update

cryptoai/agents/crypto_agent.py

@@ -217,6 +217,87 @@ class CryptoAgent:
 
 请确保回复为有效的JSON格式，分析要精准专业。"""
     
+    def analyze_symbol(self, symbol: str) -> Dict[str, Any]:
+        """
+        分析单个交易对
+        """
+        results = {}
+
+        print(f"\n开始分析{symbol}...")
+            
+        # 获取并处理数据
+        raw_data = self.fetch_historical_data(symbol, days=self.crypto_config['historical_days'])
+        
+        if not raw_data.empty:
+            processed_data = self.process_data(symbol, raw_data)
+            
+            # 准备市场数据
+            market_data = {
+                "symbol": symbol,
+                "current_price": float(processed_data['close'].iloc[-1]),
+                # "price_change_24h": float(processed_data['close'].iloc[-1] - processed_data['close'].iloc[-24]),
+                # "price_change_percentage_24h": float((processed_data['close'].iloc[-1] - processed_data['close'].iloc[-24]) / processed_data['close'].iloc[-24] * 100),
+                "historical_prices": processed_data['close'].tail(100).tolist(),
+                "volumes": processed_data['volume'].tail(100).tolist(),
+                "technical_indicators": {
+                    "rsi": float(processed_data['RSI'].iloc[-1]),
+                    "macd": float(processed_data['MACD'].iloc[-1]),
+                    "macd_signal": float(processed_data['MACD_Signal'].iloc[-1]),
+                    "bollinger_upper": float(processed_data['Bollinger_Upper'].iloc[-1]),
+                    "bollinger_lower": float(processed_data['Bollinger_Lower'].iloc[-1]),
+                    "ma5": float(processed_data['MA5'].iloc[-1]),
+                    "ma10": float(processed_data['MA10'].iloc[-1]),
+                    "ma20": float(processed_data['MA20'].iloc[-1]),
+                    "ma50": float(processed_data['MA50'].iloc[-1]),
+                    "atr": float(processed_data['ATR'].iloc[-1])
+                },
+                "klines": processed_data[['open', 'high', 'low', 'close', 'volume']].tail(100).to_dict('records')
+            }
+            
+            # 将市场数据格式化为适合大模型的格式
+            formatted_data = self._format_market_data(market_data)
+            
+            # 构建提示词
+            prompt = self._build_market_analysis_prompt(formatted_data)
+            
+            # 调用API获取分析和交易建议
+            response, usage = self.deepseek_api.call_model(prompt, task_type="交易分析", symbol=symbol)
+            
+            # 解析响应
+            analysis_result = self.deepseek_api.extract_json_from_response(response)
+            
+            # 添加token使用信息
+            if usage:
+                analysis_result["_token_usage"] = usage
+            
+            # 整合结果
+            results[symbol] = {
+                "analysis": analysis_result,
+                "timestamp": datetime.now().isoformat()
+            }
+            
+            # 保存分析结果到数据库
+            try:
+                # 保存到数据库
+                saved = self.db_manager.save_analysis_result(
+                    agent='crypto',
+                    symbol=symbol,
+                    time_interval=self.time_interval,
+                    analysis_result=analysis_result
+                )
+                if saved:
+                    print(f"{symbol}分析结果已保存到数据库")
+                else:
+                    print(f"{symbol}分析结果保存到数据库失败")
+            except Exception as e:
+                print(f"保存{symbol}分析结果到数据库时出错: {e}")
+            
+            print(f"{symbol}分析完成")
+        else:
+            print(f"跳过{symbol}分析，无法获取数据")
+        
+        return results
+    
     def analyze_all_symbols(self) -> Dict[str, Dict[str, Any]]:
         """
         分析所有支持的交易对
@@ -229,81 +310,13 @@ class CryptoAgent:
         for symbol in self.symbols:
             print(f"\n开始分析{symbol}...")
             
