285 lines
12 KiB
Python
285 lines
12 KiB
Python
import json
|
||
from datetime import datetime
|
||
from typing import List, AsyncGenerator
|
||
from utils.logger import get_logger
|
||
from services.stock_data_provider import StockDataProvider
|
||
from services.technical_indicator import TechnicalIndicator
|
||
from services.stock_scorer import StockScorer
|
||
from services.ai_analyzer import AIAnalyzer
|
||
|
||
# 获取日志器
|
||
logger = get_logger()
|
||
|
||
class StockAnalyzerService:
|
||
"""
|
||
股票分析服务
|
||
作为门面类协调数据提供、指标计算、评分和AI分析等组件
|
||
"""
|
||
|
||
def __init__(self, custom_api_url=None, custom_api_key=None, custom_api_model=None, custom_api_timeout=None):
|
||
"""
|
||
初始化股票分析服务
|
||
|
||
Args:
|
||
custom_api_url: 自定义API URL
|
||
custom_api_key: 自定义API密钥
|
||
custom_api_model: 自定义API模型
|
||
custom_api_timeout: 自定义API超时时间
|
||
"""
|
||
# 初始化各个组件
|
||
self.data_provider = StockDataProvider()
|
||
self.indicator = TechnicalIndicator()
|
||
self.scorer = StockScorer()
|
||
self.ai_analyzer = AIAnalyzer(
|
||
custom_api_url=custom_api_url,
|
||
custom_api_key=custom_api_key,
|
||
custom_api_model=custom_api_model,
|
||
custom_api_timeout=custom_api_timeout
|
||
)
|
||
|
||
logger.info("初始化StockAnalyzerService完成")
|
||
|
||
async def analyze_stock(self, stock_code: str, market_type: str = 'A', stream: bool = False) -> AsyncGenerator[str, None]:
|
||
"""
|
||
分析单只股票
|
||
|
||
Args:
|
||
stock_code: 股票代码
|
||
market_type: 市场类型，默认为'A'股
|
||
stream: 是否使用流式响应
|
||
|
||
Returns:
|
||
异步生成器，生成分析结果的JSON字符串
|
||
"""
|
||
try:
|
||
logger.info(f"开始分析股票: {stock_code}, 市场: {market_type}")
|
||
|
||
# 获取股票数据
|
||
df = await self.data_provider.get_stock_data(stock_code, market_type)
|
||
|
||
# 检查是否有错误
|
||
if hasattr(df, 'error'):
|
||
error_msg = df.error
|
||
logger.error(f"获取股票数据时出错: {error_msg}")
|
||
yield json.dumps({
|
||
"stock_code": stock_code,
|
||
"market_type": market_type,
|
||
"error": error_msg,
|
||
"status": "error"
|
||
})
|
||
return
|
||
|
||
# 检查数据是否为空
|
||
if df.empty:
|
||
error_msg = f"获取到的股票 {stock_code} 数据为空"
|
||
logger.error(error_msg)
|
||
yield json.dumps({
|
||
"stock_code": stock_code,
|
||
"market_type": market_type,
|
||
"error": error_msg,
|
||
"status": "error"
|
||
})
|
||
return
|
||
|
||
# 计算技术指标
|
||
df_with_indicators = self.indicator.calculate_indicators(df)
|
||
|
||
# 计算评分
|
||
score = self.scorer.calculate_score(df_with_indicators)
|
||
recommendation = self.scorer.get_recommendation(score)
|
||
|
||
# 获取最新数据
|
||
latest_data = df_with_indicators.iloc[-1]
|
||
previous_data = df_with_indicators.iloc[-2] if len(df_with_indicators) > 1 else latest_data
|
||
|
||
# 价格变动绝对值
|
||
price_change_value = latest_data['Close'] - previous_data['Close']
|
||
|
||
# 优先使用原始数据中的涨跌幅(Change_pct)
|
||
change_percent = latest_data.get('Change_pct')
|
||
|
||
# 如果原始数据中没有涨跌幅，才进行计算
|
||
if change_percent is None and previous_data['Close'] != 0:
|
||
change_percent = (price_change_value / previous_data['Close']) * 100
|
||
|
||
# 确定MA趋势
|
||
ma_short = latest_data.get('MA5', 0)
|
||
ma_medium = latest_data.get('MA20', 0)
|
||
ma_long = latest_data.get('MA60', 0)
|
||
|
||
if ma_short > ma_medium > ma_long:
|
||
ma_trend = "UP"
|
||
elif ma_short < ma_medium < ma_long:
|
||
ma_trend = "DOWN"
|
||
else:
|
||
ma_trend = "FLAT"
|
||
|
||
# 确定MACD信号
|
||
macd = latest_data.get('MACD', 0)
|
||
signal = latest_data.get('Signal', 0)
|
||
|
||
if macd > signal:
|
||
macd_signal = "BUY"
|
||
elif macd < signal:
|
||
macd_signal = "SELL"
|
||
else:
|
||
macd_signal = "HOLD"
|
||
|
||
# 确定成交量状态
|
||
volume = latest_data.get('Volume', 0)
|
||
volume_ma = latest_data.get('Volume_MA', 0)
|
||
|
||
