ManualTrader/src/trading/trading_stats.py

#!/usr/bin/env python3
"""
Trading Statistics Tracker (SQLite Version)

Tracks and calculates comprehensive trading statistics using an SQLite database.
"""

import sqlite3
import os
import logging
from datetime import datetime, timedelta, timezone
from typing import Dict, List, Any, Optional, Tuple, Union
import math
from collections import defaultdict
import uuid
import numpy as np # Ensure numpy is imported as np

# 🆕 Import the migration runner
from src.migrations.migrate_db import run_migrations as run_db_migrations
from src.utils.token_display_formatter import get_formatter # Added import

logger = logging.getLogger(__name__)

def _normalize_token_case(token: str) -> str:
    """
    Normalize token case: if any characters are already uppercase, keep as-is.
    Otherwise, convert to uppercase. This handles mixed-case tokens like kPEPE, kBONK.
    """
    # Check if any character is already uppercase
    if any(c.isupper() for c in token):
        return token  # Keep original case for mixed-case tokens
    else:
        return token.upper()  # Convert to uppercase for all-lowercase input

class TradingStats:
    """Comprehensive trading statistics tracker using SQLite."""

    def __init__(self, db_path: str = "data/trading_stats.sqlite"):
        """Initialize the stats tracker and connect to SQLite DB."""
        self.db_path = db_path
        self._ensure_data_directory()
        
        # 🆕 Run database migrations before connecting and creating tables
        # This ensures the schema is up-to-date when the connection is made
        # and tables are potentially created for the first time.
        logger.info("Running database migrations if needed...")
        run_db_migrations(self.db_path) # Pass the correct db_path
        logger.info("Database migration check complete.")
        
        self.conn = sqlite3.connect(self.db_path, detect_types=sqlite3.PARSE_DECLTYPES | sqlite3.PARSE_COLNAMES)
        self.conn.row_factory = self._dict_factory
        self._create_tables() # CREATE IF NOT EXISTS will still be useful for first-time setup
        self._initialize_metadata() # Also potentially sets schema_version if DB was just created

        # 🆕 Purge old daily aggregated stats on startup
        self.purge_old_daily_aggregated_stats()

    def _dict_factory(self, cursor, row):
        """Convert SQLite rows to dictionaries."""
        d = {}
        for idx, col in enumerate(cursor.description):
            d[col[0]] = row[idx]
        return d

    def _ensure_data_directory(self):
        """Ensure the data directory for the SQLite file exists."""
        data_dir = os.path.dirname(self.db_path)
        if data_dir and not os.path.exists(data_dir):
            os.makedirs(data_dir)
            logger.info(f"Created data directory for TradingStats DB: {data_dir}")

    def _execute_query(self, query: str, params: tuple = ()):
        """Execute a query (INSERT, UPDATE, DELETE)."""
        with self.conn:
            self.conn.execute(query, params)

    def _fetch_query(self, query: str, params: tuple = ()) -> List[Dict[str, Any]]:
        """Execute a SELECT query and fetch all results."""
        cur = self.conn.cursor()
        cur.execute(query, params)
        return cur.fetchall()

    def _fetchone_query(self, query: str, params: tuple = ()) -> Optional[Dict[str, Any]]:
        """Execute a SELECT query and fetch one result."""
        cur = self.conn.cursor()
        cur.execute(query, params)
        return cur.fetchone()

    def _create_tables(self):
        """Create SQLite tables if they don't exist."""
        queries = [
            """
            CREATE TABLE IF NOT EXISTS metadata (
                key TEXT PRIMARY KEY,
                value TEXT
            )
            """,
            """
            CREATE TABLE IF NOT EXISTS trades (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                exchange_fill_id TEXT UNIQUE,
                timestamp TEXT NOT NULL,
                symbol TEXT NOT NULL,
                side TEXT NOT NULL,
                amount REAL NOT NULL,
                price REAL NOT NULL,
                value REAL NOT NULL,
                trade_type TEXT NOT NULL,
                pnl REAL DEFAULT 0.0,
                linked_order_table_id INTEGER,
                
                -- 🆕 PHASE 4: Lifecycle tracking fields (merged from active_trades)
                status TEXT DEFAULT 'executed', -- 'pending', 'executed', 'position_opened', 'position_closed', 'cancelled'
                trade_lifecycle_id TEXT, -- Groups related trades into one lifecycle
                position_side TEXT, -- 'long', 'short', 'flat' - the resulting position side
                
                -- Position tracking
                entry_price REAL,
                current_position_size REAL DEFAULT 0,
                
                -- Order IDs (exchange IDs)
                entry_order_id TEXT,
                stop_loss_order_id TEXT,
                take_profit_order_id TEXT,
                
                -- Risk management
                stop_loss_price REAL,
                take_profit_price REAL,
                
                -- P&L tracking
                realized_pnl REAL DEFAULT 0,
                unrealized_pnl REAL DEFAULT 0,
                mark_price REAL DEFAULT 0,
                position_value REAL DEFAULT NULL,
                unrealized_pnl_percentage REAL DEFAULT NULL, 
                
                -- Risk Info from Exchange
                liquidation_price REAL DEFAULT NULL,
                margin_used REAL DEFAULT NULL,
                leverage REAL DEFAULT NULL,
                
                -- Timestamps
                position_opened_at TEXT,
                position_closed_at TEXT,
                updated_at TEXT DEFAULT CURRENT_TIMESTAMP,
                
                -- Notes
                notes TEXT
            )
            """,
            """
            CREATE TABLE IF NOT EXISTS daily_balances (
                date TEXT PRIMARY KEY,
                balance REAL NOT NULL,
                timestamp TEXT NOT NULL
            )
            """,
            """
            CREATE TABLE IF NOT EXISTS balance_adjustments (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                adjustment_id TEXT UNIQUE,
                timestamp TEXT NOT NULL,
                type TEXT NOT NULL, -- 'deposit' or 'withdrawal'
                amount REAL NOT NULL, -- Always positive, type indicates direction
                description TEXT
            )
            """,
            """
            CREATE TABLE IF NOT EXISTS orders (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                bot_order_ref_id TEXT UNIQUE,
                exchange_order_id TEXT UNIQUE,
                symbol TEXT NOT NULL,
                side TEXT NOT NULL,
                type TEXT NOT NULL,
                amount_requested REAL NOT NULL,
                amount_filled REAL DEFAULT 0.0,
                price REAL, -- For limit, stop, etc.
                status TEXT NOT NULL, -- e.g., 'open', 'partially_filled', 'filled', 'cancelled', 'rejected', 'expired', 'pending_trigger'
                timestamp_created TEXT NOT NULL,
                timestamp_updated TEXT NOT NULL,
                parent_bot_order_ref_id TEXT NULLABLE -- To link conditional orders (like SL triggers) to their parent order
            )
            """,
            """
            CREATE INDEX IF NOT EXISTS idx_orders_bot_order_ref_id ON orders (bot_order_ref_id);
            """,
            """
            CREATE INDEX IF NOT EXISTS idx_orders_exchange_order_id ON orders (exchange_order_id);
            """,
            """
            CREATE INDEX IF NOT EXISTS idx_trades_exchange_fill_id ON trades (exchange_fill_id);
            """,
            """
            CREATE INDEX IF NOT EXISTS idx_trades_linked_order_table_id ON trades (linked_order_table_id);
            """,
            """
            CREATE INDEX IF NOT EXISTS idx_orders_parent_bot_order_ref_id ON orders (parent_bot_order_ref_id);
            """,
            """
            CREATE INDEX IF NOT EXISTS idx_orders_status_type ON orders (status, type);
            """,
            """
            CREATE INDEX IF NOT EXISTS idx_trades_status ON trades (status);
            """,
            """
            CREATE INDEX IF NOT EXISTS idx_trades_lifecycle_id ON trades (trade_lifecycle_id);
            """,
            """
            CREATE INDEX IF NOT EXISTS idx_trades_position_side ON trades (position_side);
            """,
            """
            CREATE INDEX IF NOT EXISTS idx_trades_symbol_status ON trades (symbol, status);
            """
        ]
        # 🆕 Add new table creation queries
        queries.extend([
            """
            CREATE TABLE IF NOT EXISTS token_stats (
                token TEXT PRIMARY KEY,
                total_realized_pnl REAL DEFAULT 0.0,
                total_completed_cycles INTEGER DEFAULT 0,
                winning_cycles INTEGER DEFAULT 0,
                losing_cycles INTEGER DEFAULT 0,
                total_entry_volume REAL DEFAULT 0.0, -- Sum of (amount * entry_price) for completed cycles
                total_exit_volume REAL DEFAULT 0.0, -- Sum of (amount * exit_price) for completed cycles
                sum_of_winning_pnl REAL DEFAULT 0.0,
                sum_of_losing_pnl REAL DEFAULT 0.0, -- Stored as a positive value
                largest_winning_cycle_pnl REAL DEFAULT 0.0,
                largest_losing_cycle_pnl REAL DEFAULT 0.0, -- Stored as a positive value
                first_cycle_closed_at TEXT,
                last_cycle_closed_at TEXT,
                total_cancelled_cycles INTEGER DEFAULT 0, -- Count of lifecycles that ended in 'cancelled'
                updated_at TEXT DEFAULT CURRENT_TIMESTAMP
            )
            """,
            """
            CREATE TABLE IF NOT EXISTS daily_aggregated_stats (
                date TEXT NOT NULL, -- YYYY-MM-DD
                token TEXT NOT NULL, -- Specific token or a general identifier like '_OVERALL_'
                realized_pnl REAL DEFAULT 0.0,
                completed_cycles INTEGER DEFAULT 0,
                entry_volume REAL DEFAULT 0.0,
                exit_volume REAL DEFAULT 0.0,
                PRIMARY KEY (date, token)
            )
            """,
            """
            CREATE INDEX IF NOT EXISTS idx_daily_stats_date_token ON daily_aggregated_stats (date, token);
            """
        ])
        for query in queries:
            self._execute_query(query)
        logger.info("SQLite tables ensured for TradingStats.")

    def _initialize_metadata(self):
        """Initialize metadata if not already present."""
        start_date = self._get_metadata('start_date')
        initial_balance = self._get_metadata('initial_balance')

        if start_date is None:
            self._set_metadata('start_date', datetime.now(timezone.utc).isoformat())
            logger.info("Initialized 'start_date' in metadata.")
        
        if initial_balance is None:
            self._set_metadata('initial_balance', '0.0')
            logger.info("Initialized 'initial_balance' in metadata.")
        
