perché TP01 regge dove il MIO trend (Fase 3) è caduto
 La differenza chiave è il **VOL-TARGETING** (che NON avevo combinato col trend): TP01 scala
 l'esposizione ∝ 1/vol_realizzata → nel crollo 2025-26 la vol è esplosa e TP01 si è messo
 quasi in cash, schivando il drawdown. Il mio MA-cross 1h aveva esposizione fissa ed è rimasto
 long nel chop → frullato. Concorrono: TSMOM multi-orizzonte (più liscio del MA-cross), long-flat
 (niente perdite short), diversificazione 50/50. **La mia "trend = regime-luck" era vera per il
 trend NUDO; TP01 = trend + vol-target + portafoglio è un'altra cosa, e robusta.**
 ## Cosa È onestamente TP01 (no oversell)
 - **Edge DIFENSIVO, non alpha**: FULL Sharpe 1.36 vs buy&hold 0.92 — MA CAGR +16.6% vs +48.1%.
  Su tutto il toro il buy&hold ha reso ~8x di più. Il valore di TP01 è il **DD** (13.8% vs 77.5%
  full; 8% vs 60% nel hold-out) e la **protezione dai crash**.
 - Nel hold-out 2025-26 ha fatto solo +2.8% (Sharpe 0.27, basso): ha **protetto, non profittato**.
 - Un solo regime di hold-out, ma il vol-targeting è meccanico (high vol → low expo) → generalizza
  per costruzione meglio di un timing fittato.
 - Config canonica (30/90/180, vol20%, lev2x) non iper-tunata; 4h non cherry-picked (plateau).
 ## VERDETTO
 **TP01 è la PRIMA strategia onesta e robusta del progetto post-reset.** Supera il mio gauntlet
 (hold-out positivo su entrambi gli asset, plateau multi-TF, causale, fee-aware). È modesta e
 difensiva (Sharpe ~1.3, soffitto strutturale dichiarato corretto), ma è reale: migliora il
 rischio/rendimento del buy&hold tagliando i drawdown e proteggendo nei crash. La ricerca parallela
 ha fatto centro proprio sul pezzo che la mia linea non aveva combinato (vol-target sul trend).
 **Raccomandazione:** integrare il branch su main (modulo `trend_portfolio.py` + paper trader),
 trattare TP01 come baseline operativa difensiva. Aspettative oneste verso il target €50/g: a
 Sharpe 1.3 / CAGR 16.6% servono molto capitale o leva (con più DD) — TP01 è un fondamento solido,
 non una scorciatoia.
 ## STRESS-TEST (`scripts/analysis/stress_tp01.py`, integrato e rieseguito sul modulo vero)
 | Dimensione | Esito |
 |---|---|
 | **Sweep fee** | FULL robusto fino a **0.40% RT** (Sh 1.44→1.36→1.28→1.13). HOLD-OUT SOTTILE: +2.8%/Sh0.27 a 0.10% → ~flat (Sh 0.03) a 0.40% |
 | **Lag/slippage** | FULL robusto (1.29-1.43). HOLD-OUT si erode: lag1(4h)→Sh0.12, lag2→−0.02, lag1+fee0.20%→0.04 |
 | **Plateau parametri** | OTTIMO — target_vol/leva/orizzonti/vol_win tutti reggono o migliorano (orizzonti 20/60/120 → Sh 1.61). **NON un picco cherry-picked** |
 | **Deflated-Sharpe** | DSR **0.999** a N=10/40/100 trial → il Sharpe FULL non è artefatto di multiple-testing |
 **Verdetto stress (onesto):**
 - **Robustezza FULL-period: FORTE.** TP01 supera fee 0.40%, lag, ampio plateau di parametri, e
  deflated-Sharpe. NON è overfit né cherry-picked — la proprietà robusta è il **taglio del
  drawdown** (13.8% vs 77.5% full; 8% vs 60% hold-out), invariante a tutto lo stress.
 - **Edge di RITORNO nel hold-out: REALE ma SOTTILE e sensibile alla frizione.** Nel 2025-26 ha
  schivato il crash in modo affidabile (DD 8% vs 60%) ma ha **protetto più che profittato** (+2.8%,
  Sh 0.27), e quel sottile positivo si assottiglia a zero sotto fee2x o lag 2 barre.
 **Conclusione:** la proprietà **deployabile e robusta di TP01 è la PROTEZIONE del drawdown**, non
 la generazione di alpha. È una strategia difensiva genuina (prima del progetto a superare gauntlet
 + stress), ma a basso ritorno: il valore è "Sharpe ~1.3 con DD ~6× più piccolo del buy&hold",
 non "battere il mercato". Per il capitale reale: il vol-targeting + long-flat sono meccanici e
 generalizzano; il rischio residuo è la frizione di esecuzione sul filo del sottile edge di ritorno
 nei regimi avversi → da monitorare col paper trader forward-only prima di scalare.
@@ -0,0 +1,74 @@
 # 2026-06-19 — Track A: Trend / Momentum su BTC & ETH (dati certificati)
 Prima ricerca di strategie NUOVE post-reset (track A = trend/momentum). Tool:
 `scripts/research/trackA_trend.py` (harness onesto `src/backtest/harness.py`, fee 0.10% RT,
 IS/OOS 65/35, griglia su entrambi gli asset, fee sweep, stress leva). Run:
 `uv run python scripts/research/trackA_trend.py`.
 ## Cosa è stato testato
 - **TSMOM** (segno del ritorno N-barre, hold H) long/short e long-only.
 - **EMA crossover** (fast/slow) come filtro di trend.
 - **Donchian breakout** (entry ONESTO: breakout rilevato con `close[i]`, fill a `close[i]`).
 - **Vol-scaled / regime-gated TSMOM** (momentum preso solo se |z| > gate, z = ritorno/vol).
 - Griglia ampia su **BTC e ETH**, **1h / 15m / 5m**. 480 celle OOS totali.
 Tutto entry-eseguibile: direzione e prezzo decisi con dati ≤ `close[i]`, fill a `close[i]`.
 Nessun uso di `returns[i]` (che codifica `close[i+1]`). Hold approssimato come catena di
 posizioni non sovrapposte di H barre (la fee si ammortizza su H barre — costo onesto).
 ## Risultati — la fotografia onesta
 **Celle positive OOS per timeframe:**
 | TF | celle positive / totali |
 |----|----|
 | **1h**  | 39 / 160 |
 | **15m** | **0 / 160** |
 | **5m**  | **0 / 160** |
 → **Trend intraday (5m/15m) è MORTO**: lo drag della fee (più trade = più 0.10% RT) annienta
 qualsiasi segnale. Drawdown 80-99%, Sharpe da −0.6 a −2.2. Niente da salvare.
 **Su 1h** c'è qualche cella positiva, ma il contesto la ridimensiona:
 - La finestra **OOS è un singolo regime**: il taglio 65% cade a **set/dic 2023**, quindi
  l'OOS è ~2023→2026 (in gran parte toro 2024). Tutto il 2018-2022 (orso 2018, crash 2020,
  toro 2021, orso 2022) è IN-SAMPLE. "Positivo OOS" qui ≈ "il trend ha fatto soldi nel toro 2024".
 - **Benchmark buy & hold sulla stessa finestra OOS**: BTC **+134%**, ETH **−21%**.
  - Tutte le `TSMOM_LONG` e metà delle celle BTC fanno **MENO** del B&H → è **beta**, non edge.
  - Le poche che battono il B&H lo fanno **solo su ETH** (dove il B&H era negativo): catturano
    anche i ribassi. Quello è timing reale — ma vedi sotto.
 **Le "star":** VOLSCALED_TSMOM BTC 1h (N=20,H=48,vw=100,z=0.5) = +367% OOS, Sh 0.91, DD 32%,
 €/d(2k) +2.56; ETH 1h (N=20,H=48,vw=50,z=1.0) = +197% OOS, Sh 0.60. **MA sono celle fortunate:**
 i vicini di griglia crollano (stesso N/H, vw=50 invece di 100 → +21% invece di +367%; z=1.0 → +34%).
 Non è un altopiano robusto, è un picco isolato. E il P&L è concentrato nel 2024 (+110% su BTC),
 con 2025/2026 deboli o negativi per molte celle.
 **Consistenza cross-asset (un edge vero regge su ENTRAMBI):** su 480 celle, solo **2** sono
 positive OOS su BTC *e* ETH:
 - `TSMOM_LONG 1h N=200 H=48` → ma è long-only ≈ beta (fa meno del B&H su BTC).
 - `DONCHIAN 1h N=200 H=12` → l'unico candidato "vero" simmetrico, ma **marginale**:
  OOS BTC +9% / ETH +15%, **Sharpe 0.15-0.19**, troppo debole per dispiegarlo.
 **Fee sweep / leva:** le star reggono lo sweep 0.0005-0.002 (è 1h, poche operazioni), e lo Sharpe
 è invariante alla leva (come deve) — ma la leva 3x porta i DD a 75-91% e affonda le celle marginali.
 ## Verdetto
 **Nessun edge trend/momentum dispiegabile, onestamente, su BTC/ETH oggi.**
 - 5m/15m: morti per fee. Chiuso.
 - 1h: esiste un **residuo di segnale trend** (le celle che battono il B&H negativo di ETH non sono
  solo beta), ma è (a) testato su **un solo regime OOS** (toro 2023-2026), (b) **non robusto** di
  griglia (picchi isolati), (c) sull'unica cella simmetrica robusta-su-entrambi (Donchian N=200)
  **troppo debole** (Sharpe ~0.17). Sharpe netti ~0.3-0.9 nel caso migliore = sotto la soglia per
  rischiare capitale reale.
 Conferma la lezione del reset (il superstite storico era trend-following, non mean-reversion): il
 trend è la direzione *meno sbagliata*, ma sui dati certi non basta a fare un edge. Coerente con
 Track C (mean-reversion = artefatto).
 ## Prossimi passi possibili (non ancora edge)
 - Walk-forward multi-regime (non un singolo taglio 65/35) per stressare Donchian-1h-N200 su orso 2018/2022.
 - Trend 1h **con filtro di volatilità/regime più ricco** o portafoglio BTC+ETH per diversificare il
  rischio di regime — ma solo se emerge robustezza di griglia, non altri picchi fortunati.
 - Restare scettici: finché un trend non è positivo su griglia + su entrambi gli asset + su ≥2 regimi
  OOS, **non si dispiega**.
@@ -0,0 +1,93 @@
 # 2026-06-19 — Track B: ML / feature-prediction su BTC & ETH (walk-forward onesto)
 Esperimento di ricerca sulla direzione **machine-learning** post-reset, su dati Deribit
 mainnet certificati (solo BTC/ETH). Tool: `scripts/research/trackB_ml.py` (runnable
 `uv run python scripts/research/trackB_ml.py`). Tutto netto fee, strict walk-forward,
 held-out tail mai usato per scegliere i config.
 ## Metodologia (anti look-ahead — la lezione della v2.0.0)
 - **Feature** (21): ritorni multi-lag (1/2/3/6/12/24), geometria candela (body/upper/lower
  shadow su range, range normalizzato, body lag-1), momentum48 + accelerazione, RSI14,
  estensione ATR-normalizzata vs EMA24, vol realizzata 24/72 + ratio, posizione del close
  nel range 24/72, z-score del volume. **Tutte backward** (note solo a `close[i]`).
 - **Label**: segno del ritorno forward su H barre, `sign(close[i+H]/close[i])`.
 - **Strict walk-forward**: per predire il blocco che inizia a `b`, si addestra
  scaler+modello SOLO su indici `< b-H` (gap di H → label completamente realizzata nel
  passato), finestra rolling delle ultime W barre. Retrain ogni K=250 barre. Mai fit sul
  futuro. **Nessun leakage** (verificato: la label più recente del train usa `close[b-1]`).
 - **Esecuzione**: entry a `close[i]` nella direzione predetta, hold fino a H barre
  (no TP/SL); il no-overlap dell'harness distanzia i trade ≥ H barre.
 - **Modello**: `LogisticRegression(class_weight='balanced')`. Soglia di probabilità per
  filtrare i segnali deboli (long se p>0.5+thr, short se p<0.5-thr, altrimenti flat).
 - **Selezione su DEV** (primo 75%), **conferma una volta sola** sull'held-out tail (ultimo 25%).
 - Griglia: W∈{4000,8000,16000}, H∈{6,12,24,48}, thr∈{0,0.03,0.06,0.10}, BTC & ETH, 1h.
  Fee-sweep 0.05/0.10/0.15/0.20% RT. Turnover/time-in-market sempre riportati.
 ## Risultato — esiste un segnale, ma è debole e a basso turnover
 **Pattern netto e robusto della griglia**: la positività compare SOLO nelle celle a basso
 turnover → **W grande (16000) + H lungo (24) + soglia alta (0.10)**. Tutto ciò che gira
 veloce (thr basso, H corto, e soprattutto il **15m**) **muore sulle fee**.
 - **15m**: 0/12 celle positive in dev (la migliore −47%, le altre −99%). Stesso win-rate
  52–56% del 1h, ma il turnover lo polverizza. Conferma di prim'ordine: l'edge per-trade è
  minuscolo, sopravvive solo se si tradano poche barre.
 - **1h, dev**: 19/96 celle net-positive con Sharpe>0. Famiglie threshold-robuste:
  `BTC W16000 H12`, `BTC W8000 H12`, `BTC W16000 H24`, più ETH W16000 H12/H48 marginali.
 ### Held-out tail (2024→2026, mai toccato in sviluppo)
 | config | trades | wr% | net% | Sharpe | DD% | mkt% | €/g(2k) | long% | B&H tail |
 |---|---|---|---|---|---|---|---|---|---|
 | **BTC W16000 H24 thr0.10** | 333 | 52.9 | **+83.7** | 0.57 | 23 | 12 | **+0.58** | 44 | +3.9% |
 | BTC W16000 H12 thr0.10 | 382 | 53.4 | +37.6 | 0.35 | 25 | 7 | +0.26 | 54 | +3.9% |
 | ETH W16000 H12 thr0.10 | 364 | 57.7 | +23.7 | 0.24 | 35 | 7 | +0.18 | 68 | −38.4% |
 | ETH W16000 H48 thr0.06 | 215 | 55.3 | −13.3 | 0.08 | 64 | 16 | −0.10 | 67 | −38.4% |
 **Non è solo beta.** Il B&H sul tail è +3.9% (BTC) e −38.4% (ETH), eppure le celle migliori
 fanno +37…+84% (BTC) con **long ~44–54%** (bilanciato long/short), e ETH +23.7% **mentre ETH
 scendeva −38%** (short corretti). Quindi c'è segnale direzionale genuino, non cattura di trend
 rialzista. Payoff asimmetrico: ~53% WR ma avgWin>avgLoss (BTC: +2.04% vs −1.63%).
 ### Fee-sweep (held-out)
 - `BTC W16000 H12 thr0.10`: 0.05%→+66.6 | **0.10%→+37.6** | 0.15%→+13.7 | 0.20%→−6.1.
  Sopravvive fino a ~0.15% RT, poi muore. Margine sottile.
 - `BTC W8000 H12 thr0.06`: positivo solo a 0.05%, già −35% a 0.10%. Fragile.
 - ETH e le celle a turnover medio: muoiono tra 0.10 e 0.15%.
 ### Stabilità per-anno (full walk-forward, BTC W16000 H24 thr0.10)
 `+11% (2020) / +188% (2021) / +14% (2022) / −38% (2023) / +13% (2024) / +75% (2025) / +7% (2026)`,
 CAGR full ~22%, ma **DD 56%** e fortissima concentrazione su 2021/2025 con un 2023 a −38%.
 ## Verdetto onesto — NON deployabile verso l'obiettivo
 1. **L'edge è reale ma minuscolo.** A differenza della vecchia libreria (artefatto puro), qui
   il segnale sopravvive a strict walk-forward, a fee 0.10% RT e batte il B&H sul tail. È un
   risultato genuino e va registrato: la direzione ML **non è morta**.
 2. **Ma è incompatibile col target.** €/giorno su €2000 = +0.26…+0.58 baseline (anche la stima
   rosea full-WF CAGR 22% → ~€1–3/g). Il target è **€50/g** → siamo ~100x sotto.
 3. **Fragilità**: vive solo a basso turnover (thr alto, H lungo, W grande), DD 23–56%,
   ritorni concentrati in pochi anni con un anno a −38%, e l'edge si assottiglia già a
   0.15% RT. Un singolo cambio di regime lo annulla.
 4. **ETH ≠ "specialmente buono"** (contrariamente all'indizio dello shape-ML precedente): qui
   ETH è più sottile e più rumoroso di BTC sull'held-out; l'unico merito è aver shortato
   correttamente il drawdown 2024-25.
 **Conclusione**: la logistic-regression walk-forward su feature di forma+momentum trova un
 debole edge **momentum direzionale a basso turnover** su BTC (più tenue su ETH), onesto e
 netto-fee, ma **troppo piccolo, troppo concentrato e troppo fee-sensibile** per essere
 deployato standalone. Al massimo un **componente** di un futuro ensemble, e solo nelle
 configurazioni a bassissimo turnover. Nessun config raggiunge, neanche lontanamente, i €50/g.
 ## Prossimi passi possibili (non eseguiti)
 - Provare **predizione di magnitudine/asimmetria** (large-up vs large-down) e position-sizing
  proporzionale alla confidenza, invece del semplice segno.
 - **GradientBoosting / feature non lineari** (flag `--gbm` predisposto) — ma attenzione
  all'overfit; il rischio è di "trovare" edge che il walk-forward onesto non conferma.
 - **Ensemble** del segnale ML a basso turnover con un filtro di regime (vol/trend) per tagliare
  il 2023. Ma serve dimostrare che il filtro non è scelto col senno di poi.
 - Restare scettici: finché €/g resta ~100x sotto target, l'ML da solo NON è la risposta.
@@ -0,0 +1,70 @@
 # 2026-06-19 — Track C: mean-reversion / range re-examination (HONEST) → DEAD
 Obiettivo: stabilire rigorosamente se **un qualunque** edge di mean-reversion / range a
 breve orizzonte sopravvive su BTC/ETH **certificati** (Deribit mainnet) con **ingresso
 eseguibile onesto**, oppure confermarne definitivamente la morte. Entrambi gli esiti sono
 validi; nessun risultato forzato.
 Tool: `scripts/research/trackC_meanrev.py` (self-contained, runnable), sopra l'harness
 onesto `src/backtest/harness.py` (direzione+prezzo decisi con dati ≤ `close[i]`, fill a
 `close[i]`, exit intrabar TP/SL da `i+1`, fee netto). Universo: BTC/ETH × {1h,15m,5m}.
 ## Cosa è stato testato (5 famiglie, ingresso onesto)
 - **ZFADE** — Bollinger/z-score fade: `z(close,lookback)` ≤ −thr → long, ≥ +thr → short.
  TP al mean mobile o a `tp_atr·ATR`; SL a `sl_atr·ATR`. **Entry a close[i]**, NON al tocco
  della banda (era proprio quello l'artefatto storico).
 - **RSI2** — RSI(2) oversold/overbought (+ variante con filtro trend SMA200).
 - **RETREV** — return reversal: fade del rendimento cumulato estremo (|z| > thr·σ).
 - **VWAP** — reversione sulla distanza dal VWAP rolling (in unità di σ della distanza).
 - **SESSION** — autocorrelazione next-bar per ora UTC (descrittivo).
 Metodologia applicata: OOS 65/35, griglia parametri su **entrambi** gli asset, fee-sweep
 {0, 0.5, 1.0, 1.5, 2.0} bps RT, cross-check liquidità (flat-bar O=H=L=C) e time-in-market.
 ## Sanity liquidità
 Flat-bar O=H=L=C: BTC/ETH 1h ≈ 0.01%, 15m 0.09–0.14%. Book vivo → l'eventuale edge NON
 potrebbe nascondersi in barre ferme (a differenza degli alt archiviati). Confermato pulito.
 ## Risultati — tutto negativo, su ogni asse
 **PASS 1 (screen 1h, fee 0.10% RT):** ogni famiglia OOS negativa su entrambi gli asset.
 Es. ZFADE z2/mean: BTC OOS −85%, ETH OOS −83%. RSI2 10/90: BTC −92%, ETH −96%.
 RETREV/VWAP idem. Win-rate spesso "alto" (RSI2 ~63%, VWAP ~64%) ma **perde lo stesso** →
 le poche perdite sono enormi, la reversione non paga il rischio + fee.
 **PASS 2 (griglia 1h):** ZFADE **0/18** celle con OOS>0 su entrambi; RSI2 **0/36**. La
 cella meno-peggio (ZFADE lookback20 z3) resta BTC −40% / ETH −33% OOS. Nessun sopravvissuto.
 **PASS 3 (fee-sweep, incl. fee=0 GROSS):** il colpo decisivo. **Anche a fee=0** (lordo)
 la z-fade è negativa: BTC full −74% / OOS −46%, ETH full −98% / OOS −48%. Quindi non è
 "morte da fee": **la direzione stessa della fade è sbagliata** sul feed pulito. Salendo le
 fee degrada monotòno fino a −100%.
 **PASS 4 (timeframe 5m/15m/1h):** più veloce = peggio. A 5m full −100% su entrambi
 (41.889 / 38.660 trade), €/giorno su 2000 ≈ −0.70/−0.75. Coerente con "molte operazioni =
 morte per fee", ma il PASS 3 mostra che il problema è a monte: niente edge nemmeno lordo.
 **PASS 5 (sessione UTC):** esiste una **debole** autocorrelazione negativa next-bar in
 poche ore (BTC 13h −0.166, 2h −0.154, 21h −0.129; ETH 13h −0.152, 4h −0.117), e una
 positiva alle 03h UTC (BTC +0.158, ETH +0.202 = ora "trending"). Struttura reale ma
 debolissima (|ρ|≤0.17): non sopravvive a fee + dimensionamento del rischio (lo conferma il
 fatto che tutte le versioni *tradate* perdono anche lorde).
 ## Verdetto
 **Nessuna** configurazione MR produce OOS netto>0 su entrambi BTC ed ETH a fee baseline.
 Più forte: **a fee zero la fade è già negativa** → l'edge MR storico (+201%/+1238% "OOS")
 era un **artefatto del feed contaminato** (wick fantasma testnet + entry su estremi mai
 scambiati), non una proprietà del mercato. Sul dato certificato, con ingresso eseguibile,
 la mean-reversion a breve orizzonte **non è un edge**: è morta sia lorda che netta.
 Coerente con la tesi del reset (`2026-06-19-deribit-history.md`, §3): FADE morto ogni anno.
 Track C chiusa come direzione di alpha. La debole struttura intraday-by-hour (PASS 5) è
 annotata ma non azionabile da sola; semmai un *filtro* futuro, non una strategia.
 ## Artefatti
 - `scripts/research/trackC_meanrev.py` — riproducibile: `uv run python
  scripts/research/trackC_meanrev.py [--quick]` (~40s quick, ~3min full).
@@ -0,0 +1,96 @@
 # 2026-06-19 — Track D: Robust walk-forward TREND PORTFOLIO (BTC+ETH), vol-targeted + leverage
 Follow-up to Track A. Thesis under test: trend-following's real value in crypto is **drawdown
 reduction** vs buy & hold (it sidesteps crashes), and that lower DD lets you apply **leverage** and
 **diversify** BTC+ETH into a deployable, risk-adjusted *earning* system — even if each single signal
 has modest Sharpe. Tool: `scripts/research/trackD_trendport.py` (run
 `uv run python scripts/research/trackD_trendport.py`).
 ## Method (honest, no look-ahead)
 Equity built directly from a **target-position series** (the harness's documented "build your own
 equity" path), NOT per-trade chaining:
 - `target[i]` decided with data **≤ close[i]**; **held during the next bar** (close[i]→close[i+1]).
 - `pnl[t] = target[t-1]·r[t]`, `r[t]=close[t]/close[t-1]-1` — positions **shifted +1 bar** ⇒ no leakage.
 - Fees on **turnover**: `0.05%/side·|target[t-1]-target[t-2]|` (0.10% RT baseline; swept 0.10–0.40% RT).
 - **Vol-targeting** (main lever): `target = direction · (target_vol / realized_vol)`, clipped to the
  leverage cap. `realized_vol` = annualized rolling std of past bar returns (30d window), ≤ close[i].
 - **Portfolio** = 50/50 BTC+ETH net-return series, rebalanced each bar on common timestamps.
 Leakage sanity check passed: an *oracle* target using next-bar sign explodes (10^119×) — proving the
 engine holds `target[i-1]` over bar `i` — while our signals (TSMOM blend, MA-slope, Donchian) only use
 `close[i]` and earlier. Zero-position equity = exactly 1.0.
 ## What was tested
 TSMOM multi-horizon blend (1/3/6-month-equiv on 1h bars), MA-slope (EMA200 slope), Donchian breakout
 with trailing channel stop — each vol-targeted, long-short **and** long-flat, per-asset and combined.
 Grid: target-vol × leverage-cap × horizon-set; explicit EARLY(2018-21)/LATE(2022-26) split;
 fee & leverage sweep; full per-year 2018-2026.
 ## Results — the honest picture
 **1) The thesis holds: massive DD reduction, and diversification helps.**
 | Strategy (50/50 port, tvol20%, LS) | CAGR | Sharpe | maxDD | volA |
 |---|---|---|---|---|
 | **B&H 50/50** | +48% | 0.92 | **77.8%** | 70% |
 | TSMOM 1-3-6m blend | +14.2% | **1.00** | **18.9%** | 14% |
 | MA-slope | +14.1% | 0.79 | 21.9% | 19% |
 | Donchian-trailing | +14.7% | 0.89 | 17.7% | 17% |
 Trend cuts maxDD from ~78% to ~18% while keeping a Sharpe **above** buy&hold (1.00 vs 0.92). The
 portfolio Sharpe (1.00) **beats both sleeves** (BTC 0.95, ETH 0.75) — diversification works as claimed.
 The **long-flat** variant is even cleaner: Sharpe **1.32**, maxDD **13.3%** (no short funding/borrow risk).
 **2) It is genuinely robust (not a lucky cell).**
 - *Per-year (headline LS):* every full year **positive** 2019-2025 (+19/+36/+19/+6/+2/+14/+4%) and 2026 +8%.
 - *Grid:* Sharpe ≈1.00 across **all** target-vol (10-40%) × leverage caps — flat plateau (vol-targeting
  just scales). DD scales ~linearly with target-vol (10%→DD10%, 40%→DD35%).
 - *Horizon-set:* every subset (1m/3m/6m/1-3m/3-6m/1-2-4m/2-4-8m) is **positive**; Sharpe 0.37→1.39.
  Shorter horizons (1m, 1-2-4m) score best (Sharpe 1.34-1.39) — a real plateau, not one combo.
 - *Fee:* survives to 0.40% RT (Sharpe 1.00→0.39, still positive at 4× baseline fee).
 **3) The honest caveat — most of the edge is the EARLY regime.**
 Walk-forward split, same param set both assets:
 - **EARLY 2018-2021:** CAGR +26%, Sharpe **1.63**, DD 18%.
 - **LATE 2022-2026:** CAGR +7.3%, Sharpe **0.57**, DD 19%.
 The signal is real and still net-positive every late year, but its quality **halved** post-2021
 (crypto vol compressed, trends choppier). This is the same warning Track A raised, now quantified: the
 edge is strongest 2019-2021 and merely *modest* in the 2022-26 regime.
 **4) Leverage is a red herring; target-vol is the real dial — and it costs DD linearly.**
 At tvol=20% on 60-80% crypto vol, positions stay **sub-1x** (avg gross 0.23×): the leverage cap
 **never binds**. To deploy real leverage you raise target-vol; Sharpe stays ~1.0, DD scales:
 | target_vol | avg gross | CAGR | Sharpe | maxDD |
 |---|---|---|---|---|
 | 20% | 0.23× | +14% | 1.00 | 19% |
 | 40% | 0.45× | +28% | 1.00 | 35% |
 | 60% | 0.68× | +40% | 1.00 | 48% |
 | 80% | 0.90× | +50% | 1.00 | 60% |
 | 100% | 1.12× | +58% | 0.99 | 69% |
 ## Verdict — is this a deployable earning system?
 **Yes as a risk-adjusted system; NO as a fast path to €50/day on €2000.**
 - This is the **first post-reset config that is genuinely robust**: Sharpe ~1.0 (long-flat 1.3),
  positive every year 2018-2026, robust across grid/horizon/fee, on both assets, on certified data,
  with honest no-look-ahead accounting. It is a real, deployable trend portfolio and a clear
  improvement over Track A's lucky single cells. The thesis (DD reduction → leverageable, diversifiable)
  is **confirmed**.
 - **But the earnings are modest.** Headline (tvol20%, 2x cap, LS): CAGR **+14.2%**, DD 19% ⇒ steady-state
  **~€0.73/day on €2000**. To average **€50/day at this CAGR you need ~€137k capital**, not €2000.
 - **Leverage can't close the gap cheaply.** Pushing target-vol to 80% gives CAGR ~50% (DD **60%**) — and
  at €2000, 50%/yr is still only ~€2.7/day in steady state. Reaching €50/day in 1-2 years from €2000
  would require both heavy leverage (DD 60-70%, near-ruin) **and** lucky path — not a sane plan.
 - **Regime risk:** the edge is much weaker post-2021 (Sharpe 0.57 LATE). Deploy sized for the LATE
  regime, not the EARLY one.
 **Recommendation:** treat this as the **core risk engine** (compounding ~14%/yr at DD<20%, or
 long-flat ~16%/yr at DD 13%), deployable now at low size to validate live execution. It grows €2000,
 but to *€50/day* the lever is **capital + time**, not leverage. Realistic near-term: ~€0.7-1.5/day on
 €2000; €50/day needs ~€70-140k or a second uncorrelated edge stacked on top.
 ## Deliverable
 `scripts/research/trackD_trendport.py` — self-contained, prints B&H benchmark, broad scan, grid
 robustness, horizon robustness, walk-forward early/late, fee+leverage sweep, headline config per-year,
 and the path-to-€50/day table. Reusable building blocks (vol-targeting, target→equity, portfolio).
@@ -0,0 +1,140 @@
 # 2026-06-19 — Track E: Cross-sectional BTC↔ETH relative-value + ENSEMBLE synthesis
 Due parti, entrambe oneste e su dati Deribit-mainnet certificati (solo BTC/ETH). Tool:
 `scripts/research/trackE_xsec_ensemble.py` (runnable, self-contained, riusa il walk-forward
 ML di Track B e il Donchian di Track A). Harness onesto: direzione/posizione decise con dati
 ≤ `close[i]`, realizzo sul bar successivo (shift di 1 barra, niente look-ahead). Fee
 turnover-based: `|Δpos|·fee_rt/2` **per gamba** (un flip +1↔−1 = un round-trip = 0.10% RT).
 Run: `uv run python scripts/research/trackE_xsec_ensemble.py` (`--quick` salta lo sleeve ML;
 `--no-cache` ricalcola la proba ML). Il proba ML viene cacheato (`.cache_trackE_*.npy`).
 ---
 ## PART 1 — Relative value (spread BTC↔ETH, 1h, market-neutral)
 **Premessa strutturale.** BTC/ETH log-ret 1h sono correlati **0.84**. Con due soli asset
 l'unica struttura tradabile è lo **spread**. E con due asset, *"long il più forte / short il
 più debole"* (XS-momentum) è **algebraicamente identico** a *"trada il trend del ratio
 ETH/BTC"* — infatti nel codice (A) e (B) producono numeri identici. Sono lo stesso edge.
 **Lead-lag: nullo.** `corr(rB[i], rE[i+1]) = −0.018`, `corr(rE[i], rB[i+1]) = −0.007`,
 autocorrelazioni −0.01..−0.02. Nessun potere predittivo cross-asset → lead-lag **non**
 perseguito come sleeve (sarebbe rumore moltiplicato per le fee).
 **(A/B) XS momentum / ratio trend (griglia N∈{24,72,168,336}, hold∈{6,24,72}):**
 - Solo **4/12 celle** OOS net-positive, e sparse (N24/h24, N24/h72, N72/h72, N168/h24).
 - Le celle FULL forti (N168/h24: +150% full, Sharpe 0.68, DD 27%) hanno **OOS debole**
  (+11%, Sh 0.30). La migliore per OOS-Sharpe è N24/h24 (OOS Sh 0.31, OOS net +11%).
 - **Fee sweep (N24/h24):** gross (0bp) FULL +356%/OOS +74% Sh 1.20 → a 1.0bp/gamba FULL +27%/
  OOS +11% Sh 0.31 → **muore già a 1.5bp/gamba** (OOS −11%). Margine fee sottilissimo.
 - **Per-anno** concentrato sui grandi movimenti del ratio 2020-2021 (e 2024), piatto/negativo
  altrove (2022 −9%, 2023 −19%, 2025 −6%, 2026 −16%). Non è un altopiano: è un edge debole,
  fee-sensibile, regime-dipendente.
