OKXTrading/okxtrading2.0.py

import os
import time
import threading
import logging
import pandas as pd
import numpy as np
import requests
import json
import hashlib
import hmac
import base64
import schedule
import okx.Account as Account
import okx.MarketData as MarketData
import okx.Trade as Trade
from datetime import datetime, timezone, timedelta
from dotenv import load_dotenv
from logging.handlers import RotatingFileHandler
from database_manager import DatabaseManager
import atexit
import signal

tdmode = 'cross'

# Load environment variables
load_dotenv()

def setup_logging():
    """Configure logging system"""
    # Set environment variable to ensure UTF-8 encoding
    os.environ['PYTHONIOENCODING'] = 'utf-8'
    
    # Create formatter
    formatter = logging.Formatter(
        '%(asctime)s - %(levelname)s - %(message)s',
        datefmt='%Y-%m-%d %H:%M:%S'
    )
    
    # File handler - using UTF-8 encoding
    file_handler = RotatingFileHandler(
        'trading_system.log',
        maxBytes=10 * 1024 * 1024,
        backupCount=5,
        encoding='utf-8'
    )
    file_handler.setFormatter(formatter)
    file_handler.setLevel(logging.INFO)
    
    # Console handler - handle encoding issues
    class SafeStreamHandler(logging.StreamHandler):
        def emit(self, record):
            try:
                msg = self.format(record)
                stream = self.stream
                if hasattr(stream, 'buffer'):
                    stream.buffer.write(msg.encode('utf-8') + b'\n')
                    stream.buffer.flush()
                else:
                    stream.write(msg + self.terminator)
                    self.flush()
            except UnicodeEncodeError:
                safe_msg = msg.encode('ascii', 'ignore').decode('ascii')
                stream.write(safe_msg + self.terminator)
                self.flush()
            except Exception:
                self.handleError(record)
    
    console_handler = SafeStreamHandler()
    console_handler.setFormatter(formatter)
    console_handler.setLevel(logging.INFO)
    
    # Configure root logger
    logging.basicConfig(
        level=logging.INFO,
        handlers=[file_handler, console_handler]
    )
    
    return logging.getLogger(__name__)

logger = setup_logging()

class APIRateLimiter:
    """API rate limiter"""
    
    def __init__(self, calls_per_second=2):
        self.min_interval = 1.0 / calls_per_second
        self.last_call = {}
        self.lock = threading.Lock()
    
    def wait(self, api_name):
        """Wait until API can be called"""
        with self.lock:
            current_time = time.time()
            if api_name in self.last_call:
                elapsed = current_time - self.last_call[api_name]
                if elapsed < self.min_interval:
                    time.sleep(self.min_interval - elapsed)
            self.last_call[api_name] = current_time

class OKXAPIClient:
    """OKX API Client (using official SDK)"""
    
    def __init__(self):
        self.api_key = os.getenv('OKX_API_KEY')
        self.secret_key = os.getenv('OKX_SECRET_KEY')
        self.password = os.getenv('OKX_PASSWORD')
        
        if not all([self.api_key, self.secret_key, self.password]):
            raise ValueError("Please set OKX API key and password")
        
        # Initialize OKX SDK client - using live trading environment
        Flag = "1"  # Live trading environment
        self.account_api = Account.AccountAPI(self.api_key, self.secret_key, self.password, False, Flag)
        self.market_api = MarketData.MarketAPI(self.api_key, self.secret_key, self.password, False, Flag)
        self.trade_api = Trade.TradeAPI(self.api_key, self.secret_key, self.password, False, Flag)
        
        # API rate limiting
        self.rate_limiter = APIRateLimiter(2)
        self.log_http = False
        
        # Cache instrument info
        self.instrument_cache = {}
    
    def get_market_data(self, symbol, timeframe='1H', limit=200):
        """Get market data"""
        self.rate_limiter.wait("market_data")
        
        try:
            result = self.market_api.get_candlesticks(
                instId=symbol,
                bar=timeframe,
                limit=str(limit)
            )
            
            if self.log_http:
                logger.debug(f"HTTP Request: GET {symbol} {timeframe} {result.get('code', 'Unknown')}")
        
            # Error checking
            if 'code' not in result or result['code'] != '0':
                error_msg = result.get('msg', 'Unknown error')
                error_code = result.get('code', 'Unknown code')
                logger.error(f"Failed to get {symbol} market data: {error_msg} (code: {error_code})")
                return None
            
            # Check if data exists
            if 'data' not in result or not result['data']:
                logger.warning(f"{symbol} market data is empty")
                return None
            
            # Create DataFrame
            columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume', 'volCcy', 'volCcyQuote', 'confirm']
            df = pd.DataFrame(result['data'], columns=columns)
            
            # Convert data types
            df['timestamp'] = pd.to_datetime(df['timestamp'].astype(np.int64), unit='ms')
            numeric_cols = ['open', 'high', 'low', 'close', 'volume', 'volCcy', 'volCcyQuote']
            df[numeric_cols] = df[numeric_cols].apply(pd.to_numeric)
            
            return df.sort_values('timestamp')
            
        except Exception as e:
            logger.error(f"Error getting market data: {e}")
            return None
    
    def get_account_balance(self):
        """Get account balance (USDT)"""
        self.rate_limiter.wait("balance")
        
        try:
            result = self.account_api.get_account_balance()
            
            if self.log_http:
                logger.debug(f"HTTP Request: GET balance {result['code']}")
            
            # Correction: code "0" indicates success
            if result['code'] != '0':
                logger.error(f"Failed to get balance: {result['msg']} (code: {result['code']})")
                return None
            
            # Extract USDT balance
            for currency in result['data'][0]['details']:
                if currency['ccy'] == 'USDT':
                    return float(currency['availBal'])
            
            return 0.0
            
        except Exception as e:
            logger.error(f"Error getting balance: {e}")
            return None
    
    def get_currency_balances(self):
        """Get all currency balances"""
        self.rate_limiter.wait("balances")
        
        try:
            result = self.account_api.get_account_balance()
            
            # Correction: code "0" indicates success
            if result['code'] != '0':
                logger.error(f"Failed to get balance: {result['msg']} (code: {result['code']})")
                return {}
            
            # Check response data structure
            if not result['data'] or len(result['data']) == 0:
                logger.error("No balance data in API response")
                return {}
            
            # Check if details field exists
            if 'details' not in result['data'][0]:
                logger.error("No details field in API response")
                return {}
            
            balances = {}
            for currency in result['data'][0]['details']:
                if float(currency.get('availBal', 0)) > 0:
                    balances[currency['ccy']] = {
                        'amount': float(currency.get('availBal', 0)),
                        'frozen': float(currency.get('frozenBal', 0))
                    }
            
            return balances
            
        except Exception as e:
            logger.error(f"Error getting currency balances: {e}")
            return {}
    
    def get_positions(self):
        """Get exchange position information (based on currency balances)"""
        try:
            # Get all currency balances
            balances = self.get_currency_balances()
            if not balances:
                return {}
            
            # Filter out non-USDT currencies as positions
            positions = {}
            for currency, balance in balances.items():
                if currency != 'USDT' and balance['amount'] > 0:
                    # Construct trading pair symbol
                    symbol = f"{currency}-USDT"
                    # Get current price to calculate position value
                    current_price = self.get_current_price(symbol)
                    if current_price:
                        positions[symbol] = {
                            'amount': balance['amount'],
                            'value': balance['amount'] * current_price,
                            'avg_price': 0.0  # Spot positions don't have average price concept
                        }
                    else:
                        positions[symbol] = {
                            'amount': balance['amount'],
                            'value': 0.0,
                            'avg_price': 0.0
                        }
            
            return positions
            
        except Exception as e:
            logger.error(f"Error getting positions: {e}")
            return {}
    
    def get_current_price(self, symbol):
        """Get current price"""
        self.rate_limiter.wait("price")
        
        try:
            result = self.market_api.get_ticker(instId=symbol)
            
            # Correction: code "0" indicates success
            if result['code'] != '0':
                logger.error(f"Failed to get price: {result['msg']} (code: {result['code']})")
                return None
            
            return float(result['data'][0]['last'])
            
        except Exception as e:
            logger.error(f"Error getting price: {e}")
            return None
    
    def get_instrument_info(self, symbol):
        """Get trading pair information"""
        if symbol in self.instrument_cache:
            return self.instrument_cache[symbol]
        
        self.rate_limiter.wait("instrument")
        
        try:
            result = self.account_api.get_instruments(instType='SPOT')
            
            if result['code'] != '0':
                logger.error(f"Failed to get instrument: {result['msg']} (code: {result['code']})")
                return None, None
            
            # Find specified trading pair
            for inst in result['data']:
                if inst['instId'] == symbol:
                    min_sz = float(inst['minSz'])
                    lot_sz = float(inst['lotSz'])
                    logger.debug(f"Got {symbol} precision: minSz={min_sz}, lotSz={lot_sz}")
                    self.instrument_cache[symbol] = (min_sz, lot_sz)
                    return min_sz, lot_sz
            
            logger.error(f"Trading pair not found: {symbol}")
            return None, None
            
        except Exception as e:
            logger.error(f"Error getting instrument info: {e}")
            return None, None
    
    def get_default_min_size(self, symbol):
        """Get default minimum order size"""
        # Set default minimum order size based on currency
        defaults = {
            'BTC-USDT': 0.0001,
            'ETH-USDT': 0.001,
            'SOL-USDT': 0.01,
            'XRP-USDT': 1.0
        }
        return defaults.get(symbol, 0.01)  # Default 0.01
    
    def create_order(self, symbol, side, amount, retries=3):
        """Create order"""
        for attempt in range(retries):
            try:
                self.rate_limiter.wait("order")
                
                # Parse trading pair symbol
                parts = symbol.split('-')
                if len(parts) != 2:
                    logger.error(f"Invalid trading pair format: {symbol}")
                    return None
                
                base_currency, quote_currency = parts
                
                # Adjust parameters based on buy/sell direction
                if side == 'buy':
                    # When buying, amount is quote currency amount (USDT amount)
                    # Use amount-based order placement
                    order_params = {
                        'instId': symbol,
                        'tdMode': tdmode,
                        'side': 'buy',
                        'ordType': 'market',
                        'sz': str(amount),  # Quote currency amount
                        'tgtCcy': 'quote_ccy'  # Specify sz as quote currency
                    }
                    logger.info(f"[{symbol}] Create buy order: amount={amount:.2f} {quote_currency}")
                        
                else:
                    # When selling, amount is base currency quantity
                    # Get precision info and adjust quantity
                    min_sz, lot_sz = self.get_instrument_info(symbol)
                    if min_sz is None:
                        min_sz = self.get_default_min_size(symbol)
                    if lot_sz is None:
                        lot_sz = min_sz  
                    
                    # Adjust quantity to appropriate precision
                    if lot_sz > 0:
                        amount = (amount / lot_sz) * lot_sz
                    
                    amount_str = f"{amount:.10f}"
                    
                    order_params = {
                        'instId': symbol,
                        'tdMode': tdmode,
                        'side': 'sell',
                        'ordType': 'market',
                        'sz': amount_str  # Base currency quantity
                    }
                    logger.info(f"[{symbol}] Create sell order: quantity={amount_str} {base_currency}")
                                                
                # Use SDK to create order
                result = self.trade_api.place_order(**order_params)
                
                if self.log_http:
                    logger.debug(f"HTTP Request: POST create order {result['code']}")
                
                # Check API response
                if result['code'] != '0':
                    logger.error(f"Failed to create order: {result['msg']} (code: {result['code']})")
                    if 'data' in result and len(result['data']) > 0:
                        for item in result['data']:
                            logger.error(f"Detailed error: {item.get('sMsg', 'Unknown')} (sCode: {item.get('sCode', 'Unknown')})")
                    # Specific error handling
                    if result['code'] == '50113':  # Insufficient permissions
                        logger.error("API key may not have trading permissions, please check API key settings")
                    elif result['code'] == '51020':  # Minimum order amount
                        logger.error("Order amount below exchange minimum requirement")
                    if attempt < retries - 1:
                        wait_time = 2 ** attempt
                        time.sleep(wait_time)
                    continue
                
                # Check order status
                if len(result['data']) > 0:
                    order_data = result['data'][0]
                    if order_data.get('sCode') != '0':
                        logger.error(f"Order creation failed: {order_data.get('sMsg', 'Unknown error')} (sCode: {order_data.get('sCode', 'Unknown')})")
                        if attempt < retries - 1:
                            wait_time = 2 ** attempt
                            time.sleep(wait_time)
                        continue
                    
                    order_id = order_data.get('ordId')
                    if order_id:
                        logger.info(f"Order created successfully: {order_id}")
                        return order_id
                    else:
                        logger.error("Order ID is empty")
                        if attempt < retries - 1:
                            wait_time = 2 ** attempt
                            time.sleep(wait_time)
                        continue
                else:
                    logger.error("No order data in API response")
                    if attempt < retries - 1:
                        wait_time = 2 ** attempt
                        time.sleep(wait_time)
                    continue
                
            except Exception as e:
                logger.error(f"Error creating order (attempt {attempt+1}/{retries}): {str(e)}")
                if attempt < retries - 1:
                    wait_time = 2 ** attempt
                    time.sleep(wait_time)
                else:
                    return None
        
        return None
    
    def get_order_status(self, symbol, order_id):
        """Get order status"""
        self.rate_limiter.wait("order_status")
        
        try:
            result = self.trade_api.get_order(instId=symbol, ordId=order_id)
            
