diff --git a/indicators.py b/indicators.py
new file mode 100644
index 0000000..0ad66e2
--- /dev/null
+++ b/indicators.py
@@ -0,0 +1,272 @@
+"""
+技术指标模块 - 计算各种技术指标用于交易决策
+"""
+import logging
+from typing import List, Dict, Optional, Tuple
+import math
+
+logger = logging.getLogger(__name__)
+
+
+class TechnicalIndicators:
+    """技术指标计算类"""
+    
+    @staticmethod
+    def calculate_rsi(prices: List[float], period: int = 14) -> Optional[float]:
+        """
+        计算RSI（相对强弱指标）
+        
+        Args:
+            prices: 价格列表（从旧到新）
+            period: 计算周期，默认14
+            
+        Returns:
+            RSI值（0-100），None表示数据不足
+        """
+        if len(prices) < period + 1:
+            return None
+        
+        gains = []
+        losses = []
+        
+        for i in range(len(prices) - period, len(prices)):
+            if i == 0:
+                continue
+            change = prices[i] - prices[i - 1]
+            if change > 0:
+                gains.append(change)
+                losses.append(0)
+            else:
+                gains.append(0)
+                losses.append(abs(change))
+        
+        if len(gains) == 0:
+            return None
+        
+        avg_gain = sum(gains) / len(gains)
+        avg_loss = sum(losses) / len(losses) if sum(losses) > 0 else 0.0001
+        
+        if avg_loss == 0:
+            return 100
+        
+        rs = avg_gain / avg_loss
+        rsi = 100 - (100 / (1 + rs))
+        
+        return rsi
+    
+    @staticmethod
+    def calculate_macd(
+        prices: List[float], 
+        fast_period: int = 12, 
+        slow_period: int = 26, 
+        signal_period: int = 9
+    ) -> Optional[Dict[str, float]]:
+        """
+        计算MACD指标
+        
+        Args:
+            prices: 价格列表（从旧到新）
+            fast_period: 快线周期，默认12
+            slow_period: 慢线周期，默认26
+            signal_period: 信号线周期，默认9
+            
+        Returns:
+            {'macd': MACD值, 'signal': 信号线值, 'histogram': 柱状图值}
+        """
+        if len(prices) < slow_period + signal_period:
+            return None
+        
+        # 计算EMA
+        def ema(data, period):
+            multiplier = 2 / (period + 1)
+            ema_values = [data[0]]
+            for price in data[1:]:
+                ema_values.append((price - ema_values[-1]) * multiplier + ema_values[-1])
+            return ema_values
+        
+        fast_ema = ema(prices, fast_period)
+        slow_ema = ema(prices, slow_period)
+        
+        # 对齐长度
+        min_len = min(len(fast_ema), len(slow_ema))
+        fast_ema = fast_ema[-min_len:]
+        slow_ema = slow_ema[-min_len:]
+        
+        # 计算MACD线
+        macd_line = [fast_ema[i] - slow_ema[i] for i in range(len(fast_ema))]
+        
+        if len(macd_line) < signal_period:
+            return None
+        
+        # 计算信号线
+        signal_line = ema(macd_line, signal_period)
+        
+        # 计算柱状图
+        macd_value = macd_line[-1]
+        signal_value = signal_line[-1]
+        histogram = macd_value - signal_value
+        
+        return {
+            'macd': macd_value,
+            'signal': signal_value,
+            'histogram': histogram
+        }
+    
+    @staticmethod
+    def calculate_bollinger_bands(
+        prices: List[float], 
+        period: int = 20, 
+        std_dev: float = 2.0
+    ) -> Optional[Dict[str, float]]:
+        """
+        计算布林带
+        
+        Args:
+            prices: 价格列表（从旧到新）
+            period: 计算周期，默认20
+            std_dev: 标准差倍数，默认2.0
+            
+        Returns:
+            {'upper': 上轨, 'middle': 中轨, 'lower': 下轨}
+        """
+        if len(prices) < period:
+            return None
+        
+        recent_prices = prices[-period:]
+        middle = sum(recent_prices) / len(recent_prices)
+        
+        # 计算标准差
+        variance = sum((p - middle) ** 2 for p in recent_prices) / len(recent_prices)
+        std = math.sqrt(variance)
+        
+        upper = middle + (std * std_dev)
+        lower = middle - (std * std_dev)
+        
+        return {
+            'upper': upper,
+            'middle': middle,
+            'lower': lower
+        }
+    
+    @staticmethod
+    def calculate_atr(
+        high_prices: List[float],
+        low_prices: List[float],
+        close_prices: List[float],
+        period: int = 14
+    ) -> Optional[float]:
+        """
+        计算ATR（平均真实波幅）
+        
+        Args:
+            high_prices: 最高价列表
+            low_prices: 最低价列表
