', methods=['GET'])
+def get_stock_score_detail(code):
+ """获取单只股票的综合评分详情（外部因素）
+
+ 通过 query 参数 tech_score 传入列表中的技术得分，确保明细与列表分数一致。
+ """
+ try:
+ from services.score_engine import compute_comprehensive_score_batch
+ tech_score = float(request.args.get('tech_score', 50))
+ stocks_input = [{'stock_code': code, 'stock_name': '', 'technical_score': tech_score}]
+ scores_map = compute_comprehensive_score_batch(stocks_input)
+ sc = scores_map.get(code)
+ if sc:
+ return jsonify({
+ 'success': True,
+ 'technical_score': sc['technical_score'],
+ 'external_score': sc['external_score'],
+ 'final_score': sc['final_score'],
+ 'verdict': sc['verdict'],
+ 'factors': sc.get('factors', {}),
+ 'summary': sc.get('summary', ''),
+ })
+ return jsonify({'success': False, 'error': '未找到评分数据'}), 404
+ except Exception as e:
+ return jsonify({'success': False, 'error': str(e)}), 500
+
+
@bp.route('/scan_status', methods=['GET'])
def get_scan_status():
"""查询扫描进度"""
diff --git a/stock-html/routes/market.py b/stock-html/routes/market.py
index d7c9f30..c00ee83 100644
--- a/stock-html/routes/market.py
+++ b/stock-html/routes/market.py
@@ -540,7 +540,7 @@ def market_sentiment():
@bp.route('/external_factors', methods=['GET'])
def external_factors():
- """获取外部因素综合数据（北向资金、美股隔夜、大宗商品、汇率）"""
+ """获取外部因素综合数据（南向资金、美股隔夜、大宗商品、汇率）"""
try:
from services.external_factors import get_all_external_factors
result = get_all_external_factors()
diff --git a/stock-html/services/external_factors.py b/stock-html/services/external_factors.py
index 458db47..1eb91e3 100644
--- a/stock-html/services/external_factors.py
+++ b/stock-html/services/external_factors.py
@@ -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,
diff --git a/stock-html/services/fund_flow_analyzer.py b/stock-html/services/fund_flow_analyzer.py
index d27ec18..23660cc 100644
--- a/stock-html/services/fund_flow_analyzer.py
+++ b/stock-html/services/fund_flow_analyzer.py
@@ -73,10 +73,105 @@ def get_fund_flow_history(stock_code, days=10):
put_db(conn)
+def _get_fund_flow_from_mairui(stock_code, days=10):
+ """从麦蕊智数API获取资金流向数据，转换为与DB记录相同的格式。
+
+ API: https://api.mairuiapi.com/hsstock/history/transaction/{code}/{licence}?lt={n}
+ 字段: zmbtdcje=主买特大单, zmbddcje=主买大单, zmbzdcje=主买中单, zmbxdcje=主买小单
+ zmstdcje=主卖特大单, zmsddcje=主卖大单, zmszdcje=主卖中单, zmsxdcje=主卖小单
+ """
+ try:
+ import requests
+ from config import Config
+ LICENCE = Config.MAIRUI_LICENCE or "5352ED2F-94E5-4E96-8B7F-B57BA75284E3"
+ url = f"https://api.mairuiapi.com/hsstock/history/transaction/{stock_code}/{LICENCE}?lt={days}"
+ resp = requests.get(url, timeout=10)
+ if resp.status_code != 200:
+ logger.warning(f"麦蕊资金流向API返回{resp.status_code}")
+ return []
+
+ data = resp.json()
+ if not data or not isinstance(data, list):
+ return []
+
+ records = []
+ for item in data:
+ # 主买总额 = 特大单+大单+中单+小单
+ buy_total = (
+ float(item.get('zmbtdcje', 0) or 0) +
+ float(item.get('zmbddcje', 0) or 0) +
+ float(item.get('zmbzdcje', 0) or 0) +
+ float(item.get('zmbxdcje', 0) or 0)
+ )
+ # 主卖总额
+ sell_total = (
+ float(item.get('zmstdcje', 0) or 0) +
+ float(item.get('zmsddcje', 0) or 0) +
+ float(item.get('zmszdcje', 0) or 0) +
+ float(item.get('zmsxdcje', 0) or 0)
+ )
+ # 主力净流入 = (特大单+大单)买 - (特大单+大单)卖
+ main_buy = float(item.get('zmbtdcje', 0) or 0) + float(item.get('zmbddcje', 0) or 0)
+ main_sell = float(item.get('zmstdcje', 0) or 0) + float(item.get('zmsddcje', 0) or 0)
+ main_net = main_buy - main_sell
+
+ # 超大单净流入
