selfrelease
79e869eeda
- 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
627 lines
23 KiB
Python
627 lines
23 KiB
Python
"""
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外部因素分析模块（P2/P3/P4/P7）
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包含：
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- P2: 南向资金（港股通跨境资金动向）
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- P3: 美股隔夜板块变化
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- P4: 大宗商品价格
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- P7: 汇率变化
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数据源：
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- P2: AKShare stock_hsgt_hist_em（南向资金历史）+ stock_hsgt_fund_flow_summary_em（今日汇总）
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- P3: 腾讯财经API（美股指数实时）
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- P4: 腾讯财经API（商品期货实时）
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- P7: 新浪财经API（人民币汇率）
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所有数据采集均带超时和异常处理，失败时返回中性评分不影响主流程。
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"""
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import logging
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import requests
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from datetime import datetime, timedelta
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logger = logging.getLogger(__name__)
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# 缓存（当日有效）
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_cache = {}
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_cache_date = {}
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def _get_cache(key):
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"""获取当日缓存"""
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today = datetime.now().strftime('%Y-%m-%d')
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if _cache_date.get(key) == today:
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return _cache.get(key)
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return None
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def _set_cache(key, value):
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"""设置当日缓存"""
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today = datetime.now().strftime('%Y-%m-%d')
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_cache[key] = value
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_cache_date[key] = today
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# ═══════════════════════════════════════════════
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# P2: 南向资金
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# ═══════════════════════════════════════════════
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def get_southbound_capital():
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"""
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获取跨境资金流向数据
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南向资金（港股通）反映内地资金配置港股的意愿，是跨境资金情绪的重要指标：
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- 南向净流入 > 0：内地资金积极配置港股，大中华区risk-on，对A股偏正面
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- 南向净流入 < 0：内地资金撤出港股，risk-off，对A股偏负面
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数据源：
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1. AKShare stock_hsgt_fund_flow_summary_em（今日汇总）
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2. AKShare stock_hsgt_hist_em（南向资金历史，用于连续天数计算）
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返回:
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dict: {
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'net_inflow': float, # 今日南向净流入（亿）
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'score': int, # 评分增减（-10 ~ +10）
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'summary': str, # 白话总结
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'reasons': list, # 评分原因
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}
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"""
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cached = _get_cache('southbound')
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if cached:
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return cached
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try:
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import akshare as ak
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import pandas as pd
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# 1. 用 stock_hsgt_fund_flow_summary_em 获取今日汇总
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today_inflow = 0
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try:
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df_summary = ak.stock_hsgt_fund_flow_summary_em()
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if df_summary is not None and not df_summary.empty:
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# 筛选南向资金行
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south_rows = df_summary[df_summary['资金方向'] == '南向']
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if not south_rows.empty:
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# 成交净买额列求和
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vals = south_rows['成交净买额'].tolist()
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today_inflow = float(sum(v for v in vals if pd.notna(v) and v != 0))
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except Exception as e:
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logger.debug(f"stock_hsgt_fund_flow_summary_em失败: {e}")
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# 2. 用 stock_hsgt_hist_em 获取南向资金历史（计算连续天数）
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consecutive_inflow = 0
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consecutive_outflow = 0
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try:
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df = ak.stock_hsgt_hist_em(symbol="南向资金")
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if df is not None and not df.empty:
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recent = df.tail(5)
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for _, row in recent[::-1].iterrows():
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val = float(row.get('当日成交净买额', 0) or 0)
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if str(val) == 'nan' or pd.isna(val):
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val = 0
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if val > 0:
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if consecutive_outflow > 0:
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break
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consecutive_inflow += 1
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elif val < 0:
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if consecutive_inflow > 0:
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break
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consecutive_outflow += 1
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except Exception as e:
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logger.debug(f"stock_hsgt_hist_em南向失败: {e}")
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# 如果今日数据也为0或NaN，说明无法获取