            # Correction: code "0" indicates success
            if result['code'] != '0':
                logger.error(f"Failed to get order status: {result['msg']} (code: {result['code']})")
                return None
            
            if len(result['data']) > 0:
                order_data = result['data'][0]
                return {
                    'state': order_data.get('state'),
                    'avgPx': float(order_data.get('avgPx', 0)),
                    'accFillSz': float(order_data.get('accFillSz', 0)),
                    'fillPx': float(order_data.get('fillPx', 0)),
                    'fillSz': float(order_data.get('fillSz', 0)),
                    'fillTime': order_data.get('fillTime')
                }
            else:
                logger.error("No order data in API response")
                return None
                
        except Exception as e:
            logger.error(f"Error getting order status: {e}")
            return None
    
    def wait_for_order_completion(self, symbol, order_id, max_attempts=10, interval=1):
        """Wait for order completion"""
        for attempt in range(max_attempts):
            order_status = self.get_order_status(symbol, order_id)
            if order_status is None:
                return None
            
            state = order_status['state']
            if state == 'filled':
                logger.info(f"Order completed: {order_id}, fill price={order_status['avgPx']:.2f}, fill quantity={order_status['accFillSz']:.10f}")
                return order_status
            elif state == 'canceled':
                logger.warning(f"Order canceled: {order_id}")
                return None
            elif state == 'partially_filled':
                logger.info(f"Order partially filled: {order_id}, filled={order_status['accFillSz']:.10f}")
                time.sleep(interval)
            else:
                logger.info(f"Order status: {state}, waiting...")
                time.sleep(interval)
        
        logger.warning(f"Order not completed within specified time: {order_id}")
        return None

class DeepSeekAnalyzer:
    """DeepSeek AI Analyzer (KEN2.0 optimized)"""
    
    def __init__(self):
        self.api_key = os.getenv('DEEPSEEK_API_KEY')
        self.api_url = 'https://api.deepseek.com/v1/chat/completions'
        
    def analyze_market(self, symbol, market_data, technical_indicators):
        """Use DeepSeek to analyze market (optimized version)"""
        if not self.api_key:
            logger.warning("DeepSeek API key not set, skipping AI analysis")
            return None
        
        try:
            # Prepare analysis data
            latest = market_data.iloc[-1]
            technical_summary = self._prepare_enhanced_technical_summary(technical_indicators, market_data)
            
            prompt = self._create_enhanced_analysis_prompt(symbol, latest, market_data, technical_summary)
            response = self._call_deepseek_api(prompt)
            
            return self._parse_deepseek_response(response)
            
        except Exception as e:
            logger.error(f"DeepSeek analysis error: {e}")
            return None
    
    def _prepare_enhanced_technical_summary(self, indicators, market_data):
        """Prepare enhanced technical indicator summary"""
        summary = []
        
        try:
            # Price information
            current_price = indicators.get('current_price', 0)
            summary.append(f"Current price: ${current_price:.4f}")
            
            # Multi-period RSI
            rsi_7 = indicators.get('rsi_7', 50)
            rsi_14 = indicators.get('rsi', 50)
            rsi_21 = indicators.get('rsi_21', 50)
            if hasattr(rsi_7, '__len__'):
                rsi_7, rsi_14, rsi_21 = rsi_7.iloc[-1], rsi_14.iloc[-1], rsi_21.iloc[-1]
            
            summary.append(f"RSI(7/14/21): {rsi_7:.1f}/{rsi_14:.1f}/{rsi_21:.1f}")
            
            # RSI status analysis
            rsi_status = []
            for rsi_val, period in [(rsi_7, 7), (rsi_14, 14), (rsi_21, 21)]:
                if rsi_val > 70:
                    rsi_status.append(f"RSI{period} overbought")
                elif rsi_val < 30:
                    rsi_status.append(f"RSI{period} oversold")
            if rsi_status:
                summary.append(f"RSI status: {', '.join(rsi_status)}")
            
            # KDJ indicator
            k, d, j = indicators.get('kdj', (50, 50, 50))
            if hasattr(k, '__len__'):
                k, d, j = k.iloc[-1], d.iloc[-1], j.iloc[-1]
            
            summary.append(f"KDJ: K={k:.1f}, D={d:.1f}, J={j:.1f}")
            if j > 80:
                summary.append("KDJ status: Overbought area")
            elif j < 20:
                summary.append("KDJ status: Oversold area")
            
            # MACD indicator
            macd_line, signal_line, histogram = indicators.get('macd', (0, 0, 0))
            if hasattr(macd_line, '__len__'):
                macd_line, signal_line, histogram = macd_line.iloc[-1], signal_line.iloc[-1], histogram.iloc[-1]
            
            summary.append(f"MACD: line={macd_line:.4f}, signal={signal_line:.4f}, histogram={histogram:.4f}")
            summary.append(f"MACD status: {'Golden cross' if macd_line > signal_line else 'Death cross'}")
            
            # Moving averages
            sma_20 = indicators.get('sma_20', 0)
            sma_50 = indicators.get('sma_50', 0)
            sma_100 = indicators.get('sma_100', 0)
            if hasattr(sma_20, '__len__'):
                sma_20, sma_50, sma_100 = sma_20.iloc[-1], sma_50.iloc[-1], sma_100.iloc[-1]
            
            price_vs_ma = []
            if current_price > sma_20: price_vs_ma.append("Above 20-day MA")
            else: price_vs_ma.append("Below 20-day MA")
            if current_price > sma_50: price_vs_ma.append("Above 50-day MA")
            else: price_vs_ma.append("Below 50-day MA")
            
            summary.append(f"Moving averages: 20-day=${sma_20:.2f}, 50-day=${sma_50:.2f}, 100-day=${sma_100:.2f}")
            summary.append(f"Price position: {', '.join(price_vs_ma)}")
            
            # Bollinger Bands
            upper, middle, lower = indicators.get('bollinger_2std', (0, 0, 0))
            if hasattr(upper, '__len__'):
                upper, middle, lower = upper.iloc[-1], middle.iloc[-1], lower.iloc[-1]
            
            bb_position = (current_price - lower) / (upper - lower) * 100 if (upper - lower) > 0 else 50
            summary.append(f"Bollinger Band position: {bb_position:.1f}% (0%=lower band, 100%=upper band)")
            
            # Trend strength
            trend_strength = indicators.get('trend_strength', 0)
            summary.append(f"Trend strength: {trend_strength:.2f} (0-1, higher means stronger trend)")
            
            # Volatility
            volatility = indicators.get('volatility', 0) * 100  # Convert to percentage
            summary.append(f"Annualized volatility: {volatility:.1f}%")
            
            # ATR
            atr = indicators.get('atr', 0)
            if hasattr(atr, '__len__'):
                atr = atr.iloc[-1]
            atr_percent = (atr / current_price * 100) if current_price > 0 else 0
            summary.append(f"ATR: {atr:.4f} ({atr_percent:.2f}%)")
                               
            # Support resistance information
            sr_levels = TechnicalAnalyzer.calculate_support_resistance(market_data)
            if sr_levels:
                summary.append(f"Static support: ${sr_levels['static_support']:.4f}, resistance: ${sr_levels['static_resistance']:.4f}")
                summary.append(f"Dynamic support: ${sr_levels['dynamic_support']:.4f}, resistance: ${sr_levels['dynamic_resistance']:.4f}")
                summary.append(f"Relative resistance: {sr_levels['current_vs_resistance']:+.2f}%, relative support: {sr_levels['current_vs_support']:+.2f}%")
            
            return "\n".join(summary)
            
        except Exception as e:
            logger.error(f"Error preparing technical summary: {e}")
            return "Technical indicator calculation exception"
    
    def _create_enhanced_analysis_prompt(self, symbol, latest, market_data, technical_summary):
        """Create enhanced analysis prompt"""
        # Calculate price changes
        price_change_24h = 0
        price_change_7d = 0
        
        if len(market_data) >= 24:
            price_change_24h = ((latest['close'] - market_data.iloc[-24]['close']) / market_data.iloc[-24]['close'] * 100)
        
        if len(market_data) >= 168:  # 7 days data (hourly)
            price_change_7d = ((latest['close'] - market_data.iloc[-168]['close']) / market_data.iloc[-168]['close'] * 100)
        
        # Calculate volume changes
        volume_trend = "Rising" if latest['volume'] > market_data['volume'].mean() else "Falling"
        
        prompt = f"""
You are a professional cryptocurrency trading AI, conducting autonomous trading in the OKX digital currency market. Please comprehensively analyze the {symbol} trading pair and execute trades.

# Core Objective
**Maximize Sharpe Ratio**
Sharpe Ratio = Average Return / Return Volatility, which means:
Quality trades (high win rate, large profit/loss ratio) → Improve Sharpe
Stable returns, control drawdowns → Improve Sharpe
Frequent trading, small profits and losses → Increase volatility, severely reduce Sharpe

【Market Overview】
- Current price: ${latest['close']:.4f}
- 24-hour change: {price_change_24h:+.2f}%
- 7-day change: {price_change_7d:+.2f}%
- Volume: {latest['volume']:.0f} ({volume_trend})
- Volume relative level: {'High' if latest['volume'] > market_data['volume'].quantile(0.7) else 'Normal' if latest['volume'] > market_data['volume'].quantile(0.3) else 'Low'}

【Multi-dimensional Technical Analysis】
{technical_summary}

【Market Environment Assessment】
Please consider comprehensively:
1. Trend direction and strength
2. Overbought/oversold status
3. Volume coordination
4. Volatility level
5. Support resistance positions
6. Multi-timeframe consistency

【Risk Management Suggestions】
- Suggested position: Adjust based on signal strength and volatility
- Stop-loss setting: Reference ATR and support levels
- Holding time: Based on trend strength

【Output Format Requirements】
Please reply strictly in the following JSON format:
{{
    "action": "BUY/SELL/HOLD",
    "confidence": "HIGH/MEDIUM/LOW",
    "position_size": "Suggested position ratio (0.1-0.5)",
    "stop_loss": "Suggested stop-loss price or percentage",
    "take_profit": "Suggested take-profit price or percentage", 
    "timeframe": "Suggested holding time (SHORT/MEDIUM/LONG)",
    "reasoning": "Detailed analysis logic and risk explanation"
}}

Please provide objective suggestions based on professional technical analysis and risk management principles.
"""

        return prompt

    def analyze_sell_proportion(self, symbol, position_info, market_analysis, current_price):
        """Analyze sell proportion"""
        if not self.api_key:
            logger.warning("DeepSeek API key not set, using default sell proportion")
            return self._get_fallback_sell_proportion(position_info, 'MEDIUM')
        
        try:
            prompt = self._create_sell_proportion_prompt(symbol, position_info, market_analysis, current_price)
            response = self._call_deepseek_api(prompt)
            return self._parse_sell_proportion_response(response)
            
        except Exception as e:
            logger.error(f"DeepSeek sell proportion analysis error: {e}")
            return self._get_fallback_sell_proportion(position_info, 'MEDIUM')
    
    def _create_sell_proportion_prompt(self, symbol, position_info, market_analysis, current_price):
        """Create sell proportion analysis prompt"""
        
        # Calculate profit situation
        entry_price = position_info['entry_price']
        profit_ratio = (current_price - entry_price) / entry_price if entry_price > 0 else 0
        profit_status = "Profit" if profit_ratio > 0 else "Loss"
        profit_amount = (current_price - entry_price) * position_info['base_amount']
        
        # Calculate position ratio
        position_ratio = position_info['position_ratio'] * 100  # Convert to percentage
        
        # Prepare technical indicator summary
        technical_summary = self._prepare_enhanced_technical_summary(
            market_analysis['technical_indicators'], 
            market_analysis['market_data']
        )
        
        prompt = f"""
You are a professional cryptocurrency trading AI, conducting autonomous trading in the OKX digital currency market. Analyze the {symbol} position situation and consider and execute sell decisions.

【Current Position Situation】
- Position quantity: {position_info['base_amount']:.8f}
- Average entry price: ${entry_price:.4f}
- Current price: ${current_price:.4f}
- Profit ratio: {profit_ratio:+.2%} ({profit_status})
- Profit amount: ${profit_amount:+.2f}
- Position ratio: {position_ratio:.2f}% (percentage of total portfolio)
- Position value: ${position_info['position_value']:.2f}

【Market Technical Analysis】
{technical_summary}

【Risk Situation】
- Current price distance to stop-loss: {position_info['distance_to_stop_loss']:.2%}
- Current price distance to take-profit: {position_info['distance_to_take_profit']:.2%}
- Market volatility: {position_info['market_volatility']:.2%}
- Trend strength: {position_info.get('trend_strength', 0):.2f}

【Sell Strategy Considerations】
Please consider the following factors to determine sell proportion:
1. Profit ratio: Large profits can consider partial profit-taking, keep some position for higher gains
2. Position ratio: Overweight positions should reduce position to lower risk, light positions can consider holding
3. Technical signals: Strong bearish signals should increase sell proportion, bullish signals can reduce sell proportion
4. Market environment: High volatility markets should be more conservative, low volatility markets can be more aggressive
5. Risk control: Close to stop-loss should sell decisively, far from stop-loss can be more flexible

【Position Management Principles】
- Profit over 20%: Consider partial profit-taking (30-70%), lock in profits
- Profit 10-20%: Decide based on signal strength (20-50%)
- Small profit (0-10%): Mainly based on technical signals (0-30%)
- Small loss (0-5%): Based on risk control (50-80%)
- Large loss (>5%): Consider stop-loss or position reduction (80-100%)

【Output Format Requirements】
Please reply strictly in the following JSON format, only containing numeric ratio (0-1):
{{
    "sell_proportion": 0.75
}}

Explanation:
- 0.1 means sell 10% of position
- 0.5 means sell 50% of position  
- 1.0 means sell all position
- 0.0 means don't sell (only in extremely bullish situations)

Please provide reasonable sell proportion suggestions based on professional analysis and risk management.
"""
        
        return prompt
    
    def _parse_sell_proportion_response(self, response):
        """Parse sell proportion response"""
        try:
            content = response['choices'][0]['message']['content']
            
            # Try to parse JSON format
            if '{' in content and '}' in content:
                json_start = content.find('{')
                json_end = content.rfind('}') + 1
                json_str = content[json_start:json_end]
                
                parsed = json.loads(json_str)
                proportion = parsed.get('sell_proportion', 0.5)
                
