selfrelease
266 lines
10 KiB
Python
266 lines
10 KiB
Python
"""
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综合评分引擎 — 整合所有影响因素到统一评分体系
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将技术面（基础50%+）与外部因素（加减分项）整合为最终评分。
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权重分配：
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- 技术面评分（compute_deep_analysis 原始分）：基础分（0-100）
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- P0 资金面：±20
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- P1 市场情绪：±10
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- P2 北向资金：±10
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- P3 美股外盘：±10
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- P4 大宗商品：±5
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- P5 公告/异动：±15
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- P6 政策面：±10
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- P7 汇率：±3
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最终评分 = 技术面基础分 + 外部因素加减分（上限100，下限0）
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"""
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import logging
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logger = logging.getLogger(__name__)
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def compute_comprehensive_score(stock_code, stock_name, technical_score, df=None):
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"""
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综合评分引擎 — 整合技术面和所有外部因素
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参数:
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stock_code: 股票代码
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stock_name: 股票名称
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technical_score: float — 技术面基础评分（0-100，来自 compute_deep_analysis）
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df: K线DataFrame（用于异动检测，可选）
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返回:
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dict: {
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'technical_score': float, # 技术面基础分
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'external_score': int, # 外部因素总加减分
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'final_score': int, # 最终综合评分（0-100）
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'verdict': str, # 最终评级
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'factors': dict, # 各因素详细数据
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'all_reasons': list, # 所有评分原因
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'summary': str, # 综合白话总结
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}
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"""
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factors = {}
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all_reasons = []
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external_score = 0
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summaries = []
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# ---- P0: 主力资金进出 ----
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try:
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from services.fund_flow_analyzer import analyze_fund_flow
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fund_result = analyze_fund_flow(stock_code, days=5)
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factors['fund_flow'] = fund_result
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external_score += fund_result.get('score', 0)
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all_reasons.extend(fund_result.get('reasons', []))
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s = fund_result.get('summary', '')
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if s and '暂无' not in s:
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summaries.append(f'资金面：{s}')
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except Exception as e:
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logger.warning(f"P0资金面分析失败: {e}")
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factors['fund_flow'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
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# ---- P1: 市场情绪指标 ----
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try:
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from services.market_sentiment import calc_market_sentiment
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sentiment_result = calc_market_sentiment()
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factors['market_sentiment'] = sentiment_result
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external_score += sentiment_result.get('score', 0)
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all_reasons.extend(sentiment_result.get('reasons', []))
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s = sentiment_result.get('sentiment', '')
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if s and '无数据' not in s:
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summaries.append(f'市场情绪：{s}（涨跌停{sentiment_result.get("limit_up_count",0)}:{sentiment_result.get("limit_down_count",0)}）')
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except Exception as e:
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logger.warning(f"P1市场情绪分析失败: {e}")
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factors['market_sentiment'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
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# ---- P2/P3/P4/P7: 外部因素（北向/美股/商品/汇率）----
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try:
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from services.external_factors import get_all_external_factors
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ext_result = get_all_external_factors()
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factors['external'] = ext_result
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external_score += ext_result.get('total_score', 0)
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all_reasons.extend(ext_result.get('all_reasons', []))
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s = ext_result.get('summary', '')
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if s:
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summaries.append(s)
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except Exception as e:
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logger.warning(f"P2-P7外部因素分析失败: {e}")
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factors['external'] = {'total_score': 0, 'summary': '分析失败', 'all_reasons': []}
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# ---- P5/P6: 公告/异动/政策 ----
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try:
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from services.news_analyzer import analyze_news_factors
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news_result = analyze_news_factors(stock_code, stock_name, df)
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factors['news'] = news_result
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external_score += news_result.get('total_score', 0)
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all_reasons.extend(news_result.get('all_reasons', []))
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s = news_result.get('summary', '')
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if s:
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summaries.append(s)
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except Exception as e:
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logger.warning(f"P5/P6消息面分析失败: {e}")
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factors['news'] = {'total_score': 0, 'summary': '分析失败', 'all_reasons': []}
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# ---- 最终评分 ----
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# 外部因素加减分上限：±40（避免喧宾夺主）
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external_score = max(-40, min(40, external_score))
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final_score = max(0, min(100, int(technical_score + external_score)))
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# 最终评级
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if final_score >= 80:
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verdict = '强烈看多'
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elif final_score >= 65:
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verdict = '看多'
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elif final_score >= 50:
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verdict = '中性偏多'
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elif final_score >= 35:
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verdict = '中性偏空'
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else:
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verdict = '看空'
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# 综合总结
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summary = ' | '.join(summaries) if summaries else '暂无外部因素数据'
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return {
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'technical_score': round(technical_score, 0),
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'external_score': external_score,
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'final_score': final_score,
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'verdict': verdict,
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'factors': factors,
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'all_reasons': all_reasons,
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'summary': summary,
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}
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def compute_comprehensive_score_batch(stocks_data):
