Update app.py

app.py

@@ -1,11 +1,11 @@
 # ============================================
-# 👑 TOMIRIS SPACE 19 v8.1 — SOL/USD MASTER (Hub-Connected)
+# 👑 TOMIRIS SPACE 19 v9.0 — SOL/USD MASTER (Hub‑Connected + Auto‑Retrain)
 # ============================================
 import os, time, threading, warnings, json, asyncio
 from typing import Dict, Any, Optional, List, Tuple
 import numpy as np, pandas as pd
 import requests
-from datetime import datetime
+from datetime import datetime, timedelta
 from collections import deque
 from fastapi import FastAPI, Query
 warnings.filterwarnings('ignore')
@@ -49,7 +49,6 @@ if HAS_FIREBASE:
 SPACE_URLS: Dict[str, str] = {
     "space_17_hub": os.getenv("SPACE17_URL", "https://tomiris-ai-name5-5.hf.space"),
     "space_18_arbiter": os.getenv("SPACE18_URL", "https://tomiris-ai-name6-6.hf.space"),
-    "space_31_perf": "https://nuxotetotmailsvoboden-tomiris-perf.hf.space"
 }
 
 HUB_URL = SPACE_URLS["space_17_hub"]
@@ -64,6 +63,8 @@ TWELVE_KEYS: List[str] = [
 SYMBOL: str = "SOL/USD"
 MT5_SYMBOL: str = "SOLUSD"
 TIMEFRAMES: List[str] = ["15min", "1h", "4h"]
+AUTO_REPORT_INTERVAL = 300               # секунд между авто‑отправками
+RETRAIN_INTERVAL = 30 * 86400            # 30 дней
 
 try:
     with open("best_config.json", "r") as f:
@@ -111,7 +112,7 @@ COMPONENT_PERF: Dict[str, Dict[str, float]] = {
 YAHOO_INTERVAL_MAP: Dict[str, str] = {"15min": "15m", "1h": "60m", "4h": "4h"}
 
 # ================= ЗАГРУЗКА МОДЕЛЕЙ =================
-print(f"🔥 SPACE 19 v8.1: Загрузка моделей для {SYMBOL}...")
+print(f"🔥 SPACE 19 v9.0: Загрузка моделей для {SYMBOL}...")
 MODELS: Dict[str, Optional[Any]] = {"xgb_daily": None, "xgb_4h": None, "lgb": None}
 
 if HAS_JOBLIB:
@@ -143,7 +144,7 @@ def get_next_twelve_key() -> str:
         return key
 
 session = requests.Session()
-session.headers.update({"User-Agent": "Tomiris-Space19-v8.1"})
+session.headers.update({"User-Agent": "Tomiris-Space19-v9.0"})
 
 def safe_float(value: Any, default: float = 0.0) -> float:
     try:
@@ -203,6 +204,20 @@ def hurst_exponent(series: pd.Series, lags: int = 20) -> float:
 def fetch_google_trends_index(keyword: str) -> float:
     return 50.0
 
+# ================= ОТПРАВКА В HUB =================
+def send_signal_to_hub(direction, confidence):
+    try:
+        session.post(f"{HUB_URL}/signal", json={
+            "space": "space_19_sol_master",
+            "symbol": SYMBOL,
+            "direction": direction,
+            "confidence": confidence,
+            "raw": json.dumps({"source": "space_19_sol_master"})
+        }, timeout=5)
+        print(f"📤 {SYMBOL}: {direction} conf={confidence:.3f} отправлен в Hub")
+    except Exception as e:
+        print(f"Ошибка отправки в Hub: {e}")
+
 # ================= DATA HUB =================
 def get_mt5_price_from_hub() -> Dict[str, Any]:
     global HUB_CACHE
@@ -405,20 +420,6 @@ def fetch_space_signal(name: str, url: str, endpoint: str = "/consilium") -> Dic
         breaker_fail(name)
         return {"active": False, "reason": str(e)[:50]}
 
-# ================= ОТПРАВКА В HUB =================
-def send_signal_to_hub(symbol: str, direction: str, confidence: float):
-    try:
-        session.post(f"{HUB_URL}/signal", json={
-            "space": "space_19_sol_master",
-            "symbol": symbol,
-            "direction": direction,
-            "confidence": confidence,
-            "raw": json.dumps({"source": "space_19_sol_master"})
-        }, timeout=5)
-        print(f"📤 {symbol}: {direction} conf={confidence:.3f} отправлен в Hub")
-    except Exception as e:
-        print(f"Ошибка отправки в Hub: {e}")
-
 # ================= ПОСТРОЕНИЕ ПРИЗНАКОВ =================
 def build_features_from_mt5(mt5_features: Dict[str, Any]) -> Dict[str, Any]:
     features: Dict[str, Any] = {}
@@ -628,7 +629,7 @@ def get_sol_signal() -> Optional[Dict[str, Any]]:
             "signal": {"direction": "WAIT", "confidence": 0.0},
             "reason": "stress_test_black_swan"
         }
-        send_signal_to_hub(SYMBOL, "WAIT", 0.0)
+        send_signal_to_hub("WAIT", 0.0)
         return result
 
     regime = detect_market_regime(model_features)
@@ -799,17 +800,80 @@ def get_sol_signal() -> Optional[Dict[str, Any]]:
         "risk": {"sl": sl, "tp": tp, "rr": round(tp_dist/(sl_dist+1e-10), 2) if direction != "WAIT" else 0},
         "meta": {
             "latency_ms": latency,
-            "model_version": "v8.1_hub_connected",
+            "model_version": "v9.0_auto_retrain",
             "features_used": len(model_features) if model_features else 0,
             "dynamic_weights": comp_weights
         }
     }
 
