Add global Windows training agent queue

README.md

@@ -89,6 +89,14 @@ powershell -ExecutionPolicy Bypass -File tools\run_torch_retrain.ps1
 powershell -ExecutionPolicy Bypass -File tools\install_windows_torch_retrainer.ps1
 ```
 
+Для удалённого запуска с телефона или с бота используется Windows training agent. Бот на `tb.kusoft.xyz` хранит очередь заданий, а Windows-машина сама подключается к интернету, забирает задания, обучает модель и загружает артефакты обратно:
+
+```powershell
+powershell -ExecutionPolicy Bypass -File tools\install_windows_training_agent.ps1 -ApiAuth "login:password" -StartNow
+```
+
+Установщик регистрирует Scheduled Task `TradeBot Windows Training Agent` при входе в Windows и удаляет старые локальные retrain-задачи, чтобы обучение запускалось через очередь, а не двумя независимыми механизмами.
+
 По умолчанию Windows-расписание переобучает PyTorch `LSTM/GRU` каждые 6 часов с `--limit 3000` на 12 spot-парах из `SYMBOLS`. Параметры можно переопределить через env: `TORCH_RETRAIN_SYMBOLS`, `TORCH_RETRAIN_LIMIT`, `TORCH_RETRAIN_LOOKBACKS`, `TORCH_RETRAIN_ARCHITECTURES`, `TORCH_RETRAIN_HIDDEN_SIZES`, `TORCH_RETRAIN_LAYERS`, `TORCH_RETRAIN_DROPOUTS`, `TORCH_RETRAIN_HORIZON`, `TORCH_RETRAIN_HORIZONS`, `TORCH_RETRAIN_CONTEXT_SYMBOLS`, `TORCH_RETRAIN_FEATURES`, `TORCH_RETRAIN_SEED`, `TORCH_RETRAIN_EPOCHS`, `TORCH_RETRAIN_PATIENCE`, `TORCH_RETRAIN_INTERVAL`, `TORCH_RETRAIN_ENV`.
 
 Если retrain запускается с `-DeployToPi`, после успешного guard он синхронизирует `runtime/lstm_forecaster.json`, `runtime/torch_retrain_guard.json` и `runtime/torch_threshold_calibration.json` на Raspberry Pi через SSH-ключ и перезапускает сервис `tradebot`. Отдельный запуск sync:

android/TradeBotMonitor/README.md

@@ -10,7 +10,7 @@
 - Параметры Torch: edge, P(up), confidence, skill, quantiles, gate, причина решения.
 - Kelly/размер позиции: текущий размер, Kelly-цель, занятая экспозиция, остаток, множители edge/P(up)/skill.
 - Обзор equity/cash/exposure/PnL и последних решений.
-- Удалённый запуск retrain через настраиваемый webhook.
+- Удалённый запуск retrain через очередь заданий на боте и закреплённый Windows-компьютер обучения.
 - Расписание retrain на телефоне: Android отправляет команду по расписанию, но обучение идёт на Windows-машине.
 - Настройки API, токена команд, тёмной/светлой темы.
 - Live-чеклист: приложение показывает, готов ли сервер к реальной торговле, и не включает live одной опасной кнопкой.
@@ -46,7 +46,7 @@ https://tb.kusoft.xyz
 
 ## Переобучение
 
-Телефон не обучает модель локально. Вкладка `Обучение` отправляет POST на указанный webhook Windows-машины. Так телефон становится пультом запуска/расписания, а тяжёлый PyTorch retrain остаётся на нормальном компьютере.
+Телефон не обучает модель локально. Вкладка `Обучение` ставит задание в очередь на `tb.kusoft.xyz`, а Windows-agent на закреплённой машине `DESKTOP-TMFDL0H` сам выходит в интернет, забирает задание, обучает модель и отправляет артефакты обратно боту. Так телефон становится пультом запуска/расписания, а тяжёлый PyTorch retrain остаётся на нормальном компьютере даже если он находится в другой сети.
