Improve Windows training agent progress

crypto_spot_bot/config.py

@@ -142,6 +142,7 @@ class Settings:
     take_profit_percent: float
     trailing_stop_percent: float
     min_hold_seconds: int
+    min_exit_net_percent: float
     entry_cooldown_seconds: int
     max_daily_drawdown_usdt: float
     min_cash_reserve_usdt: float
@@ -294,6 +295,7 @@ def load_settings(env_file: str | Path | None = None) -> Settings:
         take_profit_percent=_float_env("TAKE_PROFIT_PERCENT", 0.035),
         trailing_stop_percent=_float_env("TRAILING_STOP_PERCENT", 0.015),
         min_hold_seconds=_int_env("MIN_HOLD_SECONDS", 180),
+        min_exit_net_percent=_float_env("MIN_EXIT_NET_PERCENT", 0.20),
         entry_cooldown_seconds=_int_env("ENTRY_COOLDOWN_SECONDS", 180),
         max_daily_drawdown_usdt=_float_env("MAX_DAILY_DRAWDOWN_USDT", 6.0),
         min_cash_reserve_usdt=_float_env("MIN_CASH_RESERVE_USDT", 5.0),

crypto_spot_bot/dashboard.py

@@ -323,6 +323,7 @@ def _safe_config(settings: Settings) -> dict[str, Any]:
         "take_profit_percent": settings.take_profit_percent,
         "trailing_stop_percent": settings.trailing_stop_percent,
         "min_hold_seconds": settings.min_hold_seconds,
+        "min_exit_net_percent": settings.min_exit_net_percent,
         "entry_cooldown_seconds": settings.entry_cooldown_seconds,
         "max_daily_drawdown_usdt": settings.max_daily_drawdown_usdt,
         "min_cash_reserve_usdt": settings.min_cash_reserve_usdt,

crypto_spot_bot/strategy.py

@@ -394,6 +394,7 @@ class SpotStrategy:
         effective_take_profit = position.entry_price * (1 + take_profit_percent)
         trailing = position.trailing_stop(trailing_percent)
         estimated_exit_net_percent = _estimated_exit_net_percent(position, price, self.settings)
+        min_exit_net_percent = _min_exit_net_percent(self.settings)
         diagnostics = {
             "price": price,
             "entry_price": position.entry_price,
@@ -408,6 +409,7 @@ class SpotStrategy:
             "adaptive_rules": adaptive,
             "forecast": forecast,
             "estimated_exit_net_percent": round(estimated_exit_net_percent, 4),
+            "min_exit_net_percent": min_exit_net_percent,
             "min_exit_profit_percent": float(adaptive.get("min_exit_profit_percent", 0.0) or 0.0),
         }
         if effective_stop_loss is not None and price <= effective_stop_loss:
@@ -438,6 +440,7 @@ class SpotStrategy:
             estimated_exit_net_percent=estimated_exit_net_percent,
             stop_loss_percent=stop_loss_percent,
             min_edge_percent=self.settings.time_series_min_edge_percent,
+            min_exit_net_percent=min_exit_net_percent,
         )
         if forecast_exit is not None:
             action, confidence, reason = forecast_exit
@@ -876,6 +879,7 @@ def _torch_forecast_exit_signal(
     min_edge = max(0.0, settings.time_series_min_edge_percent)
     min_probability = _torch_min_probability(settings)
     estimated_exit_net_percent = _estimated_exit_net_percent(position, price, settings)
+    min_exit_net_percent = _min_exit_net_percent(settings)
     entry_path = str(position.entry_diagnostics.get("entry_path", ""))
     entry_edge_mode = str(position.entry_diagnostics.get("edge_mode", ""))
     rebound_fallback_position = entry_path == "rebound_fallback" or entry_edge_mode == "rebound_fallback"
@@ -899,6 +903,7 @@ def _torch_forecast_exit_signal(
         "min_probability_up": min_probability,
         "skill": skill,
         "estimated_exit_net_percent": round(estimated_exit_net_percent, 4),
+        "min_exit_net_percent": min_exit_net_percent,
         "atr_14": latest.atr_14 if latest else None,
     }
     hold_seconds = (utc_now() - position.opened_at).total_seconds()
@@ -909,13 +914,15 @@ def _torch_forecast_exit_signal(
     if price >= position.take_profit:
         return Signal(position.symbol, "SELL", 0.96, "torch_forecast: take-profit hit", diagnostics)
     if atr_trailing_stop is not None and price <= atr_trailing_stop:
-        if estimated_exit_net_percent < 0:
+        if estimated_exit_net_percent < min_exit_net_percent:
-            diagnostics["atr_exit_blocked_by_cost"] = True
+            diagnostics["atr_exit_blocked_by_min_profit"] = True
+            if estimated_exit_net_percent < 0:
+                diagnostics["atr_exit_blocked_by_cost"] = True
             return Signal(
                 position.symbol,
                 "HOLD",
                 0.45,
-                "torch_forecast: ATR trailing touched, but exit is not worth fees",
+                "torch_forecast: ATR trailing touched, but exit profit is below minimum",
                 diagnostics,
             )
         return Signal(position.symbol, "SELL", 0.94, "torch_forecast: ATR trailing stop hit", diagnostics)
@@ -956,23 +963,26 @@ def _torch_forecast_exit_signal(
             estimated_exit_net_percent=estimated_exit_net_percent,
             stop_loss_percent=stop_loss_percent,
             min_edge_percent=min_edge,
+            min_exit_net_percent=min_exit_net_percent,
         )
         if forecast_exit is not None:
             action, confidence, reason = forecast_exit
             return Signal(position.symbol, action, confidence, reason, diagnostics)
-        diagnostics["forecast_exit_blocked_by_cost"] = True
+        diagnostics["forecast_exit_blocked_by_min_profit"] = True
+        if estimated_exit_net_percent < 0:
+            diagnostics["forecast_exit_blocked_by_cost"] = True
         return Signal(
             position.symbol,
             "HOLD",
             0.44,
             (
-                "torch_forecast: forecast weakened, but exit is not worth fees; "
+                "torch_forecast: forecast weakened, but exit profit is below minimum; "
                 f"p_up={probability_up:.3f}, expected={expected_return:.4f}%"
             ),
             diagnostics,
         )
     weak_hold = expected_return < min_edge or probability_up < min_probability or skill <= 0.0
-    if weak_hold and estimated_exit_net_percent >= 0:
+    if weak_hold and estimated_exit_net_percent >= min_exit_net_percent:
         return Signal(
             position.symbol,
             "SELL",
@@ -983,6 +993,8 @@ def _torch_forecast_exit_signal(
             ),
             diagnostics,
         )
+    if weak_hold and estimated_exit_net_percent >= 0:
+        diagnostics["weak_exit_blocked_by_min_profit"] = True
     return Signal(position.symbol, "HOLD", 0.35, "torch_forecast: PyTorch hold confirmed", diagnostics)
 
 
@@ -1633,6 +1645,10 @@ def _estimated_exit_net_percent(position: Position, price: float, settings: Sett
     return gross_percent - round_trip_cost_percent
 
 
+def _min_exit_net_percent(settings: Settings) -> float:
+    return round(_clamp(settings.min_exit_net_percent, 0.0, 5.0), 4)
+
+
 def _adaptive_indicator_exit_allowed(adaptive: dict, mode_key: str, estimated_exit_net_percent: float) -> bool:
     mode = str(adaptive.get(mode_key, "normal")).lower()
     if mode != "profit_only":
@@ -1649,6 +1665,7 @@ def _forecast_exit_signal(
     estimated_exit_net_percent: float,
     stop_loss_percent: float,
     min_edge_percent: float,
+    min_exit_net_percent: float,
 ) -> tuple[str, float, str] | None:
     if not forecast.get("usable"):
         return None
@@ -1660,7 +1677,7 @@ def _forecast_exit_signal(
     if not strong_negative:
         return None
     reason = forecast.get("reason") or "ожидается снижение"
-    if estimated_exit_net_percent >= 0:
+    if estimated_exit_net_percent >= min_exit_net_percent:
         return "SELL", 0.82, f"прогноз временного ряда ухудшился: {reason}; фиксируем результат"
     loss_from_entry = ((price - position.entry_price) / position.entry_price) if position.entry_price else 0.0
     soft_loss_limit = -max(0.003, stop_loss_percent * 0.35)

tests/conftest.py

@@ -94,6 +94,7 @@ def make_settings():
             take_profit_percent=0.035,
             trailing_stop_percent=0.015,
             min_hold_seconds=180,
+            min_exit_net_percent=0.20,
             entry_cooldown_seconds=180,
             max_daily_drawdown_usdt=6.0,
             min_cash_reserve_usdt=5.0,

tests/test_strategy.py

@@ -954,6 +954,81 @@ def test_torch_forecast_holds_atr_trailing_exit_that_does_not_cover_fees(make_se
     assert signal.diagnostics["atr_exit_blocked_by_cost"] is True
 
 
+def test_torch_forecast_holds_atr_trailing_exit_below_min_profit(make_settings, tmp_path) -> None:
+    settings = make_settings(tmp_path, strategy_mode="torch_forecast", min_hold_seconds=60, min_exit_net_percent=0.20)
+    strategy = SpotStrategy(settings)
+    candles = _trend_entry_candles(close=100.35)
+    candles[-1].atr_14 = 0.6
+    position = Position(
+        1,
+        "MNTUSDT",
+        1,
+        100,
+        100,
+        0.1,
+        96,
+        120,
+        102,
+        opened_at=utc_now() - timedelta(seconds=600),
+    )
+    ticker = Ticker("MNTUSDT", 100.35, 100.34, 100.36, 10_000_000, 1000, 1.0)
+
+    signal = strategy.exit_signal(
+        position,
+        candles,
+        ticker,
+        forecast={
+            "usable": True,
+            "model": "torch_lstm",
+            "expected_return_percent": 0.4,
+            "probability_up": 0.58,
+            "skill": 0.18,
+            "block_entry": False,
+        },
+    )
+
+    assert signal.action == "HOLD"
+    assert signal.diagnostics["atr_exit_blocked_by_min_profit"] is True
+    assert signal.diagnostics["estimated_exit_net_percent"] < settings.min_exit_net_percent
+
+
+def test_torch_forecast_holds_negative_forecast_exit_below_min_profit(make_settings, tmp_path) -> None:
+    settings = make_settings(tmp_path, strategy_mode="torch_forecast", min_hold_seconds=60, min_exit_net_percent=0.20)
+    strategy = SpotStrategy(settings)
+    position = Position(
+        1,
+        "BTCUSDT",
+        1,
+        100,
+        100,
+        0.1,
+        96,
+        120,
+        100.5,
+        opened_at=utc_now() - timedelta(seconds=600),
+    )
+    ticker = Ticker("BTCUSDT", 100.35, 100.34, 100.36, 10_000_000, 1000, 1.0)
+
+    signal = strategy.exit_signal(
+        position,
+        _trend_entry_candles(close=100.35),
+        ticker,
+        forecast={
+            "usable": True,
+            "model": "torch_lstm",
+            "expected_return_percent": -0.2,
+            "probability_up": 0.40,
+            "skill": 0.18,
+            "block_entry": False,
+            "reason": "model turned down",
+        },
+    )
+
+    assert signal.action == "HOLD"
+    assert signal.diagnostics["forecast_exit_blocked_by_min_profit"] is True
+    assert signal.diagnostics["estimated_exit_net_percent"] < settings.min_exit_net_percent
+
+
 def test_torch_forecast_rebound_fallback_holds_without_model(make_settings, tmp_path) -> None:
     settings = make_settings(tmp_path, strategy_mode="torch_forecast", min_hold_seconds=180)
     strategy = SpotStrategy(settings)

tools/install_windows_training_agent.ps1

@@ -40,6 +40,15 @@ function Resolve-Python {
     throw "Python was not found. Create .venv or install Python 3.12."
 }
 
+function Resolve-WindowlessPython {
+    $python = Resolve-Python
+    $pythonw = Join-Path (Split-Path -Parent $python) "pythonw.exe"
+    if (Test-Path $pythonw) {
+        return $pythonw
+    }
+    return $python
+}
+
 if ($ApiAuth) {
     [Environment]::SetEnvironmentVariable("TRADEBOT_API_AUTH", $ApiAuth, "User")
     $env:TRADEBOT_API_AUTH = $ApiAuth
@@ -59,7 +68,7 @@ if (-not $KeepLegacyRetrainer) {
     }
 }
 
-$python = Resolve-Python
+$python = Resolve-WindowlessPython
 $currentUser = [System.Security.Principal.WindowsIdentity]::GetCurrent().Name
 $arguments = @(
     "-u",

tools/windows_training_agent.py

@@ -7,6 +7,7 @@ import json
 import os
 import platform
 import queue
+import re
 import subprocess
 import sys
 import threading
@@ -124,6 +125,7 @@ def run_retrain(args: argparse.Namespace, job_id: str, job: dict[str, Any], repo
         text=True,
         encoding="utf-8",
         errors="replace",
+        **hidden_subprocess_kwargs(),
     ) as process:
         reader = threading.Thread(target=read_output, name="training-output-reader", daemon=True)
         reader.start()
@@ -140,7 +142,7 @@ def run_retrain(args: argparse.Namespace, job_id: str, job: dict[str, Any], repo
                 log(log_path, message)
                 line_count += 1
                 if message:
-                    last_message = message[-220:]
+                    last_message = friendly_training_message(message)
             except queue.Empty:
                 pass
 
@@ -163,6 +165,78 @@ def run_retrain(args: argparse.Namespace, job_id: str, job: dict[str, Any], repo
     report_progress(args, job_id, "running", "guard", 70, "Guard завершён, подготавливаю артефакты")
 