                # Ensure proportion is within reasonable range
                proportion = max(0.0, min(1.0, proportion))
                logger.info(f"DeepSeek suggested sell proportion: {proportion:.1%}")
                return proportion
            else:
                # Text analysis: try to extract proportion from text
                import re
                patterns = [
                    r'[\"\"]([\d.]+)%[\"\"]',  # "50%"
                    r'sell\s*([\d.]+)%',        # sell 50%
                    r'([\d.]+)%',              # 50%
                    r'[\"\"]([\d.]+)[\"\"]',   # "0.5"
                ]
                
                for pattern in patterns:
                    match = re.search(pattern, content)
                    if match:
                        value = float(match.group(1))
                        if value > 1:  # If in percentage format
                            value = value / 100
                        proportion = max(0.0, min(1.0, value))
                        logger.info(f"Parsed sell proportion from text: {proportion:.1%}")
                        return proportion
                
                # Default value
                logger.warning("Unable to parse sell proportion, using default 50%")
                return 0.5
                
        except Exception as e:
            logger.error(f"Error parsing sell proportion response: {e}")
            return 0.5
    
    def _get_fallback_sell_proportion(self, position_info, decision_confidence):
        """Fallback sell proportion decision (when DeepSeek is unavailable)"""
        
        profit_ratio = position_info['profit_ratio']
        position_ratio = position_info['position_ratio']
        market_volatility = position_info.get('market_volatility', 0)
        trend_strength = position_info.get('trend_strength', 0)
        
        # Decision based on profit situation
        if profit_ratio >= 0.20:  # Profit over 20%
            base_proportion = 0.6 if decision_confidence == 'HIGH' else 0.4
        elif profit_ratio >= 0.10:  # Profit 10%-20%
            base_proportion = 0.5 if decision_confidence == 'HIGH' else 0.3
        elif profit_ratio >= 0:  # Profit 0%-10%
            base_proportion = 0.4 if decision_confidence == 'HIGH' else 0.2
        elif profit_ratio >= -0.05:  # Loss 0%-5%
            base_proportion = 0.7 if decision_confidence == 'HIGH' else 0.5
        else:  # Loss over 5%
            base_proportion = 0.9 if decision_confidence == 'HIGH' else 0.7
        
        # Adjustment based on position ratio
        if position_ratio > 0.15:  # Overweight position
            base_proportion = min(1.0, base_proportion + 0.2)
        elif position_ratio < 0.05:  # Very light position
            base_proportion = max(0.1, base_proportion - 0.1)
        
        # Adjustment based on market volatility
        if market_volatility > 0.05:  # High volatility market
            base_proportion = min(1.0, base_proportion + 0.1)
        elif market_volatility < 0.02:  # Low volatility market
            base_proportion = max(0.1, base_proportion - 0.1)
        
        # Adjustment based on trend strength
        if trend_strength > 0.7:  # Strong trend
            base_proportion = max(0.1, base_proportion - 0.1)
        elif trend_strength < 0.3:  # Weak trend
            base_proportion = min(1.0, base_proportion + 0.1)
        
        # Ensure proportion is within reasonable range
        base_proportion = max(0.1, min(1.0, base_proportion))
        
        logger.info(f"Fallback sell proportion decision: profit={profit_ratio:+.2%}, position ratio={position_ratio:.2%}, "
                    f"volatility={market_volatility:.2%}, trend strength={trend_strength:.2f}, final proportion={base_proportion:.1%}")
        return base_proportion

    def _call_deepseek_api(self, prompt, max_retries=3):
        """Call DeepSeek API (improved retry mechanism)"""
        headers = {
            'Authorization': f'Bearer {self.api_key}',
            'Content-Type': 'application/json'
        }
        
        data = {
            'model': 'deepseek-chat',
            'messages': [
                {
                    'role': 'system',
                    'content': 'You are a professional cryptocurrency trading AI, conducting autonomous trading in the OKX digital currency market, pursuing stable and sustainable returns'
                },
                {
                    'role': 'user',
                    'content': prompt
                }
            ],
            'temperature': 0.3,
            'max_tokens': 1000
        }
        
        for attempt in range(max_retries):
            try:
                response = requests.post(
                    self.api_url,
                    headers=headers,
                    json=data,
                    timeout=30  # 30 second timeout
                )
                
                if response.status_code == 200:
                    return response.json()
                elif response.status_code == 429:  # Rate limit
                    wait_time = 2 ** attempt  # Exponential backoff
                    logger.warning(f"DeepSeek API rate limit, waiting {wait_time} seconds before retry")
                    time.sleep(wait_time)
                    continue
                else:
                    logger.error(f"DeepSeek API call failed: {response.status_code} - {response.text}")
                    if attempt < max_retries - 1:
                        time.sleep(1)
                        continue
                    else:
                        raise Exception(f"API call failed: {response.status_code}")
                        
            except requests.exceptions.Timeout:
                logger.warning(f"DeepSeek API request timeout, attempt {attempt + 1}/{max_retries}")
                if attempt < max_retries - 1:
                    time.sleep(1)
                    continue
                else:
                    raise Exception("DeepSeek API request timeout")
            except Exception as e:
                logger.error(f"DeepSeek API call exception: {e}")
                if attempt < max_retries - 1:
                    time.sleep(1)
                    continue
                else:
                    raise
    
    def _parse_deepseek_response(self, response):
        """Parse DeepSeek response - enhanced version"""
        try:
            content = response['choices'][0]['message']['content'].strip()
            logger.debug(f"DeepSeek raw response: {content}")
            
            # Remove possible code block markers
            if content.startswith('```json'):
                content = content[7:]
            if content.endswith('```'):
                content = content[:-3]
            content = content.strip()
            
            # Try to parse JSON format
            if '{' in content and '}' in content:
                json_start = content.find('{')
                json_end = content.rfind('}') + 1
                json_str = content[json_start:json_end]
                
                try:
                    parsed = json.loads(json_str)
                    result = {
                        'action': parsed.get('action', 'HOLD').upper(),
                        'confidence': parsed.get('confidence', 'MEDIUM').upper(),
                        'reasoning': parsed.get('reasoning', ''),
                        'raw_response': content
                    }
                    
                    # Validate action and confidence legality
                    if result['action'] not in ['BUY', 'SELL', 'HOLD']:
                        logger.warning(f"AI returned illegal action: {result['action']}, using HOLD")
                        result['action'] = 'HOLD'
                    
                    if result['confidence'] not in ['HIGH', 'MEDIUM', 'LOW']:
                        logger.warning(f"AI returned illegal confidence: {result['confidence']}, using MEDIUM")
                        result['confidence'] = 'MEDIUM'
                    
                    logger.info(f"AI parsing successful: {result['action']} (confidence: {result['confidence']})")
                    return result
                    
                except json.JSONDecodeError as e:
                    logger.warning(f"JSON parsing failed: {e}, trying text analysis")
                    return self._parse_text_response(content)
            else:
                # Text analysis
                return self._parse_text_response(content)
                
        except Exception as e:
            logger.error(f"Error parsing DeepSeek response: {e}")
            # Return default values to avoid system crash
            return {
                'action': 'HOLD',
                'confidence': 'MEDIUM', 
                'reasoning': f'Parsing error: {str(e)}',
                'raw_response': str(response)
            }
    
    def _parse_text_response(self, text):
        """Parse text response - enhanced version"""
        text_lower = text.lower()
        
        # More precise action recognition
        action = 'HOLD'
        if any(word in text_lower for word in ['买入', '做多', 'buy', 'long', '上涨', '看涨']):
            action = 'BUY'
        elif any(word in text_lower for word in ['卖出', '做空', 'sell', 'short', '下跌', '看跌']):
            action = 'SELL'
        
        # More precise confidence level recognition
        confidence = 'MEDIUM'
        if any(word in text_lower for word in ['强烈', '高信心', 'high', '非常', '强烈建议']):
            confidence = 'HIGH'
        elif any(word in text_lower for word in ['低', '低信心', 'low', '轻微', '谨慎']):
            confidence = 'LOW'
        
        logger.info(f"Text parsing result: {action} (confidence: {confidence})")
        
        return {
            'action': action,
            'confidence': confidence,
            'reasoning': text,
            'raw_response': text
        } 

class TechnicalAnalyzer:
    """Technical Analyzer"""
    
    @staticmethod
    def calculate_indicators(df):
        """Calculate technical indicators (optimized version)"""
        if df is None or len(df) < 20:  # Lower minimum data requirement
            logger.warning("Insufficient data, unable to calculate technical indicators")
            return {}
        
        try:
            indicators = {}
            
            # KDJ indicator
            k, d, j = TechnicalAnalyzer.calculate_kdj(df)
            if k is not None:
                indicators['kdj'] = (k, d, j)
            
            # RSI indicator (multiple time periods)
            rsi_14 = TechnicalAnalyzer.calculate_rsi(df['close'], 14)
            rsi_7 = TechnicalAnalyzer.calculate_rsi(df['close'], 7)
            rsi_21 = TechnicalAnalyzer.calculate_rsi(df['close'], 21)
            if rsi_14 is not None:
                indicators['rsi'] = rsi_14
                indicators['rsi_7'] = rsi_7
                indicators['rsi_21'] = rsi_21
            
            # ATR indicator
            atr = TechnicalAnalyzer.calculate_atr(df)
            if atr is not None:
                indicators['atr'] = atr
            
            # MACD indicator
            macd_line, signal_line, histogram = TechnicalAnalyzer.calculate_macd(df['close'])
            if macd_line is not None:
                indicators['macd'] = (macd_line, signal_line, histogram)
            
            # Bollinger Bands (multiple standard deviations)
            upper1, middle1, lower1 = TechnicalAnalyzer.calculate_bollinger_bands(df['close'], 20, 1)
            upper2, middle2, lower2 = TechnicalAnalyzer.calculate_bollinger_bands(df['close'], 20, 2)
            if upper1 is not None:
                indicators['bollinger_1std'] = (upper1, middle1, lower1)
                indicators['bollinger_2std'] = (upper2, middle2, lower2)
            
            # Moving Averages (multiple periods)
            sma_20 = TechnicalAnalyzer.calculate_sma(df['close'], 20)
            sma_50 = TechnicalAnalyzer.calculate_sma(df['close'], 50)
            sma_100 = TechnicalAnalyzer.calculate_sma(df['close'], 100)
            ema_12 = TechnicalAnalyzer.calculate_ema(df['close'], 12)
            ema_26 = TechnicalAnalyzer.calculate_ema(df['close'], 26)
            if sma_20 is not None:
                indicators['sma_20'] = sma_20
                indicators['sma_50'] = sma_50
                indicators['sma_100'] = sma_100
                indicators['ema_12'] = ema_12
                indicators['ema_26'] = ema_26
            
            # Stochastic Oscillator
            k, d = TechnicalAnalyzer.calculate_stochastic(df)
            if k is not None:
                indicators['stochastic'] = (k, d)
            
            # Trend strength
            indicators['trend_strength'] = TechnicalAnalyzer.calculate_trend_strength(df)
            
            # Volatility
            indicators['volatility'] = TechnicalAnalyzer.calculate_volatility(df)
            
            # Current price
            indicators['current_price'] = df['close'].iloc[-1]
            
            return indicators
            
        except Exception as e:
            logger.error(f"Error calculating technical indicators: {e}")
            return {}
    
    @staticmethod
    def calculate_ema(prices, period):
        """Calculate Exponential Moving Average"""
        try:
            return prices.ewm(span=period, adjust=False).mean()
        except Exception as e:
            logger.error(f"Error calculating EMA: {e}")
            return None
    
    @staticmethod
    def calculate_trend_strength(df, period=20):
        """Calculate trend strength"""
        try:
            if len(df) < period:
                return 0
            
            # Use linear regression to calculate trend strength
            x = np.arange(len(df))
            y = df['close'].values
            
            # Linear regression
            slope, intercept = np.polyfit(x[-period:], y[-period:], 1)
            
            # Calculate R² value as trend strength
            y_pred = slope * x[-period:] + intercept
            ss_res = np.sum((y[-period:] - y_pred) ** 2)
            ss_tot = np.sum((y[-period:] - np.mean(y[-period:])) ** 2)
            r_squared = 1 - (ss_res / ss_tot) if ss_tot != 0 else 0
            
            return abs(r_squared)  # Take absolute value, trend strength doesn't distinguish positive/negative
            
        except Exception as e:
            logger.error(f"Error calculating trend strength: {e}")
            return 0
    
    @staticmethod
    def detect_rsi_divergence(df, rsi_period=14):
        """Detect RSI bullish divergence"""
        try:
            if len(df) < 30:  # Need sufficient data
                return False
                
            # Calculate RSI
            rsi = TechnicalAnalyzer.calculate_rsi(df['close'], rsi_period)
            if rsi is None:
                return False
                
            # Use pandas methods to simplify calculation
            # Find lowest points in recent 10 periods
            recent_lows = df['low'].tail(10)
            recent_rsi = rsi.tail(10)
            
            # Find price lowest point and RSI lowest point
            min_price_idx = recent_lows.idxmin()
            min_rsi_idx = recent_rsi.idxmin()
            
            # If price lowest point appears later than RSI lowest point, possible bullish divergence
            if min_price_idx > min_rsi_idx:
                # Check if price is making new lows while RSI is rising
                price_trend = (recent_lows.iloc[-1] < recent_lows.iloc[-5])
                rsi_trend = (recent_rsi.iloc[-1] > recent_rsi.iloc[-5])
                
                return price_trend and rsi_trend and recent_rsi.iloc[-1] < 40
                
            return False
            
        except Exception as e:
            logger.error(f"RSI divergence detection error: {e}")
            return False
        
    @staticmethod    
    def detect_macd_divergence(df):
        """Detect MACD bullish divergence"""
        try:
            if len(df) < 30:
                return False
                
            # Calculate MACD, especially focus on histogram
            _, _, histogram = TechnicalAnalyzer.calculate_macd(df['close'])
            if histogram is None:
                return False
                
