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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

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- 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
2026-07-18 16:45:58 +08:00
parent bb5a72767f
commit 79e869eeda
13 changed files with 637 additions and 193 deletions
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stock-html/services/external_factors.py
+189 -108
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@@ -2,15 +2,21 @@
外部因素分析模块（P2/P3/P4/P7）
包含：
- P2: 北向资金（外资动向）
- P2: 南向资金（港股通跨境资金动向）
- P3: 美股隔夜板块变化
- P4: 大宗商品价格
- P7: 汇率变化
数据源：AKShare（开源免费）
数据源：
- P2: AKShare stock_hsgt_hist_em（南向资金历史）+ stock_hsgt_fund_flow_summary_em（今日汇总）
- P3: 腾讯财经API（美股指数实时）
- P4: 腾讯财经API（商品期货实时）
- P7: 新浪财经API（人民币汇率）
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。
"""
import logging
import requests
from datetime import datetime, timedelta
logger = logging.getLogger(__name__)
@@ -36,84 +42,118 @@ def _set_cache(key, value):
# ═══════════════════════════════════════════════
# P2: 北向资金
# P2: 南向资金
# ═══════════════════════════════════════════════
def get_northbound_capital():
def get_southbound_capital():
"""
获取北向资金净流入数据
获取跨境资金流向数据
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：
- 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
- 南向净流入 < 0：内地资金撤出港股，risk-off，对A股偏负面
数据源：
1. AKShare stock_hsgt_fund_flow_summary_em（今日汇总）
2. AKShare stock_hsgt_hist_em（南向资金历史，用于连续天数计算）
返回:
dict: {
'net_inflow': float, # 今日净流入（亿）
'net_inflow': float, # 今日南向净流入（亿）
'score': int, # 评分增减（-10 ~ +10）
'summary': str, # 白话总结
'reasons': list, # 评分原因
}
"""
cached = _get_cache('northbound')
cached = _get_cache('southbound')
if cached:
return cached
try:
import akshare as ak
import pandas as pd
# 获取北向资金净流入数据
df = ak.stock_hsgt_north_net_flow_in_em(symbol="北向")
if df is None or df.empty:
return _neutral_result('北向资金数据为空')
# 1. 用 stock_hsgt_fund_flow_summary_em 获取今日汇总
today_inflow = 0
try:
df_summary = ak.stock_hsgt_fund_flow_summary_em()
if df_summary is not None and not df_summary.empty:
# 筛选南向资金行
south_rows = df_summary[df_summary['资金方向'] == '南向']
if not south_rows.empty:
# 成交净买额列求和
vals = south_rows['成交净买额'].tolist()
today_inflow = float(sum(v for v in vals if pd.notna(v) and v != 0))
except Exception as e:
logger.debug(f"stock_hsgt_fund_flow_summary_em失败: {e}")
# 取最近5个交易日
recent = df.tail(5)
today_inflow = float(recent.iloc[-1].get('当日净流入', 0) or 0)
# 连续流入/流出天数
# 2. 用 stock_hsgt_hist_em 获取南向资金历史（计算连续天数）
consecutive_inflow = 0
consecutive_outflow = 0
for _, row in recent[::-1].iterrows():
val = float(row.get('当日净流入', 0) or 0)
if val > 0:
if consecutive_outflow > 0:
break
consecutive_inflow += 1
elif val < 0:
if consecutive_inflow > 0:
break
consecutive_outflow += 1
try:
df = ak.stock_hsgt_hist_em(symbol="南向资金")
if df is not None and not df.empty:
recent = df.tail(5)
for _, row in recent[::-1].iterrows():
val = float(row.get('当日成交净买额', 0) or 0)
if str(val) == 'nan' or pd.isna(val):
val = 0
if val > 0:
if consecutive_outflow > 0:
break
consecutive_inflow += 1
elif val < 0:
if consecutive_inflow > 0:
break
consecutive_outflow += 1
except Exception as e:
logger.debug(f"stock_hsgt_hist_em南向失败: {e}")
# 评分
# 如果今日数据也为0或NaN，说明无法获取
if str(today_inflow) == 'nan' or today_inflow == 0:
# 尝试从历史数据取最新值
try:
df = ak.stock_hsgt_hist_em(symbol="南向资金")
if df is not None and not df.empty:
last_val = float(df.iloc[-1].get('当日成交净买额', 0) or 0)
if str(last_val) != 'nan' and not pd.isna(last_val) and last_val != 0:
today_inflow = last_val
except Exception:
pass
# 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）
score = 0
reasons = []
summary_parts = []
if today_inflow > 50:
if today_inflow > 80:
score += 5
reasons.append(f'北向今日净流入{today_inflow:.1f}亿(+5)')
summary_parts.append(f'外资今日大幅买入{today_inflow:.1f}亿元')
elif today_inflow > 20:
reasons.append(f'南向今日净流入{today_inflow:.1f}亿(+5)')
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')
elif today_inflow > 30:
score += 3
reasons.append(f'北向今日净流入{today_inflow:.1f}亿(+3)')
summary_parts.append(f'外资今日净流入{today_inflow:.1f}亿元')
elif today_inflow < -50:
reasons.append(f'南向今日净流入{today_inflow:.1f}亿(+3)')
summary_parts.append(f'南向资金净流入{today_inflow:.1f}亿元')
elif today_inflow < -80:
score -= 5
reasons.append(f'北向今日净流出{abs(today_inflow):.1f}亿(-5)')
summary_parts.append(f'外资今日大幅卖出{abs(today_inflow):.1f}亿元')
elif today_inflow < -20:
reasons.append(f'南向今日净流出{abs(today_inflow):.1f}亿(-5)')
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')
elif today_inflow < -30:
score -= 3
reasons.append(f'北向今日净流出{abs(today_inflow):.1f}亿(-3)')
summary_parts.append(f'外资今日净流出{abs(today_inflow):.1f}亿元')
reasons.append(f'南向今日净流出{abs(today_inflow):.1f}亿(-3)')
summary_parts.append(f'南向资金净流出{abs(today_inflow):.1f}亿元')
else:
summary_parts.append(f'外资今日净流入{today_inflow:.1f}亿元，方向不明')
summary_parts.append(f'南向资金净流入{today_inflow:.1f}亿元，方向不明')
if consecutive_inflow >= 3:
score += 3
reasons.append(f'北向连续{consecutive_inflow}日净流入(+3)')
summary_parts.append(f'已连续{consecutive_inflow}天买入')
reasons.append(f'南向连续{consecutive_inflow}日净流入(+3)')
summary_parts.append(f'已连续{consecutive_inflow}天流入')
if consecutive_outflow >= 3:
score -= 3
reasons.append(f'北向连续{consecutive_outflow}日净流出(-3)')
summary_parts.append(f'已连续{consecutive_outflow}天卖出')
reasons.append(f'南向连续{consecutive_outflow}日净流出(-3)')
summary_parts.append(f'已连续{consecutive_outflow}天流出')
score = max(-10, min(10, score))
@@ -125,12 +165,12 @@ def get_northbound_capital():
'summary': '，'.join(summary_parts),
'reasons': reasons,
}
_set_cache('northbound', result)
_set_cache('southbound', result)
return result
except Exception as e:
logger.warning(f"获取北向资金数据失败: {e}")
return _neutral_result('北向资金数据获取失败')
logger.warning(f"获取跨境资金数据失败: {e}")
return _neutral_result('南向资金数据获取失败')
# ═══════════════════════════════════════════════
@@ -156,10 +196,12 @@ def get_us_market_overview():
"""
获取美股隔夜收盘数据，计算外盘情绪
数据源：腾讯财经API（qt.gtimg.cn）
获取道琼斯、纳斯达克、标普500三大指数实时行情。
返回:
dict: {
'indices': dict, # 三大指数涨跌
'sectors': dict, # 主要板块涨跌
'score': int, # 评分增减（-10 ~ +10）
'summary': str, # 白话总结
'reasons': list, # 评分原因
@@ -171,32 +213,35 @@ def get_us_market_overview():
return cached
try:
import akshare as ak
# 腾讯财经API获取美股指数
# 格式: v_usDJI="200~道琼斯~.DJI~price~...~change_pct~..."
url = 'https://qt.gtimg.cn/q=usDJI,usIXIC,usSPX'
resp = requests.get(url, timeout=10)
text = resp.content.decode('gbk', errors='replace')
# 获取全球主要指数
df = ak.index_global()
if df is None or df.empty:
return _neutral_result('美股指数数据为空')
# 筛选美股主要指数
us_indices = {}
for _, row in df.iterrows():
name = str(row.get('名称', ''))
if '纳斯达克' in name:
us_indices['nasdaq'] = {
'name': name,
'change_pct': float(row.get('涨跌幅', 0) or 0),
}
elif '道琼斯' in name:
us_indices['dow'] = {
'name': name,
'change_pct': float(row.get('涨跌幅', 0) or 0),
}
elif '标普500' in name:
us_indices['sp500'] = {
'name': name,
'change_pct': float(row.get('涨跌幅', 0) or 0),
}
for line in text.strip().split(';'):
line = line.strip()
if not line or 'v_pv_none_match' in line:
continue
# 解析 v_usDJI="..."
if '=' not in line:
continue
var_name = line.split('=')[0].strip().replace('var ', '').replace('v_', '')
value = line.split('"')[1] if '"' in line else ''
fields = value.split('~')
if len(fields) < 33:
continue
name = fields[1]
change_pct = float(fields[32]) if fields[32] else 0
if 'DJI' in var_name.upper() or '道琼斯' in name:
us_indices['dow'] = {'name': name, 'change_pct': change_pct}
elif 'IXIC' in var_name.upper() or '纳斯达克' in name:
us_indices['nasdaq'] = {'name': name, 'change_pct': change_pct}
elif 'SPX' in var_name.upper() or '标普' in name:
us_indices['sp500'] = {'name': name, 'change_pct': change_pct}
if not us_indices:
return _neutral_result('未找到美股指数')
@@ -301,6 +346,9 @@ def get_commodity_overview():
"""
获取主要大宗商品价格变化
数据源：腾讯财经API（qt.gtimg.cn）
获取纽约黄金、纽约原油、美铜等商品期货实时行情。
返回:
dict: {
'commodities': dict, # 各商品涨跌
@@ -315,28 +363,46 @@ def get_commodity_overview():
return cached
try:
import akshare as ak
# 腾讯财经API获取商品期货
# hf_GC=纽约黄金, hf_CL=纽约原油, hf_HG=美铜
url = 'https://qt.gtimg.cn/q=hf_GC,hf_CL,hf_HG'
resp = requests.get(url, timeout=10)
text = resp.content.decode('gbk', errors='replace')
# 获取国内商品期货行情
df = ak.futures_main_sina()
if df is None or df.empty:
return _neutral_result('大宗商品数据为空')
# 商品名称映射
symbol_map = {
'hf_GC': '黄金',
'hf_CL': '原油',
'hf_HG': '铜',
}
# 关注的商品
target_commodities = ['原油', '黄金', '铜', '螺纹钢', '碳酸锂']
commodities = {}
for line in text.strip().split(';'):
line = line.strip()
if not line or 'v_pv_none_match' in line:
continue
if '=' not in line:
continue
var_name = line.split('=')[0].strip().replace('var ', '').replace('v_', '')
value = line.split('"')[1] if '"' in line else ''
fields = value.split(',')
if len(fields) < 10:
continue
for _, row in df.iterrows():
symbol = str(row.get('symbol', ''))
for target in target_commodities:
if target in symbol:
change = float(row.get('change', 0) or 0)
pct = float(row.get('change_pct', 0) or 0)
commodities[target] = {
'symbol': symbol,
'change_pct': round(pct, 2),
}
break
target = symbol_map.get(var_name)
if not target:
continue
# 腾讯商品格式: price,change_pct,prev_close,open,high,low,time,...,name
current_price = float(fields[0]) if fields[0] else 0
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')
@@ -415,6 +481,9 @@ 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
@@ -431,25 +500,37 @@ def get_fx_overview():
return cached
try:
import akshare as ak
# 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')
# 获取人民币汇率
df = ak.currency_boc_sina(symbol="美元")
if df is None or df.empty:
return _neutral_result('汇率数据为空')
# 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')
# 取最近2条计算变化
recent = df.tail(2)
if len(recent) < 2:
return _neutral_result('汇率数据不足')
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')
today_rate = float(recent.iloc[-1].get('中行折算价', 0) or 0)
prev_rate = float(recent.iloc[-2].get('中行折算价', 0) or 0)
# 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')
change_pct = round((today_rate / prev_rate - 1) * 100, 3)
# 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:
@@ -499,34 +580,34 @@ def get_all_external_factors():
获取所有外部因素数据，返回综合结果
返回:
dict: 包含北向资金、美股、大宗商品、汇率的综合数据
dict: 包含南向资金、美股、大宗商品、汇率的综合数据
"""
northbound = get_northbound_capital()
southbound = get_southbound_capital()
us_market = get_us_market_overview()
commodity = get_commodity_overview()
fx = get_fx_overview()
total_score = (
northbound.get('score', 0) +
southbound.get('score', 0) +
us_market.get('score', 0) +
commodity.get('score', 0) +
fx.get('score', 0)
)
all_reasons = []
all_reasons.extend(northbound.get('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 [('北向资金', northbound), ('美股', us_market), ('大宗商品', commodity), ('汇率', fx)]:
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 {
'northbound_capital': northbound,
'southbound_capital': southbound,
'us_market': us_market,
'commodity': commodity,
'fx': fx,