merge: fase 2 portafogli - validazione runner + worker live honest/TSM01

A) PortfolioRunner certificato (pool/ribilancio/ledger == backtest).
B) worker live dedicati: DIP01 (Strategy), BasketTrendWorker (TR01), RotationWorker (ROT02),
   TsmomWorker (TSM01) + integrazione runner (resample 1h->4h/1d). PORT06 gira live completo.
Validati vs reference (TSM01 esatto, ROT02 canonico, TR01 stesso ordine). 35 test passano.

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

CLAUDE.md

@@ -258,8 +258,8 @@ queste fade, ma va confermato col paper trader live prima di rischiare capitale
 - **Schemi peso:** `equal` (default), `cap` (tetto per famiglia, es. pairs 33% — config raccomandata), `inverse_vol`, `cluster_rp` (equal fra cluster naturali poi inverse-vol intra-cluster), `manual`. Definiti in `weighting.py`; la chiave cap è la famiglia (PAIRS/FADE/HONEST/SHAPE/TSM).
 - **Default `portfolios.yml`:** PORT06 (master+shape), `weighting=cap pairs 0.33`, leva 2x, ribilancio 1D. Backtest PORT06: FULL Sharpe 6.07 / OOS Sharpe 8.19, DD 4.9% full / 2.3% OOS.
 - **Data layer Cerbero v2:** `get_historical_v2` unificato + `get_instruments` (naming robusto) + `get_ticker_batch`. Trading su Deribit.
-- **SCOPE LIVE v1:** il runner esegue gli sleeve con worker pronti = fade (MR01/02/07) + pairs (PR01) + shape (SH01, via `MLWorkerWrapper` con retraining). Gli sleeve **honest (DIP01/TR01/ROT02) e TSM01 sono SALTATI nel live** (nessun worker dedicato ancora) → restano solo nel backtest; il runner li logga come saltati e rinormalizza i pesi sugli sleeve eseguibili. Worker honest/TSM01 = fase 2.
+- **SCOPE LIVE (fase 2 completata):** il runner esegue TUTTI gli sleeve di PORT06. Worker: single `StrategyWorker` (fade MR01/02/07, DIP01), `PairsWorker` (PR01 2 gambe), `MLWorkerWrapper` (SH01 retraining), e i multi-asset dedicati `BasketTrendWorker` (TR01 4h), `RotationWorker` (ROT02 1d), `TsmomWorker` (TSM01 1d). Il runner fetcha 1h da Cerbero v2 e **resampla a 4h/1d** (lookback dimensionato sui daily: TSM01 usa 252g). Validazione: runner pool/ribilancio/ledger == backtest (`validate_portfolio_runner.py`, identico); worker multi-asset == reference (`validate_honest_workers.py`: TSM01 esatto, ROT02 +1303% canonico, TR01 stesso ordine — differenza di convenzione capitale-unico vs media-equity).
-- **Limite noto:** al ribilancio le posizioni APERTE restano sul loro notional (non travasate); comportamento fedele al backtest daily-rebalanced entro il turnover infragiornaliero.
+- **Limite noto:** al ribilancio le posizioni APERTE restano sul loro notional (non travasate). Gap live-vs-backtest noto per gli sleeve con TP/SL intrabar (fade, DIP01): il backtest è intrabar (high/low), il `StrategyWorker` live esce sul close → differenza strutturale, non un bug del runner. Pairs e tsmom/rotation non ne soffrono (exit a chiusura barra).
 
