fix: 修复外部因素数据源 - 北向资金改为南向资金，修复P0-P7全部外部因素
- P0资金面：DB数据过期时用麦蕊API获取资金流向 - P1市场情绪：去掉turnover字段依赖（DB无此字段） - P2南向资金：北向实时数据已停公布，改用南向资金(港股通)替代 - P3美股：用腾讯财经API替代失效的AKShare接口 - P4大宗商品：用腾讯财经API替代，修复var_name解析 - P7汇率：用新浪财经API替代失效的AKShare接口 - 修复评分详情API技术得分硬编码问题 - 前端传入tech_score参数确保明细与列表分数一致
This commit is contained in:
selfrelease
@@ -7,7 +7,7 @@
|
||||
- 技术面评分（compute_deep_analysis 原始分）：基础分（0-100）
|
||||
- P0 资金面：±20
|
||||
- P1 市场情绪：±10
|
||||
- P2 北向资金：±10
|
||||
- P2 南向资金：±10
|
||||
- P3 美股外盘：±10
|
||||
- P4 大宗商品：±5
|
||||
- P5 公告/异动：±15
|
||||
@@ -17,9 +17,96 @@
|
||||
最终评分 = 技术面基础分 + 外部因素加减分（上限100，下限0）
|
||||
"""
|
||||
import logging
|
||||
import time
|
||||
from datetime import datetime
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ═══════════════════════════════════════════════════════
|
||||
# 市场级因素缓存（开市前预计算，日内复用）
|
||||
# ═══════════════════════════════════════════════════════
|
||||
_market_cache = {
|
||||
'data': None, # (market_score, market_factors, market_reasons, summaries)
|
||||
'timestamp': 0, # 计算时间戳
|
||||
'ttl': 4 * 3600, # 缓存有效期 4 小时
|
||||
}
|
||||
|
||||
# 个股资金面缓存（30分钟 TTL）
|
||||
_fund_flow_cache = {}
|
||||
_FUND_FLOW_TTL = 30 * 60
|
||||
|
||||
|
||||
def precompute_market_factors():
|
||||
"""预计算市场级外部因素并缓存（供定时任务在开市前调用）。
|
||||
|
||||
计算 P1 市场情绪、P2 南向、P3 美股、P4 商品、P7 汇率、P6 政策面，
|
||||
结果存入内存缓存，后续 compute_comprehensive_score_batch 直接复用。
|
||||
"""
|
||||
market_score = 0
|
||||
market_factors = {}
|
||||
market_reasons = []
|
||||
summaries = []
|
||||
|
||||
# P1: 市场情绪
|
||||
try:
|
||||
from services.market_sentiment import calc_market_sentiment
|
||||
sentiment_result = calc_market_sentiment()
|
||||
market_factors['market_sentiment'] = sentiment_result
|
||||
market_score += sentiment_result.get('score', 0)
|
||||
market_reasons.extend(sentiment_result.get('reasons', []))
|
||||
s = sentiment_result.get('sentiment', '')
|
||||
if s and '无数据' not in s:
|
||||
summaries.append(
|
||||
f'市场情绪：{s}（涨跌停{sentiment_result.get("limit_up_count", 0)}:'
|
||||
f'{sentiment_result.get("limit_down_count", 0)}）'
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"预计算P1市场情绪失败: {e}")
|
||||
market_factors['market_sentiment'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
|
||||
|
||||
# P2/P3/P4/P7: 外部因素（南向/美股/商品/汇率）
|
||||
try:
|
||||
from services.external_factors import get_all_external_factors
|
||||
ext_result = get_all_external_factors()
|
||||
market_factors['external'] = ext_result
|
||||
market_score += ext_result.get('total_score', 0)
|
||||
market_reasons.extend(ext_result.get('all_reasons', []))
|
||||
s = ext_result.get('summary', '')
|
||||
if s:
|
||||
summaries.append(s)
|
||||
except Exception as e:
|
||||
logger.warning(f"预计算P2-P7外部因素失败: {e}")
|
||||
market_factors['external'] = {'total_score': 0, 'summary': '分析失败', 'all_reasons': []}
|
||||
|
||||
# P6: 政策面（市场级，只算一次）
|
||||
try:
|
||||
from services.news_analyzer import get_policy_news, analyze_policy_impact
|
||||
policy_news = get_policy_news(days=3)
|
||||
policy_result = analyze_policy_impact(policy_news)
|
||||
market_factors['policy'] = policy_result
|
||||
market_score += policy_result.get('score', 0)
|
||||
market_reasons.extend(policy_result.get('reasons', []))
|
||||
s = policy_result.get('summary', '')
|
||||
if s and '失败' not in s:
|
||||
summaries.append(f'政策面：{s}')
|
||||
except Exception as e:
|
||||
logger.warning(f"预计算P6政策面失败: {e}")
|
||||
market_factors['policy'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
|
||||
|
||||
_market_cache['data'] = (market_score, market_factors, market_reasons, summaries)
|
||||
_market_cache['timestamp'] = time.time()
|
||||
print(f'[评分引擎] 市场级因素预计算完成 (score={market_score}, {datetime.now():%H:%M:%S})')
|
||||
return market_score, market_factors, market_reasons, summaries
|
||||
|
||||
|
||||
def _get_market_factors():
|
||||
"""获取市场级因素（优先读缓存，过期则重新计算）"""
|
||||
now = time.time()
|
||||
if _market_cache['data'] is not None and (now - _market_cache['timestamp']) < _market_cache['ttl']:
|
||||
return _market_cache['data']
|
||||
# 缓存不存在或过期，重新计算
|
||||
return precompute_market_factors()
|
||||
|
||||
|
||||
def compute_comprehensive_score(stock_code, stock_name, technical_score, df=None):
|
||||
"""
|
||||