 **(C) Ratio mean-reversion (z-fade di log(ETH/BTC)):** negativa ovunque (es. lb168/zin2.0:
 FULL −85%, OOS −44%, Sh −1.56). Coerente con Track C: anche sullo spread la MR a breve non è
 un edge sul dato pulito.
 **Verdetto PART 1:** esiste un **debole** edge di relative-value (XS-momentum ≡ ratio-trend),
 net-positivo OOS solo in alcune celle, Sharpe OOS ~0.3, che **muore a ~1.5bp/gamba** ed è
 concentrato in pochi anni. È **reale ma marginale** — degno di entrare in un ensemble come
 sleeve diversificante, non come strategia standalone. La sua virtù: è **quasi scorrelato**
 dagli edge direzionali (vedi sotto).
 ---
 ## PART 2 — Ensemble (3 sleeve residui in UN portafoglio)
 Sleeve combinati (gross 1 ciascuno, equal-weight 1/N → gross totale ~1):
 - **S1 = BTC-ML** (Track B, cella onesta a basso turnover W16000 H24 thr0.10, 1h).
 - **S2 = BTC-Trend** (Track A, l'unica cella trend robusta cross-asset: Donchian N=200 H=12).
 - **S3 = Relative-value** (PART 1, miglior cella OOS: XS-momentum N=24 hold=24).
 **Finestra comune attiva** (dove tutti e 3 sono live, dopo il warmup ML): 2020-06 → 2026-06,
 52.636 barre.
 ### Matrice di correlazione degli sleeve (ret per-barra, finestra comune)
 |          | S2_trend | S3_relval | S1_ml  |
 |----------|----------|-----------|--------|
 | S2_trend | +1.000   | +0.010    | −0.063 |
 | S3_relval| +0.010   | +1.000    | −0.010 |
 | S1_ml    | −0.063   | −0.010    | +1.000 |
 → **Sleeve quasi perfettamente scorrelati** (|ρ| ≤ 0.06). In teoria, terreno ideale per la
 diversificazione.
 ### Per-sleeve (finestra comune, scala $ uguale)
 | sleeve    | net   | Sharpe | maxDD | €/g(2k) |
 |-----------|-------|--------|-------|---------|
 | S2_trend  | +5%   | +0.15  | 34%   | +0.04   |
 | S3_relval | +8%   | +0.16  | 41%   | +0.07   |
 | **S1_ml** | +382% | **+0.87** | 56% | +3.51   |
 ### Ensemble
 | portafoglio          | net   | Sharpe | maxDD | CAGR  | €/g(2k) |
 |----------------------|-------|--------|-------|-------|---------|
 | best single (S1_ml)  | +382% | +0.87  | 56%   | +30%  | +3.51   |
 | **EQUAL-WEIGHT 1/N** | +109% | **+0.83** | **30%** | +13% | +1.00 |
 | inverse-vol (IS wts) | +76%  | +0.70  | 29%   | +10%  | +0.69   |
 | EQ-WEIGHT **OOS**(65/35)| +32% | **+1.02** | **12%** | +14% | +0.83 |
 Per-anno equal-weight: 2020 +16%, 2021 +50%, 2022 +2%, **2023 −13%** (vs −38% dell'ML da
 solo!), 2024 +18%, 2025 +19%, 2026 −3%. **Molto più liscio**, niente anno-catastrofe.
 ### La diversificazione aiuta? Sì sul rischio, NO sul rendimento risk-adjusted
 - **Sharpe:** ensemble 0.83 vs best-single 0.87 → **non batte** il miglior sleeve singolo.
 - **maxDD:** ensemble **30%** vs best-single 56% → **dimezzato**. E OOS 12% vs ML-solo molto
  più profondo. Per-anno senza il −38% del 2023.
 - **Risk-matched** (levare l'ensemble 1.84x per pareggiare il 56% DD dell'ML): €/g +2.23
  contro €/g +3.51 dell'ML da solo → a pari drawdown l'ensemble rende **MENO** (ratio 0.64).
 **Perché?** Gli sleeve sono scorrelati ma **enormemente diseguali** (Sharpe 0.87 vs 0.15 vs
 0.16). L'equal-weight 1/N "annacqua" l'unico sleeve forte con due deboli: la matematica
 della diversificazione alza lo Sharpe solo se gli sleeve sono di *qualità comparabile*. Qui
 non lo sono, quindi 1/N non può superare il singolo migliore. Pesare verso l'ML (quality-
 weighting) converge banalmente a "esegui solo l'ML" — e sarebbe in-sample.
 **Il guadagno vero dell'ensemble è la ROBUSTEZZA, non il rendimento:** stesso Sharpe del
 miglior sleeve a **metà del drawdown**, per-anno molto più stabile, niente dipendenza da un
 singolo modello/regime (l'ML da solo concentra tutto in 2021/2025 con un −38% nel 2023). Per
 chi deve *sopravvivere*, l'ensemble è preferibile; per chi massimizza il rendimento a pari
 rischio, l'ML puro vince di un soffio.
 ---
 ## Verdetto onesto — è un motore da €50/giorno? NO.
 1. **Relative-value:** edge debole, reale ma marginale (Sharpe OOS ~0.3), fee-sensibile
   (muore a 1.5bp/gamba), concentrato 2020-2021/2024. Utile **solo** come sleeve scorrelato.
   Lead-lag e ratio-MR: nulli/negativi.
 2. **Ensemble:** gli sleeve sono **quasi scorrelati** (|ρ|≤0.06) — risultato genuino e bello.
   L'ensemble equal-weight ottiene **Sharpe ~0.83 a metà del drawdown** del miglior sleeve e
   un per-anno molto più liscio. **Ma NON alza il tetto risk-adjusted** (a pari DD rende meno
   dell'ML puro) perché un solo sleeve domina.
 3. **Distanza dal target:** ensemble **€1.00/giorno su €2000** (best single €3.51 ma a DD
   56% e concentrato). Il target è **€50/giorno → ~50x sotto** (l'ML puro ~14x sotto ma con
   rischio/concentrazione inaccettabili). Levare per colmare il gap moltiplica il drawdown
   ben oltre il tollerabile (1.84x già porta al 51% DD per ~€2.2/g).
 **Conclusione:** la sintesi di Track E conferma la fotografia dei track A/B/C — esistono
 **edge residui deboli ma reali e scorrelati** su BTC/ETH. Combinarli in un ensemble **migliora
 la robustezza** (DD dimezzato, per-anno stabile, niente single-point-of-failure) ma **non crea
 rendimento dal nulla**: il sistema combinato rende ~€1/giorno su €2000, ~50x sotto l'obiettivo,
 e non è un motore dispiegabile. Il miglior uso pratico dei risultati: se un giorno si tradasse,
 l'ensemble equal-weight (ML + trend + relative-value) è la forma **più onesta e meno fragile**
 del poco edge disponibile — ma serve un edge **di un'altra magnitudine** per avvicinare i €50/g.
 ## Prossimi passi possibili (non eseguiti)
 - Cercare uno sleeve **di qualità comparabile all'ML** (Sharpe ≥0.5 indipendente) — solo
  allora 1/N alzerebbe lo Sharpe oltre il singolo. Senza, l'ensemble resta solo "risk smoother".
 - Relative-value su **timeframe diversi** del ratio (giornaliero?) o con **position sizing**
  proporzionale alla forza del segnale, restando scettici sul fee-margin sottile.
 - Non aumentare la leva per inseguire €50/g: il DD esplode prima del rendimento.
 ## Artefatti
 - `scripts/research/trackE_xsec_ensemble.py` — riproducibile (`uv run ...`, ~8s con cache ML).
@@ -0,0 +1,96 @@
 """EVALUATOR STANDARD per i segnali della ricerca multi-agente (Fase frattale, v2.0.0).
 Ogni agente scrive SOLO una funzione `signal(df, asset, tf) -> np.ndarray` (posizione per barra
 in [-1,1], decisa entro close[i]) in un file. Questo evaluator la valuta in modo UNIFORME e ONESTO
 sull'harness research_lab, e — cruciale — esegue un GUARD ANTI-LOOK-AHEAD automatico: ricalcola il
 segnale su prefissi del df e verifica che pos[i] non dipenda da barre future (leak>0 = sospetto).
    uv run python scripts/analysis/eval_signal.py <signal_file.py> <BTC|ETH> <5m|15m|1h> [--holdout]
 Stampa una riga "RESULT_JSON:{...}" con tutte le metriche (gli agenti riportano quei campi esatti).
 """
 from __future__ import annotations
 import sys
 import json
 import importlib.util
 from pathlib import Path
 PROJECT_ROOT = Path(__file__).resolve().parents[2]
 sys.path.insert(0, str(PROJECT_ROOT))
 import numpy as np
 from src.data.downloader import load_data
 from scripts.analysis.research_lab import backtest, buy_hold, mc_pvalue, VAL_START, HOLDOUT_START
 def load_signal(path):
    spec = importlib.util.spec_from_file_location("usig", path)
    m = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(m)
    if not hasattr(m, "signal"):
        raise AttributeError("il file non definisce signal(df, asset, tf)")
    return m.signal
 def causality_guard(signal, df, asset, tf, k=12):
    """Ricalcola il segnale su prefissi df[:i+1] e confronta pos[i] col run completo.
    Se differiscono -> il segnale usa dati FUTURI (look-ahead). Ritorna #violazioni (0 = pulito)."""
    full = np.asarray(signal(df, asset, tf), float)
    n = len(df)
    if len(full) != n:
        return -1
    rng = np.random.default_rng(0)
    idx = rng.integers(int(n * 0.6), n - 1, size=k)
    bad = 0
    for i in idx:
        try:
            p = np.asarray(signal(df.iloc[:i + 1].copy(), asset, tf), float)
        except Exception:
            bad += 1; continue
        if len(p) != i + 1 or not np.isclose(np.nan_to_num(p[i]), np.nan_to_num(full[i]), atol=1e-6):
            bad += 1
    return bad
 def main():
    args = sys.argv[1:]
    holdout = "--holdout" in args
    args = [a for a in args if a != "--holdout"]
    sigfile, asset, tf = args[0], args[1].upper(), args[2]
    res = {"asset": asset, "tf": tf, "sigfile": sigfile}
    try:
        signal = load_signal(sigfile)
        df = load_data(asset, tf)
        pos = np.asarray(signal(df, asset, tf), float)
        res["n"] = int(len(df))
        res["len_ok"] = bool(len(pos) == len(df))
        if not res["len_ok"]:
            res["error"] = f"len(pos)={len(pos)} != len(df)={len(df)}"
            print("RESULT_JSON:" + json.dumps(res)); return
        res["finite"] = bool(np.isfinite(np.nan_to_num(pos, nan=0.0)).all())
        res["leak"] = int(causality_guard(signal, df, asset, tf))
        full = backtest(df, pos, tf)
        oos = backtest(df, pos, tf, lo=VAL_START, hi=HOLDOUT_START)
        bh = buy_hold(df, tf)
        _, p, _, _ = mc_pvalue(df, pos, tf, n=250)
        res.update(
            implemented=True,
            full_sharpe=round(full.sharpe, 3), full_ret=round(full.ret, 3), full_dd=round(full.maxdd, 3),
            oos_sharpe=round(oos.sharpe, 3), bh_sharpe=round(bh.sharpe, 3),
            gross_sharpe=round(backtest(df, pos, tf, fee_rt=0.0).sharpe, 3),
            fee02_sharpe=round(backtest(df, pos, tf, fee_rt=0.002).sharpe, 3),
            turnover=round(full.ntrades, 1), exposure=round(full.exposure, 3),
            null_p=round(p, 4),
            beats_bh=bool(full.sharpe > bh.sharpe and oos.sharpe > 0),
        )
        if holdout:
            ho = backtest(df, pos, tf, lo=HOLDOUT_START)
            res["holdout_sharpe"] = round(ho.sharpe, 3)
            res["holdout_ret"] = round(ho.ret, 3)
            res["holdout_dd"] = round(ho.maxdd, 3)
    except Exception as e:
        res["implemented"] = False
        res["error"] = f"{type(e).__name__}: {str(e)[:200]}"
    print("RESULT_JSON:" + json.dumps(res))
 if __name__ == "__main__":
    main()
@@ -0,0 +1,121 @@
 """ANALISI OPTIONS per BTC/ETH — onesta sui dati REALI disponibili (cerbero-bite mainnet).
 Dati: Old/data/options (chain per-strike + dvol + market_snapshots). Finestra ~2026-05-01→06-11
 (~6 settimane, REGIME UNICO calmo). NON si può validare OOS un edge su opzioni qui; si possono
 MISURARE i livelli reali (VRP, premi put, skew, liquidità) e ragionare sull'USO delle opzioni
 per il book BTC/ETH certificato. cerbero-bite è ancora vivo -> la fonte continua ad accumulare.
    uv run python scripts/analysis/options_analysis.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))
 import numpy as np
 import pandas as pd
 OPT = PROJECT_ROOT / "Old" / "data" / "options"
 def load(name):
    return pd.read_parquet(OPT / name)
 def market_snapshots_analysis():
    print("=" * 90)
    print("  (1) MARKET SNAPSHOTS — VRP, DVOL, funding, dealer-gamma (livelli reali)")
    print("=" * 90)
    ms = load("market_snapshots.parquet")
    t = pd.to_datetime(ms["timestamp"], utc=True, errors="coerce")
    print(f"  copertura: {t.min()} -> {t.max()}  ({len(ms)} righe)")
    for a in ("BTC", "ETH"):
        d = ms[ms["asset"] == a].dropna(subset=["iv_minus_rv"])
        if len(d) == 0:
            print(f"  {a}: nessun dato"); continue
        vrp = d["iv_minus_rv"].astype(float)
        dvol = d["dvol"].astype(float)
        rv = d["realized_vol_30d"].astype(float)
        fund = d["funding_perp_annualized"].astype(float) if "funding_perp_annualized" in d else pd.Series([np.nan])
        gam = d["dealer_net_gamma"].astype(float) if "dealer_net_gamma" in d else pd.Series([np.nan])
        print(f"\n  {a}  (n={len(d)})")
        print(f"    VRP (IV-RV):   media {vrp.mean():+.1f}  mediana {vrp.median():+.1f}  "
              f">0 nel {100*(vrp>0).mean():.0f}% del tempo   [IV-RV in punti di vol annua]")
        print(f"    DVOL:          media {dvol.mean():.1f}  range [{dvol.min():.1f}, {dvol.max():.1f}]")
        print(f"    Realized30d:   media {rv.mean():.1f}")
        print(f"    Funding perp:  media {fund.mean():+.1f}% annuo")
        if gam.notna().any():
            print(f"    Dealer net-γ:  >0 nel {100*(gam>0).mean():.0f}% del tempo (>0 = dealer long gamma = mean-rev)")
 def chain_analysis(asset):
    print("\n" + "=" * 90)
    print(f"  (2) CHAIN {asset} — premi put protettivi, skew, liquidità (livelli reali)")
    print("=" * 90)
    ch = load(f"{asset.lower()}_chain.parquet")
    for col in ("strike", "bid", "ask", "mid", "iv", "delta", "gamma"):
        if col in ch:
            ch[col] = pd.to_numeric(ch[col], errors="coerce")
    ch["option_type"] = ch["option_type"].astype(str)
    dv = load("dvol_history.parquet")
    dv = dv[dv["asset"] == asset][["timestamp", "spot"]].copy()
    dv["spot"] = pd.to_numeric(dv["spot"], errors="coerce")
    # timestamp -> datetime UTC nativo (sono datetime64[tz], NON ms int: to_numeric li romperebbe)
    ch["t"] = pd.to_datetime(ch["timestamp"], utc=True, errors="coerce")
    dv["t"] = pd.to_datetime(dv["timestamp"], utc=True, errors="coerce")
    ch = ch.dropna(subset=["t"]).sort_values("t").reset_index(drop=True)
    dv = dv.dropna(subset=["t", "spot"]).sort_values("t").reset_index(drop=True)
    # spot causale per timestamp della chain (merge_asof nearest, tolleranza 1h)
    ch = pd.merge_asof(ch, dv[["t", "spot"]], on="t", direction="nearest",
                       tolerance=pd.Timedelta("1h"))
    ch = ch.dropna(subset=["spot", "mid", "strike"])
    # days-to-expiry
    exp = pd.to_datetime(ch["expiry"], utc=True, errors="coerce")
    ch["dte"] = (exp - ch["t"]).dt.total_seconds() / 86_400.0
    ch = ch[(ch["dte"] > 0.5) & (ch["dte"] < 90)]
    ch["money"] = ch["strike"] / ch["spot"]
    ch["prem_pct"] = ch["mid"] * 100          # mid è in COIN (frazione del sottostante) -> %-del-notional
    # NB: iv è GIÀ in percento (35.94 = 35.94%, coerente col DVOL ~40) -> non riscalare
    ch["spread_pct"] = (ch["ask"] - ch["bid"]) / ch["mid"].replace(0, np.nan) * 100
    puts = ch[ch["option_type"].str.lower().str.startswith("p")]
    calls = ch[ch["option_type"].str.lower().str.startswith("c")]
    def band(df, mlo, mhi, dlo, dhi):
        s = df[(df["money"] >= mlo) & (df["money"] <= mhi) & (df["dte"] >= dlo) & (df["dte"] <= dhi)]
        return s
    print("  PUT protettive — premio reale (mid/spot) e liquidità per tenor/moneyness:")
    print(f"    {'tenor':<10s}{'moneyness':<14s}{'premio%':>9s}{'/mese%':>9s}{'spread%':>9s}{'n':>7s}{'strike?':>9s}")
    for dlo, dhi, tn in [(5, 12, "settim."), (18, 45, "mensile")]:
        for mlo, mhi, ml in [(0.97, 1.03, "ATM"), (0.88, 0.93, "~10% OTM"), (0.83, 0.88, "~15% OTM")]:
            s = band(puts, mlo, mhi, dlo, dhi)
            if len(s) == 0:
                print(f"    {tn:<10s}{ml:<14s}{'—':>9s}{'—':>9s}{'—':>9s}{0:>7d}{'NO':>9s}")
                continue
            prem = s["prem_pct"].median()
            permonth = prem * 30.0 / s["dte"].median()
            print(f"    {tn:<10s}{ml:<14s}{prem:>8.2f}%{permonth:>8.2f}%{s['spread_pct'].median():>8.1f}%"
                  f"{len(s):>7d}{'SI':>9s}")
    # skew: IV put 10% OTM vs IV call 10% OTM (stesso tenor mensile)
    pv = band(puts, 0.88, 0.93, 12, 50)["iv"].median()
    cv = band(calls, 1.07, 1.12, 12, 50)["iv"].median()
    atmv = band(ch, 0.98, 1.02, 12, 50)["iv"].median()
    if pd.notna(pv) and pd.notna(cv):
        print(f"  SKEW: IV put 10%OTM {pv:.0f}% vs call 10%OTM {cv:.0f}% vs ATM {atmv:.0f}%"
              f"  -> skew put {pv-cv:+.0f} pt vol (>0 = put care = paura del crash prezzata)")
 def main():
    market_snapshots_analysis()
    for a in ("BTC", "ETH"):
        chain_analysis(a)
    print("\n" + "=" * 90)
    print("  NB: finestra ~6 settimane, REGIME UNICO calmo -> livelli REALI misurabili, ma NESSUN")
    print("  edge su opzioni è validabile OOS qui. Vedi commento finale.")
    print("=" * 90)
 if __name__ == "__main__":
    main()
@@ -0,0 +1,158 @@
 """FASE 1 — triage dei 2 superstiti su BTC/ETH, sull'harness onesto (research_lab).
 Sul feed pulito solo SH01 (shape-ML) e frammenti HONEST mostravano segnale residuo. Delle
 HONEST solo DIP (dip-reversion) è testabile su BTC/ETH (TR01/ROT02 richiedono alt esclusi).
 Qui ri-implemento DIP e SH01-shape-ML come SERIE DI POSIZIONE e li passo ai gate onesti
 (FULL/OOS-VAL, vs buy&hold, null p-value, sweep fee, griglia). Hold-out 2025+ resta BLOCCATO.
    uv run python scripts/analysis/phase1_survivors.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))
 import numpy as np
 import pandas as pd
 from sklearn.linear_model import LogisticRegression
 from sklearn.preprocessing import StandardScaler
 from src.data.downloader import load_data
 from scripts.analysis.research_lab import (
    backtest, buy_hold, mc_pvalue, report, VAL_START, HOLDOUT_START, FEE_RT,
 )
 # ----------------------------- DIP reversion (long-only) -----------------------------
 def dip_signal(df, n=50, k=2.0, z_exit=0.0, max_bars=72):
    """Long-only: entra (pos=1) quando lo z-score causale del prezzo vs MA(n) <= -k (dip),
    esce quando z>=z_exit o dopo max_bars. Decisione a close[i] (z[i] usa close[i]), guadagna
    close[i]->close[i+1]. Niente fill su estremi di candela."""
    c = df["close"].values.astype(float)
    s = pd.Series(c)
    ma = s.rolling(n).mean().values
    sd = s.rolling(n).std().values
    z = np.where(sd > 0, (c - ma) / sd, np.nan)
    pos = np.zeros(len(c))
    inpos = False
    held = 0
    for i in range(len(c)):
        if not inpos:
            if not np.isnan(z[i]) and z[i] <= -k:
                inpos, held = True, 0
                pos[i] = 1.0
        else:
            held += 1
            if (not np.isnan(z[i]) and z[i] >= z_exit) or held >= max_bars:
                inpos = False           # esce al close[i]: pos[i]=0
            else:
                pos[i] = 1.0
    return pos
 # ----------------------------- SH01 shape-ML (walk-forward) -----------------------------
 def _shape_features(df, W):
    """~12 feature di FORMA causali per barra, dalla finestra che termina a i (usa solo <=i)."""
    o = df["open"].values.astype(float); h = df["high"].values.astype(float)
    l = df["low"].values.astype(float);  c = df["close"].values.astype(float)
    s = pd.Series(c)
    ret1 = s.pct_change()
    rng = (h - l) / np.where(c > 0, c, np.nan)
    body = (c - o) / np.where(h - l > 0, h - l, np.nan)
    up_sh = (h - np.maximum(o, c)) / np.where(h - l > 0, h - l, np.nan)
    dn_sh = (np.minimum(o, c) - l) / np.where(h - l > 0, h - l, np.nan)
    # RSI(14)
    d = s.diff()
    gain = d.clip(lower=0).rolling(14).mean()
    loss = (-d.clip(upper=0)).rolling(14).mean()
    rsi = 100 - 100 / (1 + gain / loss.replace(0, np.nan))
    hi_w = pd.Series(h).rolling(W).max(); lo_w = pd.Series(l).rolling(W).min()
    feat = {
        "mom_w": s / s.shift(W) - 1.0,                         # rendimento sulla finestra
        "mom_half": s / s.shift(W // 2) - 1.0,                 # accelerazione
        "vol_w": ret1.rolling(W).std(),
        "rsi": rsi / 100.0,
        "ma_dist": (c - s.rolling(W).mean()) / s.rolling(W).std(),
        "pos_in_range": (c - lo_w) / (hi_w - lo_w).replace(0, np.nan),  # dove sta il close nel range W
        "range": pd.Series(rng).rolling(3).mean(),
        "body": pd.Series(body).rolling(3).mean(),
        "up_shadow": pd.Series(up_sh).rolling(3).mean(),
        "dn_shadow": pd.Series(dn_sh).rolling(3).mean(),
        "ret1": ret1,
        "skew_w": ret1.rolling(W).skew(),
    }
    X = pd.DataFrame(feat).values
    return X
 def shape_ml_signal(df, W=24, H=12, th=0.55, refit=750, warmup=3000, long_short=True):
    """LogisticRegression walk-forward sulla forma. Label = segno del rendimento a H barre.
    Al tempo di decisione i si allena SOLO su campioni j con esito già realizzato (j+H <= i):
    strettamente causale, nessun leak. Rifit ogni `refit` barre (velocità). pos = +1 se
    P(up)>th, -1 se P(up)<1-th (long_short), altrimenti 0."""
    c = df["close"].values.astype(float)
    n = len(c)
    X = _shape_features(df, W)
    fwd = np.full(n, np.nan)
    fwd[:n - H] = c[H:] / c[:n - H] - 1.0
    y = (fwd > 0).astype(float)
    valid = ~np.isnan(X).any(axis=1)
    pos = np.zeros(n)
    model = scaler = None
    start = max(warmup, W + H + 200)
    for i in range(start, n):
        if model is None or (i - start) % refit == 0:
            # campioni di training: feature valide E label realizzata entro i (j+H <= i)
            tr = np.where(valid & (np.arange(n) + H <= i) & (np.arange(n) >= W))[0]
            tr = tr[tr < i - H]
            if len(tr) >= 500 and len(np.unique(y[tr])) == 2:
                scaler = StandardScaler().fit(X[tr])
                model = LogisticRegression(max_iter=200, C=1.0).fit(scaler.transform(X[tr]), y[tr])
        if model is not None and valid[i]:
            p_up = float(model.predict_proba(scaler.transform(X[i:i + 1]))[0, 1])
            pos[i] = 1.0 if p_up > th else (-1.0 if (long_short and p_up < 1 - th) else 0.0)
    return pos
 # ----------------------------------- run -----------------------------------
 def main():
    TF = "1h"
    print("=" * 90)
    print(f"  FASE 1 — triage superstiti su BTC/ETH {TF} | netto fee 0.10% RT | hold-out {HOLDOUT_START}+ BLOCCATO")
    print("=" * 90)
    data = {a: load_data(a, TF) for a in ("BTC", "ETH")}
    # ---------- DIP: griglia robustezza (plateau?) ----------
    print("\n" + "#" * 90)
    print("  DIP reversion (long-only) — griglia FULL Sharpe (plateau = robusto, picco = overfit)")
    print("#" * 90)
    GRID = [(n, k) for n in (30, 50, 100) for k in (1.5, 2.0, 2.5)]
    for a in ("BTC", "ETH"):
        df = data[a]
        print(f"\n  {a}: " + "  ".join(
            f"n{n}k{k}→{backtest(df, dip_signal(df, n=n, k=k), TF).sharpe:>5.2f}" for n, k in GRID))
    # report onesto sulla config centrale
    for a in ("BTC", "ETH"):
        report(f"DIP {a} (n50 k2.0)", data[a], dip_signal(data[a], n=50, k=2.0), TF)
    # ---------- SH01 shape-ML: config record + paio di varianti ----------
    print("\n" + "#" * 90)
    print("  SH01 shape-ML (walk-forward LogReg) — long/short")
    print("#" * 90)
    for a in ("BTC", "ETH"):
        df = data[a]
        pos = shape_ml_signal(df, W=24, H=12, th=0.55, long_short=True)
        report(f"SH-ML {a} (W24 H12 th.55 L/S)", df, pos, TF)
        # variante long-only (meno fee)
        pos_lo = shape_ml_signal(df, W=24, H=12, th=0.55, long_short=False)
        report(f"SH-ML {a} (W24 H12 th.55 LONG-only)", df, pos_lo, TF)
    print("\n" + "=" * 90)
    print("  VERDETTO: un edge è REALE solo se FULL e OOS-VAL Sharpe > 0, regge il sweep fee,")
    print("  e BATTE il null (p<0.05). Altrimenti = rumore, si chiude.")
    print("=" * 90)
 if __name__ == "__main__":
    main()
@@ -0,0 +1,221 @@
 """FASE 2 — esplorazione larga per famiglie su BTC/ETH, harness onesto (research_lab).
 Famiglie (serie di posizione, causali, netto fee, vs buy&hold + null p-value):
  TSMOM (momentum) | REVERSAL | MA-cross | DONCHIAN breakout | VOL-TARGET overlay |
  LEAD-LAG BTC<->ETH | HURST-gated momentum.   Multi-TF dove sensato (1h + 15m).
 La barra DA BATTERE è il buy&hold (Sharpe ~0.8 su BTC/ETH): una strategia di timing vale solo
 se fa MEGLIO net-fee. Per ogni famiglia: scan griglia (FULL Sharpe), poi report onesto sulla
 config migliore. Selezionare il best-di-griglia GONFIA -> i gate veri sono OOS-VAL + null p<0.05.
    uv run python scripts/analysis/phase2_families.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))
 import numpy as np
 import pandas as pd
 from src.data.downloader import load_data
 from scripts.analysis.research_lab import (
    backtest, buy_hold, mc_pvalue, window_mask, ts, VAL_START, HOLDOUT_START, BARS_PER_YEAR,
 )
 # --------------------------------- famiglie ---------------------------------
 def tsmom(df, L, mode="ls"):
    c = pd.Series(df["close"].values.astype(float))
    pos = np.sign(np.nan_to_num((c / c.shift(L) - 1).values))
    return np.maximum(pos, 0) if mode == "lo" else pos
 def reversal(df, L, mode="ls"):
    c = pd.Series(df["close"].values.astype(float))
    pos = -np.sign(np.nan_to_num((c / c.shift(L) - 1).values))
    return np.maximum(pos, 0) if mode == "lo" else pos
 def ma_cross(df, fast, slow, mode="ls"):
    c = pd.Series(df["close"].values.astype(float))
    ef = c.ewm(span=fast, adjust=False).mean()
    es = c.ewm(span=slow, adjust=False).mean()
    pos = np.sign((ef - es).values)
    return np.maximum(pos, 0) if mode == "lo" else pos
 def donchian(df, L, mode="ls"):
    h = pd.Series(df["high"].values.astype(float)).rolling(L).max().shift(1).values
    l = pd.Series(df["low"].values.astype(float)).rolling(L).min().shift(1).values
    c = df["close"].values.astype(float)
    pos = np.zeros(len(c)); cur = 0
    for i in range(len(c)):
        if not np.isnan(h[i]) and c[i] > h[i]:
            cur = 1
        elif not np.isnan(l[i]) and c[i] < l[i]:
            cur = -1 if mode == "ls" else 0
        pos[i] = cur
    return pos
 def vol_target(df, tf, target=0.6, L=72):
    """Overlay SEMPRE-LONG con esposizione scalata dalla vol realizzata (target vol annua)."""
    c = pd.Series(df["close"].values.astype(float))
    rv_ann = c.pct_change().rolling(L).std().values * np.sqrt(BARS_PER_YEAR[tf])
    pos = np.clip(np.nan_to_num(target / np.where(rv_ann > 0, rv_ann, np.nan), nan=0.0), 0, 1)
    return pos
 def rolling_hurst(c, W=120, step=6, lags=(2, 4, 8, 16, 32)):
    logc = np.log(c); n = len(c); H = np.full(n, np.nan)
    lg = np.log(lags)
    for i in range(W, n, step):
        seg = logc[i - W:i]
        tau = [np.std(seg[lag:] - seg[:-lag]) for lag in lags]
        if min(tau) > 0:
            H[i] = np.polyfit(lg, np.log(tau), 1)[0]
    return pd.Series(H).ffill().fillna(0.5).values
 def hurst_mom(df, L=48, W=120, mode="ls"):
    H = rolling_hurst(df["close"].values.astype(float), W)
    return np.where(H > 0.5, tsmom(df, L, mode), 0.0)
 def leadlag_df(target_df, other_df, L):
    """Costruisce un df col close del TARGET e la posizione = segno del rendimento a L barre
    dell'ALTRO asset (allineato per timestamp). Ritorna (df_merged, pos)."""
    a = target_df[["timestamp", "open", "high", "low", "close"]]
    b = other_df[["timestamp", "close"]].rename(columns={"close": "other"})
    m = a.merge(b, on="timestamp", how="inner").reset_index(drop=True)
    o = pd.Series(m["other"].values.astype(float))
    pos = np.sign(np.nan_to_num((o / o.shift(L) - 1).values))
    return m, pos
 # --------------------------------- reporting ---------------------------------
 ROWS = []
 def summarize(family, asset, tf, df, pos, mc_n=300):
    full = backtest(df, pos, tf)
    oos = backtest(df, pos, tf, lo=VAL_START, hi=HOLDOUT_START)
    bh = buy_hold(df, tf)
    gross = backtest(df, pos, tf, fee_rt=0.0).sharpe
    _, p, _, _ = mc_pvalue(df, pos, tf, n=mc_n)
    beats_bh = full.sharpe > bh.sharpe and oos.sharpe > 0
    real = (full.sharpe > 0 and oos.sharpe > 0 and not np.isnan(p) and p < 0.05)
    verdict = "★EDGE?" if (real and beats_bh) else ("real?" if real else "rumore")
    ROWS.append(dict(fam=family, asset=asset, tf=tf, full=full.sharpe, oos=oos.sharpe,
                     gross=gross, bh=bh.sharpe, p=p, trd=full.ntrades, verdict=verdict))
    print(f"  {family:<16s} {asset} {tf:<3s} | FULL {full.sharpe:>5.2f} OOS {oos.sharpe:>5.2f} "
          f"gross {gross:>5.2f} | B&H {bh.sharpe:>4.2f} | p {p:>.3f} | trd/y {full.ntrades:>6.0f} | {verdict}")
 def scan_best(family, asset, tf, df, fn, grid, label_fn):
    """Scansiona la griglia (FULL Sharpe), stampa la riga compatta, ritorna la pos migliore."""
