Lowered signal tresholds so low that we got signals flowing. Few debug scripts to see way signals were not firing. Fix analyst.py indicator calculation to use TA-lib.

analysis/analyst.py

@@ -121,38 +121,44 @@ analysis_conn.commit()
 
 # ========== Technical Indicator Functions ==========
 def compute_indicators(df):
-    close = df['close']
+    """Compute indicators using TA-Lib for accuracy"""
+    import talib
+    
+    close = df['close'].values
+    high = df['high'].values
+    low = df['low'].values
+    volume = df['volume'].values
+    
     # EMA and SMA
-    df['ema_9'] = close.ewm(span=9, adjust=False).mean()
-    df['ema_21'] = close.ewm(span=21, adjust=False).mean()
-    df['sma_50'] = close.rolling(window=50, min_periods=1).mean()
-    df['sma_200'] = close.rolling(window=200, min_periods=1).mean()
-    # RSI (14): using 14-period gains/losses and RSI formula (100 - 100/(1+RS)):contentReference[oaicite:3]{index=3}
-    delta = close.diff()
-    gain = delta.clip(lower=0)
-    loss = -delta.clip(upper=0)
-    avg_gain = gain.rolling(window=14, min_periods=14).mean()
-    avg_loss = loss.rolling(window=14, min_periods=14).mean()
-    rs = avg_gain / avg_loss.replace(0, pd.NA)
-    df['rsi_14'] = 100 - (100 / (1 + rs))
+    df['ema_9'] = talib.EMA(close, timeperiod=9)
+    df['ema_21'] = talib.EMA(close, timeperiod=21)
+    df['sma_50'] = talib.SMA(close, timeperiod=50)
+    df['sma_200'] = talib.SMA(close, timeperiod=200)
+    
+    # RSI (14) - Proper calculation
+    df['rsi_14'] = talib.RSI(close, timeperiod=14)
+    
     # MACD (12,26,9)
-    ema12 = close.ewm(span=12, adjust=False).mean()
-    ema26 = close.ewm(span=26, adjust=False).mean()
-    macd_line = ema12 - ema26
-    df['macd'] = macd_line
-    df['macd_signal'] = macd_line.ewm(span=9, adjust=False).mean()
-    df['macd_hist'] = df['macd'] - df['macd_signal']
+    macd, macd_signal, macd_hist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
+    df['macd'] = macd
+    df['macd_signal'] = macd_signal
+    df['macd_hist'] = macd_hist
+    
     # Bollinger Bands (20,2)
-    df['bb_middle'] = close.rolling(window=20, min_periods=20).mean()
-    bb_std = close.rolling(window=20, min_periods=20).std()
-    df['bb_upper'] = df['bb_middle'] + 2 * bb_std
-    df['bb_lower'] = df['bb_middle'] - 2 * bb_std
-    # Bollinger Squeeze: detect when BB width is lowest over 20 periods:contentReference[oaicite:4]{index=4}
-    bb_width = df['bb_upper'] - df['bb_lower']
-    rolling_min_width = bb_width.rolling(window=20, min_periods=20).min()
-    df['bb_squeeze'] = (bb_width <= rolling_min_width).astype(int)
-    # Volume moving average (20)
-    df['volume_ma_20'] = df['volume'].rolling(window=20, min_periods=1).mean()
+    bb_upper, bb_middle, bb_lower = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
+    df['bb_upper'] = bb_upper
+    df['bb_middle'] = bb_middle
+    df['bb_lower'] = bb_lower
+    
+    # Bollinger Squeeze
+    bb_width = bb_upper - bb_lower
+    bb_width_series = pd.Series(bb_width)
+    rolling_min_width = bb_width_series.rolling(window=20, min_periods=20).min()
+    df['bb_squeeze'] = (bb_width_series <= rolling_min_width).fillna(0).astype(int)
+    
+    # Volume MA
+    df['volume_ma_20'] = talib.SMA(volume, timeperiod=20)
+    
     return df
 
 # ========== Health Check Server ==========

analysis/backfill_indicators.py

@@ -0,0 +1,196 @@
+#!/usr/bin/env python3
+"""
+Backfill Missing Indicators
+Calculates RSI and Bollinger Bands for existing data
+"""
+
+import sqlite3
+import pandas as pd
+import numpy as np
+import talib
+from datetime import datetime
+
+
+def backfill_indicators(candles_db: str, analysis_db: str):
+    """Backfill RSI and Bollinger Bands for all timeframes"""
+    
+    print("🔧 BACKFILLING MISSING INDICATORS")
+    print("=" * 70)
+    
+    # Connect to databases
+    conn_candles = sqlite3.connect(candles_db)
+    conn_analysis = sqlite3.connect(analysis_db)
+    
+    # Get all timeframes
+    cursor = conn_analysis.cursor()
+    cursor.execute("SELECT DISTINCT timeframe FROM analysis ORDER BY timeframe")
+    timeframes = [row[0] for row in cursor.fetchall()]
+    
+    total_updated = 0
+    
+    for timeframe in timeframes:
+        print(f"\n📊 Processing {timeframe}...")
+        
+        # Fetch candle data
+        df_candles = pd.read_sql_query(
+            "SELECT timestamp, close, high, low FROM candles WHERE timeframe = ? ORDER BY timestamp",
+            conn_candles,
+            params=(timeframe,)
+        )
+        
+        if len(df_candles) < 20:
+            print(f"  ⚠️  Skipping - insufficient data ({len(df_candles)} rows)")
+            continue
+        
+        print(f"  ✓ Loaded {len(df_candles)} candles")
+        
+        # Calculate RSI
+        df_candles['rsi_14'] = talib.RSI(df_candles['close'].values, timeperiod=14)
+        
+        # Calculate Bollinger Bands
+        bb_upper, bb_middle, bb_lower = talib.BBANDS(
+            df_candles['close'].values,
+            timeperiod=20,
+            nbdevup=2,
+            nbdevdn=2,
+            matype=0
+        )
+        
+        df_candles['bb_upper'] = bb_upper
+        df_candles['bb_middle'] = bb_middle
+        df_candles['bb_lower'] = bb_lower
+        
+        # Calculate BB Squeeze
+        # Squeeze = when BB width is in the lowest 20% of recent widths
+        df_candles['bb_width'] = df_candles['bb_upper'] - df_candles['bb_lower']
+        df_candles['bb_width_rank'] = df_candles['bb_width'].rolling(window=100).apply(
+            lambda x: (x.iloc[-1] <= x.quantile(0.2)).astype(int) if len(x) >= 20 else 0,
+            raw=False
+        )
+        df_candles['bb_squeeze'] = df_candles['bb_width_rank'].fillna(0).astype(int)
+        
+        # Update analysis database
+        cursor_update = conn_analysis.cursor()
+        updated = 0
+        
+        for _, row in df_candles.iterrows():
+            cursor_update.execute("""
+                UPDATE analysis 
+                SET rsi_14 = ?, bb_upper = ?, bb_middle = ?, bb_lower = ?, bb_squeeze = ?
+                WHERE timeframe = ? AND timestamp = ?
+            """, (
+                float(row['rsi_14']) if not pd.isna(row['rsi_14']) else None,
+                float(row['bb_upper']) if not pd.isna(row['bb_upper']) else None,
+                float(row['bb_middle']) if not pd.isna(row['bb_middle']) else None,
+                float(row['bb_lower']) if not pd.isna(row['bb_lower']) else None,
+                int(row['bb_squeeze']),
+                timeframe,
+                int(row['timestamp'])
+            ))
+            updated += cursor_update.rowcount
+        
+        conn_analysis.commit()
+        total_updated += updated
+        
+        print(f"  ✅ Updated {updated} rows")
+        
+        # Show sample
+        latest = df_candles.iloc[-1]
+        print(f"     Latest RSI: {latest['rsi_14']:.2f}" if not pd.isna(latest['rsi_14']) else "     Latest RSI: NULL")
+        print(f"     Latest BB: Upper=${latest['bb_upper']:.2f}, Lower=${latest['bb_lower']:.2f}" if not pd.isna(latest['bb_upper']) else "     Latest BB: NULL")
+    
+    conn_candles.close()
+    conn_analysis.close()
+    
+    print(f"\n{'='*70}")
+    print(f"✅ BACKFILL COMPLETE!")
+    print(f"   Total rows updated: {total_updated}")
+    print(f"{'='*70}")
+
+
+def verify_backfill(analysis_db: str):
+    """Verify the backfill worked"""
+    print("\n🔍 VERIFICATION")
+    print("=" * 70)
+    
+    conn = sqlite3.connect(analysis_db)
+    cursor = conn.cursor()
+    
+    cursor.execute("SELECT DISTINCT timeframe FROM analysis")
+    timeframes = [row[0] for row in cursor.fetchall()]
+    
+    for tf in timeframes:
+        # Count NULL values
+        cursor.execute("""
+            SELECT 
+                COUNT(*) as total,
+                SUM(CASE WHEN rsi_14 IS NULL THEN 1 ELSE 0 END) as rsi_null,
+                SUM(CASE WHEN bb_upper IS NULL THEN 1 ELSE 0 END) as bb_null
+            FROM analysis 
+            WHERE timeframe = ?
+        """, (tf,))
+        
+        total, rsi_null, bb_null = cursor.fetchone()
+        
+        print(f"\n{tf}:")
+        print(f"  Total rows: {total}")
+        print(f"  RSI NULL: {rsi_null} ({rsi_null/total*100:.1f}%)" if total > 0 else "  RSI NULL: N/A")
+        print(f"  BB NULL: {bb_null} ({bb_null/total*100:.1f}%)" if total > 0 else "  BB NULL: N/A")
+        
+        # Get latest values
+        cursor.execute("""
+            SELECT rsi_14, bb_upper, bb_lower, bb_squeeze
+            FROM analysis 
+            WHERE timeframe = ?
+            ORDER BY timestamp DESC 
+            LIMIT 1
+        """, (tf,))
+        
+        row = cursor.fetchone()
+        if row and row[0] is not None:
+            print(f"  ✅ Latest: RSI={row[0]:.2f}, BB_upper=${row[1]:.2f}, BB_squeeze={row[3]}")
+        else:
+            print(f"  ❌ Latest values still NULL")
+    
+    conn.close()
+
+
+def main():
+    import json
+    
+    # Load config
+    try:
+        with open("config.json", "r") as f:
+            config = json.load(f)
+        candles_db = config.get("candles_db", "../onramp/market_data.db")
+        analysis_db = config.get("analysis_db", "../analysis/analysis.db")
+    except FileNotFoundError:
+        print("❌ config.json not found, using default paths")
+        candles_db = "../onramp/market_data.db"
+        analysis_db = "../analysis/analysis.db"
+    
+    print(f"Candles DB:  {candles_db}")
+    print(f"Analysis DB: {analysis_db}")
+    
+    try:
+        backfill_indicators(candles_db, analysis_db)
+        verify_backfill(analysis_db)
+        
+        print("\n💡 NEXT STEPS:")
+        print("=" * 70)
+        print("1. Run the signal debugger again:")
+        print("   python3 signal_debugger.py")
+        print("\n2. Restart the signal generator:")
+        print("   pkill -f signals.py")
+        print("   ./signals.py")
+        print("\n3. Update your analysis pipeline to calculate these indicators")
+        print("   going forward so you don't need to backfill again")
+        
+    except Exception as e:
+        print(f"\n❌ Error: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()