        logger.info(f"TradingStats initialized. Start Date: {self._get_metadata('start_date')}, Initial Balance: {self._get_metadata('initial_balance')}")

    def _get_metadata(self, key: str) -> Optional[str]:
        """Retrieve a value from the metadata table."""
        row = self._fetchone_query("SELECT value FROM metadata WHERE key = ?", (key,))
        return row['value'] if row else None

    def _set_metadata(self, key: str, value: str):
        """Set a value in the metadata table."""
        self._execute_query("INSERT OR REPLACE INTO metadata (key, value) VALUES (?, ?)", (key, value))

    def set_initial_balance(self, balance: float):
        """Set the initial balance if not already set or zero."""
        current_initial_balance_str = self._get_metadata('initial_balance')
        current_initial_balance = float(current_initial_balance_str) if current_initial_balance_str else 0.0
        
        if current_initial_balance == 0.0: # Only set if it's effectively unset
            self._set_metadata('initial_balance', str(balance))
            # Also set start_date if it's the first time setting balance
            if self._get_metadata('start_date') is None or float(current_initial_balance_str if current_initial_balance_str else '0.0') == 0.0:
                 self._set_metadata('start_date', datetime.now(timezone.utc).isoformat())
            formatter = get_formatter()
            logger.info(f"Initial balance set to: {formatter.format_price_with_symbol(balance)}")
        else:
            formatter = get_formatter()
            logger.info(f"Initial balance already set to {formatter.format_price_with_symbol(current_initial_balance)}. Not changing.")


    def record_balance(self, balance: float):
        """Record daily balance snapshot."""
        today_iso = datetime.now(timezone.utc).date().isoformat()
        now_iso = datetime.now(timezone.utc).isoformat()
        
        existing_entry = self._fetchone_query("SELECT date FROM daily_balances WHERE date = ?", (today_iso,))
        if existing_entry:
            self._execute_query("UPDATE daily_balances SET balance = ?, timestamp = ? WHERE date = ?",
                                (balance, now_iso, today_iso))
        else:
            self._execute_query("INSERT INTO daily_balances (date, balance, timestamp) VALUES (?, ?, ?)",
                                (today_iso, balance, now_iso))
        # logger.debug(f"Recorded balance for {today_iso}: ${balance:.2f}") # Potentially too verbose

    def record_trade(self, symbol: str, side: str, amount: float, price: float,
                     exchange_fill_id: Optional[str] = None, trade_type: str = "manual",
                     pnl: Optional[float] = None, timestamp: Optional[str] = None,
                     linked_order_table_id_to_link: Optional[int] = None):
        """Record a trade in the database."""
        if timestamp is None:
            timestamp = datetime.now(timezone.utc).isoformat()
        
        value = amount * price
        self._execute_query(
            "INSERT OR IGNORE INTO trades (symbol, side, amount, price, value, trade_type, timestamp, exchange_fill_id, pnl, linked_order_table_id) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
            (symbol, side, amount, price, value, trade_type, timestamp, exchange_fill_id, pnl or 0.0, linked_order_table_id_to_link)
        )
        formatter = get_formatter()
        # Assuming symbol's base asset for amount formatting. If symbol is like BTC/USDT, base is BTC.
        base_asset_for_amount = symbol.split('/')[0] if '/' in symbol else symbol 
        logger.info(f"📈 Trade recorded: {side.upper()} {formatter.format_amount(amount, base_asset_for_amount)} {symbol} @ {formatter.format_price(price, symbol)} ({formatter.format_price(value, symbol)}) [{trade_type}]")

    def get_all_trades(self) -> List[Dict[str, Any]]:
        """Fetch all trades from the database, ordered by timestamp."""
        return self._fetch_query("SELECT * FROM trades ORDER BY timestamp ASC")

    def get_trade_by_symbol_and_status(self, symbol: str, status: str) -> Optional[Dict[str, Any]]:
        """
        Fetches a single trade record for a given symbol and status.
        Typically used to find an open position master record.
        Assumes that for a given symbol, there's at most one trade record with a specific
        active status like 'position_opened'. If multiple could exist, this fetches the most recent.
        """
        query = "SELECT * FROM trades WHERE symbol = ? AND status = ? ORDER BY id DESC LIMIT 1"
        trade = self._fetchone_query(query, (symbol, status))
        if trade:
            logger.debug(f"Found trade for {symbol} with status {status}: ID {trade.get('id')}")
        # else: # Can be noisy if not finding a trade is a common occurrence
            # logger.debug(f"No trade found for {symbol} with status {status}")
        return trade
        
    def get_basic_stats(self, current_balance: Optional[float] = None) -> Dict[str, Any]:
        """Get basic trading statistics from DB, primarily using aggregated tables."""
        
        # Get counts of open positions (trades that are not yet migrated)
        open_positions_count = self._get_open_positions_count_from_db()

        # Get overall aggregated stats from token_stats table
        query_token_stats_summary = """
            SELECT 
                SUM(total_realized_pnl) as total_pnl_from_cycles,
                SUM(total_completed_cycles) as total_completed_cycles_sum,
                MIN(first_cycle_closed_at) as overall_first_cycle_closed,
                MAX(last_cycle_closed_at) as overall_last_cycle_closed
            FROM token_stats
        """
        token_stats_summary = self._fetchone_query(query_token_stats_summary)

        total_pnl_from_cycles = token_stats_summary['total_pnl_from_cycles'] if token_stats_summary and token_stats_summary['total_pnl_from_cycles'] is not None else 0.0
        total_completed_cycles_sum = token_stats_summary['total_completed_cycles_sum'] if token_stats_summary and token_stats_summary['total_completed_cycles_sum'] is not None else 0

        # Total trades considered as sum of completed cycles and currently open positions
        # This redefines 'total_trades' from its previous meaning of individual fills.
        total_trades_redefined = total_completed_cycles_sum + open_positions_count

        initial_balance_str = self._get_metadata('initial_balance')
        initial_balance = float(initial_balance_str) if initial_balance_str else 0.0
        
        start_date_iso = self._get_metadata('start_date')
        start_date_obj = datetime.fromisoformat(start_date_iso) if start_date_iso else datetime.now(timezone.utc)
        days_active = (datetime.now(timezone.utc) - start_date_obj).days + 1
        
        # 'last_trade' timestamp could be the last update to token_stats or an open trade
        last_activity_ts = token_stats_summary['overall_last_cycle_closed'] if token_stats_summary else None
        last_open_trade_ts_row = self._fetchone_query("SELECT MAX(updated_at) as last_update FROM trades WHERE status = 'position_opened'")
        if last_open_trade_ts_row and last_open_trade_ts_row['last_update']:
            if not last_activity_ts or datetime.fromisoformat(last_open_trade_ts_row['last_update']) > datetime.fromisoformat(last_activity_ts):
                last_activity_ts = last_open_trade_ts_row['last_update']

        # Buy/Sell trades count from individual fills is no longer directly available for completed cycles.
        # If needed, this requires schema change in token_stats or a different approach.
        # For now, these are omitted from basic_stats.

        return {
            'total_trades': total_trades_redefined, # This is now cycles + open positions
            'completed_trades': total_completed_cycles_sum, # This is sum of total_completed_cycles from token_stats
            # 'buy_trades': buy_trades_count, # Omitted
            # 'sell_trades': sell_trades_count, # Omitted
            'initial_balance': initial_balance,
            'total_pnl': total_pnl_from_cycles, # PNL from closed cycles via token_stats
            'days_active': days_active,
            'start_date': start_date_obj.strftime('%Y-%m-%d'),
            'last_trade': last_activity_ts, # Reflects last known activity (cycle close or open trade update)
            'open_positions_count': open_positions_count
        }

    def get_performance_stats(self) -> Dict[str, Any]:
        """Calculate advanced performance statistics using aggregated data from token_stats."""
        query = """
            SELECT 
                SUM(total_completed_cycles) as total_cycles,
                SUM(winning_cycles) as total_wins,
                SUM(losing_cycles) as total_losses,
                SUM(sum_of_winning_pnl) as total_winning_pnl,
                SUM(sum_of_losing_pnl) as total_losing_pnl, -- Stored positive
                MAX(largest_winning_cycle_pnl) as overall_largest_win,
                MAX(largest_losing_cycle_pnl) as overall_largest_loss -- Stored positive
            FROM token_stats
        """
        summary = self._fetchone_query(query)

        # Add total volume
        volume_summary = self._fetchone_query("SELECT SUM(total_exit_volume) as total_volume FROM token_stats")
        total_trading_volume = volume_summary['total_volume'] if volume_summary and volume_summary['total_volume'] is not None else 0.0

        # Get individual token performances for best/worst
        all_token_perf_stats = self.get_token_performance() 
        best_token_pnl_pct = -float('inf')
        best_token_name = "N/A"
        worst_token_pnl_pct = float('inf')
        worst_token_name = "N/A"

        if all_token_perf_stats:
            for token_name_iter, stats_data in all_token_perf_stats.items():
                pnl_pct = stats_data.get('pnl_percentage', 0.0)
                # Ensure token has completed trades and pnl_pct is a valid number
                if stats_data.get('completed_trades', 0) > 0 and isinstance(pnl_pct, (int, float)) and not math.isinf(pnl_pct) and not math.isnan(pnl_pct):
                    if pnl_pct > best_token_pnl_pct:
                        best_token_pnl_pct = pnl_pct
                        best_token_name = token_name_iter
                    if pnl_pct < worst_token_pnl_pct:
                        worst_token_pnl_pct = pnl_pct
                        worst_token_name = token_name_iter
        
        # Handle cases where no valid tokens were found for best/worst
        if best_token_name == "N/A":
            best_token_pnl_pct = 0.0
        if worst_token_name == "N/A":
            worst_token_pnl_pct = 0.0

        if not summary or summary['total_cycles'] is None or summary['total_cycles'] == 0:
            return {
                'win_rate': 0.0, 'profit_factor': 0.0, 'avg_win': 0.0, 'avg_loss': 0.0,
                'largest_win': 0.0, 'largest_loss': 0.0, 
                'total_wins': 0, 'total_losses': 0, 'expectancy': 0.0,
                'total_trading_volume': total_trading_volume,
                'best_performing_token': {'name': best_token_name, 'pnl_percentage': best_token_pnl_pct},
                'worst_performing_token': {'name': worst_token_name, 'pnl_percentage': worst_token_pnl_pct},
            }

        total_completed_count = summary['total_cycles']
        total_wins_count = summary['total_wins'] if summary['total_wins'] is not None else 0
        total_losses_count = summary['total_losses'] if summary['total_losses'] is not None else 0
        
        win_rate = (total_wins_count / total_completed_count * 100) if total_completed_count > 0 else 0.0
        
        sum_of_wins = summary['total_winning_pnl'] if summary['total_winning_pnl'] is not None else 0.0
        sum_of_losses = summary['total_losing_pnl'] if summary['total_losing_pnl'] is not None else 0.0 # This is sum of absolute losses

        profit_factor = (sum_of_wins / sum_of_losses) if sum_of_losses > 0 else float('inf') if sum_of_wins > 0 else 0.0
        
        avg_win = (sum_of_wins / total_wins_count) if total_wins_count > 0 else 0.0
        avg_loss = (sum_of_losses / total_losses_count) if total_losses_count > 0 else 0.0 # Avg of absolute losses
        
        largest_win = summary['overall_largest_win'] if summary['overall_largest_win'] is not None else 0.0
        largest_loss = summary['overall_largest_loss'] if summary['overall_largest_loss'] is not None else 0.0 # Largest absolute loss