            # Use recent data
            recent_lows = df['low'].tail(10)
            recent_hist = histogram.tail(10)
            
            # Find price lowest point and MACD histogram lowest point
            min_price_idx = recent_lows.idxmin()
            min_hist_idx = recent_hist.idxmin()
            
            # If price lowest point appears later than histogram lowest point, possible bullish divergence
            if min_price_idx > min_hist_idx:
                # Check price trend and histogram trend
                price_trend = (recent_lows.iloc[-1] < recent_lows.iloc[-5])
                hist_trend = (recent_hist.iloc[-1] > recent_hist.iloc[-5])
                
                # Histogram rising from negative area is stronger signal
                hist_improving = recent_hist.iloc[-1] > recent_hist.iloc[-3]
                
                return price_trend and hist_trend and hist_improving
                
            return False
            
        except Exception as e:
            logger.error(f"MACD divergence detection error: {e}")
            return False
        
    @staticmethod
    def volume_confirmation(df, period=5):
        """Bottom volume confirmation"""
        try:
            if len(df) < period + 5:
                return False
                
            current_volume = df['volume'].iloc[-1]
            avg_volume = df['volume'].tail(period).mean()
            
            # Current volume significantly higher than average
            volume_ratio = current_volume / avg_volume if avg_volume > 0 else 1
            
            # Price falling but volume increasing (possible bottom accumulation)
            price_change = (df['close'].iloc[-1] - df['close'].iloc[-2]) / df['close'].iloc[-2]
            
            if volume_ratio > 1.5 and price_change < 0:
                return True
                
            return False
        except Exception as e:
            logger.error(f"Volume confirmation error: {e}")
            return False
        
    @staticmethod
    def detect_hammer_pattern(df, lookback=5):
        """Detect hammer reversal pattern"""
        try:
            if len(df) < lookback + 1:
                return False
            
            latest = df.iloc[-1]
            body_size = abs(latest['close'] - latest['open'])
            total_range = latest['high'] - latest['low']
            
            # Avoid division by zero
            if total_range == 0:
                return False
            
            # Hammer characteristics: lower shadow at least 2x body, very short upper shadow
            lower_shadow = min(latest['open'], latest['close']) - latest['low']
            upper_shadow = latest['high'] - max(latest['open'], latest['close'])
            
            is_hammer = (lower_shadow >= 2 * body_size and 
                        upper_shadow <= body_size * 0.5 and
                        body_size > 0)
            
            # Needs to be in downtrend
            prev_trend = df['close'].iloc[-lookback] > df['close'].iloc[-1]
            
            return is_hammer and prev_trend
            
        except Exception as e:
            logger.error(f"Hammer pattern detection error: {e}")
            return False
        
    @staticmethod
    def detect_reversal_patterns(df):
        """Detect bottom reversal patterns"""
        reversal_signals = []
        
        try:
            # RSI bullish divergence
            if TechnicalAnalyzer.detect_rsi_divergence(df):
                reversal_signals.append({
                    'type': 'RSI_DIVERGENCE_BULLISH',
                    'action': 'BUY',
                    'strength': 'MEDIUM',
                    'description': 'RSI bullish divergence, momentum divergence bullish'
                })
            
            # MACD bullish divergence
            if TechnicalAnalyzer.detect_macd_divergence(df):
                reversal_signals.append({
                    'type': 'MACD_DIVERGENCE_BULLISH', 
                    'action': 'BUY',
                    'strength': 'MEDIUM',
                    'description': 'MACD histogram bullish divergence, momentum strengthening'
                })
            
            # Volume confirmation
            if TechnicalAnalyzer.volume_confirmation(df):
                reversal_signals.append({
                    'type': 'VOLUME_CONFIRMATION',
                    'action': 'BUY', 
                    'strength': 'LOW',
                    'description': 'Bottom volume increase, signs of capital entry'
                })
            
            # Add hammer pattern detection
            if TechnicalAnalyzer.detect_hammer_pattern(df):
                reversal_signals.append({
                    'type': 'HAMMER_PATTERN',
                    'action': 'BUY',
                    'strength': 'LOW', 
                    'description': 'Hammer pattern, short-term reversal signal'
                })
                
        except Exception as e:
            logger.error(f"Reversal pattern detection error: {e}")
        
        return reversal_signals
    
    @staticmethod
    def calculate_support_resistance(df, period=20):
        """Calculate support resistance levels (fixed version)"""
        try:
            # Recent high resistance
            resistance = df['high'].rolling(window=period).max().iloc[-1]
            
            # Recent low support  
            support = df['low'].rolling(window=period).min().iloc[-1]
            
            # Dynamic support resistance (based on Bollinger Bands)
            # Fix: correctly receive three return values from Bollinger Bands
            upper_bb, middle_bb, lower_bb = TechnicalAnalyzer.calculate_bollinger_bands(df['close'], 20, 2)
            
            # Check if Bollinger Band calculation successful
            if upper_bb is not None and lower_bb is not None:
                resistance_bb = upper_bb.iloc[-1] if hasattr(upper_bb, 'iloc') else upper_bb
                support_bb = lower_bb.iloc[-1] if hasattr(lower_bb, 'iloc') else lower_bb
            else:
                # If Bollinger Band calculation fails, use static values as backup
                resistance_bb = resistance
                support_bb = support
            
            current_price = df['close'].iloc[-1]
            
            return {
                'static_resistance': resistance,
                'static_support': support,
                'dynamic_resistance': resistance_bb,
                'dynamic_support': support_bb,
                'current_vs_resistance': (current_price - resistance) / resistance * 100 if resistance > 0 else 0,
                'current_vs_support': (current_price - support) / support * 100 if support > 0 else 0
            }
        except Exception as e:
            logger.error(f"Error calculating support resistance: {e}")
            # Return default values to avoid subsequent errors
            current_price = df['close'].iloc[-1] if len(df) > 0 else 0
            return {
                'static_resistance': current_price * 1.1 if current_price > 0 else 0,
                'static_support': current_price * 0.9 if current_price > 0 else 0,
                'dynamic_resistance': current_price * 1.05 if current_price > 0 else 0,
                'dynamic_support': current_price * 0.95 if current_price > 0 else 0,
                'current_vs_resistance': 0,
                'current_vs_support': 0
            }
    
    @staticmethod
    def calculate_volatility(df, period=20):
        """Calculate volatility"""
        try:
            returns = df['close'].pct_change().dropna()
            volatility = returns.rolling(window=period).std() * np.sqrt(365)  # Annualized volatility
            return volatility.iloc[-1] if len(volatility) > 0 else 0
        except Exception as e:
            logger.error(f"Error calculating volatility: {e}")
            return 0
    
    @staticmethod
    def generate_weighted_signals(df):
        """Generate weighted technical signals"""
        signals = []
        weights = {
            'KDJ_GOLDEN_CROSS': 0.15,
            'MACD_GOLDEN_CROSS': 0.20,
            'MA_GOLDEN_CROSS': 0.25,
            'RSI_OVERSOLD': 0.15,
            'BOLLINGER_LOWER_TOUCH': 0.15,
            'STOCH_GOLDEN_CROSS': 0.10
        }
        
        # Calculate various indicator signals
        technical_signals = TechnicalAnalyzer.generate_signals(df)
        
        # Calculate weighted score
        total_score = 0
        signal_count = 0
        
        for signal in technical_signals:
            weight = weights.get(signal['type'], 0.05)
            strength_multiplier = 1.0 if signal['strength'] == 'STRONG' else 0.7
            total_score += weight * strength_multiplier
            signal_count += 1
        
        # Normalize score
        normalized_score = min(1.0, total_score)
        
        # Determine action
        if normalized_score > 0.6:
            action = 'BUY'
        elif normalized_score < 0.3:
            action = 'SELL'
        else:
            action = 'HOLD'
        
        return {
            'score': normalized_score,
            'action': action,
            'signal_count': signal_count,
            'signals': technical_signals,
            'confidence': 'HIGH' if normalized_score > 0.7 or normalized_score < 0.2 else 'MEDIUM'
        }
    
    @staticmethod
    def calculate_kdj(df, n=9, m1=3, m2=3):
        """Calculate KDJ indicator"""
        try:
            if len(df) < n:
                return pd.Series([np.nan] * len(df)), pd.Series([np.nan] * len(df)), pd.Series([np.nan] * len(df))
            
            low_list = df['low'].rolling(window=n, min_periods=1).min()
            high_list = df['high'].rolling(window=n, min_periods=1).max()
            
            # Avoid division by zero error
            denominator = high_list - low_list
            denominator = denominator.replace(0, np.nan)
            
            rsv = ((df['close'] - low_list) / denominator) * 100
            rsv = rsv.fillna(50)
            
            k_series = rsv.ewm(span=m1-1, adjust=False).mean()
            d_series = k_series.ewm(span=m2-1, adjust=False).mean()
            j_series = 3 * k_series - 2 * d_series
            
            return k_series, d_series, j_series
            
        except Exception as e:
            logger.error(f"Error calculating KDJ: {e}")
            return None, None, None
    
    @staticmethod
    def calculate_rsi(prices, period=14):
        """Calculate RSI"""
        try:
            if len(prices) < period:
                return pd.Series([np.nan] * len(prices))
            
            delta = prices.diff()
            gain = (delta.where(delta > 0, 0)).rolling(window=period, min_periods=1).mean()
            loss = (-delta.where(delta < 0, 0)).rolling(window=period, min_periods=1).mean()
            
            # Avoid division by zero error
            rs = gain / loss.replace(0, np.nan)
            rsi = 100 - (100 / (1 + rs))
            return rsi.fillna(50)
            
        except Exception as e:
            logger.error(f"Error calculating RSI: {e}")
            return None
    
    @staticmethod
    def calculate_atr(df, period=14):
        """Calculate ATR"""
        try:
            if len(df) < period:
                return pd.Series([np.nan] * len(df))
            
            high_low = df['high'] - df['low']
            high_close = np.abs(df['high'] - df['close'].shift())
            low_close = np.abs(df['low'] - df['close'].shift())
            
            true_range = np.maximum(high_low, np.maximum(high_close, low_close))
            atr = true_range.rolling(window=period, min_periods=1).mean()
            return atr
            
        except Exception as e:
            logger.error(f"Error calculating ATR: {e}")
            return None
    
    @staticmethod
    def calculate_macd(prices, fast_period=12, slow_period=26, signal_period=9):
        """Calculate MACD"""
        try:
            ema_fast = prices.ewm(span=fast_period, adjust=False).mean()
            ema_slow = prices.ewm(span=slow_period, adjust=False).mean()
            macd_line = ema_fast - ema_slow
            signal_line = macd_line.ewm(span=signal_period, adjust=False).mean()
            histogram = macd_line - signal_line
            return macd_line, signal_line, histogram
        except Exception as e:
            logger.error(f"Error calculating MACD: {e}")
            return None, None, None
    
    @staticmethod
    def calculate_bollinger_bands(prices, period=20, std_dev=2):
        """Calculate Bollinger Bands"""
        try:
            if len(prices) < period:
                logger.warning(f"Data length {len(prices)} insufficient, unable to calculate {period}-period Bollinger Bands")
                return None, None, None
            
            middle = prices.rolling(window=period).mean()
            std = prices.rolling(window=period).std()
            
            # Handle NaN standard deviation
            std = std.fillna(0)
            
            upper = middle + (std * std_dev)
            lower = middle - (std * std_dev)
            
            return upper, middle, lower
        except Exception as e:
            logger.error(f"Error calculating Bollinger Bands: {e}")
            return None, None, None
    
    @staticmethod
    def calculate_sma(prices, period):
        """Calculate Simple Moving Average"""
        try:
            return prices.rolling(window=period).mean()
        except Exception as e:
            logger.error(f"Error calculating SMA: {e}")
            return None
    
    @staticmethod
    def calculate_stochastic(df, k_period=14, d_period=3):
        """Calculate Stochastic Oscillator"""
        try:
            low_min = df['low'].rolling(window=k_period).min()
            high_max = df['high'].rolling(window=k_period).max()
            k = 100 * ((df['close'] - low_min) / (high_max - low_min))
            d = k.rolling(window=d_period).mean()
            return k, d
        except Exception as e:
            logger.error(f"Error calculating Stochastic: {e}")
            return None, None
    
    @staticmethod
    def generate_signals(df):
        """Generate technical signals"""
        signals = []
        
        try:
            # KDJ
            k, d, j = TechnicalAnalyzer.calculate_kdj(df)
            if k is not None and len(k) > 1:
                latest_k, latest_d, latest_j = k.iloc[-1], d.iloc[-1], j.iloc[-1]
                prev_k, prev_d, prev_j = k.iloc[-2], d.iloc[-2], j.iloc[-2]
                
                if prev_k <= prev_d and latest_k > latest_d:
                    signals.append({'type': 'KDJ_GOLDEN_CROSS', 'action': 'BUY', 'strength': 'MEDIUM'})
                elif prev_k >= prev_d and latest_k < latest_d:
                    signals.append({'type': 'KDJ_DEATH_CROSS', 'action': 'SELL', 'strength': 'MEDIUM'})
                
                if latest_j < 20:
                    signals.append({'type': 'KDJ_OVERSOLD', 'action': 'BUY', 'strength': 'MEDIUM'})
                elif latest_j > 80:
                    signals.append({'type': 'KDJ_OVERBOUGHT', 'action': 'SELL', 'strength': 'MEDIUM'})
            
            # RSI
            rsi = TechnicalAnalyzer.calculate_rsi(df['close'])
            if rsi is not None and len(rsi) > 0:
                latest_rsi = rsi.iloc[-1]
                
                if latest_rsi < 30:
                    signals.append({'type': 'RSI_OVERSOLD', 'action': 'BUY', 'strength': 'MEDIUM'})
                elif latest_rsi > 70:
                    signals.append({'type': 'RSI_OVERBOUGHT', 'action': 'SELL', 'strength': 'MEDIUM'})
            