+ super_net = float(item.get('zmbtdcje', 0) or 0) - float(item.get('zmstdcje', 0) or 0)
+
+ # 总成交额
+ total_amount = buy_total + sell_total
+ main_net_pct = round(main_net / total_amount * 100, 2) if total_amount > 0 else 0
+ super_net_pct = round(super_net / total_amount * 100, 2) if total_amount > 0 else 0
+
+ # 大单净流入
+ big_net = float(item.get('zmbddcje', 0) or 0) - float(item.get('zmsddcje', 0) or 0)
+ big_net_pct = round(big_net / total_amount * 100, 2) if total_amount > 0 else 0
+
+ # 中单净流入
+ mid_net = float(item.get('zmbzdcje', 0) or 0) - float(item.get('zmszdcje', 0) or 0)
+ mid_net_pct = round(mid_net / total_amount * 100, 2) if total_amount > 0 else 0
+
+ # 小单净流入
+ small_net = float(item.get('zmbxdcje', 0) or 0) - float(item.get('zmsxdcje', 0) or 0)
+ small_net_pct = round(small_net / total_amount * 100, 2) if total_amount > 0 else 0
+
+ # 日期解析
+ t_str = str(item.get('t', ''))
+ date_str = t_str[:10] if t_str else ''
+
+ records.append({
+ 'date': date_str,
+ 'close_price': 0,
+ 'change_pct': 0,
+ 'main_net_inflow': round(main_net, 2),
+ 'main_net_inflow_pct': main_net_pct,
+ 'super_net_inflow': round(super_net, 2),
+ 'super_net_inflow_pct': super_net_pct,
+ 'big_net_inflow': round(big_net, 2),
+ 'big_net_inflow_pct': big_net_pct,
+ 'mid_net_inflow': round(mid_net, 2),
+ 'mid_net_inflow_pct': mid_net_pct,
+ 'small_net_inflow': round(small_net, 2),
+ 'small_net_inflow_pct': small_net_pct,
+ })
+
+ # 按日期升序排列
+ records.sort(key=lambda x: x['date'])
+ logger.info(f"麦蕊API获取{stock_code}资金流向{len(records)}条")
+ return records
+ except Exception as e:
+ logger.warning(f"麦蕊资金流向API失败({stock_code}): {e}")
+ return []
+
+
def analyze_fund_flow(stock_code, days=5):
"""
分析主力资金流向，返回资金面评分和信号
+ 数据源优先级：
+ 1. DB stock_fund_flow_history 表（有最新数据时）
+ 2. 麦蕊智数API hsstock/history/transaction（DB数据过期时补充）
+
参数:
stock_code: 股票代码
days: 分析最近几天的资金流向
@@ -91,6 +186,28 @@ def analyze_fund_flow(stock_code, days=5):
}
"""
records = get_fund_flow_history(stock_code, days=days + 5)
+
+ # 检查DB数据是否足够新（最近3天内有数据）
+ use_mairui = False
+ if len(records) < 2:
+ use_mairui = True
+ else:
+ from datetime import date
+ latest_date = records[-1].get('date', '')
+ if latest_date:
+ try:
+ latest = datetime.strptime(latest_date, '%Y-%m-%d').date()
+ if (date.today() - latest).days > 5:
+ use_mairui = True
+ except ValueError:
+ use_mairui = True
+
+ if use_mairui:
+ # 用麦蕊API获取资金流向数据
+ mairui_records = _get_fund_flow_from_mairui(stock_code, days + 5)
+ if mairui_records:
+ records = mairui_records
+
if len(records) < 2:
return {
'score': 0,
diff --git a/stock-html/services/market_sentiment.py b/stock-html/services/market_sentiment.py
index fe21444..00fa3e0 100644
--- a/stock-html/services/market_sentiment.py
+++ b/stock-html/services/market_sentiment.py
@@ -50,8 +50,7 @@ def calc_market_sentiment():
COUNT(*) FILTER (WHERE change_pct < 0) AS down_count,
COUNT(*) FILTER (WHERE change_pct = 0) AS flat_count,
COUNT(*) AS total,
- COALESCE(SUM(amount), 0) AS total_amount,
- COALESCE(PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY turnover), 0) AS turnover_median
+ COALESCE(SUM(amount), 0) AS total_amount
FROM stock_realtime_price
WHERE volume > 0 AND price > 0
""")
@@ -66,7 +65,7 @@ def calc_market_sentiment():
flat_count = int(row[4] or 0)
total = int(row[5] or 1)
total_amount = float(row[6] or 0) / 1e8 # 转为亿
- turnover_median = float(row[7] or 0)
+ turnover_median = 0 # DB无turnover字段，不再使用
