fix: 修复前端显示问题
This commit is contained in:
Cassianvale
@@ -4,10 +4,12 @@ import os
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import json
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import asyncio
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import httpx
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import re
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from typing import Dict, List, Optional, Any, Generator, AsyncGenerator
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from dotenv import load_dotenv
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from logger import get_logger
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from utils.api_utils import APIUtils
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from datetime import datetime
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# 获取日志器
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logger = get_logger()
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@@ -55,6 +57,26 @@ class AIAnalyzer:
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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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# 提取关键技术指标
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latest_data = df.iloc[-1]
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# 计算技术指标
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rsi = latest_data.get('RSI')
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price = latest_data.get('Close')
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price_change = latest_data.get('Change')
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# 确定MA趋势
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ma_trend = 'UP' if latest_data.get('MA5', 0) > latest_data.get('MA20', 0) else 'DOWN'
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# 确定MACD信号
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macd = latest_data.get('MACD', 0)
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macd_signal = latest_data.get('MACD_Signal', 0)
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macd_signal_type = 'BUY' if macd > macd_signal else 'SELL'
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# 确定成交量状态
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volume_ratio = latest_data.get('Volume_Ratio', 1)
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volume_status = 'HIGH' if volume_ratio > 1.5 else ('LOW' if volume_ratio < 0.5 else 'NORMAL')
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# AI 分析内容
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# 最近14天的股票数据记录
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recent_data = df.tail(14).to_dict('records')
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@@ -78,25 +100,19 @@ class AIAnalyzer:
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近14日交易数据：
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{recent_data}
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请分析该基金的技术面状况，包括：
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1. 趋势分析：判断基金当前的趋势方向
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2. 动量分析：基于RSI和交易量评估基金动量
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3. 支撑与阻力位：确定关键价格位
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4. 技术面总结
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5. 投资建议
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将分析结果格式化为JSON，像这样：
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{{
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"trend_analysis": "趋势分析结果...",
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"momentum_analysis": "动量分析结果...",
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"support_resistance": "支撑阻力位分析...",
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"technical_summary": "技术面总结...",
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"investment_advice": "投资建议..."
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}}
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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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else:
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elif market_type == 'US':
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prompt = f"""
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分析股票 {stock_code}：
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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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@@ -104,25 +120,55 @@ class AIAnalyzer:
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近14日交易数据：
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{recent_data}
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请分析该股票的技术面状况，包括：
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1. 趋势分析：当前趋势方向及强度
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2. 动量分析：基于MACD、RSI等指标
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3. 支撑与阻力位：关键价格位分析
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4. 成交量分析：交易量的变化及意义
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5. 波动性评估：ATR和波动率分析
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6. 技术面总结
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7. 投资建议：根据技术分析给出操作建议
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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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将分析结果格式化为JSON，像这样：
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{{
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"trend_analysis": "趋势分析结果...",
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"momentum_analysis": "动量分析结果...",
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"support_resistance": "支撑阻力位分析...",
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"volume_analysis": "成交量分析...",
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"volatility_assessment": "波动性评估...",
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"technical_summary": "技术面总结...",
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"investment_advice": "投资建议..."
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}}
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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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# 格式化API URL
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@@ -142,11 +188,27 @@ class AIAnalyzer:
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"Authorization": f"Bearer {self.API_KEY}"
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}
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# 获取当前日期作为分析日期
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analysis_date = datetime.now().strftime("%Y-%m-%d")
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# 异步请求API
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async with httpx.AsyncClient(timeout=self.API_TIMEOUT) as client:
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# 记录请求
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logger.debug(f"发送AI请求: URL={api_url}, MODEL={self.API_MODEL}, STREAM={stream}")
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# 先发送技术指标数据
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yield json.dumps({
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"stock_code": stock_code,
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"status": "analyzing",
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"rsi": rsi,
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"price": price,
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"price_change": price_change,
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"ma_trend": ma_trend,
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"macd_signal": macd_signal_type,
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"volume_status": volume_status,
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"analysis_date": analysis_date
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})
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if stream:
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# 流式响应处理
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async with client.stream("POST", api_url, json=request_data, headers=headers) as response:
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@@ -155,12 +217,17 @@ class AIAnalyzer:
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error_data = json.loads(error_text)
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error_message = error_data.get('error', {}).get('message', '未知错误')
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logger.error(f"AI API请求失败: {response.status_code} - {error_message}")
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yield json.dumps({"error": f"API请求失败: {error_message}"})
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yield json.dumps({
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"stock_code": stock_code,
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"error": f"API请求失败: {error_message}",
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"status": "error"
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})
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return
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# 处理流式响应
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buffer = ""
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collected_messages = []
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chunk_count = 0
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async for chunk in response.aiter_text():
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if chunk:
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@@ -169,6 +236,7 @@ class AIAnalyzer:
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chunk_str = chunk_str[6:] # 去除"data: "前缀
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if chunk_str == "[DONE]":
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logger.debug("收到流结束标记 [DONE]")
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continue
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try:
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@@ -178,49 +246,48 @@ class AIAnalyzer:
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content = delta.get("content", "")
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if content:
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chunk_count += 1
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buffer += content
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# 尝试提取完整的JSON
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if buffer.strip().startswith("{") and buffer.strip().endswith("}"):
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try:
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result_json = json.loads(buffer)
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yield json.dumps({
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"stock_code": stock_code,
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"analysis": result_json
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})
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buffer = "" # 重置缓冲区
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except json.JSONDecodeError:
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# JSON不完整，继续收集
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pass
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collected_messages.append(content)
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# 达到一定长度就输出
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if len(buffer) > 100:
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yield json.dumps({
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"stock_code": stock_code,
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"ai_analysis_chunk": buffer
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})
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collected_messages.append(buffer)
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buffer = ""
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# 直接发送每个内容片段，不累积
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yield json.dumps({
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"stock_code": stock_code,
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"ai_analysis_chunk": content,
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"status": "analyzing"
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})
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except json.JSONDecodeError:
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# 忽略无法解析的块
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logger.error(f"JSON解析错误，块内容: {chunk_str[:100]}...")
