fix(paper): ETH 5m allineato al tick + hardening GUI/compose

Bug principale: in scripts/run_paper_trading.py il fetch usava
end = now.replace(minute=0,...), troncando sempre all'ora. ETH è
dichiarato timeframe=5m (commit 23b7273) ma di fatto veniva
valutato 1 volta ogni 60 min — 502 poll del run 39e027df hanno
prodotto solo 43 evaluazioni/asset, tutte a HH:00. Il commento
in load_assets segnala esplicitamente che a 1h la strategia
perde -33% su 7y: regressione vs backtest.

Fix: helper _align_end_to_timeframe(now, timeframe) snappa end
al boundary nativo dell'asset. Mappa 1m/5m/15m/30m/1h/4h/1d.
Test regression in src/strategy_crypto/tests con 9 casi.

Hardening accessorio incluso nello stesso commit:
- docker-compose.yml: state/ in RW per strategy-crypto-gui
  (SQLite WAL richiede SHM writable anche da reader).
- multi_swarm_core/dashboard/nicegui_app.py: ui.timer ora
  deactivate on_disconnect su 3 pagine (index/convergence/genomes)
  per evitare leak di timer dopo client disconnect.
- strategy_crypto/frontend/data.py: retry 5s su sqlite.connect
  per cold-start race quando GUI parte prima del paper writer.
- state/validation-hardened-001.json: output WFA tooling
  multi-fold del run phase1-hardened-001.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

docker-compose.yml

@@ -88,8 +88,8 @@ services:
       <<: *swarm-env
       DASHBOARD_ROOT_PATH: /strategy_crypto_gui
     volumes:
-      # Dashboard legge solo strategy_crypto.db: state/ in read-only (WAL: vedi nota)
+      # RW richiesto: SQLite WAL mode richiede write-access dal reader per SHM.
-      - ./state:/app/state:ro
+      - ./state:/app/state
     entrypoint:
       - python
       - -m

scripts/run_paper_trading.py

@@ -33,6 +33,36 @@ from strategy_crypto.backend import PaperExecutor, PaperRepository, Portfolio
 
 PROJECT_ROOT = Path(__file__).resolve().parent.parent
 
+# Mapping timeframe stringa Cerbero -> minuti del bar. Le strategie tradano
+# sul "bar appena chiuso", quindi end deve essere snappato al boundary del
+# loro timeframe (NON sempre al top dell'ora) per evitare la regressione in
+# cui ETH 5m veniva valutato una volta sola ogni 60 min.
+_TIMEFRAME_MINUTES: dict[str, int] = {
+    "1m": 1,
+    "5m": 5,
+    "15m": 15,
+    "30m": 30,
+    "1h": 60,
+    "4h": 240,
+    "1d": 1440,
+}
+
+
+def _align_end_to_timeframe(now: datetime, timeframe: str) -> datetime:
+    """Snap ``now`` al boundary del bar timeframe (UTC, naive seconds).
+
+    Es.: now=14:37:42, tf="5m"  -> 14:35:00
+         now=14:37:42, tf="1h"  -> 14:00:00
+         now=14:00:00, tf="1h"  -> 14:00:00
+    """
+    bar_min = _TIMEFRAME_MINUTES[timeframe]
+    aligned = now.replace(second=0, microsecond=0)
+    if bar_min >= 1440:
+        return aligned.replace(hour=0, minute=0)
+    total_min = aligned.hour * 60 + aligned.minute
+    snapped = (total_min // bar_min) * bar_min
+    return aligned.replace(hour=snapped // 60, minute=snapped % 60)
+
 
 def _default_strategies_dir() -> Path:
     """Cartella JSON shippata col package strategy_crypto."""
@@ -131,9 +161,12 @@ def main() -> None:
             now = datetime.now(UTC)
             last_prices: dict[str, float] = {}
             for asset, executor in zip(assets, executors, strict=True):
-                # fetch OHLCV most recent lookback bars
+                # fetch OHLCV most recent lookback bars: end snappato al timeframe
-                end = now.replace(minute=0, second=0, microsecond=0)
+                # dell'asset, non sempre all'ora (altrimenti ETH 5m veniva valutato
-                start = end - timedelta(hours=args.lookback_bars + 1)
+                # solo ogni 60 min, regressione vs backtest tunato 5m).
+                bar_min = _TIMEFRAME_MINUTES[asset.timeframe]
+                end = _align_end_to_timeframe(now, asset.timeframe)
+                start = end - timedelta(minutes=bar_min * (args.lookback_bars + 1))
                 req = OHLCVRequest(
                     symbol=asset.symbol,
                     timeframe=asset.timeframe,

src/multi_swarm_core/multi_swarm_core/dashboard/nicegui_app.py

@@ -263,7 +263,8 @@ def index() -> None:
         refresh()
 
     select.on_value_change(on_select_change)
-    ui.timer(REFRESH_INTERVAL_S, refresh)
+    _timer = ui.timer(REFRESH_INTERVAL_S, refresh)
+    ui.context.client.on_disconnect(_timer.deactivate)
     refresh()
 