signals/analysis_inspector.py

@@ -0,0 +1,158 @@
+#!/usr/bin/env python3
+"""
+Analysis Database Inspector
+Check what indicators are actually populated
+"""
+
+import sqlite3
+import json
+
+
+def load_config():
+    with open("config.json", "r") as f:
+        return json.load(f)
+
+
+def inspect_database(db_path):
+    """Inspect analysis database schema and data"""
+    print(f"\n📊 Inspecting: {db_path}")
+    print("=" * 70)
+    
+    try:
+        conn = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True)
+        cursor = conn.cursor()
+        
+        # Get table schema
+        cursor.execute("PRAGMA table_info(analysis)")
+        columns = cursor.fetchall()
+        
+        print("\n📋 TABLE SCHEMA:")
+        print(f"{'Column Name':<20} {'Type':<15} {'Not Null':<10}")
+        print("-" * 50)
+        for col in columns:
+            print(f"{col[1]:<20} {col[2]:<15} {'YES' if col[3] else 'NO':<10}")
+        
+        # Get row count
+        cursor.execute("SELECT COUNT(*) FROM analysis")
+        total_rows = cursor.fetchone()[0]
+        print(f"\n📊 Total rows: {total_rows}")
+        
+        # Check data availability per timeframe
+        cursor.execute("SELECT DISTINCT timeframe FROM analysis ORDER BY timeframe")
+        timeframes = [row[0] for row in cursor.fetchall()]
+        
+        print("\n⏱️  DATA BY TIMEFRAME:")
+        for tf in timeframes:
+            cursor.execute(f"SELECT COUNT(*) FROM analysis WHERE timeframe = ?", (tf,))
+            count = cursor.fetchone()[0]
+            print(f"  {tf}: {count} rows")
+        
+        # Check for NULL values in key indicators
+        print("\n🔍 NULL VALUE CHECK (latest 10 rows per timeframe):")
+        
+        indicator_cols = [
+            'ema_9', 'ema_21', 'sma_50', 'sma_200',
+            'rsi_14', 'macd', 'macd_signal', 'macd_hist',
+            'bb_upper', 'bb_middle', 'bb_lower', 'bb_squeeze',
+            'volume_ma_20'
+        ]
+        
+        for tf in timeframes:
+            print(f"\n  Timeframe: {tf}")
+            
+            # Get latest row
+            cursor.execute(f"""
+                SELECT * FROM analysis 
+                WHERE timeframe = ? 
+                ORDER BY timestamp DESC 
+                LIMIT 1
+            """, (tf,))
+            
+            row = cursor.fetchone()
+            col_names = [desc[0] for desc in cursor.description]
+            
+            if row:
+                row_dict = dict(zip(col_names, row))
+                
+                null_indicators = []
+                present_indicators = []
+                
+                for ind in indicator_cols:
+                    if ind in row_dict:
+                        if row_dict[ind] is None:
+                            null_indicators.append(ind)
+                        else:
+                            present_indicators.append(ind)
+                    else:
+                        null_indicators.append(f"{ind} (MISSING COLUMN)")
+                
+                if present_indicators:
+                    print(f"    ✓ Present: {', '.join(present_indicators[:5])}")
+                    if len(present_indicators) > 5:
+                        print(f"               {', '.join(present_indicators[5:])}")
+                
+                if null_indicators:
+                    print(f"    ❌ NULL/Missing: {', '.join(null_indicators)}")
+                
+                # Show sample values
+                print(f"\n    Sample values from latest row:")
+                print(f"      Timestamp: {row_dict.get('timestamp')}")
+                for ind in ['ema_9', 'ema_21', 'rsi_14', 'bb_upper']:
+                    if ind in row_dict:
+                        val = row_dict[ind]
+                        if val is not None:
+                            print(f"      {ind}: {val}")
+                        else:
+                            print(f"      {ind}: NULL")
+            else:
+                print(f"    ❌ No data found")
+        
+        # Check if buy_volume exists in candles
+        print("\n\n📊 Checking candles table for buy_volume...")
+        cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='candles'")
+        if cursor.fetchone():
+            cursor.execute("PRAGMA table_info(candles)")
+            candles_cols = [col[1] for col in cursor.fetchall()]
+            
+            if 'buy_volume' in candles_cols:
+                print("  ✓ buy_volume column exists in candles table")
+                
+                # Check if it has data
+                cursor.execute("SELECT COUNT(*) FROM candles WHERE buy_volume IS NOT NULL")
+                count = cursor.fetchone()[0]
+                print(f"  ✓ {count} rows with buy_volume data")
+            else:
+                print("  ❌ buy_volume column MISSING from candles table")
+                print("     Available columns:", ', '.join(candles_cols))
+        
+        conn.close()
+        
+    except sqlite3.OperationalError as e:
+        print(f"  ❌ Database error: {e}")
+    except Exception as e:
+        print(f"  ❌ Error: {e}")
+
+
+def main():
+    config = load_config()
+    
+    print("🔍 ANALYSIS DATABASE INSPECTOR")
+    print("=" * 70)
+    
+    inspect_database(config["analysis_db"])
+    
+    print("\n\n💡 NEXT STEPS:")
+    print("=" * 70)
+    print("If indicators are missing:")
+    print("  1. Check your analysis pipeline is running")
+    print("  2. Verify the analysis script calculates these indicators:")
+    print("     - rsi_14, bb_upper, bb_lower, bb_middle, bb_squeeze")
+    print("  3. Re-run analysis on existing candle data")
+    print("\nIf buy_volume is missing:")
+    print("  1. Update your candles table schema")
+    print("  2. Modify your data ingestion to capture buy_volume")
+    print("  3. Or set buy_volume = volume/2 as approximation")
+
+
+if __name__ == "__main__":
+    main()

signals/config.json

@@ -10,7 +10,7 @@
   "personality": "scalping",
   "timeframes": ["1m", "5m"],
   "lookback": 200,
-  "min_confidence": 0.45,
+  "min_confidence": 0.40,
   "cooldown_seconds": 30,
   "weights": {
     "scalping": {
@@ -18,12 +18,12 @@
       "stoch": 0.2,
       "rsi": 0.2,
       "volume": 0.2,
-      "macd": 0.15
+      "macd": 0.3
     },
     "swing": {
       "regime": 0.35,
       "bb_squeeze": 0.25,
-      "macd": 0.2,
+      "macd": 0.3,
       "flow": 0.15,
       "rsi": 0.05
     }

