udpate

cryptoai/api/qwen_api.py

@@ -0,0 +1,327 @@
+import os
+import json
+import requests
+from typing import Dict, Any, List, Optional, Tuple
+import time
+import logging
+import datetime
+
+from cryptoai.utils.config_loader import ConfigLoader
+
+# 配置日志
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler("qwen_token_usage.log"),
+        logging.StreamHandler()
+    ]
+)
+
+class QwenAPI:
+    """Qwen API交互类，用于进行大语言模型调用"""
+    
+    def __init__(self):
+        """
+        初始化Qwen API
+        
+        Args:
+            api_key: Qwen API密钥
+            model: 使用的模型名称
+        """
+
+        config_loader = ConfigLoader()
+        self.qwen_config = config_loader.get_qwen_config()
+
+        self.api_key = self.qwen_config['api_key']
+        self.model = self.qwen_config['model']
+        self.base_url = "https://dashscope.aliyuncs.com/api/v1"
+        self.headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}"
+        }
+        
+        # Token 使用统计
+        self.token_usage = {
+            "total_prompt_tokens": 0,
+            "total_completion_tokens": 0,
+            "total_tokens": 0,
+            "calls": []
+        }
+        
+        # 创建日志记录器
+        self.logger = logging.getLogger("QwenAPI")
+
+    def streaming_call(self, user_prompt: str):
+        """
+        流式调用Qwen API
+        """
+
+        system_prompt = "你是一个专业的区块链分析高手"
+
+        try:
+            endpoint = f"{self.base_url}/services/aigc/text-generation/generation"
+            payload = {
+                "model": self.model,
+                "input": {
+                    "messages": [
+                        {"role": "system", "content": system_prompt},
+                        {"role": "user", "content": user_prompt}
+                    ]
+                },
+                "parameters": {
+                    "stream": True
+                }
+            }
+
+            response = requests.post(endpoint, headers=self.headers, json=payload, stream=True)
+            response.raise_for_status()
+
+            for line in response.iter_lines():
+                if line:
+                    # 解码二进制数据为字符串
+                    line = line.decode('utf-8')
+                    
+                    # 跳过空行和心跳检查行
+                    if not line or line == "data: [DONE]":
+                        continue
+                    
+                    # 移除 "data: " 前缀
+                    if line.startswith("data: "):
+                        line = line[6:]
+                    
+                    try:
+                        # 解析JSON数据
+                        data = json.loads(line)
+                        
+                        # 提取content内容
+                        if (data.get("output") and 
+                            data["output"].get("choices") and 
+                            len(data["output"]["choices"]) > 0 and 
+                            data["output"]["choices"][0].get("message") and 
+                            data["output"]["choices"][0]["message"].get("content")):
+                            
+                            content = data["output"]["choices"][0]["message"]["content"]
+                            yield content
+                    except json.JSONDecodeError as e:
+                        self.logger.error(f"解析JSON时出错: {e}, 原始数据: {line}")
+                        continue
+
+        except Exception as e:
+            self.logger.error(f"流式调用Qwen API时出错: {e}")
+            raise e
+
+    
+    def call_model(self, prompt: str, system_prompt: str = None, task_type: str = "未知任务", symbol: str = "未知", temperature: float = 0.2, max_tokens: int = 2000) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        """
+        调用Qwen大语言模型
+        
+        Args:
+            prompt: 用户提示词
+            system_prompt: 系统提示词，如果为None则使用默认值
+            task_type: 任务类型，用于记录
+            symbol: 交易对符号，用于记录
+            temperature: 采样温度，控制输出随机性
+            max_tokens: 最大生成token数
+            
+        Returns:
+            (API响应, token使用信息)
+        """
+        if system_prompt is None:
+            system_prompt = "你是一个专业的加密货币分析助手，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JSON。"
+            
+        usage_info = {}
+        
+        try:
+            endpoint = f"{self.base_url}/services/aigc/text-generation/generation"
+            
+            payload = {
+                "model": self.model,
+                "input": {
+                    "messages": [
+                        {"role": "system", "content": system_prompt},
+                        {"role": "user", "content": prompt}
+                    ]
+                },
+                "parameters": {
+                    "temperature": temperature,
+                    "max_tokens": max_tokens
+                }
+            }
+            
+            start_time = time.time()
+            response = requests.post(endpoint, headers=self.headers, json=payload)
+            response.raise_for_status()
+            response_data = response.json()
+            end_time = time.time()
+            
+            # 记录token使用情况
+            if 'usage' in response_data:
+                prompt_tokens = response_data['usage'].get('input_tokens', 0)
+                completion_tokens = response_data['usage'].get('output_tokens', 0)
+                total_tokens = prompt_tokens + completion_tokens
+                
+                usage_info = {
+                    "prompt_tokens": prompt_tokens,
+                    "completion_tokens": completion_tokens,
+                    "total_tokens": total_tokens,
+                    "task_type": task_type,
+                    "symbol": symbol,
+                    "model": self.model,
+                    "timestamp": datetime.datetime.now().isoformat(),
+                    "duration_seconds": round(end_time - start_time, 2)
+                }
+                
+                # 更新总计
+                self.token_usage["total_prompt_tokens"] += prompt_tokens
+                self.token_usage["total_completion_tokens"] += completion_tokens
