Clarify forecast model labels in dashboard

2026-06-20 21:37:34 +03:00
parent 92538850ad
commit d9c0317675
2 changed files with 155 additions and 8 deletions
@@ -221,6 +221,7 @@ def _safe_config(settings: Settings) -> dict[str, Any]:
        "time_series_lstm_units": settings.time_series_lstm_units,
        "time_series_lstm_ridge": settings.time_series_lstm_ridge,
        "time_series_lstm_model_path": str(settings.time_series_lstm_model_path),
+        "time_series_model_artifact": _time_series_model_artifact(settings),
        "stop_loss_percent": settings.stop_loss_percent,
        "take_profit_percent": settings.take_profit_percent,
        "trailing_stop_percent": settings.trailing_stop_percent,
@@ -235,6 +236,65 @@ def _safe_config(settings: Settings) -> dict[str, Any]:
    }


+def _time_series_model_artifact(settings: Settings) -> dict[str, Any]:
+    path = settings.time_series_lstm_model_path
+    try:
+        data = json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError):
+        return {
+            "available": False,
+            "type": "missing",
+            "label": "нет файла модели",
+            "symbol_count": 0,
+            "models": [],
+        }
+    if not isinstance(data, dict):
+        return {
+            "available": False,
+            "type": "invalid",
+            "label": "файл модели не распознан",
+            "symbol_count": 0,
+            "models": [],
+        }
+    symbols = data.get("symbols")
+    rows = list(symbols.values()) if isinstance(symbols, dict) else []
+    models = sorted(
+        {
+            _forecast_model_label(str(row.get("model", row.get("architecture", "lstm"))))
+            for row in rows
+            if isinstance(row, dict)
+        }
+    )
+    artifact_type = str(data.get("type", "")).strip()
+    if artifact_type == "pytorch_recurrent_forecaster":
+        label = "PyTorch LSTM/GRU"
+    elif artifact_type == "lstm_reservoir_ridge_params":
+        label = "легкий LSTM fallback"
+    else:
+        label = artifact_type or "настройки прогноза"
+    return {
+        "available": True,
+        "type": artifact_type or "unknown",
+        "label": label,
+        "created_at": data.get("created_at", ""),
+        "symbol_count": len(rows),
+        "models": models,
+    }
+
+
+def _forecast_model_label(model: str) -> str:
+    normalized = model.strip().lower()
+    if normalized == "torch_lstm":
+        return "PyTorch LSTM"
+    if normalized == "torch_gru":
+        return "PyTorch GRU"
+    if normalized == "lstm":
+        return "легкий LSTM"
+    if normalized == "gru":
+        return "GRU"
+    return model
+
+
 HTML = r"""
 <!doctype html>
 <html lang="ru">
@@ -677,12 +737,69 @@ HTML = r"""
      }
    }