            # MACD
            macd_line, signal_line, _ = TechnicalAnalyzer.calculate_macd(df['close'])
            if macd_line is not None and len(macd_line) > 1:
                latest_macd = macd_line.iloc[-1]
                latest_signal = signal_line.iloc[-1]
                prev_macd = macd_line.iloc[-2]
                prev_signal = signal_line.iloc[-2]
                
                if prev_macd <= prev_signal and latest_macd > latest_signal:
                    signals.append({'type': 'MACD_GOLDEN_CROSS', 'action': 'BUY', 'strength': 'MEDIUM'})
                elif prev_macd >= prev_signal and latest_macd < latest_signal:
                    signals.append({'type': 'MACD_DEATH_CROSS', 'action': 'SELL', 'strength': 'MEDIUM'})
            
            # Bollinger Bands
            upper, middle, lower = TechnicalAnalyzer.calculate_bollinger_bands(df['close'])
            if upper is not None:
                latest_close = df['close'].iloc[-1]
                if latest_close <= lower.iloc[-1]:
                    signals.append({'type': 'BOLlinger_LOWER_TOUCH', 'action': 'BUY', 'strength': 'MEDIUM'})
                elif latest_close >= upper.iloc[-1]:
                    signals.append({'type': 'BOLlinger_UPPER_TOUCH', 'action': 'SELL', 'strength': 'MEDIUM'})
            
            # Moving Averages
            sma_short = TechnicalAnalyzer.calculate_sma(df['close'], 50)
            sma_long = TechnicalAnalyzer.calculate_sma(df['close'], 200)
            if sma_short is not None and sma_long is not None and len(sma_short) > 1:
                latest_short = sma_short.iloc[-1]
                latest_long = sma_long.iloc[-1]
                prev_short = sma_short.iloc[-2]
                prev_long = sma_long.iloc[-2]
                
                if prev_short <= prev_long and latest_short > latest_long:
                    signals.append({'type': 'MA_GOLDEN_CROSS', 'action': 'BUY', 'strength': 'MEDIUM'})
                elif prev_short >= prev_long and latest_short < latest_long:
                    signals.append({'type': 'MA_DEATH_CROSS', 'action': 'SELL', 'strength': 'MEDIUM'})
            
            # Stochastic
            k, d = TechnicalAnalyzer.calculate_stochastic(df)
            if k is not None and len(k) > 1:
                latest_k = k.iloc[-1]
                latest_d = d.iloc[-1]
                prev_k = k.iloc[-2]
                prev_d = d.iloc[-2]
                
                if latest_k < 20:
                    signals.append({'type': 'STOCH_OVERSOLD', 'action': 'BUY', 'strength': 'MEDIUM'})
                elif latest_k > 80:
                    signals.append({'type': 'STOCH_OVERBOUGHT', 'action': 'SELL', 'strength': 'MEDIUM'})
                
                if prev_k <= prev_d and latest_k > latest_d and latest_k < 80:
                    signals.append({'type': 'STOCH_GOLDEN_CROSS', 'action': 'BUY', 'strength': 'MEDIUM'})
                elif prev_k >= prev_d and latest_k < latest_d and latest_k > 20:
                    signals.append({'type': 'STOCH_DEATH_CROSS', 'action': 'SELL', 'strength': 'MEDIUM'})
            
        except Exception as e:
            logger.error(f"Error generating technical signals: {e}")
        
        return signals

class RiskManager:
    """Risk Control Manager (Dynamic Position Management Version)"""
    
    def __init__(self, api):
        self.api = api
        self.max_total_position = float(os.getenv('MAX_TOTAL_POSITION', '1'))  # Maximum total position 100%
        self.max_single_position = float(os.getenv('MAX_SINGLE_POSITION', '0.3'))  # Single currency maximum position 30%
        self.stop_loss = float(os.getenv('STOP_LOSS', '0.05'))  # Stop-loss 5%
        self.take_profit = float(os.getenv('TAKE_PROFIT', '0.15'))  # Take-profit 15%        
        self.max_daily_trades = int(os.getenv('MAX_DAILY_TRADES', '10'))  # Maximum daily trades
        self.dust_threshold_value = 1.0  # Dust position value threshold (USDT)
        self.low_position_ratio = 0.05  # Low position ratio threshold (5%)
        
        self.trailing_stop_percent = float(os.getenv('TRAILING_STOP_PERCENT', '0.03'))  # Trailing stop percentage
        self.monitor_interval = int(os.getenv('MONITOR_INTERVAL', '300'))  # Monitoring interval seconds
        
        # Dynamic position parameters
        self.volatility_adjustment = True
        self.trend_adjustment = True
        self.market_sentiment_adjustment = True
        
        # Trade records
        self.daily_trade_count = {}
        self.db = DatabaseManager()
    
    def calculate_total_assets(self):
        """Calculate total assets (in USDT)"""
        try:
            # Get all currency balances
            balances = self.api.get_currency_balances()
            if not balances:
                logger.error("Unable to get currency balances")
                return 0.0, 0.0, {}

            total_usdt_value = 0.0
            position_details = {}

            # Calculate USDT balance (including available and frozen)
            usdt_balance = balances.get('USDT', {})
            usdt_avail = usdt_balance.get('amount', 0.0)
            usdt_frozen = usdt_balance.get('frozen', 0.0)
            total_usdt = usdt_avail + usdt_frozen
            total_usdt_value += total_usdt

            # Calculate value of other currencies
            for currency, balance_info in balances.items():
                if currency != 'USDT':
                    avail = balance_info.get('amount', 0.0)
                    frozen = balance_info.get('frozen', 0.0)
                    total_amount = avail + frozen
                    if total_amount > 0:
                        symbol = f"{currency}-USDT"
                        current_price = self.api.get_current_price(symbol)
                        if current_price:
                            value = total_amount * current_price
                            total_usdt_value += value
                            position_details[symbol] = {
                                'amount': total_amount,  # Total quantity (available + frozen)
                                'value': value,
                                'current_price': current_price
                            }
                        else:
                            logger.warning(f"Unable to get {currency} price, skipping value calculation")

            logger.debug(f"Total asset calculation: USDT={total_usdt:.2f}(available{usdt_avail:.2f}+frozen{usdt_frozen:.2f}), position value={total_usdt_value - total_usdt:.2f}, total={total_usdt_value:.2f}")

            return total_usdt_value, usdt_avail, position_details

        except Exception as e:
            logger.error(f"Error calculating total assets: {e}")
            return 0.0, 0.0, {}
    
    def get_position_ratio(self, symbol):
        """Get specified currency's position ratio"""
        try:
            total_assets, usdt_balance, positions = self.calculate_total_assets()
            if total_assets == 0:
                return 0.0
            
            position_value = positions.get(symbol, {}).get('value', 0.0)
            return position_value / total_assets
            
        except Exception as e:
            logger.error(f"Error calculating position ratio: {e}")
            return 0.0
    
    def get_available_usdt_ratio(self):
        """Get available USDT ratio"""
        try:
            total_assets, usdt_balance, _ = self.calculate_total_assets()
            if total_assets == 0:
                return 0.0
            
            return usdt_balance / total_assets
            
        except Exception as e:
            logger.error(f"Error calculating USDT ratio: {e}")
            return 0.0
    
    def get_position_size(self, symbol, confidence, current_price):
        """Calculate position size based on confidence level and USDT availability"""
        try:
            total_assets, usdt_balance, positions = self.calculate_total_assets()
            
            if total_assets == 0:
                logger.warning("Total assets is 0, unable to calculate position")
                return 0.0
            
            # Get current position value
            current_pos_value = positions.get(symbol, {}).get('value', 0.0)
            
            # Calculate USDT available ratio
            usdt_ratio = usdt_balance / total_assets
            
            # Adjust maximum position based on USDT availability
            adjusted_max_single = self.max_single_position * min(1.0, usdt_ratio * 3)  # More USDT allows larger positions
            
            # Calculate maximum additional position
            max_single_add = max(0, total_assets * adjusted_max_single - current_pos_value)
            max_total_add = max(0, total_assets * self.max_total_position - (total_assets - usdt_balance))
            
            max_add = min(max_single_add, max_total_add, usdt_balance)
            
            # Adjust based on confidence level
            multiplier = {
                'HIGH': 0.8,   # High confidence uses 80% of available amount
                'MEDIUM': 0.5, # Medium confidence uses 50%
                'LOW': 0.2     # Low confidence uses 20%
            }.get(confidence, 0.2)
            
            position_size = max_add * multiplier
                                  
            # Ensure doesn't exceed available USDT balance
            position_size = min(position_size, usdt_balance)
            
            logger.info(f"Position calculation: {symbol}, total assets=${total_assets:.2f}, USDT=${usdt_balance:.2f}, "
                       f"current position=${current_pos_value:.2f}, suggested position=${position_size:.2f}")
            
            return position_size
            
        except Exception as e:
            logger.error(f"Error calculating position size: {e}")
            return 0.0
    
    def get_dynamic_position_size(self, symbol, confidence, technical_indicators, current_price):
        """Dynamic position calculation"""
        try:
            # Base position calculation
            base_size = self.get_position_size(symbol, confidence, current_price)
            if base_size == 0:
                return 0.0
            
            # Get market state parameters
            volatility = technical_indicators.get('volatility', 0)
            trend_strength = technical_indicators.get('trend_strength', 0)
            market_state = self.assess_market_state(technical_indicators)
            
            # Volatility adjustment (high volatility reduces position)
            volatility_factor = self._calculate_volatility_factor(volatility)
            
            # Trend strength adjustment (strong trend increases position)
            trend_factor = self._calculate_trend_factor(trend_strength)
            
            # Market state adjustment
            market_factor = self._calculate_market_factor(market_state)
            
            # Confidence level adjustment
            confidence_factor = self._calculate_confidence_factor(confidence)
            
            # Calculate dynamic position
            dynamic_size = base_size * volatility_factor * trend_factor * market_factor * confidence_factor
                                   
            logger.info(f"Dynamic position calculation: {symbol}, base={base_size:.2f}, "
                       f"volatility factor={volatility_factor:.2f}, trend factor={trend_factor:.2f}, "
                       f"market factor={market_factor:.2f}, confidence factor={confidence_factor:.2f}, "
                       f"final={dynamic_size:.2f}")
            
            return dynamic_size
            
        except Exception as e:
            logger.error(f"Dynamic position calculation error: {e}")
            return self.get_position_size(symbol, confidence, current_price)
    
    def _calculate_volatility_factor(self, volatility):
        """Calculate volatility adjustment factor"""
        if not self.volatility_adjustment:
            return 1.0
        
        # Annualized volatility conversion and adjustment
        if volatility > 0.8:  # 80%+ annualized volatility
            return 0.3
        elif volatility > 0.6:  # 60-80%
            return 0.5
        elif volatility > 0.4:  # 40-60%
            return 0.7
        elif volatility > 0.2:  # 20-40%
            return 0.9
        else:  # <20%
            return 1.0
    
    def _calculate_trend_factor(self, trend_strength):
        """Calculate trend strength adjustment factor"""
        if not self.trend_adjustment:
            return 1.0
        
        if trend_strength > 0.7:  # Strong trend
            return 1.3
        elif trend_strength > 0.4:  # Medium trend
            return 1.1
        elif trend_strength > 0.2:  # Weak trend
            return 1.0
        else:  # No trend
            return 0.8
    
    def _calculate_market_factor(self, market_state):
        """Calculate market state adjustment factor"""
        if not self.market_sentiment_adjustment:
            return 1.0
        
        factors = {
            'STRONG_BULL': 1.2,
            'BULL': 1.1,
            'NEUTRAL': 1.0,
            'BEAR': 0.7,
            'STRONG_BEAR': 0.5
        }
        return factors.get(market_state, 1.0)
    
    def _calculate_confidence_factor(self, confidence):
        """Calculate confidence level adjustment factor"""
        factors = {
            'HIGH': 1.0,
            'MEDIUM': 0.7,
            'LOW': 0.4
        }
        return factors.get(confidence, 0.5)
    
    def assess_market_state(self, technical_indicators):
        """Assess market state"""
        try:
            # Get key indicators
            rsi = technical_indicators.get('rsi', 50)
            if hasattr(rsi, '__len__'):
                rsi = rsi.iloc[-1]
            
            macd_line, signal_line, _ = technical_indicators.get('macd', (0, 0, 0))
            if hasattr(macd_line, '__len__'):
                macd_line, signal_line = macd_line.iloc[-1], signal_line.iloc[-1]
            
            sma_20 = technical_indicators.get('sma_20', 0)
            sma_50 = technical_indicators.get('sma_50', 0)
            current_price = technical_indicators.get('current_price', 0)
            
            if hasattr(sma_20, '__len__'):
                sma_20, sma_50 = sma_20.iloc[-1], sma_50.iloc[-1]
            
            # Calculate bullish signal score
            bull_signals = 0
            total_signals = 0
            
            # RSI signal
            if rsi > 50: bull_signals += 1
            total_signals += 1
            
            # MACD signal
            if macd_line > signal_line: bull_signals += 1
            total_signals += 1
            
            # Moving average signal
            if current_price > sma_20: bull_signals += 1
            if current_price > sma_50: bull_signals += 1
            total_signals += 2
            
