add token log
This commit is contained in:
aaron
parent
303f938bd7
commit
4958b031c2
Binary file not shown.
@ -490,8 +490,45 @@ class CryptoAgent:
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print(f"分析结果已保存到：{results_file}")
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# 导出 DeepSeek API token 使用情况
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self._export_token_usage()
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return results
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def _export_token_usage(self) -> None:
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"""
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导出 DeepSeek API token 使用情况
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"""
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try:
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# 每天导出一次详细的JSON数据
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today = datetime.now().strftime("%Y%m%d")
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token_usage_dir = os.path.join(self.data_config['storage_path'], 'token_usage')
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os.makedirs(token_usage_dir, exist_ok=True)
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json_file = os.path.join(token_usage_dir, f"deepseek_token_usage_{today}.json")
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# 如果文件不存在，导出一次
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if not os.path.exists(json_file):
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exported_file = self.deepseek_api.export_token_usage(json_file)
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if exported_file:
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print(f"DeepSeek API token 使用情况已导出到：{exported_file}")
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# 每次都导出CSV格式的统计数据
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csv_file = os.path.join(token_usage_dir, f"deepseek_token_usage_{today}.csv")
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self.deepseek_api.export_token_usage(csv_file, "csv")
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# 输出当前使用情况统计
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stats = self.deepseek_api.get_token_usage_stats()
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print(f"DeepSeek API token 使用统计：")
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print(f"- 总调用次数: {stats['total_calls']}")
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print(f"- 总token数: {stats['total_tokens']}")
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print(f"- 输入token: {stats['total_prompt_tokens']}")
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print(f"- 输出token: {stats['total_completion_tokens']}")
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print(f"- 平均每次调用: {stats['average_tokens_per_call']:.2f} tokens")
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except Exception as e:
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print(f"导出 token 使用情况时出错: {e}")
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def start_agent(self, run_once: bool = False) -> None:
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"""
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启动智能体
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@ -504,6 +541,8 @@ class CryptoAgent:
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try:
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if run_once:
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self.run_analysis_cycle()
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# 导出最终的token使用情况
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self._export_token_usage()
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else:
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while True:
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self.run_analysis_cycle()
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@ -515,11 +554,15 @@ class CryptoAgent:
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except KeyboardInterrupt:
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print("\n智能体已停止")
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# 导出最终的token使用情况
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self._export_token_usage()
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except Exception as e:
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print(f"智能体运行时出错: {e}")
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import traceback
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traceback.print_exc()
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# 发生错误时也尝试导出token使用情况
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self._export_token_usage()
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def send_notifications(self, symbol: str, analysis_data: Dict[str, Any]) -> bool:
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"""
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@ -1,9 +1,20 @@
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import os
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import json
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import requests
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from typing import Dict, Any, List, Optional
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from typing import Dict, Any, List, Optional, Tuple
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import time
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import logging
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import datetime
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler("deepseek_token_usage.log"),
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logging.StreamHandler()
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]
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)
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class DeepSeekAPI:
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"""DeepSeek API交互类，用于进行市场分析和预测"""
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@ -24,6 +35,17 @@ class DeepSeekAPI:
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"Authorization": f"Bearer {api_key}"
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}
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# Token 使用统计
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self.token_usage = {
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"total_prompt_tokens": 0,
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"total_completion_tokens": 0,
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"total_tokens": 0,
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"calls": []
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}
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# 创建日志记录器
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self.logger = logging.getLogger("DeepSeekAPI")
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def analyze_market_data(self, market_data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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分析市场数据
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@ -41,10 +63,16 @@ class DeepSeekAPI:
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prompt = self._build_market_analysis_prompt(formatted_data)
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# 调用API获取分析
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response = self._call_api(prompt)
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response, usage = self._call_api(prompt, task_type="市场分析", symbol=market_data.get("symbol", "未知"))
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# 解析响应
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return self._parse_analysis_response(response)
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result = self._parse_analysis_response(response)
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# 添加token使用信息
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if usage:
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result["_token_usage"] = usage
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return result
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def predict_price_trend(self, symbol: str, historical_data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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@ -64,10 +92,16 @@ class DeepSeekAPI:
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prompt = self._build_price_prediction_prompt(symbol, formatted_data)
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# 调用API获取预测
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response = self._call_api(prompt)
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response, usage = self._call_api(prompt, task_type="价格预测", symbol=symbol)
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# 解析响应
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return self._parse_prediction_response(response)
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result = self._parse_prediction_response(response)
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# 添加token使用信息
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if usage:
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result["_token_usage"] = usage
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return result
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def generate_trading_strategy(self, symbol: str, analysis_result: Dict[str, Any], risk_level: str) -> Dict[str, Any]:
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"""
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@ -85,21 +119,112 @@ class DeepSeekAPI:
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prompt = self._build_trading_strategy_prompt(symbol, analysis_result, risk_level)
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# 调用API获取策略
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response = self._call_api(prompt)
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response, usage = self._call_api(prompt, task_type="交易策略", symbol=symbol)
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# 解析响应
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return self._parse_strategy_response(response)
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result = self._parse_strategy_response(response)
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def _call_api(self, prompt: str) -> Dict[str, Any]:
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# 添加token使用信息
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if usage:
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result["_token_usage"] = usage
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return result
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def get_token_usage_stats(self) -> Dict[str, Any]:
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"""
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获取Token使用统计信息
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Returns:
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包含使用统计的字典
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"""
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return {
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"total_prompt_tokens": self.token_usage["total_prompt_tokens"],
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"total_completion_tokens": self.token_usage["total_completion_tokens"],
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"total_tokens": self.token_usage["total_tokens"],
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"total_calls": len(self.token_usage["calls"]),
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"average_tokens_per_call": self.token_usage["total_tokens"] / len(self.token_usage["calls"]) if self.token_usage["calls"] else 0,
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"detailed_calls": self.token_usage["calls"][-10:] # 仅返回最近10次调用详情
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}
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def export_token_usage(self, file_path: str = None, format: str = "json") -> str:
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"""
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导出Token使用数据到文件
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Args:
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file_path: 文件路径，如果为None则自动生成
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format: 导出格式，支持'json'或'csv'
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Returns:
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导出文件的路径
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"""
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if file_path is None:
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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file_path = f"deepseek_token_usage_{timestamp}.{format}"
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try:
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if format.lower() == "json":
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with open(file_path, 'w', encoding='utf-8') as f:
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json.dump(self.token_usage, f, indent=2, ensure_ascii=False)
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elif format.lower() == "csv":
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import csv
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with open(file_path, 'w', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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# 写入表头
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writer.writerow([
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"timestamp", "task_type", "symbol", "model",
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"prompt_tokens", "completion_tokens", "total_tokens",
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"duration_seconds"
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])
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# 写入数据
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for call in self.token_usage["calls"]:
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writer.writerow([
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call.get("timestamp", ""),
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call.get("task_type", ""),
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call.get("symbol", ""),
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call.get("model", ""),
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call.get("prompt_tokens", 0),
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call.get("completion_tokens", 0),
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call.get("total_tokens", 0),
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call.get("duration_seconds", 0)
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])
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# 写入总计
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writer.writerow([])
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writer.writerow([
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f"总计 (调用次数: {len(self.token_usage['calls'])})",
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"", "", "",
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self.token_usage["total_prompt_tokens"],
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self.token_usage["total_completion_tokens"],
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self.token_usage["total_tokens"],
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""
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])
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else:
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raise ValueError(f"不支持的格式: {format}，仅支持 'json' 或 'csv'")
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self.logger.info(f"Token使用数据已导出到: {file_path}")
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return file_path
