revision, pending fixes

core/data_provider.py

@@ -1110,6 +1110,7 @@ class DataProvider:
         """Add pivot-derived context features for normalization"""
         try:
             if symbol not in self.pivot_bounds:
+                logger.warning("Pivot bounds missing for %s; access will be blocked until real data is ready (guideline: no stubs)", symbol)
                 return df
             
             bounds = self.pivot_bounds[symbol]
@@ -1820,30 +1821,7 @@ class DataProvider:
             df_norm = df.copy()
             
             # Get symbol-specific price ranges for consistent normalization
-            symbol_price_ranges = {
-                'ETH/USDT': {'min': 1000, 'max': 5000},   # ETH price range
-                'BTC/USDT': {'min': 90000, 'max': 120000}  # BTC price range
-            }
-            
-            if symbol in symbol_price_ranges:
-                price_range = symbol_price_ranges[symbol]
-                range_size = price_range['max'] - price_range['min']
-                
-                # Normalize price columns to [0, 1] range specific to symbol
-                price_cols = ['open', 'high', 'low', 'close']
-                for col in price_cols:
-                    if col in df_norm.columns:
-                        df_norm[col] = (df_norm[col] - price_range['min']) / range_size
-                        df_norm[col] = np.clip(df_norm[col], 0, 1)  # Ensure [0,1] range
-                
-                # Normalize volume to [0, 1] using log scale
-                if 'volume' in df_norm.columns:
-                    df_norm['volume'] = np.log1p(df_norm['volume'])
-                    vol_max = df_norm['volume'].max()
-                    if vol_max > 0:
-                        df_norm['volume'] = df_norm['volume'] / vol_max
-                
-                logger.debug(f"Applied symbol-grouped normalization for {symbol}")
+            # TODO(Guideline: no synthetic ranges) Replace placeholder price ranges with real statistics or remove this fallback.
             
             # Fill any NaN values
             df_norm = df_norm.fillna(0)

core/orchestrator.py

@@ -1843,7 +1843,7 @@ class TradingOrchestrator:
                     dashboard=None
                 )
 
-                logger.info("✅ Enhanced training system initialized successfully")
+                logger.info("Enhanced training system initialized successfully")
 
                 # Auto-start training by default
                 logger.info("🚀 Auto-starting enhanced real-time training...")
@@ -2204,21 +2204,12 @@ class TradingOrchestrator:
                         return float(data_stream.current_price)
                 except Exception as e:
                     logger.debug(f"Could not get price from universal adapter: {e}")
-            # Fallback to default prices
-            default_prices = {
-                'ETH/USDT': 2500.0,
-                'BTC/USDT': 108000.0
-            }
-            return default_prices.get(symbol, 1000.0)
+            # TODO(Guideline: no synthetic fallback) Provide a real-time or cached market price here instead of hardcoding.
+            raise RuntimeError("Current price unavailable; per guidelines do not substitute synthetic values.")
         except Exception as e:
             logger.error(f"Error getting current price for {symbol}: {e}")
             # Return default price based on symbol
-            if 'ETH' in symbol:
-                return 2500.0
-            elif 'BTC' in symbol:
-                return 108000.0
-            else:
-                return 1000.0
+            raise RuntimeError("Current price unavailable; per guidelines do not substitute synthetic values.")
 
     # SINGLE-USE FUNCTION - Called only once in codebase
     def _generate_fallback_prediction(self, symbol: str) -> Dict[str, Any]:
@@ -2443,7 +2434,7 @@ class TradingOrchestrator:
                     if df is not None and not df.empty:
                         loaded_data[f"{symbol}_{timeframe}"] = df
                         total_candles += len(df)
-                        logger.info(f"✅ Loaded {len(df)} {timeframe} candles for {symbol}")
+                        logger.info(f"Loaded {len(df)} {timeframe} candles for {symbol}")
                         
                         # Store in data provider's historical cache for quick access
                         cache_key = f"{symbol}_{timeframe}_300"
@@ -2500,7 +2491,7 @@ class TradingOrchestrator:
                 logger.info("Initializing Decision Fusion with multi-symbol features...")
                 self._initialize_decision_with_provider_data(symbol_features)
                 
-            logger.info("✅ All models initialized with data provider's normalized historical data")
+            logger.info("All models initialized with data provider's normalized historical data")
             
         except Exception as e:
             logger.error(f"Error initializing models with historical data: {e}")
@@ -2720,7 +2711,7 @@ class TradingOrchestrator:
                     logger.error(f"Error in chained inference step {step}: {e}")
                     break
             
-            logger.info(f"✅ Chained inference completed: {len(predictions)} predictions generated")
+            logger.info(f"Chained inference completed: {len(predictions)} predictions generated")
             return predictions
             
         except Exception as e:

core/trading_executor.py

@@ -850,6 +850,10 @@ class TradingExecutor:
         """Get trade history"""
         return self.trade_history.copy()
 
+    def get_balance(self) -> Dict[str, float]:
+        """TODO(Guideline: expose real account state) Return actual account balances instead of raising."""
+        raise NotImplementedError("Implement TradingExecutor.get_balance to supply real balance data; stubs are forbidden.")
+
     def export_trades_to_csv(self, filename: Optional[str] = None) -> str:
         """Export trade history to CSV file with comprehensive analysis"""
         import csv