+            close_prices: 收盘价列表
+            period: 计算周期，默认14
+            
+        Returns:
+            ATR值
+        """
+        if len(high_prices) < period + 1 or len(low_prices) < period + 1 or len(close_prices) < period + 1:
+            return None
+        
+        true_ranges = []
+        
+        for i in range(1, len(high_prices)):
+            tr1 = high_prices[i] - low_prices[i]
+            tr2 = abs(high_prices[i] - close_prices[i - 1])
+            tr3 = abs(low_prices[i] - close_prices[i - 1])
+            true_range = max(tr1, tr2, tr3)
+            true_ranges.append(true_range)
+        
+        if len(true_ranges) < period:
+            return None
+        
+        recent_tr = true_ranges[-period:]
+        atr = sum(recent_tr) / len(recent_tr)
+        
+        return atr
+    
+    @staticmethod
+    def calculate_ema(prices: List[float], period: int = 20) -> Optional[float]:
+        """
+        计算EMA（指数移动平均）
+        
+        Args:
+            prices: 价格列表（从旧到新）
+            period: 计算周期
+            
+        Returns:
+            EMA值
+        """
+        if len(prices) < period:
+            return None
+        
+        multiplier = 2 / (period + 1)
+        ema_value = prices[0]
+        
+        for price in prices[1:]:
+            ema_value = (price - ema_value) * multiplier + ema_value
+        
+        return ema_value
+    
+    @staticmethod
+    def calculate_sma(prices: List[float], period: int = 20) -> Optional[float]:
+        """
+        计算SMA（简单移动平均）
+        
+        Args:
+            prices: 价格列表（从旧到新）
+            period: 计算周期
+            
+        Returns:
+            SMA值
+        """
+        if len(prices) < period:
+            return None
+        
+        recent_prices = prices[-period:]
+        return sum(recent_prices) / len(recent_prices)
+    
+    @staticmethod
+    def detect_market_regime(
+        prices: List[float],
+        short_period: int = 20,
+        long_period: int = 50
+    ) -> str:
+        """
+        判断市场状态：趋势或震荡
+        
+        Args:
+            prices: 价格列表
+            short_period: 短期均线周期
+            long_period: 长期均线周期
+            
+        Returns:
+            'trending' 或 'ranging'
+        """
+        if len(prices) < long_period:
+            return 'unknown'
+        
+        short_ma = TechnicalIndicators.calculate_sma(prices, short_period)
+        long_ma = TechnicalIndicators.calculate_sma(prices, long_period)
+        
+        if short_ma is None or long_ma is None:
+            return 'unknown'
+        
+        # 计算价格波动率
+        recent_prices = prices[-20:]
+        if len(recent_prices) < 2:
+            return 'unknown'
+        
+        volatility = sum(abs(recent_prices[i] - recent_prices[i-1]) for i in range(1, len(recent_prices))) / len(recent_prices)
+        avg_price = sum(recent_prices) / len(recent_prices)
+        volatility_pct = (volatility / avg_price) * 100 if avg_price > 0 else 0
+        
+        # 如果短期均线明显高于或低于长期均线，且波动率较大，判断为趋势
+        ma_diff_pct = abs(short_ma - long_ma) / long_ma * 100 if long_ma > 0 else 0
+        
+        if ma_diff_pct > 2 and volatility_pct > 1:
+            return 'trending'
+        else:
+            return 'ranging'
diff --git a/unicorn_websocket.py b/unicorn_websocket.py
new file mode 100644
index 0000000..e5c9188
--- /dev/null
+++ b/unicorn_websocket.py
@@ -0,0 +1,307 @@
+"""
+Unicorn WebSocket模块 - 提供高性能实时数据流
+"""
+import asyncio
+import logging
+from typing import Dict, List, Optional, Callable
+from unicorn_binance_websocket_api.unicorn_binance_websocket_api_manager import BinanceWebSocketApiManager
+import config
+
+logger = logging.getLogger(__name__)
+
+
+class UnicornWebSocketManager:
+    """Unicorn WebSocket管理器"""
+    
+    def __init__(self, testnet: bool = False):
+        """
+        初始化Unicorn WebSocket管理器
+        
+        Args:
+            testnet: 是否使用测试网
+        """
+        self.testnet = testnet or config.USE_TESTNET
+        self.manager: Optional[BinanceWebSocketApiManager] = None
+        self.stream_ids: Dict[str, str] = {}  # symbol -> stream_id
+        self.price_callbacks: Dict[str, List[Callable]] = {}  # symbol -> callbacks
+        self.running = False
+        
+    def start(self):
+        """启动WebSocket管理器"""
+        try:
+            # 创建管理器
+            self.manager = BinanceWebSocketApiManager(
+                exchange="binance.com-futures" if not self.testnet else "binance.com-futures-testnet",