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except Exception as e:
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error_msg = f"导出Token使用数据时出错: {e}"
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self.logger.error(error_msg)
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return ""
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def _call_api(self, prompt: str, task_type: str = "未知任务", symbol: str = "未知") -> Tuple[Dict[str, Any], Dict[str, Any]]:
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"""
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调用DeepSeek API
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Args:
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prompt: 提示词
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task_type: 任务类型
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symbol: 交易对符号
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Returns:
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API响应
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(API响应, token使用信息)
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"""
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usage_info = {}
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try:
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endpoint = f"{self.base_url}/chat/completions"
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@ -113,14 +238,48 @@ class DeepSeekAPI:
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"max_tokens": 2000
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}
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start_time = time.time()
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response = requests.post(endpoint, headers=self.headers, json=payload)
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response.raise_for_status()
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response_data = response.json()
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end_time = time.time()
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return response.json()
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# 记录token使用情况
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if 'usage' in response_data:
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prompt_tokens = response_data['usage'].get('prompt_tokens', 0)
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completion_tokens = response_data['usage'].get('completion_tokens', 0)
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total_tokens = response_data['usage'].get('total_tokens', 0)
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usage_info = {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": total_tokens,
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"task_type": task_type,
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"symbol": symbol,
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"model": self.model,
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"timestamp": datetime.datetime.now().isoformat(),
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"duration_seconds": round(end_time - start_time, 2)
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}
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# 更新总计
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self.token_usage["total_prompt_tokens"] += prompt_tokens
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self.token_usage["total_completion_tokens"] += completion_tokens
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self.token_usage["total_tokens"] += total_tokens
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self.token_usage["calls"].append(usage_info)
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# 记录到日志
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self.logger.info(
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f"DeepSeek API调用 - 任务: {task_type}, 符号: {symbol}, "
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f"输入tokens: {prompt_tokens}, 输出tokens: {completion_tokens}, "
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f"总tokens: {total_tokens}, 耗时: {round(end_time - start_time, 2)}秒"
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)
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return response_data, usage_info
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except Exception as e:
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print(f"调用DeepSeek API时出错: {e}")
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return {}
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error_msg = f"调用DeepSeek API时出错: {e}"
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self.logger.error(error_msg)
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return {}, usage_info
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def _format_market_data(self, market_data: Dict[str, Any]) -> str:
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"""
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@ -273,7 +432,8 @@ class DeepSeekAPI:
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return {"error": "API响应格式不正确", "raw_response": response}
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except Exception as e:
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print(f"解析分析响应时出错: {e}")
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error_msg = f"解析分析响应时出错: {e}"
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self.logger.error(error_msg)
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return {"error": str(e), "raw_response": response}
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def _parse_prediction_response(self, response: Dict[str, Any]) -> Dict[str, Any]:
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@ -13,17 +13,17 @@ deepseek:
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crypto:
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base_currencies:
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# - "BTC"
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- "ETH"
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- "BNB"
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- "SOL"
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# - "ADA"
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# - "ETH"
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# - "BNB"
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# - "SOL"
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- "SUI"
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quote_currency: "USDT"
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time_interval: "4h" # 可选: 1m, 5m, 15m, 30m, 1h, 4h, 1d
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# 数据设置
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data:
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storage_path: "./cryptoai/data"
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historical_days: 30
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historical_days: 180
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update_interval: 60 # 数据更新间隔（分钟）
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# Agent设置
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194
cryptoai/utils/token_usage.py
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194
cryptoai/utils/token_usage.py
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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DeepSeek API Token使用情况分析工具
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"""
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import os
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import sys
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import argparse
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import json
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from typing import Dict, Any
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import pandas as pd
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from datetime import datetime