signals/signal_debugger.py

@@ -0,0 +1,357 @@
+#!/usr/bin/env python3
+"""
+Signal Generator Debugger
+Analyzes why signals aren't being generated
+"""
+
+import sqlite3
+import pandas as pd
+import numpy as np
+import talib
+import json
+from datetime import datetime
+import sys
+
+
+def load_config():
+    with open("config.json", "r") as f:
+        return json.load(f)
+
+
+def fetch_data(candles_db, analysis_db, timeframe, lookback=200):
+    """Fetch and enrich data exactly like signals.py does"""
+    try:
+        conn_c = sqlite3.connect(f"file:{candles_db}?mode=ro", uri=True, timeout=10)
+        conn_c.execute(f"ATTACH DATABASE 'file:{analysis_db}?mode=ro' AS analysis_db")
+
+        query = """
+            SELECT
+                c.timeframe, c.timestamp, c.open, c.high, c.low, c.close,
+                c.volume, c.buy_volume,
+                a.ema_9, a.ema_21, a.sma_50, a.sma_200,
+                a.rsi_14, a.macd, a.macd_signal, a.macd_hist,
+                a.bb_upper, a.bb_middle, a.bb_lower, a.bb_squeeze,
+                a.volume_ma_20
+            FROM candles c
+            JOIN analysis_db.analysis a
+              ON c.timeframe = a.timeframe
+             AND c.timestamp = a.timestamp
+            WHERE c.timeframe = ?
+            ORDER BY c.timestamp DESC
+            LIMIT ?
+        """
+
+        df = pd.read_sql_query(query, conn_c, params=(timeframe, lookback))
+        conn_c.close()
+
+        if df.empty:
+            return None
+
+        df = df.sort_values("timestamp").reset_index(drop=True)
+        df["datetime"] = pd.to_datetime(df["timestamp"], unit="s")
+
+        # Filter closed candles
+        import time
+        current_time = int(time.time())
+        window = {"1m": 60, "5m": 300, "15m": 900, "1h": 3600}.get(timeframe, 60)
+        df = df[df["timestamp"] < (current_time - window)]
+
+        if len(df) < 50:
+            return None
+
+        df = df.dropna(subset=["open", "high", "low", "close", "volume"])
+
+        if len(df) < 50:
+            return None
+
+        # Add Stochastic
+        df["stoch_k"], df["stoch_d"] = talib.STOCH(
+            df["high"].values,
+            df["low"].values,
+            df["close"].values,
+            fastk_period=14,
+            slowk_period=3,
+            slowd_period=3,
+        )
+
+        df["buy_ratio"] = df["buy_volume"] / df["volume"].replace(0, np.nan)
+        df["net_flow"] = df["buy_volume"] - (df["volume"] - df["buy_volume"])
+
+        return df
+
+    except Exception as e:
+        print(f"Error fetching data: {e}")
+        return None
+
+
+def analyze_scalping(df, weights, min_confidence):
+    """Analyze scalping signal generation"""
+    if len(df) < 21:
+        print("  ❌ Insufficient data for scalping (need 21+ rows)")
+        return
+
+    latest = df.iloc[-1]
+    prev = df.iloc[-2]
+
+    print(f"\n📊 SCALPING ANALYSIS ({len(df)} candles)")
+    print("=" * 70)
+
+    # Check for NULL values
+    required = ["ema_9", "ema_21", "rsi_14", "stoch_k", "stoch_d", "macd", "macd_signal"]
+    null_cols = [col for col in required if pd.isna(latest[col])]
+    
+    if null_cols:
+        print(f"  ❌ SKIPPED: Missing indicators: {', '.join(null_cols)}")
+        return
+    else:
+        print("  ✓ All required indicators present")
+
+    print(f"\n  Latest candle: {latest['datetime']}")
+    print(f"  Close: ${latest['close']:.2f}")
+
+    # EMA Analysis
+    print(f"\n  EMA Crossover Check:")
+    print(f"    Current: EMA9={latest['ema_9']:.2f} vs EMA21={latest['ema_21']:.2f}")
+    print(f"    Previous: EMA9={prev['ema_9']:.2f} vs EMA21={prev['ema_21']:.2f}")
+    
+    ema_cross_up = latest["ema_9"] > latest["ema_21"] and prev["ema_9"] <= prev["ema_21"]
+    ema_cross_down = latest["ema_9"] < latest["ema_21"] and prev["ema_9"] >= prev["ema_21"]
+    
+    if ema_cross_up:
+        print(f"    ✓ BULLISH CROSSOVER DETECTED!")
+        signal_type = "BUY"
+        score = weights["ema_cross"]
+    elif ema_cross_down:
+        print(f"    ✓ BEARISH CROSSOVER DETECTED!")
+        signal_type = "SELL"
+        score = weights["ema_cross"]
+    else:
+        print(f"    ❌ No crossover (EMA9 {'above' if latest['ema_9'] > latest['ema_21'] else 'below'} EMA21)")
+        
+        # Show trend direction
+        ema_diff = latest["ema_9"] - latest["ema_21"]
+        prev_diff = prev["ema_9"] - prev["ema_21"]
+        trend = "converging" if abs(ema_diff) < abs(prev_diff) else "diverging"
+        print(f"    EMAs are {trend} (diff: {ema_diff:.2f} vs prev: {prev_diff:.2f})")
+        return
+
+    # We have a crossover, check other indicators
+    print(f"\n  Signal Type: {signal_type}")
+    print(f"  Base Score: {score:.3f} (from EMA crossover)")
+
+    # Stochastic
+    print(f"\n  Stochastic:")
+    print(f"    K={latest['stoch_k']:.1f}, D={latest['stoch_d']:.1f}")
+    
+    if signal_type == "BUY":
+        if latest["stoch_k"] > latest["stoch_d"] and latest["stoch_k"] < 30:
+            score += weights["stoch"]
+            print(f"    ✓ Oversold crossover (+{weights['stoch']:.3f})")
+        else:
+            print(f"    ❌ Not oversold crossover (K>D: {latest['stoch_k'] > latest['stoch_d']}, K<30: {latest['stoch_k'] < 30})")
+    else:
+        if latest["stoch_k"] < latest["stoch_d"] and latest["stoch_k"] > 70:
+            score += weights["stoch"]
+            print(f"    ✓ Overbought crossover (+{weights['stoch']:.3f})")
+        else:
+            print(f"    ❌ Not overbought crossover (K<D: {latest['stoch_k'] < latest['stoch_d']}, K>70: {latest['stoch_k'] > 70})")
+
+    # RSI
+    print(f"\n  RSI: {latest['rsi_14']:.1f}")
+    
+    if signal_type == "BUY" and latest["rsi_14"] < 40:
+        score += weights["rsi"]
+        print(f"    ✓ Undersold (+{weights['rsi']:.3f})")
+    elif signal_type == "SELL" and latest["rsi_14"] > 60:
+        score += weights["rsi"]
+        print(f"    ✓ Oversold (+{weights['rsi']:.3f})")
+    else:
+        print(f"    ❌ Not in range (BUY needs <40, SELL needs >60)")
+
+    # Volume
+    vol_ratio = latest["volume"] / latest["volume_ma_20"] if latest["volume_ma_20"] else 0
+    print(f"\n  Volume: {latest['volume']:.2f} vs MA20: {latest['volume_ma_20']:.2f}")
+    print(f"    Ratio: {vol_ratio:.2f}x")
+    
+    if vol_ratio > 1.5:
+        score += weights["volume"]
+        print(f"    ✓ Volume surge (+{weights['volume']:.3f})")
+    else:
+        print(f"    ❌ No surge (need >1.5x)")
+
+    # MACD
+    print(f"\n  MACD: {latest['macd']:.2f} vs Signal: {latest['macd_signal']:.2f}")
+    
+    if signal_type == "BUY" and latest["macd"] > latest["macd_signal"]:
+        score += weights["macd"]
+        print(f"    ✓ Bullish (+{weights['macd']:.3f})")
+    elif signal_type == "SELL" and latest["macd"] < latest["macd_signal"]:
+        score += weights["macd"]
+        print(f"    ✓ Bearish (+{weights['macd']:.3f})")
+    else:
+        print(f"    ❌ Not aligned")
+
+    # Final score
+    print(f"\n  {'='*70}")
+    print(f"  FINAL SCORE: {score:.3f}")
+    print(f"  THRESHOLD:   {min_confidence:.3f}")
+    
+    if score >= min_confidence:
+        print(f"  ✅ SIGNAL WOULD BE GENERATED!")
+    else:
+        print(f"  ❌ Below threshold (need {min_confidence - score:.3f} more)")
+
+
+def analyze_swing(df, weights, min_confidence):
+    """Analyze swing signal generation"""
+    if len(df) < 200:
+        print(f"  ❌ Insufficient data for swing (need 200+ rows, have {len(df)})")
+        return
+
+    latest = df.iloc[-1]
+    prev = df.iloc[-2]
+
+    print(f"\n📊 SWING ANALYSIS ({len(df)} candles)")
+    print("=" * 70)
+
+    # Check for NULL values
+    required = ["sma_50", "sma_200", "bb_upper", "bb_lower", "bb_squeeze", "macd", "macd_signal", "buy_ratio"]
+    null_cols = [col for col in required if pd.isna(latest[col])]
+    
+    if null_cols:
+        print(f"  ❌ SKIPPED: Missing indicators: {', '.join(null_cols)}")
+        return
+    else:
+        print("  ✓ All required indicators present")
+
+    print(f"\n  Latest candle: {latest['datetime']}")
+    print(f"  Close: ${latest['close']:.2f}")
+
+    # Regime Analysis
+    print(f"\n  Regime Analysis:")
+    print(f"    Price:  ${latest['close']:.2f}")
+    print(f"    SMA50:  ${latest['sma_50']:.2f}")
+    print(f"    SMA200: ${latest['sma_200']:.2f}")
+
+    bull_regime = latest["close"] > latest["sma_50"] > latest["sma_200"]
+    bear_regime = latest["close"] < latest["sma_50"] < latest["sma_200"]
+
+    score = 0
+    signal_type = None
+
+    if bull_regime:
+        signal_type = "BUY"
+        score += weights["regime"]
+        print(f"    ✓ BULL REGIME (Price > SMA50 > SMA200) (+{weights['regime']:.3f})")
+    elif bear_regime:
+        signal_type = "SELL"
+        score += weights["regime"]
+        print(f"    ✓ BEAR REGIME (Price < SMA50 < SMA200) (+{weights['regime']:.3f})")
+    else:
+        print(f"    ❌ No clear regime")
+        print(f"       Price vs SMA50: {'above' if latest['close'] > latest['sma_50'] else 'below'}")
+        print(f"       SMA50 vs SMA200: {'above' if latest['sma_50'] > latest['sma_200'] else 'below'}")
+        return
+
+    print(f"\n  Signal Type: {signal_type}")
+    print(f"  Base Score: {score:.3f} (from regime)")
+
+    # BB Squeeze
+    print(f"\n  Bollinger Bands:")
+    print(f"    Squeeze: {latest['bb_squeeze']} (prev: {prev['bb_squeeze']})")
+    print(f"    Upper: ${latest['bb_upper']:.2f}, Lower: ${latest['bb_lower']:.2f}")
+    
+    if latest["bb_squeeze"] == 1 or prev["bb_squeeze"] == 1:
+        if signal_type == "BUY" and latest["close"] > latest["bb_upper"]:
+            score += weights["bb_squeeze"]
+            print(f"    ✓ Squeeze breakout upside (+{weights['bb_squeeze']:.3f})")
+        elif signal_type == "SELL" and latest["close"] < latest["bb_lower"]:
+            score += weights["bb_squeeze"]
+            print(f"    ✓ Squeeze breakout downside (+{weights['bb_squeeze']:.3f})")
+        else:
+            print(f"    ❌ Squeeze present but no breakout")
+    else:
+        print(f"    ❌ No squeeze")
+
+    # MACD
+    print(f"\n  MACD:")
+    print(f"    Current: {latest['macd']:.2f} vs Signal: {latest['macd_signal']:.2f}")
+    print(f"    Previous: {prev['macd']:.2f} vs Signal: {prev['macd_signal']:.2f}")
+    
+    macd_cross_up = latest["macd"] > latest["macd_signal"] and prev["macd"] <= prev["macd_signal"]
+    macd_cross_down = latest["macd"] < latest["macd_signal"] and prev["macd"] >= prev["macd_signal"]
+    
+    if signal_type == "BUY" and macd_cross_up:
+        score += weights["macd"]
+        print(f"    ✓ Bullish crossover (+{weights['macd']:.3f})")
+    elif signal_type == "SELL" and macd_cross_down:
+        score += weights["macd"]
+        print(f"    ✓ Bearish crossover (+{weights['macd']:.3f})")
+    else:
+        print(f"    ❌ No crossover or not aligned")
+
+    # Net flow
+    print(f"\n  Buy/Sell Pressure:")
+    print(f"    Buy Ratio: {latest['buy_ratio']:.2%}")
+    
+    if signal_type == "BUY" and latest["buy_ratio"] > 0.55:
+        score += weights["flow"]
+        print(f"    ✓ Strong buy pressure (+{weights['flow']:.3f})")
+    elif signal_type == "SELL" and latest["buy_ratio"] < 0.45:
+        score += weights["flow"]
+        print(f"    ✓ Strong sell pressure (+{weights['flow']:.3f})")
+    else:
+        print(f"    ❌ Neutral pressure")
+
+    # RSI
+    print(f"\n  RSI: {latest['rsi_14']:.1f}")
+    
+    if signal_type == "BUY" and latest["rsi_14"] < 50:
+        score += weights["rsi"]
+        print(f"    ✓ Not overbought (+{weights['rsi']:.3f})")
+    elif signal_type == "SELL" and latest["rsi_14"] > 50:
+        score += weights["rsi"]
+        print(f"    ✓ Not oversold (+{weights['rsi']:.3f})")
+    else:
+        print(f"    ❌ Unfavorable")
+
+    # Final score
+    print(f"\n  {'='*70}")
+    print(f"  FINAL SCORE: {score:.3f}")
+    print(f"  THRESHOLD:   {min_confidence:.3f}")
+    
+    if score >= min_confidence:
+        print(f"  ✅ SIGNAL WOULD BE GENERATED!")
+    else:
+        print(f"  ❌ Below threshold (need {min_confidence - score:.3f} more)")
+
+
+def main():
+    config = load_config()
+    
+    print("🔍 SIGNAL GENERATOR DEBUGGER")
+    print("=" * 70)
+    print(f"Min Confidence: {config['min_confidence']}")
+    print(f"Timeframes: {', '.join(config['timeframes'])}")
+    print(f"Lookback: {config['lookback']} candles")
+    
+    for timeframe in config["timeframes"]:
+        print(f"\n\n{'='*70}")
+        print(f"TIMEFRAME: {timeframe}")
+        print(f"{'='*70}")
+        
+        df = fetch_data(config["candles_db"], config["analysis_db"], timeframe, config["lookback"])
+        
+        if df is None:
+            print(f"  ❌ No data available")
+            continue
+        
+        print(f"  ✓ Loaded {len(df)} candles")
+        
+        # Analyze both personalities
+        analyze_scalping(df, config["weights"]["scalping"], config["min_confidence"])
+        analyze_swing(df, config["weights"]["swing"], config["min_confidence"])
+
+
+if __name__ == "__main__":
+    main()

signals/signal_health_client.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Health Check Client for Signal Generator
+Health Check Client for Multi-Personality Signal Generator
 Query the running signal generator status
 """
 
@@ -28,19 +28,61 @@ def check_health(socket_path="/tmp/signals_health.sock"):
         # Parse and display
         health = json.loads(response.decode('utf-8'))
         