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if str(today_inflow) == 'nan' or today_inflow == 0:
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# 尝试从历史数据取最新值
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try:
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df = ak.stock_hsgt_hist_em(symbol="南向资金")
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if df is not None and not df.empty:
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last_val = float(df.iloc[-1].get('当日成交净买额', 0) or 0)
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if str(last_val) != 'nan' and not pd.isna(last_val) and last_val != 0:
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today_inflow = last_val
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except Exception:
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pass
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# 评分（基于南向资金，逻辑与北向一致：净流入=正面，净流出=负面）
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score = 0
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reasons = []
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summary_parts = []
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if today_inflow > 80:
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score += 5
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reasons.append(f'南向今日净流入{today_inflow:.1f}亿(+5)')
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summary_parts.append(f'南向资金大幅流入{today_inflow:.1f}亿元，跨境资金情绪偏暖')
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elif today_inflow > 30:
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score += 3
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reasons.append(f'南向今日净流入{today_inflow:.1f}亿(+3)')
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summary_parts.append(f'南向资金净流入{today_inflow:.1f}亿元')
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elif today_inflow < -80:
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score -= 5
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reasons.append(f'南向今日净流出{abs(today_inflow):.1f}亿(-5)')
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summary_parts.append(f'南向资金大幅流出{abs(today_inflow):.1f}亿元，跨境资金情绪偏冷')
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elif today_inflow < -30:
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score -= 3
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reasons.append(f'南向今日净流出{abs(today_inflow):.1f}亿(-3)')
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summary_parts.append(f'南向资金净流出{abs(today_inflow):.1f}亿元')
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else:
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summary_parts.append(f'南向资金净流入{today_inflow:.1f}亿元，方向不明')
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if consecutive_inflow >= 3:
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score += 3
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reasons.append(f'南向连续{consecutive_inflow}日净流入(+3)')
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summary_parts.append(f'已连续{consecutive_inflow}天流入')
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if consecutive_outflow >= 3:
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score -= 3
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reasons.append(f'南向连续{consecutive_outflow}日净流出(-3)')
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summary_parts.append(f'已连续{consecutive_outflow}天流出')
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score = max(-10, min(10, score))
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result = {
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'net_inflow': round(today_inflow, 2),
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'consecutive_inflow': consecutive_inflow,
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'consecutive_outflow': consecutive_outflow,
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'score': score,
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'summary': '，'.join(summary_parts),
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'reasons': reasons,
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}
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_set_cache('southbound', result)
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return result
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except Exception as e:
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logger.warning(f"获取跨境资金数据失败: {e}")
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return _neutral_result('南向资金数据获取失败')
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# ═══════════════════════════════════════════════
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# P3: 美股隔夜板块变化
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# ═══════════════════════════════════════════════
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# 美股板块 → A股板块映射
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US_A_SECTOR_MAP = {
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'科技': ['半导体', '软件', '消费电子', '芯片', 'IT服务'],
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'新能源车': ['锂电池', '汽车零部件', '新能源车', '充电桩'],
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'金融': ['银行', '保险', '券商'],
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'能源': ['石油开采', '化工', '页岩气'],
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'医药': ['创新药', '医疗器械', '生物制品', 'CXO'],
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'消费': ['白酒', '食品饮料', '零售', '免税'],
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'房地产': ['房地产', '建材', '家居'],
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'工业': ['机械', '军工', '工业4.0'],
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'材料': ['有色金属', '钢铁', '化工新材料'],
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'公用事业': ['电力', '环保', '水务'],
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}
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def get_us_market_overview():
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"""
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获取美股隔夜收盘数据，计算外盘情绪
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数据源：腾讯财经API（qt.gtimg.cn）
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获取道琼斯、纳斯达克、标普500三大指数实时行情。
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返回:
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dict: {
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'indices': dict, # 三大指数涨跌
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'score': int, # 评分增减（-10 ~ +10）
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'summary': str, # 白话总结
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'reasons': list, # 评分原因
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'affected_a_sectors': dict, # 对A股板块的影响
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}
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"""
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cached = _get_cache('us_market')
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if cached:
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return cached
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try:
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# 腾讯财经API获取美股指数
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# 格式: v_usDJI="200~道琼斯~.DJI~price~...~change_pct~..."