 
@@ -353,7 +354,8 @@ def convergence() -> None:
         refresh()
 
     select.on_value_change(on_select_change)
-    ui.timer(REFRESH_INTERVAL_S, refresh)
+    _timer = ui.timer(REFRESH_INTERVAL_S, refresh)
+    ui.context.client.on_disconnect(_timer.deactivate)
     refresh()
 
 
@@ -535,7 +537,8 @@ def genomes() -> None:
     select.on_value_change(on_select_change)
     top_k_select.on_value_change(lambda _: refresh())
     top_table.on("selection", on_row_selected)
-    ui.timer(REFRESH_INTERVAL_S, refresh)
+    _timer = ui.timer(REFRESH_INTERVAL_S, refresh)
+    ui.context.client.on_disconnect(_timer.deactivate)
     refresh()
 
 

src/strategy_crypto/strategy_crypto/frontend/data.py

@@ -7,6 +7,7 @@ from __future__ import annotations
 
 import json
 import sqlite3
+import time
 from pathlib import Path
 from typing import Any
 
@@ -14,9 +15,18 @@ import pandas as pd  # type: ignore[import-untyped]
 
 
 def _paper_conn(db_path: str | Path) -> sqlite3.Connection:
-    conn = sqlite3.connect(str(db_path))
+    # Cold-start race: GUI può avviarsi prima che il paper writer crei il file.
+    db_path_str = str(db_path)
+    deadline = time.monotonic() + 5.0
+    while True:
+        try:
+            conn = sqlite3.connect(db_path_str, timeout=5.0)
             conn.row_factory = sqlite3.Row
             return conn
+        except sqlite3.OperationalError:
+            if time.monotonic() >= deadline:
+                raise
+            time.sleep(1.0)
 
 
 def paper_runs_df(db_path: str | Path) -> pd.DataFrame:

src/strategy_crypto/tests/test_paper_runner_alignment.py

@@ -0,0 +1,70 @@
+"""Regression guard: end-of-window snap deve seguire il timeframe dell'asset.
+
+Bug originale (scripts/run_paper_trading.py): ``end = now.replace(minute=0,...)``
+snappava sempre all'ora; ETH 5m veniva quindi valutato 1 volta ogni 60 min
+invece di ogni 5 min, riducendo la fedelta' al backtest tunato 5m.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from datetime import UTC, datetime
+from pathlib import Path
+
+import pytest
+
+_REPO_ROOT = Path(__file__).resolve().parents[3]
+_RUNNER_PATH = _REPO_ROOT / "scripts" / "run_paper_trading.py"
+
+
+def _load_runner_module():
+    spec = importlib.util.spec_from_file_location("run_paper_trading", _RUNNER_PATH)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules["run_paper_trading"] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+@pytest.fixture(scope="module")
+def runner():
+    return _load_runner_module()
+
+
+@pytest.mark.parametrize(
+    "now, tf, expected",
+    [
+        # 5m: snap al boundary di 5 min, NON all'ora
+        (datetime(2026, 5, 18, 14, 37, 42, tzinfo=UTC), "5m", datetime(2026, 5, 18, 14, 35, tzinfo=UTC)),
+        (datetime(2026, 5, 18, 14, 35, 0, tzinfo=UTC), "5m", datetime(2026, 5, 18, 14, 35, tzinfo=UTC)),
+        (datetime(2026, 5, 18, 14, 34, 59, tzinfo=UTC), "5m", datetime(2026, 5, 18, 14, 30, tzinfo=UTC)),
+        # 1h: comportamento storico preservato
+        (datetime(2026, 5, 18, 14, 37, 42, tzinfo=UTC), "1h", datetime(2026, 5, 18, 14, 0, tzinfo=UTC)),
+        (datetime(2026, 5, 18, 14, 0, 0, tzinfo=UTC), "1h", datetime(2026, 5, 18, 14, 0, tzinfo=UTC)),
+        # 15m / 4h
+        (datetime(2026, 5, 18, 14, 22, 0, tzinfo=UTC), "15m", datetime(2026, 5, 18, 14, 15, tzinfo=UTC)),
+        (datetime(2026, 5, 18, 14, 22, 0, tzinfo=UTC), "4h", datetime(2026, 5, 18, 12, 0, tzinfo=UTC)),
+    ],
+)
+def test_align_end_to_timeframe(runner, now, tf, expected) -> None:
+    assert runner._align_end_to_timeframe(now, tf) == expected
+
+
+def test_align_end_5m_advances_every_5_minutes(runner) -> None:
+    """Bug-regression: chiamate consecutive a 5 min di distanza devono
+    produrre end DIVERSI per tf=5m (prima del fix erano identici)."""
+    a = datetime(2026, 5, 18, 14, 30, 0, tzinfo=UTC)
+    b = datetime(2026, 5, 18, 14, 35, 0, tzinfo=UTC)
+    c = datetime(2026, 5, 18, 14, 40, 0, tzinfo=UTC)
+    ends = {runner._align_end_to_timeframe(t, "5m") for t in (a, b, c)}
+    assert len(ends) == 3
+
+
+def test_align_end_1h_stable_within_hour(runner) -> None:
+    """Per tf=1h, chiamate dentro la stessa ora devono dare lo stesso end."""
+    ends = {
+        runner._align_end_to_timeframe(datetime(2026, 5, 18, 14, m, 0, tzinfo=UTC), "1h")
+        for m in (0, 15, 30, 45, 59)
+    }
+    assert ends == {datetime(2026, 5, 18, 14, 0, tzinfo=UTC)}