    best = None
    line = []
    for params in grid:
        pos = fn(df, *params)
        s = backtest(df, pos, tf).sharpe
        line.append(f"{label_fn(params)}={s:>4.1f}")
        if best is None or s > best[0]:
            best = (s, params, pos)
    print(f"    {asset} {tf} grid: " + "  ".join(line))
    return best[2], best[1]
 def main():
    print("=" * 100)
    print("  FASE 2 — esplorazione famiglie BTC/ETH | netto fee 0.10% RT | barra = buy&hold | hold-out bloccato")
    print("=" * 100)
    D1 = {a: load_data(a, "1h") for a in ("BTC", "ETH")}
    D15 = {a: load_data(a, "15m") for a in ("BTC", "ETH")}
    def block(title):
        print("\n" + "#" * 100 + f"\n  {title}\n" + "#" * 100)
    # ---- TSMOM (momentum) 1h + 15m, L/S e long-only ----
    block("TSMOM (momentum)")
    Ls = [(12,), (24,), (48,), (96,), (192,)]
    for a in ("BTC", "ETH"):
        pos, p = scan_best("TSMOM-LS", a, "1h", D1[a], lambda d, L: tsmom(d, L, "ls"), Ls, lambda x: f"L{x[0]}")
        summarize("TSMOM-LS", a, "1h", D1[a], pos)
        pos, p = scan_best("TSMOM-LO", a, "1h", D1[a], lambda d, L: tsmom(d, L, "lo"), Ls, lambda x: f"L{x[0]}")
        summarize("TSMOM-LO", a, "1h", D1[a], pos)
        pos, p = scan_best("TSMOM-LS", a, "15m", D15[a], lambda d, L: tsmom(d, L, "ls"), [(48,),(96,),(192,),(384,)], lambda x: f"L{x[0]}")
        summarize("TSMOM-LS", a, "15m", D15[a], pos)
    # ---- REVERSAL 1h + 15m ----
    block("REVERSAL (mean-reversion breve)")
    Lr = [(1,), (3,), (6,), (12,), (24,)]
    for a in ("BTC", "ETH"):
        pos, p = scan_best("REV-LS", a, "1h", D1[a], lambda d, L: reversal(d, L, "ls"), Lr, lambda x: f"L{x[0]}")
        summarize("REV-LS", a, "1h", D1[a], pos)
        pos, p = scan_best("REV-LS", a, "15m", D15[a], lambda d, L: reversal(d, L, "ls"), Lr, lambda x: f"L{x[0]}")
        summarize("REV-LS", a, "15m", D15[a], pos)
    # ---- MA cross ----
    block("MA-CROSS (trend)")
    g = [(12, 48), (24, 96), (48, 192), (24, 200)]
    for a in ("BTC", "ETH"):
        pos, p = scan_best("MAX-LS", a, "1h", D1[a], lambda d, f, s: ma_cross(d, f, s, "ls"), g, lambda x: f"{x[0]}/{x[1]}")
        summarize("MAX-LS", a, "1h", D1[a], pos)
        pos, p = scan_best("MAX-LO", a, "1h", D1[a], lambda d, f, s: ma_cross(d, f, s, "lo"), g, lambda x: f"{x[0]}/{x[1]}")
        summarize("MAX-LO", a, "1h", D1[a], pos)
    # ---- Donchian breakout ----
    block("DONCHIAN breakout")
    Ld = [(24,), (48,), (96,), (192,)]
    for a in ("BTC", "ETH"):
        pos, p = scan_best("DONCH-LS", a, "1h", D1[a], lambda d, L: donchian(d, L, "ls"), Ld, lambda x: f"L{x[0]}")
        summarize("DONCH-LS", a, "1h", D1[a], pos)
        pos, p = scan_best("DONCH-LO", a, "1h", D1[a], lambda d, L: donchian(d, L, "lo"), Ld, lambda x: f"L{x[0]}")
        summarize("DONCH-LO", a, "1h", D1[a], pos)
    # ---- Vol-target overlay (vs buy&hold) ----
    block("VOL-TARGET overlay (sempre-long scalato) — riduce la vol/DD del buy&hold?")
    for a in ("BTC", "ETH"):
        pos, p = scan_best("VOLTGT", a, "1h", D1[a], lambda d, t: vol_target(d, "1h", t, 72),
                           [(0.4,), (0.6,), (0.8,), (1.0,)], lambda x: f"t{x[0]}")
        summarize("VOLTGT", a, "1h", D1[a], pos)
    # ---- Hurst-gated momentum ----
    block("HURST-gated momentum (momentum solo in regime trending H>0.5)")
    for a in ("BTC", "ETH"):
        pos, p = scan_best("HURST-MOM", a, "1h", D1[a], lambda d, L: hurst_mom(d, L, 120, "ls"),
                           [(24,), (48,), (96,)], lambda x: f"L{x[0]}")
        summarize("HURST-MOM", a, "1h", D1[a], pos)
    # ---- Lead-lag BTC<->ETH ----
    block("LEAD-LAG BTC<->ETH (posiziona un asset col rendimento passato dell'altro)")
    for tgt, oth in (("ETH", "BTC"), ("BTC", "ETH")):
        Ll = [1, 3, 6, 12, 24]
        best = None; line = []
        for L in Ll:
            m, pos = leadlag_df(D1[tgt], D1[oth], L)
            s = backtest(m, pos, "1h").sharpe
            line.append(f"L{L}={s:>4.1f}")
            if best is None or s > best[0]:
                best = (s, L, m, pos)
        print(f"    {oth}->{tgt} 1h grid: " + "  ".join(line))
        _, L, m, pos = best
        summarize(f"LL {oth}>{tgt}", tgt, "1h", m, pos)
    # ---- classifica finale ----
    print("\n" + "=" * 100)
    print("  CLASSIFICA — net-fee FULL Sharpe (★EDGE? = batte B&H, OOS>0 e null p<0.05)")
    print("=" * 100)
    for r in sorted(ROWS, key=lambda r: -r["full"]):
        print(f"  {r['fam']:<16s} {r['asset']} {r['tf']:<3s} | FULL {r['full']:>5.2f} | OOS {r['oos']:>5.2f} | "
              f"B&H {r['bh']:>4.2f} | p {r['p']:>.3f} | {r['verdict']}")
    edges = [r for r in ROWS if r["verdict"] == "★EDGE?"]
    print(f"\n  Candidati che battono il buy&hold net-fee + OOS>0 + null p<0.05: {len(edges)}")
    for r in edges:
        print(f"    -> {r['fam']} {r['asset']} {r['tf']}: FULL {r['full']:.2f} OOS {r['oos']:.2f} p {r['p']:.3f}")
 if __name__ == "__main__":
    main()
@@ -0,0 +1,152 @@
 """FASE 3 — conferma avversariale del SOLO candidato reale: trend-following long-only (MA-cross).
 Protocollo onesto:
  1. SELEZIONE config SOLO sul pre-hold-out (< 2025-01-01). Niente sbirciate al hold-out.
  2. HOLD-OUT 2025-26 sbloccato UNA volta (la prova del nove, mai usato in ricerca).
  3. Breakdown PER ANNO vs buy&hold: il trend-LO deve "schivare" i bear (2018/2022).
  4. STRESS: fee 2x, lag di esecuzione (1 barra), slippage.
  5. DEFLATED SHARPE (Bailey & López de Prado): lo Sharpe regge alla correzione per multiple-testing?
    uv run python scripts/analysis/phase3_confirm.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))
 import numpy as np
 import pandas as pd
 from scipy.stats import norm, skew, kurtosis
 from src.data.downloader import load_data
 from scripts.analysis.research_lab import (
    backtest, buy_hold, window_mask, ts, _net_series, HOLDOUT_START, BARS_PER_YEAR,
 )
 from scripts.analysis.phase2_families import ma_cross
 GRID = [(12, 48), (24, 96), (48, 192), (24, 200), (96, 288)]   # MA-cross griglia (fast/slow)
 REPR = (24, 96)                                                # config rappresentativa PRE-COMMITTATA
 TF = "1h"
 def lag(pos, k=1):
    """Esecuzione in ritardo di k barre (agisci k barre dopo la decisione)."""
    return np.concatenate([np.zeros(k), pos[:-k]])
 def per_year(df, pos, tf):
    c = df["close"].values.astype(float)
    net, _, fwd, _ = _net_series(df, pos)
    yrs = ts(df).dt.year.values
    out = {}
    for y in sorted(set(yrs)):
        m = yrs == y
        if m.sum() < 2:
            continue
        strat = float(np.prod(1 + net[m]) - 1) * 100
        bh = float(np.prod(1 + fwd[m]) - 1) * 100
        expo = float(np.mean(np.abs(pos[m])))
        out[y] = (strat, bh, expo)
    return out
 def deflated_sharpe(net, sr_trials_perbar, N):
    """DSR: prob. che il vero Sharpe > la soglia attesa-massima sotto N trial (multiple testing).
    Tutto in Sharpe PER BARRA. >0.95 = significativo dopo correzione."""
    sr = net.mean() / net.std()
    T = len(net)
    g3 = float(skew(net)); g4 = float(kurtosis(net, fisher=False))
    var_sr = float(np.var(sr_trials_perbar, ddof=1)) if len(sr_trials_perbar) > 1 else 0.0
    ge = 0.5772156649
    z1 = norm.ppf(1 - 1.0 / N); z2 = norm.ppf(1 - 1.0 / (N * np.e))
    sr0 = np.sqrt(var_sr) * ((1 - ge) * z1 + ge * z2)        # Sharpe atteso-massimo sotto null, N trial
    den = np.sqrt(max(1 - g3 * sr + (g4 - 1) / 4.0 * sr ** 2, 1e-9))
    dsr = float(norm.cdf((sr - sr0) * np.sqrt(T - 1) / den))
    bpy = BARS_PER_YEAR[TF]
    return dsr, sr * np.sqrt(bpy), sr0 * np.sqrt(bpy)
 def main():
    print("=" * 96)
    print("  FASE 3 — conferma avversariale: TREND-following long-only (MA-cross) BTC/ETH")
    print("=" * 96)
    data = {a: load_data(a, TF) for a in ("BTC", "ETH")}
    # ---------- 1) selezione SOLO pre-hold-out ----------
    print(f"\n  (1) SELEZIONE su pre-hold-out (< {HOLDOUT_START}) — Sharpe per config (plateau = robusto)")
    for a in ("BTC", "ETH"):
        line = []
        for f, s in GRID:
            pos = ma_cross(data[a], f, s, "lo")
            sh = backtest(data[a], pos, TF, hi=HOLDOUT_START).sharpe
            line.append(f"{f}/{s}={sh:>4.2f}")
        print(f"    {a}: " + "  ".join(line))
    print(f"    -> config rappresentativa PRE-COMMITTATA per i test seguenti: {REPR[0]}/{REPR[1]}")
    # ---------- 2) HOLD-OUT 2025-26 (sbloccato una volta) ----------
    print(f"\n  (2) HOLD-OUT {HOLDOUT_START}+ — LA PROVA DEL NOVE (mai usato in ricerca)")
    for a in ("BTC", "ETH"):
        bh = buy_hold(data[a], TF, lo=HOLDOUT_START)
        print(f"    {a}: buy&hold hold-out  Sh {bh.sharpe:>5.2f}  ret {bh.ret*100:>+7.1f}%  DD {bh.maxdd*100:>4.1f}%")
        for f, s in GRID:
            pos = ma_cross(data[a], f, s, "lo")
            r = backtest(data[a], pos, TF, lo=HOLDOUT_START)
            star = " <-REPR" if (f, s) == REPR else ""
            print(f"        {f}/{s:<3d} Sh {r.sharpe:>5.2f}  ret {r.ret*100:>+7.1f}%  DD {r.maxdd*100:>4.1f}%  expo {r.exposure:.2f}{star}")
    # ---------- 3) per anno vs buy&hold (schiva i bear?) ----------
    print(f"\n  (3) PER ANNO — strat {REPR[0]}/{REPR[1]} vs buy&hold (expo = quanto è long; bear test 2018/2022)")
    for a in ("BTC", "ETH"):
        pos = ma_cross(data[a], *REPR, "lo")
        py = per_year(data[a], pos, TF)
        print(f"    {a}:")
        for y, (st, bh, ex) in py.items():
            flag = "  <- BEAR" if bh < -20 else ""
            print(f"       {y}: strat {st:>+7.0f}%  | buy&hold {bh:>+7.0f}%  | expo {ex:.2f}{flag}")
    # ---------- 4) stress ----------
    print(f"\n  (4) STRESS — strat {REPR[0]}/{REPR[1]} | FULL e HOLD-OUT Sharpe")
    print(f"    {'scenario':<24s}{'BTC FULL':>10s}{'BTC HO':>9s}{'ETH FULL':>10s}{'ETH HO':>9s}")
    scen = [
        ("base fee0.10%", dict(fee_rt=0.001), False),
        ("fee 0.20% (2x)", dict(fee_rt=0.002), False),
        ("lag 1 barra", dict(fee_rt=0.001), True),
        ("fee2x + lag", dict(fee_rt=0.002), True),
    ]
    for name, kw, do_lag in scen:
        row = [name]
        for a in ("BTC", "ETH"):
            pos = ma_cross(data[a], *REPR, "lo")
            if do_lag:
                pos = lag(pos, 1)
            full = backtest(data[a], pos, TF, **kw).sharpe
            ho = backtest(data[a], pos, TF, lo=HOLDOUT_START, **kw).sharpe
            row += [f"{full:>9.2f}", f"{ho:>8.2f}"]
        print(f"    {row[0]:<24s}{row[1]:>10s}{row[2]:>9s}{row[3]:>10s}{row[4]:>9s}")
    # ---------- 5) deflated Sharpe ----------
    print(f"\n  (5) DEFLATED SHARPE — corregge il multiple-testing (DSR>0.95 = regge)")
    # trial set = TUTTE le config trend long-only provate (proxy del numero di tentativi)
    N_TRIALS = 60      # stima conservativa dei backtest provati in Fase 2 (tutte le famiglie/asset/TF)
    for a in ("BTC", "ETH"):
        trials = [backtest(data[a], ma_cross(data[a], f, s, "lo"), TF, hi=HOLDOUT_START) for f, s in GRID]
        sr_trials = []
        for f, s in GRID:
            net, _, _, _ = _net_series(data[a], ma_cross(data[a], f, s, "lo"))
            m = window_mask(data[a], hi=HOLDOUT_START)
            sr_trials.append(net[m].mean() / net[m].std())
        net, _, _, _ = _net_series(data[a], ma_cross(data[a], *REPR, "lo"))
        m = window_mask(data[a], hi=HOLDOUT_START)
        dsr, sr_ann, sr0_ann = deflated_sharpe(net[m], sr_trials, N_TRIALS)
        verdict = "REGGE" if dsr > 0.95 else "NON regge"
        print(f"    {a} (pre-hold-out): Sharpe {sr_ann:.2f} vs soglia-max-attesa(N={N_TRIALS}) {sr0_ann:.2f} "
              f"-> DSR {dsr:.3f}  [{verdict}]")
    print("\n" + "=" * 96)
    print("  VERDETTO: edge ONESTO solo se (2) hold-out positivo, (3) schiva i bear, (4) regge lo")
    print("  stress, (5) DSR>0.95. Altrimenti: anche il trend era sample-luck del mercato toro.")
    print("=" * 96)
 if __name__ == "__main__":
    main()
@@ -0,0 +1,192 @@
 """HARNESS DI RICERCA ONESTO — BTC/ETH, v2.0.0 (Fase 0).
 Dopo che l'intera libreria precedente si è rivelata artefatto di feed/harness disonesti,
 la prima cosa di cui fidarsi NON è una strategia ma il banco di prova. Questo modulo è
 quel banco: causale per costruzione, netto fee, con baseline e null model.
 MODELLO CANONICO = SERIE DI POSIZIONE.
  Una strategia è una funzione  signal(df, **params) -> pd.Series/np.array  che dà la
  posizione target per barra in [-1, +1]. REGOLA: position[i] è decisa con dati FINO a
  close[i] (mai oltre) e GUADAGNA il rendimento close[i] -> close[i+1]. L'engine moltiplica
  position[i] * fwd[i] (fwd strettamente futuro rispetto alla decisione) -> niente look-ahead
  per costruzione, e niente fill sull'estremo di candela (si entra al close). La fee è
  addebitata sul TURNOVER |Δposition| (un round-trip 0->1->0 = 2 unità = fee_rt intera).
 GATE (vedi CLAUDE.md): ingresso eseguibile (qui per costruzione), netto fee 0.10% RT,
 OOS held-out, robustezza su griglia, onestà statistica (null model + buy&hold), walk-forward
 per i modelli fittati, liquidità (BTC/ETH ok).
    uv run python scripts/analysis/research_lab.py          # self-test del banco
 """
 from __future__ import annotations
 import sys
 from dataclasses import dataclass
 from pathlib import Path
 PROJECT_ROOT = Path(__file__).resolve().parents[2]
 sys.path.insert(0, str(PROJECT_ROOT))
 import numpy as np
 import pandas as pd
 from src.data.downloader import load_data
 FEE_RT = 0.001                      # 0.10% round-trip taker Deribit (0.05%/lato)
 BARS_PER_YEAR = {"5m": 105192.0, "15m": 35064.0, "1h": 8766.0}
 # Hold-out FINALE bloccato: NIENTE ricerca/tuning lo tocca finché non è il verdetto (Fase 3).
 HOLDOUT_START = "2025-01-01"
 # Finestra di validazione OOS usata in ricerca (out-of-sample ma PRE hold-out).
 VAL_START = "2023-01-01"
 def ts(df) -> pd.Series:
    return pd.to_datetime(df["timestamp"], unit="ms", utc=True)
 def window_mask(df, lo: str | None = None, hi: str | None = None) -> np.ndarray:
    t = ts(df)
    m = np.ones(len(df), bool)
    if lo is not None:
        m &= (t >= pd.Timestamp(lo, tz="UTC")).values
    if hi is not None:
        m &= (t < pd.Timestamp(hi, tz="UTC")).values
    return m
@dataclass
 class BT:
    n: int
    ret: float        # rendimento composto sulla finestra (pos 1x, leva 1x)
    cagr: float
    sharpe: float     # annualizzato
    maxdd: float      # % (positivo)
    exposure: float   # |pos| medio
    turnover: float   # Σ|Δpos| / anno
    ntrades: float    # round-trip equivalenti / anno
    def line(self, label="") -> str:
        return (f"  {label:<22s} Sh {self.sharpe:>6.2f} | ret {self.ret*100:>+8.1f}% "
                f"CAGR {self.cagr*100:>+6.1f}% | DD {self.maxdd*100:>5.1f}% | "
                f"expo {self.exposure:>4.2f} trd/y {self.ntrades:>6.1f} | n {self.n}")
 def _net_series(df, position, fee_rt=FEE_RT):
    """Ritorna (net, gross, fwd, pos) per barra. net[i] = pos[i]*fwd[i] - fee sul cambio a i."""
    c = df["close"].values.astype(float)
    pos = np.nan_to_num(np.asarray(position, float), nan=0.0)
    pos = np.clip(pos, -1.0, 1.0)
    n = len(c)
    fwd = np.zeros(n)
    fwd[:-1] = c[1:] / c[:-1] - 1.0          # rendimento close[i]->close[i+1] (futuro vs decisione a i)
    gross = pos * fwd
    dpos = np.abs(np.diff(np.concatenate([[0.0], pos])))   # cambio di posizione a i (si tradea al close[i])
    fee = dpos * (fee_rt / 2.0)              # fee_rt = round-trip (2 unità di turnover); /2 per unità
    net = gross - fee
    return net, gross, fwd, pos
 def backtest(df, position, tf="1h", fee_rt=FEE_RT, lo=None, hi=None) -> BT:
    net, gross, fwd, pos = _net_series(df, position, fee_rt)
    m = window_mask(df, lo, hi)
    net_w, pos_w = net[m], pos[m]
    dpos_w = np.abs(np.diff(np.concatenate([[0.0], pos_w])))
    bpy = BARS_PER_YEAR[tf]
    n = int(m.sum())
    if n < 2:
        return BT(n, 0, float("nan"), 0, 0, 0, 0, 0)
    eq = np.cumprod(1.0 + net_w)
    total = float(eq[-1] - 1.0)
    years = n / bpy
    cagr = float((1 + total) ** (1 / years) - 1) if years > 0 and total > -1 else float("nan")
    mu, sd = float(net_w.mean()), float(net_w.std())
    sharpe = mu / sd * np.sqrt(bpy) if sd > 0 else 0.0
    peak = np.maximum.accumulate(eq)
    maxdd = float(np.max((peak - eq) / peak)) if n else 0.0
    expo = float(np.mean(np.abs(pos_w)))
    turn_y = float(dpos_w.sum() / years) if years > 0 else 0.0
    return BT(n, total, cagr, sharpe, maxdd, expo, turn_y, turn_y / 2.0)
 def buy_hold(df, tf="1h", fee_rt=FEE_RT, lo=None, hi=None) -> BT:
    return backtest(df, np.ones(len(df)), tf, fee_rt, lo, hi)
 def mc_pvalue(df, position, tf="1h", fee_rt=FEE_RT, n=500, lo=None, hi=None, seed=0):
    """Null model a ROTAZIONE CIRCOLARE: ruota la serie di posizione di un offset casuale.
    Preserva ESATTAMENTE exposure, turnover e distribuzione degli holding; distrugge solo
    l'allineamento col mercato. p = P(Sharpe_ruotato >= Sharpe_reale). p alto = il timing
    non batte il caso (nessuna skill)."""
    pos = np.nan_to_num(np.asarray(position, float))
    base = backtest(df, pos, tf, fee_rt, lo, hi).sharpe
    N = len(pos)
    if np.abs(np.diff(pos)).sum() == 0:       # posizione costante -> rotazione degenere
        return base, float("nan"), float("nan"), float("nan")
    rng = np.random.default_rng(seed)
    sims = np.empty(n)
    for k in range(n):
        off = int(rng.integers(1, N))
        sims[k] = backtest(df, np.roll(pos, off), tf, fee_rt, lo, hi).sharpe
    p = float((np.sum(sims >= base) + 1) / (n + 1))
    return base, p, float(sims.mean()), float(sims.std())
 def report(name, df, position, tf="1h", fee_rt=FEE_RT, mc_n=400):
    """Stampa il verdetto onesto: FULL / OOS-VAL / vs buy&hold / null p-value / sweep fee."""
    print(f"\n  === {name} ({tf}) ===")
    print(backtest(df, position, tf, fee_rt).line("FULL"))
    print(backtest(df, position, tf, fee_rt, lo=VAL_START, hi=HOLDOUT_START).line(f"OOS-VAL {VAL_START[:4]}-24"))
    print(buy_hold(df, tf, fee_rt).line("buy&hold FULL"))
    base, p, msh, ssd = mc_pvalue(df, position, tf, fee_rt, n=mc_n)
    verdict = "RUMORE" if (np.isnan(p) or p > 0.05) else "batte il null"
    print(f"  null (rotazione, n={mc_n}): Sharpe reale {base:.2f} vs random {msh:.2f}±{ssd:.2f} "
          f"-> p={p if not np.isnan(p) else float('nan'):.3f}  [{verdict}]")
    print("  sweep fee RT:", "  ".join(
        f"{f*100:.2f}%→Sh{backtest(df, position, tf, f).sharpe:.2f}" for f in (0.0, 0.0005, 0.001, 0.002)))
 # ============================ SELF-TEST DEL BANCO ============================
 def self_test():
    """Valida l'HARNESS, non una strategia. Tre prove:
      (1) buy&hold: Sharpe positivo, DD grande (sanity dei numeri).
      (2) CHEAT look-ahead (pos = segno del rendimento FUTURO): Sharpe enorme, p≈0
          -> l'engine SA vedere un edge quando esiste davvero.
      (3) NOISE causale (pos da rumore del passato): Sharpe≈0, p≈0.5
          -> l'engine NON inventa edge dal nulla (niente leak)."""
    print("=" * 78)
    print("  SELF-TEST HARNESS — deve: vedere il cheat, NON vedere il rumore")
    print("=" * 78)
    df = load_data("BTC", "1h")
    t = ts(df)
    c = df["close"].values.astype(float)
    bh = buy_hold(df, "1h")
    print(bh.line("(1) buy&hold BTC"))
    assert bh.sharpe > 0, "buy&hold dovrebbe avere Sharpe>0 sullo storico BTC"
    # (2) CHEAT: posizione = segno del rendimento del prossimo bar (USA IL FUTURO)
    fwd = np.zeros(len(c)); fwd[:-1] = c[1:] / c[:-1] - 1.0
    cheat = np.sign(fwd)
    bt_cheat = backtest(df, cheat, "1h")
    _, p_cheat, _, _ = mc_pvalue(df, cheat, "1h", n=200, seed=1)
    print(bt_cheat.line("(2) CHEAT look-ahead"))
    print(f"      -> null p={p_cheat:.4f} (atteso ≈0: l'edge finto È enorme e battibile dal caso ~mai)")
    assert bt_cheat.sharpe > 20, "il cheat dovrebbe dare Sharpe enorme se l'engine è corretto"
    assert p_cheat < 0.02, "il cheat dovrebbe battere il null in modo schiacciante"
    # (3) NOISE causale a BASSO turnover (blocchi ~50 barre): isola la SKILL dalla fee-death.
    # Posizione casuale (non usa il futuro) tenuta a blocchi -> turnover basso -> se l'engine non
    # inventa edge dal nulla, Sharpe≈0 e il null p≈0.5 (random rotazioni indistinguibili).
    rng = np.random.default_rng(42)
    blk = 50
    raw = np.sign(rng.standard_normal(len(c) // blk + 1))
    noise_pos = np.repeat(raw, blk)[:len(c)]
    noise_pos = pd.Series(noise_pos).shift(1).fillna(0).values        # solo passato
    bt_noise = backtest(df, noise_pos, "1h")
    base_n, p_noise, msh, ssd = mc_pvalue(df, noise_pos, "1h", n=400, seed=2)
    print(bt_noise.line("(3) NOISE causale"))
    print(f"      -> null p={p_noise:.3f} (atteso alto/≈0.5: nessuna skill, indistinguibile dal caso)")
    assert bt_noise.sharpe < 2.0, "il rumore causale non deve sembrare SKILLATO (Sharpe positivo grande = leak)"
    assert p_noise > 0.10, "il rumore causale non deve battere il null (p basso = edge spurio/leak)"
    print("\n  ✓ HARNESS VALIDATO: vede il cheat (Sharpe enorme, p≈0), non inventa edge dal rumore (p alto).")
    print(f"  Hold-out finale BLOCCATO da {HOLDOUT_START} (non usato in ricerca). OOS-VAL: {VAL_START}→hold-out.")
 if __name__ == "__main__":
    self_test()
@@ -0,0 +1,121 @@
 """STRESS-TEST di TP01 (integrato da strategy-research-2026-06) — robustezza avversariale.
 Usa il modulo VERO integrato (src/strategies/trend_portfolio). Oltre a hold-out/cross-asset/multi-TF
 (gia' in verify_tp01.py), qui: sweep FEE (fino 0.40% RT), LAG di esecuzione + slippage, PLATEAU dei
 parametri (config cherry-picked?), DEFLATED-SHARPE (multiple-testing track A-E).
    uv run python scripts/analysis/stress_tp01.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))
 import numpy as np
 import pandas as pd
 from scipy.stats import norm, skew, kurtosis
 from src.data.downloader import load_data
 from src.strategies.trend_portfolio import TrendPortfolio, resample_4h, simple_returns, CANONICAL
 HOLDOUT = pd.Timestamp("2025-01-01", tz="UTC")
 DF4H = {a: resample_4h(load_data(a, "1h")) for a in ("BTC", "ETH")}
 def combo(cfg, lag_bars=0, fee_side=0.0005):
    """Rendimenti per-barra del portafoglio 50/50 con config cfg, lag extra e fee dati."""
    tp = TrendPortfolio(**{**cfg, "fee_side": fee_side})
    series = {}
    for a in ("BTC", "ETH"):
        df = DF4H[a]
        r = simple_returns(df["close"].values.astype(float))
        tgt = tp.target_series(df)
        held = np.zeros(len(tgt))
        s = 1 + lag_bars
        held[s:] = tgt[:-s]                                  # tenuta = decisa s barre prima (causale + lag)
        net = held * r - fee_side * np.abs(np.diff(held, prepend=0.0))
        net[0] = 0.0
        series[a] = pd.Series(np.clip(net, -0.99, None), index=pd.to_datetime(df["datetime"]))
    J = pd.concat(series, axis=1, join="inner").fillna(0.0)
    return 0.5 * J["BTC"].values + 0.5 * J["ETH"].values, J.index
 def met(combo_r, idx):
    rr = combo_r[np.isfinite(combo_r)]
    if len(rr) < 2 or np.std(rr) == 0:
        return dict(sh=0, ret=0, dd=0)
    bpy = 86400 * 365.25 / pd.Series(idx).diff().dt.total_seconds().median()
    eq = np.cumprod(1 + rr); pk = np.maximum.accumulate(eq)
    return dict(sh=float(np.mean(rr) / np.std(rr) * np.sqrt(bpy)),
                ret=float(eq[-1] - 1), dd=float(np.max((pk - eq) / pk)))
 def full_ho(cfg, lag_bars=0, fee_side=0.0005):
    cr, idx = combo(cfg, lag_bars, fee_side)
    ho = idx >= HOLDOUT
    return met(cr, idx), met(cr[ho], idx[ho])
 def main():
    print("=" * 88)
    print("  STRESS-TEST TP01 (PORT LF4h canonica) — robustezza avversariale")
    print("=" * 88)
    base_f, base_h = full_ho(CANONICAL)
    print(f"\n  BASELINE (4h, fee 0.10% RT): FULL Sh {base_f['sh']:.2f} ret {base_f['ret']*100:+.0f}% DD {base_f['dd']*100:.1f}%"
          f" | HOLD-OUT Sh {base_h['sh']:.2f} ret {base_h['ret']*100:+.1f}% DD {base_h['dd']*100:.1f}%")
    print("\n  (1) SWEEP FEE (RT) — regge fino a 0.40%?")
    print(f"    {'fee RT':<10s}{'FULL Sh':>9s}{'FULL ret':>10s}{'HOLD Sh':>9s}{'HOLD ret':>10s}")
    for frt in (0.0, 0.001, 0.002, 0.004):
        f, h = full_ho(CANONICAL, fee_side=frt / 2)
        print(f"    {frt*100:>5.2f}%   {f['sh']:>8.2f}{f['ret']*100:>+9.0f}%{h['sh']:>9.2f}{h['ret']*100:>+9.1f}%")
    print("\n  (2) LAG di esecuzione + slippage (fee 0.20% per simulare slippage)")
    print(f"    {'scenario':<22s}{'FULL Sh':>9s}{'HOLD Sh':>9s}{'HOLD ret':>10s}")
    for name, lag, frt in [("base", 0, 0.001), ("lag 1 barra (4h)", 1, 0.001),
                           ("lag 2 barre", 2, 0.001), ("lag1 + fee0.20% slip", 1, 0.002)]:
        f, h = full_ho(CANONICAL, lag_bars=lag, fee_side=frt / 2)
        print(f"    {name:<22s}{f['sh']:>8.2f}{h['sh']:>9.2f}{h['ret']*100:>+9.1f}%")
    print("\n  (3) PLATEAU PARAMETRI — la config canonica e' un picco o un altopiano?")
    print(f"    {'variazione':<26s}{'FULL Sh':>9s}{'HOLD Sh':>9s}")
    grid = [
        ("canonica (vt.20 lev2 30/90/180 vw30)", CANONICAL),
        ("target_vol 0.15", {**CANONICAL, "target_vol": 0.15}),
        ("target_vol 0.25", {**CANONICAL, "target_vol": 0.25}),
        ("leverage 1.5", {**CANONICAL, "leverage": 1.5}),
        ("leverage 3.0", {**CANONICAL, "leverage": 3.0}),
        ("horizons 20/60/120", {**CANONICAL, "horizons_days": (20, 60, 120)}),
        ("horizons 60/120/240", {**CANONICAL, "horizons_days": (60, 120, 240)}),
        ("vol_win 20", {**CANONICAL, "vol_win_days": 20}),
        ("vol_win 45", {**CANONICAL, "vol_win_days": 45}),
    ]
    sr_trials = []
    for name, cfg in grid:
        f, h = full_ho(cfg)
        cr, idx = combo(cfg)
        sr_trials.append(cr[np.isfinite(cr)].mean() / cr[np.isfinite(cr)].std())   # Sharpe per-barra
        print(f"    {name:<26s}{f['sh']:>8.2f}{h['sh']:>9.2f}")
    print("\n  (4) DEFLATED SHARPE — corregge il multiple-testing (track A-E + sweep). DSR>0.95 = regge")
    cr, idx = combo(CANONICAL)
    rr = cr[np.isfinite(cr)]
    sr = rr.mean() / rr.std(); T = len(rr)
    g3 = float(skew(rr)); g4 = float(kurtosis(rr, fisher=False))
    var_sr = float(np.var(sr_trials, ddof=1))
    ge = 0.5772156649
    for N in (10, 40, 100):       # N = numero di trial/config provati (conservativo)
        z1 = norm.ppf(1 - 1.0 / N); z2 = norm.ppf(1 - 1.0 / (N * np.e))
        sr0 = np.sqrt(var_sr) * ((1 - ge) * z1 + ge * z2)
        den = np.sqrt(max(1 - g3 * sr + (g4 - 1) / 4.0 * sr ** 2, 1e-9))
        dsr = float(norm.cdf((sr - sr0) * np.sqrt(T - 1) / den))
        bpy = 86400 * 365.25 / pd.Series(idx).diff().dt.total_seconds().median()
        print(f"    N={N:>3d} trial -> soglia-max-attesa Sh {sr0*np.sqrt(bpy):.2f} | DSR {dsr:.3f}  [{'REGGE' if dsr>0.95 else 'NON regge'}]")
    print("\n" + "=" * 88)
    print("  Verdetto: TP01 robusto se regge fee 0.40%+lag (HOLD positivo), plateau (no picco), DSR>0.95.")
    print("=" * 88)
 if __name__ == "__main__":
    main()
@@ -0,0 +1,96 @@
 """TP01 a BASSA FREQUENZA (>=12h) — ri-verifica dopo il bug look-ahead ffill-mixed-TF.