+                self.token_usage["total_tokens"] += total_tokens
+                self.token_usage["calls"].append(usage_info)
+                
+                # 记录到日志
+                self.logger.info(
+                    f"Qwen API调用 - 任务: {task_type}, 符号: {symbol}, "
+                    f"输入tokens: {prompt_tokens}, 输出tokens: {completion_tokens}, "
+                    f"总tokens: {total_tokens}, 耗时: {round(end_time - start_time, 2)}秒"
+                )
+            
+            return response_data, usage_info
+        
+        except Exception as e:
+            error_msg = f"调用Qwen API时出错: {e}"
+            self.logger.error(error_msg)
+            return {}, usage_info
+    
+    def extract_text_from_response(self, response: Dict[str, Any]) -> str:
+        """
+        从响应中提取文本数据
+        """
+        try:
+            if 'output' in response and 'choices' in response['output'] and len(response['output']['choices']) > 0:
+                content = response['output']['choices'][0]['message']['content']
+
+                # 如果内容以```markdown开头，则去掉```
+                if content.startswith('```markdown'):
+                    content = content[len('```markdown'):]
+                # 如果内容以```结尾，则去掉```
+                if content.endswith('```'):
+                    content = content[:-len('```')]
+                    
+                return content
+            else:
+                return {"error": "无法从响应中提取文本", "raw_content": response}
+        except Exception as e:
+            return {"error": str(e), "raw_content": response}
+        
+    
+    def extract_json_from_response(self, response: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        从响应中提取JSON数据
+        
+        Args:
+            response: API响应
+            
+        Returns:
+            提取的JSON数据
+        """
+        try:
+            if 'output' in response and 'choices' in response['output'] and len(response['output']['choices']) > 0:
+                content = response['output']['choices'][0]['message']['content']
+                
+                # 尝试从响应中提取JSON
+                start_idx = content.find('{')
+                end_idx = content.rfind('}') + 1
+                
+                if start_idx != -1 and end_idx != -1:
+                    json_str = content[start_idx:end_idx]
+                    return json.loads(json_str)
+                
+                return {"error": "无法从响应中提取JSON", "raw_content": content}
+            
+            return {"error": "API响应格式不正确", "raw_response": response}
+        
+        except Exception as e:
+            error_msg = f"解析响应时出错: {e}"
+            self.logger.error(error_msg)
+            return {"error": str(e), "raw_response": response}
+    
+    def get_token_usage_stats(self) -> Dict[str, Any]:
+        """
+        获取Token使用统计信息
+        
+        Returns:
+            包含使用统计的字典
+        """
+        return {
+            "total_prompt_tokens": self.token_usage["total_prompt_tokens"],
+            "total_completion_tokens": self.token_usage["total_completion_tokens"],
+            "total_tokens": self.token_usage["total_tokens"],
+            "total_calls": len(self.token_usage["calls"]),
+            "average_tokens_per_call": self.token_usage["total_tokens"] / len(self.token_usage["calls"]) if self.token_usage["calls"] else 0,
+            "detailed_calls": self.token_usage["calls"][-10:]  # 仅返回最近10次调用详情
+        }
+    
+    def export_token_usage(self, file_path: str = None, format: str = "json") -> str:
+        """
+        导出Token使用数据到文件
+        
+        Args:
+            file_path: 文件路径，如果为None则自动生成
+            format: 导出格式，支持'json'或'csv'
+            
+        Returns:
+            导出文件的路径
+        """
+        if file_path is None:
+            timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+            file_path = f"qwen_token_usage_{timestamp}.{format}"
+        
+        try:
+            if format.lower() == "json":
+                with open(file_path, 'w', encoding='utf-8') as f:
+                    json.dump(self.token_usage, f, indent=2, ensure_ascii=False)
+            elif format.lower() == "csv":
+                import csv
+                
+                with open(file_path, 'w', newline='', encoding='utf-8') as f:
+                    writer = csv.writer(f)
+                    # 写入表头
+                    writer.writerow([
+                        "timestamp", "task_type", "symbol", "model", 
+                        "prompt_tokens", "completion_tokens", "total_tokens", 
+                        "duration_seconds"
+                    ])
+                    
+                    # 写入数据
+                    for call in self.token_usage["calls"]:
+                        writer.writerow([
+                            call.get("timestamp", ""),
+                            call.get("task_type", ""),
+                            call.get("symbol", ""),
+                            call.get("model", ""),
+                            call.get("prompt_tokens", 0),
+                            call.get("completion_tokens", 0),
+                            call.get("total_tokens", 0),
+                            call.get("duration_seconds", 0)
+                        ])
+                    
+                    # 写入总计
+                    writer.writerow([])
+                    writer.writerow([
+                        f"总计 (调用次数: {len(self.token_usage['calls'])})",
+                        "", "", "",
+                        self.token_usage["total_prompt_tokens"],
+                        self.token_usage["total_completion_tokens"],
+                        self.token_usage["total_tokens"],
+                        ""
+                    ])
+            else:
+                raise ValueError(f"不支持的格式: {format}，仅支持 'json' 或 'csv'")
+                
+            self.logger.info(f"Token使用数据已导出到: {file_path}")
+            return file_path
+            
+        except Exception as e:
+            error_msg = f"导出Token使用数据时出错: {e}"
+            self.logger.error(error_msg)
+            return "" 