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url = 'https://qt.gtimg.cn/q=usDJI,usIXIC,usSPX'
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resp = requests.get(url, timeout=10)
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text = resp.content.decode('gbk', errors='replace')
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us_indices = {}
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for line in text.strip().split(';'):
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line = line.strip()
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if not line or 'v_pv_none_match' in line:
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continue
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# 解析 v_usDJI="..."
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if '=' not in line:
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continue
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var_name = line.split('=')[0].strip().replace('var ', '').replace('v_', '')
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value = line.split('"')[1] if '"' in line else ''
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fields = value.split('~')
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if len(fields) < 33:
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continue
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name = fields[1]
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change_pct = float(fields[32]) if fields[32] else 0
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if 'DJI' in var_name.upper() or '道琼斯' in name:
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us_indices['dow'] = {'name': name, 'change_pct': change_pct}
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elif 'IXIC' in var_name.upper() or '纳斯达克' in name:
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us_indices['nasdaq'] = {'name': name, 'change_pct': change_pct}
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elif 'SPX' in var_name.upper() or '标普' in name:
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us_indices['sp500'] = {'name': name, 'change_pct': change_pct}
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if not us_indices:
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return _neutral_result('未找到美股指数')
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# 计算综合涨跌
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avg_change = sum(v['change_pct'] for v in us_indices.values()) / len(us_indices)
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# 评分
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score = 0
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reasons = []
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summary_parts = []
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if avg_change > 2:
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score += 5
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reasons.append(f'美股三大指数平均涨幅{avg_change:.1f}%(+5)')
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summary_parts.append(f'美股大涨，平均涨幅{avg_change:.1f}%')
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elif avg_change > 0.5:
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score += 2
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reasons.append(f'美股偏强，平均涨幅{avg_change:.1f}%(+2)')
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summary_parts.append(f'美股小幅上涨，平均{avg_change:.1f}%')
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elif avg_change < -2:
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score -= 5
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reasons.append(f'美股三大指数平均跌幅{abs(avg_change):.1f}%(-5)')
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summary_parts.append(f'美股大跌，平均跌幅{abs(avg_change):.1f}%')
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elif avg_change < -0.5:
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score -= 2
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reasons.append(f'美股偏弱，平均跌幅{abs(avg_change):.1f}%(-2)')
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summary_parts.append(f'美股小幅下跌，平均{avg_change:.1f}%')
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else:
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summary_parts.append(f'美股基本平盘，平均变化{avg_change:.1f}%')
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# 对A股板块的影响
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affected_sectors = {}
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if avg_change > 1:
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for us_sector, a_sectors in US_A_SECTOR_MAP.items():
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affected_sectors[us_sector] = {
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'a_sectors': a_sectors,
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'direction': '利好',
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'note': f'美股{us_sector}板块偏强，A股{"、".join(a_sectors[:3])}可能高开',
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}
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elif avg_change < -1:
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for us_sector, a_sectors in US_A_SECTOR_MAP.items():