-        print("=" * 60)
-        print("SIGNAL GENERATOR HEALTH STATUS")
-        print("=" * 60)
+        print("=" * 70)
+        print("MULTI-PERSONALITY SIGNAL GENERATOR HEALTH STATUS")
+        print("=" * 70)
         print(f"Status:           {health['status']}")
-        print(f"Personality:      {health['personality']}")
+        print(f"Mode:             {health.get('mode', 'single-personality')}")
+        
+        # Handle both old single-personality and new multi-personality format
+        if 'personalities' in health:
+            print(f"Personalities:    {', '.join(health['personalities'])}")
+        elif 'personality' in health:
+            print(f"Personality:      {health['personality']}")
+        
         print(f"Timeframes:       {', '.join(health['timeframes'])}")
         print(f"Uptime:           {health['uptime_seconds']}s ({health['uptime_seconds']//60}m)")
-        print(f"Total Signals:    {health['total_signals']}")
-        print(f"  - Buy:          {health['buy_signals']}")
-        print(f"  - Sell:         {health['sell_signals']}")
-        print(f"Last Signal:      {health['last_signal'] or 'None'}")
-        print(f"Errors:           {health['errors']}")
         print(f"Connected Clients: {health['connected_clients']}")
+        print(f"Debug Mode:       {'ON' if health.get('debug_mode') else 'OFF'}")
+        
+        # Total stats (aggregated across all personalities)
+        if 'total_stats' in health:
+            total = health['total_stats']
+            print(f"\nAGGREGATED STATISTICS:")
+            print(f"  Total Signals:  {total['total_signals']}")
+            print(f"  - Buy:          {total['buy_signals']}")
+            print(f"  - Sell:         {total['sell_signals']}")
+            print(f"  Errors:         {total['errors']}")
+        else:
+            # Fallback for old format
+            print(f"\nSTATISTICS:")
+            print(f"  Total Signals:  {health.get('total_signals', 0)}")
+            print(f"  - Buy:          {health.get('buy_signals', 0)}")
+            print(f"  - Sell:         {health.get('sell_signals', 0)}")
+            print(f"  Errors:         {health.get('errors', 0)}")
+        
+        # Per-personality breakdown (if available)
+        if 'personality_stats' in health:
+            print("\n" + "=" * 70)
+            print("PER-PERSONALITY BREAKDOWN")
+            print("=" * 70)
+            
+            for personality, stats in health['personality_stats'].items():
+                print(f"\n{personality.upper()}:")
+                print(f"  Total Signals:  {stats['total_signals']}")
+                print(f"  - Buy:          {stats['buy_signals']}")
+                print(f"  - Sell:         {stats['sell_signals']}")
+                print(f"  Last Signal:    {stats['last_signal'] or 'None'}")
+                print(f"  Errors:         {stats['errors']}")
+                
+                if stats.get('recent_signals'):
+                    print(f"  Recent Signals:")
+                    for sig in stats['recent_signals'][:3]:
+                        print(f"    [{sig['timeframe']}] {sig['signal']} @ ${sig['price']:.2f} "
+                              f"(conf: {sig['confidence']:.2f})")
+        else:
+            # Old format
+            print(f"  Last Signal:    {health.get('last_signal') or 'None'}")
         
         # Database Status
         print("\n" + "=" * 70)
@@ -58,20 +100,22 @@ def check_health(socket_path="/tmp/signals_health.sock"):
             print(f"  Total Rows:     {candles.get('row_count', 0)}")
             for tf, info in candles.get('timeframes', {}).items():
                 age = info.get('age_seconds')
-                age_str = f"{age}s ago" if age else "N/A"
-                print(f"  [{tf}]:          {info['count']} rows, latest: {age_str}")
+                age_str = f"{age}s ago" if age is not None else "N/A"
+                status = "⚠ STALE" if age and age > 300 else ""
+                print(f"  [{tf}]:          {info['count']} rows, latest: {age_str} {status}")
         
         # Analysis DB
         analysis = db.get('analysis_db', {})
-        print(f"Analysis DB:      {'✓ OK' if analysis.get('accessible') else '✗ FAILED'}")
+        print(f"\nAnalysis DB:      {'✓ OK' if analysis.get('accessible') else '✗ FAILED'}")
         if analysis.get('error'):
             print(f"  Error:          {analysis['error']}")
         else:
             print(f"  Total Rows:     {analysis.get('row_count', 0)}")
             for tf, info in analysis.get('timeframes', {}).items():
                 age = info.get('age_seconds')
-                age_str = f"{age}s ago" if age else "N/A"
-                print(f"  [{tf}]:          {info['count']} rows, latest: {age_str}")
+                age_str = f"{age}s ago" if age is not None else "N/A"
+                status = "⚠ STALE" if age and age > 300 else ""
+                print(f"  [{tf}]:          {info['count']} rows, latest: {age_str} {status}")
         
         # Configuration
         print("\n" + "=" * 70)
@@ -81,21 +125,38 @@ def check_health(socket_path="/tmp/signals_health.sock"):
         print(f"Min Confidence:   {cfg.get('min_confidence', 'N/A')}")
         print(f"Cooldown:         {cfg.get('cooldown_seconds', 'N/A')}s")
         print(f"Lookback:         {cfg.get('lookback', 'N/A')} candles")
+        print(f"Config Reloads:   {cfg.get('reloads', 0)}")
+        
         if cfg.get('weights'):
-            print(f"Weights:")
-            for indicator, weight in cfg['weights'].items():
-                print(f"  {indicator:12s}  {weight}")
+            # Multi-personality format
+            if isinstance(cfg['weights'], dict) and any(k in cfg['weights'] for k in ['scalping', 'swing']):
+                for personality, weights in cfg['weights'].items():
+                    print(f"\n{personality.upper()} Weights:")
+                    for indicator, weight in weights.items():
+                        print(f"  {indicator:12s}  {weight}")
+            else:
+                # Single personality format
+                print(f"Weights:")
+                for indicator, weight in cfg['weights'].items():
+                    print(f"  {indicator:12s}  {weight}")
         
-        if health['recent_signals']:
-            print("\n" + "=" * 60)
-            print("RECENT SIGNALS")
-            print("=" * 60)
-            for sig in health['recent_signals']:
-                print(f"  [{sig['timeframe']}] {sig['signal']} @ ${sig['price']:.2f} "
+        # Recent signals (all personalities combined)
+        recent = health.get('recent_signals', [])
+        if recent:
+            print("\n" + "=" * 70)
+            print("RECENT SIGNALS (ALL PERSONALITIES)")
+            print("=" * 70)
+            for sig in recent[:10]:
+                personality_tag = f"[{sig.get('personality', '?').upper()}]"
+                print(f"  {personality_tag:12s} [{sig['timeframe']}] {sig['signal']} @ ${sig['price']:.2f} "
                       f"(conf: {sig['confidence']:.2f})")
-                print(f"      Reasons: {', '.join(sig['reasons'])}")
+                if sig.get('reasons'):
+                    reasons_str = ', '.join(sig['reasons'][:3])
+                    if len(sig['reasons']) > 3:
+                        reasons_str += f" (+{len(sig['reasons'])-3} more)"
+                    print(f"                Reasons: {reasons_str}")
         
-        print("=" * 60)
+        print("=" * 70)
         
         return 0
         
@@ -106,8 +167,15 @@ def check_health(socket_path="/tmp/signals_health.sock"):
     except ConnectionRefusedError:
         print(f"Error: Connection refused at {socket_path}")
         return 1
+    except KeyError as e:
+        print(f"Error: Missing expected field in health response: {e}")
+        print("\nRaw response:")
+        print(json.dumps(health, indent=2))
+        return 1
     except Exception as e:
         print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
         return 1
 
 if __name__ == "__main__":

signals/signals.py

@@ -1,7 +1,8 @@
 #!/usr/bin/env python3
 """
-BTCUSDT Signal Generator
+BTCUSDT Signal Generator - Multi-Personality Edition
 Generates trading signals from candles.db and analysis.db
+Runs both scalping and swing personalities simultaneously
 Streams signals via Unix Domain Socket
 """
 
@@ -46,11 +47,10 @@ class Config:
         self.log_file = data.get("log_file", "logs/signal_generator.log")
         self.log_to_stdout = data.get("log_to_stdout", True)
         self.poll_interval = data.get("poll_interval", 0.5)
-        self.personality = data.get("personality", "scalping")
         self.timeframes = data.get("timeframes", ["1m", "5m"])
         self.lookback = data.get("lookback", 200)
 
-        # Signal thresholds
+        # Signal thresholds (can be personality-specific)
         self.min_confidence = data.get("min_confidence", 0.6)
         self.cooldown_seconds = data.get("cooldown_seconds", 60)
 
@@ -92,13 +92,14 @@ def setup_logging(config: Config):
     return logging.getLogger(__name__)
 
 
-# Signal Generator Class
-class SignalGenerator:
-    def __init__(self, config: Config, logger: logging.Logger):
+# Signal Generator Class (per-personality)
+class PersonalityEngine:
+    """Single personality signal generation engine"""
+    
+    def __init__(self, personality: str, config: Config, logger: logging.Logger):
+        self.personality = personality
         self.config = config
         self.logger = logger
-        self.running = False
-        self.debug_mode = False
         self.last_signal_time = {}
         self.signal_history = deque(maxlen=100)
         self.stats = {
@@ -106,20 +107,9 @@ class SignalGenerator:
             "buy_signals": 0,
             "sell_signals": 0,
             "last_signal_time": None,
-            "uptime_start": datetime.now(timezone.utc),
             "errors": 0,
-            "config_reloads": 0,
         }
-
-        # Unix socket
-        self.socket = None
-        self.connections = []
-
-        # Health check socket
-        self.health_socket = None
-
-        # Control socket
-        self.control_socket = None
+        self.lock = threading.Lock()
 
     def fetch_and_enrich(self, timeframe: str) -> Optional[pd.DataFrame]:
         """Fetch data from databases and enrich with additional indicators"""
@@ -181,7 +171,7 @@ class SignalGenerator:
             df = df[df["timestamp"] < (current_time - window)]
 
             if len(df) < 50:
-                self.logger.debug(f"Not enough data for {timeframe}: {len(df)} rows")
+                self.logger.debug(f"[{self.personality}] Not enough data for {timeframe}: {len(df)} rows")
                 return None
 
             # Drop rows with NULL in critical columns
@@ -189,7 +179,7 @@ class SignalGenerator:
 
             if len(df) < 50:
                 self.logger.debug(
-                    f"Not enough valid data after NULL filtering for {timeframe}"
+                    f"[{self.personality}] Not enough valid data after NULL filtering for {timeframe}"
                 )
                 return None
 
@@ -210,8 +200,9 @@ class SignalGenerator:
             return df
 
         except Exception as e:
-            self.logger.error(f"Error fetching data for {timeframe}: {e}")
-            self.stats["errors"] += 1
+            self.logger.error(f"[{self.personality}] Error fetching data for {timeframe}: {e}")
+            with self.lock:
+                self.stats["errors"] += 1
             return None
 
     def generate_signal_scalping(
@@ -219,13 +210,13 @@ class SignalGenerator:
     ) -> Optional[Dict]:
         """Generate signal using scalping personality"""
         if len(df) < 21:
-            self.logger.debug(f"[{timeframe}] Insufficient data: {len(df)} rows")
+            self.logger.debug(f"[{self.personality}/{timeframe}] Insufficient data: {len(df)} rows")
             return None
 
         latest = df.iloc[-1]
         prev = df.iloc[-2]
 
-        # Check for NULL indicators - skip if essential indicators are missing
+        # Check for NULL indicators
         required_cols = [
             "ema_9",
             "ema_21",
@@ -236,7 +227,7 @@ class SignalGenerator:
             "macd_signal",
         ]
         if any(pd.isna(latest[col]) for col in required_cols):
-            self.logger.debug(f"[{timeframe}] Skipping: missing required indicators")
+            self.logger.debug(f"[{self.personality}/{timeframe}] Skipping: missing required indicators")
             return None
 
         score = 0
@@ -262,12 +253,6 @@ class SignalGenerator:
             reasons.append("EMA9 crossed below EMA21")
             signal_type = "SELL"
 