state/validation-hardened-001.json

@@ -0,0 +1,324 @@
+{
+  "run_id": "7f65bd1832d94c638b588aab02fb223e",
+  "run_name": "phase1-hardened-001",
+  "n_folds": 4,
+  "top_k_requested": 5,
+  "top_k_evaluated": 5,
+  "symbol": "BTC-PERPETUAL",
+  "timeframe": "1h",
+  "start": "2018-09-01T00:00:00+00:00",
+  "end": "2026-01-01T00:00:00+00:00",
+  "ohlcv_bars": 64297,
+  "results": [
+    {
+      "genome_id": "9cf506b83bec55f6",
+      "fitness_is": 0.2847465321576384,
+      "sharpe_is": 0.6809931251900289,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.4454407113532186,
+          "sharpe": 0.940612398713799,
+          "dsr": 0.09856838950479485,
+          "dsr_pvalue": 0.9014316104952051,
+          "return": 0.12691347502077277,
+          "max_dd": 0.08467873586477132,
+          "n_trades": 50,
+          "test_start": "2022-05-02 12:00:00+00:00",
+          "test_end": "2023-04-02 08:00:00+00:00"
+        },
+        {
+          "fold": 1,
+          "fitness": 0.3348901947844796,
+          "sharpe": 0.6215567075501345,
+          "dsr": 0.05615871498414856,
+          "dsr_pvalue": 0.9438412850158514,
+          "return": 0.1669550204052912,
+          "max_dd": 0.2425926649805225,
+          "n_trades": 60,
+          "test_start": "2023-04-02 09:00:00+00:00",
+          "test_end": "2024-03-02 05:00:00+00:00"
+        },
+        {
+          "fold": 2,
+          "fitness": 0.08496628060413243,
+          "sharpe": -0.291593157960215,
+          "dsr": 0.006828013272159182,
+          "dsr_pvalue": 0.9931719867278408,
+          "return": -0.06496567446731383,
+          "max_dd": 0.1933746053658072,
+          "n_trades": 72,
+          "test_start": "2024-03-02 06:00:00+00:00",
+          "test_end": "2025-01-31 02:00:00+00:00"
+        },
+        {
+          "fold": 3,
+          "fitness": 0.10547685784422405,
+          "sharpe": -0.04385303190774091,
+          "dsr": 0.013045759744378084,
+          "dsr_pvalue": 0.986954240255622,
+          "return": -0.003908970230222186,
+          "max_dd": 0.05825300658307936,
+          "n_trades": 31,
+          "test_start": "2025-01-31 03:00:00+00:00",
+          "test_end": "2025-12-31 23:00:00+00:00"
+        }
+      ],
+      "fitness_oos_mean": 0.24269351114651366,
+      "fitness_oos_min": 0.08496628060413243,
+      "fitness_oos_max": 0.4454407113532186,
+      "fitness_oos_std": 0.15273583802070415,
+      "sharpe_oos_mean": 0.3066807290989944,
+      "sharpe_oos_min": -0.291593157960215,
+      "robust_score": 0.08496628060413243
+    },
+    {
+      "genome_id": "9f1f7ffa40b4900a",
+      "fitness_is": 0.2653851546757454,
+      "sharpe_is": 0.7183647852569522,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.0,
+          "sharpe": 0.0,
+          "dsr": 0.0,
+          "dsr_pvalue": 1.0,
+          "return": 0.0,
+          "max_dd": 0.0,
+          "n_trades": 0,
+          "test_start": "2022-05-02 12:00:00+00:00",
+          "test_end": "2023-04-02 08:00:00+00:00"
+        },
+        {
+          "fold": 1,
+          "fitness": 0.0,
+          "sharpe": 0.0,
+          "dsr": 0.0,
+          "dsr_pvalue": 1.0,
+          "return": 0.0,
+          "max_dd": 0.0,
+          "n_trades": 0,
+          "test_start": "2023-04-02 09:00:00+00:00",