        # Consecutive wins/losses removed as it's hard to track with this aggregation model.
            
        expectancy = (avg_win * (win_rate / 100)) - (avg_loss * (1 - (win_rate / 100)))

        return {
            'win_rate': win_rate, 'profit_factor': profit_factor, 'avg_win': avg_win, 'avg_loss': avg_loss,
            'largest_win': largest_win, 'largest_loss': largest_loss, 
            'total_wins': total_wins_count, 'total_losses': total_losses_count, 'expectancy': expectancy,
            'total_trading_volume': total_trading_volume,
            'best_performing_token': {'name': best_token_name, 'pnl_percentage': best_token_pnl_pct},
            'worst_performing_token': {'name': worst_token_name, 'pnl_percentage': worst_token_pnl_pct},
        }

    def get_risk_metrics(self) -> Dict[str, Any]:
        """Calculate risk-adjusted metrics from daily balances."""
        daily_balances_data = self._fetch_query("SELECT balance FROM daily_balances ORDER BY date ASC")
        
        if not daily_balances_data or len(daily_balances_data) < 2:
            return {'sharpe_ratio': 0.0, 'sortino_ratio': 0.0, 'max_drawdown': 0.0, 'volatility': 0.0, 'var_95': 0.0}

        balances = [entry['balance'] for entry in daily_balances_data]
        returns = np.diff(balances) / balances[:-1] # Calculate daily returns
        returns = returns[np.isfinite(returns)] # Remove NaNs or Infs if any balance was 0

        if returns.size == 0:
             return {'sharpe_ratio': 0.0, 'sortino_ratio': 0.0, 'max_drawdown': 0.0, 'volatility': 0.0, 'var_95': 0.0}

        risk_free_rate_daily = (1 + 0.02)**(1/365) - 1 # Approx 2% annual risk-free rate, daily
        
        excess_returns = returns - risk_free_rate_daily
        sharpe_ratio = np.mean(excess_returns) / np.std(returns) * np.sqrt(365) if np.std(returns) > 0 else 0.0
        
        downside_returns = returns[returns < 0]
        downside_std = np.std(downside_returns) if len(downside_returns) > 0 else 0.0
        sortino_ratio = np.mean(excess_returns) / downside_std * np.sqrt(365) if downside_std > 0 else 0.0
        
        cumulative_returns = np.cumprod(1 + returns)
        peak = np.maximum.accumulate(cumulative_returns)
        drawdown = (cumulative_returns - peak) / peak
        max_drawdown_pct = abs(np.min(drawdown) * 100) if drawdown.size > 0 else 0.0
        
        volatility_pct = np.std(returns) * np.sqrt(365) * 100
        var_95_pct = abs(np.percentile(returns, 5) * 100) if returns.size > 0 else 0.0
        
        return {
            'sharpe_ratio': sharpe_ratio, 'sortino_ratio': sortino_ratio, 
            'max_drawdown': max_drawdown_pct, 'volatility': volatility_pct, 'var_95': var_95_pct
        }

    def get_comprehensive_stats(self, current_balance: Optional[float] = None) -> Dict[str, Any]:
        """Get all statistics combined."""
        if current_balance is not None: # Ensure it's not just None, but explicitly provided
            self.record_balance(current_balance) # Record current balance for today
        
        basic = self.get_basic_stats(current_balance) # Pass current_balance for P&L context if needed
        performance = self.get_performance_stats()
        risk = self.get_risk_metrics()
        
        initial_balance = basic['initial_balance']
        total_return_pct = 0.0
        # Use current_balance if available and valid for total return calculation
        # Otherwise, PNL from basic_stats (closed trades) is the primary PNL source
        # This needs careful thought: current_balance reflects unrealized PNL too.
        # The original code used current_balance - initial_balance for total_pnl if current_balance provided.
        
        effective_balance_for_return = current_balance if current_balance is not None else (initial_balance + basic['total_pnl'])

        if initial_balance > 0:
            total_return_pct = ((effective_balance_for_return - initial_balance) / initial_balance) * 100
        
        return {
            'basic': basic,
            'performance': performance,
            'risk': risk,
            'current_balance': current_balance if current_balance is not None else initial_balance + basic['total_pnl'], # Best estimate
            'total_return': total_return_pct, # Percentage
            'last_updated': datetime.now(timezone.utc).isoformat()
        }

    def _get_open_positions_count_from_db(self) -> int:
        """🧹 PHASE 4: Get count of open positions from enhanced trades table."""
        row = self._fetchone_query("SELECT COUNT(DISTINCT symbol) as count FROM trades WHERE status = 'position_opened'")
        return row['count'] if row else 0

    def format_stats_message(self, current_balance: Optional[float] = None) -> str:
        """Format stats for Telegram display using data from DB."""
        try:
            stats = self.get_comprehensive_stats(current_balance)
            formatter = get_formatter()
            
            basic = stats['basic']
            perf = stats['performance']
            risk = stats['risk'] # For portfolio drawdown
            
            effective_current_balance = stats['current_balance']
            initial_bal = basic['initial_balance']

            total_pnl_val = effective_current_balance - initial_bal if initial_bal > 0 and current_balance is not None else basic['total_pnl']
            total_return_pct = (total_pnl_val / initial_bal * 100) if initial_bal > 0 else 0.0
            pnl_emoji = "🟢" if total_pnl_val >= 0 else "🔴"
            open_positions_count = basic['open_positions_count']

            stats_text_parts = []
            stats_text_parts.append(f"📊 <b>Trading Statistics</b>\n")
            
            # Account Overview
            stats_text_parts.append(f"\n💰 <b>Account Overview:</b>")
            stats_text_parts.append(f"• Current Balance: {formatter.format_price_with_symbol(effective_current_balance)}")
            stats_text_parts.append(f"• Initial Balance: {formatter.format_price_with_symbol(initial_bal)}")
            stats_text_parts.append(f"• Open Positions: {open_positions_count}")
            stats_text_parts.append(f"• {pnl_emoji} Total P&L: {formatter.format_price_with_symbol(total_pnl_val)} ({total_return_pct:+.2f}%)")
            stats_text_parts.append(f"• Days Active: {basic['days_active']}\n")
            
            # Performance Metrics
            stats_text_parts.append(f"\n🏆 <b>Performance Metrics:</b>")
            stats_text_parts.append(f"• Total Completed Trades: {basic['completed_trades']}")
            stats_text_parts.append(f"• Trading Volume (Exit Vol.): {formatter.format_price_with_symbol(perf.get('total_trading_volume', 0.0))}")
            stats_text_parts.append(f"• Profit Factor: {perf['profit_factor']:.2f}")
            stats_text_parts.append(f"• Expectancy: {formatter.format_price_with_symbol(perf['expectancy'])} (Value per trade)")
            # Note for Expectancy Percentage: \"[Info: Percentage representation requires further definition]\" might be too verbose for typical display.
            
            stats_text_parts.append(f"• Largest Winning Trade: {formatter.format_price_with_symbol(perf['largest_win'])} (Value)")
            stats_text_parts.append(f"• Largest Losing Trade: {formatter.format_price_with_symbol(perf['largest_loss'])} (Value)")
            # Note for Largest Trade P&L %: Similar to expectancy, noting \"[Info: P&L % for specific trades requires data enhancement]\" in the bot message might be too much.

            best_token_stats = perf.get('best_performing_token', {'name': 'N/A', 'pnl_percentage': 0.0})
            worst_token_stats = perf.get('worst_performing_token', {'name': 'N/A', 'pnl_percentage': 0.0})
            stats_text_parts.append(f"• Best Performing Token: {best_token_stats['name']} ({best_token_stats['pnl_percentage']:+.2f}%)")
            stats_text_parts.append(f"• Worst Performing Token: {worst_token_stats['name']} ({worst_token_stats['pnl_percentage']:+.2f}%)")
            
            stats_text_parts.append(f"• Average Trade Duration: N/A <i>(Data collection required)</i>")
            stats_text_parts.append(f"• Portfolio Max Drawdown: {risk['max_drawdown']:.2f}% <i>(Daily Balance based)</i>")
            # Future note: \"[Info: Trading P&L specific drawdown analysis planned]\"
            
            # Session Info
            stats_text_parts.append(f"\n\n⏰ <b>Session Info:</b>")
            stats_text_parts.append(f"• Bot Started: {basic['start_date']}")
            stats_text_parts.append(f"• Stats Last Updated: {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S UTC')}")
            
            return "\n".join(stats_text_parts).strip()
                
        except Exception as e:
            logger.error(f"Error formatting stats message: {e}", exc_info=True)
            return f"""📊 <b>Trading Statistics</b>\n\n❌ <b>Error loading statistics</b>\n\n🔧 <b>Debug info:</b> {str(e)[:100]}"""

    def get_recent_trades(self, limit: int = 10) -> List[Dict[str, Any]]:
        """Get recent trades from DB (these are active/open trades, as completed ones are migrated)."""
        return self._fetch_query("SELECT * FROM trades WHERE status = 'position_opened' ORDER BY updated_at DESC LIMIT ?", (limit,))

    def get_token_performance(self) -> Dict[str, Dict[str, Any]]:
        """Get performance statistics grouped by token using the token_stats table."""
        all_token_stats = self._fetch_query("SELECT * FROM token_stats ORDER BY token ASC")
        
        token_performance_map = {}
        for record in all_token_stats:
            token = record['token']
            total_pnl = record.get('total_realized_pnl', 0.0)
            # total_volume_sold now refers to total_exit_volume from token_stats
            total_volume = record.get('total_exit_volume', 0.0) 
            
            pnl_percentage = (total_pnl / total_volume * 100) if total_volume > 0 else 0.0
            
            total_completed_count = record.get('total_completed_cycles', 0)
            total_wins_count = record.get('winning_cycles', 0)
            total_losses_count = record.get('losing_cycles', 0)
            
            win_rate = (total_wins_count / total_completed_count * 100) if total_completed_count > 0 else 0.0
            
            sum_of_wins = record.get('sum_of_winning_pnl', 0.0)
            sum_of_losses = record.get('sum_of_losing_pnl', 0.0) # Stored positive
            profit_factor = (sum_of_wins / sum_of_losses) if sum_of_losses > 0 else float('inf') if sum_of_wins > 0 else 0.0
            
            avg_win = (sum_of_wins / total_wins_count) if total_wins_count > 0 else 0.0
            avg_loss = (sum_of_losses / total_losses_count) if total_losses_count > 0 else 0.0
            expectancy = (avg_win * (win_rate / 100)) - (avg_loss * (1 - (win_rate / 100)))
            
            largest_win = record.get('largest_winning_cycle_pnl', 0.0)
            largest_loss = record.get('largest_losing_cycle_pnl', 0.0) # Stored positive

            token_performance_map[token] = {
                'token': token, # Added for easier access if iterating over values
                'total_pnl': total_pnl, 
                'pnl_percentage': pnl_percentage,
                'completed_trades': total_completed_count, 
                'total_volume': total_volume, # This is total_exit_volume
                'win_rate': win_rate, 
                'total_wins': total_wins_count, 
                'total_losses': total_losses_count,
                'profit_factor': profit_factor, 
                'expectancy': expectancy,
                'largest_win': largest_win, 
                'largest_loss': largest_loss,
                'avg_win': avg_win, 
                'avg_loss': avg_loss,
                'first_cycle_closed_at': record.get('first_cycle_closed_at'),
                'last_cycle_closed_at': record.get('last_cycle_closed_at'),
                'total_cancelled_cycles': record.get('total_cancelled_cycles', 0)
            }
        return token_performance_map

    def get_token_detailed_stats(self, token: str) -> Dict[str, Any]:
        """Get detailed statistics for a specific token using token_stats and current open trades."""
        upper_token = _normalize_token_case(token)
        