 
 ## Live-торговля
 

android/TradeBotMonitor/app/build.gradle.kts

@@ -10,7 +10,7 @@ android {
         applicationId = "xyz.kusoft.tradebotmonitor"
         minSdk = 26
         targetSdk = 36
-        versionCode = 5
+        versionCode = 6
-        versionName = "0.2.2"
+        versionName = "0.2.3"
     }
 }

android/TradeBotMonitor/app/src/main/java/xyz/kusoft/tradebotmonitor/AppPrefs.kt

@@ -23,10 +23,6 @@ class AppPrefs(context: Context) {
         get() = prefs.getString("command_token", "") ?: ""
         set(value) = prefs.edit().putString("command_token", value.trim()).apply()
 
-    var retrainWebhookUrl: String
-        get() = prefs.getString("retrain_webhook_url", "") ?: ""
-        set(value) = prefs.edit().putString("retrain_webhook_url", value.trim()).apply()
-
     var selectedSymbol: String
         get() = prefs.getString("selected_symbol", "BTCUSDT") ?: "BTCUSDT"
         set(value) = prefs.edit().putString("selected_symbol", value).apply()

android/TradeBotMonitor/app/src/main/java/xyz/kusoft/tradebotmonitor/MainActivity.kt

@@ -565,13 +565,20 @@ class MainActivity : Activity() {
         return section("Открытые позиции", box)
     }
 
-    private fun trainingComputerPanel(): View =
+    private fun trainingComputerPanel(retrain: JSONObject): View =
         LinearLayout(this).apply {
+            val coordination = retrain.optJSONObject("coordination") ?: JSONObject()
+            val activeJob = coordination.optJSONObject("active_job")
+            val agentOnline = coordination.optBoolean("agent_online", false)
             orientation = LinearLayout.VERTICAL
             addView(text("Компьютер обучения", 12f, Typeface.NORMAL, palette.muted))
             addView(text(prefs.trainingComputerName, 18f, Typeface.BOLD, palette.green).top(dp(5)))
             addView(text(prefs.trainingComputerPath, 12f, Typeface.NORMAL, palette.muted).top(dp(4)))
             addView(keyValueLine("Статус", if (prefs.trainingComputerPinned) "закреплен" else "не закреплен", if (prefs.trainingComputerPinned) palette.green else palette.amber).top(dp(8)))
+            addView(keyValueLine("Связь агента", if (agentOnline) "онлайн" else "нет связи", if (agentOnline) palette.green else palette.amber).top(dp(4)))
+            if (activeJob != null) {
+                addView(keyValueLine("Задание", activeJob.optStringClean("status").ifBlank { "активно" }).top(dp(4)))
+            }
             addView(text("Переобучение Torch закреплено за этой Windows-машиной. Телефон только управляет запуском и расписанием, Pi только исполняет бота.", 12f, Typeface.NORMAL, palette.muted).top(dp(8)))
             addView(actionRow(
                 "Закрепить эту машину" to {
@@ -583,24 +590,17 @@ class MainActivity : Activity() {
         }
 
     private fun retrainSettingsBlock(retrain: JSONObject, backtest: JSONObject): View {
-        val webhookInput = input("URL запуска retrain на Windows", prefs.retrainWebhookUrl)
         return LinearLayout(this).apply {
             orientation = LinearLayout.VERTICAL
             addView(keyValueLine("Доступно", yesNo(retrain.optBoolean("available", false))))
             addView(keyValueLine("Принята новая модель", yesNo(retrain.optBoolean("accepted", false))).top(dp(4)))
-            addView(trainingComputerPanel().top(dp(12)))
+            addView(trainingComputerPanel(retrain).top(dp(12)))
             addView(thinDivider().top(dp(12)))
             val replay = backtest.optJSONObject("full_replay") ?: JSONObject()
             addView(keyValueLine("Replay сделок", replay.optInt("trades", 0).toString()).top(dp(4)))
             addView(keyValueLine("Replay PnL", signedMoney(replay.optDouble("net_pnl", 0.0)), colorForSigned(replay.optDouble("net_pnl", 0.0))).top(dp(4)))
-            addView(webhookInput.top(dp(12)))
             addView(actionRow(
-                "Сохранить URL" to {
-                    prefs.retrainWebhookUrl = webhookInput.text.toString()
-                    toast("URL retrain сохранен")
-                },
                 "Запустить сейчас" to {
-                    prefs.retrainWebhookUrl = webhookInput.text.toString()
                     triggerRetrainNow()
                 },
             ).top(dp(10)))
@@ -956,7 +956,7 @@ class MainActivity : Activity() {
     private fun triggerRetrainNow() {
         executor.execute {
             try {
-                val message = RetrainCommandClient(prefs.retrainWebhookUrl, prefs.commandToken).triggerNow()
+                val message = TradeBotApi(prefs.apiBaseUrl, prefs.commandToken).requestRetrain()
                 mainHandler.post { toast(message.take(180)) }
             } catch (error: Exception) {
                 mainHandler.post { toast(error.message ?: "retrain не запущен") }

android/TradeBotMonitor/app/src/main/java/xyz/kusoft/tradebotmonitor/RetrainScheduler.kt

@@ -42,8 +42,8 @@ class RetrainAlarmReceiver : BroadcastReceiver() {
         Executors.newSingleThreadExecutor().execute {
             try {
                 val prefs = AppPrefs(context)
-                if (prefs.retrainScheduleEnabled && prefs.retrainWebhookUrl.isNotBlank()) {
+                if (prefs.retrainScheduleEnabled) {
-                    RetrainCommandClient(prefs.retrainWebhookUrl, prefs.commandToken).triggerNow()
+                    TradeBotApi(prefs.apiBaseUrl, prefs.commandToken).requestRetrain()
                 }