+    function modelName(model) {
+      const key = String(model || '').toLowerCase();
+      const names = {
+        torch_lstm: 'PyTorch LSTM',
+        torch_gru: 'PyTorch GRU',
+        lstm: 'Легкий LSTM',
+        naive: 'Baseline',
+        drift: 'Drift',
+        ewma: 'EWMA',
+        ar1: 'AR(1)',
+        ar3: 'AR(3)',
+        none: '-'
+      };
+      return names[key] || String(model || '-');
+    }
+
+    function modelReason(reason) {
+      return String(reason || '')
+        .replaceAll('torch_lstm', 'PyTorch LSTM')
+        .replaceAll('torch_gru', 'PyTorch GRU')
+        .replaceAll('модель lstm', 'модель легкий LSTM');
+    }
+
+    function modelArtifactSummary(config) {
+      if (!config.time_series_lstm_enabled) {
+        return 'выкл';
+      }
+      const artifact = config.time_series_model_artifact || {};
+      if (!artifact.available) {
+        return artifact.label || 'нет файла модели';
+      }
+      const parts = [artifact.label || 'модель прогноза'];
+      if (Number(artifact.symbol_count || 0) > 0) {
+        parts.push(`${artifact.symbol_count} пар`);
+      }
+      if (Array.isArray(artifact.models) && artifact.models.length) {
+        parts.push(artifact.models.join(', '));
+      }
+      const date = shortDateTime(artifact.created_at);
+      if (date) {
+        parts.push(date);
+      }
+      return parts.join(' · ');
+    }
+
+    function shortDateTime(value) {
+      if (!value) return '';
+      const date = new Date(value);
+      if (Number.isNaN(date.getTime())) return '';
+      return date.toLocaleString('ru-RU', {
+        day: '2-digit',
+        month: '2-digit',
+        hour: '2-digit',
+        minute: '2-digit'
+      });
+    }
+
    function forecastHtml(forecast) {
      if (!forecast || !forecast.usable) {
-        return `<div class="forecast-line"><div class="forecast-chip"><b>Прогноз</b>${escapeHtml(forecast?.reason || 'нет данных')}</div></div>`;
+        return `<div class="forecast-line"><div class="forecast-chip"><b>Прогноз</b>${escapeHtml(modelReason(forecast?.reason || 'нет данных'))}</div></div>`;
      }
      return `<div class="forecast-line">
-        <div class="forecast-chip"><b>Модель</b>${escapeHtml(forecast.model || '-')}</div>
+        <div class="forecast-chip"><b>Модель</b>${escapeHtml(modelName(forecast.model || '-'))}</div>
        <div class="forecast-chip"><b>P роста</b>${num((forecast.probability_up || 0) * 100, 1)}%</div>
        <div class="forecast-chip"><b>Ожидание</b><span class="${signedClass(forecast.expected_return_percent || 0)}">${signedNum(forecast.expected_return_percent, 3)}%</span></div>
        <div class="forecast-chip"><b>Волат.</b>${num(forecast.volatility_percent, 3)}%</div>
@@ -781,7 +898,7 @@ HTML = r"""
      const expected = Number(forecast.expected_return_percent);
      const probability = Number(forecast.probability_up);
      const skill = Number(forecast.skill);
-      const model = escapeHtml(forecast.model || '-');
+      const model = escapeHtml(modelName(forecast.model || '-'));
      const expectedText = Number.isFinite(expected) ? `${signedNum(expected, 3)}%` : '-';
      const probabilityText = Number.isFinite(probability) ? `P${num(probability * 100, 1)}%` : 'P-';
      const skillText = Number.isFinite(skill) ? `S${num(skill, 3)}` : 'S-';
@@ -817,10 +934,8 @@ HTML = r"""
        ['Модельный горизонт', `${config.time_series_forecast_horizon} свечи`],
        ['Walk-forward окно', `${config.time_series_validation_window} свечей`],
        ['Мин. edge прогноза', `${num(config.time_series_min_edge_percent, 3)}%`],
-        ['LSTM-кандидат', yesNo(config.time_series_lstm_enabled)],
-        ['LSTM окно / юниты', `${config.time_series_lstm_lookback} / ${config.time_series_lstm_units}`],
-        ['LSTM ridge', `${num(config.time_series_lstm_ridge, 5)}`],
-        ['LSTM файл', config.time_series_lstm_model_path || '-'],
+        ['Нейропрогноз', modelArtifactSummary(config)],
+        ['Файл модели', config.time_series_lstm_model_path || '-'],
        ['Лимит в позициях', money(config.max_total_exposure_usdt)],
        ['Лимит позиций', `${config.max_open_positions} всего / ${config.max_positions_per_symbol} на пару`],
        ['Стоп / цель', `${num(config.stop_loss_percent * 100, 2)}% / ${num(config.take_profit_percent * 100, 2)}%`],
@@ -831,7 +946,7 @@ HTML = r"""
        ['Поток данных', yesNo(config.websocket_enabled)]
      ];
      document.getElementById('configGrid').innerHTML = keys.map(([k, v]) => `
-        <div class="config-item"><span class="muted">${k}</span><strong>${v}</strong></div>
+        <div class="config-item"><span class="muted">${escapeHtml(k)}</span><strong>${escapeHtml(String(v))}</strong></div>
      `).join('');
    }
    function renderFastMode(config) {
@@ -1,6 +1,9 @@
 from __future__ import annotations

+import json
+
 from crypto_spot_bot.dashboard import _apply_fast_trading
+from crypto_spot_bot.dashboard import _safe_config
 from crypto_spot_bot.storage import Storage


@@ -15,3 +18,32 @@ def test_apply_fast_trading_updates_runtime_and_env(make_settings, tmp_path) ->
    assert settings.fast_trading_enabled is True
    assert storage.get_runtime("fast_trading_enabled") is True
    assert "FAST_TRADING_ENABLED=true" in settings.env_file_path.read_text(encoding="utf-8")
+
+
+def test_safe_config_summarizes_torch_forecast_artifact(make_settings, tmp_path) -> None:
+    artifact_path = tmp_path / "lstm_forecaster.json"
+    artifact_path.write_text(
+        json.dumps(
+            {
+                "type": "pytorch_recurrent_forecaster",
+                "created_at": "2026-06-20T18:15:05+00:00",
+                "symbols": {
+                    "BTCUSDT": {"model": "torch_lstm"},
+                    "ETHUSDT": {"model": "torch_gru"},
+                },
+            }
+        ),
+        encoding="utf-8",
+    )
+    settings = make_settings(tmp_path, time_series_lstm_model_path=artifact_path)
+
+    config = _safe_config(settings)
+
+    assert config["time_series_model_artifact"] == {
+        "available": True,
+        "type": "pytorch_recurrent_forecaster",
+        "label": "PyTorch LSTM/GRU",
+        "created_at": "2026-06-20T18:15:05+00:00",
+        "symbol_count": 2,
+        "models": ["PyTorch GRU", "PyTorch LSTM"],
+    }