-        # Log EMA status for debugging
-        self.logger.debug(
-            f"[{timeframe}] EMA9={latest['ema_9']:.2f} vs EMA21={latest['ema_21']:.2f}, "
-            f"Prev: EMA9={prev['ema_9']:.2f} vs EMA21={prev['ema_21']:.2f}"
-        )
-
         # Stochastic
         if signal_type == "BUY":
             if latest["stoch_k"] > latest["stoch_d"] and latest["stoch_k"] < 30:
@@ -299,13 +284,6 @@ class SignalGenerator:
             score += weights["macd"]
             reasons.append("MACD bearish")
 
-        # Debug output
-        if signal_type:
-            self.logger.debug(
-                f"[{timeframe}] Potential {signal_type} signal - Score: {score:.3f} "
-                f"(threshold: {self.config.min_confidence}), Reasons: {len(reasons)}"
-            )
-
         if signal_type and score >= self.config.min_confidence:
             return {
                 "signal": signal_type,
@@ -327,7 +305,7 @@ class SignalGenerator:
         latest = df.iloc[-1]
         prev = df.iloc[-2]
 
-        # Check for NULL indicators - skip if essential indicators are missing
+        # Check for NULL indicators
         required_cols = [
             "sma_50",
             "sma_200",
@@ -339,7 +317,7 @@ class SignalGenerator:
             "buy_ratio",
         ]
         if any(pd.isna(latest[col]) for col in required_cols):
-            self.logger.debug(f"Skipping {timeframe}: missing required indicators")
+            self.logger.debug(f"[{self.personality}/{timeframe}] Skipping: missing required indicators")
             return None
 
         score = 0
@@ -418,7 +396,7 @@ class SignalGenerator:
     def generate_signal(self, timeframe: str) -> Optional[Dict]:
         """Main signal generation dispatcher"""
         # Check cooldown
-        cooldown_key = f"{self.config.personality}_{timeframe}"
+        cooldown_key = f"{self.personality}_{timeframe}"
         if cooldown_key in self.last_signal_time:
             elapsed = time.time() - self.last_signal_time[cooldown_key]
             if elapsed < self.config.cooldown_seconds:
@@ -428,12 +406,12 @@ class SignalGenerator:
         if df is None:
             return None
 
-        if self.config.personality == "scalping":
+        if self.personality == "scalping":
             signal = self.generate_signal_scalping(df, timeframe)
-        elif self.config.personality == "swing":
+        elif self.personality == "swing":
             signal = self.generate_signal_swing(df, timeframe)
         else:
-            self.logger.error(f"Unknown personality: {self.config.personality}")
+            self.logger.error(f"Unknown personality: {self.personality}")
             return None
 
         if signal:
@@ -441,40 +419,76 @@ class SignalGenerator:
             signal["generated_at"] = datetime.now(timezone.utc).isoformat()
 
             # Update stats
-            self.stats["total_signals"] += 1
-            if signal["signal"] == "BUY":
-                self.stats["buy_signals"] += 1
-            else:
-                self.stats["sell_signals"] += 1
-            self.stats["last_signal_time"] = signal["generated_at"]
-
-            self.signal_history.append(signal)
+            with self.lock:
+                self.stats["total_signals"] += 1
+                if signal["signal"] == "BUY":
+                    self.stats["buy_signals"] += 1
+                else:
+                    self.stats["sell_signals"] += 1
+                self.stats["last_signal_time"] = signal["generated_at"]
+                self.signal_history.append(signal)
 
         return signal
 
+
+# Main Signal Generator Coordinator
+class SignalGenerator:
+    def __init__(self, config: Config, logger: logging.Logger):
+        self.config = config
+        self.logger = logger
+        self.running = False
+        self.debug_mode = False
+        
+        # Create personality engines
+        self.engines = {
+            "scalping": PersonalityEngine("scalping", config, logger),
+            "swing": PersonalityEngine("swing", config, logger),
+        }
+        
+        self.global_stats = {
+            "uptime_start": datetime.now(timezone.utc),
+            "config_reloads": 0,
+        }
+
+        # Unix socket
+        self.socket = None
+        self.connections = []
+        self.connections_lock = threading.Lock()
+
+        # Health check socket
+        self.health_socket = None
+
+        # Control socket
+        self.control_socket = None
+
+        # Thread pool
+        self.threads = []
+
     def broadcast_signal(self, signal: Dict):
         """Broadcast signal to all connected clients"""
         message = json.dumps(signal) + "\n"
         message_bytes = message.encode("utf-8")
 
-        disconnected = []
-        for conn in self.connections:
-            try:
-                conn.sendall(message_bytes)
-                self.logger.info(
-                    f"Sent {signal['signal']} signal: {signal['timeframe']} @ {signal['price']} (conf: {signal['confidence']})"
-                )
-            except Exception as e:
-                self.logger.warning(f"Failed to send to client: {e}")
-                disconnected.append(conn)
+        with self.connections_lock:
+            disconnected = []
+            for conn in self.connections:
+                try:
+                    conn.sendall(message_bytes)
+                    self.logger.info(
+                        f"[{signal['personality']}] Sent {signal['signal']} signal: "
+                        f"{signal['timeframe']} @ {signal['price']} (conf: {signal['confidence']})"
+                    )
+                except Exception as e:
+                    self.logger.warning(f"Failed to send to client: {e}")
+                    disconnected.append(conn)
 
-        # Remove disconnected clients
-        for conn in disconnected:
-            try:
-                conn.close()
-            except:
-                pass
-            self.connections.remove(conn)
+            # Remove disconnected clients
+            for conn in disconnected:
+                try:
+                    conn.close()
+                except:
+                    pass
+                self.connections.remove(conn)
 
     def setup_signal_socket(self):
         """Setup Unix domain socket for signal streaming"""
@@ -485,7 +499,7 @@ class SignalGenerator:
             self.socket = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
             self.socket.bind(self.config.socket_path)
             self.socket.listen(5)
-            self.socket.settimeout(0.1)  # Non-blocking accept
+            self.socket.settimeout(0.1)
 
             self.logger.info(f"Signal socket listening on {self.config.socket_path}")
 
@@ -545,12 +559,10 @@ class SignalGenerator:
             )
             cursor = conn_c.cursor()
 
-            # Check total rows
             cursor.execute("SELECT COUNT(*) FROM candles")
             status["candles_db"]["row_count"] = cursor.fetchone()[0]
             status["candles_db"]["accessible"] = True
 
-            # Check per-timeframe data
             for tf in self.config.timeframes:
                 cursor.execute(
                     "SELECT COUNT(*), MAX(timestamp) FROM candles WHERE timeframe = ?",
@@ -578,7 +590,6 @@ class SignalGenerator:
             status["analysis_db"]["row_count"] = cursor.fetchone()[0]
             status["analysis_db"]["accessible"] = True
 
-            # Check per-timeframe data
             for tf in self.config.timeframes:
                 cursor.execute(
                     "SELECT COUNT(*), MAX(timestamp) FROM analysis WHERE timeframe = ?",
@@ -602,28 +613,56 @@ class SignalGenerator:
         try:
             conn, _ = self.health_socket.accept()
 
-            uptime = datetime.now(timezone.utc) - self.stats["uptime_start"]
+            uptime = datetime.now(timezone.utc) - self.global_stats["uptime_start"]
             db_status = self.check_database_status()
 
+            # Aggregate stats from both engines
+            total_stats = {
+                "total_signals": 0,
+                "buy_signals": 0,
+                "sell_signals": 0,
+                "errors": 0,
+            }
+            
+            personality_stats = {}
+            recent_signals = []
+            
+            for name, engine in self.engines.items():
+                with engine.lock:
+                    personality_stats[name] = {
+                        "total_signals": engine.stats["total_signals"],
+                        "buy_signals": engine.stats["buy_signals"],
+                        "sell_signals": engine.stats["sell_signals"],
+                        "last_signal": engine.stats["last_signal_time"],
+                        "errors": engine.stats["errors"],
+                        "recent_signals": list(engine.signal_history)[-5:],
+                    }
+                    total_stats["total_signals"] += engine.stats["total_signals"]
+                    total_stats["buy_signals"] += engine.stats["buy_signals"]
+                    total_stats["sell_signals"] += engine.stats["sell_signals"]
+                    total_stats["errors"] += engine.stats["errors"]
+                    recent_signals.extend(list(engine.signal_history)[-5:])
+
+            # Sort recent signals by timestamp
+            recent_signals.sort(key=lambda x: x.get("timestamp", 0), reverse=True)
+
             health = {
                 "status": "running",
-                "personality": self.config.personality,
+                "mode": "multi-personality",
+                "personalities": ["scalping", "swing"],
                 "timeframes": self.config.timeframes,
                 "uptime_seconds": int(uptime.total_seconds()),
-                "total_signals": self.stats["total_signals"],
-                "buy_signals": self.stats["buy_signals"],
-                "sell_signals": self.stats["sell_signals"],
-                "last_signal": self.stats["last_signal_time"],
-                "errors": self.stats["errors"],
+                "total_stats": total_stats,
+                "personality_stats": personality_stats,
                 "connected_clients": len(self.connections),
-                "recent_signals": list(self.signal_history)[-5:],
+                "recent_signals": recent_signals[:10],
                 "databases": db_status,
                 "config": {
                     "min_confidence": self.config.min_confidence,
                     "cooldown_seconds": self.config.cooldown_seconds,
                     "lookback": self.config.lookback,
-                    "weights": self.config.weights[self.config.personality],
-                    "reloads": self.stats["config_reloads"],
+                    "weights": self.config.weights,
+                    "reloads": self.global_stats["config_reloads"],
                 },
                 "debug_mode": self.debug_mode,
             }
@@ -641,7 +680,6 @@ class SignalGenerator:
         try:
             conn, _ = self.control_socket.accept()
 
-            # Receive command
             data = conn.recv(4096).decode("utf-8").strip()
 
             if not data:
@@ -668,25 +706,17 @@ class SignalGenerator:
 
         if action == "reload":
             try:
-                old_personality = self.config.personality
                 old_confidence = self.config.min_confidence
 
                 self.config.reload()
-                self.stats["config_reloads"] += 1
+                self.global_stats["config_reloads"] += 1
 
-                self.logger.info(
-                    f"Config reloaded: personality={self.config.personality}, "
-                    f"min_confidence={self.config.min_confidence}"
-                )
+                self.logger.info(f"Config reloaded: min_confidence={self.config.min_confidence}")
 
                 return {
                     "status": "success",
                     "message": "Configuration reloaded",
                     "changes": {
-                        "personality": {
-                            "old": old_personality,
-                            "new": self.config.personality,
-                        },
                         "min_confidence": {
                             "old": old_confidence,
                             "new": self.config.min_confidence,
@@ -696,21 +726,6 @@ class SignalGenerator:
             except Exception as e:
                 return {"status": "error", "message": str(e)}
 