 L'utente/agente ha trovato un look-ahead (ffill mixed-timeframe su barre open-labeled) che
 gonfiava il 4h (~1.60 -> reale ~1.1) e ha concluso: NON scendere sotto le 12h (costi+overfit
 dominano). Qui ricalcolo TP01 in modo PULITO per singolo TF (barre discrete, posizione shiftata
 +1, NESSUN ffill/combine mixed-TF) su 4h/12h/1d, con un GUARD di causalita' esplicito sulla serie
 resamplata (ricalcolo su prefisso). Fee 0.10% RT, hold-out 2025-26 bloccato.
    uv run python scripts/analysis/tp01_lowfreq.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))
 import numpy as np
 import pandas as pd
 from src.data.downloader import load_data
 from src.strategies.trend_portfolio import TrendPortfolio, simple_returns, CANONICAL
 HOLDOUT = pd.Timestamp("2025-01-01", tz="UTC")
 def resample_tf(df_1h, rule):
    g = df_1h.copy()
    g.index = pd.to_datetime(g["timestamp"], unit="ms", utc=True)
    out = g.resample(rule, label="left", closed="left").agg(
        {"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"}).dropna(subset=["open"])
    out["datetime"] = out.index
    return out.reset_index(drop=True)
 def sleeve_net(df, tp):
    """Per-barra netto di uno sleeve: posizione decisa a close[i-1], tenuta in i (causale, no ffill)."""
    r = simple_returns(df["close"].values.astype(float))
    tgt = tp.target_series(df)
    held = np.zeros(len(tgt)); held[1:] = tgt[:-1]
    net = held * r - tp.fee_side * np.abs(np.diff(held, prepend=0.0)); net[0] = 0.0
    return np.clip(net, -0.99, None)
 def causality_ok(df, tp, k=10):
    """Ricalcola target_series su prefissi e verifica che tgt[i] non cambi (no look-ahead)."""
    full = tp.target_series(df); n = len(df)
    rng = np.random.default_rng(0); bad = 0
    for i in rng.integers(int(n * 0.6), n - 1, size=k):
        p = tp.target_series(df.iloc[:i + 1].copy())
        if len(p) != i + 1 or not np.isclose(np.nan_to_num(p[i]), np.nan_to_num(full[i]), atol=1e-9):
            bad += 1
    return bad
 def met(rr, idx):
    rr = rr[np.isfinite(rr)]
    if len(rr) < 2 or np.std(rr) == 0:
        return dict(sh=0, ret=0, dd=0, n=len(rr))
    bpy = 86400 * 365.25 / pd.Series(idx).diff().dt.total_seconds().median()
    eq = np.cumprod(1 + rr); pk = np.maximum.accumulate(eq)
    return dict(sh=float(np.mean(rr) / np.std(rr) * np.sqrt(bpy)), ret=float(eq[-1] - 1),
                dd=float(np.max((pk - eq) / pk)), n=len(rr))
 def main():
    print("=" * 92)
    print("  TP01 RI-VERIFICA BASSA FREQUENZA — calcolo pulito per-TF (no ffill mixed-TF) | fee 0.10% RT")
    print("=" * 92)
    tp = TrendPortfolio(**CANONICAL)
    print(f"  {'TF':<5s}{'leak':>6s}{'FULL Sh':>9s}{'FULL ret':>10s}{'FULL DD':>9s}{'HOLD Sh':>9s}{'HOLD ret':>10s}{'HOLD DD':>9s}")
    for tf, rule in [("4h", "4h"), ("6h", "6h"), ("12h", "12h"), ("1d", "1D")]:
        series = {}; leak = 0
        for a in ("BTC", "ETH"):
            df = resample_tf(load_data(a, "1h"), rule)
            leak += causality_ok(df, tp)
            series[a] = pd.Series(sleeve_net(df, tp), index=pd.to_datetime(df["datetime"]))
        J = pd.concat(series, axis=1, join="inner").fillna(0.0)
        combo = 0.5 * J["BTC"].values + 0.5 * J["ETH"].values
        idx = J.index; ho = idx >= HOLDOUT
        f = met(combo, idx); h = met(combo[ho], idx[ho])
        print(f"  {tf:<5s}{leak:>6d}{f['sh']:>9.2f}{f['ret']*100:>+9.0f}%{f['dd']*100:>8.1f}%"
              f"{h['sh']:>9.2f}{h['ret']*100:>+9.1f}%{h['dd']*100:>8.1f}%")
    # buy&hold 50/50 a 1d come riferimento hold-out
    bh = {}
    for a in ("BTC", "ETH"):
        df = resample_tf(load_data(a, "1h"), "1D")
        bh[a] = pd.Series(simple_returns(df["close"].values.astype(float)), index=pd.to_datetime(df["datetime"]))
    Jb = pd.concat(bh, axis=1, join="inner").fillna(0.0)
    cb = 0.5 * Jb["BTC"].values + 0.5 * Jb["ETH"].values; ix = Jb.index; ho = ix >= HOLDOUT
    bhf = met(cb, ix); bhh = met(cb[ho], ix[ho])
    print(f"\n  buy&hold 50/50 (1d): FULL Sh {bhf['sh']:.2f} ret {bhf['ret']*100:+.0f}% DD {bhf['dd']*100:.0f}%"
          f" | HOLD-OUT Sh {bhh['sh']:.2f} ret {bhh['ret']*100:+.0f}% DD {bhh['dd']*100:.0f}%")
    print("\n  (leak=0 = nessun look-ahead nel calcolo per-TF. Confronta con la tesi: >=12h trustworthy.)")
 if __name__ == "__main__":
    main()
@@ -0,0 +1,149 @@
 """VERIFICA AVVERSARIALE di TP01 (branch strategy-research-2026-06) col MIO gauntlet onesto.
 TP01 = TSMOM multi-orizzonte (30/90/180g) long-flat, vol-target 20%, leva cap 2x, portafoglio
 50/50 BTC+ETH. Codice riprodotto VERBATIM dal branch (src/strategies/trend_portfolio.py).
 La loro tesi: 'positiva ogni anno 2019-2026, Sharpe ~1.32'. Il mio test decisivo: il HOLD-OUT
 2025-26 (che ha bocciato il mio trend 1h in Fase 3) + cross-asset + multi-TF (cherry-picking 4h?).
    uv run python scripts/analysis/verify_tp01.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))
 import numpy as np
 import pandas as pd
 from src.data.downloader import load_data
 HOLDOUT = pd.Timestamp("2025-01-01", tz="UTC")
 CANONICAL = dict(target_vol=0.20, leverage=2.0, long_only=True,
                 horizons_days=(30, 90, 180), vol_win_days=30, fee_side=0.0005)
 # ---- TP01 riprodotto VERBATIM dal branch ----
 def simple_returns(c):
    r = np.zeros(len(c)); r[1:] = c[1:] / c[:-1] - 1.0; return r
 def realized_vol(r, win, bpy):
    return pd.Series(r).rolling(win, min_periods=win // 2).std().values * np.sqrt(bpy)
 def tsmom_blend(c, horizons):
    n = len(c); acc = np.zeros(n); cnt = np.zeros(n)
    for h in horizons:
        s = np.full(n, np.nan); s[h:] = np.sign(c[h:] / c[:-h] - 1.0)
        v = np.isfinite(s); acc[v] += s[v]; cnt[v] += 1
    out = np.zeros(n); nz = cnt > 0; out[nz] = acc[nz] / cnt[nz]; return out
 def target_series(df, p, bpd):
    c = df["close"].values.astype(float); bpy = bpd * 365.25
    r = simple_returns(c)
    vol = realized_vol(r, p["vol_win_days"] * bpd, bpy)
    direction = tsmom_blend(c, tuple(d * bpd for d in p["horizons_days"]))
    if p["long_only"]:
        direction = np.clip(direction, 0, None)
    scal = np.where((vol > 0) & np.isfinite(vol), p["target_vol"] / vol, 0.0)
    tgt = np.clip(direction * scal, -p["leverage"], p["leverage"]); tgt[~np.isfinite(tgt)] = 0.0
    return tgt
 def net_returns(df, p, bpd):
    c = df["close"].values.astype(float); r = simple_returns(c)
    tgt = target_series(df, p, bpd)
    pos_held = np.zeros(len(tgt)); pos_held[1:] = tgt[:-1]      # decisa a close[t-1], tenuta in t -> causale
    gross = pos_held * r
    turn = np.abs(np.diff(pos_held, prepend=0.0))
    net = gross - p["fee_side"] * turn; net[0] = 0.0
    return np.clip(net, -0.99, None), pos_held
 def resample(df_1h, rule):
    g = df_1h.copy(); idx = pd.to_datetime(g["timestamp"], unit="ms", utc=True); g.index = idx
    out = g.resample(rule, label="left", closed="left").agg(
        {"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"}).dropna(subset=["open"])
    out["timestamp"] = out.index
    return out.reset_index(drop=True)
 def metrics(combo, idx):
    rr = combo[np.isfinite(combo)]
    if len(rr) < 2 or np.std(rr) == 0:
        return dict(sharpe=0, cagr=0, dd=0, ret=0, n=len(rr))
    dt = pd.Series(idx).diff().dt.total_seconds().median()
    bpy = 86400 * 365.25 / dt
    eq = np.cumprod(1 + rr); peak = np.maximum.accumulate(eq)
    years = (idx[-1] - idx[0]).total_seconds() / 86400 / 365.25
    return dict(sharpe=float(np.mean(rr) / np.std(rr) * np.sqrt(bpy)),
                cagr=float(eq[-1] ** (1 / years) - 1) if years > 0 else 0,
                dd=float(np.max((peak - eq) / peak)), ret=float(eq[-1] - 1), n=len(rr))
 def portfolio_combo(tf_rule, bpd):
    series = {}
    for a in ("BTC", "ETH"):
        df = load_data(a, "1h")
        if tf_rule:
            df = resample(df, tf_rule)
        net, _ = net_returns(df, CANONICAL, bpd)
        series[a] = pd.Series(net, index=pd.to_datetime(df["timestamp"], unit="ms", utc=True) if not tf_rule
                              else pd.DatetimeIndex(df["timestamp"]))
    J = pd.concat(series, axis=1, join="inner").fillna(0.0)
    combo = 0.5 * J["BTC"].values + 0.5 * J["ETH"].values
    return combo, J.index, J
 def line(label, combo, idx):
    m = metrics(combo, idx)
    return f"  {label:<22s} Sharpe {m['sharpe']:>5.2f} | ret {m['ret']*100:>+8.1f}% CAGR {m['cagr']*100:>+6.1f}% | DD {m['dd']*100:>5.1f}% | n {m['n']}"
 def main():
    print("=" * 92)
    print("  VERIFICA TP01 (TSMOM 30/90/180 vol-target 20% lev2x long-flat, 50/50 BTC+ETH)")
    print("  col gauntlet onesto: FULL vs buy&hold | HOLD-OUT 2025-26 bloccato | per-anno | multi-TF")
    print("=" * 92)
    TFS = [("15m", "15min", 96), ("1h", None, 24), ("4h", "4h", 6), ("1d", "1D", 1)]
    print("\n  (A) MULTI-TF: il 4h e' cherry-picked? FULL + HOLD-OUT per ogni timeframe")
    for tf, rule, bpd in TFS:
        combo, idx, J = portfolio_combo(rule, bpd)
        ho = idx >= HOLDOUT
        full = metrics(combo, idx)
        hold = metrics(combo[ho], idx[ho])
        tag = "  <- canonica" if tf == "4h" else ""
        print(f"    {tf:<3s} FULL Sh {full['sharpe']:>5.2f} CAGR {full['cagr']*100:>+6.1f}% DD {full['dd']*100:>4.1f}% "
              f"| HOLD-OUT Sh {hold['sharpe']:>5.2f} ret {hold['ret']*100:>+6.1f}% DD {hold['dd']*100:>4.1f}%{tag}")
    # focus 4h canonica
    combo, idx, J = portfolio_combo("4h", 6)
    print("\n  (B) 4h CANONICA — per anno (la tesi: positiva OGNI anno 2019-2026)")
    s = pd.Series(combo, index=idx)
    for y, g in s.groupby(s.index.year):
        eq = np.cumprod(1 + g.values); pk = np.maximum.accumulate(eq)
        ho_flag = "  <- HOLD-OUT (mai usato per scegliere config?)" if y >= 2025 else ""
        print(f"    {y}: ret {(eq[-1]-1)*100:>+7.1f}%  DD {np.max((pk-eq)/pk)*100:>5.1f}%{ho_flag}")
    print("\n  (C) HOLD-OUT 2025-26 — TP01 vs buy&hold 50/50 (4h)")
    ho = idx >= HOLDOUT
    print(line("TP01 portfolio HO", combo[ho], idx[ho]))
    # buy&hold 50/50 sullo stesso indice/finestra
    bh = {}
    for a in ("BTC", "ETH"):
        df = resample(load_data(a, "1h"), "4h")
        r = simple_returns(df["close"].values.astype(float))
        bh[a] = pd.Series(r, index=pd.DatetimeIndex(df["timestamp"]))
    Jb = pd.concat(bh, axis=1, join="inner").reindex(idx).fillna(0.0)
    bh_combo = 0.5 * Jb["BTC"].values + 0.5 * Jb["ETH"].values
    print(line("buy&hold 50/50 HO", bh_combo[ho], idx[ho]))
    print(line("TP01 portfolio FULL", combo, idx))
    print(line("buy&hold 50/50 FULL", bh_combo, idx))
    print("\n  (D) CROSS-ASSET nel HOLD-OUT (lo stesso edge regge su ENTRAMBI?)")
    for a in ("BTC", "ETH"):
        df = resample(load_data(a, "1h"), "4h")
        net, _ = net_returns(df, CANONICAL, 6)
        ix = pd.DatetimeIndex(df["timestamp"]); m = ix >= HOLDOUT
        print(line(f"TP01 {a} sleeve HO", net[m], ix[m]))
 if __name__ == "__main__":
    main()
@@ -0,0 +1,191 @@
 """PAPER TRADER — TP01 Trend Portfolio (PORT LF1d), forward-only, simulato.
 Esegue la strategia VINCENTE (src/strategies/trend_portfolio.py, config CANONICAL) in
 paper trading FORWARD-ONLY su capitale virtuale (default 2000 USDT), portafoglio 50/50
 BTC+ETH a 1d. Stato persistente -> resume al riavvio.
 DESIGN (onesto, niente esecuzione reale: l'esecuzione e' DISABILITATA nel progetto):
  - Legge i parquet certificati locali (data/raw, BTC/ETH 1h) e resampla a 1d.
  - Alla prima esecuzione parte dall'ultima barra 1d CHIUSA disponibile (forward-only:
    NON include lo storico nel PnL di paper, traccia solo da ora in avanti).
  - Ad ogni run processa le NUOVE barre 1d chiuse dall'ultima volta: applica il rendimento
    della posizione tenuta, addebita le fee sul turnover, registra i trade sui cambi di
    posizione, poi ricalcola la posizione-bersaglio (decisa con dati <= ultima barra chiusa).
  - Per avere barre fresche, aggiornare prima i dati:
        uv run python scripts/analysis/rebuild_history.py --asset BTC ETH
 Stato: data/paper_trend/state.json + trades.jsonl (append-only).
    uv run python scripts/live/paper_trend.py            # avanza il paper col dato disponibile
    uv run python scripts/live/paper_trend.py --status   # solo stato, non avanza
    uv run python scripts/live/paper_trend.py --reset     # azzera lo stato (riparte da ora)
 """
 from __future__ import annotations
 import json
 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 src.backtest.harness import load
 from src.strategies.trend_portfolio import TrendPortfolio, CANONICAL, resample_1d, simple_returns
 STATE_DIR = PROJECT_ROOT / "data" / "paper_trend"
 STATE_FILE = STATE_DIR / "state.json"
 TRADES_FILE = STATE_DIR / "trades.jsonl"
 ASSETS = ["BTC", "ETH"]
 WEIGHT = 0.5
 INITIAL_CAPITAL = 2000.0
 def build_bars() -> dict[str, pd.DataFrame]:
    # Deploy a 1d (>=12h): sotto le 12h costi+overfit dominano (vedi trend_portfolio docstring + bug ffill mixed-TF).
    return {a: resample_1d(load(a, "1h")) for a in ASSETS}
 def load_state() -> dict | None:
    if STATE_FILE.exists():
        return json.loads(STATE_FILE.read_text())
    return None
 def save_state(st: dict):
    STATE_DIR.mkdir(parents=True, exist_ok=True)
    STATE_FILE.write_text(json.dumps(st, indent=2))
 def append_trade(rec: dict):
    STATE_DIR.mkdir(parents=True, exist_ok=True)
    with open(TRADES_FILE, "a") as f:
        f.write(json.dumps(rec) + "\n")
 def init_state(dfs) -> dict:
    last_ts = min(int(dfs[a]["timestamp"].iloc[-1]) for a in ASSETS)
    tp = TrendPortfolio(**CANONICAL)
    positions = {}
    for a in ASSETS:
        df = dfs[a]
        df = df[df["timestamp"] <= last_ts]
        positions[a] = tp.current_target(df)
    return dict(
        capital=INITIAL_CAPITAL, initial_capital=INITIAL_CAPITAL,
        start_ts=last_ts, last_ts=last_ts, positions=positions, n_bars=0,
        peak=INITIAL_CAPITAL, max_dd=0.0,
    )
 def advance(st: dict, dfs: dict) -> dict:
    """Processa tutte le barre 1d chiuse DOPO st['last_ts']."""
    tp = TrendPortfolio(**CANONICAL)
    # precompute per-asset: timestamps, returns, target series (causale)
    data = {}
    for a in ASSETS:
        df = dfs[a]
        c = df["close"].values.astype(float)
        data[a] = dict(
            ts=df["timestamp"].values.astype("int64"),
            dt=pd.to_datetime(df["datetime"]).values,
            r=simple_returns(c),
            tgt=tp.target_series(df),
        )
    # common new timestamps after last_ts (present in both assets)
    common = sorted(set(data["BTC"]["ts"]).intersection(data["ETH"]["ts"]))
    new_ts = [t for t in common if t > st["last_ts"]]
    if not new_ts:
        return st
    pos = dict(st["positions"])
    cap = st["capital"]
    peak = st.get("peak", cap)
    max_dd = st.get("max_dd", 0.0)
    idx = {a: {int(t): i for i, t in enumerate(data[a]["ts"])} for a in ASSETS}
    for t in new_ts:
        # 1) apply held position return over this bar, charge turnover fees vs new target
        combo = 0.0
        new_pos = {}
        for a in ASSETS:
            i = idx[a][int(t)]
            r = float(data[a]["r"][i])
            held = pos[a]
            new_t = float(data[a]["tgt"][i])
            turn = abs(new_t - held)
            net = held * r - CANONICAL["fee_side"] * turn
            combo += WEIGHT * net
            new_pos[a] = new_t
            # record a trade when the SIGN of position changes (entry/exit/flip)
            if np.sign(new_t) != np.sign(held):
                append_trade(dict(
                    ts=int(t), dt=str(pd.Timestamp(data[a]["dt"][i])),
                    asset=a, action="ENTRY" if new_t != 0 else "EXIT",
                    from_pos=round(held, 4), to_pos=round(new_t, 4),
                    capital=round(cap, 2),
                ))
        cap *= (1.0 + max(combo, -0.99))
        peak = max(peak, cap)
        max_dd = max(max_dd, (peak - cap) / peak if peak > 0 else 0.0)
        pos = new_pos
    st.update(capital=cap, last_ts=int(new_ts[-1]), positions=pos,
              n_bars=st.get("n_bars", 0) + len(new_ts), peak=peak, max_dd=max_dd)
    return st
 def print_status(st: dict, dfs: dict):
    start = pd.Timestamp(st["start_ts"], unit="ms", tz="UTC")
    last = pd.Timestamp(st["last_ts"], unit="ms", tz="UTC")
    days = (last - start).total_seconds() / 86400
    cap = st["capital"]
    ret = cap / st["initial_capital"] - 1
    daily = (cap - st["initial_capital"]) / days if days > 0 else 0.0
    print("=" * 72)
    print("  PAPER TRADER — TP01 Trend Portfolio (PORT LF1d, 50/50 BTC+ETH, 1d)")
    print("=" * 72)
    print(f"  start         {start:%Y-%m-%d %H:%M} UTC")
    print(f"  last bar      {last:%Y-%m-%d %H:%M} UTC   ({days:.1f} giorni, {st['n_bars']} barre 1d)")
    print(f"  capitale      {cap:,.2f} USDT   (start {st['initial_capital']:,.0f})")
    print(f"  ritorno       {ret*100:+.2f}%   |  €/giorno {daily:+.2f}   |  maxDD {st['max_dd']*100:.1f}%")
    print(f"  posizioni now { 'flat' if all(p==0 for p in st['positions'].values()) else '' }")
    for a in ASSETS:
        p = st["positions"][a]
        state = "FLAT" if p == 0 else ("LONG" if p > 0 else "SHORT")
        print(f"     {a}: {state:<5s} target {p:+.3f}x  (frazione di equity dello sleeve)")
    # what the strategy decides at the latest available closed bar
    print("  ── prossima decisione (ultima barra chiusa disponibile) ──")
    tp = TrendPortfolio(**CANONICAL)
    for a in ASSETS:
        w = tp.current_target(dfs[a])
        print(f"     {a}: target {w:+.3f}x")
    if TRADES_FILE.exists():
        n = sum(1 for _ in open(TRADES_FILE))
        print(f"  trade registrati: {n}  ({TRADES_FILE})")
 def main():
    argv = sys.argv[1:]
    dfs = build_bars()
    if "--reset" in argv:
        if STATE_FILE.exists():
            STATE_FILE.unlink()
        if TRADES_FILE.exists():
            TRADES_FILE.unlink()
        print("stato azzerato.")
    st = load_state()
    if st is None:
        st = init_state(dfs)
        save_state(st)
        print("paper trader inizializzato (forward-only da ora).\n")
    elif "--status" not in argv:
        st = advance(st, dfs)
        save_state(st)
    print_status(st, dfs)
 if __name__ == "__main__":
    main()
@@ -0,0 +1,320 @@
 """TRACK A — TREND / MOMENTUM research on certified BTC/ETH (Deribit mainnet).
 Honest harness only (src.backtest.harness). Rules enforced:
  * Direction & entry price decided with data <= close[i]; fill at close[i].
  * Net of fees (0.10% RT baseline) + fee sweep + leverage stress.
  * IS / OOS split (65/35). Grid robustness across params AND both assets.
 Run:  uv run python scripts/research/trackA_trend.py
 This script is deliberately skeptical: it prints full grids so the reader can see
 whether an "edge" is a single lucky cell or a robust neighborhood. The verdict at the
 end is printed from the actual numbers, not asserted.
 """
 from __future__ import annotations
 import sys
 from pathlib import Path
 import numpy as np
 import pandas as pd
 sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
 from src.backtest.harness import load, backtest_signals, oos_split
 ASSETS = ["BTC", "ETH"]
 TFS = ["1h", "15m", "5m"]
 FEE = 0.001
 # ---------------------------------------------------------------------------
 # Signal builders. Each returns a list[dict|None] of length len(df).
 # All features use ONLY data up to and including close[i]. Entry fills at close[i].
 # Position is approximated as a chained, non-overlapping hold of `hold` bars whose
 # direction is recomputed at each (free) bar -> amortizes fee over `hold` bars while
 # staying honest about responsiveness.
 # ---------------------------------------------------------------------------
 def sig_tsmom(df, lookback, hold, long_only=False):
    c = df["close"].values
    n = len(c)
    ent = [None] * n
    dirs = np.where(c[lookback:] > c[:-lookback], 1, -1)
    for k, d in enumerate(dirs):
        if long_only and d < 0:
            continue
        ent[lookback + k] = {"dir": int(d), "max_bars": hold}
    return ent
 def _ema(x, span):
    return pd.Series(x).ewm(span=span, adjust=False).mean().values
 def sig_ema_cross(df, fast, slow, hold, long_only=False):
    c = df["close"].values
    n = len(c)
    ef = _ema(c, fast)
    es = _ema(c, slow)
    ent = [None] * n
    for i in range(slow, n):
        d = 1 if ef[i] > es[i] else -1
        if long_only and d < 0:
            ent[i] = None
            continue
        ent[i] = {"dir": d, "max_bars": hold}
    return ent
 def sig_donchian(df, lookback, hold, long_only=False):
    """Breakout: close[i] strictly above prior `lookback` highs -> long; below lows -> short.
    Detection AND entry both at close[i] (honest)."""
    c = df["close"].values
    h = df["high"].values
    l = df["low"].values
    n = len(c)
    ent = [None] * n
    # prior-window high/low EXCLUDING current bar (shift by 1) -> honest
    hh = pd.Series(h).rolling(lookback).max().shift(1).values
    ll = pd.Series(l).rolling(lookback).min().shift(1).values
    for i in range(lookback, n):
        if not np.isfinite(hh[i]):
            continue
        if c[i] > hh[i]:
            d = 1
        elif c[i] < ll[i]:
            d = -1
        else:
            continue
        if long_only and d < 0:
            continue
        ent[i] = {"dir": d, "max_bars": hold}
    return ent
 def sig_vol_scaled_tsmom(df, lookback, hold, vol_win, z_gate):
    """Momentum gated by trend strength: only take a position when |past return| exceeds
    z_gate * rolling stdev of bar returns (regime gate). Honest: all <= close[i]."""
    c = df["close"].values
    n = len(c)
    logret = np.zeros(n)
    logret[1:] = np.diff(np.log(c))
    vol = pd.Series(logret).rolling(vol_win).std().values
    ent = [None] * n
    start = max(lookback, vol_win) + 1
    for i in range(start, n):
        r = np.log(c[i] / c[i - lookback])
        v = vol[i] * np.sqrt(lookback)
        if not np.isfinite(v) or v == 0:
            continue
        z = r / v
        if abs(z) < z_gate:
            continue
        d = 1 if z > 0 else -1
        ent[i] = {"dir": d, "max_bars": hold}
    return ent
 # ---------------------------------------------------------------------------
 # Evaluation helpers
 # ---------------------------------------------------------------------------
 def eval_is_oos(df, entries, asset, tf, fee=FEE, lev=1.0):
    cut = oos_split(df, 0.65)
    full = backtest_signals(df, entries, fee_rt=fee, leverage=lev, asset=asset, tf=tf)
    ent_is = [e if i < cut else None for i, e in enumerate(entries)]
    ent_oos = [e if i >= cut else None for i, e in enumerate(entries)]
    m_is = backtest_signals(df, ent_is, fee_rt=fee, leverage=lev, asset=asset, tf=tf)
    m_oos = backtest_signals(df, ent_oos, fee_rt=fee, leverage=lev, asset=asset, tf=tf)
    return full, m_is, m_oos
 def buy_hold(df, cut=None):
    c = df["close"].values
    if cut is None:
        cut = oos_split(df, 0.65)
    return c[-1] / c[0] - 1, c[-1] / c[cut] - 1  # (full, oos)
 def print_benchmarks():
    print("\n" + "=" * 110)
    print("# BUY & HOLD BENCHMARK (the bar any long/short trend edge must clear)")
    print("#   NOTE: OOS window is the LAST 35% = ~late-2023 -> 2026, a single (mostly bull) regime.")
    print("#   2018-2022 (bear+crash+bull+bear) is ENTIRELY in-sample. 'positive OOS' is weak evidence.")
    print("=" * 110)
    for tf in TFS:
        for asset in ASSETS:
            df = load(asset, tf)
            cut = oos_split(df, 0.65)
            bf, bo = buy_hold(df, cut)
            print(f"  {asset} {tf:>3s}  OOS starts {df['datetime'].iloc[cut].date()}  "
                  f"B&H full={bf*100:>+7.0f}%  B&H OOS={bo*100:>+7.0f}%")
 def line(label, m):
    print(f"    {label:<30s} tr={m.n_trades:>6d} wr={m.win_rate:>4.1f}% "
          f"ret={m.net_return*100:>+8.0f}% CAGR={m.cagr*100:>+6.1f}% "
          f"Sh={m.sharpe:>5.2f} DD={m.max_dd*100:>4.1f}% mkt={m.time_in_market*100:>3.0f}% "
          f"€/d={m.daily_profit(2000):>+6.2f}")
 # ---------------------------------------------------------------------------
 # Experiments
 # ---------------------------------------------------------------------------
 def run_grid(name, builder, param_grid, builder_kwargs_fn, tfs=TFS, assets=ASSETS):
    """Generic grid runner. Prints OOS-focused table. Returns list of result dicts."""
    print("\n" + "=" * 110)
    print(f"# {name}")
    print("=" * 110)
    results = []
    for tf in tfs:
        for asset in assets:
            df = load(asset, tf)
            print(f"\n  -- {asset} {tf}  (n={len(df)}) --")
            for params in param_grid:
                ent = builder(df, **builder_kwargs_fn(params))
                full, m_is, m_oos = eval_is_oos(df, ent, asset, tf)
                tag = ",".join(f"{k}={v}" for k, v in params.items())
                line(f"{tag} [OOS]", m_oos)
                results.append(dict(name=name, asset=asset, tf=tf, params=params,
                                    full=full, is_=m_is, oos=m_oos))
    return results
 def summarize_survivors(all_results):
    print("\n" + "#" * 110)
    print("# SURVIVOR SCREEN — positive OOS net return AND positive full-sample, Sharpe(OOS)>0")
    print("#" * 110)
    survivors = [r for r in all_results
                 if r["oos"].net_return > 0 and r["full"].net_return > 0
                 and r["oos"].sharpe > 0 and r["oos"].n_trades >= 20]
    if not survivors:
        print("  NONE. No config is net-positive OOS with positive full-sample and Sharpe>0.")
        return []
    survivors.sort(key=lambda r: r["oos"].sharpe, reverse=True)
    # precompute B&H OOS per (asset,tf)
    bh = {}
    for tf in TFS:
        for a in ASSETS:
            bh[(a, tf)] = buy_hold(load(a, tf))[1]
    print("  (BEATS B&H = OOS return exceeds buy&hold over same OOS window; otherwise it's just beta)")
    for r in survivors[:40]:
        tag = ",".join(f"{k}={v}" for k, v in r["params"].items())
        bho = bh[(r["asset"], r["tf"])]
        beat = "BEATS B&H" if r["oos"].net_return > bho else "<= B&H (beta)"
        print(f"  {r['name'][:18]:<18s} {r['asset']} {r['tf']:>3s} {tag:<28s} "
              f"OOS: ret={r['oos'].net_return*100:>+7.0f}% Sh={r['oos'].sharpe:>4.2f} "
              f"DD={r['oos'].max_dd*100:>4.0f}% €/d={r['oos'].daily_profit(2000):>+5.2f} | "
              f"B&H={bho*100:>+5.0f}% {beat}")
    return survivors
 def robustness_report(survivors):
    """For top survivors, check fee sweep + leverage stress + cross-asset consistency."""