+          "test_end": "2024-03-02 05:00:00+00:00"
+        },
+        {
+          "fold": 2,
+          "fitness": 0.0,
+          "sharpe": 0.0,
+          "dsr": 0.0,
+          "dsr_pvalue": 1.0,
+          "return": 0.0,
+          "max_dd": 0.0,
+          "n_trades": 0,
+          "test_start": "2024-03-02 06:00:00+00:00",
+          "test_end": "2025-01-31 02:00:00+00:00"
+        },
+        {
+          "fold": 3,
+          "fitness": 0.0,
+          "sharpe": 0.0,
+          "dsr": 0.0,
+          "dsr_pvalue": 1.0,
+          "return": 0.0,
+          "max_dd": 0.0,
+          "n_trades": 0,
+          "test_start": "2025-01-31 03:00:00+00:00",
+          "test_end": "2025-12-31 23:00:00+00:00"
+        }
+      ],
+      "fitness_oos_mean": 0.0,
+      "fitness_oos_min": 0.0,
+      "fitness_oos_max": 0.0,
+      "fitness_oos_std": 0.0,
+      "sharpe_oos_mean": 0.0,
+      "sharpe_oos_min": 0.0,
+      "robust_score": 0.0
+    },
+    {
+      "genome_id": "aff702f86c30e30f",
+      "fitness_is": 0.22070023288031862,
+      "sharpe_is": 0.49220575419744833,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.0,
+          "sharpe": 0.0,
+          "dsr": 0.0,
+          "dsr_pvalue": 1.0,
+          "return": 0.0,
+          "max_dd": 0.0,
+          "n_trades": 0,
+          "test_start": "2022-05-02 12:00:00+00:00",
+          "test_end": "2023-04-02 08:00:00+00:00"
+        },
+        {
+          "fold": 1,
+          "fitness": 0.0,
+          "sharpe": -1.3160615555061568,
+          "dsr": 2.0480110912755528e-14,
+          "dsr_pvalue": 0.9999999999999796,
+          "return": -0.004197667464114874,
+          "max_dd": 0.004197667464114874,
+          "n_trades": 1,
+          "test_start": "2023-04-02 09:00:00+00:00",
+          "test_end": "2024-03-02 05:00:00+00:00"
+        },
+        {
+          "fold": 2,
+          "fitness": 0.0,
+          "sharpe": 0.0,
+          "dsr": 0.0,
+          "dsr_pvalue": 1.0,
+          "return": 0.0,
+          "max_dd": 0.0,
+          "n_trades": 0,
+          "test_start": "2024-03-02 06:00:00+00:00",
+          "test_end": "2025-01-31 02:00:00+00:00"
+        },
+        {
+          "fold": 3,
+          "fitness": 0.0,
+          "sharpe": 0.0,
+          "dsr": 0.0,
+          "dsr_pvalue": 1.0,
+          "return": 0.0,
+          "max_dd": 0.0,
+          "n_trades": 0,
+          "test_start": "2025-01-31 03:00:00+00:00",
+          "test_end": "2025-12-31 23:00:00+00:00"
+        }
+      ],
+      "fitness_oos_mean": 0.0,
+      "fitness_oos_min": 0.0,
+      "fitness_oos_max": 0.0,
+      "fitness_oos_std": 0.0,
+      "sharpe_oos_mean": -0.3290153888765392,
+      "sharpe_oos_min": -1.3160615555061568,
+      "robust_score": 0.0
+    },
+    {
+      "genome_id": "43236904205071d5",
+      "fitness_is": 0.22070023288031862,
+      "sharpe_is": 0.49220575419744833,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.0,
+          "sharpe": 0.0,
+          "dsr": 0.0,
+          "dsr_pvalue": 1.0,
+          "return": 0.0,
+          "max_dd": 0.0,
+          "n_trades": 0,
+          "test_start": "2022-05-02 12:00:00+00:00",
+          "test_end": "2023-04-02 08:00:00+00:00"
+        },
+        {
+          "fold": 1,
+          "fitness": 0.0,
+          "sharpe": -1.3160615555061568,