@@ -75,7 +162,7 @@ def compute_comprehensive_score(stock_code, stock_name, technical_score, df=None
|
||||
logger.warning(f"P1市场情绪分析失败: {e}")
|
||||
factors['market_sentiment'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
|
||||
|
||||
# ---- P2/P3/P4/P7: 外部因素（北向/美股/商品/汇率）----
|
||||
# ---- P2/P3/P4/P7: 外部因素（南向/美股/商品/汇率）----
|
||||
try:
|
||||
from services.external_factors import get_all_external_factors
|
||||
ext_result = get_all_external_factors()
|
||||
@@ -139,7 +226,7 @@ def compute_comprehensive_score_batch(stocks_data):
|
||||
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。
|
||||
|
||||
优化点：
|
||||
- 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
|
||||
- P1 市场情绪、P2 南向、P3 美股、P4 商品、P7 汇率、P6 政策 → 市场级，只算一次
|
||||
- P0 资金面 → 个股级，逐只从DB读取
|
||||
- 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
|
||||
|
||||
@@ -152,57 +239,8 @@ def compute_comprehensive_score_batch(stocks_data):
|
||||
返回:
|
||||
dict: {stock_code: {technical_score, external_score, final_score, verdict, factors, all_reasons, summary}}
|
||||
"""
|
||||
# ---- 市场级因素（只计算一次）----
|
||||
market_score = 0
|
||||
market_factors = {}
|
||||
market_reasons = []
|
||||
summaries = []
|
||||
|
||||
# P1: 市场情绪
|
||||
try:
|
||||
from services.market_sentiment import calc_market_sentiment
|
||||
sentiment_result = calc_market_sentiment()
|
||||
market_factors['market_sentiment'] = sentiment_result
|
||||
market_score += sentiment_result.get('score', 0)
|
||||
market_reasons.extend(sentiment_result.get('reasons', []))
|
||||
s = sentiment_result.get('sentiment', '')
|
||||
if s and '无数据' not in s:
|
||||
summaries.append(
|
||||
f'市场情绪：{s}（涨跌停{sentiment_result.get("limit_up_count", 0)}:'
|
||||
f'{sentiment_result.get("limit_down_count", 0)}）'
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"批量P1市场情绪分析失败: {e}")
|
||||
market_factors['market_sentiment'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
|
||||
|
||||
# P2/P3/P4/P7: 外部因素（北向/美股/商品/汇率）
|
||||
try:
|
||||
from services.external_factors import get_all_external_factors
|
||||
ext_result = get_all_external_factors()
|
||||
market_factors['external'] = ext_result
|
||||
market_score += ext_result.get('total_score', 0)
|
||||
market_reasons.extend(ext_result.get('all_reasons', []))
|
||||
s = ext_result.get('summary', '')
|
||||
if s:
|
||||
summaries.append(s)
|
||||
except Exception as e:
|
||||
logger.warning(f"批量P2-P7外部因素分析失败: {e}")
|
||||
market_factors['external'] = {'total_score': 0, 'summary': '分析失败', 'all_reasons': []}
|
||||
|
||||
# P6: 政策面（市场级，只算一次）
|
||||
try:
|
||||
from services.news_analyzer import get_policy_news, analyze_policy_impact
|
||||
policy_news = get_policy_news(days=3)
|
||||
policy_result = analyze_policy_impact(policy_news)
|
||||
market_factors['policy'] = policy_result
|
||||
market_score += policy_result.get('score', 0)
|
||||
market_reasons.extend(policy_result.get('reasons', []))
|
||||
s = policy_result.get('summary', '')
|
||||
if s and '失败' not in s:
|
||||
summaries.append(f'政策面：{s}')
|
||||
except Exception as e:
|
||||
logger.warning(f"批量P6政策面分析失败: {e}")
|
||||
market_factors['policy'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
|
||||
# ---- 市场级因素（从缓存读取，开市前由定时任务预计算）----
|
||||
market_score, market_factors, market_reasons, summaries = _get_market_factors()
|
||||
|
||||
# ---- 为每只股票计算个股级因素 ----
|
||||
results = {}
|
||||
@@ -219,10 +257,16 @@ def compute_comprehensive_score_batch(stocks_data):
|
||||
}
|
||||
stock_reasons = list(market_reasons)
|
||||
|
||||
# P0: 资金面（个股级，从DB读取）
|
||||
# P0: 资金面（个股级，从DB读取，带30分钟缓存）
|
||||
try:
|
||||
from services.fund_flow_analyzer import analyze_fund_flow
|
||||
fund_result = analyze_fund_flow(code, days=5)
|
||||
now = time.time()
|
||||
cached_ff = _fund_flow_cache.get(code)
|
||||
if cached_ff and (now - cached_ff[1]) < _FUND_FLOW_TTL:
|
||||
fund_result = cached_ff[0]
|
||||
else:
|
||||
from services.fund_flow_analyzer import analyze_fund_flow
|
||||
fund_result = analyze_fund_flow(code, days=5)
|
||||
_fund_flow_cache[code] = (fund_result, now)
|
||||
stock_factors['fund_flow'] = fund_result
|
||||
stock_external += fund_result.get('score', 0)
|
||||
stock_reasons.extend(fund_result.get('reasons', []))
|
||||
|
||||
Reference in New Issue
Block a user