cryptoai/config/config.yaml

@@ -16,6 +16,11 @@ deepseek:
   api_key: "sk-9f6b56f08796435d988cf202e37f6ee3"
   model: "deepseek-chat"  # 使用的模型
 
+# Qwen API设置
+qwen:
+  api_key: "sk-caa199589f1c451aaac471fad2986e28"
+  model: "qwen-max"  # 使用的模型
+
 # AllTick API设置（用于获取黄金数据）
 alltick:
   api_key: "ee66d8e2868fd988fffacec40d078df8-c-app"

cryptoai/main.py

@@ -24,7 +24,6 @@ def main():
         # return
 
         print("定时程序启动")
-        CryptoAgent().start_agent()
         # 设置 08:00, 20:00 运行一次
         schedule.every().day.at("00:00").do(CryptoAgent().start_agent)
         schedule.every().day.at("08:00").do(CryptoAgent().start_agent)
@@ -32,11 +31,11 @@ def main():
         schedule.every().day.at("16:00").do(CryptoAgent().start_agent)
         schedule.every().day.at("20:00").do(CryptoAgent().start_agent)
 
-        schedule.every().day.at("00:00").do(GoldAgent().start_agent)
+        # schedule.every().day.at("00:00").do(GoldAgent().start_agent)
-        schedule.every().day.at("08:00").do(GoldAgent().start_agent)
+        # schedule.every().day.at("08:00").do(GoldAgent().start_agent)
-        schedule.every().day.at("12:00").do(GoldAgent().start_agent)
+        # schedule.every().day.at("12:00").do(GoldAgent().start_agent)
-        schedule.every().day.at("16:00").do(GoldAgent().start_agent)
+        # schedule.every().day.at("16:00").do(GoldAgent().start_agent)
-        schedule.every().day.at("20:00").do(GoldAgent().start_agent)
+        # schedule.every().day.at("20:00").do(GoldAgent().start_agent)
 
         while True:
             schedule.run_pending()

cryptoai/utils/config_loader.py

@@ -72,6 +72,10 @@ class ConfigLoader:
         """获取DeepSeek配置"""
         return self.get_config('deepseek')
     
+    def get_qwen_config(self) -> Dict[str, Any]:
+        """获取Qwen配置"""
+        return self.get_config('qwen')
+    
     def get_gold_config(self) -> Dict[str, Any]:
         """获取黄金配置"""
         return self.get_config('gold')