# 涨跌停比
up_down_ratio = round(limit_up / limit_down, 1) if limit_down > 0 else float(limit_up)
diff --git a/stock-html/services/scheduler.py b/stock-html/services/scheduler.py
index 582952d..382e4bb 100644
--- a/stock-html/services/scheduler.py
+++ b/stock-html/services/scheduler.py
@@ -575,16 +575,33 @@ def job_afternoon_trade():
print(f"[定时任务] ===== 午后交易任务结束 {datetime.now()} =====")
+def job_precompute_market_factors():
+ """开市前预热市场级外部因素缓存（09:15执行）
+
+ 预计算 P1市场情绪、P2北向、P3美股、P4商品、P7汇率、P6政策面，
+ 结果存入 score_engine 内存缓存，后续全景扫描和评分直接复用。
+ """
+ print(f"[定时任务] ===== 开市前预热外部因素 {datetime.now()} =====")
+ try:
+ from services.score_engine import precompute_market_factors
+ precompute_market_factors()
+ print("[定时任务] 外部因素预热完成")
+ except Exception as e:
+ print(f"[定时任务] 外部因素预热失败: {e}")
+
+
def run_scheduler():
"""运行定时任务调度器"""
global _is_running
# 设置定时任务 — v7最优时点: 09:35买入 / 13:40卖出
+ schedule.every().day.at("09:15").do(job_precompute_market_factors) # 开市前预热外部因素
schedule.every().day.at("09:35").do(job_morning_trade)
schedule.every().day.at("13:40").do(job_afternoon_trade)
schedule.every().day.at("15:05").do(trigger_closing_update) # 收盘更新持仓价格
print("[定时任务] 调度器已启动 (v7最优时点)")
+ print("[定时任务] - 09:15 开市前预热外部因素缓存")
print("[定时任务] - 09:35 早盘交易（使用昨日扫描数据 — 最优买入时点）")
print("[定时任务] - 13:40 午后交易（使用当日中午扫描数据 — 最优卖出时点）")
print("[定时任务] - 15:05 收盘更新持仓价格")
diff --git a/stock-html/services/score_engine.py b/stock-html/services/score_engine.py
index 8316bec..ca0112a 100644
--- a/stock-html/services/score_engine.py
+++ b/stock-html/services/score_engine.py
@@ -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', []))
diff --git a/stock-html/static/css/scan.css b/stock-html/static/css/scan.css
index 2e304b7..e65a413 100644
--- a/stock-html/static/css/scan.css
+++ b/stock-html/static/css/scan.css
@@ -1041,3 +1041,80 @@
color: var(--text-muted);
background: rgba(255,255,255,0.06);
}
+
+/* 综合评分明细 */
+.scan-score-breakdown {
+ display: flex;
+ gap: 12px;
+ padding: 10px 14px;
+ margin: 8px 0;
+ background: rgba(255,255,255,0.04);
+ border-radius: 10px;
+ flex-wrap: wrap;
+}
+.score-breakdown-item {
+ display: flex;
+ flex-direction: column;
+ gap: 2px;
+ min-width: 60px;
+}
+.score-breakdown-item .breakdown-label {
+ font-size: 10px;
+ color: var(--text-muted);
+}
+.score-breakdown-item .breakdown-value {
+ font-size: 16px;
+ font-weight: 700;
+ color: var(--text-primary);
+}
+.score-breakdown-item .breakdown-value.pos { color: var(--success); }
+.score-breakdown-item .breakdown-value.neg { color: var(--danger); }
+.score-breakdown-item.highlight .breakdown-value {
+ color: var(--warning);
+ font-size: 18px;
+}
+
+/* 外部因素详情面板 */
+.scan-external-factors {
+ padding: 10px 14px;
+ margin-bottom: 8px;
+ background: rgba(255,255,255,0.03);
+ border-radius: 10px;
+ border: 1px solid var(--border);
+}
+.scan-external-factors.loading {
+ display: flex;
+ align-items: center;
+ gap: 8px;
+ color: var(--text-muted);
+ font-size: 12px;
+}
+.ext-factor-item {
+ display: flex;
+ align-items: center;
+ gap: 8px;
+ padding: 4px 0;
+ font-size: 12px;
+ border-bottom: 1px solid rgba(255,255,255,0.03);
+}
+.ext-factor-item:last-child { border-bottom: none; }
+.ext-factor-label {
+ flex-shrink: 0;
+ min-width: 80px;
+ color: var(--text-secondary);
+ font-weight: 600;
+}
+.ext-factor-score {
+ flex-shrink: 0;
+ min-width: 32px;
+ text-align: center;
+ font-weight: 700;
+ font-variant-numeric: tabular-nums;
+}
+.ext-factor-score.pos { color: var(--success); }