    if not survivors:
        return
    print("\n" + "#" * 110)
    print("# ROBUSTNESS: fee sweep (0.0005/0.001/0.0015/0.002) + leverage (1x/2x/3x) on top survivors")
    print("#" * 110)
    seen = set()
    for r in survivors[:8]:
        key = (r["name"], r["asset"], r["tf"], tuple(r["params"].items()))
        if key in seen:
            continue
        seen.add(key)
        df = load(r["asset"], r["tf"])
        # rebuild entries
        builder = BUILDERS[r["name"]]
        ent = builder(df, **KW_FN[r["name"]](r["params"]))
        tag = ",".join(f"{k}={v}" for k, v in r["params"].items())
        print(f"\n  {r['name']} {r['asset']} {r['tf']} {tag}")
        print("    fee sweep (OOS net return):")
        for fee in (0.0005, 0.001, 0.0015, 0.002):
            _, _, m_oos = eval_is_oos(df, ent, r["asset"], r["tf"], fee=fee)
            flag = "" if m_oos.net_return > 0 else "  <-- DIES"
            print(f"      fee={fee:.4f}: OOS ret={m_oos.net_return*100:>+8.0f}% Sh={m_oos.sharpe:>4.2f}{flag}")
        print("    leverage stress (OOS, fee=0.001):")
        for lev in (1.0, 2.0, 3.0):
            _, _, m_oos = eval_is_oos(df, ent, r["asset"], r["tf"], lev=lev)
            print(f"      {lev:.0f}x: OOS ret={m_oos.net_return*100:>+8.0f}% "
                  f"Sh={m_oos.sharpe:>4.2f} DD={m_oos.max_dd*100:>4.0f}% €/d={m_oos.daily_profit(2000):>+5.2f}")
        # yearly OOS
        _, _, m_oos = eval_is_oos(df, ent, r["asset"], r["tf"])
        print("    OOS yearly:")
        for y in sorted(m_oos.yearly):
            print(f"      {y}: {m_oos.yearly[y]*100:>+7.1f}%")
 # registry so robustness_report can rebuild entries
 BUILDERS = {
    "TSMOM": sig_tsmom,
    "TSMOM_LONG": sig_tsmom,
    "EMA_CROSS": sig_ema_cross,
    "DONCHIAN": sig_donchian,
    "VOLSCALED_TSMOM": sig_vol_scaled_tsmom,
 }
 KW_FN = {
    "TSMOM": lambda p: dict(lookback=p["N"], hold=p["H"]),
    "TSMOM_LONG": lambda p: dict(lookback=p["N"], hold=p["H"], long_only=True),
    "EMA_CROSS": lambda p: dict(fast=p["f"], slow=p["s"], hold=p["H"]),
    "DONCHIAN": lambda p: dict(lookback=p["N"], hold=p["H"]),
    "VOLSCALED_TSMOM": lambda p: dict(lookback=p["N"], hold=p["H"], vol_win=p["vw"], z_gate=p["z"]),
 }
 def main():
    pd.set_option("display.width", 200)
    print_benchmarks()
    all_results = []
    # ---- 1. TSMOM (long/short) ----
    tsmom_grid = [dict(N=n, H=h) for n in (10, 20, 50, 100, 200) for h in (6, 12, 24, 48)]
    all_results += run_grid("TSMOM", sig_tsmom, tsmom_grid,
                            KW_FN["TSMOM"])
    # ---- 2. TSMOM long-only (crypto has strong upward drift; honest to test) ----
    all_results += run_grid("TSMOM_LONG", lambda df, **k: sig_tsmom(df, long_only=True, **k),
                            [dict(N=n, H=h) for n in (20, 50, 100, 200) for h in (12, 24, 48)],
                            KW_FN["TSMOM"])
    # ---- 3. EMA crossover ----
    ema_grid = [dict(f=f, s=s, H=h)
                for (f, s) in ((10, 30), (20, 50), (20, 100), (50, 200))
                for h in (12, 24, 48)]
    all_results += run_grid("EMA_CROSS", sig_ema_cross, ema_grid, KW_FN["EMA_CROSS"])
    # ---- 4. Donchian breakout ----
    don_grid = [dict(N=n, H=h) for n in (20, 50, 100, 200) for h in (12, 24, 48)]
    all_results += run_grid("DONCHIAN", sig_donchian, don_grid, KW_FN["DONCHIAN"])
    # ---- 5. Vol-scaled / regime-gated TSMOM ----
    vs_grid = [dict(N=n, H=h, vw=vw, z=z)
               for n in (20, 50, 100) for h in (24, 48)
               for vw in (50, 100) for z in (0.5, 1.0)]
    all_results += run_grid("VOLSCALED_TSMOM", sig_vol_scaled_tsmom, vs_grid,
                            KW_FN["VOLSCALED_TSMOM"])
    # ---- survivor screen + robustness ----
    survivors = summarize_survivors(all_results)
    robustness_report(survivors)
    # ---- cross-asset robustness note ----
    print("\n" + "#" * 110)
    print("# CROSS-ASSET / CROSS-TF CONSISTENCY of survivors (a real edge holds on BOTH BTC & ETH)")
    print("#" * 110)
    from collections import defaultdict
    by_strat = defaultdict(list)
    for r in survivors:
        by_strat[(r["name"], r["tf"], tuple(r["params"].items()))].append(r["asset"])
    both = [(k, v) for k, v in by_strat.items() if set(v) >= {"BTC", "ETH"}]
    if not both:
        print("  No single (strategy, tf, params) cell is an OOS survivor on BOTH BTC and ETH.")
        print("  => any apparent edge is asset/regime-specific, not a robust trend edge.")
    else:
        for (name, tf, params), assets in both:
            print(f"  {name} {tf} {dict(params)} survives on: {assets}")
    print("\nDONE. Read the survivor screen + robustness above for the honest verdict.")
 if __name__ == "__main__":
    main()
@@ -0,0 +1,398 @@
 """TRACK B — Machine-learning / feature-prediction on BTC & ETH (Deribit-certified).
 Honest, strict walk-forward ML research. The whole point is to NOT repeat the death of
 the old library (look-ahead). Everything here obeys:
  * Features for bar i use ONLY data <= close[i] (all rolling windows are backward).
  * Labels (sign of forward return over H bars) use close[i+H]; in walk-forward we only
    train on samples whose label is FULLY realized in the past relative to the prediction
    bar (a gap of H is enforced between train-end and the prediction block).
  * Scaler + model are fit ONLY on past data, retrained periodically, never on the future.
  * Net of fees (fee_rt sweep 0.0005 .. 0.002, baseline 0.001). Turnover reported.
  * Grid over W (lookback for training), H (horizon), threshold, asset, tf.
  * A final held-out segment (last HELD_OUT_FRAC) is NEVER used to choose configs;
    configs are selected on the DEV portion, then confirmed once on the held-out tail.
 Run:  uv run python scripts/research/trackB_ml.py
      uv run python scripts/research/trackB_ml.py --quick      (smaller grid, faster)
      uv run python scripts/research/trackB_ml.py --gbm        (also try GradientBoosting)
 Entry convention (harness): for a signalled bar i we open at close[i] in the predicted
 direction and hold up to H bars (max_bars=H, no TP/SL) — a pure test of directional sign.
 No-overlap is enforced by the harness, so trades are naturally spaced >= H bars.
 """
 from __future__ import annotations
 import argparse
 import sys
 import time
 import warnings
 from pathlib import Path
 sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
 import numpy as np
 import pandas as pd
 from sklearn.linear_model import LogisticRegression
 from sklearn.preprocessing import StandardScaler
 from src.backtest.harness import backtest_signals, load
 warnings.filterwarnings("ignore")
 HELD_OUT_FRAC = 0.25      # final tail reserved for confirmation only
 RETRAIN_K = 250          # retrain every K bars (block prediction)
 MIN_TRAIN = 400          # minimum usable training samples
 # ---------------------------------------------------------------------------
 # Feature engineering — ALL backward-looking (safe at close[i])
 # ---------------------------------------------------------------------------
 def _rsi(close: pd.Series, n: int = 14) -> pd.Series:
    d = close.diff()
    up = d.clip(lower=0).ewm(alpha=1 / n, adjust=False).mean()
    dn = (-d.clip(upper=0)).ewm(alpha=1 / n, adjust=False).mean()
    rs = up / dn.replace(0, np.nan)
    return (100 - 100 / (1 + rs)).fillna(50.0)
 def _atr(df: pd.DataFrame, n: int = 14) -> pd.Series:
    h, l, c = df["high"], df["low"], df["close"]
    pc = c.shift(1)
    tr = pd.concat([(h - l), (h - pc).abs(), (l - pc).abs()], axis=1).max(axis=1)
    return tr.ewm(alpha=1 / n, adjust=False).mean()
 def build_features(df: pd.DataFrame) -> tuple[np.ndarray, list[str], np.ndarray]:
    """Return (X, names, warmup_valid_mask). Every column known at close[i]."""
    c = df["close"].astype(float)
    h = df["high"].astype(float)
    l = df["low"].astype(float)
    o = df["open"].astype(float)
    v = df["volume"].astype(float)
    logc = np.log(c)
    feats: dict[str, pd.Series] = {}
    # multi-lag simple returns (ret[i] uses close[i],close[i-k] -> known at i)
    for k in (1, 2, 3, 6, 12, 24):
        feats[f"ret{k}"] = c.pct_change(k)
    # candle geometry (current bar fully known at its close)
    rng = (h - l).replace(0, np.nan)
    feats["body"] = (c - o) / rng
    feats["upsh"] = (h - np.maximum(c, o)) / rng
    feats["dnsh"] = (np.minimum(c, o) - l) / rng
    feats["range_n"] = (h - l) / c
    # one-lag candle geometry
    feats["body1"] = ((c - o) / rng).shift(1)
    # momentum/acceleration
    feats["mom48"] = c.pct_change(48)
    feats["accel"] = c.pct_change(6) - c.pct_change(12)
    # RSI
    feats["rsi14"] = _rsi(c, 14) / 100.0
    # ATR-normalized extension from a trend baseline
    ema = c.ewm(span=24, adjust=False).mean()
    atr = _atr(df, 14)
    feats["ext_atr"] = (c - ema) / atr.replace(0, np.nan)
    # realized vol (std of 1-bar returns)
    r1 = c.pct_change()
    feats["rvol24"] = r1.rolling(24).std()
    feats["rvol72"] = r1.rolling(72).std()
    feats["vol_ratio"] = feats["rvol24"] / feats["rvol72"].replace(0, np.nan)
    # position of close within recent window (0=low,1=high)
    for w in (24, 72):
        lo = l.rolling(w).min()
        hi = h.rolling(w).max()
        feats[f"pos{w}"] = (c - lo) / (hi - lo).replace(0, np.nan)
    # volume z-score
    vlog = np.log1p(v)
    feats["volz"] = (vlog - vlog.rolling(72).mean()) / vlog.rolling(72).std().replace(0, np.nan)
    names = list(feats.keys())
    X = np.column_stack([feats[k].to_numpy(dtype=float) for k in names])
    valid = np.isfinite(X).all(axis=1)
    return X, names, valid
 def forward_labels(df: pd.DataFrame, H: int):
    """label[i] = 1 if close[i+H] > close[i] else 0 ; fwd[i] = forward return."""
    c = df["close"].to_numpy(float)
    n = len(c)
    fwd = np.full(n, np.nan)
    fwd[: n - H] = c[H:] / c[: n - H] - 1.0
    y = (fwd > 0).astype(float)
    lab_valid = np.isfinite(fwd)
    return y, fwd, lab_valid
 # ---------------------------------------------------------------------------
 # Strict walk-forward probability
 # ---------------------------------------------------------------------------
 def walk_forward_proba(X, y, feat_valid, lab_valid, warmup, W, H, K, model_factory):
    """Return proba_up[i] for all i (NaN where not predicted). No leakage:
    when predicting block starting at b, training labels must be realized: i + H <= b-1,
    i.e. train indices < b - H. Training window is the last W such indices."""
    n = len(y)
    proba = np.full(n, np.nan)
    start = warmup + W + H
    b = start
    while b < n:
        end_block = min(b + K, n)
        train_hi = b - H                      # exclusive; ensures label realized by b-1
        train_lo = max(warmup, train_hi - W)
        idx = np.arange(train_lo, train_hi)
        idx = idx[feat_valid[idx] & lab_valid[idx]]
        if len(idx) >= MIN_TRAIN:
            ytr = y[idx]
            if np.unique(ytr).size == 2:
                Xtr = X[idx]
                sc = StandardScaler().fit(Xtr)
                model = model_factory()
                model.fit(sc.transform(Xtr), ytr)
                # predict the block (features known at each bar's own close)
                blk = np.arange(b, end_block)
                fv = feat_valid[blk]
                if fv.any():
                    pb = model.predict_proba(sc.transform(X[blk[fv]]))[:, 1]
                    proba[blk[fv]] = pb
        b = end_block
    return proba
 def proba_to_entries(proba, threshold, H, n):
    """Long if proba>0.5+thr, short if proba<0.5-thr, else flat. Hold H bars."""
    entries = [None] * n
    hi = 0.5 + threshold
    lo = 0.5 - threshold
    for i in range(n):
        p = proba[i]
        if not np.isfinite(p):
            continue
        if p > hi:
            entries[i] = {"dir": 1, "tp": None, "sl": None, "max_bars": H}
        elif p < lo:
            entries[i] = {"dir": -1, "tp": None, "sl": None, "max_bars": H}
    return entries
 def mask_entries(entries, lo, hi):
    """Keep only entries with index in [lo, hi); others -> None (for IS/OOS split)."""
    out = [None] * len(entries)
    for i in range(lo, min(hi, len(entries))):
        out[i] = entries[i]
    return out
 def trade_stats(df, entries, H):
    """Replicate harness no-overlap to get per-trade gross returns -> avg win/loss + long frac."""
    c = df["close"].to_numpy(float)
    n = len(c)
    grosses = []
    dirs = []
    busy = -1
    for i in range(n):
        e = entries[i]
        if e is None or i <= busy:
            continue
        j = min(i + H, n - 1)
        g = (c[j] - c[i]) / c[i] * e["dir"]
        grosses.append(g)
        dirs.append(e["dir"])
        busy = j
    g = np.array(grosses)
    if len(g) == 0:
        return 0, 0.0, 0.0, 0.0, 0.0
    wins = g[g > 0]
    losses = g[g <= 0]
    avg_w = wins.mean() if len(wins) else 0.0
    avg_l = losses.mean() if len(losses) else 0.0
    long_frac = float(np.mean(np.array(dirs) > 0))
    return len(g), avg_w, avg_l, g.mean(), long_frac
 def buy_hold(df, lo, hi):
    """Buy & hold net return over [lo,hi) bars (beta benchmark)."""
    c = df["close"].to_numpy(float)
    hi = min(hi, len(c))
    if hi - lo < 2:
        return 0.0
    return c[hi - 1] / c[lo] - 1.0
 # ---------------------------------------------------------------------------
 # Driver
 # ---------------------------------------------------------------------------
 def run():
    ap = argparse.ArgumentParser()
    ap.add_argument("--quick", action="store_true", help="smaller grid (faster)")
    ap.add_argument("--gbm", action="store_true", help="also try GradientBoosting on best LR cells")
    ap.add_argument("--tf", default="1h")
    args = ap.parse_args()
    assets = ["BTC", "ETH"]
    tf = args.tf
    if args.quick:
        Ws = [8000]
        Hs = [12, 24]
        thresholds = [0.0, 0.05, 0.10]
    else:
        Ws = [4000, 8000, 16000]
        Hs = [6, 12, 24, 48]
        thresholds = [0.0, 0.03, 0.06, 0.10]
    def lr_factory():
        return LogisticRegression(C=1.0, max_iter=300, class_weight="balanced")
    print("=" * 100)
    print(f"TRACK B — walk-forward ML  tf={tf}  retrain_K={RETRAIN_K}  held_out_tail={HELD_OUT_FRAC:.0%}")
    print(f"  Ws={Ws}  Hs={Hs}  thresholds={thresholds}  model=LogisticRegression(balanced)")
    print("=" * 100)
    # cache features per asset
    cache = {}
    for a in assets:
        df = load(a, tf)
        X, names, fvalid = build_features(df)
        warmup = int(np.argmax(fvalid)) if fvalid.any() else 0
        cache[a] = (df, X, names, fvalid, warmup)
    print(f"features ({len(names)}): {names}\n")
    # ---- DEV grid search (configs chosen ONLY on dev portion) ----------------
    results = []  # dict rows
    t0 = time.time()
    for a in assets:
        df, X, names, fvalid, warmup = cache[a]
        n = len(df)
        dev_hi = int(n * (1 - HELD_OUT_FRAC))   # dev = [0, dev_hi), held = [dev_hi, n)
        for W in Ws:
            for H in Hs:
                y, _fwd, lvalid = forward_labels(df, H)
                proba = walk_forward_proba(X, y, fvalid, lvalid, warmup, W, H,
                                           RETRAIN_K, lr_factory)
                for thr in thresholds:
                    ent_full = proba_to_entries(proba, thr, H, n)
                    ent_dev = mask_entries(ent_full, warmup, dev_hi)
                    m = backtest_signals(df, ent_dev, fee_rt=0.001, asset=a, tf=tf)
                    nt, aw, al, gmean, lf = trade_stats(df, ent_dev, H)
                    results.append(dict(asset=a, W=W, H=H, thr=thr, seg="DEV",
                                        m=m, nt=nt, aw=aw, al=al, gmean=gmean,
                                        proba=proba))
        print(f"  [{a}] dev grid done ({time.time()-t0:.0f}s)")
    # print dev table
    print("\n--- DEV walk-forward (config selection set) ---")
    hdr = f"{'asset':5} {'W':>6} {'H':>3} {'thr':>5} {'trd':>5} {'wr%':>5} {'net%':>8} {'CAGR%':>7} {'Shrp':>6} {'DD%':>5} {'mkt%':>5} {'avgW%':>6} {'avgL%':>6} {'€/d':>6}"
    print(hdr)
    for r in sorted(results, key=lambda r: -r["m"].sharpe):
        m = r["m"]
        print(f"{r['asset']:5} {r['W']:>6} {r['H']:>3} {r['thr']:>5.2f} {m.n_trades:>5} "
              f"{m.win_rate:>5.1f} {m.net_return*100:>+8.1f} {m.cagr*100:>+7.1f} {m.sharpe:>6.2f} "
              f"{m.max_dd*100:>5.1f} {m.time_in_market*100:>5.0f} {r['aw']*100:>+6.2f} {r['al']*100:>+6.2f} "
              f"{m.daily_profit(2000):>+6.2f}")
    # ---- selection: positive net AND sharpe>0 on dev, then robustness ----------
    pos = [r for r in results if r["m"].net_return > 0 and r["m"].sharpe > 0 and r["m"].n_trades >= 30]
    pos.sort(key=lambda r: -r["m"].sharpe)
    print(f"\n{len(pos)}/{len(results)} dev cells net-positive with Sharpe>0 & >=30 trades.")
    # robustness: a config family (asset,W,H) is robust if positive across thresholds
    fam = {}
    for r in results:
        fam.setdefault((r["asset"], r["W"], r["H"]), []).append(r)
    robust_fams = []
    for key, rs in fam.items():
        npos = sum(1 for r in rs if r["m"].net_return > 0 and r["m"].sharpe > 0)
        if npos >= max(2, int(0.6 * len(rs))):
            robust_fams.append((key, npos, len(rs)))
    robust_fams.sort(key=lambda x: -x[1])
    print("\nThreshold-robust (asset,W,H) families [>=60% thresholds net+ & Sharpe>0]:")
    if not robust_fams:
        print("  NONE.")
    for key, npos, tot in robust_fams:
        print(f"  {key}: {npos}/{tot} thresholds positive")
    # ---- HELD-OUT confirmation on best robust cells ---------------------------
    print("\n" + "=" * 100)
    print("HELD-OUT TAIL CONFIRMATION (never used for selection)")
    print("=" * 100)
    # choose up to 6 best dev cells that belong to a robust family
    robust_keys = {k for k, _, _ in robust_fams}
    cand = [r for r in pos if (r["asset"], r["W"], r["H"]) in robust_keys][:6]
    if not cand:
        cand = pos[:6]
    if not cand:
        print("No positive dev cells to confirm. ML did not beat fees on dev.")
    print(hdr)
    held_rows = []
    for r in cand:
        a, W, H, thr = r["asset"], r["W"], r["H"], r["thr"]
        df = cache[a][0]
        n = len(df)
        dev_hi = int(n * (1 - HELD_OUT_FRAC))
        ent_full = proba_to_entries(r["proba"], thr, H, n)
        ent_held = mask_entries(ent_full, dev_hi, n)
        m = backtest_signals(df, ent_held, fee_rt=0.001, asset=a, tf=tf)
        nt, aw, al, gmean, lf = trade_stats(df, ent_held, H)
        bh = buy_hold(df, dev_hi, n)
        held_rows.append((r, m, aw, al, lf, bh))
        print(f"{a:5} {W:>6} {H:>3} {thr:>5.2f} {m.n_trades:>5} {m.win_rate:>5.1f} "
              f"{m.net_return*100:>+8.1f} {m.cagr*100:>+7.1f} {m.sharpe:>6.2f} {m.max_dd*100:>5.1f} "
              f"{m.time_in_market*100:>5.0f} {aw*100:>+6.2f} {al*100:>+6.2f} {m.daily_profit(2000):>+6.2f}  "
              f"long={lf*100:>3.0f}% B&H={bh*100:>+7.1f}%")
    # ---- FEE SWEEP on the held-out winners ------------------------------------
    print("\n--- FEE SWEEP (held-out tail) on confirmed cells ---")
    fees = [0.0005, 0.001, 0.0015, 0.002]
    print("  (B&H = buy&hold over held-out tail; if net% << B&H the 'edge' is just beta)")
    for r, _, _, _, _, _ in held_rows[:4]:
        a, W, H, thr = r["asset"], r["W"], r["H"], r["thr"]
        df = cache[a][0]
        n = len(df)
        dev_hi = int(n * (1 - HELD_OUT_FRAC))
        ent_held = mask_entries(proba_to_entries(r["proba"], thr, H, n), dev_hi, n)
        line = f"  {a} W{W} H{H} thr{thr:.2f}: "
        for f in fees:
            m = backtest_signals(df, ent_held, fee_rt=f, asset=a, tf=tf)
            line += f"[{f*100:.2f}%]net={m.net_return*100:>+6.1f}% Shrp={m.sharpe:>+4.2f}  "
        print(line)
    # ---- per-year on the single best held-out cell ----------------------------
    if held_rows:
        held_rows.sort(key=lambda x: -x[1].sharpe)
        r, m, aw, al, lf, bh = held_rows[0]
        a, W, H, thr = r["asset"], r["W"], r["H"], r["thr"]
        print(f"\n--- Per-year (best held-out): {a} W{W} H{H} thr{thr:.2f} ---")
        df = cache[a][0]
        n = len(df)
        dev_hi = int(n * (1 - HELD_OUT_FRAC))
        # full walk-forward per-year (dev+held) to see regime stability
        mfull = backtest_signals(df, mask_entries(proba_to_entries(r["proba"], thr, H, n),
                                                   cache[a][4], n), fee_rt=0.001, asset=a, tf=tf)
        mfull.print_summary(f"{a} W{W}H{H}thr{thr:.2f} FULL-WF")
        mfull.print_yearly()
    print(f"\nTotal runtime {time.time()-t0:.0f}s")
    print("\n" + "=" * 100)
    print("VERDICT (see docs/diary/2026-06-19-trackB-ml.md for the full write-up)")
    print("=" * 100)
    print(
        "  * A weak but REAL low-turnover directional signal exists on BTC (thinner on ETH):\n"
        "    large train window (W~16000) + long horizon (H~24) + high prob threshold (~0.10).\n"
        "  * It beats fees at 0.10% RT AND beats buy&hold on the held-out tail with a balanced\n"
        "    long/short mix (so it is NOT just bull-market beta). Payoff: ~53% WR, avgWin>avgLoss.\n"
        "  * BUT: high-turnover cells (low thr / short H / 15m) ALL die on fees -> the edge is small.\n"
        "    Returns concentrate in a few years (2021,2025) with a -38% year (2023); DD 23-56%.\n"
        "  * EUR/day on 2000 ~= +0.3..+0.6 baseline. Target is 50/day -> ~100x short. NOT deployable\n"
        "    standalone; at best a small component, and only the lowest-turnover configs are honest."
    )
 if __name__ == "__main__":
    run()
@@ -0,0 +1,380 @@
 """TRACK C — Mean-reversion / range re-examination on CLEAN BTC/ETH (Deribit mainnet).
 HONEST harness only. The OLD 'fade' library (Bollinger fade, Donchian fade, return
 reversal) was an ARTIFACT of look-ahead + ghost wicks on a contaminated feed; on the
 rebuilt+certified data those are negative every year. This script asks, skeptically:
    Does ANY short-horizon mean-reversion / range edge survive on clean BTC/ETH with a
    genuinely EXECUTABLE entry (direction + price decided with data <= close[i],
    fill at close[i]), net of realistic Deribit fees, out-of-sample and grid-robust?
 Methodology enforced here:
  * Entry decided with data through close[i]; fill at close[i] (harness guarantees it).
    No entering "at the band edge" / candle extreme only known intrabar.
  * NET fees fee_rt=0.001 baseline + sweep {0.0005, 0.0015, 0.002}.
  * OOS 65/35 split + parameter grid across BOTH BTC & ETH.
  * Liquidity/plausibility cross-check: time-in-market, avg bars, and whether the edge
    concentrates in flat (O=H=L=C heavy) periods.
 Run:
    uv run python scripts/research/trackC_meanrev.py            # full (slow, all TFs)
    uv run python scripts/research/trackC_meanrev.py --quick    # 1h + 15m only
 """
 from __future__ import annotations
 import argparse
 import sys
 import time
 from pathlib import Path
 import numpy as np
 import pandas as pd
 sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
 from src.backtest.harness import load, backtest_signals, oos_split, Metrics
 # ===========================================================================
 # Indicator helpers — ALL causal: value at index i uses ONLY data through i.
 # ===========================================================================
 def zscore(close: np.ndarray, lookback: int) -> np.ndarray:
    s = pd.Series(close)
    ma = s.rolling(lookback).mean()
    sd = s.rolling(lookback).std(ddof=0)
    z = (s - ma) / sd
    return z.values, ma.values, sd.values
 def rsi(close: np.ndarray, period: int) -> np.ndarray:
    s = pd.Series(close)
    d = s.diff()
    up = d.clip(lower=0.0)
    dn = (-d).clip(lower=0.0)
    # Wilder smoothing via ewm alpha=1/period (causal)
    ru = up.ewm(alpha=1.0 / period, adjust=False).mean()
    rd = dn.ewm(alpha=1.0 / period, adjust=False).mean()
    rs = ru / rd.replace(0, np.nan)
    out = 100 - 100 / (1 + rs)
    return out.values
 def atr(df: pd.DataFrame, period: int) -> 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).ewm(alpha=1.0 / period, adjust=False).mean().values
 # ===========================================================================
 # Signal generators — each returns a list[dict|None] length len(df).
 # Direction/levels decided strictly with data through close[i].
 # ===========================================================================
 def sig_zfade(df, lookback=20, z=2.0, tp_mode="mean", tp_atr=1.0, sl_atr=2.0,
              max_bars=24, atr_p=14):
    """Bollinger / z-score fade. z<-thr -> long (reversion up); z>+thr -> short.
    TP at the moving mean (tp_mode='mean') or at tp_atr*ATR toward the mean.
    SL at sl_atr*ATR beyond entry. Entry at close[i]."""
    c = df["close"].values
    z_arr, ma, _ = zscore(c, lookback)
    a = atr(df, atr_p)
    n = len(c)
    out = [None] * n
    for i in range(lookback, n):
        zi = z_arr[i]
        if not np.isfinite(zi) or not np.isfinite(a[i]):
            continue
        px = c[i]
        if zi <= -z:
            direction = 1
            tp = ma[i] if tp_mode == "mean" else px + tp_atr * a[i]
            sl = px - sl_atr * a[i] if sl_atr else None
        elif zi >= z:
            direction = -1
            tp = ma[i] if tp_mode == "mean" else px - tp_atr * a[i]
            sl = px + sl_atr * a[i] if sl_atr else None
        else:
            continue
        # guardrail: never set TP on wrong side of entry
        if direction == 1 and tp <= px:
            tp = px + tp_atr * a[i]
        if direction == -1 and tp >= px:
            tp = px - tp_atr * a[i]
        out[i] = {"dir": direction, "tp": tp, "sl": sl, "max_bars": max_bars}
    return out
 def sig_rsi2(df, period=2, lo=10, hi=90, tp_atr=1.0, sl_atr=2.0, max_bars=12,
             atr_p=14, sma_filter=0):
    """RSI(2)-style oversold/overbought reversion. RSI<lo -> long, RSI>hi -> short.
    Optional trend filter: only long above SMA(sma_filter), only short below."""
    c = df["close"].values
    r = rsi(c, period)
    a = atr(df, atr_p)
    sma = pd.Series(c).rolling(sma_filter).mean().values if sma_filter else None
    n = len(c)
    out = [None] * n
    for i in range(max(period, atr_p, sma_filter), n):
        ri = r[i]
        if not np.isfinite(ri) or not np.isfinite(a[i]):
            continue
        px = c[i]
        if ri <= lo:
            if sma is not None and not (px > sma[i]):
                continue
            out[i] = {"dir": 1, "tp": px + tp_atr * a[i],
                      "sl": px - sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
        elif ri >= hi:
            if sma is not None and not (px < sma[i]):
                continue
            out[i] = {"dir": -1, "tp": px - tp_atr * a[i],
                      "sl": px + sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
    return out
 def sig_retrev(df, ret_lb=1, thr_sigma=2.0, vol_lb=50, tp_atr=1.0, sl_atr=2.0,
               max_bars=6, atr_p=14):
    """Return reversal: fade an extreme cumulative return over the last ret_lb bars.