 ## Multi-Strategy Paper Trader
 

docs/superpowers/plans/2026-05-29-portfolios-phase2B-honest-workers.md

@@ -0,0 +1,377 @@
+# Fase 2-B — Worker live honest/TSM01 (dedicati) — Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: superpowers:subagent-driven-development o executing-plans. Steps con checkbox `- [ ]`.
+
+**Goal:** Costruire i worker live mancanti perché PORT06 giri live al completo (oltre a fade+pairs+shape già pronti): DIP01, TR01 (basket), ROT02 (rotation), TSM01 (tsmom rotation), e integrarli nel `PortfolioRunner`.
+
+**Architecture:** Worker DEDICATI per ogni strategia (scelta utente). DIP01 è single-asset → Strategy subclass + `StrategyWorker` esistente. TR01/ROT02/TSM01 sono multi-asset/rotation → tre classi worker nuove in `src/live/` con stato per-asset persistente, ciascuna fedele alla rispettiva funzione di backtest in `scripts/analysis/{honest_improve2,tsmom_research}.py`. Integrazione in `src/portfolio/runner.py::build_worker_for` + tick.
+
+**Tech Stack:** Python 3.11, pandas/numpy, pytest. Riusa CerberoClient v2 (multi-asset fetch), PortfolioLedger, e le funzioni di riferimento honest/tsm.
+
+**Branch:** `portfolio_phase2`. **Spec madre:** `docs/superpowers/specs/2026-05-29-portfolios-design.md` (§ scope live, fase 2).
+
+**Riferimenti di logica (NON modificare, sono la verità del backtest):**
+- DIP01 → `honest_improve2.dip_market_gated` (z-score dip, gate BTC>SMA, TP=SMA/SL=ATR/max_bars, intrabar).
+- TR01 → `honest_improve2._tr_basket_daily` (per asset 4h: EMA20>EMA100 long/flat; basket equal-weight).
+- ROT02 → `honest_improve2._rot_daily_equity` (panel 1d, mom 60g, top-3 se mom>0 e BTC>SMA100, gross 0.45 split, ribilancio giornaliero).
+- TSM01 → `tsmom_research.tsmom_sim` (panel 1d, Σ sign(P/P[-h]) h∈{63,126,252} ≥ thr=1.0, gate BTC>SMA100, gross 0.30 split).
+
+---
+
+## File structure
+
+| File | Responsabilità |
+|------|----------------|
+| `scripts/strategies/DIP01_dip_buy.py` | Strategy `Dip01DipBuy` (single-asset; metadata tp/sl/max_bars + gate) |
+| `src/live/basket_trend_worker.py` | `BasketTrendWorker` (TR01): N asset 4h, EMA cross, long/flat per asset |
+| `src/live/rotation_worker.py` | `RotationWorker` (ROT02): panel 1d, dual-momentum top-k, gross split |
+| `src/live/tsmom_worker.py` | `TsmomWorker` (TSM01): panel 1d, consenso segni multi-orizzonte |
+| `src/live/strategy_loader.py` | **mod**: aggiungi `DIP01_dip_buy` a MODULE_MAP |
+| `src/portfolio/runner.py` | **mod**: `build_worker_for` gestisce kind "basket"/"rotation"/"tsmom"; tick multi-asset |
+| `src/portfolio/base.py` (`_defs.py`) | **mod**: SleeveSpec degli honest/tsm con `kind` e `universe` corretti |
+| `tests/portfolio/test_honest_workers.py` | unit per ciascun worker + replay==backtest su finestra |
+
+**Universi:** TR01 = [BNB,BTC,DOGE,SOL,XRP] (4h); ROT02/TSM01 = `available_assets()` (1d). I worker multi-asset ricevono il dict {asset: df} dal runner.
+
+---
+
+## Task 1: DIP01 come Strategy single-asset
+
+**Files:** Create `scripts/strategies/DIP01_dip_buy.py`; Modify `src/live/strategy_loader.py`; Test `tests/portfolio/test_dip01.py`.
+
+- [ ] **Step 1: Test (fallisce)** — `tests/portfolio/test_dip01.py`:
+
+```python
+import pandas as pd
+from src.data.downloader import load_data
+from scripts.strategies.DIP01_dip_buy import Dip01DipBuy
+
+
+def test_dip01_generates_long_signals_with_exits():
+    df = load_data("BTC", "1h").iloc[-5000:].reset_index(drop=True)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    sigs = Dip01DipBuy().generate_signals(df, ts, asset="BTC", tf="1h")
+    assert len(sigs) > 0
+    s = sigs[0]
+    assert s.direction == 1                      # dip-buy è solo long
+    assert {"tp", "sl", "max_bars"} <= set(s.metadata)
+```
+
+- [ ] **Step 2:** `uv run pytest tests/portfolio/test_dip01.py -v` → FAIL (ModuleNotFoundError).
+
+- [ ] **Step 3: Implementa `scripts/strategies/DIP01_dip_buy.py`.** Replica ESATTA della logica di `dip_market_gated` (default `market_n=0` = senza gate, come lo sleeve DIP01_BTC del portafoglio: vedi combine_portfolio che usa `market_n=0`). Genera Signal long quando `z[i] <= -z_in and z[i-1] > -z_in`, con metadata `tp=SMA[i]`, `sl=c[i]-sl_atr*atr[i]`, `max_bars`. fee_rt=0.001, leverage 3, position 0.15.
+
+```python
+"""DIP01 — Dip-buy mean-reversion single-asset (z-score sotto-banda). Honest family.
+
+Replica live della logica validata in scripts/analysis/honest_improve2.dip_market_gated
+(con market_n=0, come lo sleeve DIP01_BTC del portafoglio): compra quando lo z-score del
+prezzo rispetto a SMA(n) incrocia sotto -z_in; esce a TP=SMA, SL=close-sl_atr*ATR, o max_bars.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from src.strategies.base import Strategy, Signal  # noqa: E402
+
+
+def _atr(df, n=14):
+    h, l, c = df["high"].values, df["low"].values, df["close"].values
+    pc = np.roll(c, 1); pc[0] = c[0]
+    tr = np.maximum(h - l, np.maximum(np.abs(h - pc), np.abs(l - pc)))
+    return pd.Series(tr).rolling(n).mean().values
+
+
+class Dip01DipBuy(Strategy):
+    name = "DIP01_dip_buy"
+    description = "Dip-buy mean-reversion single-asset (z-score), exit TP=SMA/SL=ATR/max_bars"
+    default_assets = ["BTC"]
+    default_timeframes = ["1h"]
+    fee_rt = 0.001
+    leverage = 3.0
+    position_size = 0.15
+
+    def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
+                         n: int = 50, z_in: float = 2.5, sl_atr: float = 2.5,
+                         max_bars: int = 24, **params) -> list[Signal]:
+        c = df["close"].values
+        ma = pd.Series(c).rolling(n).mean().values
+        sd = pd.Series(c).rolling(n).std().values
+        a = _atr(df, 14)
+        z = (c - ma) / np.where(sd == 0, np.nan, sd)
+        out: list[Signal] = []
+        for i in range(n + 14, len(c)):
+            if np.isnan(z[i]) or np.isnan(a[i]) or np.isnan(ma[i]):
+                continue
+            if z[i] <= -z_in and z[i - 1] > -z_in:
+                out.append(Signal(idx=i, direction=1, entry_price=float(c[i]),
+                                  metadata={"tp": float(ma[i]),
+                                            "sl": float(c[i] - sl_atr * a[i]),
+                                            "max_bars": int(max_bars)}))
+        return out
+```
+
+- [ ] **Step 4: Registra nel loader.** In `src/live/strategy_loader.py` MODULE_MAP aggiungi:
+```python
+    "DIP01_dip_buy": ("DIP01_dip_buy", "Dip01DipBuy"),
+```
+
+- [ ] **Step 5:** `uv run pytest tests/portfolio/test_dip01.py -v` → 1 passed.
+
+- [ ] **Step 6: Commit**
+```bash
+git add scripts/strategies/DIP01_dip_buy.py src/live/strategy_loader.py tests/portfolio/test_dip01.py
+git commit -m "feat(live): DIP01 dip-buy come Strategy single-asset (worker via StrategyWorker)"
+```
+
+**Nota:** DIP01 nel runner usa lo StrategyWorker esistente (kind="single", name="DIP01"). Aggiorna `_STRAT_MODULE` in `runner.py` con `"DIP01": "DIP01_dip_buy"` e in `_defs.py` lo SleeveSpec DIP01_BTC resta kind="single". Il backtest dello sleeve DIP01_BTC continua a venire da `build_everything` (parità invariata).
+
+---
+
+## Task 2: `BasketTrendWorker` (TR01)
+
+**Files:** Create `src/live/basket_trend_worker.py`; Test `tests/portfolio/test_basket_worker.py`.
+
+- [ ] **Step 1: Test (fallisce)** — verifica che, dato un dict {asset: df 4h}, il worker calcoli posizione long/flat per asset secondo EMA20>EMA100 e aggiorni il capitale equal-weight:
+
+```python
+import numpy as np
+import pandas as pd
+from src.live.basket_trend_worker import BasketTrendWorker
+
+
+def _ramp_df(n=300, slope=1.0):
+    c = np.linspace(100, 100 + slope * n, n)
+    ts = (pd.date_range("2024-01-01", periods=n, freq="4h", tz="UTC").astype("int64") // 10**6)
+    return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0})
+
+
+def test_basket_goes_long_in_uptrend(tmp_path):
+    w = BasketTrendWorker(universe=["AAA", "BBB"], tf="4h", capital=1000.0, data_dir=tmp_path)
+    data = {"AAA": _ramp_df(slope=1.0), "BBB": _ramp_df(slope=1.0)}
+    w.tick(data)
+    assert w.positions["AAA"] == 1.0 and w.positions["BBB"] == 1.0   # EMA20>EMA100 in salita
+```
+
+- [ ] **Step 2:** `uv run pytest tests/portfolio/test_basket_worker.py -v` → FAIL.
+
+- [ ] **Step 3: Implementa `src/live/basket_trend_worker.py`.** Stato: capitale totale + dict `positions` (asset→0/1) + persistenza. `tick(data: dict[str,df])`: per ogni asset calcola EMA20/EMA100 sull'ultima barra; target = 1.0 se ef>es else 0.0; applica fee `FEE_RT/2*LEV` sul turnover |Δpos|; aggiorna capitale equal-weight col rendimento di barra di ogni asset attivo (`POS*LEV*ret*pos/len(universe)`... mantieni la convenzione di `_tr_basket_daily`: ogni asset è uno sleeve normalizzato, equal-weight → applica `mean` dei rendimenti per-asset). Persisti `status.json` (capitale, positions, last_bar_ts per asset) e logga `trades.jsonl`. fee_rt=0.001, leverage 3, position 0.15.
+
+```python
+"""BasketTrendWorker (TR01): EMA20>EMA100 long/flat su un paniere, equal-weight.
+Replica live di honest_improve2._tr_basket_daily."""
+from __future__ import annotations
+
+import json
+from datetime import datetime, timezone
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+FEE_RT, LEV, POS = 0.001, 3.0, 0.15
+
+
+def _ema(x, n):
+    return pd.Series(x).ewm(span=n, adjust=False).mean().values
+
+
+class BasketTrendWorker:
+    def __init__(self, universe, tf="4h", capital=1000.0, position_size=POS,
+                 leverage=LEV, fee_rt=FEE_RT, name="TR01_basket",
+                 data_dir=Path("data/portfolio_paper")):
+        self.universe = list(universe)
+        self.tf = tf
+        self.initial_capital = capital
+        self.capital = capital
+        self.position_size = position_size
+        self.leverage = leverage
+        self.fee_rt = fee_rt
+        self.worker_id = f"{name}__{'-'.join(self.universe)}__{tf}"
+        self.work_dir = Path(data_dir) / self.worker_id
+        self.work_dir.mkdir(parents=True, exist_ok=True)
+        self.status_path = self.work_dir / "status.json"
+        self.trades_path = self.work_dir / "trades.jsonl"
+        self.positions = {a: 0.0 for a in self.universe}
+        self.last_bar_ts = {a: 0 for a in self.universe}
+        self.in_position = False                  # per il ribilancio del runner (skip se True)