+                throw_exception_if_unrepairable=True,
+                high_performance=True
+            )
+            
+            self.running = True
+            logger.info(f"Unicorn WebSocket管理器启动成功 (测试网: {self.testnet})")
+            return True
+        except Exception as e:
+            logger.error(f"启动Unicorn WebSocket管理器失败: {e}")
+            return False
+    
+    def stop(self):
+        """停止WebSocket管理器"""
+        self.running = False
+        if self.manager:
+            # 停止所有流
+            for stream_id in self.stream_ids.values():
+                try:
+                    self.manager.stop_stream(stream_id)
+                except:
+                    pass
+            
+            # 停止管理器
+            try:
+                self.manager.stop_manager_with_all_streams()
+            except:
+                pass
+            
+            logger.info("Unicorn WebSocket管理器已停止")
+    
+    def subscribe_ticker(self, symbols: List[str], callback: Callable) -> Dict[str, str]:
+        """
+        订阅交易对的价格流
+        
+        Args:
+            symbols: 交易对列表
+            callback: 价格更新回调函数 callback(symbol, price_data)
+            
+        Returns:
+            交易对到stream_id的映射
+        """
+        if not self.manager:
+            logger.error("WebSocket管理器未启动")
+            return {}
+        
+        stream_ids = {}
+        
+        try:
+            # 构建流名称列表
+            streams = []
+            for symbol in symbols:
+                # 转换为小写（币安要求）
+                symbol_lower = symbol.lower()
+                # 订阅ticker流
+                stream_name = f"{symbol_lower}@ticker"
+                streams.append(stream_name)
+                
+                # 注册回调
+                if symbol not in self.price_callbacks:
+                    self.price_callbacks[symbol] = []
+                self.price_callbacks[symbol].append(callback)
+            
+            # 创建多路复用流
+            if streams:
+                stream_id = self.manager.create_stream(
+                    ["arr"],
+                    streams,
+                    output="UnicornFy"
+                )
+                
+                # 记录stream_id
+                for symbol in symbols:
+                    self.stream_ids[symbol] = stream_id
+                    stream_ids[symbol] = stream_id
+                
+                logger.info(f"订阅 {len(symbols)} 个交易对的价格流")
+            
+            return stream_ids
+            
+        except Exception as e:
+            logger.error(f"订阅价格流失败: {e}")
+            return {}
+    
+    def subscribe_kline(
+        self, 
+        symbols: List[str], 
+        interval: str = "5m",
+        callback: Optional[Callable] = None
+    ) -> Dict[str, str]:
+        """
+        订阅K线数据流
+        
+        Args:
+            symbols: 交易对列表
+            interval: K线周期（1m, 5m, 15m等）
+            callback: K线更新回调函数 callback(symbol, kline_data)
+            
+        Returns:
+            交易对到stream_id的映射
+        """
+        if not self.manager:
+            logger.error("WebSocket管理器未启动")
+            return {}
+        
+        stream_ids = {}
+        
+        try:
+            # 构建流名称列表
+            streams = []
+            for symbol in symbols:
+                symbol_lower = symbol.lower()
+                stream_name = f"{symbol_lower}@kline_{interval}"
+                streams.append(stream_name)
+            
+            # 创建多路复用流
+            if streams:
+                stream_id = self.manager.create_stream(
+                    ["arr"],
+                    streams,
+                    output="UnicornFy"
+                )
+                
+                for symbol in symbols:
+                    stream_ids[symbol] = stream_id
+                
+                logger.info(f"订阅 {len(symbols)} 个交易对的K线流 ({interval})")
+            
+            return stream_ids
+            
+        except Exception as e:
+            logger.error(f"订阅K线流失败: {e}")
+            return {}
+    
+    def get_realtime_price(self, symbol: str) -> Optional[float]:
+        """
+        获取实时价格（从WebSocket流缓冲区中）
+        
+        Args:
+            symbol: 交易对
+            
+        Returns:
+            实时价格，如果未订阅则返回None
+        """
+        if not self.manager or symbol not in self.stream_ids:
+            return None
+        
+        try:
+            stream_id = self.stream_ids[symbol]
+            # 从流缓冲区获取最新数据
+            data = self.manager.pop_stream_data_from_stream_buffer(stream_id)
+            