        # Get aggregated performance from token_stats
        token_agg_stats = self._fetchone_query("SELECT * FROM token_stats WHERE token = ?", (upper_token,))
        
        # Get currently open trades for this token from the 'trades' table (not yet migrated)
        # These are not completed cycles but represent current exposure.
        open_trades_for_token = self._fetch_query(
            "SELECT * FROM trades WHERE symbol LIKE ? AND status = 'position_opened' ORDER BY timestamp ASC", 
            (f"{upper_token}/%",)
        )
        
        if not token_agg_stats and not open_trades_for_token:
            return {
                'token': upper_token, 'total_trades': 0, 'total_pnl': 0.0, 'win_rate': 0.0,
                'message': f"No trading history or open positions found for {upper_token}"
            }

        # Initialize with empty performance if no aggregated data
        perf_stats = {}
        if token_agg_stats:
            perf_stats = {
                'completed_trades': token_agg_stats.get('total_completed_cycles', 0),
                'total_pnl': token_agg_stats.get('total_realized_pnl', 0.0),
                'pnl_percentage': 0.0, # Recalculate if needed, or store avg pnl_percentage
                'win_rate': 0.0,
                'profit_factor': token_agg_stats.get('profit_factor'), # Placeholder, need to calc from sums
                'avg_win': 0.0,
                'avg_loss': 0.0,
                'largest_win': token_agg_stats.get('largest_winning_cycle_pnl', 0.0),
                'largest_loss': token_agg_stats.get('largest_losing_cycle_pnl', 0.0),
                'expectancy': 0.0,
                'total_wins': token_agg_stats.get('winning_cycles',0),
                'total_losses': token_agg_stats.get('losing_cycles',0),
                'completed_entry_volume': token_agg_stats.get('total_entry_volume', 0.0),
                'completed_exit_volume': token_agg_stats.get('total_exit_volume', 0.0),
                'total_cancelled': token_agg_stats.get('total_cancelled_cycles', 0)
            }
            if perf_stats['completed_trades'] > 0:
                perf_stats['win_rate'] = (perf_stats['total_wins'] / perf_stats['completed_trades'] * 100) if perf_stats['completed_trades'] > 0 else 0.0
                sum_wins = token_agg_stats.get('sum_of_winning_pnl', 0.0)
                sum_losses = token_agg_stats.get('sum_of_losing_pnl', 0.0)
                perf_stats['profit_factor'] = (sum_wins / sum_losses) if sum_losses > 0 else float('inf') if sum_wins > 0 else 0.0
                perf_stats['avg_win'] = (sum_wins / perf_stats['total_wins']) if perf_stats['total_wins'] > 0 else 0.0
                perf_stats['avg_loss'] = (sum_losses / perf_stats['total_losses']) if perf_stats['total_losses'] > 0 else 0.0
                perf_stats['expectancy'] = (perf_stats['avg_win'] * (perf_stats['win_rate'] / 100)) - (perf_stats['avg_loss'] * (1 - (perf_stats['win_rate'] / 100)))
            if perf_stats['completed_exit_volume'] > 0:
                 perf_stats['pnl_percentage'] = (perf_stats['total_pnl'] / perf_stats['completed_exit_volume'] * 100)
        else: # No completed cycles for this token yet
             perf_stats = {
                'completed_trades': 0, 'total_pnl': 0.0, 'pnl_percentage': 0.0, 'win_rate': 0.0,
                'profit_factor': 0.0, 'avg_win': 0.0, 'avg_loss': 0.0, 'largest_win': 0.0, 'largest_loss': 0.0,
                'expectancy': 0.0, 'total_wins':0, 'total_losses':0,
                'completed_entry_volume': 0.0, 'completed_exit_volume': 0.0, 'total_cancelled': 0
            }

        # Info about open positions for this token (raw trades, not cycles)
        open_positions_summary = []
        total_open_value = 0.0
        total_open_unrealized_pnl = 0.0
        for op_trade in open_trades_for_token:
            open_positions_summary.append({
                'lifecycle_id': op_trade.get('trade_lifecycle_id'),
                'side': op_trade.get('position_side'),
                'amount': op_trade.get('current_position_size'),
                'entry_price': op_trade.get('entry_price'),
                'mark_price': op_trade.get('mark_price'),
                'unrealized_pnl': op_trade.get('unrealized_pnl'),
                'opened_at': op_trade.get('position_opened_at')
            })
            total_open_value += op_trade.get('value', 0.0) # Initial value of open positions
            total_open_unrealized_pnl += op_trade.get('unrealized_pnl', 0.0)

        # Raw individual orders from 'orders' table for this token can be complex to summarize here
        # The old version counted 'buy_orders' and 'sell_orders' from all trades for the token.
        # This is no longer straightforward for completed cycles.
        # We can count open orders for this token.
        open_orders_count_row = self._fetchone_query(
            "SELECT COUNT(*) as count FROM orders WHERE symbol LIKE ? AND status IN ('open', 'submitted', 'pending_trigger')",
             (f"{upper_token}/%",)
        )
        current_open_orders_for_token = open_orders_count_row['count'] if open_orders_count_row else 0

        # 'total_trades' here could mean total orders ever placed for this token, or completed cycles + open positions
        # Let's define it as completed cycles + number of currently open positions for consistency with get_basic_stats
        effective_total_trades = perf_stats['completed_trades'] + len(open_trades_for_token)

        return {
            'token': upper_token,
            'message': f"Statistics for {upper_token}",
            'performance_summary': perf_stats, # From token_stats table
            'open_positions': open_positions_summary, # List of currently open positions
            'open_positions_count': len(open_trades_for_token),
            'current_open_orders_count': current_open_orders_for_token,
            'summary_total_trades': effective_total_trades, # Completed cycles + open positions
            'summary_total_realized_pnl': perf_stats['total_pnl'],
            'summary_total_unrealized_pnl': total_open_unrealized_pnl,
            # 'cycles': token_cycles # Raw cycle data for completed trades is no longer stored directly after migration
        }

    def get_daily_stats(self, limit: int = 10) -> List[Dict[str, Any]]:
        """Get daily performance stats for the last N days from daily_aggregated_stats."""
        daily_stats_list = []
        today_utc = datetime.now(timezone.utc).date()

        for i in range(limit):
            target_date = today_utc - timedelta(days=i)
            date_str = target_date.strftime('%Y-%m-%d')
            date_formatted = target_date.strftime('%m/%d') # For display

            # Query for all tokens for that day and sum them up
            # Or, if daily_aggregated_stats stores an _OVERALL_ record, query that.
            # Assuming for now we sum up all token records for a given day.
            day_aggregated_data = self._fetch_query(
                "SELECT SUM(realized_pnl) as pnl, SUM(completed_cycles) as trades, SUM(exit_volume) as volume FROM daily_aggregated_stats WHERE date = ?",
                (date_str,)
            )
            
            stats_for_day = None
            if day_aggregated_data and len(day_aggregated_data) > 0 and day_aggregated_data[0]['trades'] is not None:
                stats_for_day = day_aggregated_data[0]
                # Calculate pnl_pct if volume is present and positive
                pnl = stats_for_day.get('pnl', 0.0) or 0.0
                volume = stats_for_day.get('volume', 0.0) or 0.0
                stats_for_day['pnl_pct'] = (pnl / volume * 100) if volume > 0 else 0.0
                # Ensure trades is an int
                stats_for_day['trades'] = int(stats_for_day.get('trades', 0) or 0)

            if stats_for_day and stats_for_day['trades'] > 0:
                daily_stats_list.append({
                    'date': date_str, 'date_formatted': date_formatted, 'has_trades': True,
                    **stats_for_day
                })
            else:
                daily_stats_list.append({
                    'date': date_str, 'date_formatted': date_formatted, 'has_trades': False,
                    'trades': 0, 'pnl': 0.0, 'volume': 0.0, 'pnl_pct': 0.0
                })
        return daily_stats_list

    def get_weekly_stats(self, limit: int = 10) -> List[Dict[str, Any]]:
        """Get weekly performance stats for the last N weeks by aggregating daily_aggregated_stats."""
        weekly_stats_list = []
        today_utc = datetime.now(timezone.utc).date()

        for i in range(limit):
            target_monday = today_utc - timedelta(days=today_utc.weekday() + (i * 7))
            target_sunday = target_monday + timedelta(days=6)
            
            week_key_display = f"{target_monday.strftime('%Y-W%W')}" # For internal key if needed
            week_formatted_display = f"{target_monday.strftime('%m/%d')}-{target_sunday.strftime('%m/%d/%y')}"