    Extreme = |ret| > thr_sigma * rolling std of that return. Entry at close[i]."""
    c = df["close"].values
    s = pd.Series(c)
    ret = np.log(s / s.shift(ret_lb))
    sd = ret.rolling(vol_lb).std(ddof=0)
    a = atr(df, atr_p)
    n = len(c)
    out = [None] * n
    rv = ret.values
    sv = sd.values
    for i in range(vol_lb + ret_lb, n):
        if not np.isfinite(rv[i]) or not np.isfinite(sv[i]) or sv[i] == 0 or not np.isfinite(a[i]):
            continue
        z = rv[i] / sv[i]
        px = c[i]
        if z <= -thr_sigma:
            out[i] = {"dir": 1, "tp": px + tp_atr * a[i],
                      "sl": px - sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
        elif z >= thr_sigma:
            out[i] = {"dir": -1, "tp": px - tp_atr * a[i],
                      "sl": px + sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
    return out
 def sig_vwap(df, sess_bars=24, thr=2.0, tp_atr=1.0, sl_atr=2.0, max_bars=12, atr_p=14):
    """Rolling-VWAP distance reversion. Distance in std-of-distance units over a
    rolling session window. Far above VWAP -> short, far below -> long. Entry close[i]."""
    c = df["close"].values
    v = df["volume"].values.astype(float)
    tp = (df["high"].values + df["low"].values + c) / 3.0
    pv = pd.Series(tp * v)
    vol = pd.Series(v)
    vwap = (pv.rolling(sess_bars).sum() / vol.rolling(sess_bars).sum()).values
    dist = pd.Series(c - vwap)
    dsd = dist.rolling(sess_bars).std(ddof=0).values
    a = atr(df, atr_p)
    n = len(c)
    out = [None] * n
    for i in range(sess_bars * 2, n):
        if not np.isfinite(vwap[i]) or not np.isfinite(dsd[i]) or dsd[i] == 0 or not np.isfinite(a[i]):
            continue
        z = (c[i] - vwap[i]) / dsd[i]
        px = c[i]
        if z <= -thr:
            out[i] = {"dir": 1, "tp": px + tp_atr * a[i],
                      "sl": px - sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
        elif z >= thr:
            out[i] = {"dir": -1, "tp": px - tp_atr * a[i],
                      "sl": px + sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
    return out
 # ===========================================================================
 # Evaluation utilities
 # ===========================================================================
 def flat_fraction(df: pd.DataFrame) -> float:
    o, h, l, c = df["open"], df["high"], df["low"], df["close"]
    return float(((h == l) & (o == c)).mean())
 def run_split(df, sigfn, params, fee_rt=0.001, leverage=1.0):
    """Run full / IS / OOS for a single config. Returns (full, is_, oos)."""
    entries = sigfn(df, **params)
    cut = oos_split(df, 0.65)
    full = backtest_signals(df, entries, fee_rt=fee_rt, leverage=leverage)
    df_is = df.iloc[:cut].reset_index(drop=True)
    df_oos = df.iloc[cut:].reset_index(drop=True)
    is_ = backtest_signals(df_is, sigfn(df_is, **params), fee_rt=fee_rt, leverage=leverage)
    oos = backtest_signals(df_oos, sigfn(df_oos, **params), fee_rt=fee_rt, leverage=leverage)
    return full, is_, oos
 def hdr(title):
    print("\n" + "=" * 92)
    print(title)
    print("=" * 92)
 # ===========================================================================
 # Main
 # ===========================================================================
 def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--quick", action="store_true", help="1h+15m only (skip slow 5m)")
    args = ap.parse_args()
    t0 = time.time()
    tfs = ["1h", "15m"] if args.quick else ["1h", "15m", "5m"]
    assets = ["BTC", "ETH"]
    # preload + liquidity sanity
    data = {}
    hdr("DATA / LIQUIDITY SANITY (flat-bar fraction O=H=L=C; should be ~0 on clean BTC/ETH)")
    for a in assets:
        for tf in tfs:
            df = load(a, tf)
            data[(a, tf)] = df
            print(f"  {a} {tf:>3s}: {len(df):>7d} bars  {df['datetime'].iloc[0].date()}→"
                  f"{df['datetime'].iloc[-1].date()}  flat={flat_fraction(df)*100:5.2f}%")
    # -------------------------------------------------------------------
    # PASS 1 — broad screen per family on 1h, both assets (IS/OOS).
    # -------------------------------------------------------------------
    hdr("PASS 1 — FAMILY SCREEN on 1h (honest entry, fee_rt=0.001, lev=1). "
        "Look for OOS>0 on BOTH assets.")
    families = {
        "ZFADE   z2/mean ": (sig_zfade, dict(lookback=20, z=2.0, tp_mode="mean", sl_atr=2.0, max_bars=24)),
        "ZFADE   z2.5/atr": (sig_zfade, dict(lookback=20, z=2.5, tp_mode="atr", tp_atr=1.5, sl_atr=2.0, max_bars=24)),
        "ZFADE   z3/mean ": (sig_zfade, dict(lookback=40, z=3.0, tp_mode="mean", sl_atr=3.0, max_bars=48)),
        "RSI2    10/90   ": (sig_rsi2, dict(period=2, lo=10, hi=90, tp_atr=1.0, sl_atr=2.0, max_bars=12)),
        "RSI2    5/95     ": (sig_rsi2, dict(period=2, lo=5, hi=95, tp_atr=1.5, sl_atr=2.5, max_bars=12)),
        "RSI2    +trend   ": (sig_rsi2, dict(period=2, lo=10, hi=90, tp_atr=1.0, sl_atr=2.0, max_bars=12, sma_filter=200)),
        "RETREV  2sig/6b ": (sig_retrev, dict(ret_lb=1, thr_sigma=2.0, tp_atr=1.0, sl_atr=2.0, max_bars=6)),
        "RETREV  3sig/12b": (sig_retrev, dict(ret_lb=3, thr_sigma=3.0, tp_atr=1.5, sl_atr=2.5, max_bars=12)),
        "VWAP    2/sess24": (sig_vwap, dict(sess_bars=24, thr=2.0, tp_atr=1.0, sl_atr=2.0, max_bars=12)),
    }
    for name, (fn, params) in families.items():
        line = f"  {name} | "
        for a in assets:
            df = data[(a, "1h")]
            full, is_, oos = run_split(df, fn, params)
            line += (f"{a}: IS={is_.net_return*100:>+6.0f}% OOS={oos.net_return*100:>+6.0f}% "
                     f"(tr={oos.n_trades:>4d} wr={oos.win_rate:>4.1f} shrp={oos.sharpe:>+4.1f} "
                     f"mkt={oos.time_in_market*100:>3.0f}% ab={oos.avg_bars:>4.1f})  ")
        print(line)
    # -------------------------------------------------------------------
    # PASS 2 — parameter GRID on the two most-promising families (z-fade, rsi2),
    # require OOS>0 on BOTH assets to count a cell as "surviving".
    # -------------------------------------------------------------------
    hdr("PASS 2 — GRID ROBUSTNESS (1h). A cell 'survives' only if OOS net>0 on BOTH BTC AND ETH.")
    def grid(fn, base, sweep, tf="1h"):
        keys = list(sweep.keys())
        survivors = []
        total = 0
        rows = []
        from itertools import product
        for combo in product(*[sweep[k] for k in keys]):
            params = dict(base)
            params.update(dict(zip(keys, combo)))
            total += 1
            res = {}
            for a in assets:
                _, is_, oos = run_split(data[(a, tf)], fn, params)
                res[a] = (is_, oos)
            ok = all(res[a][1].net_return > 0 for a in assets)
            both_oos = np.mean([res[a][1].net_return for a in assets]) * 100
            rows.append((params, res, ok))
            if ok:
                survivors.append((params, res))
        print(f"  {fn.__name__}: {len(survivors)}/{total} cells with OOS>0 on BOTH assets")
        # show best few by mean OOS
        rows.sort(key=lambda r: np.mean([r[1][a][1].net_return for a in assets]), reverse=True)
        for params, res, ok in rows[:6]:
            tag = "OK " if ok else "  -"
            pp = {k: params[k] for k in sweep}
            s = f"   {tag} {pp} | "
            for a in assets:
                oos = res[a][1]
                s += f"{a} OOS={oos.net_return*100:>+6.0f}% (wr={oos.win_rate:>4.1f} shrp={oos.sharpe:>+4.1f}) "
            print(s)
        return survivors
    zsurv = grid(sig_zfade,
                 dict(tp_mode="mean", max_bars=24),
                 dict(lookback=[20, 40, 60], z=[2.0, 2.5, 3.0], sl_atr=[2.0, 3.0]))
    rsurv = grid(sig_rsi2,
                 dict(period=2, tp_atr=1.0),
                 dict(lo=[5, 10, 15], hi=[85, 90, 95], sl_atr=[2.0, 3.0], max_bars=[6, 12]))
    # -------------------------------------------------------------------
    # PASS 3 — FEE SWEEP on whatever looks least-bad (z-fade z2/mean) to show fee
    # sensitivity (MR is high-frequency: fees are first-order).
    # -------------------------------------------------------------------
    hdr("PASS 3 — FEE SWEEP (z-fade lookback=20 z=2 mean, 1h). fee=0 is GROSS: is there\n"
        "          ANY edge before fees, or is the fade direction itself wrong on clean data?")
    fees = [0.0, 0.0005, 0.001, 0.0015, 0.002]
    base = dict(lookback=20, z=2.0, tp_mode="mean", sl_atr=2.0, max_bars=24)
    for a in assets:
        df = data[(a, "1h")]
        line = f"  {a}: "
        for f in fees:
            full, is_, oos = run_split(df, sig_zfade, base, fee_rt=f)
            line += f"fee={f*1000:.1f}bp→ full={full.net_return*100:>+6.0f}% OOS={oos.net_return*100:>+6.0f}%  "
        print(line)
    # -------------------------------------------------------------------
    # PASS 4 — faster TFs (15m, 5m) on the canonical z-fade, to test the "more MR
    # opportunities" hypothesis vs the "fee death" reality.
    # -------------------------------------------------------------------
    hdr("PASS 4 — z-fade across timeframes (lookback=20 z=2 mean). Faster TF = more fees.")
    for tf in tfs:
        for a in assets:
            df = data[(a, tf)]
            full, is_, oos = run_split(df, sig_zfade, base)
            print(f"  {a} {tf:>3s}: full={full.net_return*100:>+7.0f}% IS={is_.net_return*100:>+7.0f}% "
                  f"OOS={oos.net_return*100:>+7.0f}% tr={full.n_trades:>5d} wr={full.win_rate:>4.1f}% "
                  f"shrp={full.sharpe:>+4.1f} mkt={full.time_in_market*100:>3.0f}% €/d={full.daily_profit(2000):>+5.2f}")
    # -------------------------------------------------------------------
    # PASS 5 — SESSION / overnight effect (UTC hour-of-day) on 1h returns.
    # Pure descriptive: is there a systematically mean-reverting hour bucket?
    # -------------------------------------------------------------------
    hdr("PASS 5 — UTC hour-of-day next-bar return autocorrelation (descriptive, no trade).")
    for a in assets:
        df = data[(a, "1h")]
        c = df["close"].values
        ret = pd.Series(np.log(c[1:] / c[:-1]))   # ret[k] = log(c[k+1]/c[k])
        prev = ret.shift(1)
        hours = df["datetime"].dt.hour.values[1:1 + len(ret)]
        tmp = pd.DataFrame({"h": hours[:len(ret)], "r": ret.values, "p": prev.values}).dropna()
        # autocorr of consecutive bar returns per hour bucket (negative = mean-reverting)
        ac = tmp.groupby("h").apply(lambda g: g["r"].corr(g["p"]) if len(g) > 30 else np.nan)
        worst = ac.nsmallest(3)
        best = ac.nlargest(3)
        print(f"  {a}: most mean-reverting UTC hours (neg autocorr): "
              + ", ".join(f"{int(h)}h={v:+.3f}" for h, v in worst.items())
              + " | most trending: "
              + ", ".join(f"{int(h)}h={v:+.3f}" for h, v in best.items()))
    # -------------------------------------------------------------------
    # VERDICT
    # -------------------------------------------------------------------
    hdr("VERDICT")
    n_surv = len(zsurv) + len(rsurv)
    if n_surv == 0:
        print("  No grid cell produced OOS net>0 on BOTH BTC and ETH at baseline fees.")
        print("  => Consistent with the reset thesis: the old MR 'edge' was a feed artifact.")
        print("     On clean Deribit data with honest executable entry, short-horizon MR is NOT")
        print("     a robust net-positive edge. (See per-pass tables above for the evidence.)")
    else:
        print(f"  {n_surv} grid cell(s) survived OOS>0 on both assets. Inspect above; then stress")
        print("  with fee sweep / faster TFs before believing. Surviving configs:")
        for params, res in (zsurv + rsurv):
            ms = np.mean([res[a][1].net_return for a in assets]) * 100
            print(f"    {params}  meanOOS={ms:+.0f}%")
    print(f"\n  (elapsed {time.time()-t0:.0f}s)")
 if __name__ == "__main__":
    main()
@@ -0,0 +1,176 @@
 """TRACK D on DIFFERENT TIMEFRAMES — per-year PnL and per-year max drawdown.
 Takes the winning config (TSMOM 1-3-6 month blend, vol-target 20%, leverage cap 2x,
 50/50 BTC+ETH portfolio) and runs it across timeframes 15m / 1h / 4h / 1d.
 Honesty preserved: same building blocks as trackD_trendport.py (positions shifted +1 bar,
 fee 0.10% RT on turnover, vol-targeting on past-only realized vol). Horizons are kept
 CALENDAR-consistent across TFs (1/3/6 months -> bars = months*30*bars_per_day), so we test
 the SAME economic strategy sampled at different frequencies, not different strategies.
 4h/1d are RESAMPLED from the certified 1h feed (00:00 UTC boundaries).
 Run:  uv run python scripts/research/trackD_timing.py
 """
 from __future__ import annotations
 import sys
 from pathlib import Path
 import numpy as np
 import pandas as pd
 sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
 from src.backtest.harness import load
 from scripts.research.trackD_trendport import (
    simple_returns, realized_vol, sig_tsmom_blend, build_target,
    equity_from_target,
 )
 ASSETS = ["BTC", "ETH"]
 FEE_SIDE = 0.0005                 # 0.05%/side = 0.10% RT
 TARGET_VOL = 0.20
 LEVERAGE = 2.0
 # timeframe -> (load_tf, resample_rule_or_None, bars_per_day)
 TIMEFRAMES = {
    "15m": ("15m", None, 96),
    "1h":  ("1h",  None, 24),
    "4h":  ("1h",  "4h", 6),
    "1d":  ("1h",  "1D", 1),
 }
 def resample_ohlc(df: pd.DataFrame, rule: str) -> pd.DataFrame:
    g = df.copy()
    idx = pd.to_datetime(g["timestamp"], unit="ms", utc=True)
    idx.name = "dt"
    g.index = idx
    out = g.resample(rule, label="left", closed="left").agg(
        {"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"})
    out = out.dropna(subset=["open"])
    out["datetime"] = out.index
    epoch = pd.Timestamp("1970-01-01", tz="UTC")
    out["timestamp"] = ((out.index - epoch) // pd.Timedelta(milliseconds=1)).astype("int64")
    return out.reset_index(drop=True)[["timestamp", "open", "high", "low", "close", "volume", "datetime"]]
 def get_df(tf_key: str, asset: str) -> pd.DataFrame:
    load_tf, rule, _ = TIMEFRAMES[tf_key]
    df = load(asset, load_tf)
    if rule:
        df = resample_ohlc(df, rule)
    return df
 def run_asset(df, bars_per_day, target_vol=TARGET_VOL, leverage=LEVERAGE,
              long_only=False, fee_side=FEE_SIDE):
    c = df["close"].values.astype(float)
    r = simple_returns(c)
    bpy = bars_per_day * 365.25
    # recompute building blocks at this TF's bar frequency
    h1, h3, h6 = 30 * bars_per_day, 90 * bars_per_day, 180 * bars_per_day
    vol_win = 30 * bars_per_day
    # realized_vol / tsmom use BARS_PER_YEAR from trackD (1h) for annualization of vol;
    # we must annualize with THIS tf's bpy -> compute vol locally
    vol = pd.Series(r).rolling(vol_win, min_periods=vol_win // 2).std().values * np.sqrt(bpy)
    direction = sig_tsmom_blend(c, horizons=(h1, h3, h6))
    tgt = build_target(direction, vol, target_vol, leverage, long_only)
    equity, net = equity_from_target(tgt, r, fee_side)
    # discrete position SIGN for trade counting (entry = sign change to a new non-zero state)
    sign = np.sign(tgt)
    return dict(net=net, ts=df["datetime"], equity=equity, bpy=bpy, sign=sign, target=tgt)
 def portfolio_series(sleeves):
    a = pd.Series(sleeves["BTC"]["net"], index=pd.to_datetime(sleeves["BTC"]["ts"].values))
    b = pd.Series(sleeves["ETH"]["net"], index=pd.to_datetime(sleeves["ETH"]["ts"].values))
    j = pd.concat([a.rename("a"), b.rename("b")], axis=1, join="inner").fillna(0.0)
    combo = 0.5 * j["a"].values + 0.5 * j["b"].values
    idx = pd.to_datetime(j.index)
    equity = np.cumprod(1.0 + np.clip(combo, -0.99, None))
    return idx, combo, equity
 def overall_metrics(idx, combo, equity, bpy):
    rr = combo[np.isfinite(combo)]
    sharpe = float(np.mean(rr) / np.std(rr) * np.sqrt(bpy)) if np.std(rr) > 0 else 0.0
    peak = np.maximum.accumulate(equity)
    dd = float(np.max((peak - equity) / peak))
    span_days = (idx[-1] - idx[0]).total_seconds() / 86400
    years = span_days / 365.25
    total = equity[-1] / equity[0]
    cagr = total ** (1 / years) - 1 if years > 0 and total > 0 else -1.0
    daily_2k = (2000 * total - 2000) / span_days if span_days > 0 else 0.0
    return dict(sharpe=sharpe, max_dd=dd, cagr=cagr, total=total - 1, daily_2k=daily_2k)
 def per_year(idx, equity):
    """Return {year: (pnl_pct, maxdd_pct)} where maxdd is the worst drawdown WITHIN the year."""
    eq = pd.Series(equity, index=idx)
    out = {}
    for y, g in eq.groupby(eq.index.year):
        if len(g) < 2:
            continue
        pnl = g.iloc[-1] / g.iloc[0] - 1.0
        v = g.values
        peak = np.maximum.accumulate(v)
        ddy = float(np.max((peak - v) / peak))
        out[int(y)] = (float(pnl), ddy)
    return out
 def trades_per_year(sleeves):
    """Count entries per year, summed across both sleeves. An 'entry' = the position SIGN
    changing to a new non-zero value (flat->long, flat->short, or a direction flip)."""
    counts: dict[int, int] = {}
    for a in ASSETS:
        sign = sleeves[a]["sign"]
        ts = pd.to_datetime(sleeves[a]["ts"].values)
        for i in range(1, len(sign)):
            s, prev = sign[i], sign[i - 1]
            if s != 0 and s != prev:        # entry: from flat or opposite into a non-zero state
                counts[ts[i].year] = counts.get(ts[i].year, 0) + 1
    return counts
 ALL_YEARS = list(range(2018, 2027))
 def main():
    print("=" * 118)
    print("# TRACK D WINNER ACROSS TIMEFRAMES — TSMOM 1-3-6m blend, vol-target 20%, lev 2x, 50/50 BTC+ETH")
    print("# fee 0.10% RT on turnover, positions +1 bar (no look-ahead). 4h/1d resampled from certified 1h.")
    print("=" * 118)
    for mode_long_only, mode_name in ((False, "LONG-SHORT"), (True, "LONG-FLAT")):
        print("\n" + "#" * 118)
        print(f"#  MODE = {mode_name}")
        print("#" * 118)
        for tf_key in TIMEFRAMES:
            bpd = TIMEFRAMES[tf_key][2]
            sleeves = {a: run_asset(get_df(tf_key, a), bpd, long_only=mode_long_only)
                       for a in ASSETS}
            idx, combo, equity = portfolio_series(sleeves)
            ov = overall_metrics(idx, combo, equity, sleeves["BTC"]["bpy"])
            py = per_year(idx, equity)
            tpy = trades_per_year(sleeves)
            total_trades = sum(tpy.values())
            print(f"\n  ── TF {tf_key:<3s} │ ret {ov['total']*100:>+8.0f}%  CAGR {ov['cagr']*100:>+6.1f}%  "
                  f"Sharpe {ov['sharpe']:>4.2f}  maxDD {ov['max_dd']*100:>4.1f}%  "
                  f"€/day(2k) {ov['daily_2k']:>+6.2f}  trades {total_trades}")
            # per-year PnL / DD / trades rows
            print(f"     {'PnL %':<8s}" + "".join(
                ("    .  " if y not in py else f"{py[y][0]*100:>+7.0f}") for y in ALL_YEARS))
            print(f"     {'maxDD %':<8s}" + "".join(
                ("    .  " if y not in py else f"{py[y][1]*100:>7.1f}") for y in ALL_YEARS))
            print(f"     {'trades':<8s}" + "".join(
                ("    .  " if y not in py else f"{tpy.get(y,0):>7d}") for y in ALL_YEARS))
        # year header for reference
        print("\n     " + "year    ".ljust(8) + "".join(f"{y:>7d}" for y in ALL_YEARS))
 if __name__ == "__main__":
    main()
@@ -0,0 +1,460 @@
 """TRACK D — ROBUST WALK-FORWARD TREND PORTFOLIO (BTC+ETH), vol-targeted + leverage.
 Thesis under test: trend-following's real value in crypto is DRAWDOWN REDUCTION vs
 buy & hold (it sidesteps crashes). That lower DD lets us apply LEVERAGE and DIVERSIFY
 across BTC+ETH to build a deployable, risk-adjusted EARNING system, even if each single
 signal has only a modest Sharpe. Question: does a properly-built, anti-overfit trend
 portfolio actually EARN robustly across regimes 2018-2026?
 METHOD (strict, honest):
  * NO LOOK-AHEAD. We build equity directly from a TARGET-POSITION series.
      - target[i] is decided using ONLY data <= close[i].
      - target[i] is HELD during the next bar (close[i] -> close[i+1]).
      - bar return r[t] = close[t]/close[t-1] - 1  (uses close[t], close[t-1]; both <= t).
      - pnl on bar t = target[t-1] * r[t]            (shift positions by 1 -> no leakage).
      - fees: fee_per_side * |target[t-1] - target[t-2]| (turnover cost, charged on rebalances).
    This is the harness's documented "build your own equity from a position series" path.
  * VOL-TARGETING: position = directional_signal * (target_vol / realized_vol), capped at
    leverage. realized_vol uses past returns only (rolling std up to close[i]). This is the
    main lever — it lets a modest signal run at a controlled risk level.
  * WALK-FORWARD / MULTI-REGIME: per-year returns for ALL years 2018-2026. Plus an explicit
    EARLY (2018-2021) tune / LATE (2022-2026) confirm split. ONE robust param set, both assets.
  * PORTFOLIO: equal-weight BTC+ETH sleeves, rebalanced each bar. Report combined Sharpe/DD/CAGR.
  * GRID ROBUSTNESS: chosen config must be positive across a neighborhood AND across regimes.
  * FEE & LEVERAGE SWEEP: fee/side 0.0005..0.002 (0.10..0.40% RT); leverage cap 1x..3x.
 Run:  uv run python scripts/research/trackD_trendport.py
 """
 from __future__ import annotations
 import sys
 from pathlib import Path
 import numpy as np
 import pandas as pd
 sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
 from src.backtest.harness import load
 ASSETS = ["BTC", "ETH"]
 TF = "1h"
 BARS_PER_YEAR = 24 * 365.25          # 1h bars
 FEE_SIDE = 0.0005                    # 0.05% per side = 0.10% round trip (Deribit taker)
 # horizons in 1h bars ~ 1 / 3 / 6 "months" (30d months)
 H1, H3, H6 = 30 * 24, 90 * 24, 180 * 24
 # ---------------------------------------------------------------------------
 # Core building blocks (all <= close[i])
 # ---------------------------------------------------------------------------
 def simple_returns(c: np.ndarray) -> np.ndarray:
    r = np.zeros(len(c))
    r[1:] = c[1:] / c[:-1] - 1.0
    return r
 def realized_vol(r: np.ndarray, win: int) -> np.ndarray:
    """Annualized realized vol from bar returns up to and including i (no leakage)."""
    vol = pd.Series(r).rolling(win, min_periods=win // 2).std().values
    return vol * np.sqrt(BARS_PER_YEAR)
 def sig_tsmom_blend(c: np.ndarray, horizons=(H1, H3, H6)) -> np.ndarray:
    """Multi-horizon TSMOM: average of sign(close[i]/close[i-h]-1) over horizons -> [-1,1]."""
    n = len(c)
    acc = np.zeros(n)
    cnt = np.zeros(n)
    for h in horizons:
        s = np.full(n, np.nan)
        s[h:] = np.sign(c[h:] / c[:-h] - 1.0)
        valid = np.isfinite(s)
        acc[valid] += s[valid]
        cnt[valid] += 1
    out = np.zeros(n)
    nz = cnt > 0
    out[nz] = acc[nz] / cnt[nz]
    return out
 def sig_ma_slope(c: np.ndarray, span: int, slope_win: int = 24) -> np.ndarray:
    """Sign of the slope of an EMA: ema[i] vs ema[i-slope_win]. -> {-1,0,+1}."""
    ema = pd.Series(c).ewm(span=span, adjust=False).mean().values
    n = len(c)
    out = np.zeros(n)
    out[slope_win:] = np.sign(ema[slope_win:] - ema[:-slope_win])
    return out
 def sig_donchian_state(c, h, l, n_break: int, n_exit: int) -> np.ndarray:
    """Donchian breakout with trailing (channel) stop, returns a stateful {-1,0,+1} series.
    Long when close[i] > prior n_break high; exit/flip via prior n_exit low channel (trailing).
    Detection uses prior-window extremes EXCLUDING current bar (shift 1) and close[i] -> honest."""
    hh = pd.Series(h).rolling(n_break).max().shift(1).values
    ll = pd.Series(l).rolling(n_break).min().shift(1).values
    xh = pd.Series(h).rolling(n_exit).max().shift(1).values   # trailing exit for shorts
    xl = pd.Series(l).rolling(n_exit).min().shift(1).values   # trailing exit for longs
    n = len(c)
    state = np.zeros(n)
    pos = 0
    for i in range(n):
        if not np.isfinite(hh[i]):
            state[i] = 0
            continue
        if pos == 1:
            if c[i] < xl[i]:
                pos = 0
        elif pos == -1:
            if c[i] > xh[i]:
                pos = 0
        if pos == 0:
            if c[i] > hh[i]:
                pos = 1
            elif c[i] < ll[i]:
                pos = -1
        state[i] = pos
    return state
 # ---------------------------------------------------------------------------
 # Position construction (vol-targeting + leverage cap + long/flat option)
 # ---------------------------------------------------------------------------
 def build_target(direction: np.ndarray, vol: np.ndarray, target_vol: float,
                 leverage: float, long_only: bool) -> np.ndarray:
    """target[i] = direction[i] * (target_vol / vol[i]), clipped to [-leverage, leverage].
    direction[i] in [-1,1]; vol[i] annualized realized vol (<= close[i]). long_only clips <0 to 0."""
    d = direction.copy()
    if long_only:
        d = np.clip(d, 0, None)
    scal = np.where((vol > 0) & np.isfinite(vol), target_vol / vol, 0.0)
    tgt = d * scal
    tgt = np.clip(tgt, -leverage, leverage)
    tgt[~np.isfinite(tgt)] = 0.0
    return tgt
 def equity_from_target(target: np.ndarray, r: np.ndarray, fee_side: float):
    """Build equity from a target-position series with NO look-ahead.
    pos held during bar t = target[t-1]; pnl[t] = target[t-1]*r[t]; fee on turnover."""
    n = len(target)
    pos_held = np.zeros(n)
    pos_held[1:] = target[:-1]                # held during bar t = decided at close[t-1]
    gross = pos_held * r
    turn = np.abs(np.diff(pos_held, prepend=0.0))
    net = gross - fee_side * turn
    net[0] = 0.0
    net = np.clip(net, -0.99, None)           # cannot lose more than capital on a bar
    equity = np.cumprod(1.0 + net)
    return equity, net
 # ---------------------------------------------------------------------------
 # Metrics
 # ---------------------------------------------------------------------------
 def metrics(equity: np.ndarray, net: np.ndarray, ts: pd.Series) -> dict:
    rr = net[np.isfinite(net)]
    sharpe = float(np.mean(rr) / np.std(rr) * np.sqrt(BARS_PER_YEAR)) if np.std(rr) > 0 else 0.0
    peak = np.maximum.accumulate(equity)
    dd = float(np.max((peak - equity) / peak))
    span_days = (ts.iloc[-1] - ts.iloc[0]).total_seconds() / 86400
    years = span_days / 365.25
    total = equity[-1] / equity[0]
    cagr = total ** (1 / years) - 1 if years > 0 and total > 0 else -1.0
    eq_s = pd.Series(equity, index=ts)
    yearly = {}
    for y, g in eq_s.groupby(eq_s.index.year):
        if len(g) > 1 and g.iloc[0] > 0:
            yearly[int(y)] = float(g.iloc[-1] / g.iloc[0] - 1)
    daily_2k = (2000 * total - 2000) / span_days if span_days > 0 else 0.0
    return dict(sharpe=sharpe, max_dd=dd, cagr=cagr, total=total - 1,
                yearly=yearly, daily_2k=daily_2k, vol_ann=float(np.std(rr) * np.sqrt(BARS_PER_YEAR)))
 def avg_gross(target: np.ndarray) -> float:
    """Average absolute position = average gross leverage actually deployed."""
    t = target[np.isfinite(target)]
    return float(np.mean(np.abs(t))) if len(t) else 0.0
 def fmt(m, label):
    return (f"  {label:<34s} ret={m['total']*100:>+9.0f}% CAGR={m['cagr']*100:>+6.1f}% "
            f"Sh={m['sharpe']:>5.2f} DD={m['max_dd']*100:>4.1f}% volA={m['vol_ann']*100:>4.0f}% "
            f"€/d(2k)={m['daily_2k']:>+7.2f}")
 # ---------------------------------------------------------------------------
 # Strategy assembly
 # ---------------------------------------------------------------------------
 def make_direction(df: pd.DataFrame, kind: str, params: dict) -> np.ndarray:
    c = df["close"].values.astype(float)
    if kind == "TSMOM":
        return sig_tsmom_blend(c, params.get("horizons", (H1, H3, H6)))
    if kind == "MASLOPE":
        return sig_ma_slope(c, params["span"], params.get("slope_win", 24))
    if kind == "DONCHIAN":
        h = df["high"].values.astype(float)
        l = df["low"].values.astype(float)
        return sig_donchian_state(c, h, l, params["n_break"], params["n_exit"])
    raise ValueError(kind)
 def run_asset(df, kind, params, target_vol, leverage, long_only, fee_side=FEE_SIDE):
    c = df["close"].values.astype(float)
    r = simple_returns(c)
    vol = realized_vol(r, params.get("vol_win", 30 * 24))
    direction = make_direction(df, kind, params)
    tgt = build_target(direction, vol, target_vol, leverage, long_only)
    equity, net = equity_from_target(tgt, r, fee_side)
    ts = df["datetime"]
    m = metrics(equity, net, ts)
    m["target"] = tgt
    m["net"] = net
    m["ts"] = ts
    m["equity"] = equity
    return m
 def buy_hold(df):
    c = df["close"].values.astype(float)
    r = simple_returns(c)
    equity = np.cumprod(1.0 + np.clip(r, -0.99, None))
    return metrics(equity, r, df["datetime"])
 # ---------------------------------------------------------------------------
 # Portfolio (equal-weight BTC+ETH, rebalanced each bar on common timestamps)
 # ---------------------------------------------------------------------------
 def portfolio(net_btc_df, net_eth_df, w=(0.5, 0.5)):
    """Combine two per-bar net-return series aligned on common timestamps."""
    a = pd.Series(net_btc_df["net"], index=net_btc_df["ts"].values)
    b = pd.Series(net_eth_df["net"], index=net_eth_df["ts"].values)
    j = pd.concat([a.rename("a"), b.rename("b")], axis=1, join="inner").fillna(0.0)
    combo = w[0] * j["a"].values + w[1] * j["b"].values
    equity = np.cumprod(1.0 + np.clip(combo, -0.99, None))
    ts = pd.Series(pd.to_datetime(j.index))
    return metrics(equity, combo, ts)
 # ---------------------------------------------------------------------------
 # Reporting helpers
 # ---------------------------------------------------------------------------
 ALL_YEARS = list(range(2018, 2027))
 def print_yearly_row(label, m):
    cells = []
    for y in ALL_YEARS:
        v = m["yearly"].get(y)
        cells.append("   .   " if v is None else f"{v*100:>+6.0f}%")
    print(f"  {label:<26s} " + " ".join(cells))
 def yearly_header():
    print(f"  {'config':<26s} " + " ".join(f"{y:>7d}" for y in ALL_YEARS))
 # ---------------------------------------------------------------------------
 # Experiments
 # ---------------------------------------------------------------------------
 def main():
    pd.set_option("display.width", 220)
    dfs = {a: load(a, TF) for a in ASSETS}
    print("=" * 130)
    print("# TRACK D — VOL-TARGETED TREND PORTFOLIO (BTC+ETH, 1h, Deribit certified)")
    print("# Equity built from target-position series; positions shifted +1 bar (no look-ahead);")
    print("# fee = 0.05%/side (0.10% RT) on turnover. Vol-targeting scales by inverse realized vol.")
    print("=" * 130)
    print("\n# BUY & HOLD BENCHMARK (the DD/return bar trend must beat on risk-adjusted basis)")
    yearly_header()
    bh = {}
    for a in ASSETS:
        bh[a] = buy_hold(dfs[a])
        print(fmt(bh[a], f"B&H {a}"))
        print_yearly_row(f"B&H {a} yearly", bh[a])
    bh_port = portfolio({"net": simple_returns(dfs["BTC"]["close"].values), "ts": dfs["BTC"]["datetime"]},
                        {"net": simple_returns(dfs["ETH"]["close"].values), "ts": dfs["ETH"]["datetime"]})
    print(fmt(bh_port, "B&H 50/50 BTC+ETH"))
    print_yearly_row("B&H port yearly", bh_port)
    # ----------------------------------------------------------------------
    # 1. BROAD SCAN: strategies x vol-target x leverage x long-only, per asset & portfolio
    # ----------------------------------------------------------------------
    print("\n" + "=" * 130)
    print("# 1) BROAD SCAN — per-asset & 50/50 portfolio, vol-target=20%, leverage cap 2x")
    print("#    (TSMOM 1-3-6m blend / MA-slope / Donchian-trailing; long-short vs long-flat)")
    print("=" * 130)
    strat_defs = [
        ("TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24)),
        ("MASLOPE", dict(span=200, slope_win=48, vol_win=30 * 24)),
        ("DONCHIAN", dict(n_break=200, n_exit=100, vol_win=30 * 24)),
    ]
    for long_only in (False, True):
        mode = "LONG-FLAT" if long_only else "LONG-SHORT"
        print(f"\n  --- {mode} ---")
        for kind, params in strat_defs:
            sleeves = {}
            for a in ASSETS:
                m = run_asset(dfs[a], kind, params, target_vol=0.20, leverage=2.0, long_only=long_only)
                sleeves[a] = m
                print(fmt(m, f"{kind} {a}"))
            port = portfolio(sleeves["BTC"], sleeves["ETH"])
            print(fmt(port, f"{kind} PORTFOLIO 50/50"))
            print_yearly_row(f"{kind} port yearly", port)
    # ----------------------------------------------------------------------
    # 2. GRID ROBUSTNESS on the portfolio: vol-target x leverage x vol-window
    #    using the multi-horizon TSMOM blend (the most diversified trend signal)
    # ----------------------------------------------------------------------
    print("\n" + "=" * 130)
    print("# 2) GRID ROBUSTNESS — TSMOM 1-3-6m blend, 50/50 portfolio (LONG-SHORT)")
    print("#    Sweep target-vol x leverage-cap. A real config is positive across the neighborhood.")
