feat: auto-queue orderbook retrain at coverage gate

.env.example

@@ -126,6 +126,7 @@ TORCH_ORDERBOOK_DB=runtime/orderbook_observations.sqlite3
 TORCH_ORDERBOOK_MIN_SAMPLES_PER_BUCKET=20
 TORCH_ORDERBOOK_MIN_COVERED_BUCKETS=240
 TORCH_ORDERBOOK_MIN_SYMBOLS=2
+TORCH_ORDERBOOK_AUTO_CHECK_SECONDS=3600
 
 # Forward-only gate for an offline-approved shadow model. Promotion remains an
 # explicit authenticated API action after every check has passed.

README.md

@@ -250,6 +250,7 @@ Live-исполнение ведет журнал order intent до отправ
 - `GET /api/training/market-observations/manifest` — training-token manifest для инкрементальной синхронизации forward L1-данных.
 - `GET /api/training/shadow` — состояние изолированной shadow-модели и повторного forward-gate.
 - `POST /api/training/shadow/promote` — атомарное продвижение shadow-модели; возвращает `409`, пока forward-gate не пройден.
+- `POST /api/training/retrain/auto` — ограниченная training-token команда Windows-agent; ставит только orderbook-retrain без произвольных параметров.
 - `GET /api/trades` — последние сделки.
 - `GET /api/signals` — последние сигналы стратегии.
 - `GET /api/events` — события.

crypto_spot_bot/__init__.py

@@ -1,3 +1,3 @@
 """Crypto spot trading bot package."""
 
-__version__ = "1.1.0"
+__version__ = "1.1.1"

crypto_spot_bot/dashboard.py

@@ -223,6 +223,17 @@ def create_app(settings: Settings | None = None) -> FastAPI:
     ) -> dict[str, Any]:
         return training.request_retrain(payload)
 
+    @app.post("/api/training/retrain/auto")
+    async def training_retrain_auto(
+        _: None = Depends(authorizer.require_training),
+    ) -> dict[str, Any]:
+        return training.request_retrain(
+            {
+                "source": "windows-agent-auto",
+                "parameters": {"use_orderbook": True},
+            }
+        )
+
     @app.post("/api/training/heartbeat")
     async def training_heartbeat(
         payload: dict[str, Any] | None = None,

tools/windows_training_agent.py

@@ -38,6 +38,7 @@ SHADOW_ARTIFACT_NAMES = (
     "torch_shadow_guard.json",
     "torch_shadow_calibration.json",
 )
+_LAST_ORDERBOOK_AUTO_CHECK = 0.0
 
 
 def main() -> None:
@@ -63,6 +64,7 @@ def poll_once(args: argparse.Namespace, repo_root: Path, runtime_dir: Path, log_
     api_json(args, "/api/training/heartbeat", worker)
     claim = api_json(args, "/api/training/claim", worker)
     if not claim.get("claimed"):
+        maybe_auto_queue_orderbook(args, repo_root, runtime_dir, log_path)
         return
     job = claim.get("job") if isinstance(claim.get("job"), dict) else {}
     job_id = str(job.get("id") or "")
@@ -352,6 +354,45 @@ def prepare_orderbook_data(
     return result
 
 
+def maybe_auto_queue_orderbook(
+    args: argparse.Namespace,
+    repo_root: Path,
+    runtime_dir: Path,
+    log_path: Path,
+) -> None:
+    global _LAST_ORDERBOOK_AUTO_CHECK
+    try:
+        interval_seconds = max(
+            300,
+            int(os.environ.get("TORCH_ORDERBOOK_AUTO_CHECK_SECONDS", "3600") or 3600),
+        )
+    except ValueError:
+        interval_seconds = 3600
+    now = time.monotonic()
+    if _LAST_ORDERBOOK_AUTO_CHECK and now - _LAST_ORDERBOOK_AUTO_CHECK < interval_seconds:
+        return
+    _LAST_ORDERBOOK_AUTO_CHECK = now
+    marker_path = runtime_dir / "orderbook_auto_queue.json"
+    if marker_path.is_file() or (runtime_dir / "lstm_forecaster.shadow.json").is_file():
+        return
+    status = prepare_orderbook_data(args, repo_root, {}, log_path)
+    if status.get("state") != "ready":
+        return
+    response = api_json(args, "/api/training/retrain/auto", {})
+    if not response.get("queued"):
+        log(log_path, f"Automatic orderbook retrain was not queued: {response.get('reason', 'unknown')}")
+        return
+    marker = {
+        "queued_at": datetime.now().astimezone().isoformat(timespec="seconds"),
+        "job_id": (response.get("job") or {}).get("id"),
+        "coverage": status,
+    }
+    marker_tmp = marker_path.with_suffix(".tmp")
+    marker_tmp.write_text(json.dumps(marker, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+    marker_tmp.replace(marker_path)
+    log(log_path, f"Automatically queued orderbook retrain job {marker['job_id']}")
+
+
 def friendly_training_message(message: str) -> str:
     cleaned = message.strip()
     if not cleaned: