759 lines
32 KiB
Python
759 lines
32 KiB
Python
import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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import os
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import requests
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from typing import Dict, List, Optional, Tuple, Generator
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from dotenv import load_dotenv
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import json
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from logger import get_logger
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from utils.api_utils import APIUtils
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# 获取日志器
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logger = get_logger()
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class StockAnalyzer:
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def __init__(self, initial_cash=1000000, custom_api_url=None, custom_api_key=None, custom_api_model=None, custom_api_timeout=None):
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# 加载环境变量
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load_dotenv()
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# 设置 API 配置，优先使用自定义配置，否则使用环境变量
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self.API_URL = custom_api_url or os.getenv('API_URL')
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self.API_KEY = custom_api_key or os.getenv('API_KEY')
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self.API_MODEL = custom_api_model or os.getenv('API_MODEL', 'gpt-3.5-turbo')
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self.API_TIMEOUT = int(custom_api_timeout or os.getenv('API_TIMEOUT', 60))
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logger.debug(f"初始化StockAnalyzer: API_URL={self.API_URL}, API_MODEL={self.API_MODEL}, API_KEY={'已提供' if self.API_KEY else '未提供'}, API_TIMEOUT={self.API_TIMEOUT}")
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# 配置参数
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self.params = {
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'ma_periods': {'short': 5, 'medium': 20, 'long': 60},
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'rsi_period': 14,
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'bollinger_period': 20,
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'bollinger_std': 2,
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'volume_ma_period': 20,
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'atr_period': 14
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}
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def get_stock_data(self, stock_code, market_type='A', start_date=None, end_date=None):
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"""获取股票或基金数据"""
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import akshare as ak
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if start_date is None:
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start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
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if end_date is None:
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end_date = datetime.now().strftime('%Y%m%d')
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try:
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# 验证股票代码格式
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if market_type == 'A':
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# 上海证券交易所股票代码以6开头
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# 深圳证券交易所股票代码以0或3开头
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# 科创板股票代码以688开头
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# 北京证券交易所股票代码以8开头
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valid_prefixes = ['0', '3', '6', '688', '8']
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valid_format = False
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for prefix in valid_prefixes:
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if stock_code.startswith(prefix):
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valid_format = True
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break
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if not valid_format:
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error_msg = f"无效的A股股票代码格式: {stock_code}。A股代码应以0、3、6、688或8开头"
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logger.error(f"[股票代码格式错误] {error_msg}")
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raise ValueError(error_msg)
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df = ak.stock_zh_a_hist(
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symbol=stock_code,
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start_date=start_date,
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end_date=end_date,
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adjust="qfq"
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)
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elif market_type == 'HK':
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df = ak.stock_hk_daily(
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symbol=stock_code,
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adjust="qfq"
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)
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elif market_type == 'US':
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df = ak.stock_us_hist(
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symbol=stock_code,
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start_date=start_date,
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end_date=end_date,
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adjust="qfq"
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)
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elif market_type == 'ETF':
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df = ak.fund_etf_hist_em(
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symbol=stock_code,
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period="daily",
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start_date=start_date,
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end_date=end_date,
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adjust="qfq"
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)
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elif market_type == 'LOF':
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df = ak.fund_lof_hist_em(
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symbol=stock_code,
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period="daily",
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start_date=start_date,
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end_date=end_date,
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adjust="qfq"
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)
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else:
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raise ValueError(f"不支持的市场类型: {market_type}")
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# 重命名列名以匹配分析需求
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df = df.rename(columns={
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"日期": "date",
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"开盘": "open",
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"收盘": "close",
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"最高": "high",
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"最低": "low",
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"成交量": "volume"
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})
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# 确保日期格式正确
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df['date'] = pd.to_datetime(df['date'])
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# 数据类型转换
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numeric_columns = ['open', 'close', 'high', 'low', 'volume']
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df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce')
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# 删除空值
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df = df.dropna()
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return df.sort_values('date')
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# except ValueError as ve:
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# # 捕获格式验证错误
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# logger.error(f"[股票代码格式错误] {str(ve)}")
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# raise Exception(f"股票代码格式错误: {str(ve)}")
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except Exception as e:
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logger.error(f"[获取数据失败] {str(e)}")
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raise Exception(f"获取数据失败: {str(e)}")
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def calculate_ema(self, series, period):
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"""计算指数移动平均线"""
