463 lines
16 KiB
Python
463 lines
16 KiB
Python
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, 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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def __init__(self, api_key: str, model: str = "deepseek-moe-16b-chat"):
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"""
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初始化DeepSeek API
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Args:
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api_key: DeepSeek API密钥
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model: 使用的模型名称
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"""
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self.api_key = api_key
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self.model = model
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self.base_url = "https://api.deepseek.com/v1"
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self.headers = {
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"Content-Type": "application/json",
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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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Args:
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market_data: 包含市场数据的字典，例如价格、交易量等
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Returns:
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分析结果
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"""
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# 将市场数据格式化为适合大模型的格式
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formatted_data = self._format_market_data(market_data)
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# 构建提示词
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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, 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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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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预测价格趋势
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Args:
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symbol: 交易对符号，例如 'BTCUSDT'
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historical_data: 历史数据
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Returns:
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预测结果
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"""
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# 格式化历史数据
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formatted_data = self._format_historical_data(symbol, historical_data)
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# 构建提示词
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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, 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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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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生成交易策略
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Args:
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symbol: 交易对符号，例如 'BTCUSDT'
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analysis_result: 分析结果
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risk_level: 风险等级，'low', 'medium', 'high'
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Returns:
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交易策略
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"""
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# 构建提示词
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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, 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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result = self._parse_strategy_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 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, 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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payload = {
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"model": self.model,
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"messages": [
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{"role": "system", "content": "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。"},
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{"role": "user", "content": prompt}
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],
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"temperature": 0.2, # 低温度使输出更加确定性
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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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# 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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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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格式化市场数据为适合大模型的格式
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Args:
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market_data: 市场数据
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Returns:
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格式化的数据字符串
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"""
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# 这里可以根据实际情况调整格式化方式
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return json.dumps(market_data, indent=2)
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def _format_historical_data(self, symbol: str, historical_data: Dict[str, Any]) -> str:
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"""
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格式化历史数据为适合大模型的格式
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Args:
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symbol: 交易对符号
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historical_data: 历史数据
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Returns:
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格式化的数据字符串
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"""
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# 可以根据实际情况调整格式化方式
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return json.dumps(historical_data, indent=2)
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def _build_market_analysis_prompt(self, formatted_data: str) -> str:
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"""
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构建市场分析提示词
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Args:
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formatted_data: 格式化的市场数据
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Returns:
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提示词
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"""
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return f"""请分析以下加密货币市场数据，并提供详细的市场分析。请使用中文回复。
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数据:
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{formatted_data}
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请包括以下内容:
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1. 市场总体趋势
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2. 主要支撑位和阻力位
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3. 交易量分析
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4. 市场情绪评估
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5. 关键技术指标解读（如RSI、MACD等）
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请以JSON格式回复，包含以下字段:
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- market_trend: 市场趋势 (牛市, 熊市, 震荡)
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- support_levels: 支撑位列表
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- resistance_levels: 阻力位列表
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- volume_analysis: 交易量分析
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- market_sentiment: 市场情绪
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- technical_indicators: 技术指标分析
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- summary: 总结
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请确保回复为有效的JSON格式，并使用中文进行分析。"""
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def _build_price_prediction_prompt(self, symbol: str, formatted_data: str) -> str:
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"""
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构建价格预测提示词
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Args:
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symbol: 交易对符号
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formatted_data: 格式化的历史数据
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Returns:
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提示词
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"""
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return f"""请基于以下{symbol}的历史数据，预测未来24小时、7天和30天的价格走势。请使用中文回复。
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历史数据:
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{formatted_data}
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请考虑市场趋势、技术指标、历史模式和当前市场情况，提供详细的预测分析。
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请以JSON格式回复，包含以下字段:
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- symbol: 交易对符号
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- current_price: 当前价格
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- prediction_24h: 24小时预测 (包含 price_range价格区间, trend趋势, confidence置信度)
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- prediction_7d: 7天预测 (包含 price_range价格区间, trend趋势, confidence置信度)
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- prediction_30d: 30天预测 (包含 price_range价格区间, trend趋势, confidence置信度)
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- key_factors: 影响预测的关键因素
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- risk_assessment: 风险评估
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请确保回复为有效的JSON格式，并使用中文进行分析。"""
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def _build_trading_strategy_prompt(self, symbol: str, analysis_result: Dict[str, Any], risk_level: str) -> str:
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"""
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构建交易策略提示词
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Args:
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symbol: 交易对符号
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analysis_result: 分析结果
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risk_level: 风险等级
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Returns:
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提示词
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"""
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analysis_json = json.dumps(analysis_result, indent=2)
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return f"""请基于以下{symbol}的市场分析结果，生成一个风险等级为{risk_level}的交易策略。请使用中文回复。
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分析结果:
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{analysis_json}
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请考虑市场趋势、技术指标、风险等级和当前市场情况，提供详细的交易策略。
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请以JSON格式回复，包含以下字段:
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- symbol: 交易对符号
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- risk_level: 风险等级 (low低风险, medium中风险, high高风险)
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- position: 建议仓位 (买入、卖出、持有)
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- entry_points: 入场点列表
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- exit_points: 出场点列表
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- stop_loss: 止损位
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- take_profit: 止盈位
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- time_frame: 建议的交易时间框架
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- strategy_type: 策略类型 (例如：趋势跟踪、反转、突破等)
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- reasoning: 策略推理过程
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请确保回复为有效的JSON格式，并使用中文进行分析。"""
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def _parse_analysis_response(self, response: Dict[str, Any]) -> Dict[str, Any]:
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"""
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解析分析响应
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Args:
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response: API响应
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Returns:
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解析后的分析结果
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"""
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try:
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if 'choices' in response and len(response['choices']) > 0:
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content = response['choices'][0]['message']['content']
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# 尝试从响应中提取JSON
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start_idx = content.find('{')
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end_idx = content.rfind('}') + 1
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if start_idx != -1 and end_idx != -1:
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json_str = content[start_idx:end_idx]
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return json.loads(json_str)
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return {"error": "无法从响应中提取JSON", "raw_content": content}
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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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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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"""
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解析预测响应
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Args:
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response: API响应
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Returns:
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解析后的预测结果
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"""
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# 与_parse_analysis_response相同的实现
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return self._parse_analysis_response(response)
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def _parse_strategy_response(self, response: Dict[str, Any]) -> Dict[str, Any]:
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"""
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解析策略响应
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Args:
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response: API响应
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Returns:
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解析后的策略结果
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"""
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# 与_parse_analysis_response相同的实现
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return self._parse_analysis_response(response) |