diff --git a/scripts/analysis/validate_honest_workers.py b/scripts/analysis/validate_honest_workers.py
new file mode 100644
index 0000000..06d6ffb
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+++ b/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()