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return series.ewm(span=period, adjust=False).mean()
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def calculate_rsi(self, series, period):
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"""计算RSI指标"""
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delta = series.diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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return 100 - (100 / (1 + rs))
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def calculate_macd(self, series):
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"""计算MACD指标"""
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exp1 = series.ewm(span=12, adjust=False).mean()
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exp2 = series.ewm(span=26, adjust=False).mean()
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macd = exp1 - exp2
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signal = macd.ewm(span=9, adjust=False).mean()
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hist = macd - signal
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return macd, signal, hist
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def calculate_bollinger_bands(self, series, period, std_dev):
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"""计算布林带"""
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middle = series.rolling(window=period).mean()
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std = series.rolling(window=period).std()
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upper = middle + (std * std_dev)
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lower = middle - (std * std_dev)
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return upper, middle, lower
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def calculate_atr(self, df, period):
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"""计算ATR指标"""
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high = df['high']
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low = df['low']
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close = df['close'].shift(1)
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tr1 = high - low
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tr2 = abs(high - close)
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tr3 = abs(low - close)
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tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
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return tr.rolling(window=period).mean()
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def calculate_indicators(self, df):
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"""计算技术指标"""
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try:
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# 计算移动平均线
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df['MA5'] = self.calculate_ema(df['close'], self.params['ma_periods']['short'])
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df['MA20'] = self.calculate_ema(df['close'], self.params['ma_periods']['medium'])
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df['MA60'] = self.calculate_ema(df['close'], self.params['ma_periods']['long'])
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# 计算RSI
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df['RSI'] = self.calculate_rsi(df['close'], self.params['rsi_period'])
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# 计算MACD
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df['MACD'], df['Signal'], df['MACD_hist'] = self.calculate_macd(df['close'])
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# 计算布林带
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df['BB_upper'], df['BB_middle'], df['BB_lower'] = self.calculate_bollinger_bands(
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df['close'],
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self.params['bollinger_period'],
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self.params['bollinger_std']
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)
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# 成交量分析
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df['Volume_MA'] = df['volume'].rolling(window=self.params['volume_ma_period']).mean()
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df['Volume_Ratio'] = df['volume'] / df['Volume_MA']
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# 计算ATR和波动率
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df['ATR'] = self.calculate_atr(df, self.params['atr_period'])
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df['Volatility'] = df['ATR'] / df['close'] * 100
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# 动量指标
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df['ROC'] = df['close'].pct_change(periods=10) * 100
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return df
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except Exception as e:
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print(f"计算技术指标时出错: {str(e)}")
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raise
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def calculate_score(self, df):
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"""计算评分"""
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try:
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score = 0
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latest = df.iloc[-1]
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# 趋势得分 (30分)
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if latest['MA5'] > latest['MA20']:
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score += 15
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if latest['MA20'] > latest['MA60']:
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score += 15
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# RSI得分 (20分)
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if 30 <= latest['RSI'] <= 70:
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score += 20
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elif latest['RSI'] < 30: # 超卖
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score += 15
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# MACD得分 (20分)
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if latest['MACD'] > latest['Signal']:
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score += 20
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# 成交量得分 (30分)
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if latest['Volume_Ratio'] > 1.5:
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score += 30
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elif latest['Volume_Ratio'] > 1:
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score += 15
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return score
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except Exception as e:
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print(f"计算评分时出错: {str(e)}")
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raise
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def get_ai_analysis(self, df, stock_code, market_type='A', stream=False):
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"""使用 OpenAI 进行 AI 分析"""
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try:
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logger.info(f"开始AI分析 {stock_code}, 流式模式: {stream}")
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recent_data = df.tail(14).to_dict('records')
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technical_summary = {
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'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward',
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'volatility': f"{df.iloc[-1]['Volatility']:.2f}%",
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'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing',
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'rsi_level': df.iloc[-1]['RSI']
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}
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# 根据市场类型调整分析提示
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if market_type in ['ETF', 'LOF']:
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prompt = f"""
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分析基金 {stock_code}：
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技术指标概要：
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{technical_summary}
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近14日交易数据：
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{recent_data}
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请提供：
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1. 净值走势分析（包含支撑位和压力位）
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2. 成交量分析及其对净值的影响
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3. 风险评估（包含波动率和折溢价分析）
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4. 短期和中期净值预测
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5. 关键价格位分析
