a

backend/api/routes/recommendations.py

@@ -36,7 +36,7 @@ async def get_recommendations(
     """
     try:
         if type == 'realtime':
-            # 实时生成推荐
+            # 从Redis缓存读取推荐（如果存在）
             import sys
             from pathlib import Path
             current_file = Path(__file__)
@@ -58,43 +58,59 @@ async def get_recommendations(
             sys.path.insert(0, str(project_root))
             
             from binance_client import BinanceClient
-            from market_scanner import MarketScanner
-            from risk_manager import RiskManager
-            from trade_recommender import TradeRecommender
             import config
             
-            # 初始化组件
+            # 创建客户端（用于访问Redis）
             client = BinanceClient(
                 api_key=config.BINANCE_API_KEY,
                 api_secret=config.BINANCE_API_SECRET,
                 testnet=config.USE_TESTNET
             )
             
-            await client.connect()
-            
+            # 连接Redis（如果还没有连接）
             try:
-                scanner = MarketScanner(client)
-                risk_manager = RiskManager(client)
-                recommender = TradeRecommender(client, scanner, risk_manager)
+                await client.redis_cache.connect()
+            except:
+                pass
             
-                # 生成推荐
-                recommendations = await recommender.generate_recommendations(
-                    min_signal_strength=min_signal_strength,
-                    max_recommendations=limit
-                )
+            # 从Redis读取推荐
+            recommendations = []
+            try:
+                import time
+                cache_key = "recommendations:realtime"
+                cached_data = await client.redis_cache.hgetall(cache_key)
+                if cached_data:
+                    current_time = time.time()
+                    max_age = 3600 * 2  # 推荐最大保留时间：2小时
                     
-                # 方向过滤
-                if direction:
-                    recommendations = [r for r in recommendations if r.get('direction') == direction]
+                    # 过滤过期推荐
+                    for rec in cached_data.values():
+                        if isinstance(rec, dict):
+                            rec_timestamp = rec.get('timestamp', 0)
+                            # 如果推荐时间超过2小时，跳过
+                            if current_time - rec_timestamp > max_age:
+                                continue
+                            recommendations.append(rec)
                     
-                return {
-                    "success": True,
-                    "count": len(recommendations),
-                    "type": "realtime",
-                    "data": recommendations
-                }
-            finally:
-                await client.disconnect()
+                    # 按时间戳降序排序（最新的在前）
+                    recommendations.sort(key=lambda x: x.get('timestamp', 0), reverse=True)
+                    logger.info(f"从Redis读取到 {len(recommendations)} 个有效推荐（已过滤过期）")
+            except Exception as e:
+                logger.debug(f"从Redis读取推荐失败: {e}")
+            
+            # 方向过滤
+            if direction:
+                recommendations = [r for r in recommendations if r.get('direction') == direction]
+            
+            # 限制返回数量
+            recommendations = recommendations[:limit]
+            
+            return {
+                "success": True,
+                "count": len(recommendations),
+                "type": "realtime",
+                "data": recommendations
+            }
         
         elif type == 'bookmarked':
             # 从数据库查询已标记的推荐
@@ -470,10 +486,12 @@ async def generate_recommendations(
             risk_manager = RiskManager(client)
             recommender = TradeRecommender(client, scanner, risk_manager)
             
-            # 生成推荐
+            # 生成推荐（增量添加到Redis缓存）
             recommendations = await recommender.generate_recommendations(
                 min_signal_strength=min_signal_strength,
-                max_recommendations=max_recommendations
+                max_recommendations=max_recommendations,
+                add_to_cache=True,  # 添加到Redis缓存
+                min_quality_score=0.0  # 不过滤，保留所有推荐
             )
             
             return {
@@ -494,6 +512,52 @@ async def generate_recommendations(
         raise HTTPException(status_code=500, detail=f"生成推荐失败: {str(e)}")
 