             } finally {
                 pending.finish()

android/TradeBotMonitor/app/src/main/java/xyz/kusoft/tradebotmonitor/TradeBotApi.kt

@@ -52,11 +52,22 @@ class TradeBotApi(
         postJson("/api/control/stop")
     }
 
+    fun requestRetrain(): String {
+        val response = postJson("/api/training/retrain", "{\"source\":\"android\"}")
+        if (response.optBoolean("queued", false)) {
+            return "Задание переобучения отправлено на закрепленный компьютер"
+        }
+        return when (response.optStringClean("reason")) {
+            "active_job_exists" -> "Задание переобучения уже выполняется или ждет агента"
+            else -> "Задание переобучения принято ботом"
+        }
+    }
+
     private fun getJson(path: String): JSONObject = request(path, "GET")
 
-    private fun postJson(path: String): JSONObject = request(path, "POST")
+    private fun postJson(path: String, body: String = "{}"): JSONObject = request(path, "POST", body)
 
-    private fun request(path: String, method: String): JSONObject {
+    private fun request(path: String, method: String, body: String = "{}"): JSONObject {
         val root = baseUrl.trim().trimEnd('/')
         val url = URL(root + path)
         val connection = (url.openConnection() as HttpURLConnection).apply {
@@ -68,7 +79,7 @@ class TradeBotApi(
             if (method == "POST") {
                 doOutput = true
                 setRequestProperty("Content-Type", "application/json")
-                outputStream.use { stream -> stream.write("{}".toByteArray(StandardCharsets.UTF_8)) }
+                outputStream.use { stream -> stream.write(body.toByteArray(StandardCharsets.UTF_8)) }
             }
         }
         val code = connection.responseCode
@@ -216,35 +227,6 @@ class TradeBotApi(
     }
 }
 
-class RetrainCommandClient(
-    private val webhookUrl: String,
-    private val token: String,
-) {
-    fun triggerNow(): String {
-        if (webhookUrl.isBlank()) {
-            throw IllegalStateException("URL запуска retrain не задан")
-        }
-        val connection = (URL(webhookUrl).openConnection() as HttpURLConnection).apply {
-            requestMethod = "POST"
-            connectTimeout = 6000
-            readTimeout = 15000
-            doOutput = true
-            setRequestProperty("Content-Type", "application/json")
-            setRequestProperty("Accept", "application/json,text/plain,*/*")
-            applyAuthHeaders(this, token)
-            outputStream.use { stream -> stream.write("{\"source\":\"android\"}".toByteArray(StandardCharsets.UTF_8)) }
-        }
-        val code = connection.responseCode
-        val stream = if (code in 200..299) connection.inputStream else connection.errorStream
-        val text = stream?.bufferedReader(StandardCharsets.UTF_8)?.use(BufferedReader::readText).orEmpty()
-        connection.disconnect()
-        if (code !in 200..299) {
-            throw IllegalStateException("Retrain HTTP $code: ${text.take(240)}")
-        }
-        return text.ifBlank { "Команда retrain отправлена" }
-    }
-}
-
 private fun applyAuthHeaders(connection: HttpURLConnection, token: String) {
     val value = token.trim()
     if (value.isBlank()) return

crypto_spot_bot/dashboard.py

@@ -4,7 +4,7 @@ import json
 from contextlib import asynccontextmanager
 from typing import Any
 
-from fastapi import FastAPI, Response
+from fastapi import FastAPI, HTTPException, Response
 from fastapi.responses import JSONResponse, PlainTextResponse
 
 from crypto_spot_bot.analytics import analytics_snapshot
@@ -19,6 +19,7 @@ from crypto_spot_bot.reconciliation import reconciliation_snapshot
 from crypto_spot_bot.storage import Storage
 from crypto_spot_bot.strategy import SpotStrategy
 from crypto_spot_bot.time_series import TimeSeriesForecaster
+from crypto_spot_bot.training_coordination import TrainingCoordinator
 
 
 WEB_UI_REMOVED_MESSAGE = "Web UI removed. Use the Android TradeBot AI app and /api/* endpoints."
@@ -42,6 +43,7 @@ def create_app(settings: Settings | None = None) -> FastAPI:
     learner = TradeLearner(settings, storage)
     forecaster = TimeSeriesForecaster(settings)
     bot = CryptoSpotBot(settings, storage, market, broker, strategy, pattern_analyzer, learner, forecaster)
+    training = TrainingCoordinator(settings.time_series_lstm_model_path.parent)
 
     @asynccontextmanager
     async def lifespan(_: FastAPI):
@@ -56,6 +58,7 @@ def create_app(settings: Settings | None = None) -> FastAPI:
     app.state.storage = storage
     app.state.bot = bot
     app.state.market = market
+    app.state.training = training
 
     @app.get("/", response_class=PlainTextResponse, status_code=410)
     async def index() -> str:
@@ -114,7 +117,39 @@ def create_app(settings: Settings | None = None) -> FastAPI:
 
     @app.get("/api/retrain")
     async def retrain() -> dict[str, Any]:
-        return _runtime_json(settings, "torch_retrain_guard.json")
+        data = _runtime_json(settings, "torch_retrain_guard.json")
+        data["coordination"] = training.status()
+        return data
+
+    @app.get("/api/training/status")
+    async def training_status() -> dict[str, Any]:
+        return training.status()
+
+    @app.post("/api/training/retrain")
+    async def training_retrain(payload: dict[str, Any] | None = None) -> dict[str, Any]:
+        return training.request_retrain(payload)
+