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6. 申购赎回建议（包含止损位）
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请基于技术指标和市场表现进行分析，给出具体数据支持。
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"""
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elif market_type == 'US':
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prompt = f"""
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分析美股 {stock_code}：
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技术指标概要：
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{technical_summary}
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近14日交易数据：
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{recent_data}
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请提供：
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1. 趋势分析（包含支撑位和压力位，美元计价）
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2. 成交量分析及其含义
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3. 风险评估（包含波动率和美股市场特有风险）
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4. 短期和中期目标价位（美元）
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5. 关键技术位分析
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6. 具体交易建议（包含止损位）
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请基于技术指标和美股市场特点进行分析，给出具体数据支持。
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"""
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elif market_type == 'HK':
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prompt = f"""
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分析港股 {stock_code}：
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技术指标概要：
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{technical_summary}
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近14日交易数据：
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{recent_data}
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请提供：
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1. 趋势分析（包含支撑位和压力位，港币计价）
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2. 成交量分析及其含义
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3. 风险评估（包含波动率和港股市场特有风险）
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4. 短期和中期目标价位（港币）
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5. 关键技术位分析
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6. 具体交易建议（包含止损位）
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请基于技术指标和港股市场特点进行分析，给出具体数据支持。
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"""
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else: # A股
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prompt = f"""
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分析A股 {stock_code}：
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技术指标概要：
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{technical_summary}
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近14日交易数据：
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{recent_data}
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请提供：
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1. 趋势分析（包含支撑位和压力位）
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2. 成交量分析及其含义
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3. 风险评估（包含波动率分析）
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4. 短期和中期目标价位
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5. 关键技术位分析
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6. 具体交易建议（包含止损位）
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请基于技术指标和A股市场特点进行分析，给出具体数据支持。
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"""
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logger.debug(f"生成的AI分析提示词: {self._truncate_json_for_logging(prompt, 100)}...")
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# 检查API配置
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if not self.API_URL:
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error_msg = "API URL未配置，无法进行AI分析"
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logger.error(f"[API配置错误] {error_msg}")
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return error_msg if not stream else (yield json.dumps({"error": error_msg}))
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if not self.API_KEY:
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error_msg = "API Key未配置，无法进行AI分析"
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logger.error(f"[API配置错误] {error_msg}")
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return error_msg if not stream else (yield json.dumps({"error": error_msg}))
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# 标准化API URL
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api_url = APIUtils.format_api_url(self.API_URL)
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logger.debug(f"标准化后的API URL: {api_url}")
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# 构建请求头和请求体
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headers = {
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"Authorization": f"Bearer {self.API_KEY}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": self.API_MODEL,
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"messages": [{"role": "user", "content": prompt}]
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}
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# 流式处理设置
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if stream:
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logger.debug(f"配置流式参数，使用API URL: {api_url}")
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payload["stream"] = True # 明确设置stream参数为True
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try:
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logger.debug(f"发起流式API请求: {api_url}")
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logger.debug(f"请求载荷: {self._truncate_json_for_logging(payload)}")
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response = requests.post(
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api_url,
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headers=headers,
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json=payload,
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timeout=self.API_TIMEOUT, # 增加超时时间
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stream=True
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)
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logger.debug(f"API流式响应状态码: {response.status_code}")
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if response.status_code == 200:
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logger.info(f"成功获取API流式响应，开始处理")
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yield from self._process_ai_stream(response, stock_code)
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else:
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try:
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error_response = response.json()
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error_text = self._truncate_json_for_logging(error_response)
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except:
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error_text = response.text[:500] if response.text else "无响应内容"
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error_msg = f"API请求失败: 状态码 {response.status_code}, 响应: {error_text}"
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logger.error(f"[API请求失败] {error_msg}")
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yield json.dumps({"stock_code": stock_code, "error": error_msg})
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except Exception as e:
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error_msg = f"流式API请求异常: {str(e)}"
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logger.error(f"[流式API异常] {error_msg}")
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logger.exception(e)
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yield json.dumps({"stock_code": stock_code, "error": error_msg})
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else:
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# 非流式处理
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logger.debug(f"发起非流式API请求: {api_url}")
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try:
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response = requests.post(
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api_url,
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headers=headers,
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json=payload,
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timeout=self.API_TIMEOUT
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)
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logger.debug(f"API非流式响应状态码: {response.status_code}")
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if response.status_code == 200:
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api_response = response.json()
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content = api_response['choices'][0]['message']['content']
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logger.info(f"成功获取AI分析结果，长度: {len(content)}")
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logger.debug(f"AI分析结果前100字符: {content[:100]}...")