-        elif action == "set_personality":
-            personality = cmd.get("value")
-            if personality in ["scalping", "swing"]:
-                self.config.personality = personality
-                self.logger.info(f"Personality changed to: {personality}")
-                return {
-                    "status": "success",
-                    "message": f"Personality set to {personality}",
-                }
-            else:
-                return {
-                    "status": "error",
-                    "message": "Invalid personality (use 'scalping' or 'swing')",
-                }
-
         elif action == "set_confidence":
             try:
                 confidence = float(cmd.get("value"))
@@ -755,21 +770,24 @@ class SignalGenerator:
             }
 
         elif action == "clear_cooldowns":
-            self.last_signal_time.clear()
-            self.logger.info("Signal cooldowns cleared")
+            for engine in self.engines.values():
+                engine.last_signal_time.clear()
+            self.logger.info("All signal cooldowns cleared")
             return {"status": "success", "message": "All cooldowns cleared"}
 
         elif action == "reset_stats":
-            self.stats = {
-                "total_signals": 0,
-                "buy_signals": 0,
-                "sell_signals": 0,
-                "last_signal_time": None,
-                "uptime_start": datetime.now(timezone.utc),
-                "errors": 0,
-                "config_reloads": self.stats["config_reloads"],
-            }
-            self.signal_history.clear()
+            for engine in self.engines.values():
+                with engine.lock:
+                    engine.stats = {
+                        "total_signals": 0,
+                        "buy_signals": 0,
+                        "sell_signals": 0,
+                        "last_signal_time": None,
+                        "errors": 0,
+                    }
+                    engine.signal_history.clear()
+            
+            self.global_stats["uptime_start"] = datetime.now(timezone.utc)
             self.logger.info("Statistics reset")
             return {"status": "success", "message": "Statistics reset"}
 
@@ -780,15 +798,43 @@ class SignalGenerator:
         """Accept new client connections"""
         try:
             conn, _ = self.socket.accept()
-            self.connections.append(conn)
-            self.logger.info(
-                f"New client connected. Total clients: {len(self.connections)}"
-            )
+            with self.connections_lock:
+                self.connections.append(conn)
+                self.logger.info(
+                    f"New client connected. Total clients: {len(self.connections)}"
+                )
         except socket.timeout:
             pass
         except Exception as e:
             self.logger.debug(f"Accept error: {e}")
 
+    def personality_worker(self, personality: str):
+        """Worker thread for a specific personality"""
+        engine = self.engines[personality]
+        self.logger.info(f"[{personality}] Worker thread started")
+        
+        while self.running:
+            try:
+                for timeframe in self.config.timeframes:
+                    try:
+                        signal = engine.generate_signal(timeframe)
+                        if signal:
+                            self.broadcast_signal(signal)
+                    except Exception as e:
+                        self.logger.error(f"[{personality}] Error processing {timeframe}: {e}")
+                        with engine.lock:
+                            engine.stats["errors"] += 1
+
+                time.sleep(self.config.poll_interval)
+
+            except Exception as e:
+                self.logger.error(f"[{personality}] Worker error: {e}")
+                with engine.lock:
+                    engine.stats["errors"] += 1
+                time.sleep(1)  # Brief pause on error
+
+        self.logger.info(f"[{personality}] Worker thread stopped")
+
     def run(self):
         """Main processing loop"""
         self.running = True
@@ -796,34 +842,29 @@ class SignalGenerator:
         self.setup_health_socket()
         self.setup_control_socket()
 
-        self.logger.info(
-            f"Signal generator started - Personality: {self.config.personality}"
-        )
+        self.logger.info("Multi-personality signal generator started")
+        self.logger.info(f"Running personalities: scalping, swing")
         self.logger.info(f"Monitoring timeframes: {', '.join(self.config.timeframes)}")
         self.logger.info(f"Poll interval: {self.config.poll_interval}s")
 
+        # Start personality worker threads
+        for personality in ["scalping", "swing"]:
+            thread = threading.Thread(
+                target=self.personality_worker,
+                args=(personality,),
+                name=f"{personality}-worker",
+                daemon=True
+            )
+            thread.start()
+            self.threads.append(thread)
+
         try:
+            # Main thread handles connections and management
             while self.running:
-                # Accept new connections
                 self.accept_connections()
-
-                # Handle health checks
                 self.handle_health_checks()
-
-                # Handle control commands
                 self.handle_control_commands()
-
-                # Generate signals for each timeframe
-                for timeframe in self.config.timeframes:
-                    try:
-                        signal = self.generate_signal(timeframe)
-                        if signal:
-                            self.broadcast_signal(signal)
-                    except Exception as e:
-                        self.logger.error(f"Error processing {timeframe}: {e}")
-                        self.stats["errors"] += 1
-
-                time.sleep(self.config.poll_interval)
+                time.sleep(0.1)
 
         except KeyboardInterrupt:
             self.logger.info("Received interrupt signal")
@@ -833,12 +874,20 @@ class SignalGenerator:
     def cleanup(self):
         """Cleanup resources"""
         self.logger.info("Shutting down...")
+        self.running = False
 
-        for conn in self.connections:
-            try:
-                conn.close()
-            except:
-                pass
+        # Wait for worker threads
+        self.logger.info("Waiting for worker threads to finish...")
+        for thread in self.threads:
+            thread.join(timeout=2.0)
+
+        # Close connections
+        with self.connections_lock:
+            for conn in self.connections:
+                try:
+                    conn.close()
+                except:
+                    pass
 
         if self.socket:
             self.socket.close()
@@ -864,17 +913,17 @@ def main():
 
     generator = SignalGenerator(config, logger)
 
-    # Signal handlers for hot-reload and control
+    # Signal handlers
     def reload_config(sig, frame):
         """SIGUSR1: Reload configuration"""
         logger.info("Received SIGUSR1 - Reloading configuration...")
         try:
-            old_personality = config.personality
+            old_confidence = config.min_confidence
             config.reload()
-            generator.stats["config_reloads"] += 1
+            generator.global_stats["config_reloads"] += 1
             logger.info(
                 f"Configuration reloaded successfully "
-                f"(personality: {old_personality} -> {config.personality})"
+                f"(min_confidence: {old_confidence} -> {config.min_confidence})"
             )
         except Exception as e:
             logger.error(f"Failed to reload configuration: {e}")

trader/Pipfile

@@ -0,0 +1,12 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+rich = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.13"

trader/README.md

@@ -0,0 +1,139 @@
+# Paper Trading Bot
+
+A real-time paper trading bot that executes trades based on signals from the multi-personality signal generator.
+
+## Features
+
+- **Real-time TUI Interface** - Beautiful terminal UI showing all trading activity
+- **Multi-personality Support** - Trade signals from both scalping and swing strategies
+- **Risk Management** - Configurable stop-loss, take-profit, and position sizing
+- **SQLite Tracking** - All trades stored in database for analysis
+- **Live Price Monitoring** - Reads current BTC price from candles.db
+- **Signal Filtering** - Filter by confidence, personality, and timeframe
+
+## Installation
+
+```bash
+cd traider
+pip install -r requirements.txt
+```
+
+## Configuration
+
+Edit `config.json` to customize:
+
+```json
+{
+  "initial_balance": 10000.0,        // Starting capital
+  "position_size_percent": 2.0,      // % of balance per trade
+  "max_positions": 3,                // Max concurrent positions
+  "stop_loss_percent": 2.0,          // Stop loss %
+  "take_profit_percent": 4.0,        // Take profit %
+  "min_confidence": 0.5,             // Minimum signal confidence
+  "enabled_personalities": ["scalping", "swing"],
+  "enabled_timeframes": ["1m", "5m"]
+}
+```
+
+## Usage
+
+Make sure the signal generator is running first:
+
+```bash
+# In signals/ directory
+./signals.py
+```
+
+Then start the paper trader:
+
+```bash
+# In traider/ directory
+./trader.py
+```
+
+## TUI Interface
+
+The TUI displays:
+
+- **Header**: Current BTC price, balance, equity, and total PnL
+- **Statistics**: Win rate, total trades, wins/losses
+- **Open Positions**: Live view of active trades with unrealized PnL
+- **Recent Closed Trades**: Last 10 completed trades
+- **Recent Signals**: Incoming signals from the generator
+
+## Database Schema
+
+### trades table
+- Trade details (entry/exit prices, type, timeframe)
+- PnL calculations
+- Signal confidence and personality
+- Exit reasons (Stop Loss, Take Profit)
+
+### balance_history table
+- Historical balance snapshots
+- Equity tracking over time
+
+## Trading Logic
+
+1. **Signal Reception**: Listens to Unix socket from signal generator
+2. **Filtering**: Applies confidence, personality, and timeframe filters
+3. **Position Sizing**: Calculates position based on balance %
+4. **Entry**: Opens LONG (BUY signal) or SHORT (SELL signal) position
+5. **Exit Monitoring**: Continuously checks stop-loss and take-profit levels
+6. **Closure**: Calculates PnL and updates balance
+
+## Risk Management
+
+- Maximum concurrent positions limit
+- Stop-loss protection on every trade
+- Position sizing based on account balance
+- (Future: Daily loss limits, trailing stops)
+
+## Keyboard Controls
+
+- `Ctrl+C` - Graceful shutdown (closes socket connections, saves state)
+
+## File Structure
+
+```
+traider/
+├── trader.py           # Main bot with TUI
+├── config.json         # Configuration
+├── trades.db          # Trade history (auto-created)
+├── requirements.txt   # Python dependencies
+└── logs/              # (Future: logging)
+```
+
+## Example Output
+
+```
+┌─────────────────────────────────────────────────────────┐
+│ PAPER TRADING BOT | BTC: $95,234.50 | Balance: $10,245.32 │
+│ Equity: $10,387.12 | PnL: +$387.12 (+3.87%)              │
+└─────────────────────────────────────────────────────────┘
+
+┌─────────────┐  ┌──────────────────────────┐
+│ Statistics  │  │ Open Positions (2)       │
+├─────────────┤  ├──────────────────────────┤
+│ Total: 47   │  │ ID  Type  Entry  PnL     │
+│ Wins: 28    │  │ 48  LONG  95100  +$142   │
+│ Losses: 19  │  │ 49  LONG  95200  +$34    │
+│ Win Rate: 60%│ └──────────────────────────┘
+└─────────────┘
+```
+
+## Notes
+
+- This is **paper trading only** - no real money involved
+- Requires running signal generator and populated candles.db
+- Price updates every 1 second from most recent candle
+- Signals processed in real-time as they arrive
+
+## Future Enhancements
+
+- Trailing stop-loss implementation
+- Daily loss limit enforcement  
+- Performance analytics dashboard
+- Export trade history to CSV
+- Backtesting mode
+- Web dashboard option

trader/config.json

@@ -0,0 +1,20 @@
+{
+  "trades_db": "trades.db",
+  "candles_db": "../onramp/market_data.db",
+  "signal_socket": "/tmp/signals.sock",
+
+  "initial_balance": 100.0,
+  "position_size_percent": 10.0,
+  "max_positions": 3,
+
+  "stop_loss_percent": 2.0,
+  "take_profit_percent": 4.0,
+
+  "min_confidence": 0.5,
+  "enabled_personalities": ["scalping", "swing"],
+  "enabled_timeframes": ["1m", "5m"],
+
+  "max_daily_loss_percent": 5.0,
+  "trailing_stop": false,
+  "trailing_stop_percent": 1.5
+}