+    @app.post("/api/training/heartbeat")
+    async def training_heartbeat(payload: dict[str, Any] | None = None) -> dict[str, Any]:
+        return training.heartbeat(payload)
+
+    @app.post("/api/training/claim")
+    async def training_claim(payload: dict[str, Any] | None = None) -> dict[str, Any]:
+        return training.claim(payload)
+
+    @app.post("/api/training/jobs/{job_id}/artifacts/chunk")
+    async def training_artifact_chunk(job_id: str, payload: dict[str, Any]) -> dict[str, Any]:
+        try:
+            return training.save_artifact_chunk(job_id, payload)
+        except ValueError as exc:
+            raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+    @app.post("/api/training/jobs/{job_id}/complete")
+    async def training_complete(job_id: str, payload: dict[str, Any] | None = None) -> dict[str, Any]:
+        try:
+            return training.complete(job_id, payload)
+        except ValueError as exc:
+            raise HTTPException(status_code=404, detail=str(exc)) from exc
 
     @app.get("/api/config")
     async def config() -> dict[str, Any]:

crypto_spot_bot/training_coordination.py

@@ -0,0 +1,268 @@
+from __future__ import annotations
+
+import base64
+import hashlib
+import json
+import os
+import uuid
+from datetime import UTC
+from datetime import datetime
+from datetime import timedelta
+from pathlib import Path
+from threading import Lock
+from typing import Any
+
+
+ALLOWED_TRAINING_ARTIFACTS = {
+    "lstm_forecaster.json",
+    "torch_retrain_guard.json",
+    "torch_threshold_calibration.json",
+}
+RUNNING_TIMEOUT = timedelta(hours=12)
+ONLINE_WINDOW = timedelta(minutes=3)
+
+
+class TrainingCoordinator:
+    def __init__(self, runtime_dir: Path) -> None:
+        self.runtime_dir = runtime_dir
+        self.state_path = runtime_dir / "training_coordination.json"
+        self.upload_root = runtime_dir / ".training_uploads"
+        self._lock = Lock()
+
+    def status(self) -> dict[str, Any]:
+        with self._lock:
+            state = self._load_state()
+            self._expire_stale_jobs(state)
+            self._save_state(state)
+            return self._public_status(state)
+
+    def request_retrain(self, payload: dict[str, Any] | None = None) -> dict[str, Any]:
+        payload = payload or {}
+        with self._lock:
+            state = self._load_state()
+            self._expire_stale_jobs(state)
+            existing = self._active_job(state)
+            if existing is not None:
+                self._save_state(state)
+                return {"queued": False, "reason": "active_job_exists", "job": existing, "status": self._public_status(state)}
+
+            now = _now()
+            job = {
+                "id": str(uuid.uuid4()),
+                "status": "pending",
+                "requested_at": now,
+                "requested_by": str(payload.get("source") or "api"),
+                "parameters": _safe_parameters(payload.get("parameters")),
+                "message": "",
+                "artifacts": [],
+            }
+            state.setdefault("jobs", []).append(job)
+            self._trim_jobs(state)
+            self._save_state(state)
+            return {"queued": True, "job": job, "status": self._public_status(state)}
+
+    def heartbeat(self, payload: dict[str, Any] | None = None) -> dict[str, Any]:
+        payload = payload or {}
+        with self._lock:
+            state = self._load_state()
+            worker = self._worker_from_payload(payload)
+            state["worker"] = worker
+            self._save_state(state)
+            return {"ok": True, "worker": worker, "status": self._public_status(state)}
+
+    def claim(self, payload: dict[str, Any] | None = None) -> dict[str, Any]:
+        payload = payload or {}
+        with self._lock:
+            state = self._load_state()
+            self._expire_stale_jobs(state)
+            worker = self._worker_from_payload(payload)
+            state["worker"] = worker
+            job = self._oldest_pending_job(state)
+            if job is None:
+                self._save_state(state)
+                return {"claimed": False, "job": None, "status": self._public_status(state)}
+
+            now = _now()
+            job["status"] = "running"
+            job["claimed_at"] = now
+            job["claimed_by"] = worker["id"]
+            job["worker"] = worker
+            self._save_state(state)
+            return {"claimed": True, "job": job, "status": self._public_status(state)}
+
+    def save_artifact_chunk(self, job_id: str, payload: dict[str, Any]) -> dict[str, Any]:
+        name = Path(str(payload.get("name") or "")).name
+        if name not in ALLOWED_TRAINING_ARTIFACTS:
+            raise ValueError(f"artifact is not allowed: {name}")
+        index = int(payload.get("index", -1))
+        total = int(payload.get("total", 0))
+        sha256 = str(payload.get("sha256") or "").strip().lower()
+        if index < 0 or total <= 0 or index >= total:
+            raise ValueError("invalid artifact chunk index")
+        if not sha256:
+            raise ValueError("artifact sha256 is required")
+        try:
+            chunk = base64.b64decode(str(payload.get("data_base64") or ""), validate=True)
+        except (ValueError, TypeError) as exc:
+            raise ValueError("invalid artifact chunk payload") from exc
+
+        chunk_dir = self.upload_root / job_id / name