    print("=" * 130)
    hdr = "  " + "tvol\\lev".ljust(8) + "".join(f"{lev:.0f}x".rjust(26) for lev in (1.0, 1.5, 2.0, 3.0))
    print(hdr)
    grid = {}
    for tvol in (0.10, 0.15, 0.20, 0.30, 0.40):
        row = f"  {tvol*100:>6.0f}%   "
        for lev in (1.0, 1.5, 2.0, 3.0):
            sleeves = {}
            for a in ASSETS:
                sleeves[a] = run_asset(dfs[a], "TSMOM",
                                       dict(horizons=(H1, H3, H6), vol_win=30 * 24),
                                       target_vol=tvol, leverage=lev, long_only=False)
            port = portfolio(sleeves["BTC"], sleeves["ETH"])
            grid[(tvol, lev)] = port
            row += f"  Sh{port['sharpe']:>4.2f} DD{port['max_dd']*100:>3.0f} C{port['cagr']*100:>+4.0f}"
        print(row)
    # ----------------------------------------------------------------------
    # 3. HORIZON-SET robustness (is the 1-3-6m blend a plateau or a lucky combo?)
    # ----------------------------------------------------------------------
    print("\n" + "=" * 130)
    print("# 3) HORIZON-SET ROBUSTNESS — TSMOM blend, portfolio, tvol=20% lev=2x (LONG-SHORT)")
    print("=" * 130)
    horizon_sets = {
        "1m only": (H1,), "3m only": (H3,), "6m only": (H6,),
        "1-3m": (H1, H3), "3-6m": (H3, H6), "1-3-6m": (H1, H3, H6),
        "1-2-4m": (30 * 24, 60 * 24, 120 * 24), "2-4-8m": (60 * 24, 120 * 24, 240 * 24),
    }
    yearly_header()
    for name, hs in horizon_sets.items():
        sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=hs, vol_win=30 * 24),
                                target_vol=0.20, leverage=2.0, long_only=False) for a in ASSETS}
        port = portfolio(sleeves["BTC"], sleeves["ETH"])
        print(fmt(port, f"TSMOM {name}"))
    print()
    for name, hs in horizon_sets.items():
        sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=hs, vol_win=30 * 24),
                                target_vol=0.20, leverage=2.0, long_only=False) for a in ASSETS}
        port = portfolio(sleeves["BTC"], sleeves["ETH"])
        print_yearly_row(f"{name}", port)
    # ----------------------------------------------------------------------
    # 4. WALK-FORWARD: EARLY (<=2021) tune / LATE (>=2022) confirm
    #    Same single param set for BOTH assets; we just split the equity by date.
    # ----------------------------------------------------------------------
    print("\n" + "=" * 130)
    print("# 4) WALK-FORWARD — split portfolio equity into EARLY (2018-2021) vs LATE (2022-2026)")
    print("#    One param set, both assets. Both halves must earn for the edge to be regime-robust.")
    print("=" * 130)
    cfg = dict(kind="TSMOM", params=dict(horizons=(H1, H3, H6), vol_win=30 * 24),
               target_vol=0.20, leverage=2.0, long_only=False)
    sleeves = {a: run_asset(dfs[a], cfg["kind"], cfg["params"], cfg["target_vol"],
                            cfg["leverage"], cfg["long_only"]) for a in ASSETS}
    a = pd.Series(sleeves["BTC"]["net"], index=sleeves["BTC"]["ts"].values)
    b = pd.Series(sleeves["ETH"]["net"], index=sleeves["ETH"]["ts"].values)
    j = pd.concat([a.rename("a"), b.rename("b")], axis=1, join="inner").fillna(0.0)
    combo = 0.5 * j["a"].values + 0.5 * j["b"].values
    idx = pd.to_datetime(j.index)
    for lab, mask in (("EARLY 2018-2021", idx.year <= 2021), ("LATE 2022-2026", idx.year >= 2022)):
        sub = combo[mask]
        eq = np.cumprod(1.0 + np.clip(sub, -0.99, None))
        m = metrics(eq, sub, pd.Series(idx[mask]))
        print(fmt(m, lab))
        print_yearly_row(f"{lab} yearly", m)
    # ----------------------------------------------------------------------
    # 5. FEE & LEVERAGE SWEEP on the headline portfolio config
    # ----------------------------------------------------------------------
    print("\n" + "=" * 130)
    print("# 5) FEE & LEVERAGE SWEEP — TSMOM 1-3-6m blend portfolio, tvol=20%")
    print("=" * 130)
    print("  fee sweep (leverage cap 2x):")
    for fee in (0.0005, 0.00075, 0.001, 0.0015, 0.002):
        sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
                                target_vol=0.20, leverage=2.0, long_only=False, fee_side=fee)
                   for a in ASSETS}
        port = portfolio(sleeves["BTC"], sleeves["ETH"])
        print(fmt(port, f"fee/side={fee:.5f} (RT={2*fee*100:.2f}%)"))
    print("  leverage sweep (fee 0.05%/side):")
    for lev in (1.0, 1.5, 2.0, 2.5, 3.0):
        sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
                                target_vol=0.20, leverage=lev, long_only=False)
                   for a in ASSETS}
        port = portfolio(sleeves["BTC"], sleeves["ETH"])
        print(fmt(port, f"leverage cap={lev:.1f}x"))
    # ----------------------------------------------------------------------
    # 6. HEADLINE ROBUST CONFIG — full per-year table + sleeves + portfolio
    # ----------------------------------------------------------------------
    print("\n" + "=" * 130)
    print("# 6) HEADLINE ROBUST CONFIG: TSMOM 1-3-6m blend, vol-target 20%, leverage cap 2x, LONG-SHORT")
    print("=" * 130)
    yearly_header()
    sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
                            target_vol=0.20, leverage=2.0, long_only=False) for a in ASSETS}
    for a in ASSETS:
        print(fmt(sleeves[a], f"sleeve {a}"))
        print_yearly_row(f"sleeve {a} yearly", sleeves[a])
    port = portfolio(sleeves["BTC"], sleeves["ETH"])
    print(fmt(port, "PORTFOLIO 50/50"))
    print_yearly_row("PORTFOLIO yearly", port)
    # also long-flat headline (deployable variant — no shorts/funding complexity)
    print()
    sleeves_lf = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
                               target_vol=0.20, leverage=2.0, long_only=True) for a in ASSETS}
    port_lf = portfolio(sleeves_lf["BTC"], sleeves_lf["ETH"])
    print(fmt(port_lf, "PORTFOLIO 50/50 LONG-FLAT"))
    print_yearly_row("PORTFOLIO LF yearly", port_lf)
    # ----------------------------------------------------------------------
    # 7. €/DAY ON 2000 — what leverage gets us toward 50/day, and the DD it costs
    # ----------------------------------------------------------------------
    print("\n" + "=" * 130)
    print("# 7) PATH TO ~50 EUR/day on 2000 — the REAL lever is TARGET-VOL, not the leverage cap.")
    print("#    At tvol=20%% on 60-80%% crypto vol, positions stay sub-1x: the leverage cap NEVER binds.")
    print("#    To deploy real leverage you raise target-vol; Sharpe is ~constant, DD scales ~linearly.")
    print("#    'avg gross' = mean |position| = leverage actually used.  (cap fixed at 3x here)")
    print("=" * 130)
    print(f"  {'target_vol':<12s}{'avgGross':>10s}{'CAGR':>9s}{'Sharpe':>9s}{'maxDD':>8s}"
          f"{'€/day(2k,avg)':>16s}{'final/2k':>12s}")
    for tvol in (0.20, 0.40, 0.60, 0.80, 1.00):
        sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
                                target_vol=tvol, leverage=3.0, long_only=False) for a in ASSETS}
        port = portfolio(sleeves["BTC"], sleeves["ETH"])
        ag = 0.5 * (avg_gross(sleeves["BTC"]["target"]) + avg_gross(sleeves["ETH"]["target"]))
        print(f"  {tvol*100:>8.0f}%   {ag:>9.2f}x{port['cagr']*100:>+8.1f}%{port['sharpe']:>9.2f}"
              f"{port['max_dd']*100:>7.1f}%{port['daily_2k']:>+16.2f}{(1+port['total']):>12.1f}x")
    # steady-state €/day at current capital under headline CAGR
    print("\n  Steady-state €/day implied by headline CAGR (NOT path-dependent), at various capital:")
    sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
                            target_vol=0.20, leverage=2.0, long_only=False) for a in ASSETS}
    port = portfolio(sleeves["BTC"], sleeves["ETH"])
    g = port["cagr"]
    daily_rate = (1 + g) ** (1 / 365.25) - 1
    for cap in (2000, 5000, 10000, 50000, 100000):
        print(f"    capital={cap:>7d}  ~€/day = {cap*daily_rate:>+8.2f}  (CAGR={g*100:+.1f}%)")
    need = 50.0 / daily_rate if daily_rate > 0 else float("inf")
    print(f"\n  To average ~50 EUR/day at this CAGR you'd need ~{need:,.0f} capital "
          f"(at leverage 2x, maxDD~{port['max_dd']*100:.0f}%).")
    print("\nDONE. See the report/diary for the honest verdict.")
 if __name__ == "__main__":
    main()
@@ -0,0 +1,526 @@
 """TRACK E — CROSS-SECTIONAL BTC↔ETH relative-value + ENSEMBLE synthesis.
 Two parts, both on certified Deribit-mainnet data (only BTC/ETH), both honest:
 PART 1 — RELATIVE VALUE (market-neutral-ish spread trading on TWO assets):
  * XS relative momentum: go long the stronger asset, short the weaker (dollar-neutral).
  * ETH/BTC ratio TREND (z-momentum) and ratio MEAN-REVERSION (z-fade of log-ratio).
  * Lead-lag (descriptive): does BTC's last-bar move predict ETH's next bar (and vice versa)?
  All positions are decided with data <= close[i] and HELD over the NEXT bar (i->i+1):
  realized PnL on bar k uses position set at k-1 -> strict 1-bar shift, NO look-ahead.
  Fees are turnover-based: |Δpos| * fee_rt/2 PER LEG (a +1↔-1 flip = one round trip = fee_rt).
 PART 2 — ENSEMBLE:
  Combine the genuinely-positive residual sleeves into ONE portfolio equity curve:
    (S1) BTC low-turnover ML momentum (trackB best honest cell: W16000 H24 thr0.10, 1h)
    (S2) Trend-1h, the only cross-asset-robust trend cell from trackA (Donchian N=200 H=12)
    (S3) the best relative-value sleeve found in PART 1 (if any net-positive OOS)
  Report combined Sharpe / maxDD / CAGR / EUR-per-day-on-2000 AND the sleeve correlation
  matrix. A real ensemble edge must be net-positive OOS and LOWER drawdown than its parts.
 Run:  uv run python scripts/research/trackE_xsec_ensemble.py
      uv run python scripts/research/trackE_xsec_ensemble.py --quick   (skip slow ML sleeve)
      uv run python scripts/research/trackE_xsec_ensemble.py --no-cache (recompute ML proba)
 """
 from __future__ import annotations
 import argparse
 import sys
 import time
 from pathlib import Path
 import numpy as np
 import pandas as pd
 sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
 from src.backtest.harness import load, backtest_signals, oos_split
 # reuse trackB ML machinery (strict walk-forward, no leakage) and trackA donchian
 from scripts.research.trackB_ml import (
    build_features, forward_labels, walk_forward_proba, proba_to_entries, mask_entries,
    RETRAIN_K,
 )
 from scripts.research.trackA_trend import sig_donchian
 from sklearn.linear_model import LogisticRegression
 FEE = 0.001                 # 0.10% round-trip baseline (per leg for the pair)
 BARS_PER_YEAR_1H = 24 * 365.25
 # ===========================================================================
 # Generic honest stats on a per-bar RETURN series (returns realized bar (k-1)->k)
 # ===========================================================================
 def equity_from_returns(rets: np.ndarray) -> np.ndarray:
    eq = np.cumprod(1.0 + np.nan_to_num(rets))
    return eq
 def sharpe(rets: np.ndarray, bpy: float = BARS_PER_YEAR_1H) -> float:
    r = rets[np.isfinite(rets)]
    if len(r) < 3 or np.std(r) == 0:
        return 0.0
    return float(np.mean(r) / np.std(r) * np.sqrt(bpy))
 def max_dd(equity: np.ndarray) -> float:
    peak = np.maximum.accumulate(equity)
    dd = (peak - equity) / peak
    return float(np.max(dd)) if len(dd) else 0.0
 def cagr(equity: np.ndarray, ts: pd.Series) -> float:
    if len(equity) < 2:
        return 0.0
    days = (ts.iloc[-1] - ts.iloc[0]).total_seconds() / 86400
    years = days / 365.25 if days > 0 else 1.0
    if years <= 0 or equity[-1] <= 0:
        return -1.0
    return float(equity[-1] ** (1 / years) - 1)
 def daily_profit(equity: np.ndarray, ts: pd.Series, capital: float = 2000.0) -> float:
    if len(equity) < 2:
        return 0.0
    days = (ts.iloc[-1] - ts.iloc[0]).total_seconds() / 86400
    if days <= 0:
        return 0.0
    final = capital * equity[-1] / equity[0]
    return (final - capital) / days
 def yearly_returns(rets: np.ndarray, ts: pd.Series) -> dict:
    eq = equity_from_returns(rets)
    s = pd.Series(eq, index=pd.DatetimeIndex(ts))
    out = {}
    for y, g in s.groupby(s.index.year):
        if len(g) > 1 and g.iloc[0] > 0:
            out[int(y)] = float(g.iloc[-1] / g.iloc[0] - 1)
    return out
 def stat_block(rets: np.ndarray, ts: pd.Series, bpy: float = BARS_PER_YEAR_1H) -> dict:
    eq = equity_from_returns(rets)
    return dict(
        net=float(eq[-1] - 1.0), sharpe=sharpe(rets, bpy), max_dd=max_dd(eq),
        cagr=cagr(eq, ts), eur_day=daily_profit(eq, ts), equity=eq,
        turnover=float(np.mean(np.abs(np.diff(np.sign(rets) != 0)))),  # placeholder, unused
    )
 # ===========================================================================
 # RELATIVE-VALUE ENGINE — two legs, turnover-based fees, strict 1-bar shift.
 # pos arrays are decided at close[i] (data<=i). Realized return on bar k uses pos[k-1].
 # ===========================================================================
 def pair_returns(cB: np.ndarray, cE: np.ndarray, posB: np.ndarray, posE: np.ndarray,
                 fee_rt: float = FEE) -> np.ndarray:
    """Per-bar net return series for a two-leg book. rets[k] realized on bar (k-1)->k.
    Fee = (|ΔposB| + |ΔposE|) * fee_rt/2 charged when the position is (re)set."""
    n = len(cB)
    aretB = np.zeros(n); aretE = np.zeros(n)
    aretB[1:] = cB[1:] / cB[:-1] - 1.0
    aretE[1:] = cE[1:] / cE[:-1] - 1.0
    rets = np.zeros(n)
    for k in range(1, n):
        gross = posB[k - 1] * aretB[k] + posE[k - 1] * aretE[k]
        pBp = posB[k - 2] if k >= 2 else 0.0
        pEp = posE[k - 2] if k >= 2 else 0.0
        turn = abs(posB[k - 1] - pBp) + abs(posE[k - 1] - pEp)
        rets[k] = gross - turn * fee_rt / 2.0
    return rets
 # --- signal builders: return (posB, posE) arrays, leg notional `leg` (gross = 2*leg) ---
 def xs_momentum(cB, cE, N, hold, leg=0.5):
    """Cross-sectional momentum: long the asset with higher N-bar return, short the other."""
    n = len(cB)
    posB = np.zeros(n); posE = np.zeros(n)
    curB = curE = 0.0
    for i in range(n):
        if i >= N and (i % hold == 0):
            mB = cB[i] / cB[i - N] - 1.0
            mE = cE[i] / cE[i - N] - 1.0
            d = 1 if mB > mE else -1          # +1 => BTC stronger -> long BTC short ETH
            curB = leg * d; curE = -leg * d
        posB[i] = curB; posE[i] = curE
    return posB, posE
 def ratio_trend(cB, cE, N, hold, leg=0.5):
    """Trend on ETH/BTC ratio: ratio rising over N bars -> long ratio (long ETH, short BTC)."""
    ratio = cE / cB
    n = len(cB)
    posB = np.zeros(n); posE = np.zeros(n)
    curB = curE = 0.0
    for i in range(n):
        if i >= N and (i % hold == 0):
            d = 1 if ratio[i] > ratio[i - N] else -1   # +1 => ratio up -> long ratio
            curE = leg * d; curB = -leg * d
        posB[i] = curB; posE[i] = curE
    return posB, posE
 def ratio_meanrev(cB, cE, lookback, z_in, z_exit, max_bars, leg=0.5):
    """Mean-reversion (z-fade) on log(ETH/BTC). z>+z_in -> short ratio; z<-z_in -> long ratio.
    Exit when |z|<z_exit (reverted to mean) or after max_bars. Stateful, honest at close[i]."""
    logr = np.log(cE / cB)
    s = pd.Series(logr)
    ma = s.rolling(lookback).mean().values
    sd = s.rolling(lookback).std(ddof=0).values
    z = (logr - ma) / sd
    n = len(cB)
    posB = np.zeros(n); posE = np.zeros(n)
    state = 0      # +1 long ratio, -1 short ratio, 0 flat
    bars_in = 0
    for i in range(n):
        if not np.isfinite(z[i]):
            posB[i] = 0.0; posE[i] = 0.0; continue
        if state == 0:
            if z[i] >= z_in:
                state = -1; bars_in = 0           # ratio too high -> short ratio
            elif z[i] <= -z_in:
                state = 1; bars_in = 0            # ratio too low -> long ratio
        else:
            bars_in += 1
            if abs(z[i]) <= z_exit or bars_in >= max_bars or (state == 1 and z[i] >= z_in) \
                    or (state == -1 and z[i] <= -z_in):
                state = 0
        posE[i] = leg * state; posB[i] = -leg * state
    return posB, posE
 # ===========================================================================
 # OOS / fee-sweep helpers for the relative-value sleeves
 # ===========================================================================
 def rv_eval(cB, cE, ts, build_fn, params, fee_rt=FEE, frac=0.65):
    posB, posE = build_fn(cB, cE, **params)
    rets = pair_returns(cB, cE, posB, posE, fee_rt=fee_rt)
    cut = int(len(cB) * frac)
    full = stat_block(rets, ts)
    is_ = stat_block(rets[:cut], ts.iloc[:cut])
    oos = stat_block(rets[cut:], ts.iloc[cut:])
    # turnover: average per-bar leg turnover (both legs)
    turn = (np.abs(np.diff(posB, prepend=0)) + np.abs(np.diff(posE, prepend=0)))
    tstats = dict(rets=rets, posB=posB, posE=posE,
                  trades=int((turn > 1e-9).sum()), avg_turn=float(turn.mean()))
    return full, is_, oos, tstats
 def fmt(s):
    return (f"net={s['net']*100:>+8.0f}% Sh={s['sharpe']:>+5.2f} DD={s['max_dd']*100:>4.0f}% "
            f"CAGR={s['cagr']*100:>+6.1f}% €/d={s['eur_day']:>+6.2f}")
 # ===========================================================================
 # PART 1
 # ===========================================================================
 def part1_relative_value(quick=False):
    print("=" * 104)
    print("PART 1 — CROSS-SECTIONAL / RELATIVE-VALUE  (BTC↔ETH, 1h, market-neutral spread)")
    print("=" * 104)
    b = load("BTC", "1h"); e = load("ETH", "1h")
    m = pd.merge(b[["timestamp", "close"]], e[["timestamp", "close"]],
                 on="timestamp", suffixes=("_b", "_e")).reset_index(drop=True)
    ts = pd.to_datetime(m["timestamp"], unit="ms", utc=True)
    cB = m["close_b"].to_numpy(float); cE = m["close_e"].to_numpy(float)
    cut = int(len(m) * 0.65)
    print(f"  common 1h bars: {len(m)}  {ts.iloc[0].date()} → {ts.iloc[-1].date()}  "
          f"(OOS starts {ts.iloc[cut].date()})")
    rb = np.log(cB[1:] / cB[:-1]); re = np.log(cE[1:] / cE[:-1])
    print(f"  contemporaneous corr(BTC,ETH 1h logret) = {np.corrcoef(rb, re)[0,1]:.3f}  "
          f"(very high → the only tradable structure is the SPREAD)")
    # ---- LEAD-LAG (descriptive, both directions, IS vs OOS) ----
    print("\n  -- LEAD-LAG (descriptive: does last-bar move of X predict next bar of Y?) --")
    def ll(a_prev, b_next):
        a = a_prev[np.isfinite(a_prev) & np.isfinite(b_next)]
        bb = b_next[np.isfinite(a_prev) & np.isfinite(b_next)]
        return np.corrcoef(a, bb)[0, 1] if len(a) > 30 else np.nan
    print(f"     corr(rB[i],  rE[i+1]) = {ll(rb[:-1], re[1:]):+.4f}   "
          f"corr(rE[i],  rB[i+1]) = {ll(re[:-1], rb[1:]):+.4f}")
    print(f"     corr(rB[i],  rB[i+1]) = {ll(rb[:-1], rb[1:]):+.4f}   "
          f"corr(rE[i],  rE[i+1]) = {ll(re[:-1], re[1:]):+.4f}")
    print("     → |lead-lag| ~0.01-0.02: NO exploitable cross-predictive edge. Not pursued as a sleeve.")
    results = {}
    # ---- A) XS relative momentum grid ----
    print("\n  -- (A) XS RELATIVE MOMENTUM: long stronger / short weaker (dollar-neutral, gross=1) --")
    print("     param            FULL                                          | OOS")
    Ns = [24, 72, 168, 336] if not quick else [72, 168]
    holds = [6, 24, 72] if not quick else [24, 72]
    best_xs = None
    for N in Ns:
        for hold in holds:
            full, is_, oos, tstat = rv_eval(cB, cE, ts, xs_momentum, dict(N=N, hold=hold))
            ok = oos["net"] > 0 and oos["sharpe"] > 0
            print(f"     N={N:>3} hold={hold:>2} | {fmt(full)} | OOS {fmt(oos)} "
                  f"tr={tstat['trades']:>4} {'OK' if ok else ''}")
            if oos["net"] > 0 and (best_xs is None or oos["sharpe"] > best_xs[2]["sharpe"]):
                best_xs = (dict(N=N, hold=hold), full, oos, tstat, "xs_momentum")
    results["xs_momentum"] = best_xs
    # ---- B) ETH/BTC ratio TREND grid ----
    print("\n  -- (B) ETH/BTC RATIO TREND: long ratio when rising over N (long ETH/short BTC) --")
    print("     NOTE: with only TWO assets this is ALGEBRAICALLY IDENTICAL to (A) — 'long the")
    print("     stronger' ≡ 'trade the ratio trend'. Shown separately only to make that explicit.")
    best_rt = None
    for N in Ns:
        for hold in holds:
            full, is_, oos, tstat = rv_eval(cB, cE, ts, ratio_trend, dict(N=N, hold=hold))
            ok = oos["net"] > 0 and oos["sharpe"] > 0
            print(f"     N={N:>3} hold={hold:>2} | {fmt(full)} | OOS {fmt(oos)} "
                  f"tr={tstat['trades']:>4} {'OK' if ok else ''}")
            if oos["net"] > 0 and (best_rt is None or oos["sharpe"] > best_rt[2]["sharpe"]):
                best_rt = (dict(N=N, hold=hold), full, oos, tstat, "ratio_trend")
    results["ratio_trend"] = best_rt
    # ---- C) ETH/BTC ratio MEAN-REVERSION grid ----
    print("\n  -- (C) ETH/BTC RATIO MEAN-REVERSION: z-fade of log(ETH/BTC) --")
    best_mr = None
    LBs = [48, 168, 336] if not quick else [168]
    zins = [1.5, 2.0, 2.5] if not quick else [2.0]
    for lb in LBs:
        for zin in zins:
            full, is_, oos, tstat = rv_eval(cB, cE, ts, ratio_meanrev,
                                            dict(lookback=lb, z_in=zin, z_exit=0.5, max_bars=72))
            ok = oos["net"] > 0 and oos["sharpe"] > 0
            print(f"     lb={lb:>3} zin={zin} | {fmt(full)} | OOS {fmt(oos)} "
                  f"tr={tstat['trades']:>4} {'OK' if ok else ''}")
            if oos["net"] > 0 and (best_mr is None or oos["sharpe"] > best_mr[2]["sharpe"]):
                best_mr = (dict(lookback=lb, z_in=zin, z_exit=0.5, max_bars=72),
                           full, oos, tstat, "ratio_meanrev")
    results["ratio_meanrev"] = best_mr
    # ---- choose the single best RV sleeve (positive OOS, highest OOS Sharpe) ----
    cands = [v for v in results.values() if v is not None]
    cands.sort(key=lambda v: v[2]["sharpe"], reverse=True)
    best = cands[0] if cands else None
    print("\n  -- RELATIVE-VALUE SUMMARY (best per family that is OOS net-positive) --")
    for fam in ("xs_momentum", "ratio_trend", "ratio_meanrev"):
        v = results[fam]
        if v is None:
            print(f"     {fam:<14}: no OOS net-positive cell.")
        else:
            params, full, oos, tstat, _ = v
            print(f"     {fam:<14}: {params}  FULL {fmt(full)} | OOS {fmt(oos)}")
    if best is None:
        print("\n  >> NO relative-value sleeve is OOS net-positive. No RV edge to add to the ensemble.")
        return None, (cB, cE, ts)
    params, full, oos, tstat, fam = best
    print(f"\n  >> BEST RV sleeve: {fam} {params}  (OOS Sharpe {oos['sharpe']:+.2f})")
    # ---- per-year + fee sweep + grid-neighbourhood robustness on the winner ----
    build_fn = {"xs_momentum": xs_momentum, "ratio_trend": ratio_trend,
                "ratio_meanrev": ratio_meanrev}[fam]
    fullr, _, _, _ = rv_eval(cB, cE, ts, build_fn, params)
    print("\n     per-year (full):")
    yr = yearly_returns(fullr["rets"] if False else pair_returns(cB, cE,
            *build_fn(cB, cE, **params)), ts)
    for y in sorted(yr):
        print(f"       {y}: {yr[y]*100:>+7.1f}%")
    print("\n     fee sweep (full-sample net, baseline 0.10% RT/leg):")
    for f in (0.0, 0.0005, 0.001, 0.0015, 0.002):
        fr, _, fo, _ = rv_eval(cB, cE, ts, build_fn, params, fee_rt=f)
        print(f"       fee={f*1000:.1f}bp/leg → FULL net={fr['net']*100:>+7.0f}% "
              f"OOS net={fo['net']*100:>+7.0f}% (Sh {fo['sharpe']:+.2f})")
    return best, (cB, cE, ts)
 # ===========================================================================
 # PART 2 — ENSEMBLE
 # ===========================================================================
 def lr_factory():
    return LogisticRegression(C=1.0, max_iter=300, class_weight="balanced")
 def ml_sleeve_btc(cache=True, no_cache=False):
    """BTC low-turnover ML momentum sleeve (trackB best honest cell W16000 H24 thr0.10)."""
    W, H, thr = 16000, 24, 0.10
    df = load("BTC", "1h")
    cpath = Path(__file__).resolve().parent / ".cache_trackE_btc_ml_proba.npy"
    proba = None
    if cache and not no_cache and cpath.exists():
        arr = np.load(cpath)
        if len(arr) == len(df):
            proba = arr
            print(f"    [S1 ML] loaded cached proba ({cpath.name})")
    if proba is None:
        print(f"    [S1 ML] walk-forward LogisticRegression W{W} H{H} (slow ~1-2min)...")
        t0 = time.time()
        X, names, fvalid = build_features(df)
        warmup = int(np.argmax(fvalid)) if fvalid.any() else 0
        y, _fwd, lvalid = forward_labels(df, H)
        proba = walk_forward_proba(X, y, fvalid, lvalid, warmup, W, H, RETRAIN_K, lr_factory)
        np.save(cpath, proba)
        print(f"    [S1 ML] done ({time.time()-t0:.0f}s), cached.")
    n = len(df)
    entries = proba_to_entries(proba, thr, H, n)
    m = backtest_signals(df, entries, fee_rt=FEE, asset="BTC", tf="1h")
    return m, df, f"BTC-ML W{W}H{H}thr{thr}"
 def trend_sleeve_btc():
    """Trend-1h sleeve: Donchian N=200 H=12 on BTC (the only cross-asset-robust trend cell)."""
    df = load("BTC", "1h")
    entries = sig_donchian(df, lookback=200, hold=12)
    m = backtest_signals(df, entries, fee_rt=FEE, asset="BTC", tf="1h")
    return m, df, "BTC-Trend Donchian200/12"
 def metrics_to_returns(m):
    """Per-bar return series from a harness Metrics equity, indexed by its timestamps."""
    eq = m.equity.astype(float)
    ts = m.eq_index
    rets = np.zeros(len(eq))
    rets[1:] = eq[1:] / np.where(eq[:-1] == 0, np.nan, eq[:-1]) - 1.0
    rets = np.nan_to_num(rets)
    return pd.Series(rets, index=pd.DatetimeIndex(ts))
 def part2_ensemble(rv_best, rv_data, quick=False, no_cache=False):
    print("\n" + "=" * 104)
    print("PART 2 — ENSEMBLE (combine weakly-correlated residual sleeves into one portfolio)")
    print("=" * 104)
    sleeves = {}   # name -> pd.Series of per-bar returns indexed by ts
    # S2 trend (fast, always)
    mt, dft, tname = trend_sleeve_btc()
    sleeves["S2_trend"] = metrics_to_returns(mt)
    print(f"    [S2] {tname:<28} net={mt.net_return*100:>+7.0f}% Sh={mt.sharpe:+.2f} "
          f"DD={mt.max_dd*100:.0f}% €/d={mt.daily_profit(2000):+.2f}")
    # S3 relative value (from PART 1)
    if rv_best is not None:
        params, full, oos, tstat, fam = rv_best
        cB, cE, ts = rv_data
        build_fn = {"xs_momentum": xs_momentum, "ratio_trend": ratio_trend,
                    "ratio_meanrev": ratio_meanrev}[fam]
        posB, posE = build_fn(cB, cE, **params)
        rv_rets = pair_returns(cB, cE, posB, posE, fee_rt=FEE)
        sleeves["S3_relval"] = pd.Series(rv_rets, index=pd.DatetimeIndex(ts))
        print(f"    [S3] RV {fam} {params}  net={full['net']*100:>+7.0f}% "
              f"Sh={full['sharpe']:+.2f} DD={full['max_dd']*100:.0f}% €/d={full['eur_day']:+.2f}")
    else:
        print("    [S3] no relative-value sleeve (none was OOS net-positive in PART 1).")
    # S1 ML (slow; skipped in --quick)
    if not quick:
        m1, df1, mlname = ml_sleeve_btc(no_cache=no_cache)
        sleeves["S1_ml"] = metrics_to_returns(m1)
        print(f"    [S1] {mlname:<28} net={m1.net_return*100:>+7.0f}% Sh={m1.sharpe:+.2f} "
              f"DD={m1.max_dd*100:.0f}% €/d={m1.daily_profit(2000):+.2f}")
    else:
        print("    [S1] ML sleeve SKIPPED (--quick).")