-            # 获取并处理数据
+            single_result = self.analyze_symbol(symbol)
-            raw_data = self.fetch_historical_data(symbol, days=self.crypto_config['historical_days'])
+            results[symbol] = single_result[symbol]
-            
-            if not raw_data.empty:
-                processed_data = self.process_data(symbol, raw_data)
-                
-                # 准备市场数据
-                market_data = {
-                    "symbol": symbol,
-                    "current_price": float(processed_data['close'].iloc[-1]),
-                    # "price_change_24h": float(processed_data['close'].iloc[-1] - processed_data['close'].iloc[-24]),
-                    # "price_change_percentage_24h": float((processed_data['close'].iloc[-1] - processed_data['close'].iloc[-24]) / processed_data['close'].iloc[-24] * 100),
-                    "historical_prices": processed_data['close'].tail(100).tolist(),
-                    "volumes": processed_data['volume'].tail(100).tolist(),
-                    "technical_indicators": {
-                        "rsi": float(processed_data['RSI'].iloc[-1]),
-                        "macd": float(processed_data['MACD'].iloc[-1]),
-                        "macd_signal": float(processed_data['MACD_Signal'].iloc[-1]),
-                        "bollinger_upper": float(processed_data['Bollinger_Upper'].iloc[-1]),
-                        "bollinger_lower": float(processed_data['Bollinger_Lower'].iloc[-1]),
-                        "ma5": float(processed_data['MA5'].iloc[-1]),
-                        "ma10": float(processed_data['MA10'].iloc[-1]),
-                        "ma20": float(processed_data['MA20'].iloc[-1]),
-                        "ma50": float(processed_data['MA50'].iloc[-1]),
-                        "atr": float(processed_data['ATR'].iloc[-1])
-                    },
-                    "klines": processed_data[['open', 'high', 'low', 'close', 'volume']].tail(100).to_dict('records')
-                }
-                
-                # 将市场数据格式化为适合大模型的格式
-                formatted_data = self._format_market_data(market_data)
-                
-                # 构建提示词
-                prompt = self._build_market_analysis_prompt(formatted_data)
-                
-                # 调用API获取分析和交易建议
-                response, usage = self.deepseek_api.call_model(prompt, task_type="交易分析", symbol=symbol)
-                
-                # 解析响应
-                analysis_result = self.deepseek_api.extract_json_from_response(response)
-                
-                # 添加token使用信息
-                if usage:
-                    analysis_result["_token_usage"] = usage
-                
-                # 整合结果
-                results[symbol] = {
-                    "analysis": analysis_result,
-                    "timestamp": datetime.now().isoformat()
-                }
-                
-                # 保存分析结果到数据库
-                try:
-                    # 保存到数据库
-                    saved = self.db_manager.save_analysis_result(
-                        agent='crypto',
-                        symbol=symbol,
-                        time_interval=self.time_interval,
-                        analysis_result=analysis_result
-                    )
-                    if saved:
-                        print(f"{symbol}分析结果已保存到数据库")
-                    else:
-                        print(f"{symbol}分析结果保存到数据库失败")
-                except Exception as e:
-                    print(f"保存{symbol}分析结果到数据库时出错: {e}")
-                
-                print(f"{symbol}分析完成")
-            else:
-                print(f"跳过{symbol}分析，无法获取数据")
         
         return results
     
     
-    def run_analysis_cycle(self) -> Dict[str, Any]:
+    def run_analysis_cycle(self, symbol: Optional[str] = None) -> Dict[str, Any]:
         """
         运行一个完整的分析周期
         
@@ -312,7 +325,10 @@ class CryptoAgent:
         """
         print(f"开始新的分析周期，时间：{datetime.now().isoformat()}")
         
-        results = self.analyze_all_symbols()
+        if symbol:
+            results = self.analyze_symbol(symbol)
+        else:
+            results = self.analyze_all_symbols()
         
         # 保存分析结果
         timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
@@ -343,7 +359,7 @@ class CryptoAgent:
         把分析的JSON结果调用大模型转化成交易建议
         """
         prompt = f"""
-        请对以下加密货币市场分析的JSON结果进行深入分析， 转化成包含：分析时间，技术分析，支撑位，压力位， 建议买点，卖点，止损位，止盈位，仓位建议， 增加适当的emoji便于阅读， 简单明了。
+        请对以下加密货币市场分析的JSON结果进行深入分析， 转化成包含：分析时间，分析时间级别，技术分析，支撑位，压力位， 建议买点，卖点，止损位，止盈位，仓位建议， 增加适当的emoji便于阅读， 简单明了。
 
         分析 JSON 结果:
         {results}
@@ -392,7 +408,7 @@ class CryptoAgent:
         except Exception as e:
             print(f"导出 token 使用情况时出错: {e}")
     