+        chunk_dir.mkdir(parents=True, exist_ok=True)
+        (chunk_dir / f"{index:06d}.part").write_bytes(chunk)
+
+        if not all((chunk_dir / f"{part:06d}.part").is_file() for part in range(total)):
+            return {"complete": False, "received": index + 1, "total": total}
+
+        target_tmp = self.runtime_dir / f".{name}.{job_id}.tmp"
+        digest = hashlib.sha256()
+        with target_tmp.open("wb") as output:
+            for part in range(total):
+                data = (chunk_dir / f"{part:06d}.part").read_bytes()
+                digest.update(data)
+                output.write(data)
+        if digest.hexdigest().lower() != sha256:
+            target_tmp.unlink(missing_ok=True)
+            raise ValueError("artifact sha256 mismatch")
+
+        self.runtime_dir.mkdir(parents=True, exist_ok=True)
+        os.replace(target_tmp, self.runtime_dir / name)
+        _remove_tree(chunk_dir)
+
+        with self._lock:
+            state = self._load_state()
+            job = self._job_by_id(state, job_id)
+            if job is not None:
+                artifacts = job.setdefault("artifacts", [])
+                artifacts = [item for item in artifacts if item.get("name") != name]
+                artifacts.append({"name": name, "sha256": sha256, "uploaded_at": _now()})
+                job["artifacts"] = artifacts
+                self._save_state(state)
+        return {"complete": True, "name": name, "sha256": sha256}
+
+    def complete(self, job_id: str, payload: dict[str, Any] | None = None) -> dict[str, Any]:
+        payload = payload or {}
+        with self._lock:
+            state = self._load_state()
+            job = self._job_by_id(state, job_id)
+            if job is None:
+                raise ValueError(f"training job not found: {job_id}")
+            success = bool(payload.get("success", payload.get("status") == "completed"))
+            job["status"] = "completed" if success else "failed"
+            job["completed_at"] = _now()
+            job["message"] = str(payload.get("message") or "")
+            if isinstance(payload.get("summary"), dict):
+                job["summary"] = payload["summary"]
+            self._save_state(state)
+            return {"ok": True, "job": job, "status": self._public_status(state)}
+
+    def _load_state(self) -> dict[str, Any]:
+        try:
+            data = json.loads(self.state_path.read_text(encoding="utf-8"))
+        except (OSError, json.JSONDecodeError):
+            data = {}
+        if not isinstance(data, dict):
+            data = {}
+        data.setdefault("jobs", [])
+        return data
+
+    def _save_state(self, state: dict[str, Any]) -> None:
+        self.runtime_dir.mkdir(parents=True, exist_ok=True)
+        tmp = self.state_path.with_suffix(".tmp")
+        tmp.write_text(json.dumps(state, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+        os.replace(tmp, self.state_path)
+
+    def _worker_from_payload(self, payload: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "id": str(payload.get("worker_id") or payload.get("id") or "windows-training-host"),
+            "name": str(payload.get("name") or "DESKTOP-TMFDL0H"),
+            "path": str(payload.get("path") or "C:\\Repos\\TradeBot"),
+            "version": str(payload.get("version") or "1"),
+            "last_seen_at": _now(),
+        }
+
+    def _public_status(self, state: dict[str, Any]) -> dict[str, Any]:
+        worker = state.get("worker") if isinstance(state.get("worker"), dict) else {}
+        last_seen = _parse_time(str(worker.get("last_seen_at") or ""))
+        active = self._active_job(state)
+        latest = _latest_job(state)
+        return {
+            "available": True,
+            "agent_online": bool(last_seen and datetime.now(UTC) - last_seen <= ONLINE_WINDOW),
+            "worker": worker,
+            "active_job": active,
+            "latest_job": latest,
+            "pending_jobs": sum(1 for job in state.get("jobs", []) if job.get("status") == "pending"),
+        }
+
+    def _active_job(self, state: dict[str, Any]) -> dict[str, Any] | None:
+        for job in reversed(state.get("jobs", [])):
+            if job.get("status") in {"pending", "running"}:
+                return job
+        return None
+
+    def _oldest_pending_job(self, state: dict[str, Any]) -> dict[str, Any] | None:
+        for job in state.get("jobs", []):
+            if job.get("status") == "pending":
+                return job
+        return None
+
+    def _job_by_id(self, state: dict[str, Any], job_id: str) -> dict[str, Any] | None:
+        for job in state.get("jobs", []):
+            if job.get("id") == job_id:
+                return job
+        return None
+
+    def _expire_stale_jobs(self, state: dict[str, Any]) -> None:
+        now = datetime.now(UTC)
+        for job in state.get("jobs", []):
+            if job.get("status") != "running":
+                continue
+            claimed_at = _parse_time(str(job.get("claimed_at") or ""))
+            if claimed_at and now - claimed_at > RUNNING_TIMEOUT:
+                job["status"] = "failed"
+                job["completed_at"] = _now()
+                job["message"] = "training worker timeout"
+
+    def _trim_jobs(self, state: dict[str, Any]) -> None:
+        jobs = state.get("jobs", [])
+        if isinstance(jobs, list) and len(jobs) > 30:
+            state["jobs"] = jobs[-30:]
+
+
+def _safe_parameters(value: Any) -> dict[str, Any]:
+    if not isinstance(value, dict):
+        return {}
+    allowed = {"symbols", "limit", "lookbacks", "architectures", "hidden_sizes", "layers", "dropouts", "epochs"}
+    return {key: value[key] for key in allowed if key in value}
+
+
+def _latest_job(state: dict[str, Any]) -> dict[str, Any] | None:
+    jobs = state.get("jobs", [])
+    if not jobs:
+        return None
+    latest = jobs[-1]
+    return latest if isinstance(latest, dict) else None
+
+
+def _now() -> str:
+    return datetime.now(UTC).isoformat(timespec="seconds")
+
+
+def _parse_time(value: str) -> datetime | None:
+    if not value:
+        return None
+    try:
+        return datetime.fromisoformat(value.replace("Z", "+00:00"))
+    except ValueError:
+        return None
+
+
+def _remove_tree(path: Path) -> None:
+    if not path.exists():
+        return
+    for child in path.iterdir():
+        if child.is_dir():
+            _remove_tree(child)
+        else:
+            child.unlink(missing_ok=True)
+    path.rmdir()

tests/test_training_coordination.py

@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+import base64
+import hashlib
+
+from crypto_spot_bot.training_coordination import TrainingCoordinator
+
+
+def test_training_coordinator_claims_and_completes_job(tmp_path) -> None:
+    coordinator = TrainingCoordinator(tmp_path)
+
+    requested = coordinator.request_retrain({"source": "android"})
+    job_id = requested["job"]["id"]
+    heartbeat = coordinator.heartbeat({"worker_id": "win-1", "name": "DESKTOP-TMFDL0H"})
+    claimed = coordinator.claim({"worker_id": "win-1", "name": "DESKTOP-TMFDL0H"})
+
+    assert requested["queued"] is True
+    assert heartbeat["status"]["agent_online"] is True
+    assert claimed["claimed"] is True
+    assert claimed["job"]["id"] == job_id
+    assert coordinator.status()["active_job"]["status"] == "running"
+
+    completed = coordinator.complete(job_id, {"success": True, "message": "ok"})
+
+    assert completed["job"]["status"] == "completed"
+    assert coordinator.status()["active_job"] is None
+
+
+def test_training_coordinator_accepts_chunked_artifact_upload(tmp_path) -> None:
+    coordinator = TrainingCoordinator(tmp_path)
+    job = coordinator.request_retrain({"source": "test"})["job"]
+    payload = b'{"type":"pytorch_recurrent_forecaster","symbols":{}}\n'
+    sha256 = hashlib.sha256(payload).hexdigest()
+    first = payload[:20]
+    second = payload[20:]
+
+    part_1 = coordinator.save_artifact_chunk(
+        job["id"],
+        {
+            "name": "lstm_forecaster.json",
+            "index": 0,
+            "total": 2,
+            "sha256": sha256,
+            "data_base64": base64.b64encode(first).decode("ascii"),
+        },
+    )
+    part_2 = coordinator.save_artifact_chunk(
+        job["id"],
+        {
+            "name": "lstm_forecaster.json",
+            "index": 1,
+            "total": 2,
+            "sha256": sha256,
+            "data_base64": base64.b64encode(second).decode("ascii"),
+        },
+    )
+
+    assert part_1["complete"] is False
+    assert part_2["complete"] is True
+    assert (tmp_path / "lstm_forecaster.json").read_bytes() == payload
+    assert coordinator.status()["latest_job"]["artifacts"][0]["sha256"] == sha256

tools/install_windows_training_agent.ps1

@@ -0,0 +1,98 @@
+[CmdletBinding()]
+param(
+    [string]$TaskName = "TradeBot Windows Training Agent",
+    [string]$ApiBaseUrl = "https://tb.kusoft.xyz",
+    [string]$ApiAuth = "",
+    [int]$PollSeconds = 60,
+    [string]$RepoRoot = "",
+    [switch]$StartNow,
+    [switch]$KeepLegacyRetrainer
+)
+
+$ErrorActionPreference = "Stop"
+
+if (-not $RepoRoot) {
+    $RepoRoot = (Resolve-Path (Join-Path $PSScriptRoot "..")).Path
+}
+$Agent = Join-Path $RepoRoot "tools\windows_training_agent.py"
+if (-not (Test-Path $Agent)) {
+    throw "Windows training agent not found: $Agent"
+}
+
+function Resolve-Python {
+    $venvPython = Join-Path $RepoRoot ".venv\Scripts\python.exe"
+    if (Test-Path $venvPython) {
+        return $venvPython
+    }
+
+    $userPython = Join-Path $env:LOCALAPPDATA "Programs\TradeBotPython312\python.exe"
+    if (Test-Path $userPython) {
+        return $userPython
+    }
+
+    foreach ($candidate in @("python.exe", "python")) {
+        $command = Get-Command $candidate -ErrorAction SilentlyContinue
+        if ($command) {
+            return $command.Source
+        }
+    }
+    throw "Python was not found. Create .venv or install Python 3.12."
+}
+
+if ($ApiAuth) {
+    [Environment]::SetEnvironmentVariable("TRADEBOT_API_AUTH", $ApiAuth, "User")
+    $env:TRADEBOT_API_AUTH = $ApiAuth
+}
+[Environment]::SetEnvironmentVariable("TRADEBOT_API_BASE_URL", $ApiBaseUrl, "User")
+[Environment]::SetEnvironmentVariable("TRADEBOT_TRAINING_WORKER_NAME", $env:COMPUTERNAME, "User")
+$env:TRADEBOT_API_BASE_URL = $ApiBaseUrl
+$env:TRADEBOT_TRAINING_WORKER_NAME = $env:COMPUTERNAME
+
+if (-not $KeepLegacyRetrainer) {
+    foreach ($legacyName in @("TradeBot PyTorch Forecaster Retrainer", "TradeBot LSTM Retrainer")) {
+        $legacyTask = Get-ScheduledTask -TaskName $legacyName -ErrorAction SilentlyContinue
+        if ($legacyTask) {
+            Unregister-ScheduledTask -TaskName $legacyName -Confirm:$false
+            Write-Host "Removed legacy scheduled task '$legacyName'."