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continue
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# 处理最后的缓冲区
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if buffer:
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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)}")
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# 如果buffer不为空且不以换行符结束，发送一个换行符
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if buffer and not buffer.endswith('\n'):
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logger.debug("发送换行符")
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yield json.dumps({
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"stock_code": stock_code,
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"ai_analysis_chunk": buffer
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"ai_analysis_chunk": "\n",
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"status": "analyzing"
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})
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collected_messages.append(buffer)
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# 尝试从整个内容中提取JSON
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full_content = "".join(collected_messages)
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# 完整的分析内容
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full_content = buffer
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# 如果没有成功解析JSON，返回原始内容
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if not full_content.strip().startswith("{"):
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yield json.dumps({
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"stock_code": stock_code,
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"raw_analysis": full_content
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})
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# 尝试从分析内容中提取投资建议
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recommendation = self._extract_recommendation(full_content)
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# 计算分析评分
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score = self._calculate_analysis_score(full_content, technical_summary)
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# 发送完成状态和评分、建议
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yield json.dumps({
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"stock_code": stock_code,
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"status": "completed",
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"score": score,
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"recommendation": recommendation
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})
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else:
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# 非流式响应处理
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response = await client.post(api_url, json=request_data, headers=headers)
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@@ -229,32 +296,100 @@ class AIAnalyzer:
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error_data = response.json()
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error_message = error_data.get('error', {}).get('message', '未知错误')
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logger.error(f"AI API请求失败: {response.status_code} - {error_message}")
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yield json.dumps({"error": f"API请求失败: {error_message}"})
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yield json.dumps({
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"stock_code": stock_code,
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"error": f"API请求失败: {error_message}",
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"status": "error"
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})
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return
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response_data = response.json()
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analysis_text = response_data.get("choices", [{}])[0].get("message", {}).get("content", "")
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try:
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# 尝试解析JSON
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analysis_json = json.loads(analysis_text)
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yield json.dumps({
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"stock_code": stock_code,
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"analysis": analysis_json
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})
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except json.JSONDecodeError:
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# 返回原始文本
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yield json.dumps({
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"stock_code": stock_code,
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"raw_analysis": analysis_text
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})
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logger.info(f"完成对 {stock_code} 的AI分析")
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# 尝试从分析内容中提取投资建议
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recommendation = self._extract_recommendation(analysis_text)
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# 计算分析评分
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score = self._calculate_analysis_score(analysis_text, technical_summary)
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# 发送完整的分析结果
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yield json.dumps({
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"stock_code": stock_code,
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"status": "completed",
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"analysis": analysis_text,
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"score": score,
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"recommendation": recommendation,
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"rsi": rsi,
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"price": price,
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"price_change": price_change,
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"ma_trend": ma_trend,
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"macd_signal": macd_signal_type,
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"volume_status": volume_status,
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"analysis_date": analysis_date
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})
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except Exception as e:
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logger.error(f"AI分析 {stock_code} 时出错: {str(e)}")
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logger.exception(e)
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yield json.dumps({"error": f"分析出错: {str(e)}"})
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logger.error(f"AI分析出错: {str(e)}", exc_info=True)
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yield json.dumps({
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"stock_code": stock_code,
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"error": f"分析出错: {str(e)}",
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"status": "error"
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})