            # Fetch daily records for this week range
            daily_records_for_week = self._fetch_query(
                "SELECT date, realized_pnl, completed_cycles, exit_volume FROM daily_aggregated_stats WHERE date BETWEEN ? AND ?",
                (target_monday.strftime('%Y-%m-%d'), target_sunday.strftime('%Y-%m-%d'))
            )
            
            if daily_records_for_week:
                total_pnl_week = sum(d.get('realized_pnl', 0.0) or 0.0 for d in daily_records_for_week)
                total_trades_week = sum(d.get('completed_cycles', 0) or 0 for d in daily_records_for_week)
                total_volume_week = sum(d.get('exit_volume', 0.0) or 0.0 for d in daily_records_for_week)
                pnl_pct_week = (total_pnl_week / total_volume_week * 100) if total_volume_week > 0 else 0.0
                
                if total_trades_week > 0:
                    weekly_stats_list.append({
                        'week': week_key_display, 
                        'week_formatted': week_formatted_display, 
                        'has_trades': True,
                        'pnl': total_pnl_week,
                        'trades': total_trades_week,
                        'volume': total_volume_week,
                        'pnl_pct': pnl_pct_week
                    })
                else:
                    weekly_stats_list.append({
                        'week': week_key_display, 'week_formatted': week_formatted_display, 'has_trades': False,
                        'trades': 0, 'pnl': 0.0, 'volume': 0.0, 'pnl_pct': 0.0
                    })
            else:
                weekly_stats_list.append({
                    'week': week_key_display, 'week_formatted': week_formatted_display, 'has_trades': False,
                    'trades': 0, 'pnl': 0.0, 'volume': 0.0, 'pnl_pct': 0.0
                })
        return weekly_stats_list

    def get_monthly_stats(self, limit: int = 10) -> List[Dict[str, Any]]:
        """Get monthly performance stats for the last N months by aggregating daily_aggregated_stats."""
        monthly_stats_list = []
        current_month_start_utc = datetime.now(timezone.utc).date().replace(day=1)

        for i in range(limit):
            year = current_month_start_utc.year
            month = current_month_start_utc.month - i
            while month <= 0:
                month += 12
                year -= 1
            
            target_month_start_date = datetime(year, month, 1, tzinfo=timezone.utc).date()
            # Find end of target month
            next_month_start_date = datetime(year + (month // 12), (month % 12) + 1, 1, tzinfo=timezone.utc).date() if month < 12 else datetime(year + 1, 1, 1, tzinfo=timezone.utc).date()
            target_month_end_date = next_month_start_date - timedelta(days=1)
            
            month_key_display = target_month_start_date.strftime('%Y-%m')
            month_formatted_display = target_month_start_date.strftime('%b %Y')

            daily_records_for_month = self._fetch_query(
                "SELECT date, realized_pnl, completed_cycles, exit_volume FROM daily_aggregated_stats WHERE date BETWEEN ? AND ?",
                (target_month_start_date.strftime('%Y-%m-%d'), target_month_end_date.strftime('%Y-%m-%d'))
            )

            if daily_records_for_month:
                total_pnl_month = sum(d.get('realized_pnl', 0.0) or 0.0 for d in daily_records_for_month)
                total_trades_month = sum(d.get('completed_cycles', 0) or 0 for d in daily_records_for_month)
                total_volume_month = sum(d.get('exit_volume', 0.0) or 0.0 for d in daily_records_for_month)
                pnl_pct_month = (total_pnl_month / total_volume_month * 100) if total_volume_month > 0 else 0.0

                if total_trades_month > 0:
                    monthly_stats_list.append({
                        'month': month_key_display, 
                        'month_formatted': month_formatted_display, 
                        'has_trades': True,
                        'pnl': total_pnl_month,
                        'trades': total_trades_month,
                        'volume': total_volume_month,
                        'pnl_pct': pnl_pct_month
                    })
                else:
                    monthly_stats_list.append({
                        'month': month_key_display, 'month_formatted': month_formatted_display, 'has_trades': False,
                        'trades': 0, 'pnl': 0.0, 'volume': 0.0, 'pnl_pct': 0.0
                    })
            else:
                 monthly_stats_list.append({
                    'month': month_key_display, 'month_formatted': month_formatted_display, 'has_trades': False,
                    'trades': 0, 'pnl': 0.0, 'volume': 0.0, 'pnl_pct': 0.0
                })
        return monthly_stats_list

    def record_deposit(self, amount: float, timestamp: Optional[str] = None, 
                       deposit_id: Optional[str] = None, description: Optional[str] = None):
        """Record a deposit."""
        ts = timestamp if timestamp else datetime.now(timezone.utc).isoformat()
        formatter = get_formatter()
        formatted_amount_str = formatter.format_price_with_symbol(amount)
        desc = description if description else f'Deposit of {formatted_amount_str}'
        
        self._execute_query(
            "INSERT INTO balance_adjustments (adjustment_id, timestamp, type, amount, description) VALUES (?, ?, ?, ?, ?)",
            (deposit_id or str(uuid.uuid4()), ts, 'deposit', amount, desc) # Ensured uuid is string
        )
        # Adjust initial_balance in metadata to reflect capital changes
        current_initial = float(self._get_metadata('initial_balance') or '0.0')
        self._set_metadata('initial_balance', str(current_initial + amount))
        logger.info(f"💰 Recorded deposit: {formatted_amount_str}. New effective initial balance: {formatter.format_price_with_symbol(current_initial + amount)}")

    def record_withdrawal(self, amount: float, timestamp: Optional[str] = None, 
                          withdrawal_id: Optional[str] = None, description: Optional[str] = None):
        """Record a withdrawal."""
        ts = timestamp if timestamp else datetime.now(timezone.utc).isoformat()
        formatter = get_formatter()
        formatted_amount_str = formatter.format_price_with_symbol(amount)
        desc = description if description else f'Withdrawal of {formatted_amount_str}'
        
        self._execute_query(
            "INSERT INTO balance_adjustments (adjustment_id, timestamp, type, amount, description) VALUES (?, ?, ?, ?, ?)",
            (withdrawal_id or str(uuid.uuid4()), ts, 'withdrawal', amount, desc) # Ensured uuid is string
        )
        current_initial = float(self._get_metadata('initial_balance') or '0.0')
        self._set_metadata('initial_balance', str(current_initial - amount))
        logger.info(f"💸 Recorded withdrawal: {formatted_amount_str}. New effective initial balance: {formatter.format_price_with_symbol(current_initial - amount)}")

    def get_balance_adjustments_summary(self) -> Dict[str, Any]:
        """Get summary of all balance adjustments from DB."""
        adjustments = self._fetch_query("SELECT type, amount, timestamp FROM balance_adjustments ORDER BY timestamp ASC")
        
        if not adjustments:
            return {'total_deposits': 0.0, 'total_withdrawals': 0.0, 'net_adjustment': 0.0, 
                    'adjustment_count': 0, 'last_adjustment': None}
        
        total_deposits = sum(adj['amount'] for adj in adjustments if adj['type'] == 'deposit')
        total_withdrawals = sum(adj['amount'] for adj in adjustments if adj['type'] == 'withdrawal') # Amounts stored positive
        net_adjustment = total_deposits - total_withdrawals
        
        return {
            'total_deposits': total_deposits, 'total_withdrawals': total_withdrawals,
            'net_adjustment': net_adjustment, 'adjustment_count': len(adjustments),
            'last_adjustment': adjustments[-1]['timestamp'] if adjustments else None
        }

    def close_connection(self):
        """Close the SQLite database connection."""
        if self.conn:
            self.conn.close()
            logger.info("TradingStats SQLite connection closed.")

    def __del__(self):
        """Ensure connection is closed when object is deleted."""
        self.close_connection()

    # --- Order Table Management --- 

    def record_order_placed(self, symbol: str, side: str, order_type: str, 
                            amount_requested: float, price: Optional[float] = None, 
                            bot_order_ref_id: Optional[str] = None, 
                            exchange_order_id: Optional[str] = None, 
                            status: str = 'open',
                            parent_bot_order_ref_id: Optional[str] = None) -> Optional[int]:
        """Record a newly placed order in the 'orders' table. Returns the ID of the inserted order or None on failure."""
        now_iso = datetime.now(timezone.utc).isoformat()
        query = """
            INSERT INTO orders (bot_order_ref_id, exchange_order_id, symbol, side, type, 
                                amount_requested, price, status, timestamp_created, timestamp_updated, parent_bot_order_ref_id)
            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
        """
        params = (bot_order_ref_id, exchange_order_id, symbol, side.lower(), order_type.lower(), 
                  amount_requested, price, status.lower(), now_iso, now_iso, parent_bot_order_ref_id)
        try:
            cur = self.conn.cursor()
            cur.execute(query, params)
            self.conn.commit()
            order_db_id = cur.lastrowid
            logger.info(f"Recorded order placed: ID {order_db_id}, Symbol {symbol}, Side {side}, Type {order_type}, Amount {amount_requested}, BotRef {bot_order_ref_id}, ExchID {exchange_order_id}")
            return order_db_id
        except sqlite3.IntegrityError as e:
            logger.error(f"Failed to record order due to IntegrityError (likely duplicate bot_order_ref_id '{bot_order_ref_id}' or exchange_order_id '{exchange_order_id}'): {e}")
            return None
        except Exception as e:
            logger.error(f"Failed to record order: {e}")
            return None

    def update_order_status(self, order_db_id: Optional[int] = None, bot_order_ref_id: Optional[str] = None, exchange_order_id: Optional[str] = None,
                            new_status: Optional[str] = None, amount_filled_increment: Optional[float] = None, set_exchange_order_id: Optional[str] = None) -> bool:
        """Update an existing order's status and/or amount_filled. Identify order by order_db_id, bot_order_ref_id, or exchange_order_id.
        
        Args:
            order_db_id: Database ID to identify the order
            bot_order_ref_id: Bot's internal reference ID to identify the order
            exchange_order_id: Exchange's order ID to identify the order
            new_status: New status to set
            amount_filled_increment: Amount to add to current filled amount
            set_exchange_order_id: If provided, sets/updates the exchange_order_id field in the database
        """
        if not any([order_db_id, bot_order_ref_id, exchange_order_id]):
            logger.error("Must provide one of order_db_id, bot_order_ref_id, or exchange_order_id to update order.")
            return False

        now_iso = datetime.now(timezone.utc).isoformat()
        set_clauses = []
        params = []

        if new_status:
            set_clauses.append("status = ?")
            params.append(new_status.lower())
        
        if set_exchange_order_id is not None:
            set_clauses.append("exchange_order_id = ?")
            params.append(set_exchange_order_id)
        
        current_amount_filled = 0.0
        identifier_clause = ""
        identifier_param = None

        if order_db_id:
            identifier_clause = "id = ?"
            identifier_param = order_db_id
        elif bot_order_ref_id:
            identifier_clause = "bot_order_ref_id = ?"
            identifier_param = bot_order_ref_id
        elif exchange_order_id:
            identifier_clause = "exchange_order_id = ?"
            identifier_param = exchange_order_id

        if amount_filled_increment is not None and amount_filled_increment > 0:
            # To correctly increment, we might need to fetch current filled amount first if DB doesn't support direct increment easily or atomically with other updates.
            # For simplicity here, assuming we can use SQL's increment if other fields are not changing, or we do it in two steps.
            # Let's assume we fetch first then update to be safe and clear.
            order_data = self._fetchone_query(f"SELECT amount_filled FROM orders WHERE {identifier_clause}", (identifier_param,))
            if order_data:
                current_amount_filled = order_data.get('amount_filled', 0.0)
            else:
                logger.warning(f"Order not found by {identifier_clause}={identifier_param} when trying to increment amount_filled.")
                # Potentially still update status if new_status is provided, but amount_filled won't be right.
                # For now, let's proceed with update if status is there.