+        }
+    }
+}
+
+$python = Resolve-Python
+$arguments = @(
+    "-u",
+    "`"$Agent`"",
+    "--repo-root", "`"$RepoRoot`"",
+    "--api-base-url", "`"$ApiBaseUrl`"",
+    "--poll-seconds", $PollSeconds.ToString()
+) -join " "
+
+$action = New-ScheduledTaskAction -Execute $python -Argument $arguments -WorkingDirectory $RepoRoot
+$trigger = New-ScheduledTaskTrigger -AtLogOn -User ([System.Security.Principal.WindowsIdentity]::GetCurrent().Name)
+$principal = New-ScheduledTaskPrincipal `
+    -UserId ([System.Security.Principal.WindowsIdentity]::GetCurrent().Name) `
+    -LogonType Interactive `
+    -RunLevel Limited
+$settings = New-ScheduledTaskSettingsSet `
+    -StartWhenAvailable `
+    -MultipleInstances IgnoreNew `
+    -AllowStartIfOnBatteries `
+    -DontStopIfGoingOnBatteries `
+    -ExecutionTimeLimit (New-TimeSpan -Days 30)
+
+Register-ScheduledTask `
+    -TaskName $TaskName `
+    -Action $action `
+    -Trigger $trigger `
+    -Principal $principal `
+    -Settings $settings `
+    -Description "Keeps the TradeBot Windows training agent online and polls the public bot API for retrain jobs." `
+    -Force | Out-Null
+
+if ($StartNow) {
+    Start-ScheduledTask -TaskName $TaskName
+}
+
+Write-Host "Registered scheduled task '$TaskName' for Windows logon."
+Write-Host "Agent API: $ApiBaseUrl"
+Write-Host "Agent script: $Agent"

tools/windows_training_agent.py

@@ -0,0 +1,221 @@
+from __future__ import annotations
+
+import argparse
+import base64
+import hashlib
+import json
+import os
+import platform
+import subprocess
+import sys
+import time
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+from urllib.error import HTTPError
+from urllib.error import URLError
+from urllib.request import Request
+from urllib.request import urlopen
+
+
+ARTIFACT_NAMES = (
+    "lstm_forecaster.json",
+    "torch_retrain_guard.json",
+    "torch_threshold_calibration.json",
+)
+
+
+def main() -> None:
+    args = parse_args()
+    repo_root = Path(args.repo_root).resolve()
+    runtime_dir = repo_root / "runtime"
+    runtime_dir.mkdir(parents=True, exist_ok=True)
+    log_path = Path(args.log_file).resolve() if args.log_file else runtime_dir / "windows_training_agent.log"
+
+    log(log_path, f"TradeBot Windows training agent started for {args.api_base_url}")
+    while True:
+        try:
+            poll_once(args, repo_root, runtime_dir, log_path)
+        except Exception as exc:  # noqa: BLE001 - agent must keep running.
+            log(log_path, f"ERROR: {exc}")
+        if args.once:
+            break
+        time.sleep(max(5, args.poll_seconds))
+
+
+def poll_once(args: argparse.Namespace, repo_root: Path, runtime_dir: Path, log_path: Path) -> None:
+    worker = worker_payload(args, repo_root)
+    api_json(args, "/api/training/heartbeat", worker)
+    claim = api_json(args, "/api/training/claim", worker)
+    if not claim.get("claimed"):
+        return
+    job = claim.get("job") if isinstance(claim.get("job"), dict) else {}
+    job_id = str(job.get("id") or "")
+    if not job_id:
+        return
+    log(log_path, f"Claimed retrain job {job_id}")
+    success = False
+    message = ""
+    summary: dict[str, Any] = {}
+    try:
+        run_retrain(job, repo_root, log_path)
+        summary = read_json(runtime_dir / "torch_retrain_guard.json")
+        for name in ARTIFACT_NAMES:
+            path = runtime_dir / name
+            if path.is_file():
+                upload_artifact(args, job_id, path, log_path)
+        success = True
+        message = "training completed"
+        log(log_path, f"Completed retrain job {job_id}")
+    except Exception as exc:  # noqa: BLE001 - report failure to the bot.