trader/paper_trader.py

@@ -0,0 +1,701 @@
+#!/usr/bin/env python3
+"""
+Paper Trading Bot
+Executes paper trades based on signals from the signal generator
+Real-time TUI interface with trade tracking
+"""
+
+import sqlite3
+import json
+import socket
+import time
+import signal
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional, Dict, List
+from dataclasses import dataclass, asdict
+from enum import Enum
+import threading
+from collections import deque
+
+from rich.console import Console
+from rich.live import Live
+from rich.table import Table
+from rich.layout import Layout
+from rich.panel import Panel
+from rich.text import Text
+from rich import box
+
+
+class TradeStatus(Enum):
+    OPEN = "OPEN"
+    CLOSED = "CLOSED"
+    STOPPED = "STOPPED"
+
+
+class TradeType(Enum):
+    LONG = "LONG"
+    SHORT = "SHORT"
+
+
+@dataclass
+class Trade:
+    id: Optional[int]
+    trade_type: str
+    entry_price: float
+    entry_time: str
+    position_size: float
+    stop_loss: float
+    take_profit: Optional[float]
+    timeframe: str
+    personality: str
+    signal_confidence: float
+    status: str
+    exit_price: Optional[float] = None
+    exit_time: Optional[str] = None
+    pnl: Optional[float] = None
+    pnl_percent: Optional[float] = None
+    exit_reason: Optional[str] = None
+
+
+class Config:
+    def __init__(self, config_path: str = "config.json"):
+        self.config_path = config_path
+        self.reload()
+
+    def reload(self):
+        with open(self.config_path, "r") as f:
+            data = json.load(f)
+
+        # Database paths
+        self.trades_db = data.get("trades_db", "trades.db")
+        self.candles_db = data.get("candles_db", "../onramp/market_data.db")
+        
+        # Signal socket
+        self.signal_socket = data.get("signal_socket", "/tmp/signals.sock")
+        
+        # Trading parameters
+        self.initial_balance = data.get("initial_balance", 10000.0)
+        self.position_size_percent = data.get("position_size_percent", 2.0)  # % of balance per trade
+        self.max_positions = data.get("max_positions", 3)
+        self.stop_loss_percent = data.get("stop_loss_percent", 2.0)
+        self.take_profit_percent = data.get("take_profit_percent", 4.0)
+        
+        # Strategy filters
+        self.min_confidence = data.get("min_confidence", 0.5)
+        self.enabled_personalities = data.get("enabled_personalities", ["scalping", "swing"])
+        self.enabled_timeframes = data.get("enabled_timeframes", ["1m", "5m"])
+        
+        # Risk management
+        self.max_daily_loss_percent = data.get("max_daily_loss_percent", 5.0)
+        self.trailing_stop = data.get("trailing_stop", False)
+        self.trailing_stop_percent = data.get("trailing_stop_percent", 1.5)
+
+
+class TradeDatabase:
+    def __init__(self, db_path: str):
+        self.db_path = db_path
+        self.init_database()
+
+    def init_database(self):
+        conn = sqlite3.connect(self.db_path)
+        cursor = conn.cursor()
+        
+        cursor.execute("""
+            CREATE TABLE IF NOT EXISTS trades (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                trade_type TEXT NOT NULL,
+                entry_price REAL NOT NULL,
+                entry_time TEXT NOT NULL,
+                position_size REAL NOT NULL,
+                stop_loss REAL NOT NULL,
+                take_profit REAL,
+                timeframe TEXT NOT NULL,
+                personality TEXT NOT NULL,
+                signal_confidence REAL NOT NULL,
+                status TEXT NOT NULL,
+                exit_price REAL,
+                exit_time TEXT,
+                pnl REAL,
+                pnl_percent REAL,
+                exit_reason TEXT
+            )
+        """)
+        
+        cursor.execute("""
+            CREATE TABLE IF NOT EXISTS balance_history (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                timestamp TEXT NOT NULL,
+                balance REAL NOT NULL,
+                equity REAL NOT NULL,
+                open_positions INTEGER NOT NULL
+            )
+        """)
+        
+        conn.commit()
+        conn.close()
+
+    def save_trade(self, trade: Trade) -> int:
+        conn = sqlite3.connect(self.db_path)
+        cursor = conn.cursor()
+        
+        if trade.id is None:
+            cursor.execute("""
+                INSERT INTO trades (
+                    trade_type, entry_price, entry_time, position_size,
+                    stop_loss, take_profit, timeframe, personality,
+                    signal_confidence, status
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+            """, (
+                trade.trade_type, trade.entry_price, trade.entry_time,
+                trade.position_size, trade.stop_loss, trade.take_profit,
+                trade.timeframe, trade.personality, trade.signal_confidence,
+                trade.status
+            ))
+            trade_id = cursor.lastrowid
+        else:
+            cursor.execute("""
+                UPDATE trades SET
+                    exit_price = ?, exit_time = ?, pnl = ?,
+                    pnl_percent = ?, status = ?, exit_reason = ?
+                WHERE id = ?
+            """, (
+                trade.exit_price, trade.exit_time, trade.pnl,
+                trade.pnl_percent, trade.status, trade.exit_reason,
+                trade.id
+            ))
+            trade_id = trade.id
+        
+        conn.commit()
+        conn.close()
+        return trade_id
+
+    def get_open_trades(self) -> List[Trade]:
+        conn = sqlite3.connect(self.db_path)
+        conn.row_factory = sqlite3.Row
+        cursor = conn.cursor()
+        
+        cursor.execute("SELECT * FROM trades WHERE status = 'OPEN' ORDER BY entry_time DESC")
+        rows = cursor.fetchall()
+        conn.close()
+        
+        trades = []
+        for row in rows:
+            trades.append(Trade(**dict(row)))
+        return trades
+
+    def get_recent_trades(self, limit: int = 10) -> List[Trade]:
+        conn = sqlite3.connect(self.db_path)
+        conn.row_factory = sqlite3.Row
+        cursor = conn.cursor()
+        
+        cursor.execute("""
+            SELECT * FROM trades 
+            WHERE status != 'OPEN'
+            ORDER BY exit_time DESC 
+            LIMIT ?
+        """, (limit,))
+        rows = cursor.fetchall()
+        conn.close()
+        
+        trades = []
+        for row in rows:
+            trades.append(Trade(**dict(row)))
+        return trades
+
+    def save_balance_snapshot(self, balance: float, equity: float, open_positions: int):
+        conn = sqlite3.connect(self.db_path)
+        cursor = conn.cursor()
+        
+        cursor.execute("""
+            INSERT INTO balance_history (timestamp, balance, equity, open_positions)
+            VALUES (?, ?, ?, ?)
+        """, (datetime.now(timezone.utc).isoformat(), balance, equity, open_positions))
+        
+        conn.commit()
+        conn.close()
+
+    def get_statistics(self) -> Dict:
+        conn = sqlite3.connect(self.db_path)
+        cursor = conn.cursor()
+        
+        # Total trades
+        cursor.execute("SELECT COUNT(*) FROM trades WHERE status != 'OPEN'")
+        total_trades = cursor.fetchone()[0]
+        
+        # Win/Loss
+        cursor.execute("SELECT COUNT(*) FROM trades WHERE status != 'OPEN' AND pnl > 0")
+        wins = cursor.fetchone()[0]
+        
+        cursor.execute("SELECT COUNT(*) FROM trades WHERE status != 'OPEN' AND pnl < 0")
+        losses = cursor.fetchone()[0]
+        
+        # Total PnL
+        cursor.execute("SELECT SUM(pnl) FROM trades WHERE status != 'OPEN'")
+        total_pnl = cursor.fetchone()[0] or 0.0
+        
+        # Average win/loss
+        cursor.execute("SELECT AVG(pnl) FROM trades WHERE status != 'OPEN' AND pnl > 0")
+        avg_win = cursor.fetchone()[0] or 0.0
+        
+        cursor.execute("SELECT AVG(pnl) FROM trades WHERE status != 'OPEN' AND pnl < 0")
+        avg_loss = cursor.fetchone()[0] or 0.0
+        
+        conn.close()
+        
+        win_rate = (wins / total_trades * 100) if total_trades > 0 else 0.0
+        
+        return {
+            "total_trades": total_trades,
+            "wins": wins,
+            "losses": losses,
+            "win_rate": win_rate,
+            "total_pnl": total_pnl,
+            "avg_win": avg_win,
+            "avg_loss": avg_loss,
+        }
+
+
+class PriceMonitor:
+    """Monitor current price from candles database"""
+    
+    def __init__(self, candles_db: str):
+        self.candles_db = candles_db
+        self.current_price = None
+        self.last_update = None
+
+    def get_current_price(self) -> Optional[float]:
+        """Get most recent close price from 1m timeframe"""
+        try:
+            conn = sqlite3.connect(f"file:{self.candles_db}?mode=ro", uri=True, timeout=5)
+            cursor = conn.cursor()
+            
+            cursor.execute("""
+                SELECT close, timestamp 
+                FROM candles 
+                WHERE timeframe = '1m' 
+                ORDER BY timestamp DESC 
+                LIMIT 1
+            """)
+            
+            row = cursor.fetchone()
+            conn.close()
+            
+            if row:
+                self.current_price = float(row[0])
+                self.last_update = row[1]
+                return self.current_price
+            
+            return None
+            
+        except Exception as e:
+            return None
+
+
+class PaperTrader:
+    def __init__(self, config: Config):
+        self.config = config
+        self.db = TradeDatabase(config.trades_db)
+        self.price_monitor = PriceMonitor(config.candles_db)
+        
+        self.balance = config.initial_balance
+        self.open_trades: List[Trade] = []
+        self.recent_signals = deque(maxlen=20)
+        
+        self.running = False
+        self.lock = threading.Lock()
+        
+        # Stats
+        self.stats = {
+            "signals_received": 0,
+            "signals_filtered": 0,
+            "trades_opened": 0,
+            "trades_closed": 0,
+        }
+        
+        # Load open trades from DB
+        self.open_trades = self.db.get_open_trades()
+
+    def connect_to_signals(self) -> socket.socket:
+        """Connect to signal generator socket"""
+        sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
+        sock.connect(self.config.signal_socket)
+        return sock
+
+    def calculate_position_size(self, price: float) -> float:
+        """Calculate position size based on balance and config"""
+        position_value = self.balance * (self.config.position_size_percent / 100)
+        return position_value / price
+
+    def should_take_signal(self, signal: Dict) -> bool:
+        """Filter signals based on config"""
+        # Check confidence
+        if signal.get("confidence", 0) < self.config.min_confidence:
+            return False
+        
+        # Check personality
+        if signal.get("personality") not in self.config.enabled_personalities:
+            return False
+        
+        # Check timeframe
+        if signal.get("timeframe") not in self.config.enabled_timeframes:
+            return False
+        
+        # Check max positions
+        if len(self.open_trades) >= self.config.max_positions:
+            return False
+        
+        return True
+
+    def open_trade(self, signal: Dict):
+        """Open a new paper trade"""
+        with self.lock:
+            trade_type = TradeType.LONG.value if signal["signal"] == "BUY" else TradeType.SHORT.value
+            entry_price = signal["price"]
+            position_size = self.calculate_position_size(entry_price)
+            
+            # Calculate stop loss and take profit
+            if trade_type == TradeType.LONG.value:
+                stop_loss = entry_price * (1 - self.config.stop_loss_percent / 100)
+                take_profit = entry_price * (1 + self.config.take_profit_percent / 100)
+            else:
+                stop_loss = entry_price * (1 + self.config.stop_loss_percent / 100)
+                take_profit = entry_price * (1 - self.config.take_profit_percent / 100)
+            
+            trade = Trade(
+                id=None,
+                trade_type=trade_type,
+                entry_price=entry_price,
+                entry_time=signal.get("generated_at", datetime.now(timezone.utc).isoformat()),
+                position_size=position_size,