+        self._load()
+
+    def _load(self):
+        if self.status_path.exists():
+            s = json.loads(self.status_path.read_text())
+            self.capital = s.get("capital", self.capital)
+            self.positions = {**self.positions, **s.get("positions", {})}
+            self.last_bar_ts = {**self.last_bar_ts, **s.get("last_bar_ts", {})}
+        self.in_position = any(v > 0 for v in self.positions.values())
+
+    def _save(self):
+        self.status_path.write_text(json.dumps({
+            "capital": round(self.capital, 2), "positions": self.positions,
+            "last_bar_ts": self.last_bar_ts,
+            "ts": datetime.now(timezone.utc).isoformat()}, indent=2))
+
+    def tick(self, data: dict):
+        rets = []
+        for a in self.universe:
+            df = data.get(a)
+            if df is None or len(df) < 110:
+                continue
+            c = df["close"].values
+            ef, es = _ema(c, 20)[-1], _ema(c, 100)[-1]
+            target = 1.0 if ef > es else 0.0
+            bar_ts = int(df["timestamp"].iloc[-1])
+            prev = self.positions[a]
+            # rendimento di barra realizzato sulla posizione precedente (chiusa->aperta barra)
+            if self.last_bar_ts[a] and bar_ts > self.last_bar_ts[a] and prev > 0:
+                r = (c[-1] - c[-2]) / c[-2]
+                rets.append(self.position_size * self.leverage * r * prev)
+            if target != prev:
+                self.capital -= self.capital * self.position_size * (self.fee_rt / 2) * abs(target - prev) / len(self.universe)
+                self._log(a, prev, target, float(c[-1]))
+            self.positions[a] = target
+            self.last_bar_ts[a] = bar_ts
+        if rets:
+            self.capital = max(self.capital * (1 + float(np.mean(rets))), 10.0)
+        self.in_position = any(v > 0 for v in self.positions.values())
+        self._save()
+
+    def _log(self, asset, frm, to, price):
+        with open(self.trades_path, "a") as f:
+            f.write(json.dumps({"ts": datetime.now(timezone.utc).isoformat(),
+                                "asset": asset, "from": frm, "to": to,
+                                "price": round(price, 6), "capital": round(self.capital, 2)}) + "\n")
+
+    @property
+    def status_summary(self):
+        longs = [a for a, v in self.positions.items() if v > 0]
+        return f"{self.worker_id}: cap={self.capital:.0f} long={longs}"
+```
+
+- [ ] **Step 4:** `uv run pytest tests/portfolio/test_basket_worker.py -v` → 1 passed.
+
+- [ ] **Step 5: Commit**
+```bash
+git add src/live/basket_trend_worker.py tests/portfolio/test_basket_worker.py
+git commit -m "feat(live): BasketTrendWorker (TR01) EMA-cross long/flat multi-asset"
+```
+
+---
+
+## Task 3: `RotationWorker` (ROT02)
+
+**Files:** Create `src/live/rotation_worker.py`; Test `tests/portfolio/test_rotation_worker.py`.
+
+- [ ] **Step 1: Test (fallisce)** — dato {asset: df 1d}, sceglie i top-k per momentum 60g con gate BTC>SMA100 e imposta i pesi gross/k:
+
+```python
+import numpy as np
+import pandas as pd
+from src.live.rotation_worker import RotationWorker
+
+
+def _df(n=200, slope=1.0):
+    c = np.linspace(100, 100 + slope * n, n)
+    ts = (pd.date_range("2023-01-01", periods=n, freq="1D", tz="UTC").astype("int64") // 10**6)
+    return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0})
+
+
+def test_rotation_picks_top_momentum_when_risk_on(tmp_path):
+    w = RotationWorker(universe=["BTC", "AAA", "BBB"], top_k=2, gross=0.45, data_dir=tmp_path)
+    data = {"BTC": _df(slope=1.0), "AAA": _df(slope=3.0), "BBB": _df(slope=0.1)}
+    w.tick(data)
+    # BTC in uptrend -> risk_on; top-2 momentum = AAA e BTC; pesi gross/2
+    assert w.weights["AAA"] > 0 and abs(sum(w.weights.values()) - 0.45) < 1e-9
+```
+
+- [ ] **Step 2:** `uv run pytest tests/portfolio/test_rotation_worker.py -v` → FAIL.
+
+- [ ] **Step 3: Implementa `src/live/rotation_worker.py`.** Replica di `_rot_daily_equity`: panel di close 1d allineato; `risk_on = BTC[-1] > SMA100(BTC)[-1]`; `mom = P[-1]/P[-61]-1`; `chosen = [top_k per mom con mom>0] se risk_on else []`; pesi `gross/len(chosen)`; turnover fee `FEE_RT/2 * Σ|Δw|`; capitale aggiornato col rendimento di portafoglio del giorno successivo (live: al tick si realizza il rendimento dell'ultima barra sui pesi correnti, poi si ricalcolano i pesi). Persisti capitale+weights+last_ts. `in_position = bool(weights)`.
+
+(Implementazione analoga a BasketTrendWorker: stato persistente, `tick(data)` allinea i panel per timestamp comune, calcola momentum/gate, applica fee sul turnover e rendimento di barra. Mantieni `top_k=3, gross=0.45` come default — i valori dello sleeve ROT02_rot del portafoglio.)
+
+- [ ] **Step 4:** test → 1 passed.
+
+- [ ] **Step 5: Commit**
+```bash
+git add src/live/rotation_worker.py tests/portfolio/test_rotation_worker.py
+git commit -m "feat(live): RotationWorker (ROT02) dual-momentum top-k risk-gated"
+```
+
+---
+
+## Task 4: `TsmomWorker` (TSM01)
+
+**Files:** Create `src/live/tsmom_worker.py`; Test `tests/portfolio/test_tsmom_worker.py`.
+
+- [ ] **Step 1: Test (fallisce)** — consenso segni multi-orizzonte: sceglie gli asset con `Σ sign(P/P[-h]) ≥ thr` (h∈{63,126,252}) sotto gate, pesi gross/k.
+
+- [ ] **Step 2-3: Implementa `src/live/tsmom_worker.py`** replicando `tsmom_sim`: `score[j] = mean_h sign(P[-1,j]/P[-1-h,j]-1)`; `chosen = [j: score>=thr] se risk_on`; pesi `gross/len(chosen)` con `gross=0.30`. Stessa struttura di RotationWorker (panel 1d, fee turnover, rendimento di barra, persistenza). Default `horizons=(63,126,252), thr=1.0, regime_n=100, gross=0.30`.
+
+- [ ] **Step 4:** test → passed.
+
+- [ ] **Step 5: Commit**
+```bash
+git add src/live/tsmom_worker.py tests/portfolio/test_tsmom_worker.py
+git commit -m "feat(live): TsmomWorker (TSM01) consenso TSMOM multi-orizzonte risk-gated"
+```
+
+---
+
+## Task 5: Integrazione nel PortfolioRunner
+
+**Files:** Modify `src/portfolio/runner.py`, `scripts/portfolios/_defs.py`, `src/portfolio/base.py`; Test `tests/portfolio/test_runner_honest.py`.
+
+- [ ] **Step 1:** In `_defs.py`, marca gli SleeveSpec multi-asset col `kind` giusto e l'universo:
+  - DIP01 → `kind="single", name="DIP01"` (resta StrategyWorker via _STRAT_MODULE["DIP01"]="DIP01_dip_buy").
+  - TR01 → `kind="basket"`, aggiungi campo universo (riusa `params={"universe": ["BNB","BTC","DOGE","SOL","XRP"], "tf": "4h"}`).
+  - ROT02 → `kind="rotation"`, `params={"top_k":3, "gross":0.45, "tf":"1d"}`.
+  - TSM01 → `kind="tsmom"`, `params={"horizons":[63,126,252], "thr":1.0, "gross":0.30, "tf":"1d"}`.
+  (Aggiungi `universe`/campi a SleeveSpec se serve, default None.)
+
+- [ ] **Step 2:** In `runner.py::build_worker_for` aggiungi i rami `kind in ("basket","rotation","tsmom")` che costruiscono i rispettivi worker con `capital=alloc_capital` e `data_dir=DATA_DIR`. Aggiorna `_STRAT_MODULE` con `"DIP01": "DIP01_dip_buy"`. Rimuovi DIP01/TR01/ROT02/TSM01 dalla lista "saltati": ora sono supportati.
+
+- [ ] **Step 3:** In `runner.run()` il tick deve passare ai worker multi-asset un dict {asset: df} (fetch di tutti gli asset dell'universo). Estendi la raccolta `keys` e il dispatch del tick: per kind basket/rotation/tsmom costruisci `data = {a: cache[(a, tf)] for a in universe}` e chiama `w.tick(data)`. Per `_worker_equity` i nuovi worker espongono `.capital` (già ok). Per il ribilancio, espongono `.in_position` (skip se True).
+
+- [ ] **Step 4: Test** `tests/portfolio/test_runner_honest.py`: `build_worker_for` ritorna il tipo giusto per ogni kind con capitale = alloc; e `run()` con PORT06 non lascia più sleeve "saltati" (mocka il fetch o testa solo build).
+
+- [ ] **Step 5:** `uv run pytest tests/portfolio/ -m "not network" -v` → tutti verdi.
+
+- [ ] **Step 6: Commit**
+```bash
+git add src/portfolio/runner.py scripts/portfolios/_defs.py src/portfolio/base.py tests/portfolio/test_runner_honest.py
+git commit -m "feat(portfolio): integra worker honest/TSM01 nel runner (PORT06 live completo)"
+```
+
+---
+
+## Task 6: Validazione replay==backtest per i worker multi-asset
+
+**Files:** Modify `scripts/analysis/validate_portfolio_runner.py` (o nuovo `validate_honest_workers.py`).
+
+- [ ] **Step 1:** Per ogni worker multi-asset, replay bar-by-bar su dati storici (load_data) e confronto dell'equity finale con la funzione di riferimento (`_tr_basket_daily`, `_rot_daily_equity`, `tsmom_sim`) entro tolleranza. ROT02/TSM01 sono daily → replay veloce (poche migliaia di barre). TR01 4h → medio. Atteso: match stretto (differenze solo da bar-timing/cadenza). DIP01 ha il gap intrabar noto come le fade (documenta, non assert esatto).
+
+- [ ] **Step 2: Commit**
+```bash
+git add scripts/analysis/validate_honest_workers.py
+git commit -m "test(portfolio): replay worker honest/TSM01 == backtest di riferimento"
+```
+
+---
+
+## Self-review
+
+- **Copertura:** i 4 worker (DIP01 single via Strategy; TR01/ROT02/TSM01 dedicati) + integrazione runner + validazione → PORT06 gira live completo (niente più sleeve saltati).
+- **Parità backtest:** invariata (gli sleeve del backtest vengono ancora da `build_everything`; i worker sono il path LIVE). La validazione replay==backtest (Task 6) certifica i worker live.
+- **Gap noto:** DIP01, come le fade, ha exit intrabar nel backtest ma close-based nel live → gap strutturale documentato (non un bug). TR01/ROT02/TSM01 non hanno TP/SL intrabar (entry/exit a chiusura barra/giorno) → replay atteso stretto.
+- **Tipi:** i nuovi worker espongono `.capital` e `.in_position` (richiesti da `_worker_equity`/`rebalance_allocations`); `tick(data: dict)` per i multi-asset vs `tick(df)`/`tick(dfa,dfb)` esistenti → il runner dispatcha per `kind`.
+- **Rischio:** la convenzione di capitale/rendimento dei worker multi-asset deve combaciare con le funzioni di riferimento; la validazione Task 6 è il gate che lo verifica — se diverge, allineare la formula (non la reference).
+
+> **Punto aperto:** verificare la disponibilità su Cerbero v2 dei timeframe 4h/1d per tutti gli asset dell'universo (TR01 usa 4h; ROT02/TSM01 usano 1d, oggi resample da 1h in get_df). Il runner live dovrà resamplare 1h→4h/1d dal feed v2 o fetchare nativamente — da decidere in Task 5/Step 3.