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affected_sectors[us_sector] = {
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'a_sectors': a_sectors,
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'direction': '利空',
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'note': f'美股{us_sector}板块偏弱，A股{"、".join(a_sectors[:3])}可能低开',
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}
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score = max(-10, min(10, score))
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result = {
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'indices': us_indices,
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'avg_change': round(avg_change, 2),
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'score': score,
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'summary': '，'.join(summary_parts),
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'reasons': reasons,
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'affected_a_sectors': affected_sectors,
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}
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_set_cache('us_market', result)
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return result
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except Exception as e:
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logger.warning(f"获取美股数据失败: {e}")
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return _neutral_result('美股数据获取失败')
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# ═══════════════════════════════════════════════
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# P4: 大宗商品价格
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# ═══════════════════════════════════════════════
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# 大宗商品 → A股板块影响映射
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COMMODITY_A_SECTOR_MAP = {
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'原油': {
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'beneficiary': ['石油开采', '油服', '化工'],
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'victim': ['航空', '物流', '化工下游'],
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'direction': '油价涨→开采受益，航空受损',
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},
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'黄金': {
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'beneficiary': ['黄金股', '珠宝', '有色'],
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'victim': [],
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'direction': '金价涨→黄金企业受益',
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},
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'铜': {
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'beneficiary': ['铜矿', '有色', '电缆'],
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'victim': [],
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'direction': '铜价涨→铜企受益',
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},
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'螺纹钢': {
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'beneficiary': ['钢铁', '钢矿'],
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'victim': ['基建', '地产'],
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'direction': '钢价涨→钢企受益，基建成本增',
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},
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'碳酸锂': {
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'beneficiary': ['锂矿', '锂电池'],
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'victim': ['新能源车'],
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'direction': '锂价涨→锂矿受益，新能源车成本增',
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},
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}
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def get_commodity_overview():
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"""
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获取主要大宗商品价格变化
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数据源：腾讯财经API（qt.gtimg.cn）
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获取纽约黄金、纽约原油、美铜等商品期货实时行情。
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返回:
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dict: {
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'commodities': dict, # 各商品涨跌
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'score': int, # 评分增减（-5 ~ +5）
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'summary': str, # 白话总结
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'reasons': list, # 评分原因
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'affected_sectors': dict, # 对A股板块影响
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}
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"""
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cached = _get_cache('commodity')
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if cached:
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return cached
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try:
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# 腾讯财经API获取商品期货
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# hf_GC=纽约黄金, hf_CL=纽约原油, hf_HG=美铜