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return content
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else:
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try:
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error_response = response.json()
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error_text = self._truncate_json_for_logging(error_response)
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except:
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error_text = response.text[:500] if response.text else "无响应内容"
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error_msg = f"API请求失败: 状态码 {response.status_code}, 响应: {error_text}"
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logger.error(f"[API请求失败] {error_msg}")
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return error_msg
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except Exception as e:
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error_msg = f"非流式API请求异常: {str(e)}"
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logger.error(f"[非流式API异常] {error_msg}")
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logger.exception(e)
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return error_msg
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except Exception as e:
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error_msg = f"AI 分析过程中发生错误: {str(e)}"
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logger.error(f"[AI分析异常] {error_msg}")
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logger.exception(e)
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if stream:
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logger.debug("在流式模式下返回异常信息")
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error_json = json.dumps({"stock_code": stock_code, "error": error_msg})
|
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logger.info(f"流式异常输出: {error_json}")
|
||
yield error_json
|
||
else:
|
||
return error_msg
|
||
|
||
def _truncate_json_for_logging(self, json_obj, max_length=500):
|
||
"""截断JSON对象用于日志记录，避免日志过大
|
||
|
||
Args:
|
||
json_obj: 要截断的JSON对象
|
||
max_length: 最大字符长度，默认500
|
||
|
||
Returns:
|
||
str: 截断后的JSON字符串
|
||
"""
|
||
json_str = json.dumps(json_obj, ensure_ascii=False)
|
||
if len(json_str) <= max_length:
|
||
return json_str
|
||
return json_str[:max_length] + f"... [截断，总长度: {len(json_str)}字符]"
|
||
|
||
def _process_ai_stream(self, response, stock_code) -> Generator[str, None, None]:
|
||
"""处理AI流式响应"""
|
||
logger.info(f"开始处理股票 {stock_code} 的AI流式响应\n")
|
||
buffer = ""
|
||
chunk_count = 0
|
||
|
||
try:
|
||
for line in response.iter_lines():
|
||
if line:
|
||
line = line.decode('utf-8')
|
||
|
||
# 跳过保持连接的空行
|
||
if line.strip() == '':
|
||
logger.debug("跳过空行")
|
||
continue
|
||
|
||
# 数据行通常以"data: "开头
|
||
if line.startswith('data: '):
|
||
data_content = line[6:] # 移除 "data: " 前缀
|
||
|
||
# 检查是否为流的结束
|
||
if data_content.strip() == '[DONE]':
|
||
logger.debug("收到流结束标记 [DONE]")
|
||
break
|
||
|
||
try:
|
||
json_data = json.loads(data_content)
|
||
|
||
# 检查 choices 列表是否为空
|
||
if 'choices' in json_data and json_data['choices']:
|
||
delta = json_data['choices'][0].get('delta', {})
|
||
content = delta.get('content', '')
|
||
|
||
if content:
|
||
chunk_count += 1
|
||
buffer += content
|
||
|
||
# 创建包含AI分析片段的JSON
|
||
chunk_json = json.dumps({
|
||
"stock_code": stock_code,
|
||