-    def start_agent(self) -> None:
+    def start_agent(self, symbol: Optional[str] = None) -> None:
         """
         启动智能体
         
@@ -402,11 +418,13 @@ class CryptoAgent:
         print("启动加密货币分析智能体...")
         
         try:
-            self.run_analysis_cycle()
+            if symbol:
+                self.run_analysis_cycle(symbol)
+            else:
+                self.run_analysis_cycle()
                 # 导出最终的token使用情况
             self._export_token_usage()
                 
-        
         except KeyboardInterrupt:
             print("\n智能体已停止")
             # 导出最终的token使用情况

cryptoai/agents/gold_agent.py

@@ -276,7 +276,7 @@ class GoldAgent:
                         "seasonal_factor": float(processed_data['seasonal_factor'].iloc[-1]),
                         "gold_bull_signal": float(processed_data['gold_bull_signal'].iloc[-1])
                     },
-                    "klines": processed_data[['open', 'high', 'low', 'close', 'volume']].tail(30).to_dict('records')
+                    "klines": processed_data[['open', 'high', 'low', 'close', 'volume']].tail(100).to_dict('records')
                 }
                 
                 # 将市场数据格式化为适合大模型的格式
@@ -332,7 +332,34 @@ class GoldAgent:
         # 导出 DeepSeek API token 使用情况
         self._export_token_usage()
 
-        return results
+        # 把分析的JSON结果调用大模型转化成交易建议
+        trading_suggestions = self.convert_analysis_to_trading_suggestions(results)
+        print(f"交易建议：{trading_suggestions}")
+
+        if self.dingtalk_bot:
+            self.dingtalk_bot.send_markdown(title="黄金交易建议", text=trading_suggestions)
+
+        return results, trading_suggestions
+    
+    def convert_analysis_to_trading_suggestions(self, results: Dict[str, Any]) -> str:
+        """
+        把分析的JSON结果调用大模型转化成交易建议
+        """
+        prompt = f"""
+        请对以下黄金市场分析的JSON结果进行深入分析， 转化成包含：分析时间，技术分析，支撑位，压力位， 建议买点，卖点，止损位，止盈位，仓位建议， 增加适当的emoji便于阅读， 简单明了。
+
+        分析 JSON 结果:
+        {results}
+        """
+
+        system_prompt = """
+        你是一个专业的黄金分析助手，你擅长分析市场趋势、预测价格走向和提供交易建议，请始终使用中文回复，并确保输出格式规范的Markdown。
+        """
+        response, usage = self.deepseek_api.call_model(prompt, system_prompt=system_prompt, task_type="交易建议")
+
+        message = self.deepseek_api.extract_text_from_response(response)
+
+        return message
     
     def _export_token_usage(self) -> None:
         """

cryptoai/config/config.yaml

@@ -25,13 +25,13 @@ alltick:
 # 加密货币设置
 crypto:
   base_currencies:
-    - "BTC"
+    # - "BTC"
-    - "ETH"
+    # - "ETH"
     # - "SOL"
     # - "SUI"
-    # - "XRP"
+    - "CETUS"
   quote_currency: "USDT"
-  time_interval: "1h"  # 可选: 1m, 5m, 15m, 30m, 1h, 4h, 1d
+  time_interval: "4h"  # 可选: 1m, 5m, 15m, 30m, 1h, 4h, 1d
   historical_days: 30
 
 # 黄金市场分析配置
@@ -39,9 +39,9 @@ gold:
   # 黄金交易对
   symbols: ["GOLD"]
   # 历史数据天数
-  historical_days: 180
+  historical_days: 30
   # 时间间隔
-  time_interval: "1d"
+  time_interval: "4h"
 
 
 # 数据设置

cryptoai/main.py

@@ -18,7 +18,7 @@ from cryptoai.utils.config_loader import ConfigLoader
 
 def main():
     try:
-        print("程序启动")
+        print("定时程序启动")
         # 设置每个4小时运行一次
         schedule.every(4).hours.do(CryptoAgent().start_agent)
 
@@ -26,7 +26,10 @@ def main():
         while True:
             schedule.run_pending()
             time.sleep(1)
-        # CryptoAgent().start_agent()
+
+            
+        # GoldAgent().start_agent()
+        # CryptoAgent().start_agent('BTCUSDT')
     
     except KeyboardInterrupt:
         print("\n程序已退出")