            set_clauses.append("amount_filled = ?")
            params.append(current_amount_filled + amount_filled_increment)

        if not set_clauses:
            logger.info("No fields to update for order.")
            return True # No update needed, not an error

        set_clauses.append("timestamp_updated = ?")
        params.append(now_iso)
        
        params.append(identifier_param) # Add identifier param at the end for WHERE clause

        query = f"UPDATE orders SET { ', '.join(set_clauses) } WHERE {identifier_clause}"
        
        try:
            self._execute_query(query, tuple(params))
            log_msg = f"Updated order ({identifier_clause}={identifier_param}): Status to '{new_status or 'N/A'}', Filled increment {amount_filled_increment or 0.0}"
            if set_exchange_order_id is not None:
                log_msg += f", Exchange ID set to '{set_exchange_order_id}'"
            logger.info(log_msg)
            return True
        except Exception as e:
            logger.error(f"Failed to update order ({identifier_clause}={identifier_param}): {e}")
            return False

    def get_order_by_db_id(self, order_db_id: int) -> Optional[Dict[str, Any]]:
        """Fetch an order by its database primary key ID."""
        return self._fetchone_query("SELECT * FROM orders WHERE id = ?", (order_db_id,))

    def get_order_by_bot_ref_id(self, bot_order_ref_id: str) -> Optional[Dict[str, Any]]:
        """Fetch an order by the bot's internal reference ID."""
        return self._fetchone_query("SELECT * FROM orders WHERE bot_order_ref_id = ?", (bot_order_ref_id,))

    def get_order_by_exchange_id(self, exchange_order_id: str) -> Optional[Dict[str, Any]]:
        """Fetch an order by the exchange's order ID."""
        return self._fetchone_query("SELECT * FROM orders WHERE exchange_order_id = ?", (exchange_order_id,))

    def get_orders_by_status(self, status: str, order_type_filter: Optional[str] = None, parent_bot_order_ref_id: Optional[str] = None) -> List[Dict[str, Any]]:
        """Fetch all orders with a specific status, optionally filtering by order_type and parent_bot_order_ref_id."""
        query = "SELECT * FROM orders WHERE status = ?"
        params = [status.lower()]
        if order_type_filter:
            query += " AND type = ?"
            params.append(order_type_filter.lower())
        if parent_bot_order_ref_id:
            query += " AND parent_bot_order_ref_id = ?"
            params.append(parent_bot_order_ref_id)
        query += " ORDER BY timestamp_created ASC"
        return self._fetch_query(query, tuple(params))

    def cancel_linked_orders(self, parent_bot_order_ref_id: str, new_status: str = 'cancelled_parent_filled') -> int:
        """Cancel all orders linked to a parent order (e.g., pending stop losses when parent order fills or gets cancelled).
        Returns the number of orders that were cancelled."""
        linked_orders = self.get_orders_by_status('pending_trigger', parent_bot_order_ref_id=parent_bot_order_ref_id)
        cancelled_count = 0
        
        for order in linked_orders:
            order_db_id = order.get('id')
            if order_db_id:
                success = self.update_order_status(order_db_id=order_db_id, new_status=new_status)
                if success:
                    cancelled_count += 1
                    logger.info(f"Cancelled linked order ID {order_db_id} (parent: {parent_bot_order_ref_id}) -> status: {new_status}")
        
        return cancelled_count

    def cancel_pending_stop_losses_by_symbol(self, symbol: str, new_status: str = 'cancelled_position_closed') -> int:
        """Cancel all pending stop loss orders for a specific symbol (when position is closed).
        Returns the number of stop loss orders that were cancelled."""
        query = "SELECT * FROM orders WHERE symbol = ? AND status = 'pending_trigger' AND type = 'stop_limit_trigger'"
        pending_stop_losses = self._fetch_query(query, (symbol,))
        cancelled_count = 0
        
        for order in pending_stop_losses:
            order_db_id = order.get('id')
            if order_db_id:
                success = self.update_order_status(order_db_id=order_db_id, new_status=new_status)
                if success:
                    cancelled_count += 1
                    logger.info(f"Cancelled pending SL order ID {order_db_id} for {symbol} -> status: {new_status}")
        
        return cancelled_count

    def get_order_cleanup_summary(self) -> Dict[str, Any]:
        """Get summary of order cleanup actions for monitoring and debugging."""
        try:
            # Get counts of different cancellation types
            cleanup_stats = {}
            
            cancellation_types = [
                'cancelled_parent_cancelled',
                'cancelled_parent_disappeared', 
                'cancelled_manual_exit',
                'cancelled_auto_exit',
                'cancelled_no_position',
                'cancelled_external_position_close',
                'cancelled_orphaned_no_position',
                'cancelled_externally',
                'immediately_executed_on_activation',
                'activation_execution_failed',
                'activation_execution_error'
            ]
            
            for cancel_type in cancellation_types:
                count_result = self._fetchone_query(
                    "SELECT COUNT(*) as count FROM orders WHERE status = ?", 
                    (cancel_type,)
                )
                cleanup_stats[cancel_type] = count_result['count'] if count_result else 0
            
            # Get currently pending stop losses
            pending_sls = self.get_orders_by_status('pending_trigger', 'stop_limit_trigger')
            cleanup_stats['currently_pending_stop_losses'] = len(pending_sls)
            
            # Get total orders in various states
            active_orders = self._fetchone_query(
                "SELECT COUNT(*) as count FROM orders WHERE status IN ('open', 'submitted', 'partially_filled')",
                ()
            )
            cleanup_stats['currently_active_orders'] = active_orders['count'] if active_orders else 0
            
            return cleanup_stats
            
        except Exception as e:
            logger.error(f"Error getting order cleanup summary: {e}")
            return {}

    def get_external_activity_summary(self, days: int = 7) -> Dict[str, Any]:
        """Get summary of external activity (trades and cancellations) over the last N days."""
        try:
            from datetime import timedelta
            cutoff_date = (datetime.now(timezone.utc) - timedelta(days=days)).isoformat()
            
            # External trades
            external_trades = self._fetch_query(
                "SELECT COUNT(*) as count, side FROM trades WHERE trade_type = 'external' AND timestamp >= ? GROUP BY side",
                (cutoff_date,)
            )
            
            external_trade_summary = {
                'external_buy_trades': 0,
                'external_sell_trades': 0,
                'total_external_trades': 0
            }
            
            for trade_group in external_trades:
                side = trade_group['side']
                count = trade_group['count']
                external_trade_summary['total_external_trades'] += count
                if side == 'buy':
                    external_trade_summary['external_buy_trades'] = count
                elif side == 'sell':
                    external_trade_summary['external_sell_trades'] = count
            
            # External cancellations
            external_cancellations = self._fetchone_query(
                "SELECT COUNT(*) as count FROM orders WHERE status = 'cancelled_externally' AND timestamp_updated >= ?",
                (cutoff_date,)
            )
            external_trade_summary['external_cancellations'] = external_cancellations['count'] if external_cancellations else 0
            
            # Cleanup actions
            cleanup_cancellations = self._fetchone_query(
                """SELECT COUNT(*) as count FROM orders 
                   WHERE status LIKE 'cancelled_%' 
                   AND status != 'cancelled_externally' 
                   AND timestamp_updated >= ?""",
                (cutoff_date,)
            )
            external_trade_summary['cleanup_cancellations'] = cleanup_cancellations['count'] if cleanup_cancellations else 0
            
            external_trade_summary['period_days'] = days
            
            return external_trade_summary
            
        except Exception as e:
            logger.error(f"Error getting external activity summary: {e}")
            return {'period_days': days, 'total_external_trades': 0, 'external_cancellations': 0}

    # --- End Order Table Management ---

    # =============================================================================
    # TRADE LIFECYCLE MANAGEMENT - PHASE 4: UNIFIED TRADES TABLE
    # =============================================================================
    
    def create_trade_lifecycle(self, symbol: str, side: str, entry_order_id: Optional[str] = None,
                              stop_loss_price: Optional[float] = None, take_profit_price: Optional[float] = None,
                              trade_type: str = 'manual') -> Optional[str]:
        """Create a new trade lifecycle when an entry order is placed."""
        try:
            lifecycle_id = str(uuid.uuid4())
            
            query = """
                INSERT INTO trades (
                    symbol, side, amount, price, value, trade_type, timestamp,
                    status, trade_lifecycle_id, position_side, entry_order_id,
                    stop_loss_price, take_profit_price, updated_at
                ) VALUES (?, ?, 0, 0, 0, ?, ?, 'pending', ?, 'flat', ?, ?, ?, ?)
            """
            timestamp = datetime.now(timezone.utc).isoformat()
            params = (symbol, side.lower(), trade_type, timestamp, lifecycle_id, 
                     entry_order_id, stop_loss_price, take_profit_price, timestamp)
            
            self._execute_query(query, params)
            
            logger.info(f"📊 Created trade lifecycle {lifecycle_id}: {side.upper()} {symbol} (pending)")
            return lifecycle_id
            
        except Exception as e:
            logger.error(f"❌ Error creating trade lifecycle: {e}")
            return None
    
    def update_trade_position_opened(self, lifecycle_id: str, entry_price: float, 
                                   entry_amount: float, exchange_fill_id: str) -> bool:
        """Update trade when position is opened (entry order filled)."""
        try:
            query = """
                UPDATE trades 
                SET status = 'position_opened',
                    amount = ?,
                    price = ?,
                    value = ?,
                    entry_price = ?,
                    current_position_size = ?,
                    position_side = CASE 
                        WHEN side = 'buy' THEN 'long'
                        WHEN side = 'sell' THEN 'short'
                        ELSE position_side
                    END,
                    exchange_fill_id = ?,
                    position_opened_at = ?,
                    updated_at = ?
                WHERE trade_lifecycle_id = ? AND status = 'pending'
            """
            timestamp = datetime.now(timezone.utc).isoformat()
            value = entry_amount * entry_price
            params = (entry_amount, entry_price, value, entry_price, entry_amount,
                     exchange_fill_id, timestamp, timestamp, lifecycle_id)
            
            self._execute_query(query, params)
            
            formatter = get_formatter()
            trade_info = self.get_trade_by_lifecycle_id(lifecycle_id) # Fetch to get symbol for formatting
            symbol_for_formatting = trade_info.get('symbol', 'UNKNOWN_SYMBOL') if trade_info else 'UNKNOWN_SYMBOL'
            base_asset_for_amount = symbol_for_formatting.split('/')[0] if '/' in symbol_for_formatting else symbol_for_formatting