 
+@router.post("/clear-cache")
+async def clear_recommendations_cache():
+    """
+    清理Redis中的推荐缓存
+    """
+    try:
+        import sys
+        from pathlib import Path
+        current_file = Path(__file__)
+        backend_path = current_file.parent.parent.parent
+        project_root = backend_path.parent
+        trading_system_path = project_root / 'trading_system'
+        
+        if not trading_system_path.exists():
+            alternative_path = backend_path / 'trading_system'
+            if alternative_path.exists():
+                trading_system_path = alternative_path
+        
+        sys.path.insert(0, str(trading_system_path))
+        sys.path.insert(0, str(project_root))
+        
+        from binance_client import BinanceClient
+        import config
+        
+        client = BinanceClient(
+            api_key=config.BINANCE_API_KEY,
+            api_secret=config.BINANCE_API_SECRET,
+            testnet=config.USE_TESTNET
+        )
+        
+        try:
+            await client.redis_cache.connect()
+            cache_key = "recommendations:realtime"
+            await client.redis_cache.delete(cache_key)
+            
+            return {
+                "success": True,
+                "message": "推荐缓存已清理"
+            }
+        finally:
+            await client.disconnect()
+    except Exception as e:
+        logger.error(f"清理推荐缓存失败: {e}")
+        raise HTTPException(status_code=500, detail=f"清理推荐缓存失败: {str(e)}")
+
+
 @router.post("/bookmark")
 async def bookmark_recommendation(recommendation_data: dict):
     """