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"""
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批量计算综合评分 — 市场级因素只计算一次，个股级因素逐只计算。
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优化点：
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- P1 市场情绪、P2 北向、P3 美股、P4 商品、P7 汇率、P6 政策 → 市场级，只算一次
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- P0 资金面 → 个股级，逐只从DB读取
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- P5 公告/异动 → 批量模式跳过（需AKShare API + LLM，太慢），在深度分析时补充
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参数:
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stocks_data: list[dict]，每个元素包含:
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- stock_code: str 股票代码
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- stock_name: str 股票名称（可选）
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- technical_score: float 技术得分（0-100）
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返回:
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dict: {stock_code: {technical_score, external_score, final_score, verdict, factors, all_reasons, summary}}
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"""
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# ---- 市场级因素（只计算一次）----
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market_score = 0
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market_factors = {}
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market_reasons = []
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summaries = []
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# P1: 市场情绪
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try:
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from services.market_sentiment import calc_market_sentiment
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sentiment_result = calc_market_sentiment()
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market_factors['market_sentiment'] = sentiment_result
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market_score += sentiment_result.get('score', 0)
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market_reasons.extend(sentiment_result.get('reasons', []))
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s = sentiment_result.get('sentiment', '')
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if s and '无数据' not in s:
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summaries.append(
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f'市场情绪：{s}（涨跌停{sentiment_result.get("limit_up_count", 0)}:'
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f'{sentiment_result.get("limit_down_count", 0)}）'
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)
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except Exception as e:
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logger.warning(f"批量P1市场情绪分析失败: {e}")
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market_factors['market_sentiment'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
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# P2/P3/P4/P7: 外部因素（北向/美股/商品/汇率）
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try:
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from services.external_factors import get_all_external_factors
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ext_result = get_all_external_factors()
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market_factors['external'] = ext_result
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market_score += ext_result.get('total_score', 0)
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market_reasons.extend(ext_result.get('all_reasons', []))
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s = ext_result.get('summary', '')
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if s:
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summaries.append(s)
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except Exception as e:
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logger.warning(f"批量P2-P7外部因素分析失败: {e}")
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market_factors['external'] = {'total_score': 0, 'summary': '分析失败', 'all_reasons': []}
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# P6: 政策面（市场级，只算一次）
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try:
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from services.news_analyzer import get_policy_news, analyze_policy_impact
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policy_news = get_policy_news(days=3)
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policy_result = analyze_policy_impact(policy_news)
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market_factors['policy'] = policy_result
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market_score += policy_result.get('score', 0)
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market_reasons.extend(policy_result.get('reasons', []))
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s = policy_result.get('summary', '')
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if s and '失败' not in s:
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summaries.append(f'政策面：{s}')
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except Exception as e:
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logger.warning(f"批量P6政策面分析失败: {e}")
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market_factors['policy'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
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# ---- 为每只股票计算个股级因素 ----
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results = {}
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for stock in stocks_data:
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code = stock.get('stock_code', '')
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name = stock.get('stock_name', '')
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tech_score = stock.get('technical_score', 50)
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stock_external = market_score
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stock_factors = {
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'market_sentiment': market_factors.get('market_sentiment', {}),
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'external': market_factors.get('external', {}),
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'policy': market_factors.get('policy', {}),
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}
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stock_reasons = list(market_reasons)
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# P0: 资金面（个股级，从DB读取）
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try:
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from services.fund_flow_analyzer import analyze_fund_flow
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fund_result = analyze_fund_flow(code, days=5)
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stock_factors['fund_flow'] = fund_result
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stock_external += fund_result.get('score', 0)
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stock_reasons.extend(fund_result.get('reasons', []))
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except Exception as e:
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logger.warning(f"批量P0资金面分析失败 {code}: {e}")
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stock_factors['fund_flow'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
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# P5: 公告/异动 — 批量模式跳过（需AKShare API + LLM，在深度分析时补充）
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stock_factors['news'] = {
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'total_score': 0, 'summary': '批量模式跳过，请使用深度分析查看',
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'all_reasons': [],
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}
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# 外部得分上限 ±40
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stock_external = max(-40, min(40, stock_external))
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final_score = max(0, min(100, int(tech_score + stock_external)))
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# 评级
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if final_score >= 80:
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verdict = '强烈看多'
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elif final_score >= 65:
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verdict = '看多'
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elif final_score >= 50:
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verdict = '中性偏多'
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elif final_score >= 35:
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verdict = '中性偏空'
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else:
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verdict = '看空'
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results[code] = {
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'technical_score': round(tech_score, 0),
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'external_score': stock_external,
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'final_score': final_score,
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'verdict': verdict,
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'factors': stock_factors,
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'all_reasons': stock_reasons,
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'summary': ' | '.join(summaries) if summaries else '',
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}
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return results
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