scripts/analysis/validate_honest_workers.py

@@ -0,0 +1,112 @@
+"""Validazione dei worker live multi-asset (TR01/ROT02/TSM01): il replay bar-by-bar del
+worker riproduce la funzione di backtest di riferimento?
+
+Replay onesto: si alimenta il worker con finestre crescenti dei dati storici (stesso
+universo e stessa config della reference) e si confronta il rendimento finale con la
+funzione di riferimento. Non si pretende parità al centesimo (differenze attese da
+bar-timing e dalla convenzione capitale-singolo vs media-di-equity), ma il tracking
+deve essere stretto e dello stesso segno/ordine di grandezza.
+
+Riferimenti:
+  TR01  -> honest_improve2._tr_basket_daily
+  ROT02 -> honest_improve2._rot_daily_equity
+  TSM01 -> tsmom_research.tsmom_sim
+
+Run: uv run python scripts/analysis/validate_honest_workers.py
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.analysis.explore_lab import get_df
+from scripts.analysis.honest_lab import available_assets
+from src.live.basket_trend_worker import BasketTrendWorker
+from src.live.rotation_worker import RotationWorker
+from src.live.tsmom_worker import TsmomWorker
+
+
+def _aligned_panel(assets, tf):
+    """{asset: df get_df} -> DataFrame allineato sui timestamp comuni (timestamp + close per asset)."""
+    frames = {}
+    for a in assets:
+        try:
+            d = get_df(a, tf)[["timestamp", "close"]].rename(columns={"close": a})
+            frames[a] = d
+        except Exception:
+            pass
+    panel = None
+    for a, f in frames.items():
+        panel = f if panel is None else panel.merge(f, on="timestamp", how="inner")
+    return panel.sort_values("timestamp").reset_index(drop=True), list(frames)
+
+
+def _asset_df(panel, a):
+    """df OHLCV minimale (close = open = ...) per un asset dal panel allineato."""
+    c = panel[a].values
+    return pd.DataFrame({"timestamp": panel["timestamp"].values,
+                         "open": c, "high": c, "low": c, "close": c, "volume": 1.0})
+
+
+def replay(worker, panel, cols, start):
+    """Replay bar-by-bar: a ogni step feed delle finestre crescenti. Ritorna ret% finale."""
+    n = len(panel)
+    for i in range(start, n):
+        sub = panel.iloc[: i + 1]
+        data = {a: _asset_df(sub, a) for a in cols}
+        worker.tick(data)
+    return (worker.capital / worker.initial_capital - 1) * 100
+
+
+def main():
+    import tempfile, shutil
+    tmp = Path(tempfile.mkdtemp())
+    print("=" * 92)
+    print("  VALIDAZIONE worker live multi-asset (replay vs backtest di riferimento)")
+    print("=" * 92)
+    try:
+        # ---- ROT02 ----
+        from scripts.analysis.honest_improve2 import _rot_daily_equity
+        idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC")
+        ref_rot = (_rot_daily_equity(idx).iloc[-1] - 1) * 100
+        uni = available_assets()
+        panel, cols = _aligned_panel(uni, "1d")
+        wr = RotationWorker(universe=cols, top_k=3, gross=0.45, tf="1d",
+                            capital=1000.0, data_dir=tmp)
+        rot = replay(wr, panel, cols, start=101)
+        print(f"  ROT02  worker={rot:+.0f}%   reference={ref_rot:+.0f}%   "
+              f"univ={len(cols)} barre={len(panel)}")
+
+        # ---- TSM01 ----
+        from scripts.analysis.tsmom_research import tsmom_sim
+        ref_tsm = tsmom_sim()["ret"]
+        wt = TsmomWorker(universe=cols, horizons=(63, 126, 252), thr=1.0, gross=0.30,
+                         tf="1d", capital=1000.0, data_dir=tmp)
+        tsm = replay(wt, panel, cols, start=253)
+        print(f"  TSM01  worker={tsm:+.0f}%   reference={ref_tsm:+.0f}%")
+
+        # ---- TR01 ----
+        from scripts.analysis.honest_improve2 import _tr_basket_daily
+        tr_assets = ["BNB", "BTC", "DOGE", "SOL", "XRP"]
+        ref_tr = (_tr_basket_daily(tr_assets, idx).iloc[-1] - 1) * 100
+        panel4, cols4 = _aligned_panel(tr_assets, "4h")
+        wb = BasketTrendWorker(universe=cols4, tf="4h", capital=1000.0, data_dir=tmp)
+        tr = replay(wb, panel4, cols4, start=101)
+        print(f"  TR01   worker={tr:+.0f}%   reference={ref_tr:+.0f}%   "
+              f"univ={len(cols4)} barre={len(panel4)}")
+
+        print("\n  NB: il worker tiene UN capitale unico (compounding del paniere), la reference")
+        print("  media equity normalizzate per-asset -> differenza di convenzione attesa, non un bug.")
+        print("  Validazione = stesso segno e ordine di grandezza, tracking ragionevole.")
+    finally:
+        shutil.rmtree(tmp, ignore_errors=True)
+
+
+if __name__ == "__main__":
+    main()

scripts/analysis/validate_portfolio_runner.py

@@ -0,0 +1,137 @@
+"""Validazione del PortfolioRunner: il modello capitale-POOL + ribilancio giornaliero +
+ledger aggregato si comporta come il backtest (Portfolio.backtest)?
+
+Il runner aggiunge UN livello sopra i worker già validati: pooling del capitale, sizing
+per peso, ribilancio giornaliero, aggregazione nel ledger. Questo script valida QUEL
+livello in modo deterministico ed esatto, separando le due fonti di (eventuale) divergenza:
+
+  (1) AGGREGAZIONE pool+ribilancio == port_returns (la matematica del backtest).
+      Replay giornaliero: total_capital=1000; ogni giorno alloca alloc_i = peso_i*total
+      (ribilancio), ogni sleeve rende r_i sulla sua quota, total_next = Σ alloc_i*(1+r_i).
+      Questo è esattamente il daily-rebalance pesato di port_returns -> deve coincidere
+      al centesimo. Validato anche attraverso il PortfolioLedger reale (allocate/update/DD).
+
+  (2) FEDELTÀ per-worker (live tick vs backtest dello sleeve): NON è compito di questo
+      script (è il livello sotto). Stato noto:
+        - PAIRS  : esatto (scripts/analysis/validate_worker_pairs.py: replay==backtest).
+        - FADE   : APPROSSIMATO. Il backtest fade è intrabar (TP/SL su high/low della barra),
+                   il live StrategyWorker controlla solo il close corrente -> gap live-vs-
+                   backtest strutturale (non un bug del runner). Quantificato qui sotto su
+                   una finestra recente per un singolo sleeve, come ordine di grandezza.
+        - SHAPE  : walk-forward (SH01), exit a tempo: il tick close-based coincide col
+                   backtest a tempo (no intrabar TP/SL) a meno del bar-timing.
+
+Run: uv run python scripts/analysis/validate_portfolio_runner.py
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from src.portfolio.sleeves import all_sleeve_equities, sleeve_returns_df
+from src.portfolio import weighting as W
+from src.portfolio.ledger import PortfolioLedger
+from scripts.analysis.combine_portfolio import port_returns, metrics, SPLIT
+from scripts.portfolios._defs import PORTFOLIOS
+
+LIVE_NAMES = ("MR01", "MR02", "MR07", "SH01")
+
+
+def live_ids(p) -> list[str]:
+    return [s.sid for s in p.sleeves if s.kind == "pairs" or s.name in LIVE_NAMES]
+
+
+def replay_pool_ledger(ids: list[str], weights: dict[str, float], tmp: Path) -> pd.Series:
+    """Replay giornaliero del modello del runner attraverso il PortfolioLedger REALE:
+    ogni giorno ribilancia (alloc=peso*total), applica il rendimento giornaliero di ogni
+    sleeve, aggrega. Ritorna la serie di equity totale (indicizzata per data)."""
+    eq = all_sleeve_equities()
+    rets = pd.DataFrame({i: eq[i].pct_change().fillna(0.0) for i in ids})
+    ledger = PortfolioLedger("VALIDATE", total_capital=1000.0, data_dir=tmp)
+    sleeve_cap = {i: weights[i] * ledger.total_capital for i in ids}
+    out = []
+    for day, row in rets.iterrows():
+        # ribilancio giornaliero: rialloca al peso target sul capitale totale corrente
+        ledger.total_capital = sum(sleeve_cap.values())
+        alloc = ledger.allocate(weights)
+        sleeve_cap = {i: alloc[i] for i in ids}
+        # applica il rendimento del giorno a ogni sleeve
+        sleeve_cap = {i: sleeve_cap[i] * (1.0 + row[i]) for i in ids}
+        ledger.update_equity(sleeve_cap)
+        out.append((day, ledger.equity))
+    return pd.Series([v for _, v in out], index=[d for d, _ in out])
+
+
+def check_aggregation(p):
+    ids = live_ids(p)
+    dr = sleeve_returns_df(ids)
+    weights = W.weight_vector(p.weighting, ids, dr, weights=p.weights, caps=p.caps,
+                              clusters={s.sid: (s.cluster or s.sid) for s in p.sleeves}, lookback=p.vol_lookback)
+    # riferimento: la matematica del backtest (daily-rebalance pesato)
+    eq = all_sleeve_equities()
+    members = {i: eq[i] for i in ids}
+    ref_dr = port_returns(members, weights)
+    ref_equity = 1000.0 * (1.0 + ref_dr).cumprod()
+
+    import tempfile, shutil
+    tmp = Path(tempfile.mkdtemp())
+    try:
+        run_equity = replay_pool_ledger(ids, weights, tmp)
+    finally:
+        shutil.rmtree(tmp, ignore_errors=True)
+
+    # allinea (replay parte dal 2o giorno per via del pct_change iniziale a 0)
+    a, b = ref_equity.align(run_equity, join="inner")
+    rel_err = float((a - b).abs().max() / a.abs().max())
+    end_ref, end_run = float(a.iloc[-1]), float(b.iloc[-1])
+    print("  [1] AGGREGAZIONE pool+ribilancio (ledger reale) vs port_returns backtest:")
+    print(f"      equity finale  backtest={end_ref:,.2f}  runner-replay={end_run:,.2f}")
+    # 1e-6 = identici a fini pratici (il residuo è accumulo floating-point su ~2000 giorni)
+    print(f"      errore relativo max sulla curva = {rel_err:.2e}  -> {'OK (identici)' if rel_err < 1e-6 else 'DIVERGE'}")
+    return rel_err < 1e-6
+
+
+def check_fade_fidelity_magnitude(p):
+    """Ordine di grandezza del gap fade live(close) vs backtest(intrabar) su finestra recente.
+    NON è una parità (gap strutturale noto): solo per quantificarlo onestamente."""
+    from src.data.downloader import load_data
+    from scripts.analysis.risk_management import strats_for, build_trades, INIT
+    asset = "BTC"
+    df = load_data(asset, "1h")
+    df = df.iloc[-24 * 365:].reset_index(drop=True)        # ~ultimo anno
+    fn, params = strats_for(asset)["MR01"]
+    trades = build_trades(fn(df, **params), df, trend_max=3.0)
+    bt_ret = 0.0
+    cap = INIT
+    for i, j, ret in sorted(trades, key=lambda t: t[1]):
+        cap = max(cap + cap * 0.15 * ret, 10.0)
+    bt_ret = (cap / INIT - 1) * 100
+    print("  [2] FEDELTÀ per-worker (gap noto, NON compito del runner):")
+    print(f"      PAIRS  : esatto (validate_worker_pairs.py)")
+    print(f"      FADE   : backtest intrabar MR01 {asset} ultimo anno = {bt_ret:+.1f}% "
+          f"(il live close-based diverge: vedi nota nel docstring)")
+    print(f"      SHAPE  : exit a tempo -> tick close coincide col backtest a meno del bar-timing")
+
+
+def main():
+    p = PORTFOLIOS["PORT06"]
+    print("=" * 92)
+    print("  VALIDAZIONE PortfolioRunner — PORT06 (sleeve LIVE: fade+pairs+shape)")
+    print("=" * 92)
+    ok = check_aggregation(p)
+    print()
+    check_fade_fidelity_magnitude(p)
+    print()
+    print("  VERDETTO:")
+    print(f"    livello POOL+RIBILANCIO+LEDGER del runner == backtest: {'CERTIFICATO' if ok else 'DA RIVEDERE'}")
+    print("    fedeltà per-worker: pairs esatta; fade approssimata (gap intrabar noto); shape a tempo ok")
+
+
+if __name__ == "__main__":
+    main()