trading_system/redis_cache.py

@@ -219,3 +219,176 @@ class RedisCache:
     def is_connected(self) -> bool:
         """检查是否已连接"""
         return self._connected and self.redis is not None
+    
+    async def hset(self, name: str, key: str, value: Any, ttl: int = None):
+        """
+        设置Hash字段
+        
+        Args:
+            name: Hash名称
+            key: 字段名
+            value: 字段值
+            ttl: 过期时间（秒），如果设置，会对整个Hash设置TTL
+        """
+        if self.redis and self._connected:
+            try:
+                await self.redis.hset(name, key, json.dumps(value))
+                # 如果设置了TTL，对整个Hash设置过期时间
+                if ttl:
+                    await self.redis.expire(name, ttl)
+                return True
+            except Exception as e:
+                logger.debug(f"Redis Hash设置失败 {name}.{key}: {e}")
+                # Redis失败时，尝试重新连接
+                if not self._connected:
+                    await self.connect()
+                    if self.redis and self._connected:
+                        try:
+                            await self.redis.hset(name, key, json.dumps(value))
+                            if ttl:
+                                await self.redis.expire(name, ttl)
+                            return True
+                        except:
+                            pass
+        
+        # 降级到内存缓存
+        if name not in self._memory_cache:
+            self._memory_cache[name] = {}
+        self._memory_cache[name][key] = value
+        return False
+    
+    async def hget(self, name: str, key: str) -> Optional[Any]:
+        """
+        获取Hash字段
+        
+        Args:
+            name: Hash名称
+            key: 字段名
+            
+        Returns:
+            字段值，如果不存在则返回None
+        """
+        if self.redis and self._connected:
+            try:
+                data = await self.redis.hget(name, key)
+                if data:
+                    return json.loads(data)
+            except Exception as e:
+                logger.debug(f"Redis Hash获取失败 {name}.{key}: {e}")
+        
+        # 降级到内存缓存
+        if name in self._memory_cache and key in self._memory_cache[name]:
+            return self._memory_cache[name][key]
+        
+        return None
+    
+    async def hgetall(self, name: str) -> Dict[str, Any]:
+        """
+        获取Hash所有字段
+        
+        Args:
+            name: Hash名称
+            
+        Returns:
+            所有字段的字典
+        """
+        if self.redis and self._connected:
+            try:
+                data = await self.redis.hgetall(name)
+                result = {}
+                for k, v in data.items():
+                    if isinstance(k, bytes):
+                        k = k.decode('utf-8')
+                    if isinstance(v, bytes):
+                        v = json.loads(v.decode('utf-8'))
+                    else:
+                        v = json.loads(v)
+                    result[k] = v
+                return result
+            except Exception as e:
+                logger.debug(f"Redis Hash获取全部失败 {name}: {e}")
+        
+        # 降级到内存缓存
+        if name in self._memory_cache:
+            return self._memory_cache[name].copy()
+        
+        return {}
+    
+    async def hdel(self, name: str, *keys: str):
+        """
+        删除Hash字段
+        
+        Args:
+            name: Hash名称
+            *keys: 要删除的字段名
+        """
+        if self.redis and self._connected:
+            try:
+                await self.redis.hdel(name, *keys)
+            except Exception as e:
+                logger.debug(f"Redis Hash删除失败 {name}: {e}")
+        
+        # 同时删除内存缓存
+        if name in self._memory_cache:
+            for key in keys:
+                if key in self._memory_cache[name]:
+                    del self._memory_cache[name][key]
+    
+    async def zadd(self, name: str, mapping: Dict[str, float], ttl: int = None):
+        """
+        添加Sorted Set成员
+        
+        Args:
+            name: Sorted Set名称
+            mapping: {member: score} 字典
+            ttl: 过期时间（秒）
+        """
+        if self.redis and self._connected:
+            try:
+                await self.redis.zadd(name, mapping)
+                if ttl:
+                    await self.redis.expire(name, ttl)
+                return True
+            except Exception as e:
+                logger.debug(f"Redis Sorted Set添加失败 {name}: {e}")
+        
+        return False
+    
+    async def zrange(self, name: str, start: int = 0, end: int = -1, desc: bool = False, withscores: bool = False) -> List:
+        """
+        获取Sorted Set成员（按分数排序）
+        
+        Args:
+            name: Sorted Set名称
+            start: 起始索引
+            end: 结束索引
+            desc: 是否降序（默认False，升序）
+            withscores: 是否包含分数
+            
+        Returns:
+            成员列表
+        """
+        if self.redis and self._connected:
+            try:
+                if desc:
+                    return await self.redis.zrevrange(name, start, end, withscores=withscores)
+                else:
+                    return await self.redis.zrange(name, start, end, withscores=withscores)
+            except Exception as e:
+                logger.debug(f"Redis Sorted Set获取失败 {name}: {e}")
+        
+        return []
+    
+    async def zrem(self, name: str, *members: str):
+        """
+        删除Sorted Set成员
+        
+        Args:
+            name: Sorted Set名称
+            *members: 要删除的成员
+        """
+        if self.redis and self._connected:
+            try:
+                await self.redis.zrem(name, *members)
+            except Exception as e:
+                logger.debug(f"Redis Sorted Set删除失败 {name}: {e}")

trading_system/trade_recommender.py

@@ -3,6 +3,7 @@
 """
 import asyncio
 import logging
+import time
 from typing import List, Dict, Optional
 from datetime import datetime, timedelta
 try:
@@ -66,50 +67,155 @@ class TradeRecommender:
     async def generate_recommendations(
         self,
         min_signal_strength: int = 5,
-        max_recommendations: int = 20
+        max_recommendations: int = 20,
+        add_to_cache: bool = True,
+        min_quality_score: float = 0.0
     ) -> List[Dict]:
         """
-        生成交易推荐
+        生成交易推荐（支持增量添加到Redis缓存）
         
         Args:
             min_signal_strength: 最小信号强度（默认5，低于此强度的不推荐）
-            max_recommendations: 最大推荐数量
+            max_recommendations: 最大推荐数量（单次生成）
+            add_to_cache: 是否添加到Redis缓存（默认True）
+            min_quality_score: 最小质量分数（用于过滤，默认0.0表示不过滤）
             
         Returns:
             推荐列表
         """
         logger.info("开始生成交易推荐...")
         