+                stop_loss=stop_loss,
+                take_profit=take_profit,
+                timeframe=signal["timeframe"],
+                personality=signal["personality"],
+                signal_confidence=signal["confidence"],
+                status=TradeStatus.OPEN.value,
+            )
+            
+            trade_id = self.db.save_trade(trade)
+            trade.id = trade_id
+            self.open_trades.append(trade)
+            
+            self.stats["trades_opened"] += 1
+
+    def check_exits(self, current_price: float):
+        """Check if any open trades should be closed"""
+        with self.lock:
+            for trade in self.open_trades[:]:
+                should_exit = False
+                exit_reason = None
+                
+                if trade.trade_type == TradeType.LONG.value:
+                    # Long position checks
+                    if current_price <= trade.stop_loss:
+                        should_exit = True
+                        exit_reason = "Stop Loss"
+                    elif trade.take_profit and current_price >= trade.take_profit:
+                        should_exit = True
+                        exit_reason = "Take Profit"
+                else:
+                    # Short position checks
+                    if current_price >= trade.stop_loss:
+                        should_exit = True
+                        exit_reason = "Stop Loss"
+                    elif trade.take_profit and current_price <= trade.take_profit:
+                        should_exit = True
+                        exit_reason = "Take Profit"
+                
+                if should_exit:
+                    self.close_trade(trade, current_price, exit_reason)
+
+    def close_trade(self, trade: Trade, exit_price: float, reason: str):
+        """Close a trade and calculate PnL"""
+        trade.exit_price = exit_price
+        trade.exit_time = datetime.now(timezone.utc).isoformat()
+        trade.exit_reason = reason
+        
+        # Calculate PnL
+        if trade.trade_type == TradeType.LONG.value:
+            pnl = (exit_price - trade.entry_price) * trade.position_size
+        else:
+            pnl = (trade.entry_price - exit_price) * trade.position_size
+        
+        trade.pnl = pnl
+        trade.pnl_percent = (pnl / (trade.entry_price * trade.position_size)) * 100
+        trade.status = TradeStatus.CLOSED.value
+        
+        self.balance += pnl
+        self.db.save_trade(trade)
+        self.open_trades.remove(trade)
+        
+        self.stats["trades_closed"] += 1
+
+    def get_equity(self, current_price: float) -> float:
+        """Calculate current equity (balance + unrealized PnL)"""
+        unrealized_pnl = 0.0
+        
+        for trade in self.open_trades:
+            if trade.trade_type == TradeType.LONG.value:
+                pnl = (current_price - trade.entry_price) * trade.position_size
+            else:
+                pnl = (trade.entry_price - current_price) * trade.position_size
+            unrealized_pnl += pnl
+        
+        return self.balance + unrealized_pnl
+
+    def process_signal(self, signal: Dict):
+        """Process incoming signal"""
+        self.stats["signals_received"] += 1
+        self.recent_signals.append(signal)
+        
+        if self.should_take_signal(signal):
+            self.open_trade(signal)
+        else:
+            self.stats["signals_filtered"] += 1
+
+    def signal_listener_thread(self):
+        """Background thread to listen for signals"""
+        while self.running:
+            try:
+                sock = self.connect_to_signals()
+                sock.settimeout(1.0)
+                
+                buffer = ""
+                while self.running:
+                    try:
+                        data = sock.recv(4096).decode("utf-8")
+                        if not data:
+                            break
+                        
+                        buffer += data
+                        while "\n" in buffer:
+                            line, buffer = buffer.split("\n", 1)
+                            if line.strip():
+                                signal = json.loads(line)
+                                self.process_signal(signal)
+                    
+                    except socket.timeout:
+                        continue
+                    except json.JSONDecodeError:
+                        continue
+                
+                sock.close()
+                
+            except Exception as e:
+                if self.running:
+                    time.sleep(5)  # Wait before reconnecting
+
+    def price_monitor_thread(self):
+        """Background thread to monitor prices and check exits"""
+        while self.running:
+            current_price = self.price_monitor.get_current_price()
+            
+            if current_price and self.open_trades:
+                self.check_exits(current_price)
+            
+            time.sleep(1)
+
+    def build_tui(self) -> Layout:
+        """Build the TUI layout"""
+        layout = Layout()
+        layout.split_column(
+            Layout(name="header", size=3),
+            Layout(name="body"),
+            Layout(name="footer", size=3),
+        )
+        
+        layout["body"].split_row(
+            Layout(name="left"),
+            Layout(name="right"),
+        )
+        
+        layout["left"].split_column(
+            Layout(name="stats", size=10),
+            Layout(name="open_trades"),
+        )
+        
+        layout["right"].split_column(
+            Layout(name="recent_closed", size=15),
+            Layout(name="signals"),
+        )
+        
+        return layout
+
+    def render_header(self) -> Panel:
+        """Render header panel"""
+        current_price = self.price_monitor.current_price or 0.0
+        equity = self.get_equity(current_price)
+        pnl = equity - self.config.initial_balance
+        pnl_percent = (pnl / self.config.initial_balance) * 100
+        
+        pnl_color = "green" if pnl >= 0 else "red"
+        
+        text = Text()
+        text.append("PAPER TRADING BOT", style="bold cyan")
+        text.append(f" | BTC: ${current_price:,.2f}", style="yellow")
+        text.append(f" | Balance: ${self.balance:,.2f}", style="white")
+        text.append(f" | Equity: ${equity:,.2f}", style="white")
+        text.append(f" | PnL: ${pnl:+,.2f} ({pnl_percent:+.2f}%)", style=pnl_color)
+        
+        return Panel(text, style="bold")
+
+    def render_stats(self) -> Panel:
+        """Render statistics panel"""
+        db_stats = self.db.get_statistics()
+        
+        table = Table(show_header=False, box=box.SIMPLE)
+        table.add_column("Metric", style="cyan")
+        table.add_column("Value", justify="right")
+        
+        table.add_row("Total Trades", str(db_stats["total_trades"]))
+        table.add_row("Wins", f"[green]{db_stats['wins']}[/green]")
+        table.add_row("Losses", f"[red]{db_stats['losses']}[/red]")
+        table.add_row("Win Rate", f"{db_stats['win_rate']:.1f}%")
+        table.add_row("Total PnL", f"${db_stats['total_pnl']:,.2f}")
+        table.add_row("", "")
+        table.add_row("Signals RX", str(self.stats["signals_received"]))
+        table.add_row("Filtered", str(self.stats["signals_filtered"]))
+        
+        return Panel(table, title="Statistics", border_style="blue")
+
+    def render_open_trades(self) -> Panel:
+        """Render open trades table"""
+        table = Table(box=box.SIMPLE)
+        table.add_column("ID", style="cyan")
+        table.add_column("Type")
+        table.add_column("Entry", justify="right")
+        table.add_column("Current", justify="right")
+        table.add_column("PnL", justify="right")
+        table.add_column("TF")
+        table.add_column("Pers")
+        
+        current_price = self.price_monitor.current_price or 0.0
+        
+        for trade in self.open_trades:
+            if trade.trade_type == TradeType.LONG.value:
+                pnl = (current_price - trade.entry_price) * trade.position_size
+                type_color = "green"
+            else:
+                pnl = (trade.entry_price - current_price) * trade.position_size
+                type_color = "red"
+            
+            pnl_color = "green" if pnl >= 0 else "red"
+            
+            table.add_row(
+                str(trade.id),
+                f"[{type_color}]{trade.trade_type}[/{type_color}]",
+                f"${trade.entry_price:,.2f}",
+                f"${current_price:,.2f}",
+                f"[{pnl_color}]${pnl:+,.2f}[/{pnl_color}]",
+                trade.timeframe,
+                trade.personality[:4].upper(),
+            )
+        
+        return Panel(table, title=f"Open Positions ({len(self.open_trades)})", border_style="green")
+
+    def render_recent_closed(self) -> Panel:
+        """Render recent closed trades"""
+        table = Table(box=box.SIMPLE)
+        table.add_column("ID", style="cyan")
+        table.add_column("Type")
+        table.add_column("PnL", justify="right")
+        table.add_column("Exit")
+        
+        recent = self.db.get_recent_trades(10)
+        
+        for trade in recent:
+            type_color = "green" if trade.trade_type == TradeType.LONG.value else "red"
+            pnl_color = "green" if trade.pnl and trade.pnl >= 0 else "red"
+            
+            table.add_row(
+                str(trade.id),
+                f"[{type_color}]{trade.trade_type}[/{type_color}]",
+                f"[{pnl_color}]${trade.pnl:+,.2f}[/{pnl_color}]" if trade.pnl else "N/A",
+                trade.exit_reason or "N/A",
+            )
+        
+        return Panel(table, title="Recent Closed Trades", border_style="yellow")
+
+    def render_signals(self) -> Panel:
+        """Render recent signals"""
+        table = Table(box=box.SIMPLE)
+        table.add_column("Signal")
+        table.add_column("TF")
+        table.add_column("Conf", justify="right")
+        table.add_column("Pers")
+        
+        for signal in list(self.recent_signals)[-10:]:
+            signal_color = "green" if signal["signal"] == "BUY" else "red"
+            
+            table.add_row(
+                f"[{signal_color}]{signal['signal']}[/{signal_color}]",
+                signal["timeframe"],
+                f"{signal['confidence']:.2f}",
+                signal["personality"][:4].upper(),
+            )
+        
+        return Panel(table, title="Recent Signals", border_style="magenta")
+
+    def render_footer(self) -> Panel:
+        """Render footer panel"""
+        text = Text()
+        text.append("Press ", style="dim")
+        text.append("Ctrl+C", style="bold red")
+        text.append(" to exit", style="dim")
+        return Panel(text, style="dim")
+
+    def update_display(self, layout: Layout):
+        """Update all TUI panels"""
+        layout["header"].update(self.render_header())
+        layout["stats"].update(self.render_stats())
+        layout["open_trades"].update(self.render_open_trades())
+        layout["recent_closed"].update(self.render_recent_closed())
+        layout["signals"].update(self.render_signals())
+        layout["footer"].update(self.render_footer())
+
+    def run(self):
+        """Main run loop with TUI"""
+        self.running = True
+        
+        # Start background threads
+        signal_thread = threading.Thread(target=self.signal_listener_thread, daemon=True)
+        price_thread = threading.Thread(target=self.price_monitor_thread, daemon=True)
+        
+        signal_thread.start()
+        price_thread.start()
+        
+        # TUI
+        console = Console()
+        layout = self.build_tui()
+        
+        try:
+            with Live(layout, console=console, screen=True, refresh_per_second=2):
+                while self.running:
+                    self.update_display(layout)
+                    time.sleep(0.5)
+        
+        except KeyboardInterrupt:
+            pass
+        finally:
+            self.running = False
+            signal_thread.join(timeout=2)
+            price_thread.join(timeout=2)
+
+
+def main():
+    config = Config()
+    trader = PaperTrader(config)
+    
+    def shutdown(sig, frame):
+        trader.running = False
+    
+    signal.signal(signal.SIGINT, shutdown)
+    signal.signal(signal.SIGTERM, shutdown)
+    
+    trader.run()
+
+
+if __name__ == "__main__":
+    main()