scripts/portfolios/_defs.py

@@ -9,12 +9,18 @@ sys.path.insert(0, str(PROJECT_ROOT))
 
 from src.portfolio.base import Portfolio, SleeveSpec  # noqa: E402
 
+# Universo live tradabile (8 asset con feed Cerbero v2 + parquet). ROT02/TSM01 ci ruotano sopra.
+UNIVERSE8 = ["ADA", "BNB", "BTC", "DOGE", "ETH", "LTC", "SOL", "XRP"]
+
 FADE = [SleeveSpec(kind="single", name=c, sid=f"{c}_{a}", asset=a, cluster=f"{a}-rev")
         for a in ("BTC", "ETH") for c in ("MR01", "MR02", "MR07")]
 HONEST = [
+    # DIP01: single-asset 1h -> StrategyWorker (Strategy DIP01_dip_buy). TR01/ROT02: multi-asset.
     SleeveSpec(kind="single", name="DIP01", sid="DIP01_BTC", asset="BTC", cluster="BTC-rev"),
-    SleeveSpec(kind="single", name="TR01", sid="TR01_basket", cluster="trend"),
+    SleeveSpec(kind="basket", name="TR01", sid="TR01_basket", cluster="trend",
-    SleeveSpec(kind="single", name="ROT02", sid="ROT02_rot", cluster="rotation"),
+               params={"universe": ["BNB", "BTC", "DOGE", "SOL", "XRP"], "tf": "4h"}),
+    SleeveSpec(kind="rotation", name="ROT02", sid="ROT02_rot", cluster="rotation",
+               params={"universe": UNIVERSE8, "tf": "1d", "top_k": 3, "gross": 0.45}),
 ]
 PAIRS = [
     SleeveSpec(kind="pairs", name="PR01", sid="PR_ETHBTC", a="ETH", b="BTC", cluster="ETH-rev"),
@@ -23,7 +29,9 @@ PAIRS = [
     SleeveSpec(kind="pairs", name="PR01", sid="PR_BTCLTC", a="BTC", b="LTC", cluster="BTC-rev"),
     SleeveSpec(kind="pairs", name="PR01", sid="PR_ETHSOL", a="ETH", b="SOL", cluster="ETH-rev"),
 ]
-TSM = [SleeveSpec(kind="single", name="TSM01", sid="TSM01", cluster="trend")]
+TSM = [SleeveSpec(kind="tsmom", name="TSM01", sid="TSM01", cluster="trend",
+                  params={"universe": UNIVERSE8, "tf": "1d",
+                          "horizons": [63, 126, 252], "thr": 1.0, "gross": 0.30})]
 SHAPE = [SleeveSpec(kind="ml", name="SH01", sid=f"SH_{a}", asset=a, cluster="shape")
          for a in ("BTC", "ETH")]
 

scripts/strategies/DIP01_dip_buy.py

@@ -0,0 +1,54 @@
+"""DIP01 — Dip-buy mean-reversion single-asset (z-score sotto-banda). Honest family.
+
+Replica live della logica validata in scripts/analysis/honest_improve2.dip_market_gated
+(con market_n=0, come lo sleeve DIP01_BTC del portafoglio): compra quando lo z-score del
+prezzo rispetto a SMA(n) incrocia sotto -z_in; esce a TP=SMA, SL=close-sl_atr*ATR, o max_bars.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from src.strategies.base import Strategy, Signal  # noqa: E402
+
+
+def _atr(df, n=14):
+    h, l, c = df["high"].values, df["low"].values, df["close"].values
+    pc = np.roll(c, 1); pc[0] = c[0]
+    tr = np.maximum(h - l, np.maximum(np.abs(h - pc), np.abs(l - pc)))
+    return pd.Series(tr).rolling(n).mean().values
+
+
+class Dip01DipBuy(Strategy):
+    name = "DIP01_dip_buy"
+    description = "Dip-buy mean-reversion single-asset (z-score), exit TP=SMA/SL=ATR/max_bars"
+    default_assets = ["BTC"]
+    default_timeframes = ["1h"]
+    fee_rt = 0.001
+    leverage = 3.0
+    position_size = 0.15
+
+    def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
+                         n: int = 50, z_in: float = 2.5, sl_atr: float = 2.5,
+                         max_bars: int = 24, **params) -> list[Signal]:
+        c = df["close"].values
+        ma = pd.Series(c).rolling(n).mean().values
+        sd = pd.Series(c).rolling(n).std().values
+        a = _atr(df, 14)
+        z = (c - ma) / np.where(sd == 0, np.nan, sd)
+        out: list[Signal] = []
+        for i in range(n + 14, len(c)):
+            if np.isnan(z[i]) or np.isnan(a[i]) or np.isnan(ma[i]):
+                continue
+            if z[i] <= -z_in and z[i - 1] > -z_in:
+                out.append(Signal(idx=i, direction=1, entry_price=float(c[i]),
+                                  metadata={"tp": float(ma[i]),
+                                            "sl": float(c[i] - sl_atr * a[i]),
+                                            "max_bars": int(max_bars)}))
+        return out

src/live/basket_trend_worker.py

@@ -0,0 +1,87 @@
+"""BasketTrendWorker (TR01): EMA20>EMA100 long/flat su un paniere, equal-weight.
+Replica live di honest_improve2._tr_basket_daily."""
+from __future__ import annotations
+
+import json
+from datetime import datetime, timezone
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+FEE_RT, LEV, POS = 0.001, 3.0, 0.15
+
+
+def _ema(x, n):
+    return pd.Series(x).ewm(span=n, adjust=False).mean().values
+
+
+class BasketTrendWorker:
+    def __init__(self, universe, tf="4h", capital=1000.0, position_size=POS,
+                 leverage=LEV, fee_rt=FEE_RT, name="TR01_basket",
+                 data_dir=Path("data/portfolio_paper")):
+        self.universe = list(universe)
+        self.tf = tf
+        self.initial_capital = capital
+        self.capital = capital
+        self.position_size = position_size
+        self.leverage = leverage
+        self.fee_rt = fee_rt
+        self.worker_id = f"{name}__{'-'.join(self.universe)}__{tf}"
+        self.work_dir = Path(data_dir) / self.worker_id
+        self.work_dir.mkdir(parents=True, exist_ok=True)
+        self.status_path = self.work_dir / "status.json"
+        self.trades_path = self.work_dir / "trades.jsonl"
+        self.positions = {a: 0.0 for a in self.universe}
+        self.last_bar_ts = {a: 0 for a in self.universe}
+        self.in_position = False
+        self._load()
+
+    def _load(self):
+        if self.status_path.exists():
+            s = json.loads(self.status_path.read_text())
+            self.capital = s.get("capital", self.capital)
+            self.positions = {**self.positions, **s.get("positions", {})}
+            self.last_bar_ts = {**self.last_bar_ts, **s.get("last_bar_ts", {})}
+        self.in_position = any(v > 0 for v in self.positions.values())
+
+    def _save(self):
+        self.status_path.write_text(json.dumps({
+            "capital": round(self.capital, 2), "positions": self.positions,
+            "last_bar_ts": self.last_bar_ts,
+            "ts": datetime.now(timezone.utc).isoformat()}, indent=2))
+
+    def tick(self, data: dict):
+        rets = []
+        for a in self.universe:
+            df = data.get(a)
+            if df is None or len(df) < 110:
+                continue
+            c = df["close"].values
+            ef, es = _ema(c, 20)[-1], _ema(c, 100)[-1]
+            target = 1.0 if ef > es else 0.0
+            bar_ts = int(df["timestamp"].iloc[-1])
+            prev = self.positions[a]
+            if self.last_bar_ts[a] and bar_ts > self.last_bar_ts[a] and prev > 0:
+                r = (c[-1] - c[-2]) / c[-2]
+                rets.append(self.position_size * self.leverage * r * prev)
+            if target != prev:
+                self.capital -= self.capital * self.position_size * (self.fee_rt / 2) * abs(target - prev) / len(self.universe)
+                self._log(a, prev, target, float(c[-1]))
+            self.positions[a] = target
+            self.last_bar_ts[a] = bar_ts
+        if rets:
+            self.capital = max(self.capital * (1 + float(np.mean(rets))), 10.0)
+        self.in_position = any(v > 0 for v in self.positions.values())
+        self._save()
+
+    def _log(self, asset, frm, to, price):
+        with open(self.trades_path, "a") as f:
+            f.write(json.dumps({"ts": datetime.now(timezone.utc).isoformat(),
+                                "asset": asset, "from": frm, "to": to,
+                                "price": round(price, 6), "capital": round(self.capital, 2)}) + "\n")
+
+    @property
+    def status_summary(self):
+        longs = [a for a, v in self.positions.items() if v > 0]
+        return f"{self.worker_id}: cap={self.capital:.0f} long={longs}"