+            if data and isinstance(data, dict):
+                # 解析ticker数据
+                if 'event_type' in data and data['event_type'] == '24hrTicker':
+                    price_data = data.get('data', {})
+                    if 'c' in price_data:  # 最新价格
+                        return float(price_data['c'])
+                elif 'data' in data and isinstance(data['data'], dict):
+                    if 'c' in data['data']:  # 最新价格
+                        return float(data['data']['c'])
+                    elif 'close' in data['data']:  # K线收盘价
+                        return float(data['data']['close'])
+            
+            return None
+            
+        except Exception as e:
+            logger.debug(f"获取 {symbol} 实时价格失败: {e}")
+            return None
+    
+    async def process_stream_data(self):
+        """
+        处理WebSocket流数据（异步）
+        """
+        if not self.manager:
+            return
+        
+        while self.running:
+            try:
+                # 处理所有流的数据
+                for symbol, stream_id in list(self.stream_ids.items()):
+                    try:
+                        # 获取流数据（非阻塞）
+                        stream_data = self.manager.pop_stream_data_from_stream_buffer(stream_id)
+                        if stream_data:
+                            # 处理数据
+                            await self._handle_stream_data(symbol, stream_data)
+                    except Exception as e:
+                        logger.debug(f"处理 {symbol} 流数据失败: {e}")
+                
+                # 短暂休眠，避免CPU占用过高
+                await asyncio.sleep(0.1)
+                
+            except Exception as e:
+                logger.error(f"处理WebSocket流数据失败: {e}")
+                await asyncio.sleep(1)
+    
+    async def _handle_stream_data(self, symbol: str, data: Dict):
+        """
+        处理单个流的数据
+        
+        Args:
+            symbol: 交易对
+            data: 流数据
+        """
+        try:
+            if not data or not isinstance(data, dict):
+                return
+            
+            # 处理ticker数据
+            if 'event_type' in data and data['event_type'] == '24hrTicker':
+                price_data = data.get('data', {})
+                if 'c' in price_data:  # 最新价格
+                    price = float(price_data['c'])
+                    # 调用所有注册的回调
+                    if symbol in self.price_callbacks:
+                        for callback in self.price_callbacks[symbol]:
+                            try:
+                                if asyncio.iscoroutinefunction(callback):
+                                    await callback(symbol, price, price_data)
+                                else:
+                                    callback(symbol, price, price_data)
+                            except Exception as e:
+                                logger.debug(f"回调函数执行失败: {e}")
+            
+            # 处理K线数据
+            elif 'event_type' in data and data['event_type'] == 'kline':
+                kline_data = data.get('data', {})
+                if 'k' in kline_data:
+                    kline = kline_data['k']
+                    # 可以在这里处理K线更新
+                    logger.debug(f"{symbol} K线更新: {kline.get('c', 'N/A')}")
+                    
+        except Exception as e:
+            logger.debug(f"处理 {symbol} 流数据失败: {e}")
+    
+    def unsubscribe(self, symbol: str):
+        """
+        取消订阅交易对
+        
+        Args:
+            symbol: 交易对
+        """
+        if symbol in self.stream_ids:
+            stream_id = self.stream_ids[symbol]
+            try:
+                self.manager.stop_stream(stream_id)
+                del self.stream_ids[symbol]
+                if symbol in self.price_callbacks:
+                    del self.price_callbacks[symbol]
+                logger.info(f"取消订阅 {symbol}")
+            except Exception as e:
+                logger.error(f"取消订阅 {symbol} 失败: {e}")
+    
+    def get_stream_statistics(self) -> Dict:
+        """
+        获取流统计信息
+        
+        Returns:
+            统计信息字典
+        """
+        if not self.manager:
+            return {}
+        
+        try:
+            stats = {
+                'total_streams': len(self.stream_ids),
+                'active_streams': len([s for s in self.stream_ids.values() if s]),
+                'subscribed_symbols': list(self.stream_ids.keys())
+            }
+            return stats
+        except Exception as e:
+            logger.error(f"获取流统计信息失败: {e}")
+            return {}