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url = 'https://qt.gtimg.cn/q=hf_GC,hf_CL,hf_HG'
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resp = requests.get(url, timeout=10)
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text = resp.content.decode('gbk', errors='replace')
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# 商品名称映射
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symbol_map = {
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'hf_GC': '黄金',
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'hf_CL': '原油',
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'hf_HG': '铜',
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}
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commodities = {}
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for line in text.strip().split(';'):
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line = line.strip()
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if not line or 'v_pv_none_match' in line:
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continue
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if '=' not in line:
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continue
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var_name = line.split('=')[0].strip().replace('var ', '').replace('v_', '')
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value = line.split('"')[1] if '"' in line else ''
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fields = value.split(',')
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if len(fields) < 10:
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continue
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target = symbol_map.get(var_name)
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if not target:
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continue
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# 腾讯商品格式: price,change_pct,prev_close,open,high,low,time,...,name
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current_price = float(fields[0]) if fields[0] else 0
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change_pct = float(fields[1]) if fields[1] else 0
|
||
name = fields[-1].rstrip(';"')
|
||
|
||
commodities[target] = {
|
||
'price': current_price,
|
||
'change_pct': round(change_pct, 2),
|
||
'name': name,
|
||
}
|
||
|
||
if not commodities:
|
||
return _neutral_result('未找到关注的大宗商品')
|
||
|
||
# 评分和影响
|
||
score = 0
|
||
reasons = []
|
||
summary_parts = []
|
||
affected = {}
|
||
|
||
for name, data in commodities.items():
|
||
pct = data['change_pct']
|
||
if abs(pct) < 0.5:
|
||
continue
|
||
|
||
mapping = COMMODITY_A_SECTOR_MAP.get(name)
|
||
if not mapping:
|
||
continue
|
||
|
||
if pct > 2:
|
||
score += 1
|
||
reasons.append(f'{name}涨{pct:.1f}%，利好{"、".join(mapping["beneficiary"][:2])}(+1)')
|
||
summary_parts.append(f'{name}大涨{pct:.1f}%')
|
||
affected[name] = {
|
||
'direction': '利好',
|
||
'beneficiary': mapping['beneficiary'],
|
||
'victim': mapping['victim'],
|
||
'note': mapping['direction'],
|
||
}
|
||
elif pct < -2:
|
||
score -= 1
|
||
reasons.append(f'{name}跌{abs(pct):.1f}%，利空{"、".join(mapping["beneficiary"][:2])}(-1)')
|
||
summary_parts.append(f'{name}大跌{pct:.1f}%')
|
||
affected[name] = {
|
||
'direction': '利空',
|
||
'beneficiary': mapping['victim'],
|
||
'victim': mapping['beneficiary'],
|
||
'note': mapping['direction'],
|
||
}
|
||
|
||
score = max(-5, min(5, score))
|
||
|
||
result = {
|
||
'commodities': commodities,
|
||
'score': score,
|
||
'summary': '，'.join(summary_parts) if summary_parts else '大宗商品整体平稳',
|
||
'reasons': reasons,
|
||
'affected_sectors': affected,
|
||
}
|
||
_set_cache('commodity', result)
|
||
return result
|
||
|
||
except Exception as e:
|
||
logger.warning(f"获取大宗商品数据失败: {e}")
|
||
return _neutral_result('大宗商品数据获取失败')
|
||
|
||
|
||
# ═══════════════════════════════════════════════
|
||
# P7: 汇率变化
|
||
# ═══════════════════════════════════════════════
|
||
|
||
# 汇率 → A股板块影响
|
||
FX_SECTOR_MAP = {
|
||
'升值': {
|
||
'beneficiary': ['航空', '造纸', '房地产'],
|
||
'victim': ['纺织', '家电出口', '电子代工'],
|
||
},
|
||
'贬值': {
|
||
'beneficiary': ['纺织', '家电', '电子代工'],
|
||
'victim': ['航空', '造纸'],
|
||
},
|
||
}
|
||
|
||
|
||
def get_fx_overview():
|
||
"""
|
||
获取人民币汇率变化
|
||
|
||
数据源：新浪财经API（hq.sinajs.cn）
|
||
获取在岸人民币兑美元实时汇率。
|
||
|
||
返回:
|
||
dict: {
|
||
'usd_cny': float, # 美元兑人民币汇率
|
||
'change_pct': float, # 涨跌幅
|
||
'direction': str, # 升值/贬值/稳定
|
||
'score': int, # 评分增减（-3 ~ +3）
|
||
'summary': str, # 白话总结
|
||
'reasons': list, # 评分原因
|
||
'affected_sectors': dict, # 对A股板块影响
|
||
}
|
||
"""
|
||
cached = _get_cache('fx')
|
||
if cached:
|
||
return cached
|
||
|
||
try:
|
||
# 新浪财经API获取在岸人民币汇率
|
||
# 格式: var hq_str_fx_susdcny="time,bid,ask,prev_close,...,name,change_pct,..."
|
||
url = 'https://hq.sinajs.cn/list=fx_susdcny'
|
||
resp = requests.get(url, timeout=10, headers={'Referer': 'https://finance.sina.com.cn'})
|
||
text = resp.content.decode('gbk', errors='replace')
|
||
|
||
# 解析汇率数据
|
||
if 'hq_str_fx_susdcny' not in text:
|
||
return _neutral_result('汇率数据解析失败')
|
||
|
||
value = text.split('"')[1] if '"' in text else ''
|
||
fields = value.split(',')
|
||
if len(fields) < 11:
|
||
return _neutral_result('汇率数据格式异常')