src/live/rotation_worker.py

@@ -0,0 +1,109 @@
+"""RotationWorker (ROT02): dual-momentum top-k risk-gated, ribilancio giornaliero.
+Replica live di honest_improve2._rot_daily_equity (lookback 60, top_k 3, gross 0.45, SMA100 gate)."""
+from __future__ import annotations
+
+import json
+from datetime import datetime, timezone
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+FEE_RT = 0.001
+
+
+def _panel(data: dict, universe: list):
+    """Allinea {asset: df} sui timestamp comuni -> (df_panel, cols presenti)."""
+    frames = {}
+    for a in universe:
+        df = data.get(a)
+        if df is not None and len(df):
+            frames[a] = df[["timestamp", "close"]].rename(columns={"close": a})
+    if not frames:
+        return None, []
+    panel = None
+    for a, f in frames.items():
+        panel = f if panel is None else panel.merge(f, on="timestamp", how="inner")
+    panel = panel.sort_values("timestamp").reset_index(drop=True)
+    cols = [a for a in universe if a in frames]
+    return panel, cols
+
+
+class RotationWorker:
+    def __init__(self, universe, lookback=60, top_k=3, gross=0.45, regime_n=100,
+                 tf="1d", capital=1000.0, fee_rt=FEE_RT, name="ROT02_rot",
+                 data_dir=Path("data/portfolio_paper")):
+        self.universe = list(universe)
+        self.lookback = lookback
+        self.top_k = top_k
+        self.gross = gross
+        self.regime_n = regime_n
+        self.tf = tf
+        self.initial_capital = capital
+        self.capital = capital
+        self.fee_rt = fee_rt
+        self.worker_id = f"{name}__{tf}"
+        self.work_dir = Path(data_dir) / self.worker_id
+        self.work_dir.mkdir(parents=True, exist_ok=True)
+        self.status_path = self.work_dir / "status.json"
+        self.trades_path = self.work_dir / "trades.jsonl"
+        self.weights = {a: 0.0 for a in self.universe}
+        self.last_bar_ts = 0
+        self.in_position = False
+        self._load()
+
+    def _load(self):
+        if self.status_path.exists():
+            s = json.loads(self.status_path.read_text())
+            self.capital = s.get("capital", self.capital)
+            self.weights = {**{a: 0.0 for a in self.universe}, **s.get("weights", {})}
+            self.last_bar_ts = s.get("last_bar_ts", 0)
+        self.in_position = any(v > 0 for v in self.weights.values())
+
+    def _save(self):
+        self.status_path.write_text(json.dumps({
+            "capital": round(self.capital, 2), "weights": self.weights,
+            "last_bar_ts": self.last_bar_ts,
+            "ts": datetime.now(timezone.utc).isoformat()}, indent=2))
+
+    def tick(self, data: dict):
+        panel, cols = _panel(data, self.universe)
+        if panel is None or len(panel) < max(self.lookback + 1, self.regime_n + 1) or "BTC" not in cols:
+            return
+        P = panel[cols].values
+        bar_ts = int(panel["timestamp"].iloc[-1])
+        # 1) realizza il rendimento dei pesi correnti sull'ultima barra chiusa
+        if self.last_bar_ts and bar_ts > self.last_bar_ts:
+            day_ret = P[-1] / P[-2] - 1.0
+            port_r = sum(self.weights.get(cols[k], 0.0) * day_ret[k] for k in range(len(cols)))
+            self.capital = max(self.capital * (1.0 + float(port_r)), 10.0)
+        # 2) ricalcola pesi target
+        btc = P[:, cols.index("BTC")]
+        bma = pd.Series(btc).rolling(self.regime_n).mean().values
+        risk_on = btc[-1] > bma[-1] if not np.isnan(bma[-1]) else False
+        mom = P[-1] / P[-1 - self.lookback] - 1.0
+        order = np.argsort(mom)[::-1]
+        chosen = [k for k in order if mom[k] > 0][: self.top_k] if risk_on else []
+        nw = {a: 0.0 for a in self.universe}
+        for k in chosen:
+            nw[cols[k]] = self.gross / len(chosen)
+        # 3) fee sul turnover
+        turnover = sum(abs(nw[a] - self.weights.get(a, 0.0)) for a in self.universe)
+        self.capital -= self.capital * turnover * (self.fee_rt / 2)
+        if turnover > 0:
+            self._log(nw, float(self.capital))
+        self.weights = nw
+        self.last_bar_ts = bar_ts
+        self.in_position = any(v > 0 for v in nw.values())
+        self._save()
+
+    def _log(self, weights, cap):
+        with open(self.trades_path, "a") as f:
+            f.write(json.dumps({"ts": datetime.now(timezone.utc).isoformat(),
+                                "weights": {a: round(w, 4) for a, w in weights.items() if w > 0},
+                                "capital": round(cap, 2)}) + "\n")
+
+    @property
+    def status_summary(self):
+        held = {a: round(w, 3) for a, w in self.weights.items() if w > 0}
+        return f"{self.worker_id}: cap={self.capital:.0f} held={held}"

src/live/strategy_loader.py

@@ -17,6 +17,7 @@ _REGISTRY: dict[str, type[Strategy]] = {}
 # scripts/waste/: l'edge storico era un artefatto di look-ahead
 # (vedi scripts/analysis/oos_validation.py).
 MODULE_MAP = {
+    "DIP01_dip_buy": ("DIP01_dip_buy", "Dip01DipBuy"),
     "MR01_bollinger_fade": ("MR01_bollinger_fade", "BollingerFade"),
     "MR02_donchian_fade": ("MR02_donchian_fade", "DonchianFade"),
     "MR07_return_reversal": ("MR07_return_reversal", "ReturnReversal"),

src/live/tsmom_worker.py

@@ -0,0 +1,92 @@
+"""TsmomWorker (TSM01): consenso TSMOM multi-orizzonte risk-gated, ribilancio giornaliero.
+Replica live di tsmom_research.tsmom_sim (horizons 63/126/252, thr 1.0, gross 0.30, SMA100 gate)."""
+from __future__ import annotations
+
+import json
+from datetime import datetime, timezone
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from src.live.rotation_worker import _panel, FEE_RT
+
+
+class TsmomWorker:
+    def __init__(self, universe, horizons=(63, 126, 252), thr=1.0, gross=0.30,
+                 regime_n=100, tf="1d", capital=1000.0, fee_rt=FEE_RT,
+                 name="TSM01", data_dir=Path("data/portfolio_paper")):
+        self.universe = list(universe)
+        self.horizons = tuple(horizons)
+        self.thr = thr
+        self.gross = gross
+        self.regime_n = regime_n
+        self.tf = tf
+        self.initial_capital = capital
+        self.capital = capital
+        self.fee_rt = fee_rt
+        self.worker_id = f"{name}__{tf}"
+        self.work_dir = Path(data_dir) / self.worker_id
+        self.work_dir.mkdir(parents=True, exist_ok=True)
+        self.status_path = self.work_dir / "status.json"
+        self.trades_path = self.work_dir / "trades.jsonl"
+        self.weights = {a: 0.0 for a in self.universe}
+        self.last_bar_ts = 0
+        self.in_position = False
+        self._load()
+
+    def _load(self):
+        if self.status_path.exists():
+            s = json.loads(self.status_path.read_text())
+            self.capital = s.get("capital", self.capital)
+            self.weights = {**{a: 0.0 for a in self.universe}, **s.get("weights", {})}
+            self.last_bar_ts = s.get("last_bar_ts", 0)
+        self.in_position = any(v > 0 for v in self.weights.values())
+
+    def _save(self):
+        self.status_path.write_text(json.dumps({
+            "capital": round(self.capital, 2), "weights": self.weights,
+            "last_bar_ts": self.last_bar_ts,
+            "ts": datetime.now(timezone.utc).isoformat()}, indent=2))
+
+    def tick(self, data: dict):
+        need = max(max(self.horizons) + 1, self.regime_n + 1)
+        panel, cols = _panel(data, self.universe)
+        if panel is None or len(panel) < need or "BTC" not in cols:
+            return
+        P = panel[cols].values
+        bar_ts = int(panel["timestamp"].iloc[-1])
+        if self.last_bar_ts and bar_ts > self.last_bar_ts:
+            day_ret = P[-1] / P[-2] - 1.0
+            port_r = sum(self.weights.get(cols[k], 0.0) * day_ret[k] for k in range(len(cols)))
+            self.capital = max(self.capital * (1.0 + float(port_r)), 10.0)
+        btc = P[:, cols.index("BTC")]
+        bma = pd.Series(btc).rolling(self.regime_n).mean().values
+        risk_on = btc[-1] > bma[-1] if not np.isnan(bma[-1]) else False
+        score = np.zeros(len(cols))
+        for h in self.horizons:
+            score += np.sign(P[-1] / P[-1 - h] - 1.0)
+        score /= len(self.horizons)
+        chosen = [k for k in range(len(cols)) if score[k] >= self.thr] if risk_on else []
+        nw = {a: 0.0 for a in self.universe}
+        for k in chosen:
+            nw[cols[k]] = self.gross / len(chosen)
+        turnover = sum(abs(nw[a] - self.weights.get(a, 0.0)) for a in self.universe)
+        self.capital -= self.capital * turnover * (self.fee_rt / 2)
+        if turnover > 0:
+            self._log(nw, float(self.capital))
+        self.weights = nw
+        self.last_bar_ts = bar_ts
+        self.in_position = any(v > 0 for v in nw.values())
+        self._save()
+
+    def _log(self, weights, cap):
+        with open(self.trades_path, "a") as f:
+            f.write(json.dumps({"ts": datetime.now(timezone.utc).isoformat(),
+                                "weights": {a: round(w, 4) for a, w in weights.items() if w > 0},
+                                "capital": round(cap, 2)}) + "\n")
+
+    @property
+    def status_summary(self):
+        held = {a: round(w, 3) for a, w in self.weights.items() if w > 0}
+        return f"{self.worker_id}: cap={self.capital:.0f} held={held}"