"ai_analysis_chunk": content
|
||
})
|
||
yield chunk_json
|
||
except json.JSONDecodeError as e:
|
||
logger.error(f"[JSON解析错误] {str(e)}, 行内容: {data_content}")
|
||
# 忽略无法解析的JSON
|
||
pass
|
||
else:
|
||
logger.warning(f"收到非'data:'开头的行: {line}")
|
||
|
||
logger.info(f"AI流式处理完成，共收到 {chunk_count} 个内容片段，总长度: {len(buffer)}")
|
||
|
||
# 如果buffer不为空，最后一次发送完整内容
|
||
if buffer and not buffer.endswith('\n'):
|
||
logger.debug("发送换行符")
|
||
yield json.dumps({"stock_code": stock_code, "ai_analysis_chunk": "\n"})
|
||
|
||
except Exception as e:
|
||
error_msg = f"处理AI流式响应时出错: {str(e)}"
|
||
logger.error(f"[流式响应异常] {error_msg}")
|
||
logger.exception(e)
|
||
yield json.dumps({"stock_code": stock_code, "error": error_msg})
|
||
|
||
|
||
def get_recommendation(self, score):
|
||
"""根据得分给出建议"""
|
||
logger.debug(f"根据评分 {score} 生成投资建议")
|
||
if score >= 80:
|
||
return '强烈推荐买入'
|
||
elif score >= 60:
|
||
return '建议买入'
|
||
elif score >= 40:
|
||
return '观望'
|
||
elif score >= 20:
|
||
return '建议卖出'
|
||
else:
|
||
return '强烈建议卖出'
|
||
|
||
def analyze_stock(self, stock_code, market_type='A', stream=False):
|
||
"""分析单只"""
|
||
logger.info(f"开始分析 {stock_code}, 市场类型: {market_type}, 流式模式: {stream}")
|
||
|
||
try:
|
||
# 获取股票数据
|
||
try:
|
||
df = self.get_stock_data(stock_code, market_type)
|
||
except Exception as e:
|
||
# 捕获股票数据获取异常
|
||
error_msg = str(e)
|
||
logger.error(f"[数据获取异常] {error_msg}")
|
||
|
||
# 格式化错误响应
|
||
error_response = {
|
||
'stock_code': stock_code,
|
||
'error': error_msg,
|
||
'status': 'error',
|
||
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||
}
|
||
|
||
if stream:
|
||
return (yield json.dumps(error_response))
|
||
else:
|
||
return error_response
|
||
|
||
# 检查数据是否为空
|
||
if df.empty:
|
||
error_msg = f" {stock_code} 数据为空"
|
||
logger.error(f"[空数据] {error_msg}")
|
||
|
||
# 格式化错误响应
|
||
error_response = {
|
||
'stock_code': stock_code,
|
||
'error': error_msg,
|
||
'status': 'error',
|
||
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||
}
|
||
|
||
if stream:
|
||
return (yield json.dumps(error_response))
|
||
else:
|
||
return error_response
|
||
|
||
# 计算技术指标
|
||
logger.debug(f"计算 {stock_code} 技术指标")
|
||
df = self.calculate_indicators(df)
|
||
|
||
# 评分系统
|
||
logger.debug(f"计算 {stock_code} 评分")
|
||
score = self.calculate_score(df)
|
||
logger.info(f"{stock_code} 评分结果: {score}")
|
||
|
||
# 获取最新数据
|
||
latest = df.iloc[-1]
|
||
prev = df.iloc[-2]
|
||
|
||
# 生成报告
|
||
report = {
|
||
'stock_code': stock_code,
|
||
'market_type': market_type, # 添加市场类型
|
||
'analysis_date': datetime.now().strftime('%Y-%m-%d'),
|
||
'score': score,
|
||
'price': latest['close'],
|
||
'price_change': (latest['close'] - prev['close']) / prev['close'] * 100,
|
||
'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
|
||
'rsi': latest['RSI'] if not pd.isna(latest['RSI']) else None,
|
||
'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL',
|
||
'volume_status': 'HIGH' if latest['Volume_Ratio'] > 1.5 else 'NORMAL',
|
||
'recommendation': self.get_recommendation(score)
|
||
}
|
||
logger.debug(f"生成 {stock_code} 基础报告: {self._truncate_json_for_logging(report, 100)}...")
|
||
|
||
if stream:
|
||
logger.info(f"以流式模式返回 {stock_code} 分析结果")
|
||
# 先返回基本报告结构
|
||
base_report = dict(report)
|
||
base_report['ai_analysis'] = ''
|
||
base_report_json = json.dumps(base_report)
|
||
logger.debug(f"基础报告JSON: {self._truncate_json_for_logging(base_report_json, 100)}...")