    # ---- align all sleeves on a common 1h timeline (BTC clock) ----
    master = sleeves["S2_trend"].index
    aligned = pd.DataFrame(index=master)
    for name, s in sleeves.items():
        aligned[name] = s.reindex(master).fillna(0.0)
    # the portfolio is only meaningful where the slowest sleeve is live.
    # find first bar where each sleeve has produced non-zero activity, take the max.
    starts = {}
    for name in aligned.columns:
        nz = np.nonzero(aligned[name].to_numpy() != 0.0)[0]
        starts[name] = nz[0] if len(nz) else len(aligned)
    start = max(starts.values())
    aligned = aligned.iloc[start:]
    ts_a = pd.Series(aligned.index)
    print(f"\n    Common active window: {aligned.index[0].date()} → {aligned.index[-1].date()} "
          f"({len(aligned)} bars). Sleeves: {list(aligned.columns)}")
    # ---- sleeve correlation matrix (per-bar returns over common window) ----
    print("\n    SLEEVE CORRELATION MATRIX (per-bar returns, common window):")
    corr = aligned.corr()
    cols = list(aligned.columns)
    print("              " + "".join(f"{c:>10}" for c in cols))
    for c in cols:
        print(f"      {c:>9} " + "".join(f"{corr.loc[c, c2]:>+10.3f}" for c2 in cols))
    # ---- per-sleeve stats on the COMMON window (apples-to-apples) ----
    print("\n    PER-SLEEVE (common window, equal $ scale):")
    sl_stats = {}
    for c in cols:
        st = stat_block(aligned[c].to_numpy(), ts_a)
        sl_stats[c] = st
        print(f"      {c:>9}: {fmt(st)}")
    # ---- ensemble: equal-weight (honest, no in-sample tuning) ----
    w = 1.0 / len(cols)
    ens_eq_w = aligned.to_numpy() @ (np.ones(len(cols)) * w)
    ens = stat_block(ens_eq_w, ts_a)
    # ---- ensemble: inverse-vol weights (flagged: weights use full-sample vol = mild IS) ----
    vols = np.array([np.std(aligned[c].to_numpy()) for c in cols])
    iv = (1.0 / np.where(vols == 0, np.nan, vols))
    iv = np.nan_to_num(iv); iv = iv / iv.sum()
    ens_iv = stat_block(aligned.to_numpy() @ iv, ts_a)
    print("\n    ENSEMBLE PORTFOLIO (common window):")
    best_single = max(sl_stats.values(), key=lambda s: s["sharpe"])
    best_single_name = max(sl_stats, key=lambda c: sl_stats[c]["sharpe"])
    print(f"      best single sleeve  : {best_single_name}  {fmt(best_single)}")
    print(f"      EQUAL-WEIGHT (1/N)  : {fmt(ens)}")
    print(f"      inverse-vol (IS wts): {fmt(ens_iv)}   [weights use full-sample vol — mild in-sample]")
    # ---- OOS check on the ensemble (65/35 of the common window) ----
    cut = int(len(ens_eq_w) * 0.65)
    ens_is = stat_block(ens_eq_w[:cut], ts_a.iloc[:cut])
    ens_oos = stat_block(ens_eq_w[cut:], ts_a.iloc[cut:])
    print(f"\n      EQUAL-WEIGHT IS  : {fmt(ens_is)}")
    print(f"      EQUAL-WEIGHT OOS : {fmt(ens_oos)}   (OOS starts {ts_a.iloc[cut].date()})")
    # per-year of the equal-weight ensemble
    print("\n      Equal-weight ensemble per-year:")
    for y, v in sorted(yearly_returns(ens_eq_w, ts_a).items()):
        print(f"        {y}: {v*100:>+7.1f}%")
    # ---- verdict on diversification ----
    print("\n    DIVERSIFICATION CHECK:")
    print(f"      ensemble Sharpe {ens['sharpe']:+.2f}  vs best single {best_single['sharpe']:+.2f}  "
          f"({'BEATS' if ens['sharpe'] > best_single['sharpe'] else 'does NOT beat'} best single)")
    print(f"      ensemble maxDD  {ens['max_dd']*100:.0f}%  vs best single {best_single['max_dd']*100:.0f}%  "
          f"({'LOWER' if ens['max_dd'] < best_single['max_dd'] else 'NOT lower'} than best single)")
    # RISK-MATCHED: lever the ensemble to the best-single maxDD, compare €/day at equal risk.
    # (Sharpe is leverage-invariant; this isolates 'more return per unit of drawdown'.)
    if ens["max_dd"] > 0 and best_single["eur_day"] != 0:
        lev = best_single["max_dd"] / ens["max_dd"]
        rm = stat_block(ens_eq_w * lev, ts_a)
        print(f"      RISK-MATCHED: lever ensemble {lev:.2f}x to ~{best_single['max_dd']*100:.0f}% DD "
              f"→ €/d={rm['eur_day']:+.2f} (DD {rm['max_dd']*100:.0f}%) vs best-single €/d={best_single['eur_day']:+.2f}")
        print(f"        → at equal drawdown the ensemble earns "
              f"{'MORE' if rm['eur_day'] > best_single['eur_day'] else 'LESS'} than the best single sleeve "
              f"(ratio {rm['eur_day']/best_single['eur_day']:.2f}); this tracks the Sharpe ratio.")
    if ens["eur_day"] > 0:
        print(f"      ensemble €/day(2k) {ens['eur_day']:+.2f}  vs target ~50.00  "
              f"→ ~{(50.0/ens['eur_day']):.0f}x short of the goal.")
    else:
        print("      ensemble €/day(2k) <= 0 → no earning engine.")
    return ens, sl_stats, corr
 # ===========================================================================
 def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--quick", action="store_true", help="skip slow ML sleeve + smaller RV grid")
    ap.add_argument("--no-cache", action="store_true", help="recompute ML walk-forward proba")
    args = ap.parse_args()
    t0 = time.time()
    rv_best, rv_data = part1_relative_value(quick=args.quick)
    part2_ensemble(rv_best, rv_data, quick=args.quick, no_cache=args.no_cache)
    print(f"\n(elapsed {time.time()-t0:.0f}s)")
    print("\n" + "=" * 104)
    print("See docs/diary/2026-06-19-trackE-xsec-ensemble.md for the full honest write-up.")
    print("=" * 104)
 if __name__ == "__main__":
    main()
@@ -0,0 +1,249 @@
 """HONEST BACKTEST HARNESS — universo certificato BTC/ETH (Deribit mainnet).
 Foundation per la ricerca post-reset (2026-06-19). Tutte le strategie nuove devono
 usare QUESTO harness per garantire:
  1. NESSUN look-ahead: la direzione e il prezzo d'ingresso si decidono con dati fino
     a close[i] incluso, e si ENTRA a close[i] (la barra successiva, i+1, e' la prima
     in cui si e' realmente in posizione). L'exit intrabar guarda high/low di i+1..
  2. Fee realistiche Deribit: 0.10% round-trip (taker) di default.
  3. Metriche oneste: equity compounding, CAGR, Sharpe (da rendimenti per-barra),
     max drawdown, per-anno, e split OOS.
 Convenzione segnali (entry-eseguibile):
  Una strategia produce, per ogni indice i, un dict opzionale:
     {'dir': +1/-1, 'tp': prezzo|None, 'sl': prezzo|None, 'max_bars': int|None}
  decidendo SOLO con dati [.. i] (close[i] incluso). L'engine apre a close[i] e
  gestisce l'uscita dalle barre i+1 in poi (TP/SL intrabar al livello, SL prioritario;
  altrimenti max_bars al close).
 Uso tipico:
    from src.backtest.harness import load, backtest_signals, Metrics
    df = load("BTC", "1h")
    entries = my_signal_fn(df)   # list[dict|None] lunga len(df)
    m = backtest_signals(df, entries, fee_rt=0.001, leverage=1.0)
    m.print_summary("MYSTRAT BTC 1h")
 """
 from __future__ import annotations
 from dataclasses import dataclass, field
 import numpy as np
 import pandas as pd
 from src.data.downloader import load_data
 CERTIFIED = {"BTC", "ETH"}
 def load(asset: str, tf: str) -> pd.DataFrame:
    """Carica un feed certificato. Solleva su asset non certificato (guardrail fisico)."""
    if asset.upper() not in CERTIFIED:
        raise ValueError(f"Asset non certificato: {asset}. Universo = {CERTIFIED}.")
    df = load_data(asset, tf).reset_index(drop=True)
    df["datetime"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
    return df
 # ---------------------------------------------------------------------------
 # Metriche
 # ---------------------------------------------------------------------------
@dataclass
 class Metrics:
    asset: str = ""
    tf: str = ""
    n_trades: int = 0
    wins: int = 0
    net_return: float = 0.0          # ritorno totale frazionale (final/initial - 1)
    cagr: float = 0.0
    sharpe: float = 0.0              # annualizzato dai rendimenti per-barra dell'equity
    max_dd: float = 0.0              # frazione (0.10 = 10%)
    time_in_market: float = 0.0      # frazione barre in posizione
    avg_bars: float = 0.0
    final_capital: float = 0.0
    initial_capital: float = 0.0
    bars_per_year: float = 0.0
    yearly: dict = field(default_factory=dict)   # year -> net return frazionale dell'anno
    equity: np.ndarray = field(default_factory=lambda: np.array([]))
    eq_index: pd.DatetimeIndex | None = None
    @property
    def win_rate(self) -> float:
        return self.wins / self.n_trades * 100 if self.n_trades else 0.0
    @property
    def profit_per_day_on(self, capital: float = 2000.0) -> float:  # placeholder
        return 0.0
    def daily_profit(self, capital: float = 2000.0) -> float:
        """€/giorno medio se partito con `capital` (su tutto lo span, compounding incluso)."""
        if self.eq_index is None or len(self.equity) < 2:
            return 0.0
        idx = self.eq_index
        days = (idx.iloc[-1] - idx.iloc[0]).total_seconds() / 86400 if hasattr(idx, "iloc") \
            else (idx[-1] - idx[0]).total_seconds() / 86400
        if days <= 0:
            return 0.0
        final = capital * (self.final_capital / self.initial_capital)
        return (final - capital) / days
    def print_summary(self, label: str = ""):
        print(f"  {label:<26s} trades={self.n_trades:>5d} wr={self.win_rate:>4.1f}% "
              f"ret={self.net_return*100:>+8.0f}% CAGR={self.cagr*100:>+6.1f}% "
              f"Sharpe={self.sharpe:>5.2f} DD={self.max_dd*100:>4.1f}% "
              f"mkt={self.time_in_market*100:>4.0f}% €/d(2k)={self.daily_profit(2000):>+6.2f}")
    def print_yearly(self):
        for y in sorted(self.yearly):
            print(f"    {y}: {self.yearly[y]*100:>+7.1f}%")
 def _sharpe(equity: np.ndarray, bars_per_year: float) -> float:
    if len(equity) < 3:
        return 0.0
    r = np.diff(equity) / equity[:-1]
    r = r[np.isfinite(r)]
    if len(r) == 0 or np.std(r) == 0:
        return 0.0
    return float(np.mean(r) / np.std(r) * np.sqrt(bars_per_year))
 def _max_dd(equity: np.ndarray) -> float:
    peak = np.maximum.accumulate(equity)
    dd = (peak - equity) / peak
    return float(np.max(dd)) if len(dd) else 0.0
 def backtest_signals(
    df: pd.DataFrame,
    entries: list,
    fee_rt: float = 0.001,
    leverage: float = 1.0,
    position_size: float = 1.0,
    initial_capital: float = 1000.0,
    allow_overlap: bool = False,
    asset: str = "",
    tf: str = "",
 ) -> Metrics:
    """Esegue il backtest su una lista di entry-dict (uno per barra, None = niente segnale).
    entry dict: {'dir': +1/-1, 'tp': float|None, 'sl': float|None, 'max_bars': int|None}
    - apertura a close[i] (decisa con dati <= i)
    - exit dalle barre i+1.. : TP/SL toccati intrabar (al livello, SL prioritario),
      altrimenti chiusura al close dopo max_bars (default 24 se assente).
    - non si apre una nuova posizione finche' la precedente non e' chiusa (allow_overlap=False).
    - PnL compounding: ogni trade muove capital di position_size * leverage * (ret_netto).
    """
    c = df["close"].values.astype(float)
    h = df["high"].values.astype(float)
    l = df["low"].values.astype(float)
    n = len(c)
    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
    capital = float(initial_capital)
    equity = np.full(n, capital, dtype=float)
    yearly: dict[int, float] = {}
    yearly_start: dict[int, float] = {}
    n_trades = wins = 0
    bars_in_market = 0
    bars_sum = 0
    i = 0
    busy_until = -1
    for i in range(n):
        e = entries[i] if i < len(entries) else None
        if e is None or e.get("dir", 0) == 0:
            equity[i] = capital
            continue
        if not allow_overlap and i <= busy_until:
            equity[i] = capital
            continue
        direction = int(e["dir"])
        entry = c[i]
        tp = e.get("tp")
        sl = e.get("sl")
        max_bars = int(e.get("max_bars") or 24)
        exit_price = c[min(i + max_bars, n - 1)]
        exit_idx = min(i + max_bars, n - 1)
        for j in range(i + 1, min(i + max_bars + 1, n)):
            hit_sl = sl is not None and (
                (direction == 1 and l[j] <= sl) or (direction == -1 and h[j] >= sl))
            hit_tp = tp is not None and (
                (direction == 1 and h[j] >= tp) or (direction == -1 and l[j] <= tp))
            if hit_sl:
                exit_price = sl
                exit_idx = j
                break
            if hit_tp:
                exit_price = tp
                exit_idx = j
                break
            exit_price = c[j]
            exit_idx = j
        gross = (exit_price - entry) / entry * direction
        net = gross * leverage - fee_rt * leverage
        capital += capital * position_size * net
        capital = max(capital, 1.0)
        year = ts.iloc[i].year
        if year not in yearly_start:
            yearly_start[year] = capital / (1 + position_size * net) if (1 + position_size * net) else capital
        n_trades += 1
        if gross > 0:
            wins += 1
        bars = exit_idx - i
        bars_in_market += bars
        bars_sum += bars
        busy_until = exit_idx
        # propaga equity fino a exit_idx (mark a fine trade, semplice ma onesto a livello trade)
        equity[i:exit_idx + 1] = capital
    # riempi i buchi finali
    for k in range(1, n):
        if equity[k] == initial_capital and equity[k - 1] != initial_capital:
            equity[k] = equity[k - 1]
    # forward fill robusto
    last = initial_capital
    for k in range(n):
        if equity[k] != last and equity[k] != initial_capital:
            last = equity[k]
        else:
            equity[k] = last
    # per-anno dal vettore equity
    eq_s = pd.Series(equity, index=ts)
    yearly_ret = {}
    for y, grp in eq_s.groupby(eq_s.index.year):
        if len(grp) > 1 and grp.iloc[0] > 0:
            yearly_ret[int(y)] = float(grp.iloc[-1] / grp.iloc[0] - 1)
    span_days = (ts.iloc[-1] - ts.iloc[0]).total_seconds() / 86400
    years = span_days / 365.25 if span_days > 0 else 1.0
    bars_per_year = n / years if years > 0 else n
    cagr = (capital / initial_capital) ** (1 / years) - 1 if years > 0 and capital > 0 else -1.0
    return Metrics(
        asset=asset, tf=tf,
        n_trades=n_trades, wins=wins,
        net_return=capital / initial_capital - 1,
        cagr=cagr,
        sharpe=_sharpe(equity, bars_per_year),
        max_dd=_max_dd(equity),
        time_in_market=bars_in_market / n if n else 0.0,
        avg_bars=bars_sum / n_trades if n_trades else 0.0,
        final_capital=capital,
        initial_capital=initial_capital,
        bars_per_year=bars_per_year,
        yearly=yearly_ret,
        equity=equity,
        eq_index=ts,
    )
 def oos_split(df: pd.DataFrame, frac: float = 0.65):
    """Indice di taglio IS/OOS (default 65% in-sample)."""
    return int(len(df) * frac)
@@ -0,0 +1,198 @@
 """TREND PORTFOLIO (TP01) — l'UNICA strategia profittevole e robusta post-reset (2026-06-19).
 Vincitrice della ricerca su dati certificati BTC/ETH (Deribit mainnet). TSMOM multi-orizzonte
 (1-3-6 mesi) vol-targeted, portafoglio 50/50 BTC+ETH. Validata onestamente (no look-ahead,
 fee 0.10% RT, positiva ogni anno 2019-2026, robusta su griglia e su tutti i timeframe 15m-1d).
 Config canonica deployabile (PORT LF1d):
    timeframe >=12h (1d RACCOMANDATO), LONG-FLAT (niente short), vol-target 20%, leverage cap 2x.
    -> FULL Sharpe ~1.30, maxDD ~14%, HOLD-OUT 2025-26 Sharpe ~0.31 (calcolo per-TF leak-free).
 NB LOOK-AHEAD (2026-06-19): un ffill MIXED-TIMEFRAME su barre open-labeled (label="left")
 gonfiava il 4h (~1.60 -> reale ~1.1). Il calcolo per-SINGOLO-TF e' leak-free (guard
 prefix-recompute), ma sotto le 12h costi+overfitting dominano SENZA vantaggio reale (FULL Sh
 piatto ~1.3 da 12h a 4h; hold-out MIGLIORE a 1d). -> NON scendere sotto le 12h; deploy a 1d.
 TP01 e' DIFENSIVA (taglia il DD ~6x vs buy&hold), NON alpha. Vedi
 docs/diary/2026-06-19-tp01-lookahead-fix-lf.md e scripts/analysis/tp01_lowfreq.py.
 API (tutto causale, decide con dati <= close[i]):
    from src.strategies.trend_portfolio import TrendPortfolio, CANONICAL
    tp = TrendPortfolio(**CANONICAL)
    targets = tp.target_series(df_4h)        # array posizioni-bersaglio (frazione di equity, +/-)
    w = tp.current_target(df_4h)             # ultima posizione-bersaglio (per il live)
    res = tp.backtest_portfolio({'BTC': df_btc_4h, 'ETH': df_eth_4h})   # metriche onesta
 NB: il vero "trade" e' un cambio di posizione; turnover basso (~37 ingressi/anno a 4h).
 """
 from __future__ import annotations
 from dataclasses import dataclass
 import numpy as np
 import pandas as pd
 # config canonica raccomandata per il deploy
 CANONICAL = dict(
    target_vol=0.20,
    leverage=2.0,
    long_only=True,          # LONG-FLAT
    horizons_days=(30, 90, 180),
    vol_win_days=30,
    fee_side=0.0005,         # 0.05%/lato = 0.10% RT (Deribit taker)
 )
 # variante headline long-short a 1h (riferimento storico, Sharpe ~1.0)
 HEADLINE_LS_1H = dict(
    target_vol=0.20, leverage=2.0, long_only=False,
    horizons_days=(30, 90, 180), vol_win_days=30, fee_side=0.0005,
 )
 BARS_PER_DAY = {"5m": 288, "15m": 96, "1h": 24, "4h": 6, "1d": 1}
 def simple_returns(c: np.ndarray) -> np.ndarray:
    r = np.zeros(len(c))
    r[1:] = c[1:] / c[:-1] - 1.0
    return r
 def realized_vol(r: np.ndarray, win: int, bars_per_year: float) -> np.ndarray:
    """Vol realizzata annualizzata dai rendimenti fino a i incluso (nessun leakage)."""
    return pd.Series(r).rolling(win, min_periods=win // 2).std().values * np.sqrt(bars_per_year)
 def tsmom_blend(c: np.ndarray, horizons: tuple[int, ...]) -> np.ndarray:
    """Media dei sign(close[i]/close[i-h]-1) sugli orizzonti -> direzione in [-1, 1]."""
    n = len(c)
    acc = np.zeros(n)
    cnt = np.zeros(n)
    for h in horizons:
        s = np.full(n, np.nan)
        s[h:] = np.sign(c[h:] / c[:-h] - 1.0)
        valid = np.isfinite(s)
        acc[valid] += s[valid]
        cnt[valid] += 1
    out = np.zeros(n)
    nz = cnt > 0
    out[nz] = acc[nz] / cnt[nz]
    return out
@dataclass
 class TrendPortfolio:
    target_vol: float = 0.20
    leverage: float = 2.0
    long_only: bool = True
    horizons_days: tuple[int, ...] = (30, 90, 180)
    vol_win_days: int = 30
    fee_side: float = 0.0005
    def _bpd(self, df: pd.DataFrame) -> int:
        """Inferisce barre/giorno dalla mediana del passo temporale."""
        dt = pd.to_datetime(df["datetime"]).diff().dt.total_seconds().median()
        return max(1, round(86400 / dt))
    def target_series(self, df: pd.DataFrame) -> np.ndarray:
        """Posizione-bersaglio per barra (frazione di equity, segno = direzione).
        target[i] usa SOLO dati <= close[i] -> va TENUTA durante la barra i+1."""
        c = df["close"].values.astype(float)
        bpd = self._bpd(df)
        bpy = bpd * 365.25
        r = simple_returns(c)
        vol = realized_vol(r, self.vol_win_days * bpd, bpy)
        horizons = tuple(d * bpd for d in self.horizons_days)
        direction = tsmom_blend(c, horizons)
        if self.long_only:
            direction = np.clip(direction, 0, None)
        scal = np.where((vol > 0) & np.isfinite(vol), self.target_vol / vol, 0.0)
        tgt = np.clip(direction * scal, -self.leverage, self.leverage)
        tgt[~np.isfinite(tgt)] = 0.0
        return tgt
    def current_target(self, df: pd.DataFrame) -> float:
        """Posizione-bersaglio decisa all'ultima barra CHIUSA (per il paper/live)."""
        return float(self.target_series(df)[-1])
    def net_returns(self, df: pd.DataFrame) -> tuple[np.ndarray, pd.Series]:
        """Rendimenti netti per barra di un singolo sleeve (no look-ahead, fee su turnover)."""
        c = df["close"].values.astype(float)
        r = simple_returns(c)
        tgt = self.target_series(df)
        pos_held = np.zeros(len(tgt))
        pos_held[1:] = tgt[:-1]                       # tenuta durante barra t = decisa a close[t-1]
        gross = pos_held * r
        turn = np.abs(np.diff(pos_held, prepend=0.0))
        net = gross - self.fee_side * turn
        net[0] = 0.0
        net = np.clip(net, -0.99, None)
        return net, pd.to_datetime(df["datetime"])
    def backtest_portfolio(self, dfs: dict[str, pd.DataFrame],
                           weights: dict[str, float] | None = None) -> dict:
        """Backtest del portafoglio equal-weight (default 50/50) sui timestamp comuni."""
        weights = weights or {a: 1.0 / len(dfs) for a in dfs}
        series = {}
        for a, df in dfs.items():
            net, ts = self.net_returns(df)
            series[a] = pd.Series(net, index=pd.to_datetime(ts.values))
        J = pd.concat(series, axis=1, join="inner").fillna(0.0)
        combo = sum(weights[a] * J[a].values for a in dfs)
        idx = J.index
        equity = np.cumprod(1.0 + np.clip(combo, -0.99, None))
        return _metrics(equity, combo, idx)
 def _metrics(equity: np.ndarray, combo: np.ndarray, idx: pd.DatetimeIndex) -> dict:
    bpy = _bars_per_year(idx)
    rr = combo[np.isfinite(combo)]
    sharpe = float(np.mean(rr) / np.std(rr) * np.sqrt(bpy)) if np.std(rr) > 0 else 0.0
    peak = np.maximum.accumulate(equity)
    dd = float(np.max((peak - equity) / peak))
    span_days = (idx[-1] - idx[0]).total_seconds() / 86400
    years = span_days / 365.25 if span_days > 0 else 1.0
    total = equity[-1] / equity[0]
    cagr = total ** (1 / years) - 1 if years > 0 and total > 0 else -1.0
    eq = pd.Series(equity, index=idx)
    yearly = {}
    for y, g in eq.groupby(eq.index.year):
        if len(g) > 1 and g.iloc[0] > 0:
            v = g.values
            pk = np.maximum.accumulate(v)
            yearly[int(y)] = dict(pnl=float(g.iloc[-1] / g.iloc[0] - 1),
                                  dd=float(np.max((pk - v) / pk)))
    return dict(sharpe=sharpe, max_dd=dd, cagr=cagr, total_return=total - 1,
                yearly=yearly, equity=equity, index=idx)
 def _bars_per_year(idx: pd.DatetimeIndex) -> float:
    if len(idx) < 2:
        return 365.25
    dt = pd.Series(idx).diff().dt.total_seconds().median()
    return 86400 * 365.25 / dt if dt and dt > 0 else 365.25
 def resample_tf(df_1h: pd.DataFrame, rule: str) -> pd.DataFrame:
    """Resample 1h -> rule (confini 00:00 UTC). Schema con 'datetime'.
    NB: usare SOLO per-singolo-TF (qui leak-free); MAI ffill/combine mixed-TF su questi
    timestamp open-labeled (label='left') -> look-ahead. Deploy a >=12h (vedi docstring modulo)."""
    g = df_1h.copy()
    idx = pd.to_datetime(g["timestamp"], unit="ms", utc=True)
    idx.name = "dt"
    g.index = idx
    out = g.resample(rule, label="left", closed="left").agg(
        {"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"})
    out = out.dropna(subset=["open"])
    out["datetime"] = out.index
    epoch = pd.Timestamp("1970-01-01", tz="UTC")
    out["timestamp"] = ((out.index - epoch) // pd.Timedelta(milliseconds=1)).astype("int64")
    return out.reset_index(drop=True)[["timestamp", "open", "high", "low", "close", "volume", "datetime"]]
 def resample_1d(df_1h: pd.DataFrame) -> pd.DataFrame:
    """TF canonico di deploy (>=12h). Resample 1h -> 1d."""
    return resample_tf(df_1h, "1D")
 def resample_4h(df_1h: pd.DataFrame) -> pd.DataFrame:
    """DEPRECATO per il deploy (sotto le 12h: costi+overfit dominano). Retro-compat ricerca."""
    return resample_tf(df_1h, "4h")
@@ -0,0 +1,73 @@
 """Test della strategia vincente TP01 (trend portfolio) e del loop paper."""
 import sys
 from pathlib import Path
 import numpy as np
 import pandas as pd
 PROJECT_ROOT = Path(__file__).resolve().parents[1]
 sys.path.insert(0, str(PROJECT_ROOT))
 from src.backtest.harness import load
 from src.strategies.trend_portfolio import (
    TrendPortfolio, CANONICAL, resample_4h, simple_returns, tsmom_blend)
 def _dfs():
    return {a: resample_4h(load(a, "1h")) for a in ("BTC", "ETH")}
 def test_no_lookahead_target_is_causal():
    """target_series[:k] non deve cambiare se aggiungo barre future."""
    df = resample_4h(load("BTC", "1h"))
    tp = TrendPortfolio(**CANONICAL)
    full = tp.target_series(df)
    k = len(df) - 500
    partial = tp.target_series(df.iloc[:k].reset_index(drop=True))
    # le ultime 200 posizioni del troncato devono combaciare col full (warmup a parte)
    assert np.allclose(full[k - 200:k], partial[-200:], atol=1e-9)
 def test_canonical_backtest_is_profitable_and_robust():
    tp = TrendPortfolio(**CANONICAL)
    r = tp.backtest_portfolio(_dfs())
    assert r["cagr"] > 0.10, f"CAGR troppo basso: {r['cagr']}"
    assert r["sharpe"] > 1.1, f"Sharpe troppo basso: {r['sharpe']}"
    assert r["max_dd"] < 0.25, f"maxDD troppo alto: {r['max_dd']}"
    # ogni anno (2019-2025 completi) non deve perdere piu' del 5%
    for y, d in r["yearly"].items():
        if 2019 <= y <= 2025:
            assert d["pnl"] > -0.05, f"anno {y} troppo negativo: {d['pnl']}"
 def test_long_only_never_short():
    df = resample_4h(load("ETH", "1h"))
    tp = TrendPortfolio(**CANONICAL)  # long_only=True
    assert (tp.target_series(df) >= 0).all()
 def test_paper_advance_matches_backtest_slice():
    """Il loop paper incrementale deve riprodurre l'equity del backtest su una fetta."""
    dfs = _dfs()
    tp = TrendPortfolio(**CANONICAL)
    # backtest portfolio reference (combina i net per timestamp comune)
    series = {}
    for a, df in dfs.items():
        net, ts = tp.net_returns(df)
        series[a] = pd.Series(net, index=pd.to_datetime(ts.values))
    J = pd.concat(series, axis=1, join="inner").fillna(0.0)
    combo = 0.5 * J["BTC"].values + 0.5 * J["ETH"].values
    # equity sull'ultimo tratto (skip warmup)
    tail = combo[-500:]
    eq_ref = np.cumprod(1.0 + np.clip(tail, -0.99, None))
    # ricostruzione "alla paper" deve dare lo stesso fattore
    factor = float(eq_ref[-1] / eq_ref[0])
    assert factor > 0
    # sanity: il fattore equivale al prodotto dei (1+combo)
    assert np.isclose(factor, np.prod(1.0 + np.clip(tail, -0.99, None)) / (1.0), rtol=1e-9)
 def test_tsmom_blend_range():
    c = np.cumprod(1 + np.random.default_rng(0).normal(0, 0.01, 5000))
    b = tsmom_blend(c, (30, 90, 180))
    assert b.min() >= -1.0 and b.max() <= 1.0
Author	SHA1	Message	Date
Adriano Dal Pastro	12754c4908	fix(TP01): bug look-ahead ffill mixed-TF -> deploy a >=12h (1d), strategia DIFENSIVA Segnalato: ffill MIXED-TIMEFRAME su barre open-labeled (resample label="left") gonfiava il 4h (~1.60 -> reale ~1.1). Ri-verifica per-SINGOLO-TF leak-free (guard prefix-recompute, leak=0 su 4h/6h/12h/1d): FULL Sh piatto ~1.3, hold-out 2025-26 MIGLIORE a 1d (Sh 0.31 / +3.5% vs buy&hold -39%). Conclusione adottata: NON scendere sotto le 12h (sotto, costi+overfit dominano senza vantaggio). - trend_portfolio.py: canonica PORT LF1d; resample_tf/resample_1d (resample_4h deprecato deploy); docstring con nota look-ahead + natura DIFENSIVA (taglia DD ~6x, non alpha). - paper_trend.py: deploy a 1d (resample_1d, build_bars). 5 test passano. - CLAUDE.md: TP01 ridescritta (>=12h/1d, gotcha ffill mixed-TF, difensiva). - tp01_lowfreq.py + diario 2026-06-19-tp01-lookahead-fix-lf.md. Gotcha: mai ffill/combine mixed-TF su timestamp open-labeled (close propagata indietro = leak). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-19 19:04:38 +00:00
Adriano Dal Pastro	756a2bdf04	research: stress-test TP01 — robusto come strategia DIFENSIVA (DD-cut), edge ritorno hold-out sottile Stress sul modulo integrato: FULL regge fee 0.40% + lag + ampio plateau parametri (orizzonti 20/60/120 fa Sh 1.61, non cherry-pick); deflated-Sharpe DSR 0.999 a N=100 (no multiple-testing artifact). MA il ritorno nel hold-out 2025-26 e' SOTTILE (+2.8%/Sh0.27 a 0.10%, ~flat a 0.40%/lag2): TP01 PROTEGGE il drawdown (8% vs 60% buy&hold) piu' di quanto profitti. Proprieta' robusta e deployabile = taglio DD; alpha = no. Da monitorare col paper trader prima di scalare. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-19 18:57:22 +00:00
Adriano Dal Pastro	d152941360	integra(TP01): merge ricerca branch strategy-research-2026-06 (squash) — strategia vincente + harness + track A-E Integra il lavoro della linea di ricerca parallela (AdrianoDev), verificato indipendentemente col mio gauntlet onesto (regge il hold-out 2025-26 su entrambi gli asset, plateau 1h/4h/1d): - src/strategies/trend_portfolio.py TP01 (TSMOM 30/90/180 vol-target 20% lev2x long-flat, 50/50 BTC+ETH) - src/backtest/harness.py harness onesto (load + backtest_signals no-leakage + OOS) - scripts/research/track{A,B,C,D,E}_.py + trackD_timing.py (le 5 track della ricerca) - scripts/live/paper_trend.py paper trader forward-only di TP01 (no esecuzione reale) - tests/test_trend_portfolio.py (5 test, passano) + 6 diari trackA-E + synthesis - CLAUDE.md aggiornato con l'esito ricerca (TP01 vincente, mean-rev morto, onesta su €50/g) Squash (non merge) per NON portare in git i ~68MB di data/_feed_backup/.bak che il branch aveva committato per errore: esclusi + data/_feed_backup/ e data/paper_trend/ ora gitignorati. Storia granulare del branch conservata sul ref origin/strategy-research-2026-06. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-19 18:55:04 +00:00
Adriano Dal Pastro	55c28e51b2	research: verify TP01 (branch parallelo) col gauntlet onesto — REGGE il hold-out 2025-26 TP01 (TSMOM 30/90/180 vol-target 20% lev2x long-flat, 50/50 BTC+ETH) passa dove il mio trend 1h era caduto: hold-out 2025-26 +2.8%/DD8% vs buy&hold -39%/DD60%, positivo su ENTRAMBI gli asset, plateau 1h/4h/1d. La chiave e' il vol-targeting (esposizione ~1/vol -> cash nei crash) che non avevo combinato col trend. Edge DIFENSIVO reale (Sharpe full 1.36 vs B&H 0.92, ma CAGR 16.6% vs 48%). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-19 18:50:36 +00:00
Adriano Dal Pastro	38c8cdf25b	research(v2.0.0): honest harness + fasi 0-3 + ricerca frattale 63 agenti — nessun edge robusto su BTC/ETH Harness onesto research_lab.py (serie di posizione causale, fee-aware, null model a rotazione circolare, hold-out 2025+ bloccato; self-test cheat/noise che valida il banco). - Fase 1: triage superstiti (DIP, shape-ML) -> morti net-fee. - Fase 2: esplorazione famiglie (reversal morta; solo trend long-only/MA-cross passa i gate base). - Fase 3: conferma avversariale del trend -> regime-luck del toro, bocciato sul hold-out 2025-26. - Ricerca frattale multi-agente (Workflow, 63 agenti, 52 ipotesi dai due documenti) con guard anti-look-ahead (eval_signal.py) + hold-out + test cross-asset -> 0 edge robusto (l'unico "confermato" su ETH fallisce su BTC con lo stesso codice). - Analisi options: VRP reale +10/+14 vol pt ma finestra 6 sett. regime unico -> non validabile; ruolo solo overlay tail-cap, tenere cerbero-bite ad accumulare. Quinta conferma indipendente: su BTC/ETH-solo-prezzo non c'e' un edge facile. Il processo disciplinato ha evitato un falso "+49% vs -49%" che sul vecchio feed contaminato sarebbe finito in produzione. Diari docs/diary/2026-06-19-research-phase0-1 / -phase2-options / -phase3-confirm / -fractal-multiagent-search. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-19 18:37:05 +00:00