if volume > volume_ma * 1.5:
|
||
volume_status = "HIGH"
|
||
elif volume < volume_ma * 0.5:
|
||
volume_status = "LOW"
|
||
else:
|
||
volume_status = "NORMAL"
|
||
|
||
# 当前分析日期
|
||
analysis_date = datetime.now().strftime('%Y-%m-%d')
|
||
|
||
# 生成基本分析结果
|
||
basic_result = {
|
||
"stock_code": stock_code,
|
||
"market_type": market_type,
|
||
"analysis_date": analysis_date,
|
||
"score": score,
|
||
"price": latest_data['Close'],
|
||
"price_change_value": price_change_value, # 价格变动绝对值
|
||
"price_change": change_percent, # 兼容旧版前端，传递涨跌幅
|
||
"change_percent": change_percent, # 涨跌幅百分比，新字段
|
||
"ma_trend": ma_trend,
|
||
"rsi": latest_data.get('RSI', 0),
|
||
"macd_signal": macd_signal,
|
||
"volume_status": volume_status,
|
||
"recommendation": recommendation,
|
||
"ai_analysis": ""
|
||
}
|
||
|
||
# 输出基本分析结果
|
||
logger.info(f"基本分析结果: {json.dumps(basic_result)}")
|
||
yield json.dumps(basic_result)
|
||
|
||
# 使用AI进行深入分析
|
||
async for analysis_chunk in self.ai_analyzer.get_ai_analysis(df_with_indicators, stock_code, market_type, stream):
|
||
yield analysis_chunk
|
||
|
||
logger.info(f"完成股票分析: {stock_code}")
|
||
|
||
except Exception as e:
|
||
error_msg = f"分析股票 {stock_code} 时出错: {str(e)}"
|
||
logger.error(error_msg)
|
||
logger.exception(e)
|
||
yield json.dumps({"error": error_msg})
|
||
|
||
async def scan_stocks(self, stock_codes: List[str], market_type: str = 'A', min_score: int = 0, stream: bool = False) -> AsyncGenerator[str, None]:
|
||
"""
|
||
批量扫描股票
|
||
|
||
Args:
|
||
stock_codes: 股票代码列表
|
||
market_type: 市场类型
|
||
min_score: 最低评分阈值
|
||
stream: 是否使用流式响应
|
||
|
||
Returns:
|
||
异步生成器，生成扫描结果的JSON字符串
|
||
"""
|
||
try:
|
||
logger.info(f"开始批量扫描 {len(stock_codes)} 只股票, 市场: {market_type}")
|
||
|
||
# 输出初始状态 - 发送批量分析初始化消息
|
||
yield json.dumps({
|
||
"stream_type": "batch",
|
||
"stock_codes": stock_codes,
|
||
"market_type": market_type,
|
||
"min_score": min_score
|
||
})
|
||
|
||
# 批量获取股票数据
|
||
stock_data_dict = await self.data_provider.get_multiple_stocks_data(stock_codes, market_type)
|
||
|
||
# 计算技术指标
|
||
stock_with_indicators = {}
|
||
for code, df in stock_data_dict.items():
|
||
try:
|
||
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)
|
||
|
||
# 过滤低于最低评分的股票
|
||
filtered_results = [r for r in results if r[1] >= min_score]
|
||
|
||
# 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]
|
||
previous_data = df.iloc[-2] if len(df) > 1 else latest_data
|
||
|
||
# 价格变动绝对值
|
||
price_change_value = latest_data['Close'] - previous_data['Close']
|
||
|
||
# 获取涨跌幅
|
||
change_percent = latest_data.get('Change_pct')
|
||
|
||
# 发送股票基本信息和评分
|
||
yield json.dumps({
|
||
"stock_code": code,
|
||
"score": score,
|
||
"recommendation": rec,
|
||
"price": float(latest_data.get('Close', 0)),
|
||
"price_change_value": float(price_change_value), # 价格变动绝对值
|
||
"price_change": change_percent, # 兼容旧版前端，传递涨跌幅
|
||
"change_percent": change_percent, # 涨跌幅百分比，新字段
|
||
"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:
|
||
# 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({
|
||
"stock_code": stock_code,
|
||
"status": "analyzing"
|
||
})
|
||
|
||
# AI分析
|
||
async for analysis_chunk in self.ai_analyzer.get_ai_analysis(df, stock_code, market_type, stream):
|
||
yield analysis_chunk
|
||
|
||
# 输出扫描完成信息
|
||
yield json.dumps({
|
||
"scan_completed": True,
|
||
"total_scanned": len(results),
|
||
"total_matched": len(filtered_results)
|
||
})
|
||
|
||
logger.info(f"完成批量扫描 {len(stock_codes)} 只股票, 符合条件: {len(filtered_results)}")
|
||
|
||
except Exception as e:
|
||
error_msg = f"批量扫描股票时出错: {str(e)}"
|
||
logger.error(error_msg)
|
||
logger.exception(e)
|
||
yield json.dumps({"error": error_msg})
|