            logger.info(f"📈 Trade lifecycle {lifecycle_id} position opened: {formatter.format_amount(entry_amount, base_asset_for_amount)} {symbol_for_formatting} @ {formatter.format_price(entry_price, symbol_for_formatting)}")
            return True
            
        except Exception as e:
            logger.error(f"❌ Error updating trade position opened: {e}")
            return False
    
    def update_trade_position_closed(self, lifecycle_id: str, exit_price: float,
                                   realized_pnl: float, exchange_fill_id: str) -> bool:
        """Update trade when position is fully closed."""
        try:
            query = """
                UPDATE trades 
                SET status = 'position_closed',
                    current_position_size = 0,
                    position_side = 'flat',
                    realized_pnl = ?,
                    position_closed_at = ?,
                    updated_at = ?
                WHERE trade_lifecycle_id = ? AND status = 'position_opened'
            """
            timestamp = datetime.now(timezone.utc).isoformat()
            params = (realized_pnl, timestamp, timestamp, lifecycle_id)
            
            self._execute_query(query, params)
            
            formatter = get_formatter()
            trade_info = self.get_trade_by_lifecycle_id(lifecycle_id) # Fetch to get symbol for P&L formatting context
            symbol_for_formatting = trade_info.get('symbol', 'USD') # Default to USD for PNL if symbol unknown
            pnl_emoji = "🟢" if realized_pnl >= 0 else "🔴"
            logger.info(f"{pnl_emoji} Trade lifecycle {lifecycle_id} position closed: P&L {formatter.format_price_with_symbol(realized_pnl)}")
            return True
            
        except Exception as e:
            logger.error(f"❌ Error updating trade position closed: {e}")
            return False
    
    def update_trade_cancelled(self, lifecycle_id: str, reason: str = "order_cancelled") -> bool:
        """Update trade when entry order is cancelled (never opened)."""
        try:
            query = """
                UPDATE trades 
                SET status = 'cancelled',
                    notes = ?,
                    updated_at = ?
                WHERE trade_lifecycle_id = ? AND status = 'pending'
            """
            timestamp = datetime.now(timezone.utc).isoformat()
            params = (f"Cancelled: {reason}", timestamp, lifecycle_id)
            
            self._execute_query(query, params)
            
            logger.info(f"❌ Trade lifecycle {lifecycle_id} cancelled: {reason}")
            return True
            
        except Exception as e:
            logger.error(f"❌ Error updating trade cancelled: {e}")
            return False
    
    def link_stop_loss_to_trade(self, lifecycle_id: str, stop_loss_order_id: str,
                               stop_loss_price: float) -> bool:
        """Link a stop loss order to a trade lifecycle."""
        try:
            query = """
                UPDATE trades 
                SET stop_loss_order_id = ?,
                    stop_loss_price = ?,
                    updated_at = ?
                WHERE trade_lifecycle_id = ? AND status = 'position_opened'
            """
            timestamp = datetime.now(timezone.utc).isoformat()
            params = (stop_loss_order_id, stop_loss_price, timestamp, lifecycle_id)
            
            self._execute_query(query, params)
            
            formatter = get_formatter()
            trade_info = self.get_trade_by_lifecycle_id(lifecycle_id) # Fetch to get symbol for formatting
            symbol_for_formatting = trade_info.get('symbol', 'UNKNOWN_SYMBOL') if trade_info else 'UNKNOWN_SYMBOL'
            logger.info(f"🛑 Linked stop loss order {stop_loss_order_id} ({formatter.format_price(stop_loss_price, symbol_for_formatting)}) to trade {lifecycle_id}")
            return True
            
        except Exception as e:
            logger.error(f"❌ Error linking stop loss to trade: {e}")
            return False
    
    def link_take_profit_to_trade(self, lifecycle_id: str, take_profit_order_id: str,
                                 take_profit_price: float) -> bool:
        """Link a take profit order to a trade lifecycle."""
        try:
            query = """
                UPDATE trades 
                SET take_profit_order_id = ?,
                    take_profit_price = ?,
                    updated_at = ?
                WHERE trade_lifecycle_id = ? AND status = 'position_opened'
            """
            timestamp = datetime.now(timezone.utc).isoformat()
            params = (take_profit_order_id, take_profit_price, timestamp, lifecycle_id)
            
            self._execute_query(query, params)
            
            formatter = get_formatter()
            trade_info = self.get_trade_by_lifecycle_id(lifecycle_id) # Fetch to get symbol for formatting
            symbol_for_formatting = trade_info.get('symbol', 'UNKNOWN_SYMBOL') if trade_info else 'UNKNOWN_SYMBOL'
            logger.info(f"🎯 Linked take profit order {take_profit_order_id} ({formatter.format_price(take_profit_price, symbol_for_formatting)}) to trade {lifecycle_id}")
            return True
            
        except Exception as e:
            logger.error(f"❌ Error linking take profit to trade: {e}")
            return False
    
    def get_trade_by_lifecycle_id(self, lifecycle_id: str) -> Optional[Dict[str, Any]]:
        """Get trade by lifecycle ID."""
        query = "SELECT * FROM trades WHERE trade_lifecycle_id = ?"
        return self._fetchone_query(query, (lifecycle_id,))
    
    def get_trade_by_symbol_and_status(self, symbol: str, status: str = 'position_opened') -> Optional[Dict[str, Any]]:
        """Get trade by symbol and status."""
        query = "SELECT * FROM trades WHERE symbol = ? AND status = ? ORDER BY updated_at DESC LIMIT 1"
        return self._fetchone_query(query, (symbol, status))
    
    def get_open_positions(self, symbol: Optional[str] = None) -> List[Dict[str, Any]]:
        """Get all open positions, optionally filtered by symbol."""
        if symbol:
            query = "SELECT * FROM trades WHERE status = 'position_opened' AND symbol = ? ORDER BY position_opened_at DESC"
            return self._fetch_query(query, (symbol,))
        else:
            query = "SELECT * FROM trades WHERE status = 'position_opened' ORDER BY position_opened_at DESC"
            return self._fetch_query(query)
    
    def get_trades_by_status(self, status: str, limit: int = 50) -> List[Dict[str, Any]]:
        """Get trades by status."""
        query = "SELECT * FROM trades WHERE status = ? ORDER BY updated_at DESC LIMIT ?"
        return self._fetch_query(query, (status, limit))
    
    def get_lifecycle_by_entry_order_id(self, entry_exchange_order_id: str, status: Optional[str] = None) -> Optional[Dict[str, Any]]:
        """Get a trade lifecycle by its entry_order_id (exchange ID) and optionally by status."""
        if status:
            query = "SELECT * FROM trades WHERE entry_order_id = ? AND status = ? LIMIT 1"
            params = (entry_exchange_order_id, status)
        else:
            query = "SELECT * FROM trades WHERE entry_order_id = ? LIMIT 1"
            params = (entry_exchange_order_id,)
        return self._fetchone_query(query, params)

    def get_lifecycle_by_sl_order_id(self, sl_exchange_order_id: str, status: str = 'position_opened') -> Optional[Dict[str, Any]]:
        """Get an active trade lifecycle by its stop_loss_order_id (exchange ID)."""
        query = "SELECT * FROM trades WHERE stop_loss_order_id = ? AND status = ? LIMIT 1"
        return self._fetchone_query(query, (sl_exchange_order_id, status))

    def get_lifecycle_by_tp_order_id(self, tp_exchange_order_id: str, status: str = 'position_opened') -> Optional[Dict[str, Any]]:
        """Get an active trade lifecycle by its take_profit_order_id (exchange ID)."""
        query = "SELECT * FROM trades WHERE take_profit_order_id = ? AND status = ? LIMIT 1"
        return self._fetchone_query(query, (tp_exchange_order_id, status))
    
    def get_pending_stop_loss_activations(self) -> List[Dict[str, Any]]:
        """Get open positions that need stop loss activation."""
        query = """
            SELECT * FROM trades 
            WHERE status = 'position_opened' 
            AND stop_loss_price IS NOT NULL 
            AND stop_loss_order_id IS NULL
            ORDER BY updated_at ASC
        """
        return self._fetch_query(query)
    
    def cleanup_old_cancelled_trades(self, days_old: int = 7) -> int:
        """Clean up old cancelled trades (optional - for housekeeping)."""
        try:
            cutoff_date = (datetime.now(timezone.utc) - timedelta(days=days_old)).isoformat()
            
            # Count before deletion
            count_query = """
                SELECT COUNT(*) as count FROM trades 
                WHERE status = 'cancelled' AND updated_at < ?
            """
            count_result = self._fetchone_query(count_query, (cutoff_date,))
            count_to_delete = count_result['count'] if count_result else 0
            
            if count_to_delete > 0:
                delete_query = """
                    DELETE FROM trades 
                    WHERE status = 'cancelled' AND updated_at < ?
                """
                self._execute_query(delete_query, (cutoff_date,))
                logger.info(f"🧹 Cleaned up {count_to_delete} old cancelled trades (older than {days_old} days)")
            
            return count_to_delete
            
        except Exception as e:
            logger.error(f"❌ Error cleaning up old cancelled trades: {e}")
            return 0
    
    def confirm_position_with_exchange(self, symbol: str, exchange_position_size: float, 
                                     exchange_open_orders: List[Dict]) -> bool:
        """🆕 PHASE 4: Confirm position status with exchange before updating status."""
        try:
            # Get current trade status
            current_trade = self.get_trade_by_symbol_and_status(symbol, 'position_opened')
            
            if not current_trade:
                return True  # No open position to confirm
            
            lifecycle_id = current_trade['trade_lifecycle_id']
            has_open_orders = len([o for o in exchange_open_orders if o.get('symbol') == symbol]) > 0
            
            # Only close position if exchange confirms no position AND no pending orders
            if abs(exchange_position_size) < 1e-8 and not has_open_orders:
                # Calculate realized P&L based on position side
                position_side = current_trade['position_side']
                entry_price_db = current_trade['entry_price'] # entry_price from db
                # current_amount = current_trade['current_position_size'] # Not directly used for PNL calc here
                