+.ext-factor-score.neg { color: var(--danger); }
+.ext-factor-summary {
+ color: var(--text-muted);
+ font-size: 11px;
+ line-height: 1.4;
+}
diff --git a/stock-html/static/js/app.js b/stock-html/static/js/app.js
index f2268cd..bb90703 100644
--- a/stock-html/static/js/app.js
+++ b/stock-html/static/js/app.js
@@ -2121,6 +2121,13 @@
recommend_rate: scanItem.recommend_rate,
has_scan_data: true,
scan_triggered_count: scanCount,
+ final_score: scanItem.final_score,
+ technical_score: scanItem.technical_score,
+ external_score: scanItem.external_score,
+ verdict: scanItem.verdict,
+ score_factors: null,
+ score_summary: '',
+ score_detail_loading: true,
realtime_loading: true,
realtime_signal_status: null,
realtime_triggered_count: null,
@@ -2158,6 +2165,24 @@
this.techSignalResult.realtime_error = err.message || '获取失败';
}
}
+ // 异步请求评分详情（外部因素），传入列表技术得分确保一致
+ try {
+ const techScore = scanItem.final_score ? Math.round(scanItem.final_score - (scanItem.external_score || 0)) : 50;
+ const scoreResp = await axios.get(`/api/stock_score_detail/${code}?tech_score=${techScore}`);
+ if (scoreResp.data.success && this.techSignalResult && this.techSignalResult.stock_code === code) {
+ this.techSignalResult.score_factors = scoreResp.data.factors || null;
+ this.techSignalResult.score_summary = scoreResp.data.summary || '';
+ this.techSignalResult.final_score = scoreResp.data.final_score;
+ this.techSignalResult.technical_score = scoreResp.data.technical_score;
+ this.techSignalResult.external_score = scoreResp.data.external_score;
+ this.techSignalResult.verdict = scoreResp.data.verdict;
+ }
+ } catch (e) {
+ console.debug('评分详情获取失败:', e);
+ }
+ if (this.techSignalResult && this.techSignalResult.stock_code === code) {
+ this.techSignalResult.score_detail_loading = false;
+ }
return;
}
try {
diff --git a/stock-html/templates/index.html b/stock-html/templates/index.html
index b8e831a..7e10663 100644
--- a/stock-html/templates/index.html
+++ b/stock-html/templates/index.html
@@ -25,7 +25,7 @@
-
+
@@ -956,6 +956,66 @@
{{ techSignalResult.recommend_rate }}%
{{ techSignalResult.recommend_reason }}
+
+
+
+ 技术得分
+ {{ techSignalResult.technical_score }}
+
+
+ 外部得分
+
+ {{ techSignalResult.external_score >= 0 ? '+' : '' }}{{ techSignalResult.external_score }}
+
+
+
+ 综合得分
+ {{ techSignalResult.final_score }}
+
+
+ 评级
+ {{ techSignalResult.verdict }}
+
+
+
+
+ 正在加载外部因素...
+
+
+
+ 💰 资金面
+
+ {{ techSignalResult.score_factors.fund_flow.score >= 0 ? '+' : '' }}{{ techSignalResult.score_factors.fund_flow.score }}
+
+ {{ techSignalResult.score_factors.fund_flow.summary }}
+
+
+ 📊 市场情绪
+
+ {{ techSignalResult.score_factors.market_sentiment.score >= 0 ? '+' : '' }}{{ techSignalResult.score_factors.market_sentiment.score }}
+
+ {{ techSignalResult.score_factors.market_sentiment.sentiment || techSignalResult.score_factors.market_sentiment.summary }}
+
+
+ 🌍 外部环境
+
+ {{ techSignalResult.score_factors.external.total_score >= 0 ? '+' : '' }}{{ techSignalResult.score_factors.external.total_score }}
+
+ {{ techSignalResult.score_factors.external.summary }}
+
+
+ 📜 政策面
+
+ {{ techSignalResult.score_factors.policy.score >= 0 ? '+' : '' }}{{ techSignalResult.score_factors.policy.score }}
+
+ {{ techSignalResult.score_factors.policy.summary }}
+
+
+ 📰 公告/异动
+ {{ techSignalResult.score_factors.news.total_score }}
+ {{ techSignalResult.score_factors.news.summary }}
+
+
@@ -2239,6 +2299,6 @@
{% endraw %}
-
+