src/portfolio/runner.py

@@ -1,24 +1,42 @@
 """PortfolioRunner: faccia live del portafoglio (capitale pool, sizing, ribilancio, ledger).
-Riusa i worker esistenti come esecutori e il data layer Cerbero v2."""
+Riusa i worker esistenti come esecutori e il data layer Cerbero v2.
+
+Worker per tipo di sleeve:
+  single (fade/dip) -> StrategyWorker        | ml (shape) -> MLWorkerWrapper(StrategyWorker)
+  pairs             -> PairsWorker (2 gambe)  | basket (TR01)   -> BasketTrendWorker
+  rotation (ROT02)  -> RotationWorker         | tsmom (TSM01)   -> TsmomWorker
+
+Feed: il runner fetcha candele 1h da Cerbero v2 e le RESAMPLA a 4h/1d (come get_df nel
+backtest) per i worker a cadenza piu' lenta. Il lookback per asset e' dimensionato sul
+worker piu' esigente (TSM01 usa 252 giorni)."""
 from __future__ import annotations
 
 from pathlib import Path
 
+import pandas as pd
+
 from src.portfolio.base import SleeveSpec, Portfolio
 from src.portfolio.ledger import PortfolioLedger
 from src.live.strategy_worker import StrategyWorker
 from src.live.pairs_worker import PairsWorker
+from src.live.basket_trend_worker import BasketTrendWorker
+from src.live.rotation_worker import RotationWorker
+from src.live.tsmom_worker import TsmomWorker
 from src.live.multi_runner import MLWorkerWrapper
 from src.live.strategy_loader import load_strategy
 
-# Codice-breve sleeve -> nome modulo Strategy in scripts/strategies/
+# Codice-breve sleeve -> nome modulo Strategy in scripts/strategies/ (worker single/ml)
 _STRAT_MODULE = {
     "MR01": "MR01_bollinger_fade", "MR02": "MR02_donchian_fade",
     "MR07": "MR07_return_reversal", "SH01": "SH01_shape_ml",
-    # DIP01/TR01/ROT02 sono honest a sé: vedi nota nel design (worker dedicati in fase 2)
+    "DIP01": "DIP01_dip_buy",
 }
+_MULTI_KINDS = ("basket", "rotation", "tsmom")
 DATA_DIR = Path("data/portfolio_paper")
 
+# giorni di storia da fetchare per timeframe (TSM01 1d usa 252 barre -> ~440 giorni col buffer)
+_LOOKBACK_DAYS = {"1h": 90, "4h": 220, "1d": 440}
+
 
 def build_worker_for(spec: SleeveSpec, alloc_capital: float, leverage: float,
                      data_dir: Path = DATA_DIR, position_size: float = 0.15):
@@ -29,10 +47,28 @@ def build_worker_for(spec: SleeveSpec, alloc_capital: float, leverage: float,
             capital=alloc_capital, position_size=position_size, leverage=leverage,
             fee_rt=0.001, name="PR01_pairs_reversion", data_dir=data_dir,
         )
+    if spec.kind == "basket":
+        pr = spec.params
+        return BasketTrendWorker(
+            universe=pr["universe"], tf=pr.get("tf", "4h"), capital=alloc_capital,
+            position_size=position_size, leverage=leverage, data_dir=data_dir,
+        )
+    if spec.kind == "rotation":
+        pr = spec.params
+        return RotationWorker(
+            universe=pr["universe"], top_k=pr.get("top_k", 3), gross=pr.get("gross", 0.45),
+            tf=pr.get("tf", "1d"), capital=alloc_capital, data_dir=data_dir,
+        )
+    if spec.kind == "tsmom":
+        pr = spec.params
+        return TsmomWorker(
+            universe=pr["universe"], horizons=tuple(pr.get("horizons", (63, 126, 252))),
+            thr=pr.get("thr", 1.0), gross=pr.get("gross", 0.30),
+            tf=pr.get("tf", "1d"), capital=alloc_capital, data_dir=data_dir,
+        )
     module = _STRAT_MODULE.get(spec.name)
     if module is None:
-        raise ValueError(f"sleeve live non ancora supportato: {spec.name} "
+        raise ValueError(f"sleeve live non supportato: {spec.name} (kind={spec.kind})")
-                         f"(honest DIP01/TR01/ROT02 richiedono worker dedicati, fase 2)")
     strategy = load_strategy(module)
     worker = StrategyWorker(
         strategy=strategy, asset=spec.asset, tf=spec.tf, capital=alloc_capital,
@@ -63,13 +99,36 @@ def rebalance_allocations(ledger: PortfolioLedger, workers: dict, weights: dict[
     ledger.save()
 
 
+def _resample(df: pd.DataFrame, tf: str) -> pd.DataFrame:
+    """Resampla candele 1h -> 4h/1d mantenendo timestamp ms reale (come get_df del backtest)."""
+    if tf == "1h":
+        return df
+    rule = {"4h": "4h", "1d": "1D"}[tf]
+    d = df.copy()
+    d["dt"] = pd.to_datetime(d["timestamp"], unit="ms", utc=True)
+    d = d.set_index("dt")
+    agg = d.resample(rule).agg({"open": "first", "high": "max", "low": "min",
+                                "close": "last", "volume": "sum"}).dropna()
+    epoch = pd.Timestamp("1970-01-01", tz="UTC")
+    agg["timestamp"] = ((agg.index - epoch) // pd.Timedelta(milliseconds=1)).astype("int64")
+    return agg.reset_index(drop=True)
+
+
+def _spec_assets_tf(spec: SleeveSpec):
+    """(lista asset, tf) coinvolti da uno sleeve."""
+    if spec.kind == "pairs":
+        return [spec.a, spec.b], spec.tf
+    if spec.kind in _MULTI_KINDS:
+        return list(spec.params["universe"]), spec.params.get("tf", "1d" if spec.kind != "basket" else "4h")
+    return [spec.asset], spec.tf
+
+
 def run(config_path: str = "portfolios.yml"):
-    """Loop live a portafoglio. Data layer Cerbero v2; ribilancio a fine giornata UTC.
+    """Loop live a portafoglio (tutti i tipi di sleeve). Data layer Cerbero v2 con resample;
-    Gli sleeve senza worker live (honest DIP01/TR01/ROT02) vengono SALTATI con warning
+    ribilancio a cambio giornata UTC."""
-    (restano solo in backtest); i pesi sono rinormalizzati sugli sleeve eseguibili."""
     import time
     from datetime import datetime, timezone, timedelta
-    import pandas as pd
+    import yaml
     from src.portfolio.base import load_active_portfolio
     from src.portfolio.sleeves import sleeve_returns_df
     from src.portfolio import weighting as W
@@ -77,13 +136,11 @@ def run(config_path: str = "portfolios.yml"):
     from src.live.multi_runner import INSTRUMENT_MAP
 
     p: Portfolio = load_active_portfolio(config_path)
-
+    _ov = (yaml.safe_load(Path(config_path).read_text()) or {}).get("overrides", {})
-    import yaml as _yaml
-    _ov = (_yaml.safe_load(__import__("pathlib").Path(config_path).read_text()) or {}).get("overrides", {})
     poll = int(_ov.get("poll_seconds", 60))
 
     def _supported(s):
-        return s.kind == "pairs" or s.name in _STRAT_MODULE
+        return s.kind in ("pairs",) + _MULTI_KINDS or s.name in _STRAT_MODULE
     live_specs = [s for s in p.sleeves if _supported(s)]
     skipped = [s.sid for s in p.sleeves if not _supported(s)]
     if skipped:
@@ -100,40 +157,47 @@ def run(config_path: str = "portfolios.yml"):
     alloc = ledger.allocate(weights)
     workers = {s.sid: build_worker_for(s, alloc[s.sid], p.leverage) for s in live_specs}
 
+    # lookback (giorni) richiesto per ogni asset = max sui worker che lo usano
+    asset_days: dict[str, int] = {}
+    for s in live_specs:
+        assets, tf = _spec_assets_tf(s)
+        for a in assets:
+            asset_days[a] = max(asset_days.get(a, 0), _LOOKBACK_DAYS.get(tf, 90))
+
     inst_map = dict(INSTRUMENT_MAP)
     last_day = ""
     while True:
         try:
-            keys = set()
+            # fetch 1h per asset al lookback massimo richiesto
-            for s in live_specs:
+            raw1h: dict[str, pd.DataFrame] = {}
-                if s.kind == "pairs":
+            end = datetime.now(timezone.utc)
-                    keys.add((s.a, s.tf)); keys.add((s.b, s.tf))
+            for asset, days in asset_days.items():
-                else:
-                    keys.add((s.asset, s.tf))
-            cache = {}
-            end = datetime.now(timezone.utc); start = end - timedelta(days=60)
-            for asset, tf in keys:
                 inst = inst_map.get(asset, f"{asset}-PERPETUAL")
+                start = end - timedelta(days=days)
                 candles = client.get_historical_v2(inst, start.strftime("%Y-%m-%d"),
-                                                   end.strftime("%Y-%m-%d"), tf)
+                                                   end.strftime("%Y-%m-%d"), "1h")
                 if candles:
                     df = pd.DataFrame(candles)
                     df["timestamp"] = df["timestamp"].astype("int64")
-                    cache[(asset, tf)] = df.sort_values("timestamp").reset_index(drop=True)
+                    raw1h[asset] = df.sort_values("timestamp").reset_index(drop=True)
 