|
||
|
||
# 新浪汇率格式: time,bid,ask,prev_close,?,mid,?,?,?,name,change_pct,...
|
||
today_rate = float(fields[5]) if fields[5] else 0 # 中间价
|
||
prev_rate = float(fields[3]) if fields[3] else 0 # 昨收价
|
||
change_pct = float(fields[10]) if fields[10] else 0 # 涨跌幅
|
||
|
||
if today_rate == 0:
|
||
today_rate = float(fields[1]) if fields[1] else 0
|
||
if prev_rate == 0:
|
||
prev_rate = float(fields[3]) if fields[3] else 0
|
||
|
||
if today_rate == 0 or prev_rate == 0:
|
||
return _neutral_result('汇率数据异常')
|
||
|
||
# 如果涨跌幅为0，自行计算
|
||
if change_pct == 0:
|
||
change_pct = round((today_rate / prev_rate - 1) * 100, 3)
|
||
|
||
# 判断方向（美元兑人民币：涨=人民币贬值，跌=人民币升值）
|
||
if change_pct > 0.1:
|
||
direction = '贬值'
|
||
score = -2
|
||
summary = f'人民币贬值{abs(change_pct):.3f}%'
|
||
reasons = [f'人民币贬值{abs(change_pct):.3f}%(-2)']
|
||
affected = FX_SECTOR_MAP['贬值']
|
||
elif change_pct < -0.1:
|
||
direction = '升值'
|
||
score = 2
|
||
summary = f'人民币升值{abs(change_pct):.3f}%'
|
||
reasons = [f'人民币升值{abs(change_pct):.3f}%(+2)']
|
||
affected = FX_SECTOR_MAP['升值']
|
||
else:
|
||
direction = '稳定'
|
||
score = 0
|
||
summary = '人民币汇率基本稳定'
|
||
reasons = []
|
||
affected = {}
|
||
|
||
score = max(-3, min(3, score))
|
||
|
||
result = {
|
||
'usd_cny': round(today_rate, 4),
|
||
'change_pct': change_pct,
|
||
'direction': direction,
|
||
'score': score,
|
||
'summary': summary,
|
||
'reasons': reasons,
|
||
'affected_sectors': affected,
|
||
}
|
||
_set_cache('fx', result)
|
||
return result
|
||
|
||
except Exception as e:
|
||
logger.warning(f"获取汇率数据失败: {e}")
|
||
return _neutral_result('汇率数据获取失败')
|
||
|
||
|
||
# ═══════════════════════════════════════════════
|
||
# 综合外部因素
|
||
# ═══════════════════════════════════════════════
|
||
|
||
def get_all_external_factors():
|
||
"""
|
||
获取所有外部因素数据，返回综合结果
|
||
|
||
返回:
|
||
dict: 包含南向资金、美股、大宗商品、汇率的综合数据
|
||
"""
|
||
southbound = get_southbound_capital()
|
||
us_market = get_us_market_overview()
|
||
commodity = get_commodity_overview()
|
||
fx = get_fx_overview()
|
||
|
||
total_score = (
|
||
southbound.get('score', 0) +
|
||
us_market.get('score', 0) +
|
||
commodity.get('score', 0) +
|
||
fx.get('score', 0)
|
||
)
|
||
|
||
all_reasons = []
|
||
all_reasons.extend(southbound.get('reasons', []))
|
||
all_reasons.extend(us_market.get('reasons', []))
|
||
all_reasons.extend(commodity.get('reasons', []))
|
||
all_reasons.extend(fx.get('reasons', []))
|
||
|
||
summaries = []
|
||
for name, data in [('南向资金', southbound), ('美股', us_market), ('大宗商品', commodity), ('汇率', fx)]:
|
||
s = data.get('summary', '')
|
||
if s and '失败' not in s and '为空' not in s:
|
||
summaries.append(f'{name}：{s}')
|
||
|
||
return {
|
||
'southbound_capital': southbound,
|
||
'us_market': us_market,
|
||
'commodity': commodity,
|
||
'fx': fx,
|
||
'total_score': total_score,
|
||
'all_reasons': all_reasons,
|
||
'summary': ' | '.join(summaries),
|
||
}
|
||
|
||
|
||
def _neutral_result(reason):
|
||
"""返回中性结果"""
|
||
return {
|
||
'score': 0,
|
||
'summary': reason,
|
||
'reasons': [],
|
||
}
|