            # Determine market state
            bull_ratio = bull_signals / total_signals
            
            if bull_ratio >= 0.8:
                return 'STRONG_BULL'
            elif bull_ratio >= 0.6:
                return 'BULL'
            elif bull_ratio >= 0.4:
                return 'NEUTRAL'
            elif bull_ratio >= 0.2:
                return 'BEAR'
            else:
                return 'STRONG_BEAR'
                
        except Exception as e:
            logger.error(f"Error assessing market state: {e}")
            return 'NEUTRAL'
    
    def is_dust_position(self, symbol, current_price, base_amount):
        """Check if dust position"""
        hold_value = base_amount * current_price if current_price else 0
        return hold_value < self.dust_threshold_value
    
    def is_low_position_ratio(self, symbol, total_assets, current_price, base_amount):
        """Check if position ratio is low"""
        hold_value = base_amount * current_price if current_price else 0
        return (hold_value / total_assets) < self.low_position_ratio if total_assets > 0 else False
    
    def can_trade(self, symbol, amount, current_price):
        """Check if can trade"""
        try:                       
            # Check daily trade count
            today = datetime.now().date()
            if symbol not in self.daily_trade_count:
                self.daily_trade_count[symbol] = {'date': today, 'count': 0}
            
            if self.daily_trade_count[symbol]['date'] != today:
                self.daily_trade_count[symbol] = {'date': today, 'count': 0}
            
            if self.daily_trade_count[symbol]['count'] >= self.max_daily_trades:
                logger.warning(f"{symbol} daily trade count reached {self.max_daily_trades} limit")
                return False
            
            return True
            
        except Exception as e:
            logger.error(f"Error checking trade conditions: {e}")
            return False
    
    def increment_trade_count(self, symbol):
        """Increment trade count"""
        try:
            today = datetime.now().date()
            if symbol in self.daily_trade_count and self.daily_trade_count[symbol]['date'] == today:
                self.daily_trade_count[symbol]['count'] += 1
        except Exception as e:
            logger.error(f"Error incrementing trade count: {e}")

class TradingStrategy:
    """Trading Strategy (Comprehensive Optimized Version)"""
    
    def __init__(self, symbol, config, api, risk_manager, deepseek):
        self.symbol = symbol
        self.config = config
        self.api = api
        self.risk_manager = risk_manager
        self.deepseek = deepseek
        self.technical_analyzer = TechnicalAnalyzer()
        self.db = DatabaseManager()  # Use default configuration
        
        # Position status
        self.base_amount = 0.0
        self.entry_price = 0.0
        
        # Dynamic stop-loss monitoring - using environment variable configured interval
        self.monitor_interval = 300  # Default 5 minutes
        if hasattr(risk_manager, 'monitor_interval'):
            self.monitor_interval = risk_manager.monitor_interval
        self.monitor_running = False
        self.monitor_thread = None
        
        # Running status control
        self.strategy_running = False
        
        # Load position status
        self.load_position()
        
        # Start dynamic stop-loss monitoring
        self.start_dynamic_stop_monitor()
        
    def load_position(self):
        """Load position status from database"""
        try:
            position_data = self.db.load_position(self.symbol)
            if position_data:
                self.base_amount = position_data['base_amount']
                self.entry_price = position_data['entry_price']
                logger.info(f"Loaded {self.symbol} position: currency quantity={self.base_amount:.10f}, entry price=${self.entry_price:.2f}")
            else:
                # If no database record, sync with exchange position
                self.sync_with_exchange()
        except Exception as e:
            logger.error(f"Error loading position status: {e}")
            self.sync_with_exchange()
    
    def save_position(self):
        """Save position status to database"""
        try:
            if self.base_amount > 0:
                self.db.save_position(self.symbol, self.base_amount, self.entry_price)
            else:
                # If position is 0, delete record
                self.db.delete_position(self.symbol)
                # Also delete dynamic stop record
                self.db.delete_dynamic_stop(self.symbol)
        except Exception as e:
            logger.error(f"Error saving position status: {e}")
    
    def sync_with_exchange(self):
        """Sync with exchange position"""
        try:
            logger.debug(f"Starting {self.symbol} position status sync")
            
            # Get all currency balances
            balances = self.api.get_currency_balances()
            if not balances:
                logger.warning("Unable to get currency balances")
                return False
            
            # Extract base currency
            base_currency = self.symbol.split('-')[0]
            
            # Update local balance
            if base_currency in balances:
                new_base_amount = balances[base_currency].get('amount', 0.0)
                
                # If position quantity changes, print log
                if abs(new_base_amount - self.base_amount) > 1e-10:
                    logger.info(f"Synced {self.symbol} latest balance: {base_currency}={new_base_amount:.10f}")
                
                # If position decreases but entry price not updated, keep original entry price (for calculating profit/loss)
                if new_base_amount < self.base_amount and self.entry_price > 0:
                    # Position decreased, reset entry price to conservative estimate (current price)
                    if new_base_amount <= 0:
                        self.entry_price = 0
                        logger.info(f"{self.symbol} position cleared, reset entry price to 0")
                    else:
                        current_price = self.api.get_current_price(self.symbol)
                        if current_price:
                            self.entry_price = current_price
                            logger.info(f"{self.symbol} partial sell, reset entry price to current price: ${current_price:.2f}")
                        else:
                            logger.warning(f"{self.symbol} partial sell but unable to get current price, keep original entry price")
                    
                # If from no position to having position, and no entry price record
                elif self.base_amount == 0 and new_base_amount > 0 and self.entry_price == 0:
                    # Try to load historical entry price from database
                    position_data = self.db.load_position(self.symbol)
                    if position_data and position_data['entry_price'] > 0:
                        self.entry_price = position_data['entry_price']
                        logger.info(f"Restored entry price from database: ${self.entry_price:.2f}")
                    else:
                        # Get current price as reference (but note this is reference price)
                        current_price = self.api.get_current_price(self.symbol)
                        if current_price:
                            self.entry_price = current_price
                            logger.info(f"Set reference entry price: ${current_price:.2f} (Note: This is reference price, actual entry price may differ)")
                
                self.base_amount = new_base_amount
                
            else:
                self.base_amount = 0.0
                # Don't clear entry price, keep for historical record analysis
                logger.info(f"Synced {self.symbol}: No position")
            
            # Save position status
            self.save_position()
            return True
                
        except Exception as e:
            logger.error(f"Error syncing exchange position: {e}")
            return False

    def get_actual_position_from_exchange(self):
        """Get actual position data from exchange"""
        try:
            balances = self.api.get_currency_balances()
            if not balances:
                return 0.0
            
            base_currency = self.symbol.split('-')[0]
            return balances.get(base_currency, {}).get('amount', 0.0)
            
        except Exception as e:
            logger.error(f"Error getting exchange position: {e}")
            return 0.0

    def analyze_market(self):
        """Comprehensive market analysis (optimized version)"""
        try:
            # Get market data
            df = self.api.get_market_data(self.symbol, self.config['timeframe'])
            if df is None or len(df) < 20:
                logger.error(f"Failed to get {self.symbol} market data")
                return None
            
            # Calculate technical indicators
            technical_indicators = self.technical_analyzer.calculate_indicators(df)
            if not technical_indicators:
                logger.warning(f"Failed to calculate {self.symbol} technical indicators")
                return None
            
            # Weighted technical signals
            weighted_signals = self.technical_analyzer.generate_weighted_signals(df)
            
            # DeepSeek AI analysis
            deepseek_analysis = self.deepseek.analyze_market(self.symbol, df, technical_indicators)
            
            # Technical signals
            technical_signals = self.technical_analyzer.generate_signals(df)
            
            return {
                'deepseek': deepseek_analysis,
                'technical_signals': technical_signals,
                'weighted_signals': weighted_signals,
                'technical_indicators': technical_indicators,
                'current_price': df['close'].iloc[-1],
                'market_data': df
            }
            
        except Exception as e:
            logger.error(f"{self.symbol} market analysis error: {e}")
            return None
    
    def make_enhanced_decision(self, analysis):
        """Enhanced trading decision"""
        if analysis is None:
            return {'action': 'HOLD', 'confidence': 'LOW', 'reason': 'Insufficient analysis data'}
        
        deepseek_signal = analysis['deepseek']
        weighted_signals = analysis['weighted_signals']
        technical_indicators = analysis['technical_indicators']
        market_data = analysis['market_data'] 
        
        # Build decision matrix
        decision_matrix = self._build_decision_matrix(deepseek_signal, weighted_signals, technical_indicators, market_data)
        
        return self._finalize_decision(decision_matrix)
    
    def _build_decision_matrix(self, ai_signal, weighted_signal, technical_indicators, market_data):
        """Build decision matrix"""
        matrix = {
            'ai_weight': 0.6,           # AI analysis weight
            'technical_weight': 0.2,     # Traditional technical indicator weight
            'reversal_weight': 0.05,     # Reversal signal weight
            'support_resistance_weight': 0.1,  # Support resistance weight
            'market_weight': 0.05    # Market state weight
        }
        
        # AI signal scoring
        ai_score = self._score_ai_signal(ai_signal) if ai_signal else 0.5
        
        # Technical signal scoring
        tech_score = self._score_technical_signal(weighted_signal)
        
        # Market state scoring
        market_score = self._score_market_condition(technical_indicators)
        
        # Reversal signal scoring
        reversal_signals = TechnicalAnalyzer.detect_reversal_patterns(market_data)
        reversal_score = 0.5
        if reversal_signals:
            # Score based on reversal signal quantity and strength
            strong_signals = [s for s in reversal_signals if s['strength'] == 'STRONG']
            reversal_score = 0.5 + (len(strong_signals) * 0.1) + (len(reversal_signals) * 0.05)
            reversal_score = min(0.9, reversal_score)
        
        # Support resistance scoring
        sr_levels = TechnicalAnalyzer.calculate_support_resistance(market_data)
        sr_score = 0.5
        if sr_levels:
            # Near support level adds points, near resistance level subtracts points
            if sr_levels['current_vs_support'] > -2:  # Very close to support
                sr_score = 0.7
            elif sr_levels['current_vs_resistance'] < 2:  # Very close to resistance
                sr_score = 0.3
        
        # Comprehensive scoring
        total_score = (
            ai_score * matrix['ai_weight'] +
            tech_score * matrix['technical_weight'] +
            reversal_score * matrix['reversal_weight'] +
            sr_score * matrix['support_resistance_weight'] +
            market_score * matrix['market_weight']
        )
        logger.info(f"[{self.symbol}] Decision matrix details: "
                f"AI score={ai_score:.3f}, technical score={tech_score:.3f}, "
                f"market score={market_score:.3f}, reversal score={reversal_score:.3f}, "
                f"support resistance score={sr_score:.3f}, total score={total_score:.3f}")
                
        # Determine action and confidence
        if total_score > 0.6:
            action = 'BUY'
            confidence = 'HIGH' if total_score > 0.75 else 'MEDIUM'
        elif total_score < 0.4:
            action = 'SELL'
            confidence = 'HIGH' if total_score < 0.25 else 'MEDIUM'
        else:
            action = 'HOLD'
            confidence = 'MEDIUM' if abs(total_score - 0.5) > 0.1 else 'LOW'
        
        return {
            'total_score': total_score,
            'action': action,
            'confidence': confidence,
            'components': {
                'ai_score': ai_score,
                'tech_score': tech_score,
                'market_score': market_score,
                'reversal_score': reversal_score,
                'sr_score': sr_score
            }
        }
    
    def _score_ai_signal(self, ai_signal):
        """AI signal scoring"""
        if not ai_signal:
            logger.warning("AI signal is empty, using neutral score")
            return 0.5
        
        action = ai_signal.get('action', 'HOLD')
        confidence = ai_signal.get('confidence', 'MEDIUM')
        
        # Correction: Redesign scoring logic to ensure score has differentiation
        if action == 'BUY':
            base_score = 0.7  # Buy base score
        elif action == 'SELL':
            base_score = 0.3  # Sell base score
        else:  # HOLD
            base_score = 0.5  # Hold neutral score
        
        # Confidence adjustment - ensure final score is within reasonable range
        confidence_adjustments = {
            'HIGH': 0.2,    # High confidence +0.2
            'MEDIUM': 0.0,   # Medium confidence unchanged
            'LOW': -0.2     # Low confidence -0.2
        }
        
        adjustment = confidence_adjustments.get(confidence, 0.0)
        final_score = base_score + adjustment
        
        # Ensure within 0-1 range
        final_score = max(0.1, min(0.9, final_score))
        
        logger.debug(f"AI signal scoring: action={action}, confidence={confidence}, "
                    f"base={base_score}, adjustment={adjustment}, final={final_score}")
        
        return final_score
    
    def _score_technical_signal(self, weighted_signal):
        """Technical signal scoring"""
        if not weighted_signal:
            logger.warning("Technical signal is empty, using neutral score")
            return 0.5
        
        action = weighted_signal.get('action', 'HOLD')
        score = weighted_signal.get('score', 0.5)
        signal_count = weighted_signal.get('signal_count', 0)
        
        # Adjust based on signal strength and quantity
        if action == 'BUY':
            # Buy signal: 0.5-1.0 range
            if score > 0.7 and signal_count >= 3:
                final_score = 0.8
            elif score > 0.6:
                final_score = 0.7
            else:
                final_score = 0.6
        elif action == 'SELL':
            # Sell signal: 0.0-0.5 range  
            if score < 0.3 and signal_count >= 3:
                final_score = 0.2
            elif score < 0.4:
                final_score = 0.3
            else:
                final_score = 0.4
        else:  # HOLD
            final_score = 0.5
        
        logger.debug(f"Technical signal scoring: action={action}, score={score}, "
                    f"signal_count={signal_count}, final={final_score}")
        
        return final_score
    
    def _score_market_condition(self, technical_indicators):
        """Market state scoring"""
        try:
            # Assess market state based on multiple indicators
            rsi = technical_indicators.get('rsi', 50)
            if hasattr(rsi, '__len__'):
                rsi = rsi.iloc[-1]
            
            trend_strength = technical_indicators.get('trend_strength', 0)
            volatility = technical_indicators.get('volatility', 0)
            
            # RSI scoring (30-70 is neutral)
            rsi_score = 0.5
            if rsi < 30:  # Oversold
                rsi_score = 0.8
            elif rsi > 70:  # Overbought
                rsi_score = 0.2              
                       