-        # 1. 扫描市场
+        # 获取账户余额（用于计算盈亏评估）
+        try:
+            balance = await self.client.get_account_balance()
+            account_balance = balance.get('total', 1000)
+            logger.debug(f"账户余额: {account_balance:.2f} USDT")
+        except Exception as e:
+            account_balance = 1000
+            logger.warning(f"获取账户余额失败，使用默认值1000 USDT: {e}")
+        
+        # 1. 从Redis读取现有推荐（如果启用缓存）
+        existing_recommendations = {}
+        if add_to_cache:
+            try:
+                # 从Redis Hash读取所有现有推荐
+                cache_key = "recommendations:realtime"
+                existing_data = await self.client.redis_cache.hgetall(cache_key)
+                for rec_key, rec_data in existing_data.items():
+                    if isinstance(rec_data, dict):
+                        existing_recommendations[rec_key] = rec_data
+                logger.info(f"从Redis读取到 {len(existing_recommendations)} 个现有推荐")
+            except Exception as e:
+                logger.debug(f"从Redis读取现有推荐失败: {e}")
+        
+        # 2. 扫描市场
         top_symbols = await self.scanner.scan_market()
         if not top_symbols:
             logger.warning("未找到符合条件的交易对")
+            # 如果市场扫描失败，返回现有缓存推荐
+            if existing_recommendations:
+                return list(existing_recommendations.values())
             return []
         
-        recommendations = []
+        new_recommendations = []
         
-        # 2. 对每个交易对进行分析
+        # 3. 对每个交易对进行分析
         for symbol_info in top_symbols:
-            if len(recommendations) >= max_recommendations:
-                break
-            
             symbol = symbol_info['symbol']
             current_price = symbol_info['price']
-            change_percent = symbol_info.get('changePercent', 0)
             
-            # 3. 分析交易信号（使用策略模块的逻辑）
+            # 4. 分析交易信号
             trade_signal = await self._analyze_trade_signal(symbol_info)
             
-            # 4. 如果信号强度足够，生成推荐
+            # 5. 如果信号强度足够，生成推荐
             if trade_signal['should_trade'] and trade_signal['strength'] >= min_signal_strength:
-                recommendation = await self._create_recommendation(
-                    symbol_info, trade_signal
+                recommendations_result = await self._create_recommendation(
+                    symbol_info, trade_signal, account_balance
                 )
-                if recommendation:
-                    recommendations.append(recommendation)
+                if recommendations_result:
+                    # _create_recommendation 返回列表（限价单和市价单）
+                    if isinstance(recommendations_result, list):
+                        new_recommendations.extend(recommendations_result)
+                    else:
+                        new_recommendations.append(recommendations_result)
         