 
+def friendly_training_message(message: str) -> str:
+    cleaned = message.strip()
+    if not cleaned:
+        return "PyTorch обучает модель"
+
+    if "Starting PyTorch recurrent retrain:" in cleaned:
+        return "PyTorch LSTM/GRU запущен: готовлю данные и варианты модели"
+
+    started = re.search(
+        r"training started: symbols=(?P<symbols>\d+) interval=(?P<interval>\d+) "
+        r"limit=(?P<limit>\d+) epochs=(?P<epochs>\d+)",
+        cleaned,
+    )
+    if started:
+        interval = started.group("interval")
+        timeframe = "1h" if interval == "60" else f"{interval}m"
+        return (
+            f"Старт обучения: {started.group('symbols')} пар, таймфрейм {timeframe}, "
+            f"история {started.group('limit')} свечей, до {started.group('epochs')} эпох"
+        )
+
+    pair_started = re.search(r"^(?P<symbol>[A-Z0-9]+): training started \((?P<index>\d+)/(?P<total>\d+)\)", cleaned)
+    if pair_started:
+        return (
+            f"{pair_started.group('symbol')}: обучение пары "
+            f"{pair_started.group('index')}/{pair_started.group('total')}"
+        )
+
+    preparing = re.search(r"^(?P<symbol>[A-Z0-9]+): preparing lookback=(?P<lookback>\d+)", cleaned)
+    if preparing:
+        return f"{preparing.group('symbol')}: готовлю окно {preparing.group('lookback')} свечей"
+
+    fitting = re.search(
+        r"^(?P<symbol>[A-Z0-9]+): fitting (?P<arch>lstm|gru) "
+        r"lookback=(?P<lookback>\d+) hidden=(?P<hidden>\d+) "
+        r"layers=(?P<layers>\d+) dropout=(?P<dropout>[0-9.]+)",
+        cleaned,
+    )
+    if fitting:
+        return (
+            f"{fitting.group('symbol')}: обучаю {fitting.group('arch').upper()}, "
+            f"окно {fitting.group('lookback')}, нейронов {fitting.group('hidden')}, "
+            f"слоёв {fitting.group('layers')}, dropout {fitting.group('dropout')}"
+        )
+
+    model = re.search(
+        r"^(?P<symbol>[A-Z0-9]+): model=torch_(?P<arch>lstm|gru).*?"
+        r"mae=(?P<mae>[0-9.]+)%.*?skill=(?P<skill>-?[0-9.]+).*?dir=(?P<direction>[0-9.]+)",
+        cleaned,
+    )
+    if model:
+        direction = float(model.group("direction")) * 100
+        skill = float(model.group("skill")) * 100
+        return (
+            f"{model.group('symbol')}: выбран {model.group('arch').upper()}, "
+            f"ошибка {model.group('mae')}%, skill {skill:.1f}%, направление {direction:.1f}%"
+        )
+
+    if "Calibrating current artifact" in cleaned:
+        return "Проверяю текущую модель на replay"
+    if "Calibrating candidate artifact" in cleaned:
+        return "Проверяю новую модель на replay"
+    if "Running retrain guard" in cleaned:
+        return "Gate сравнивает новую модель с текущей"
+    if "Candidate rejected by guard" in cleaned:
+        return "Новая модель обучилась, но gate не дал ей ходу"
+    if "Candidate accepted by guard" in cleaned:
+        return "Новая модель прошла gate и стала активной"
+
+    return cleaned[-220:]
+
+
 def training_heartbeat_message(now: float, started_at: float, last_output_at: float, last_message: str) -> str:
     elapsed = format_duration(now - started_at)
     idle_seconds = max(0.0, now - last_output_at)
@@ -296,7 +370,11 @@ 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)
+    if sys.stdout is not None:
+        try:
+            print(line, flush=True)
+        except OSError:
+            pass
     with path.open("a", encoding="utf-8") as handle:
         handle.write(line + "\n")
 
@@ -309,6 +387,18 @@ def read_json(path: Path) -> dict[str, Any]:
     return data if isinstance(data, dict) else {}
 
 
+def hidden_subprocess_kwargs() -> dict[str, Any]:
+    if os.name != "nt":
+        return {}
+    startupinfo = subprocess.STARTUPINFO()
+    startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
+    startupinfo.wShowWindow = 0
+    return {
+        "creationflags": getattr(subprocess, "CREATE_NO_WINDOW", 0),
+        "startupinfo": startupinfo,
+    }
+
+
 def quote_for_log(value: str) -> str:
     return f'"{value}"' if " " in value else value