            # Trend strength scoring
            trend_score = 0.5 + (trend_strength * 0.3)  # Strong trend favors buying
            
            # Volatility scoring (medium volatility is best)
            vol_score = 0.7 if 0.3 < volatility < 0.6 else 0.5
            
            # Comprehensive scoring
            return (rsi_score + trend_score + vol_score) / 3
            
        except Exception as e:
            logger.error(f"Market state scoring error: {e}")
            return 0.5
    
    def _finalize_decision(self, decision_matrix):
        """Final decision"""
        action = decision_matrix['action']
        confidence = decision_matrix['confidence']
        total_score = decision_matrix['total_score']
        
        reason = f"Comprehensive score: {total_score:.3f} (AI: {decision_matrix['components']['ai_score']:.3f}, "
        reason += f"Technical: {decision_matrix['components']['tech_score']:.3f}, "
        reason += f"Market: {decision_matrix['components']['market_score']:.3f},"
        reason += f"Reversal: {decision_matrix['components']['reversal_score']:.3f},"
        reason += f"Support Resistance: {decision_matrix['components']['sr_score']:.3f}),"
        
        return {
            'action': action,
            'confidence': confidence,
            'reason': reason,
            'total_score': total_score,
            'source': 'Enhanced Decision Matrix'
        }
    
    def execute_trade(self, decision, current_price):
        """Execute trade"""
        if decision['action'] == 'HOLD':
            logger.info(f"[{self.symbol}] Decision is HOLD, not executing trade")
            return
        
        # Sync exchange position
        self.sync_with_exchange()
        actual_position = self.base_amount  # Use synced position
        
        logger.info(f"[{self.symbol}] Current position: {self.base_amount:.10f}")
        
        # Only get needed total asset information
        total_assets, available_usdt, _ = self.risk_manager.calculate_total_assets()
        
        if decision['action'] == 'BUY':
            # Get market analysis for dynamic position calculation
            market_analysis = self.analyze_market()
            if market_analysis is None:
                logger.warning(f"[{self.symbol}] Unable to get market analysis, using base position")
                buy_amount = self.risk_manager.get_position_size(self.symbol, decision['confidence'], current_price)
            else:
                # Use dynamic position calculation
                buy_amount = self.risk_manager.get_dynamic_position_size(
                    self.symbol, decision['confidence'], market_analysis['technical_indicators'], current_price
                )
            
            if buy_amount == 0:
                logger.warning(f"[{self.symbol}] Calculated position size is 0, skipping buy")
                return
                                    
            # Get exchange minimum trade quantity for buy check
            min_sz, _ = self.api.get_instrument_info(self.symbol)
            if min_sz is None:
                min_sz = self.api.get_default_min_size(self.symbol)          
            
            # Calculate base currency quantity to buy
            estimated_base_amount = buy_amount / current_price if current_price > 0 else 0
            
            logger.info(f"[{self.symbol}] Pre-buy check: estimated quantity={estimated_base_amount:.10f}, "
                        f"minimum trade quantity={min_sz}, available USDT=${available_usdt:.2f}, "
                        f"buy amount=${buy_amount:.2f}")

            # Check if buy quantity meets exchange minimum requirement
            if estimated_base_amount < min_sz:
                logger.warning(f"[{self.symbol}] Estimated buy quantity {estimated_base_amount:.10f} less than minimum trade quantity {min_sz}, canceling buy")
                return                    
            
            # Check if quote currency balance is sufficient
            if available_usdt < buy_amount:
                logger.warning(f"[{self.symbol}] Quote currency balance insufficient: need ${buy_amount:.2f}, current ${available_usdt:.2f}")
                return
            
            logger.info(f"[{self.symbol}] Creating buy order: amount=${buy_amount:.2f}, price=${current_price:.2f}")                     
            order_id = self.api.create_order(
                symbol=self.symbol,
                side='buy',
                amount=buy_amount
            )
            
            if order_id:
                # Wait for order completion
                order_status = self.api.wait_for_order_completion(self.symbol, order_id)
                if order_status is None:
                    logger.error(f"[{self.symbol}] Order not completed")
                    return
                
                # Get actual fill price and quantity
                fill_price = order_status['avgPx']
                fill_amount = order_status['accFillSz']
                
                # Update position status
                self.base_amount += fill_amount
                
                # Update average entry price (weighted average)
                if self.entry_price > 0:
                    # If adding position, calculate weighted average price
                    self.entry_price = (self.entry_price * (self.base_amount - fill_amount) + fill_price * fill_amount) / self.base_amount
                    logger.info(f"[{self.symbol}] Position addition operation, updated average entry price: ${self.entry_price:.2f}")
                else:
                    self.entry_price = fill_price
                
                logger.info(f"[{self.symbol}] Buy successful: quantity={fill_amount:.10f}, price=${fill_price:.2f}")
                logger.info(f"[{self.symbol}] Updated position: {self.symbol.split('-')[0]}={self.base_amount:.10f}")
                
                # Sync exchange position
                self.sync_with_exchange()
                                            
                # Set dynamic stop-loss
                trailing_percent = self.risk_manager.trailing_stop_percent
                self.db.set_dynamic_stop(self.symbol, fill_price, trailing_percent=trailing_percent)
                logger.info(f"[{self.symbol}] Set dynamic stop-loss: initial price=${fill_price:.2f}, trailing_percent={trailing_percent:.1%}")
                
                # Note: sync_with_exchange already saves position, no need to save again here
                self.risk_manager.increment_trade_count(self.symbol)
            else:
                logger.error(f"[{self.symbol}] Buy order creation failed")
        
        elif decision['action'] == 'SELL':                                   
            if actual_position <= 0:
                logger.info(f"[{self.symbol}] Exchange actual position is 0, ignoring sell signal")
                # Update local status
                self.base_amount = 0
                self.entry_price = 0
                self.save_position()
                return
            
            # Prepare position information for AI analysis
            position_info = self._prepare_position_info(actual_position, current_price, total_assets)
            
            # Get market analysis data (for technical indicators)
            market_analysis = self.analyze_market()
            
            # Use DeepSeek to analyze sell proportion
            try:
                sell_proportion = self.deepseek.analyze_sell_proportion(
                    self.symbol, position_info, market_analysis, current_price
                )
            except Exception as e:
                logger.warning(f"[{self.symbol}] DeepSeek sell proportion analysis failed: {e}, using fallback decision")
                sell_proportion = self.deepseek._get_fallback_sell_proportion(position_info, decision['confidence'])
            
            # Calculate sell quantity based on AI suggestion
            sell_amount = actual_position * sell_proportion
            
            # Ensure sell quantity doesn't exceed actual position
            sell_amount = min(sell_amount, actual_position)
            
            logger.info(f"[{self.symbol}] AI suggested sell proportion: {sell_proportion:.1%}, calculated sell quantity: {sell_amount:.10f}")
            
            # Get exchange minimum trade quantity for sell check
            min_sz, _ = self.api.get_instrument_info(self.symbol)
            if min_sz is None:
                min_sz = self.api.get_default_min_size(self.symbol)
            
            # Check if sell quantity is less than exchange minimum trade quantity
            if sell_amount < min_sz:
                if sell_proportion < 1.0:  # Partial sell
                    logger.info(f"[{self.symbol}] Sell quantity {sell_amount:.10f} less than minimum trade quantity {min_sz}, ignoring this partial sell")
                    return
                else:  # Full sell
                    logger.info(f"[{self.symbol}] Position quantity {sell_amount:.10f} less than minimum trade quantity {min_sz}, dust position, ignoring sell")
                    return
            
            # Record sell decision details
            logger.info(f"[{self.symbol}] Final sell decision: proportion={sell_proportion:.1%}, quantity={sell_amount:.10f}, "
                    f"reason={decision.get('reason', 'AI decision')}")
            
            # Execute sell order
            order_id = self.api.create_order(
                symbol=self.symbol,
                side='sell',
                amount=sell_amount
            )
                                
            if order_id:
                # Wait for order completion
                order_status = self.api.wait_for_order_completion(self.symbol, order_id)
                if order_status is None:
                    logger.error(f"[{self.symbol}] Order not completed")
                    return
                
                # Get actual fill price and quantity
                fill_price = order_status['avgPx']
                fill_amount = order_status['accFillSz']
                
                # Calculate profit
                profit = (fill_price - self.entry_price) * fill_amount
                profit_percent = (fill_price / self.entry_price - 1) * 100 if self.entry_price > 0 else 0
                
                # Sync exchange position
                self.sync_with_exchange()
                
                logger.info(f"[{self.symbol}] Sell successful: quantity={fill_amount:.10f}, price=${fill_price:.2f}")
                logger.info(f"[{self.symbol}] Profit: ${profit:.2f} ({profit_percent:+.2f}%)")
                logger.info(f"[{self.symbol}] Updated position: {self.symbol.split('-')[0]}={self.base_amount:.10f}")
                
                # Save trade record
                self.db.save_trade_record(self.symbol, 'sell', fill_amount, fill_price, order_id)
                
                # If fully sold, reset status
                if self.base_amount <= 0:
                    self.entry_price = 0
                    self.db.delete_dynamic_stop(self.symbol)
                    logger.info(f"[{self.symbol}] Position cleared, removed dynamic stop-loss")
                else:
                    # After partial sell, update dynamic stop-loss
                    self.db.set_dynamic_stop(self.symbol, self.entry_price, trailing_percent=0.03, multiplier=2)
                    logger.info(f"[{self.symbol}] After partial sell, reset dynamic stop-loss")
                
                # Note: sync_with_exchange already saves position, no need to save again here
                self.risk_manager.increment_trade_count(self.symbol)
            else:
                logger.error(f"[{self.symbol}] Sell order creation failed")
    
    def _prepare_position_info(self, actual_position, current_price, total_assets):
        """Prepare position information for AI analysis"""
        try:
            # Calculate position value
            position_value = actual_position * current_price
            
            # Calculate position ratio
            position_ratio = position_value / total_assets if total_assets > 0 else 0
            
            # Calculate profit ratio
            profit_ratio = (current_price - self.entry_price) / self.entry_price if self.entry_price > 0 else 0
            
            # Calculate distance to stop-loss/take-profit
            stop_loss_price = self.entry_price * (1 - self.risk_manager.stop_loss) if self.entry_price > 0 else 0
            take_profit_price = self.entry_price * (1 + self.risk_manager.take_profit) if self.entry_price > 0 else 0
            
            distance_to_stop_loss = (current_price - stop_loss_price) / current_price if current_price > 0 else 0
            distance_to_take_profit = (take_profit_price - current_price) / current_price if current_price > 0 else 0
            
            # Get market volatility
            market_data = self.api.get_market_data(self.symbol, '1H', limit=24)
            if market_data is not None and len(market_data) > 0:
                market_volatility = market_data['close'].pct_change().std() * 100
            else:
                market_volatility = 0
            
            # Get trend strength
            market_analysis = self.analyze_market()
            trend_strength = 0
            if market_analysis and 'technical_indicators' in market_analysis:
                trend_strength = market_analysis['technical_indicators'].get('trend_strength', 0)
            
            position_info = {
                'base_amount': actual_position,
                'entry_price': self.entry_price,
                'current_price': current_price,
                'position_value': position_value,
                'position_ratio': position_ratio,
                'profit_ratio': profit_ratio,
                'distance_to_stop_loss': distance_to_stop_loss,
                'distance_to_take_profit': distance_to_take_profit,
                'market_volatility': market_volatility,
                'trend_strength': trend_strength,
                'total_assets': total_assets
            }
            
            logger.debug(f"[{self.symbol}] Position analysis information: ratio={position_ratio:.2%}, profit={profit_ratio:+.2%}, "
                        f"distance to stop-loss={distance_to_stop_loss:.2%}, volatility={market_volatility:.2f}%")
            
            return position_info
            
        except Exception as e:
            logger.error(f"[{self.symbol}] Error preparing position information: {e}")
            # Return basic position information
            return {
                'base_amount': actual_position,
                'entry_price': self.entry_price,
                'current_price': current_price,
                'position_value': actual_position * current_price,
                'position_ratio': 0,
                'profit_ratio': 0,
                'distance_to_stop_loss': 0,
                'distance_to_take_profit': 0,
                'market_volatility': 0,
                'trend_strength': 0,
                'total_assets': total_assets
            }
    
    def start_dynamic_stop_monitor(self):
        """Start dynamic stop-loss monitoring"""
        if self.monitor_running and self.monitor_thread and self.monitor_thread.is_alive():
            logger.info(f"[{self.symbol}] Dynamic stop-loss monitoring already running")
            return
        
        self.monitor_running = True
        self.monitor_thread = threading.Thread(target=self._dynamic_stop_monitor_loop, name=f"StopMonitor-{self.symbol}")
        self.monitor_thread.daemon = True
        self.monitor_thread.start()
        logger.info(f"[{self.symbol}] Dynamic stop-loss monitoring started")
    
    def stop_dynamic_stop_monitor(self):
        """Stop dynamic stop-loss monitoring"""
        self.monitor_running = False
        if self.monitor_thread and self.monitor_thread.is_alive():
            self.monitor_thread.join(timeout=5)
            if self.monitor_thread.is_alive():
                logger.warning(f"{self.symbol} dynamic stop-loss monitoring thread didn't exit normally")
            else:
                logger.info(f"{self.symbol} dynamic stop-loss monitoring stopped")
    
    def _dynamic_stop_monitor_loop(self):
        """Dynamic stop-loss monitoring loop"""
        while self.monitor_running:
            try:
                # Only monitor when there is position
                if self.base_amount > 0:
                    self.check_dynamic_stops()
                
                # Monitor every 5 minutes
                time.sleep(self.monitor_interval)
                
            except Exception as e:
                logger.error(f"[{self.symbol}] Dynamic stop-loss monitoring error: {e}")
                time.sleep(60)  # Wait 1 minute before continuing when error occurs
    
    def check_dynamic_stops(self):
        """Check dynamic stop-loss conditions"""
        try:
            # Get current price
            current_price = self.api.get_current_price(self.symbol)
            if not current_price:
                logger.warning(f"[{self.symbol}] Unable to get current price, skipping stop-loss check")
                return
            