|
||
logger.info(f"发送基础报告: {base_report_json}")
|
||
yield base_report_json
|
||
|
||
# 然后流式返回AI分析部分
|
||
logger.debug(f"开始获取 {stock_code} 的流式AI分析")
|
||
ai_chunks_count = 0
|
||
for ai_chunk in self.get_ai_analysis(df, stock_code, market_type, stream=True):
|
||
ai_chunks_count += 1
|
||
yield ai_chunk
|
||
logger.info(f" {stock_code} 流式AI分析完成，共发送 {ai_chunks_count} 个块")
|
||
else:
|
||
logger.info(f"以非流式模式返回 {stock_code} 分析结果")
|
||
logger.debug(f"开始获取 {stock_code} 的AI分析")
|
||
report['ai_analysis'] = self.get_ai_analysis(df, stock_code, market_type)
|
||
logger.debug(f"AI分析结果长度: {len(report['ai_analysis'])}")
|
||
return report
|
||
|
||
except Exception as e:
|
||
error_msg = f"分析 {stock_code} 时出错: {str(e)}\n"
|
||
logger.error(f"[分析异常] {error_msg}")
|
||
logger.exception(e)
|
||
|
||
# 格式化错误响应
|
||
error_response = {
|
||
'stock_code': stock_code,
|
||
'error': error_msg,
|
||
'status': 'error',
|
||
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||
}
|
||
|
||
if stream:
|
||
return (yield json.dumps(error_response))
|
||
else:
|
||
return error_response
|
||
|
||
def scan_stocks(self, stock_codes, market_type='A', min_score=60, stream=False):
|
||
"""扫描多只"""
|
||
logger.info(f"开始扫描 {len(stock_codes)} 只, 市场类型: {market_type}, 最低评分: {min_score}, 流式模式: {stream}")
|
||
|
||
if not stream:
|
||
# 非流式模式
|
||
recommended_stocks = []
|
||
stock_count = 0
|
||
error_count = 0
|
||
|
||
for stock_code in stock_codes:
|
||
stock_count += 1
|
||
logger.info(f"扫描进度: {stock_count}/{len(stock_codes)}, 当前: {stock_code}")
|
||
|
||
try:
|
||
logger.debug(f"分析: {stock_code}")
|
||
report = self.analyze_stock(stock_code, market_type)
|
||
|
||
# 检查是否有错误
|
||
if isinstance(report, dict) and 'error' in report:
|
||
error_count += 1
|
||
logger.warning(f"[扫描错误] {stock_code}: {report['error']}")
|
||
continue
|
||
|
||
# 检查评分是否达到最低要求
|
||
if report['score'] >= min_score:
|
||
logger.info(f" {stock_code} 评分 {report['score']} >= {min_score}，添加到推荐列表")
|
||
recommended_stocks.append(report)
|
||
else:
|
||
logger.debug(f" {stock_code} 评分 {report['score']} < {min_score}，不添加到推荐列表")
|
||
except Exception as e:
|
||
error_count += 1
|
||
error_msg = f"分析 {stock_code} 时出错: {str(e)}"
|
||
logger.error(f"[扫描异常] {error_msg}")
|
||
logger.exception(e)
|
||
|
||
# 添加错误信息到推荐列表，确保前端能看到错误
|
||
error_response = {
|
||
'stock_code': stock_code,
|
||
'error': error_msg,
|
||
'status': 'error',
|
||
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||
}
|
||
recommended_stocks.append(error_response)
|
||
continue
|
||
|
||
logger.info(f"扫描完成，共 {stock_count} 只，{error_count} 只出错，{len(recommended_stocks)} 只推荐")
|
||
return recommended_stocks
|
||
else:
|
||
# 流式模式
|
||
stock_count = 0
|
||
error_count = 0
|
||
|
||
for stock_code in stock_codes:
|
||
stock_count += 1
|
||
logger.info(f"流式扫描进度: {stock_count}/{len(stock_codes)}, 当前: {stock_code}")
|
||
|
||
try:
|
||
chunk_count = 0
|
||
for chunk in self.analyze_stock(stock_code, market_type, stream=True):
|
||
chunk_count += 1
|
||
# 检查是否有错误信息
|
||
try:
|
||
chunk_data = json.loads(chunk)
|
||
if 'error' in chunk_data:
|
||
error_count += 1
|
||
logger.warning(f"[流式扫描错误] {stock_code}: {chunk_data['error']}")
|
||
except:
|
||
pass
|
||
yield chunk
|
||
logger.debug(f" {stock_code} 流式分析完成，共 {chunk_count} 个块")
|
||
except Exception as e:
|
||
error_count += 1
|
||
error_msg = f"分析 {stock_code} 时出错: {str(e)}"
|
||
logger.error(f"[流式扫描异常] {error_msg}")
|
||
logger.exception(e)
|
||
|
||
# 格式化错误响应
|
||
error_response = {
|
||
'stock_code': stock_code,
|
||
'error': error_msg,
|
||
'status': 'error',
|
||
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||
}
|
||
error_json = json.dumps(error_response)
|
||
logger.info(f"流式错误输出: {error_json}")
|
||
yield error_json
|
||
|
||
logger.info(f"流式扫描完成，共处理 {stock_count} ，{error_count} 只出错")
|