-        logger.info(f"生成了 {len(recommendations)} 个交易推荐")
-        return recommendations
+        # 6. 合并新推荐和现有推荐
+        all_recommendations = {}
+        current_time = time.time()
+        max_age = 3600 * 2  # 推荐最大保留时间：2小时
+        
+        # 先添加现有推荐（过滤掉过期的）
+        for rec_key, rec_data in existing_recommendations.items():
+            rec_timestamp = rec_data.get('timestamp', 0)
+            # 如果推荐时间超过2小时，跳过
+            if current_time - rec_timestamp > max_age:
+                continue
+            all_recommendations[rec_key] = rec_data
+        
+        # 添加新推荐（如果质量足够）
+        for rec in new_recommendations:
+            # 生成推荐键：symbol_orderType
+            rec_key = f"{rec['symbol']}_{rec.get('order_type', 'LIMIT')}"
+            
+            # 计算质量分数（信号强度 + 胜率预估）
+            quality_score = rec.get('signal_strength', 0) * 10
+            if 'estimated_win_rate' in rec:
+                quality_score += rec['estimated_win_rate']
+            
+            # 如果质量分数足够，才添加或更新
+            if quality_score >= min_quality_score:
+                # 如果已存在，比较质量分数，只保留更好的
+                if rec_key in all_recommendations:
+                    existing_quality = all_recommendations[rec_key].get('signal_strength', 0) * 10
+                    if 'estimated_win_rate' in all_recommendations[rec_key]:
+                        existing_quality += all_recommendations[rec_key]['estimated_win_rate']
+                    
+                    # 如果新推荐质量更好，更新
+                    if quality_score > existing_quality:
+                        all_recommendations[rec_key] = rec
+                        logger.debug(f"更新推荐 {rec_key}: 质量分数 {existing_quality:.1f} -> {quality_score:.1f}")
+                else:
+                    # 新推荐，直接添加
+                    all_recommendations[rec_key] = rec
+                    logger.debug(f"添加新推荐 {rec_key}: 质量分数 {quality_score:.1f}")
+        
+        # 7. 按信号强度排序，保留质量最好的推荐
+        sorted_recommendations = sorted(
+            all_recommendations.values(),
+            key=lambda x: (x.get('signal_strength', 0), x.get('estimated_win_rate', 0)),
+            reverse=True
+        )
+        
+        # 限制总数（保留质量最好的）
+        max_total = max_recommendations * 3  # 允许更多推荐以便参考
+        final_recommendations = sorted_recommendations[:max_total]
+        
+        # 8. 保存到Redis缓存（如果启用）
+        if add_to_cache:
+            try:
+                cache_key = "recommendations:realtime"
+                
+                # 增量更新：只更新或添加新的推荐，保留其他推荐
+                updated_count = 0
+                for rec in final_recommendations:
+                    rec_key = f"{rec['symbol']}_{rec.get('order_type', 'LIMIT')}"
+                    # 使用hset更新或添加（如果不存在则创建）
+                    await self.client.redis_cache.hset(cache_key, rec_key, rec, ttl=3600)  # TTL 1小时
+                    updated_count += 1
+                
+                # 设置整个Hash的TTL（1小时）
+                if self.client.redis_cache.redis and self.client.redis_cache._connected:
+                    try:
+                        await self.client.redis_cache.redis.expire(cache_key, 3600)
+                    except:
+                        pass
+                
+                logger.info(f"已更新 {updated_count} 个推荐到Redis缓存（总计 {len(final_recommendations)} 个）")
+            except Exception as e:
+                logger.warning(f"保存推荐到Redis失败: {e}")
+        
+        logger.info(f"生成了 {len(new_recommendations)} 个新推荐，总计 {len(final_recommendations)} 个推荐（包含缓存）")
+        return final_recommendations
     
     async def _analyze_trade_signal(self, symbol_info: Dict) -> Dict:
         """
@@ -230,8 +336,9 @@ class TradeRecommender:
     async def _create_recommendation(
         self,
         symbol_info: Dict,
-        trade_signal: Dict
-    ) -> Optional[Dict]:
+        trade_signal: Dict,
+        account_balance: float = 1000
+    ) -> Optional[List[Dict]]:
         """
         创建推荐记录
         
@@ -291,16 +398,43 @@ class TradeRecommender:
             else:  # SELL
                 suggested_limit_price = current_price * (1 + limit_price_offset_pct / 100)
             