+          "dsr": 2.0480110912755528e-14,
+          "dsr_pvalue": 0.9999999999999796,
+          "return": -0.004197667464114874,
+          "max_dd": 0.004197667464114874,
+          "n_trades": 1,
+          "test_start": "2023-04-02 09:00:00+00:00",
+          "test_end": "2024-03-02 05:00:00+00:00"
+        },
+        {
+          "fold": 2,
+          "fitness": 0.0,
+          "sharpe": 0.0,
+          "dsr": 0.0,
+          "dsr_pvalue": 1.0,
+          "return": 0.0,
+          "max_dd": 0.0,
+          "n_trades": 0,
+          "test_start": "2024-03-02 06:00:00+00:00",
+          "test_end": "2025-01-31 02:00:00+00:00"
+        },
+        {
+          "fold": 3,
+          "fitness": 0.0,
+          "sharpe": 0.0,
+          "dsr": 0.0,
+          "dsr_pvalue": 1.0,
+          "return": 0.0,
+          "max_dd": 0.0,
+          "n_trades": 0,
+          "test_start": "2025-01-31 03:00:00+00:00",
+          "test_end": "2025-12-31 23:00:00+00:00"
+        }
+      ],
+      "fitness_oos_mean": 0.0,
+      "fitness_oos_min": 0.0,
+      "fitness_oos_max": 0.0,
+      "fitness_oos_std": 0.0,
+      "sharpe_oos_mean": -0.3290153888765392,
+      "sharpe_oos_min": -1.3160615555061568,
+      "robust_score": 0.0
+    },
+    {
+      "genome_id": "78a445d3043e0771",
+      "fitness_is": 0.20950868586433583,
+      "sharpe_is": 0.2940379509455447,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.07182313189843528,
+          "sharpe": -0.5971649832941454,
+          "dsr": 0.0029614586254508167,
+          "dsr_pvalue": 0.9970385413745492,
+          "return": -0.10657245144370586,
+          "max_dd": 0.17076725093180228,
+          "n_trades": 14,
+          "test_start": "2022-05-02 12:00:00+00:00",
+          "test_end": "2023-04-02 08:00:00+00:00"
+        },
+        {
+          "fold": 1,
+          "fitness": 0.04914620786914511,
+          "sharpe": -0.7350195766937807,
+          "dsr": 0.0007433210570883239,
+          "dsr_pvalue": 0.9992566789429117,
+          "return": -0.03472119511680272,
+          "max_dd": 0.060780295013844174,
+          "n_trades": 20,
+          "test_start": "2023-04-02 09:00:00+00:00",
+          "test_end": "2024-03-02 05:00:00+00:00"
+        },
+        {
+          "fold": 2,
+          "fitness": 0.0,
+          "sharpe": 1.2071345821102195,
+          "dsr": 0.14855640183813817,
+          "dsr_pvalue": 0.8514435981618618,
+          "return": 0.12296871148346566,
+          "max_dd": 0.05065940776643054,
+          "n_trades": 9,
+          "test_start": "2024-03-02 06:00:00+00:00",
+          "test_end": "2025-01-31 02:00:00+00:00"
+        },
+        {
+          "fold": 3,
+          "fitness": 0.0,
+          "sharpe": -1.6874561123211371,
+          "dsr": 9.510326955509528e-06,
+          "dsr_pvalue": 0.9999904896730445,
+          "return": -0.03197955364502458,
+          "max_dd": 0.032239270519921044,
+          "n_trades": 7,
+          "test_start": "2025-01-31 03:00:00+00:00",
+          "test_end": "2025-12-31 23:00:00+00:00"
+        }
+      ],
+      "fitness_oos_mean": 0.0302423349418951,
+      "fitness_oos_min": 0.0,
+      "fitness_oos_max": 0.07182313189843528,
+      "fitness_oos_std": 0.03128704497338385,
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+    }
+  ]
+}