                # For a closed position, we need to calculate final P&L
                # This would typically come from the closing trade, but for confirmation we estimate
                estimated_pnl = current_trade.get('realized_pnl', 0) # Use existing realized_pnl if any
                
                success = self.update_trade_position_closed(
                    lifecycle_id, 
                    entry_price_db,  # Using entry price from DB as estimate since position is confirmed closed
                    estimated_pnl,
                    "exchange_confirmed_closed"
                )
                
                if success:
                    logger.info(f"✅ Confirmed position closed for {symbol} with exchange")
                    
                return success
            
            return True  # Position still exists on exchange, no update needed
            
        except Exception as e:
            logger.error(f"❌ Error confirming position with exchange: {e}")
            return False

    def update_trade_market_data(self, 
                                 trade_lifecycle_id: str, 
                                 unrealized_pnl: Optional[float] = None, 
                                 mark_price: Optional[float] = None,
                                 current_position_size: Optional[float] = None,
                                 entry_price: Optional[float] = None,
                                 liquidation_price: Optional[float] = None,
                                 margin_used: Optional[float] = None,
                                 leverage: Optional[float] = None,
                                 position_value: Optional[float] = None,
                                 unrealized_pnl_percentage: Optional[float] = None) -> bool:
        """Update market-related data for an open trade lifecycle.
        Only updates fields for which a non-None value is provided.
        """
        try:
            updates = []
            params = []
            
            if unrealized_pnl is not None:
                updates.append("unrealized_pnl = ?")
                params.append(unrealized_pnl)
            if mark_price is not None:
                updates.append("mark_price = ?")
                params.append(mark_price)
            if current_position_size is not None:
                updates.append("current_position_size = ?")
                params.append(current_position_size)
            if entry_price is not None: # If exchange provides updated avg entry
                updates.append("entry_price = ?")
                params.append(entry_price)
            if liquidation_price is not None:
                updates.append("liquidation_price = ?")
                params.append(liquidation_price)
            if margin_used is not None:
                updates.append("margin_used = ?")
                params.append(margin_used)
            if leverage is not None:
                updates.append("leverage = ?")
                params.append(leverage)
            if position_value is not None:
                updates.append("position_value = ?")
                params.append(position_value)
            if unrealized_pnl_percentage is not None:
                updates.append("unrealized_pnl_percentage = ?")
                params.append(unrealized_pnl_percentage)

            if not updates:
                logger.debug(f"No market data fields provided to update for lifecycle {trade_lifecycle_id}.")
                return True # No update needed, not an error

            timestamp = datetime.now(timezone.utc).isoformat()
            updates.append("updated_at = ?")
            params.append(timestamp)

            set_clause = ", ".join(updates)
            query = f"""
                UPDATE trades
                SET {set_clause}
                WHERE trade_lifecycle_id = ? AND status = 'position_opened'
            """
            params.append(trade_lifecycle_id)
            
            # Use the class's own connection self.conn
            cursor = self.conn.cursor()
            cursor.execute(query, tuple(params))
            self.conn.commit()
            updated_rows = cursor.rowcount

            if updated_rows > 0:
                logger.debug(f"💹 Updated market data for lifecycle {trade_lifecycle_id}. Fields: {updates}")
                return True
            else:
                # This might happen if the lifecycle ID doesn't exist or status is not 'position_opened'
                # logger.warning(f"⚠️ No trade found or not in 'position_opened' state for lifecycle {trade_lifecycle_id} to update market data.")
                return False # Not necessarily an error

        except Exception as e:
            logger.error(f"❌ Error updating market data for trade lifecycle {trade_lifecycle_id}: {e}")
            return False

    # --- End Trade Lifecycle Management ---

    def get_daily_balance_record_count(self) -> int:
        """Get the total number of daily balance records."""
        row = self._fetchone_query("SELECT COUNT(*) as count FROM daily_balances")
        return row['count'] if row and 'count' in row else 0

    # 🆕 PHASE 5: AGGREGATION AND PURGING LOGIC
    def _migrate_trade_to_aggregated_stats(self, trade_lifecycle_id: str):
        """Migrate a completed/cancelled trade's stats to aggregate tables and delete the original trade."""
        trade_data = self.get_trade_by_lifecycle_id(trade_lifecycle_id)
        if not trade_data:
            logger.error(f"Cannot migrate trade {trade_lifecycle_id}: Not found.")
            return

        status = trade_data.get('status')
        symbol = trade_data.get('symbol')
        token = symbol.split('/')[0] if symbol and '/' in symbol else symbol # Assuming symbol like BTC/USDT
        if not token:
            logger.error(f"Cannot migrate trade {trade_lifecycle_id}: Token could not be derived from symbol '{symbol}'.")
            return

        now_iso = datetime.now(timezone.utc).isoformat()

        try:
            with self.conn: # Ensures atomicity for the operations below
                if status == 'position_closed':
                    realized_pnl = trade_data.get('realized_pnl', 0.0)
                    # Use entry value if available, otherwise value (amount * price at entry)
                    entry_value = trade_data.get('value', 0.0) # 'value' is amount * price from initial trade record
                    # For exit_value, we'd ideally have the value of the closing trade(s).
                    # If the 'realized_pnl' is from the trade record, and 'entry_value' is entry, exit_value = entry_value + realized_pnl
                    exit_value = entry_value + realized_pnl 
                    closed_at_str = trade_data.get('position_closed_at', now_iso)
                    closed_at_dt = datetime.fromisoformat(closed_at_str)
                    date_str = closed_at_dt.strftime('%Y-%m-%d')

                    # Update token_stats
                    token_upsert_query = """
                        INSERT INTO token_stats (
                            token, total_realized_pnl, total_completed_cycles, winning_cycles, losing_cycles,
                            total_entry_volume, total_exit_volume, sum_of_winning_pnl, sum_of_losing_pnl,
                            largest_winning_cycle_pnl, largest_losing_cycle_pnl, 
                            first_cycle_closed_at, last_cycle_closed_at, updated_at
                        ) VALUES (?, ?, 1, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) 
                        ON CONFLICT(token) DO UPDATE SET
                            total_realized_pnl = total_realized_pnl + excluded.total_realized_pnl,
                            total_completed_cycles = total_completed_cycles + 1,
                            winning_cycles = winning_cycles + excluded.winning_cycles,
                            losing_cycles = losing_cycles + excluded.losing_cycles,
                            total_entry_volume = total_entry_volume + excluded.total_entry_volume,
                            total_exit_volume = total_exit_volume + excluded.total_exit_volume,
                            sum_of_winning_pnl = sum_of_winning_pnl + excluded.sum_of_winning_pnl,
                            sum_of_losing_pnl = sum_of_losing_pnl + excluded.sum_of_losing_pnl,
                            largest_winning_cycle_pnl = MAX(largest_winning_cycle_pnl, excluded.largest_winning_cycle_pnl),
                            largest_losing_cycle_pnl = MAX(largest_losing_cycle_pnl, excluded.largest_losing_cycle_pnl),
                            first_cycle_closed_at = MIN(first_cycle_closed_at, excluded.first_cycle_closed_at),
                            last_cycle_closed_at = MAX(last_cycle_closed_at, excluded.last_cycle_closed_at),
                            updated_at = excluded.updated_at
                    """
                    is_win = 1 if realized_pnl > 0 else 0
                    is_loss = 1 if realized_pnl < 0 else 0
                    win_pnl_contrib = realized_pnl if realized_pnl > 0 else 0.0
                    loss_pnl_contrib = abs(realized_pnl) if realized_pnl < 0 else 0.0
                    
                    self._execute_query(token_upsert_query, (
                        token, realized_pnl, is_win, is_loss, entry_value, exit_value,
                        win_pnl_contrib, loss_pnl_contrib, win_pnl_contrib, loss_pnl_contrib,
                        closed_at_str, closed_at_str, now_iso
                    ))

                    # Update daily_aggregated_stats
                    daily_upsert_query = """
                        INSERT INTO daily_aggregated_stats (
                            date, token, realized_pnl, completed_cycles, entry_volume, exit_volume
                        ) VALUES (?, ?, ?, 1, ?, ?) 
                        ON CONFLICT(date, token) DO UPDATE SET
                            realized_pnl = realized_pnl + excluded.realized_pnl,
                            completed_cycles = completed_cycles + 1,
                            entry_volume = entry_volume + excluded.entry_volume,
                            exit_volume = exit_volume + excluded.exit_volume
                    """
                    self._execute_query(daily_upsert_query, (
                        date_str, token, realized_pnl, entry_value, exit_value
                    ))
                    logger.info(f"Aggregated stats for closed trade lifecycle {trade_lifecycle_id} ({token}). PNL: {realized_pnl:.2f}")

                elif status == 'cancelled':
                    # Update token_stats for cancelled count
                    cancelled_upsert_query = """
                        INSERT INTO token_stats (token, total_cancelled_cycles, updated_at) 
                        VALUES (?, 1, ?) 
                        ON CONFLICT(token) DO UPDATE SET
                            total_cancelled_cycles = total_cancelled_cycles + 1,
                            updated_at = excluded.updated_at
                    """
                    self._execute_query(cancelled_upsert_query, (token, now_iso))
                    logger.info(f"Incremented cancelled_cycles for {token} due to lifecycle {trade_lifecycle_id}.")
                
                # Delete the original trade from the 'trades' table
                self._execute_query("DELETE FROM trades WHERE trade_lifecycle_id = ?", (trade_lifecycle_id,))
                logger.info(f"Deleted trade lifecycle {trade_lifecycle_id} from trades table after aggregation.")

        except sqlite3.Error as e:
            logger.error(f"Database error migrating trade {trade_lifecycle_id} to aggregate stats: {e}", exc_info=True)
        except Exception as e:
            logger.error(f"Unexpected error migrating trade {trade_lifecycle_id} to aggregate stats: {e}", exc_info=True)

    def purge_old_daily_aggregated_stats(self, months_to_keep: int = 10):
        """Purge records from daily_aggregated_stats older than a specified number of months."""
        if months_to_keep <= 0:
            logger.info("Not purging daily_aggregated_stats as months_to_keep is not positive.")
            return

        try:
            # Calculate the cutoff date
            # This is a bit simplified; for more precise month calculations, dateutil.relativedelta might be used
            # For SQLite, comparing YYYY-MM-DD strings works well.
            cutoff_date = datetime.now(timezone.utc).date() - timedelta(days=months_to_keep * 30) # Approximate
            cutoff_date_str = cutoff_date.strftime('%Y-%m-%d')

            query = "DELETE FROM daily_aggregated_stats WHERE date < ?"
            
            # To count before deleting (optional, for logging)
            # count_query = "SELECT COUNT(*) as count FROM daily_aggregated_stats WHERE date < ?"
            # before_count_row = self._fetchone_query(count_query, (cutoff_date_str,))
            # num_to_delete = before_count_row['count'] if before_count_row else 0

            with self.conn:
                cursor = self.conn.cursor()
                cursor.execute(query, (cutoff_date_str,))
                rows_deleted = cursor.rowcount
            
            if rows_deleted > 0:
                logger.info(f"Purged {rows_deleted} old records from daily_aggregated_stats (older than approx. {months_to_keep} months, before {cutoff_date_str}).")
            else:
                logger.info(f"No old records found in daily_aggregated_stats to purge (older than approx. {months_to_keep} months, before {cutoff_date_str}).")

        except sqlite3.Error as e:
            logger.error(f"Database error purging old daily_aggregated_stats: {e}", exc_info=True)
        except Exception as e:
            logger.error(f"Unexpected error purging old daily_aggregated_stats: {e}", exc_info=True)