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def _extract_recommendation(self, analysis_text: str) -> str:
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"""从分析文本中提取投资建议"""
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# 查找投资建议部分
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investment_advice_pattern = r"##\s*投资建议\s*\n(.*?)(?:\n##|\Z)"
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match = re.search(investment_advice_pattern, analysis_text, re.DOTALL)
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if match:
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advice_text = match.group(1).strip()
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# 提取关键建议
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if "买入" in advice_text or "增持" in advice_text:
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return "买入"
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elif "卖出" in advice_text or "减持" in advice_text:
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return "卖出"
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elif "持有" in advice_text:
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return "持有"
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else:
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return "观望"
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return "观望" # 默认建议
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def _calculate_analysis_score(self, analysis_text: str, technical_summary: dict) -> int:
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"""计算分析评分"""
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score = 50 # 基础分数
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# 根据技术指标调整分数
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if technical_summary['trend'] == 'upward':
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score += 10
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else:
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score -= 10
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if technical_summary['volume_trend'] == 'increasing':
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score += 5
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else:
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score -= 5
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rsi = technical_summary['rsi_level']
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if rsi < 30: # 超卖
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score += 15
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elif rsi > 70: # 超买
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score -= 15
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# 根据分析文本中的关键词调整分数
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if "强烈买入" in analysis_text or "显著上涨" in analysis_text:
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score += 20
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elif "买入" in analysis_text or "看涨" in analysis_text:
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score += 10
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elif "强烈卖出" in analysis_text or "显著下跌" in analysis_text:
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score -= 20
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elif "卖出" in analysis_text or "看跌" in analysis_text:
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score -= 10
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# 确保分数在0-100范围内
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return max(0, min(100, score))
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def _truncate_json_for_logging(self, json_obj, max_length=500):
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"""
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@@ -159,10 +159,10 @@ class StockAnalyzerService:
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try:
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logger.info(f"开始批量扫描 {len(stock_codes)} 只股票, 市场: {market_type}")
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# 输出初始状态
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# 输出初始状态 - 发送批量分析初始化消息
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yield json.dumps({
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"status": "scanning",
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"total_stocks": len(stock_codes),
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"stream_type": "batch",
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"stock_codes": stock_codes,
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"market_type": market_type,
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"min_score": min_score
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})
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@@ -177,6 +177,12 @@ class StockAnalyzerService:
|
||||
stock_with_indicators[code] = self.indicator.calculate_indicators(df)
|
||||
except Exception as e:
|
||||
logger.error(f"计算 {code} 技术指标时出错: {str(e)}")
|
||||
# 发送错误状态
|
||||
yield json.dumps({
|
||||
"stock_code": code,
|
||||
"error": f"计算技术指标时出错: {str(e)}",
|
||||
"status": "error"
|
||||
})
|
||||
|
||||
# 评分股票
|
||||
results = self.scorer.batch_score_stocks(stock_with_indicators)
|
||||
@@ -184,30 +190,39 @@ class StockAnalyzerService:
|
||||
# 过滤低于最低评分的股票
|
||||
filtered_results = [r for r in results if r[1] >= min_score]
|
||||
|
||||
# 输出评分结果
|
||||
yield json.dumps({
|
||||
"scan_results": [
|
||||
{
|
||||
# 为每只股票发送基本评分和推荐信息
|
||||
for code, score, rec in results:
|
||||
df = stock_with_indicators.get(code)
|
||||
if df is not None and len(df) > 0:
|
||||
# 获取最新数据
|
||||
latest_data = df.iloc[-1]
|
||||
|
||||
# 发送股票基本信息和评分
|
||||
yield json.dumps({
|
||||
"stock_code": code,
|
||||
"score": score,
|
||||
"recommendation": rec
|
||||
} for code, score, rec in filtered_results
|
||||
],
|
||||
"total_matched": len(filtered_results),
|
||||
"total_scanned": len(results)
|
||||
})
|
||||
"recommendation": rec,
|
||||
"price": float(latest_data.get('Close', 0)),
|
||||
"price_change": float(latest_data.get('Change', 0)),
|
||||
"rsi": float(latest_data.get('RSI', 0)) if 'RSI' in latest_data else None,
|
||||
"ma_trend": "UP" if latest_data.get('MA5', 0) > latest_data.get('MA20', 0) else "DOWN",
|
||||
"macd_signal": "BUY" if latest_data.get('MACD', 0) > latest_data.get('MACD_Signal', 0) else "SELL",
|
||||
"volume_status": "HIGH" if latest_data.get('Volume_Ratio', 1) > 1.5 else ("LOW" if latest_data.get('Volume_Ratio', 1) < 0.5 else "NORMAL"),
|
||||
"status": "completed" if score < min_score else "waiting"
|
||||
})
|
||||
|
||||
# 如果需要进一步分析，对评分较高的股票进行AI分析
|
||||
if stream and filtered_results:
|
||||
top_stocks = filtered_results[:3] # 只分析前3只评分最高的股票
|
||||
# 只分析前5只评分最高的股票，避免分析过多导致前端卡顿
|
||||
top_stocks = filtered_results[:5]
|
||||
|
||||
for stock_code, score, _ in top_stocks:
|
||||
df = stock_with_indicators.get(stock_code)
|
||||
if df is not None:
|
||||
# 输出正在分析的股票信息
|
||||
yield json.dumps({
|
||||
"analyzing": stock_code,
|
||||
"score": score
|
||||
"stock_code": stock_code,
|
||||
"status": "analyzing"
|
||||
})
|
||||
|
||||
# AI分析
|
||||
@@ -216,7 +231,7 @@ class StockAnalyzerService:
|
||||
|
||||
# 输出扫描完成信息
|
||||
yield json.dumps({
|
||||
"status": "completed",
|
||||
"scan_completed": True,
|
||||
"total_scanned": len(results),
|
||||
"total_matched": len(filtered_results)
|
||||
})
|
||||
|
||||
Reference in New Issue
Block a user