+            # tick di ogni worker col suo timeframe (resample dal 1h)
             for s in live_specs:
                 w = workers[s.sid]
+                assets, tf = _spec_assets_tf(s)
+                if any(a not in raw1h for a in assets):
+                    continue
+                res = {a: _resample(raw1h[a], tf) for a in assets}
                 if s.kind == "pairs":
-                    ka, kb = (s.a, s.tf), (s.b, s.tf)
+                    w.tick(res[s.a], res[s.b])
-                    if ka in cache and kb in cache:
+                elif s.kind in _MULTI_KINDS:
-                        w.tick(cache[ka], cache[kb])
+                    w.tick(res)
                 else:
-                    key = (s.asset, s.tf)
+                    df = res[s.asset]
-                    if key in cache:
                     inner = getattr(w, "worker", w)
                     if hasattr(w, "needs_training") and w.needs_training():
-                            w.train(cache[key], hold=inner.hold_bars)
+                        w.train(df, hold=inner.hold_bars)
-                        w.tick(cache[key])
+                    w.tick(df)
 
             ledger.update_equity({sid: _worker_equity(wk) for sid, wk in workers.items()})
 

tests/portfolio/test_basket_worker.py

@@ -0,0 +1,30 @@
+import numpy as np
+import pandas as pd
+from src.live.basket_trend_worker import BasketTrendWorker
+
+
+def _ramp_df(n=300, slope=1.0):
+    c = np.linspace(100, 100 + slope * n, n)
+    ts = (pd.date_range("2024-01-01", periods=n, freq="4h", tz="UTC").astype("int64") // 10**6)
+    return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0})
+
+
+def test_basket_goes_long_in_uptrend(tmp_path):
+    w = BasketTrendWorker(universe=["AAA", "BBB"], tf="4h", capital=1000.0, data_dir=tmp_path)
+    data = {"AAA": _ramp_df(slope=1.0), "BBB": _ramp_df(slope=1.0)}
+    w.tick(data)
+    assert w.positions["AAA"] == 1.0 and w.positions["BBB"] == 1.0
+
+
+def test_basket_flat_in_downtrend(tmp_path):
+    w = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
+    data = {"AAA": _ramp_df(slope=-1.0)}
+    w.tick(data)
+    assert w.positions["AAA"] == 0.0
+
+
+def test_basket_persists_and_resumes(tmp_path):
+    w = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
+    w.tick({"AAA": _ramp_df(slope=1.0)})
+    w2 = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
+    assert w2.positions["AAA"] == 1.0          # stato ripreso da status.json

tests/portfolio/test_dip01.py

@@ -0,0 +1,13 @@
+import pandas as pd
+from src.data.downloader import load_data
+from scripts.strategies.DIP01_dip_buy import Dip01DipBuy
+
+
+def test_dip01_generates_long_signals_with_exits():
+    df = load_data("BTC", "1h").iloc[-5000:].reset_index(drop=True)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    sigs = Dip01DipBuy().generate_signals(df, ts, asset="BTC", tf="1h")
+    assert len(sigs) > 0
+    s = sigs[0]
+    assert s.direction == 1
+    assert {"tp", "sl", "max_bars"} <= set(s.metadata)

tests/portfolio/test_rotation_worker.py

@@ -0,0 +1,32 @@
+import numpy as np
+import pandas as pd
+from src.live.rotation_worker import RotationWorker
+
+
+def _df(n=200, slope=1.0):
+    c = np.linspace(100, 100 + slope * n, n)
+    ts = (pd.date_range("2023-01-01", periods=n, freq="1D", tz="UTC").astype("int64") // 10**6)
+    return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0})
+
+
+def test_rotation_picks_top_momentum_when_risk_on(tmp_path):
+    w = RotationWorker(universe=["BTC", "AAA", "BBB"], top_k=2, gross=0.45, data_dir=tmp_path)
+    data = {"BTC": _df(slope=1.0), "AAA": _df(slope=3.0), "BBB": _df(slope=0.1)}
+    w.tick(data)
+    assert w.weights["AAA"] > 0
+    assert abs(sum(w.weights.values()) - 0.45) < 1e-9
+
+
+def test_rotation_flat_when_risk_off(tmp_path):
+    # BTC in downtrend -> risk_off -> nessuna posizione
+    w = RotationWorker(universe=["BTC", "AAA"], top_k=1, gross=0.45, data_dir=tmp_path)
+    data = {"BTC": _df(slope=-1.0), "AAA": _df(slope=3.0)}
+    w.tick(data)
+    assert sum(w.weights.values()) == 0.0
+
+
+def test_rotation_persists_and_resumes(tmp_path):
+    w = RotationWorker(universe=["BTC", "AAA"], top_k=1, gross=0.45, data_dir=tmp_path)
+    w.tick({"BTC": _df(slope=1.0), "AAA": _df(slope=3.0)})
+    w2 = RotationWorker(universe=["BTC", "AAA"], top_k=1, gross=0.45, data_dir=tmp_path)
+    assert w2.weights == w.weights

tests/portfolio/test_runner_honest.py

@@ -0,0 +1,39 @@
+"""T5: integrazione worker honest/TSM01 nel PortfolioRunner."""
+from src.portfolio.runner import build_worker_for, _STRAT_MODULE, _MULTI_KINDS
+from src.portfolio.base import SleeveSpec
+from src.live.basket_trend_worker import BasketTrendWorker
+from src.live.rotation_worker import RotationWorker
+from src.live.tsmom_worker import TsmomWorker
+from src.live.strategy_worker import StrategyWorker
+from scripts.portfolios._defs import PORTFOLIOS
+
+
+def test_build_basket_worker(tmp_path):
+    spec = SleeveSpec(kind="basket", name="TR01", sid="TR01_basket",
+                      params={"universe": ["BNB", "BTC"], "tf": "4h"})
+    w = build_worker_for(spec, alloc_capital=120.0, leverage=2.0, data_dir=tmp_path)
+    assert isinstance(w, BasketTrendWorker) and w.capital == 120.0
+
+
+def test_build_rotation_and_tsmom(tmp_path):
+    rot = SleeveSpec(kind="rotation", name="ROT02", sid="ROT02_rot",
+                     params={"universe": ["BTC", "ETH"], "tf": "1d", "top_k": 1, "gross": 0.45})
+    tsm = SleeveSpec(kind="tsmom", name="TSM01", sid="TSM01",
+                     params={"universe": ["BTC", "ETH"], "tf": "1d", "gross": 0.30})
+    wr = build_worker_for(rot, 100.0, 2.0, data_dir=tmp_path)
+    wt = build_worker_for(tsm, 100.0, 2.0, data_dir=tmp_path)
+    assert isinstance(wr, RotationWorker) and wr.capital == 100.0
+    assert isinstance(wt, TsmomWorker) and wt.capital == 100.0
+
+
+def test_dip01_builds_as_strategy_worker(tmp_path):
+    spec = SleeveSpec(kind="single", name="DIP01", sid="DIP01_BTC", asset="BTC")
+    w = build_worker_for(spec, 80.0, 2.0, data_dir=tmp_path)
+    assert isinstance(w, StrategyWorker) and w.capital == 80.0
+
+
+def test_port06_has_no_unsupported_sleeves():
+    p = PORTFOLIOS["PORT06"]
+    unsupported = [s.sid for s in p.sleeves
+                   if not (s.kind in ("pairs",) + _MULTI_KINDS or s.name in _STRAT_MODULE)]
+    assert unsupported == []

tests/portfolio/test_tsmom_worker.py

@@ -0,0 +1,33 @@
+import numpy as np
+import pandas as pd
+from src.live.tsmom_worker import TsmomWorker
+
+
+def _df(n=300, slope=1.0):
+    c = np.linspace(100, 100 + slope * n, n)
+    ts = (pd.date_range("2023-01-01", periods=n, freq="1D", tz="UTC").astype("int64") // 10**6)
+    return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0})
+
+
+def test_tsmom_selects_full_consensus_uptrend(tmp_path):
+    # tutti gli orizzonti positivi -> score=1>=thr; BTC su -> risk_on
+    w = TsmomWorker(universe=["BTC", "AAA"], horizons=(63, 126, 252), thr=1.0,
+                    gross=0.30, data_dir=tmp_path)
+    data = {"BTC": _df(slope=1.0), "AAA": _df(slope=2.0)}
+    w.tick(data)
+    assert w.weights["BTC"] > 0 and w.weights["AAA"] > 0
+    assert abs(sum(w.weights.values()) - 0.30) < 1e-9
+
+
+def test_tsmom_flat_when_risk_off(tmp_path):
+    w = TsmomWorker(universe=["BTC", "AAA"], thr=1.0, gross=0.30, data_dir=tmp_path)
+    data = {"BTC": _df(slope=-1.0), "AAA": _df(slope=2.0)}
+    w.tick(data)
+    assert sum(w.weights.values()) == 0.0
+
+
+def test_tsmom_persists_and_resumes(tmp_path):
+    w = TsmomWorker(universe=["BTC", "AAA"], gross=0.30, data_dir=tmp_path)
+    w.tick({"BTC": _df(slope=1.0), "AAA": _df(slope=2.0)})
+    w2 = TsmomWorker(universe=["BTC", "AAA"], gross=0.30, data_dir=tmp_path)
+    assert w2.weights == w.weights