-    # Отправка в Hub вместо Arbiter
-    send_signal_to_hub(SYMBOL, result["signal"]["direction"], result["signal"]["confidence"])
+    send_signal_to_hub(result["signal"]["direction"], result["signal"]["confidence"])
     print(f"🥉 SOL/USD: {direction} | conf={confidence:.3f} | ensemble={xgb_prob:.3f} | models={models_used} | regime={regime} | latency={latency}ms")
     return result
 
+# ================= АВТО-ОТПРАВКА =================
+def auto_report():
+    while True:
+        time.sleep(AUTO_REPORT_INTERVAL)
+        try:
+            get_sol_signal()
+        except Exception as e:
+            print(f"Ошибка авто-отправки: {e}")
+
+# ================= АВТО-ДООБУЧЕНИЕ =================
+def retrain_models():
+    print("🔄 Запуск дообучения моделей SOL...")
+    try:
+        # Используем дневные данные за последний год через Yahoo (или Hub)
+        df = None
+        if HAS_YFINANCE:
+            yf_data = yf.download("SOL-USD", period="1y", interval="1d", progress=False)
+            if not yf_data.empty:
+                df = pd.DataFrame({
+                    'close': yf_data['Close'].values.flatten(),
+                    'high': yf_data['High'].values.flatten(),
+                    'low': yf_data['Low'].values.flatten(),
+                    'open': yf_data['Open'].values.flatten(),
+                    'volume': yf_data['Volume'].values.flatten()
+                }).dropna()
+        if df is None or len(df) < 200:
+            print("Недостаточно данных для дообучения")
+            return
+
+        features_list = []
+        targets = []
+        for i in range(100, len(df)-24):
+            sub_df = df.iloc[:i+1]
+            feats = build_sol_features(sub_df)
+            if not feats:
+                continue
+            features_list.append(feats)
+            target = 1 if df["close"].iloc[i+24] > df["close"].iloc[i] else 0
+            targets.append(target)
+
+        if not features_list:
+            return
+        # Приводим к единому размеру (pad до 200)
+        X = np.array([list(f.values())[:200] + [0.0]*(200 - len(f)) for f in features_list])
+        y = np.array(targets)
+
+        for model_name in ["xgb_daily", "xgb_4h"]:
+            model = MODELS.get(model_name)
+            if model and hasattr(model, 'fit'):
+                model.fit(X, y)
+                joblib.dump(model, f"{model_name}_retrained.joblib")
+                print(f"✅ {model_name} дообучена")
+        if MODELS.get("lgb") and hasattr(MODELS["lgb"], 'fit'):
+            MODELS["lgb"].fit(X, y)
+            joblib.dump(MODELS["lgb"], "lgb_retrained.joblib")
+            print("✅ LightGBM дообучена")
+    except Exception as e:
+        print(f"Ошибка дообучения: {e}")
+
+def auto_retrain():
+    while True:
+        time.sleep(RETRAIN_INTERVAL)
+        retrain_models()
+
 # ================= KEEP-ALIVE =================
 def keep_alive():
     while True:
@@ -818,21 +882,24 @@ def keep_alive():
             requests.get("http://localhost:7860/health", timeout=5)
         except:
             pass
+
 threading.Thread(target=keep_alive, daemon=True).start()
+threading.Thread(target=auto_report, daemon=True).start()
+threading.Thread(target=auto_retrain, daemon=True).start()
 
 # ================= FASTAPI =================
-app = FastAPI(title="TOMIRIS SOL/USD MASTER v8.1 Hub-Connected")
+app = FastAPI(title="TOMIRIS SOL/USD MASTER v9.0 Auto-Retrain")
 
 @app.get("/health")
 async def health():
     models_loaded = sum(1 for m in ["xgb_daily","xgb_4h","lgb"] if MODELS.get(m) is not None)
     return {
-        "space": "Space 19 v8.1 Hub-Connected",
+        "space": "Space 19 v9.0 Auto-Retrain",
         "status": "operational",
         "symbol": SYMBOL,
         "models_loaded": models_loaded,
-        "dynamic_weights": True,
-        "hub_connected": True
+        "hub_connected": True,
+        "auto_retrain": True
     }
 
 @app.get("/consilium")
@@ -889,6 +956,6 @@ async def explain():
         }
     return {"error": "No prediction yet"}
 
-print("🚀 SPACE 19 v8.1 — SOL/USD MASTER (Hub-Connected) ЗАПУЩЕН!")
-print("🥉 Ансамбль daily+4h + Meta SOL Score + Dynamic Weights + Hub Integration")
+print("🚀 SPACE 19 v9.0 — SOL/USD MASTER (Hub-Connected + Auto-Retrain) ЗАПУЩЕН!")
+print("🥉 Ансамбль daily+4h + Meta SOL Score + Dynamic Weights + Auto-Retrain")
 print("✅ Готов к бою!")