-            # 准备推荐数据
-            recommendation_data = {
+            # 添加时间戳
+            timestamp = time.time()
+            recommendation_time = datetime.now().isoformat()
+            
+            # 计算胜率预估（基于信号强度、市场状态等）
+            base_win_rate = 40 + (signal_strength * 3)  # 信号强度0-10，对应胜率40-70%
+            market_regime = symbol_info.get('marketRegime', 'ranging')
+            if market_regime == 'ranging':
+                base_win_rate += 5
+            elif market_regime == 'trending':
+                base_win_rate += 3
+            trend_4h = trade_signal.get('trend_4h')
+            if trend_4h:
+                if (direction == 'BUY' and trend_4h == 'up') or (direction == 'SELL' and trend_4h == 'down'):
+                    base_win_rate += 3
+                elif (direction == 'BUY' and trend_4h == 'down') or (direction == 'SELL' and trend_4h == 'up'):
+                    base_win_rate -= 2
+            estimated_win_rate = max(35, min(75, base_win_rate))
+            
+            # 计算盈亏USDT评估
+            position_value = account_balance * suggested_position_pct * config.TRADING_CONFIG.get('LEVERAGE', 10)
+            stop_loss_usdt = position_value * stop_loss_pct
+            take_profit_1_usdt = position_value * stop_loss_pct
+            take_profit_2_usdt = position_value * take_profit_2_pct
+            expected_pnl_1 = (estimated_win_rate / 100) * take_profit_1_usdt - ((100 - estimated_win_rate) / 100) * stop_loss_usdt
+            expected_pnl_2 = (estimated_win_rate / 100) * take_profit_2_usdt - ((100 - estimated_win_rate) / 100) * stop_loss_usdt
+            
+            # 基础推荐数据
+            base_data = {
                 'symbol': symbol,
                 'direction': direction,
                 'current_price': current_price,
                 'change_percent': symbol_info.get('changePercent', 0),
                 'recommendation_reason': trade_signal['reason'],
                 'signal_strength': signal_strength,
-                'market_regime': symbol_info.get('marketRegime'),
-                'trend_4h': trade_signal.get('trend_4h'),
+                'market_regime': market_regime,
+                'trend_4h': trend_4h,
                 'rsi': symbol_info.get('rsi'),
                 'macd_histogram': symbol_info.get('macd', {}).get('histogram') if symbol_info.get('macd') else None,
                 'bollinger_upper': symbol_info.get('bollinger', {}).get('upper') if symbol_info.get('bollinger') else None,
@@ -317,18 +451,36 @@ class TradeRecommender:
                 'suggested_leverage': config.TRADING_CONFIG.get('LEVERAGE', 10),
                 'volume_24h': symbol_info.get('volume24h'),
                 'volatility': symbol_info.get('volatility'),
-                'order_type': 'LIMIT',  # 使用限价单
-                'suggested_limit_price': suggested_limit_price  # 建议的挂单价
+                'estimated_win_rate': round(estimated_win_rate, 1),
+                'expected_pnl_1': round(expected_pnl_1, 2),
+                'expected_pnl_2': round(expected_pnl_2, 2),
+                'stop_loss_usdt': round(stop_loss_usdt, 2),
+                'take_profit_1_usdt': round(take_profit_1_usdt, 2),
+                'take_profit_2_usdt': round(take_profit_2_usdt, 2),
+                'recommendation_time': recommendation_time,
+                'timestamp': timestamp
             }
             
-            # 不再自动保存到数据库，只返回推荐数据
-            # 只有用户在前端点击"标记"时才会保存到数据库（用于复盘）
+            # 限价单推荐
+            limit_recommendation = base_data.copy()
+            limit_recommendation.update({
+                'order_type': 'LIMIT',
+                'suggested_limit_price': suggested_limit_price
+            })
+            
+            # 市价单推荐
+            market_recommendation = base_data.copy()
+            market_recommendation.update({
+                'order_type': 'MARKET',
+                'suggested_limit_price': None
+            })
+            
             logger.debug(
                 f"✓ 生成推荐: {symbol} {direction} "
-                f"(信号强度: {signal_strength}/10)"
+                f"(信号强度: {signal_strength}/10, 胜率预估: {estimated_win_rate:.1f}%)"
             )
             
-            return recommendation_data
+            return [limit_recommendation, market_recommendation]
             
         except Exception as e:
             logger.error(f"创建推荐失败 {symbol_info.get('symbol', 'unknown')}: {e}")