+        message = str(exc)
+        log(log_path, f"Job {job_id} failed: {message}")
+    finally:
+        payload = {"success": success, "message": message, "summary": summary}
+        api_json(args, f"/api/training/jobs/{job_id}/complete", payload)
+
+
+def run_retrain(job: dict[str, Any], repo_root: Path, log_path: Path) -> None:
+    script = repo_root / "tools" / "run_torch_retrain.ps1"
+    if not script.is_file():
+        raise RuntimeError(f"retrain script not found: {script}")
+    cmd = [
+        "powershell.exe",
+        "-NoProfile",
+        "-ExecutionPolicy",
+        "Bypass",
+        "-File",
+        str(script),
+    ]
+    parameters = job.get("parameters") if isinstance(job.get("parameters"), dict) else {}
+    arg_map = {
+        "symbols": "-Symbols",
+        "limit": "-Limit",
+        "lookbacks": "-Lookbacks",
+        "architectures": "-Architectures",
+        "hidden_sizes": "-HiddenSizes",
+        "layers": "-Layers",
+        "dropouts": "-Dropouts",
+        "epochs": "-Epochs",
+    }
+    for key, ps_arg in arg_map.items():
+        value = parameters.get(key)
+        if value not in (None, ""):
+            cmd.extend([ps_arg, str(value)])
+    log(log_path, "Running retrain: " + " ".join(quote_for_log(part) for part in cmd))
+    with subprocess.Popen(
+        cmd,
+        cwd=str(repo_root),
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=True,
+        encoding="utf-8",
+        errors="replace",
+    ) as process:
+        assert process.stdout is not None
+        for line in process.stdout:
+            log(log_path, line.rstrip())
+        code = process.wait()
+    if code != 0:
+        raise RuntimeError(f"retrain failed with exit code {code}")
+
+
+def upload_artifact(args: argparse.Namespace, job_id: str, path: Path, log_path: Path) -> None:
+    digest = hashlib.sha256(path.read_bytes()).hexdigest()
+    size = path.stat().st_size
+    chunk_size = max(64 * 1024, args.chunk_size)
+    total = max(1, (size + chunk_size - 1) // chunk_size)
+    log(log_path, f"Uploading {path.name}: {size} bytes, {total} chunks")
+    with path.open("rb") as source:
+        for index in range(total):
+            data = source.read(chunk_size)
+            payload = {
+                "name": path.name,
+                "index": index,
+                "total": total,
+                "sha256": digest,
+                "data_base64": base64.b64encode(data).decode("ascii"),
+            }
+            api_json(args, f"/api/training/jobs/{job_id}/artifacts/chunk", payload, timeout=120)
+
+
+def api_json(args: argparse.Namespace, path: str, payload: dict[str, Any], timeout: int = 30) -> dict[str, Any]:
+    url = args.api_base_url.rstrip("/") + path
+    body = json.dumps(payload, ensure_ascii=False).encode("utf-8")
+    headers = {"Content-Type": "application/json", "Accept": "application/json"}
+    token = args.api_auth or os.environ.get("TRADEBOT_API_AUTH", "")
+    headers.update(auth_headers(token))
+    request = Request(url, data=body, headers=headers, method="POST")
+    try:
+        with urlopen(request, timeout=timeout) as response:
+            text = response.read().decode("utf-8")
+    except HTTPError as exc:
+        detail = exc.read().decode("utf-8", errors="replace")
+        raise RuntimeError(f"HTTP {exc.code} {path}: {detail[:300]}") from exc
+    except URLError as exc:
+        raise RuntimeError(f"network error {path}: {exc.reason}") from exc
+    return json.loads(text) if text.strip() else {}
+
+
+def auth_headers(token: str) -> dict[str, str]:
+    value = token.strip()
+    if not value:
+        return {}
+    headers = {"X-TradeBot-Token": value}
+    if value.lower().startswith(("basic ", "bearer ")):
+        headers["Authorization"] = value
+    elif ":" in value:
+        encoded = base64.b64encode(value.encode("utf-8")).decode("ascii")
+        headers["Authorization"] = f"Basic {encoded}"
+    else:
+        headers["Authorization"] = f"Bearer {value}"
+    return headers
+
+
+def worker_payload(args: argparse.Namespace, repo_root: Path) -> dict[str, Any]:
+    name = args.worker_name or platform.node() or "Windows training host"
+    return {
+        "worker_id": args.worker_id or f"{name}:{repo_root}",
+        "name": name,
+        "path": str(repo_root),
+        "version": "1",
+    }
+
+
+def log(path: Path, message: str) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    stamp = datetime.now().astimezone().isoformat(timespec="seconds")
+    line = f"[{stamp}] {message}"
+    print(line, flush=True)
+    with path.open("a", encoding="utf-8") as handle:
+        handle.write(line + "\n")
+
+
+def read_json(path: Path) -> dict[str, Any]:
+    try:
+        data = json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError):
+        return {}
+    return data if isinstance(data, dict) else {}
+
+
+def quote_for_log(value: str) -> str:
+    return f'"{value}"' if " " in value else value
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Poll TradeBot for retrain jobs and execute them on Windows.")
+    parser.add_argument("--api-base-url", default=os.environ.get("TRADEBOT_API_BASE_URL", "https://tb.kusoft.xyz"))
+    parser.add_argument("--api-auth", default=os.environ.get("TRADEBOT_API_AUTH", ""))
+    parser.add_argument("--repo-root", default=str(Path(__file__).resolve().parents[1]))
+    parser.add_argument("--worker-id", default=os.environ.get("TRADEBOT_TRAINING_WORKER_ID", ""))
+    parser.add_argument("--worker-name", default=os.environ.get("TRADEBOT_TRAINING_WORKER_NAME", ""))
+    parser.add_argument("--poll-seconds", type=int, default=int(os.environ.get("TRADEBOT_TRAINING_POLL_SECONDS", "60")))
+    parser.add_argument("--chunk-size", type=int, default=int(os.environ.get("TRADEBOT_TRAINING_CHUNK_SIZE", str(512 * 1024))))
+    parser.add_argument("--log-file", default=os.environ.get("TRADEBOT_TRAINING_LOG", ""))
+    parser.add_argument("--once", action="store_true")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    main()