Remove duplicate function definitions in live_trading.py

live_trading.py

@@ -444,75 +444,6 @@ def _train_hmm_model(ticker: str, n_states: int = 3, train_years: int = 2):
         return None, None
 
 
-def get_user_sessions_from_db(user_email: str) -> List[dict]:
-    """Get all sessions for a user from database"""
-    try:
-        with Session(engine) as db_session:
-            statement = select(TradingSession).where(
-                TradingSession.user_email == user_email
-            ).order_by(TradingSession.start_time.desc()).limit(20)
-            sessions = db_session.exec(statement).all()
-            
-            result = []
-            for s in sessions:
-                if s.session_id in active_sessions:
-                    runner = active_sessions[s.session_id]
-                    result.append(runner.get_status())
-                else:
-                    elapsed = (datetime.now() - s.start_time).total_seconds() / 60
-                    remaining = max(0, s.duration_minutes - elapsed)
-                    if s.is_running and remaining <= 0:
-                        s.is_running = False
-                        s.end_time = datetime.now()
-                        db_session.add(s)
-                        db_session.commit()
-                    
-                    result.append({
-                        'session_id': s.session_id,
-                        'strategy': s.strategy,
-                        'symbol': s.symbol,
-                        'trade_amount': s.trade_amount,
-                        'is_running': s.is_running,
-                        'position': 'FLAT',
-                        'trades_count': s.trades_count,
-                        'pnl': s.total_pnl,
-                        'elapsed_minutes': round(elapsed, 1),
-                        'remaining_minutes': round(remaining, 1),
-                        'trades': []
-                    })
-            return result
-    except Exception as e:
-        print(f"Error getting sessions: {e}")
-        return []
-
-def _train_hmm_model(ticker: str, n_states: int = 3, train_years: int = 2):
-    """Trains a GaussianHMM model based on historical data."""
-    try:
-        end_date = datetime.now()
-        start_date = end_date - timedelta(days=train_years * 365)
-        
-        df = yf.download(ticker, start=start_date, end=end_date, progress=False)
-        if df.empty or len(df) < 100:
-            print("Not enough historical data to train HMM model.")
-            return None, None
-
-        df['Log_Returns'] = np.log(df['Close'] / df['Close'].shift(1))
-        df['Volatility'] = df['Log_Returns'].rolling(window=10).std()
-        df = df.dropna()
-
-        X_train = df[['Log_Returns', 'Volatility']].values * 100
-        hmm_model = GaussianHMM(n_components=n_states, covariance_type="full", n_iter=100, random_state=42)
-        hmm_model.fit(X_train)
-        
-        state_vars = [hmm_model.means_[i][1] for i in range(n_states)]
-        high_vol_state = np.argmax(state_vars)
-        
-        print(f"HMM model trained for {ticker}. High volatility state identified as: {high_vol_state}")
-        return hmm_model, high_vol_state
-    except Exception as e:
-        print(f"Error training HMM model: {e}")
-        return None, None
-
 class BaseTradingSessionRunner:
     """Base class for managing a live trading session."""