#!/usr/bin/env python3
"""
📊 System Status Check - Trading Bot V2.2
"""

import sqlite3
from datetime import datetime, timedelta
import os

DB_PATH = "trading_bot.db"

def check_status():
    """Comprehensive system status check"""

    print("=" * 70)
    print("📊 TRADING BOT V2.2 - SYSTEM STATUS")
    print("=" * 70)

    if not os.path.exists(DB_PATH):
        print(f"❌ Database not found: {DB_PATH}")
        return

    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()

    try:
        # 1. CONSECUTIVE LOSSES
        print("\n🎯 CONSECUTIVE LOSSES:")
        cursor.execute("""
            SELECT net_profit
            FROM trades
            WHERE status = 'closed'
            ORDER BY exit_time DESC
            LIMIT 20
        """)

        trades = cursor.fetchall()
        consecutive = 0
        for trade in trades:
            if trade[0] < 0:
                consecutive += 1
            else:
                break

        print(f"   Current: {consecutive}")
        print(f"   Status: {'✅ OK' if consecutive < 5 else '🚨 BLOCKED (≥5)'}")

        # 2. RECENT TRADES
        print("\n📋 LAST 10 TRADES:")
        cursor.execute("""
            SELECT ticket, entry_time, exit_time, profit, net_profit, exit_reason, regime
            FROM trades
            WHERE status = 'closed'
            ORDER BY exit_time DESC
            LIMIT 10
        """)

        for row in cursor.fetchall():
            ticket, entry, exit, profit, net_profit, reason, regime = row
            symbol = "✅" if net_profit > 0 else "❌"
            print(f"   {symbol} #{ticket}: ${net_profit:.2f} | {regime or 'unknown'} | {reason or 'unknown'}")

        # 3. PERFORMANCE STATS
        print("\n📈 PERFORMANCE (Last 20 trades):")
        cursor.execute("""
            SELECT
                COUNT(*) as total,
                SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
                SUM(CASE WHEN net_profit < 0 THEN 1 ELSE 0 END) as losses,
                SUM(net_profit) as total_pnl,
                AVG(net_profit) as avg_pnl
            FROM trades
            WHERE status = 'closed'
            ORDER BY exit_time DESC
            LIMIT 20
        """)

        row = cursor.fetchone()
        if row:
            total, wins, losses, total_pnl, avg_pnl = row
            if total > 0:
                win_rate = (wins / total) * 100
                print(f"   Total Trades: {total}")
                print(f"   Wins: {wins} | Losses: {losses}")
                print(f"   Win Rate: {win_rate:.1f}%")
                print(f"   Total P&L: ${total_pnl:.2f}")
                print(f"   Avg P&L: ${avg_pnl:.2f}")

        # 4. RANGING VS TRENDING
        print("\n🔍 REGIME BREAKDOWN (Last 20 trades):")
        cursor.execute("""
            SELECT
                regime,
                COUNT(*) as count,
                SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
                SUM(net_profit) as pnl
            FROM trades
            WHERE status = 'closed'
            ORDER BY exit_time DESC
            LIMIT 20
        """)

        cursor.execute("""
            SELECT
                regime,
                COUNT(*) as count,
                SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
                SUM(net_profit) as pnl
            FROM (
                SELECT * FROM trades
                WHERE status = 'closed'
                ORDER BY exit_time DESC
                LIMIT 20
            )
            GROUP BY regime
        """)

        for row in cursor.fetchall():
            regime, count, wins, pnl = row
            win_rate = (wins / count * 100) if count > 0 else 0
            print(f"   {regime or 'unknown'}: {count} trades, {win_rate:.1f}% WR, ${pnl:.2f}")

        # 5. TODAY'S ACTIVITY
        print("\n📅 TODAY'S TRADES:")
        today = datetime.now().date()
        cursor.execute("""
            SELECT COUNT(*), SUM(net_profit)
            FROM trades
            WHERE status = 'closed'
            AND DATE(exit_time) = ?
        """, (today.isoformat(),))

        row = cursor.fetchone()
        if row and row[0] > 0:
            print(f"   Trades: {row[0]}")
            print(f"   P&L: ${row[1]:.2f}")
        else:
            print("   No trades today")

        # 6. OPEN POSITIONS
        print("\n🔓 OPEN POSITIONS:")
        cursor.execute("""
            SELECT ticket, entry_time, entry_price, profit
            FROM trades
            WHERE status = 'open'
        """)

        open_pos = cursor.fetchall()
        if open_pos:
            for row in open_pos:
                print(f"   #{row[0]}: Entry ${row[2]:.2f}, Unrealized P&L: ${row[3]:.2f}")
        else:
            print("   No open positions")

        # 7. FINAL VERDICT
        print("\n" + "=" * 70)
        if consecutive < 5:
            print("✅ SYSTEM STATUS: READY TO TRADE")
            print("=" * 70)
            print("\n🛡️ ACTIVE PROTECTIONS:")
            print("   🛑 Ranging Filter - blocks ADX < 25")
            print("   💾 Exit Logging - all exits tracked")
            print("   📊 Drawdown Protection - monitoring")
            print("\n💡 NEXT EXPECTED ACTION:")
            print("   Waiting for trending market signal (ADX > 25)")
            print("   Session Filter active (NY + Asian sessions only)")
        else:
            print("🚨 SYSTEM STATUS: TRADING PAUSED")
            print("=" * 70)
            print(f"\n⚠️ Reason: {consecutive} consecutive losses (limit: 5)")
            print("\n💡 TO RESUME:")
            print("   1. Wait for cooldown (24h)")
            print("   2. OR: Run reset_consecutive_losses.py")
            print("   3. Restart Jupyter Kernel")

    except Exception as e:
        print(f"\n❌ ERROR: {e}")
        import traceback
        traceback.print_exc()

    finally:
        conn.close()

if __name__ == "__main__":
    check_status()