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# 添加项目根目录到Python路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from api.deepseek_api import DeepSeekAPI
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from utils.config_loader import ConfigLoader
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def get_deepseek_api() -> DeepSeekAPI:
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"""
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获取已配置的DeepSeekAPI实例
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"""
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config_loader = ConfigLoader()
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deepseek_config = config_loader.get_deepseek_config()
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if not deepseek_config or 'api_key' not in deepseek_config:
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print("错误: 未找到DeepSeek API配置或API密钥")
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sys.exit(1)
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return DeepSeekAPI(
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api_key=deepseek_config['api_key'],
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model=deepseek_config.get('model', 'deepseek-moe-16b-chat')
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)
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def show_token_usage_stats():
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"""
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显示Token使用统计信息
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"""
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api = get_deepseek_api()
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stats = api.get_token_usage_stats()
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print("\n===== DeepSeek API Token使用统计 =====")
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print(f"总输入Tokens: {stats['total_prompt_tokens']:,}")
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print(f"总输出Tokens: {stats['total_completion_tokens']:,}")
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print(f"总Tokens: {stats['total_tokens']:,}")
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print(f"API调用次数: {stats['total_calls']}")
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print(f"平均每次调用Tokens: {stats['average_tokens_per_call']:.2f}")
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if stats['total_calls'] > 0:
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print("\n最近调用记录:")
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for call in stats['detailed_calls']:
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print(f" - {call['timestamp']} | {call['symbol']} | {call['task_type']} | "
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f"输入: {call['prompt_tokens']} | 输出: {call['completion_tokens']} | "
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f"总计: {call['total_tokens']} | 耗时: {call['duration_seconds']}秒")
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print("\n注意: 这些统计数据仅反映当前程序运行期间的使用情况")
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def export_token_usage(format_type: str = "json", output_path: str = None):
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"""
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导出Token使用数据
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Args:
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format_type: 导出格式，'json'或'csv'
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output_path: 输出文件路径
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"""
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api = get_deepseek_api()
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if output_path is None:
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# 如果未指定路径，则使用默认路径
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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output_path = f"deepseek_token_usage_{timestamp}.{format_type}"
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file_path = api.export_token_usage(output_path, format_type)
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if file_path:
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print(f"\nToken使用数据已导出到: {file_path}")
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else:
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print("\n导出Token使用数据失败")
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def analyze_token_usage(json_file: str = None):
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"""
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分析Token使用数据并显示统计
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Args:
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json_file: JSON格式的Token使用数据文件
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"""
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try:
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# 加载数据
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if json_file and os.path.exists(json_file):
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with open(json_file, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
else:
|
||||
# 使用当前内存中的数据
|
||||
api = get_deepseek_api()
|
||||
data = api.token_usage
|
||||
|
||||
if not data or not data.get('calls'):
|
||||
print("没有可用的Token使用数据进行分析")
|
||||
return
|
||||
|
||||
# 转换为DataFrame进行分析
|
||||
df = pd.DataFrame(data['calls'])
|
||||
|
||||
# 按任务类型分析
|
||||
task_analysis = df.groupby('task_type').agg({
|
||||
'prompt_tokens': ['sum', 'mean'],
|
||||
'completion_tokens': ['sum', 'mean'],
|
||||
'total_tokens': ['sum', 'mean', 'count']
|
||||
})
|
||||
|
||||
# 按符号(交易对)分析
|
||||
symbol_analysis = df.groupby('symbol').agg({
|
||||
'total_tokens': ['sum', 'mean', 'count']
|
||||
})
|
||||
|
||||
# 计算总体统计
|
||||
total_tokens = data['total_tokens']
|
||||
total_calls = len(data['calls'])
|
||||
|
||||
# 显示分析结果
|
||||
print("\n===== DeepSeek API Token使用分析 =====")
|
||||
print(f"总调用次数: {total_calls}")
|
||||
print(f"总Token使用: {total_tokens:,}")
|
||||
print(f"平均每次调用Token: {total_tokens / total_calls if total_calls else 0:.2f}")
|
||||
|
||||
print("\n--- 按任务类型分析 ---")
|
||||
for task, stats in task_analysis.iterrows():
|
||||
calls = stats[('total_tokens', 'count')]
|
||||
total = stats[('total_tokens', 'sum')]
|
||||
avg = stats[('total_tokens', 'mean')]
|
||||
print(f"{task}: {calls}次调用, 共{total:,}tokens, 平均{avg:.2f}tokens/次")
|
||||
|
||||
print("\n--- 按交易对分析 ---")
|
||||
for symbol, stats in symbol_analysis.iterrows():
|
||||
calls = stats[('total_tokens', 'count')]
|
||||
total = stats[('total_tokens', 'sum')]
|
||||
avg = stats[('total_tokens', 'mean')]
|
||||
print(f"{symbol}: {calls}次调用, 共{total:,}tokens, 平均{avg:.2f}tokens/次")
|
||||
|
||||
except Exception as e:
|
||||
print(f"分析Token使用数据时出错: {e}")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
parser = argparse.ArgumentParser(description="DeepSeek API Token使用情况分析工具")
|
||||
|
||||
subparsers = parser.add_subparsers(dest="command", help="命令")
|
||||
|
||||
# 显示统计信息
|
||||
show_parser = subparsers.add_parser("show", help="显示Token使用统计")
|
||||
|
||||
# 导出使用数据
|
||||
export_parser = subparsers.add_parser("export", help="导出Token使用数据")
|
||||
export_parser.add_argument(
|
||||
"--format", "-f",
|
||||
choices=["json", "csv"],
|
||||
default="json",
|
||||
help="导出格式 (默认: json)"
|
||||
)
|
||||
export_parser.add_argument(
|
||||
"--output", "-o",
|
||||
help="输出文件路径"
|
||||
)
|
||||
|
||||
# 分析使用数据
|
||||
analyze_parser = subparsers.add_parser("analyze", help="分析Token使用数据")
|
||||
analyze_parser.add_argument(
|
||||
"--input", "-i",
|
||||
help="输入JSON文件路径，如果未指定则使用当前内存中的数据"
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.command == "show":
|
||||
show_token_usage_stats()
|
||||
elif args.command == "export":
|
||||
export_token_usage(args.format, args.output)
|
||||
elif args.command == "analyze":
|
||||
analyze_token_usage(args.input)
|
||||
else:
|
||||
# 默认显示统计信息
|
||||
show_token_usage_stats()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue
Block a user