            # Get dynamic stop-loss information
            stop_info = self.db.get_dynamic_stop(self.symbol)
            if not stop_info:
                logger.debug(f"[{self.symbol}] No dynamic stop-loss settings")
                return
            
            stop_loss_price = stop_info['current_stop_loss']
            take_profit_price = stop_info['current_take_profit']
            trailing_percent = stop_info['trailing_percent']
            
            logger.debug(f"[{self.symbol}] Dynamic stop-loss check: current price=${current_price:.2f}, stop-loss=${stop_loss_price:.2f}, take-profit=${take_profit_price:.2f}")
            
            # Check stop-loss condition
            if current_price <= stop_loss_price:
                logger.warning(f"⚠️ {self.symbol} triggered dynamic stop-loss! Current price=${current_price:.2f} <= stop-loss price=${stop_loss_price:.2f}")
                self.execute_trade({
                    'action': 'SELL',
                    'confidence': 'HIGH',
                    'reason': f'Dynamic stop-loss triggered: {current_price:.2f} <= {stop_loss_price:.2f}'
                }, current_price)
                return
            
            # Check take-profit condition
            if current_price >= take_profit_price:
                logger.info(f"🎯 {self.symbol} triggered dynamic take-profit! Current price=${current_price:.2f} >= take-profit price=${take_profit_price:.2f}")
                self.execute_trade({
                    'action': 'SELL', 
                    'confidence': 'HIGH',
                    'reason': f'Dynamic take-profit triggered: {current_price:.2f} >= {take_profit_price:.2f}'
                }, current_price)
                return
            
            # Update trailing stop-loss (only moves upward) - fix logic
            if stop_info and current_price > stop_info.get('highest_price', 0): 
                new_stop_loss = self.db.update_dynamic_stop(self.symbol, current_price, trailing_percent=trailing_percent, multiplier=2) 
                if new_stop_loss:
                    logger.info(f"[{self.symbol}] Trailing stop-loss updated: new stop-loss price=${new_stop_loss:.2f}")
                
        except Exception as e:
            logger.error(f"[{self.symbol}] Error checking dynamic stop-loss: {e}")
    
    def run(self):
        """Run strategy (using enhanced decision)"""
        self.strategy_running = True
        
        while self.strategy_running:
            try:
                logger.info(f"\n=== {self.symbol} Strategy Auto Execution ===")
                
                # Market analysis
                analysis = self.analyze_market()
                if analysis is None:
                    logger.warning("Analysis failed, waiting for next execution")
                    time.sleep(self.config['interval'])
                    continue
                
                # Make trading decision
                decision = self.make_enhanced_decision(analysis)
                logger.info(f"{self.symbol} Decision: {decision['action']} (Confidence: {decision['confidence']}, Score: {decision['total_score']:.3f})")
                
                if decision['reason']:
                    logger.info(f"Reason: {decision['reason']}")
                                            
                # Execute trade
                self.execute_trade(decision, analysis['current_price'])
                
                # Risk management check
                self.check_risk_management(analysis['current_price'])
                    
                time.sleep(self.config['interval'])
                
            except Exception as e:
                logger.error(f"Strategy execution error: {e}")
                if "Network" in str(e) or "Connection" in str(e):
                    wait_time = min(300, self.config['interval'] * 2)  # Network error wait longer
                else:
                    wait_time = self.config['interval']
                
                if self.strategy_running:
                    time.sleep(wait_time)
    
    def stop_strategy(self):
        """Stop strategy running"""
        with threading.Lock():  # Add thread lock
            self.strategy_running = False
        logger.info(f"{self.symbol} strategy stop signal sent")
           
    def check_risk_management(self, current_price):
        """Check risk management (fixed stop-loss/take-profit)"""
        if self.base_amount == 0:
            return
        
        # Calculate profit/loss percentage
        pnl_pct = (current_price - self.entry_price) / self.entry_price if self.entry_price > 0 else 0
        
        # Fixed stop-loss/take-profit check
        if pnl_pct <= -self.risk_manager.stop_loss:
            logger.warning(f"⚠️ {self.symbol} triggered fixed stop-loss ({pnl_pct:.2%})")
            self.execute_trade({
                'action': 'SELL',
                'confidence': 'HIGH',
                'reason': 'Fixed stop-loss triggered'
            }, current_price)
        elif pnl_pct >= self.risk_manager.take_profit:
            logger.info(f"🎯 {self.symbol} triggered fixed take-profit ({pnl_pct:.2%})")
            self.execute_trade({
                'action': 'SELL',
                'confidence': 'HIGH',
                'reason': 'Fixed take-profit triggered'
            }, current_price)

class MultiStrategyRunner:
    """Multi-Strategy Runner"""
    
    def __init__(self):
        self.running = False
        self.strategies = {}
        self.threads = {}
        
        # Configure each currency strategy
        self.symbol_configs = {
            'ETH-USDT': {'name': 'Ethereum', 'interval': 1800, 'timeframe': '1H'},
            'BTC-USDT': {'name': 'Bitcoin', 'interval': 1800, 'timeframe': '1H'},
            'SOL-USDT': {'name': 'Solana', 'interval': 1800, 'timeframe': '1H'},
            'XRP-USDT': {'name': 'Ripple', 'interval': 1800, 'timeframe': '1H'},
            'BNB-USDT': {'name': 'Binance Coin', 'interval': 1800, 'timeframe': '1H'},
            'OKB-USDT': {'name': 'OKB', 'interval': 1800, 'timeframe': '1H'},
            }
        
        # Initialize API client
        self.api = OKXAPIClient()
        self.deepseek = DeepSeekAnalyzer()
        self.risk_manager = RiskManager(self.api)
        
        # Initialize all strategies
        for symbol, config in self.symbol_configs.items():
            self.strategies[symbol] = TradingStrategy(symbol, config, self.api, self.risk_manager, self.deepseek)
        
        # Register exit handlers
        atexit.register(self.shutdown)
        signal.signal(signal.SIGINT, lambda s, f: self.shutdown())
        signal.signal(signal.SIGTERM, lambda s, f: self.shutdown())
    
    def start_strategy(self, symbol):
        """Start single currency strategy"""
        if symbol not in self.strategies:
            logger.error(f"Unsupported {symbol} trading pair")
            return False
        
        # Check if already running
        if symbol in self.threads and self.threads[symbol].is_alive():
            logger.info(f"{symbol} strategy already running")
            return True
        
        def strategy_worker():
            """Strategy worker thread"""
            strategy = self.strategies[symbol]
            logger.info(f"Starting {symbol} strategy")
            
            try:
                # Directly run strategy, strategy has its own loop internally
                strategy.run()
            except Exception as e:
                logger.error(f"{symbol} strategy execution error: {e}")
            finally:
                logger.info(f"{symbol} strategy thread ended")
                # Remove from active thread list when thread ends
                if symbol in self.threads:
                    del self.threads[symbol]
        
        # Create and start thread
        thread = threading.Thread(target=strategy_worker, name=f"Strategy-{symbol}")
        thread.daemon = True
        thread.start()
        self.threads[symbol] = thread
        
        logger.info(f"Started {symbol} strategy")
        return True
    
    def start_all_strategies(self):
        """Start all strategies"""
        self.running = True
        
        for symbol in self.strategies.keys():
            self.start_strategy(symbol)
            time.sleep(60)  # Each currency starts with 60 second interval           
        
        
        logger.info("All strategies started")
    
    def stop_strategy(self, symbol):
        """Stop single currency strategy"""
        if symbol in self.strategies:
            # First stop strategy
            self.strategies[symbol].stop_strategy()
            
        if symbol in self.threads:
            thread = self.threads[symbol]
            thread.join(timeout=10)  # Wait 10 seconds
            if thread.is_alive():
                logger.warning(f"Unable to stop {symbol} strategy thread")
            else:
                del self.threads[symbol]
                logger.info(f"Stopped {symbol} strategy")
        
        # Stop dynamic stop-loss monitoring
        if symbol in self.strategies:
            self.strategies[symbol].stop_dynamic_stop_monitor()

    def stop_all_strategies(self):
        """Stop all strategies"""
        self.running = False
        
        # First send stop signal to all strategies
        for symbol in list(self.strategies.keys()):
            self.strategies[symbol].stop_strategy()
        
        # Then stop threads
        for symbol in list(self.threads.keys()):
            self.stop_strategy(symbol)
        
        logger.info("All strategies stopped")
    
    def get_status(self):
        """Get system status"""
        status = {
            'running': self.running,
            'active_strategies': list(self.threads.keys()),
            'strategies_detail': {}
        }
        
        for symbol, strategy in self.strategies.items():
            status['strategies_detail'][symbol] = {
                'base_amount': strategy.base_amount,
                'entry_price': strategy.entry_price,
                'config': self.symbol_configs[symbol]
            }
        
        return status
        
    def shutdown(self):
        """System shutdown handling"""
        logger.info("System shutting down, stopping all strategies and monitoring...")
        self.stop_all_strategies()
        
        # Stop all dynamic stop-loss monitoring
        for symbol, strategy in self.strategies.items():
            try:
                strategy.stop_dynamic_stop_monitor()
                logger.info(f"Stopped {symbol} dynamic stop-loss monitoring")
            except Exception as e:
                logger.error(f"Error stopping {symbol} dynamic stop-loss monitoring: {e}")
        
        # Wait for all threads to end
        for symbol, thread in list(self.threads.items()):
            if thread.is_alive():
                thread.join(timeout=5)  # Wait 5 seconds
                if thread.is_alive():
                    logger.warning(f"{symbol} strategy thread didn't exit normally")
        
        # Ensure all resources released
        time.sleep(1)
        logger.info("System shutdown completed")
        print("👋 System safely exited, thank you for using!")

def main():
    """Main function"""
    print("=" * 60)
    print(" " * 20 + "Multi-Currency Intelligent Trading System")
    print("=" * 60)
    print("Core Features:")
    print("  • DeepSeek AI Driven Decisions")
    print("  • KDJ + RSI + ATR Technical Indicators")
    print("  • Multi-level Risk Control")
    print("  • Position Status Persistence")
    print("=" * 60)
    
    # Check environment variables
    required_env_vars = ['OKX_API_KEY', 'OKX_SECRET_KEY', 'OKX_PASSWORD']
    if not all(os.getenv(var) for var in required_env_vars):
        print("❌ Please set OKX API environment variables")
        return
    
    # Create strategy runner
    runner = MultiStrategyRunner()
    
    # Add debug mode switch
    debug_mode = False
    
    def toggle_debug_mode():
        nonlocal debug_mode
        debug_mode = not debug_mode
        runner.api.log_http = debug_mode
        print(f"Debug mode {'enabled' if debug_mode else 'disabled'}")
    
    try:
        while True:
            print("\n" + "=" * 50)
            print(" " * 15 + "Trading System Control Panel")
            print("=" * 50)
            print("1. Start All Strategies")
            print("2. Start Specific Strategy")
            print("3. Stop All Strategies")
            print("4. Stop Specific Strategy")
            print("5. View System Status")
            print("6. Manual Position Sync")
            print("7. Toggle Debug Mode")
            print("8. Exit System")
            print("=" * 50)
            
            choice = input("Please enter choice (1-8): ").strip()
            
            if choice == '1':
                print("Starting all trading strategies...")
                runner.start_all_strategies()
                print("✅ All strategies started")
            
            elif choice == '2':
                print("\nSelect currency to start:")
                symbols = list(runner.symbol_configs.keys())
                for i, symbol in enumerate(symbols, 1):
                    config = runner.symbol_configs[symbol]
                    print(f"{i}. {symbol} ({config['name']})")
                print(f"{len(symbols)+1}. Back")
                
                symbol_choice = input("Please enter choice: ").strip()
                if symbol_choice.isdigit():
                    index = int(symbol_choice) - 1
                    if 0 <= index < len(symbols):
                        symbol = symbols[index]
                        runner.start_strategy(symbol)
                        print(f"✅ Started {symbol} strategy")
                    elif index == len(symbols):
                        continue
                    else:
                        print("❌ Invalid choice")
                else:
                    print("❌ Invalid input")
                    
            elif choice == '3':
                print("Stopping all trading strategies...")
                runner.stop_all_strategies()
                print("✅ All strategies stopped")
                
            elif choice == '4':
                print("\nSelect currency to stop:")
                symbols = list(runner.symbol_configs.keys())
                for i, symbol in enumerate(symbols, 1):
                    config = runner.symbol_configs[symbol]
                    print(f"{i}. {symbol} ({config['name']})")
                print(f"{len(symbols)+1}. Back")
                
                symbol_choice = input("Please enter choice: ").strip()
                if symbol_choice.isdigit():
                    index = int(symbol_choice) - 1
                    if 0 <= index < len(symbols):
                        symbol = symbols[index]
                        runner.stop_strategy(symbol)
                        print(f"✅ Stopped {symbol} strategy")
                    elif index == len(symbols):
                        continue
                    else:
                        print("❌ Invalid choice")
                else:
                    print("❌ Invalid input")
                    
            elif choice == '5':
                status = runner.get_status()
                print(f"\nSystem Status: {'Running' if status['running'] else 'Stopped'}")
                print(f"Active Strategies: {len(status['active_strategies'])}")
                for symbol in status['active_strategies']:
                    print(f"  • {symbol}")
                
                print("\nPosition Status:")
                for symbol, detail in status['strategies_detail'].items():
                    base_amount = detail['base_amount']
                    entry_price = detail['entry_price']
                    if base_amount > 0:
                        print(f"  {symbol}: Position {base_amount:.10f} @ ${entry_price:.2f}")
                        
            elif choice == '6':
                print("Manual sync all currency positions...")
                for symbol, strategy in runner.strategies.items():
                    strategy.sync_with_exchange()
                print("✅ Sync completed")
                
            elif choice == '7':
                toggle_debug_mode()
                
            elif choice == '8':
                print("Exiting system, please wait for all strategies to stop...")
                break
                
            else:
                print("❌ Invalid choice, please re-enter")
                
    except KeyboardInterrupt:
        print("\nReceived interrupt signal, exiting...")
        
if __name__ == "__main__":
    main()