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update pay ui

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zzlgreat
2025-12-02 12:22:49 +08:00
parent bd86ccce85
commit d1a222d9e9
2 changed files with 331 additions and 169 deletions
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app_vx.py
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@@ -5381,6 +5381,114 @@ def get_comment_replies(comment_id):
}), 500
# 工具函数：解析JSON字段
def parse_json_field(field_value):
"""解析JSON字段"""
if not field_value:
return []
try:
if isinstance(field_value, str):
if field_value.startswith('['):
return json.loads(field_value)
else:
return field_value.split(',')
else:
return field_value
except:
return []
# 工具函数：获取 future_events 表字段值，支持新旧字段回退
def get_future_event_field(row, new_field, old_field):
"""
获取 future_events 表字段值，支持新旧字段回退
如果新字段存在且不为空，使用新字段；否则使用旧字段
"""
new_value = getattr(row, new_field, None) if hasattr(row, new_field) else None
old_value = getattr(row, old_field, None) if hasattr(row, old_field) else None
# 如果新字段有值（不为空字符串），使用新字段
if new_value is not None and str(new_value).strip():
return new_value
return old_value
# 工具函数：解析新的 best_matches 数据结构（含研报引用信息）
def parse_best_matches(best_matches_value):
"""
解析新的 best_matches 数据结构（含研报引用信息）
新结构示例:
[
{
"stock_code": "300451.SZ",
"company_name": "创业慧康",
"original_description": "核心标的，医疗信息化...",
"best_report_title": "报告标题",
"best_report_author": "作者",
"best_report_sentences": "相关内容",
"best_report_match_score": "好",
"best_report_match_ratio": 0.9285714285714286,
"best_report_declare_date": "2023-04-25T00:00:00",
"total_reports": 9,
"high_score_reports": 6
},
...
]
返回统一格式的股票列表，兼容旧格式
"""
if not best_matches_value:
return []
try:
# 解析 JSON
if isinstance(best_matches_value, str):
data = json.loads(best_matches_value)
else:
data = best_matches_value
if not isinstance(data, list):
return []
result = []
for item in data:
if isinstance(item, dict):
# 新结构：包含研报信息的字典
stock_info = {
'code': item.get('stock_code', ''),
'name': item.get('company_name', ''),
'description': item.get('original_description', ''),
'score': item.get('best_report_match_ratio', 0),
# 研报引用信息
'report': {
'title': item.get('best_report_title', ''),
'author': item.get('best_report_author', ''),
'sentences': item.get('best_report_sentences', ''),
'match_score': item.get('best_report_match_score', ''),
'match_ratio': item.get('best_report_match_ratio', 0),
'declare_date': item.get('best_report_declare_date', ''),
'total_reports': item.get('total_reports', 0),
'high_score_reports': item.get('high_score_reports', 0)
} if item.get('best_report_title') else None
}
result.append(stock_info)
elif isinstance(item, (list, tuple)) and len(item) >= 2:
# 旧结构：[code, name, description, score]
result.append({
'code': item[0],
'name': item[1],
'description': item[2] if len(item) > 2 else '',
'score': item[3] if len(item) > 3 else 0,
'report': None
})
return result
except Exception as e:
print(f"parse_best_matches error: {e}")
return []
# 工具函数：处理转义字符，保留 Markdown 格式
def unescape_markdown_text(text):
"""
@@ -5470,6 +5578,7 @@ def api_calendar_events():
offset = (page - 1) * per_page
# 构建基础查询 - 使用 future_events 表
# 添加新字段 second_modified_text, `second_modified_text.1`, best_matches 支持新旧回退
query = """
SELECT data_id, \
calendar_time, \
@@ -5481,7 +5590,10 @@ def api_calendar_events():
fact, \
related_stocks, \
concepts, \
inferred_tag
inferred_tag, \
second_modified_text, \
`second_modified_text.1` as second_modified_text_1, \
best_matches
FROM future_events
WHERE 1 = 1 \
"""
@@ -5552,90 +5664,114 @@ def api_calendar_events():
events_data = []
for event in events:
# 解析相关股票
# 使用新字段回退机制获取 former 和 forecast
# second_modified_text -> former
former_value = get_future_event_field(event, 'second_modified_text', 'former')
# second_modified_text.1 -> forecast
forecast_new = getattr(event, 'second_modified_text_1', None)
forecast_value = forecast_new if (forecast_new and str(forecast_new).strip()) else getattr(event, 'forecast', None)
# 解析相关股票 - 优先使用 best_matches，回退到 related_stocks
related_stocks_list = []
related_avg_chg = 0
related_max_chg = 0
related_week_chg = 0
# 处理相关股票数据
if event.related_stocks:
# 优先使用 best_matches（新结构，含研报引用）
best_matches = getattr(event, 'best_matches', None)
if best_matches and str(best_matches).strip():
# 使用新的 parse_best_matches 函数解析
parsed_stocks = parse_best_matches(best_matches)
else:
# 回退到旧的 related_stocks 处理
parsed_stocks = []
if event.related_stocks:
try:
import ast
if isinstance(event.related_stocks, str):
try:
stock_data = json.loads(event.related_stocks)
except:
stock_data = ast.literal_eval(event.related_stocks)
else:
stock_data = event.related_stocks
if stock_data:
for stock_info in stock_data:
if isinstance(stock_info, list) and len(stock_info) >= 2:
parsed_stocks.append({
'code': stock_info[0],
'name': stock_info[1],
'description': stock_info[2] if len(stock_info) > 2 else '',
'score': stock_info[3] if len(stock_info) > 3 else 0,
'report': None
})
except Exception as e:
print(f"Error parsing related_stocks for event {event.data_id}: {e}")
# 处理解析后的股票数据，获取交易信息
if parsed_stocks:
try:
import json
import ast
daily_changes = []
week_changes = []
# 使用与detail接口相同的解析逻辑
if isinstance(event.related_stocks, str):
try:
stock_data = json.loads(event.related_stocks)
except:
stock_data = ast.literal_eval(event.related_stocks)
else:
stock_data = event.related_stocks
for stock_info in parsed_stocks:
stock_code = stock_info.get('code', '')
stock_name = stock_info.get('name', '')
description = stock_info.get('description', '')
score = stock_info.get('score', 0)
report = stock_info.get('report', None)
if stock_data:
daily_changes = []
week_changes = []
if stock_code:
# 规范化股票代码，移除后缀
clean_code = stock_code.replace('.SZ', '').replace('.SH', '').replace('.BJ', '')
# 处理正确的数据格式 [股票代码, 股票名称, 描述, 分数]
for stock_info in stock_data:
if isinstance(stock_info, list) and len(stock_info) >= 2:
stock_code = stock_info[0] # 股票代码
stock_name = stock_info[1] # 股票名称
description = stock_info[2] if len(stock_info) > 2 else ''
score = stock_info[3] if len(stock_info) > 3 else 0
else:
continue
# 使用模糊匹配查询真实的交易数据
trade_query = """
SELECT F007N as close_price, F010N as change_pct, TRADEDATE
FROM ea_trade
WHERE SECCODE LIKE :stock_code_pattern
ORDER BY TRADEDATE DESC LIMIT 7 \
"""
trade_result = db.session.execute(text(trade_query),
{'stock_code_pattern': f'{clean_code}%'})
trade_data = trade_result.fetchall()
if stock_code:
# 规范化股票代码，移除后缀
clean_code = stock_code.replace('.SZ', '').replace('.SH', '').replace('.BJ', '')
daily_chg = 0
week_chg = 0
# 使用模糊匹配查询真实的交易数据
trade_query = """
SELECT F007N as close_price, F010N as change_pct, TRADEDATE
FROM ea_trade
WHERE SECCODE LIKE :stock_code_pattern
ORDER BY TRADEDATE DESC LIMIT 7 \
"""
trade_result = db.session.execute(text(trade_query),
{'stock_code_pattern': f'{clean_code}%'})
trade_data = trade_result.fetchall()
if trade_data:
# 日涨跌幅（当日）
daily_chg = float(trade_data[0].change_pct or 0)
daily_chg = 0
week_chg = 0
# 周涨跌幅（5个交易日）
if len(trade_data) >= 5:
current_price = float(trade_data[0].close_price or 0)
week_ago_price = float(trade_data[4].close_price or 0)
if week_ago_price > 0:
week_chg = ((current_price - week_ago_price) / week_ago_price) * 100
if trade_data:
# 日涨跌幅（当日）
daily_chg = float(trade_data[0].change_pct or 0)
# 收集涨跌幅数据
daily_changes.append(daily_chg)
week_changes.append(week_chg)
# 周涨跌幅（5个交易日）
if len(trade_data) >= 5:
current_price = float(trade_data[0].close_price or 0)
week_ago_price = float(trade_data[4].close_price or 0)
if week_ago_price > 0:
week_chg = ((current_price - week_ago_price) / week_ago_price) * 100
related_stocks_list.append({
'code': stock_code,
'name': stock_name,
'description': description,
'score': score,
'daily_chg': daily_chg,
'week_chg': week_chg,
'report': report # 添加研报引用信息
})
# 收集涨跌幅数据
daily_changes.append(daily_chg)
week_changes.append(week_chg)
# 计算平均收益率
if daily_changes:
related_avg_chg = round(sum(daily_changes) / len(daily_changes), 4)
related_max_chg = round(max(daily_changes), 4)
related_stocks_list.append({
'code': stock_code,
'name': stock_name,
'description': description,
'score': score,
'daily_chg': daily_chg,
'week_chg': week_chg
})
# 计算平均收益率
if daily_changes:
related_avg_chg = round(sum(daily_changes) / len(daily_changes), 4)
related_max_chg = round(max(daily_changes), 4)
if week_changes:
related_week_chg = round(sum(week_changes) / len(week_changes), 4)
if week_changes:
related_week_chg = round(sum(week_changes) / len(week_changes), 4)
except Exception as e:
print(f"Error processing related stocks for event {event.data_id}: {e}")
@@ -5660,8 +5796,9 @@ def api_calendar_events():
highlight_match = 'concepts'
# 将转义的换行符转换为真正的换行符，保留 Markdown 格式
cleaned_former = unescape_markdown_text(event.former)
cleaned_forecast = unescape_markdown_text(event.forecast)
# 使用新字段回退后的值（former_value, forecast_value）
cleaned_former = unescape_markdown_text(former_value)
cleaned_forecast = unescape_markdown_text(forecast_value)
cleaned_fact = unescape_markdown_text(event.fact)
event_dict = {
@@ -5907,6 +6044,7 @@ def api_future_event_detail(item_id):
"""未来事件详情接口 - 连接 future_events 表 (修正数据解析) - 仅限 Pro/Max 会员"""
try:
# 从 future_events 表查询事件详情
# 添加新字段 second_modified_text, `second_modified_text.1`, best_matches 支持新旧回退
query = """
SELECT data_id, \
calendar_time, \
@@ -5917,7 +6055,10 @@ def api_future_event_detail(item_id):
forecast, \
fact, \
related_stocks, \
concepts
concepts, \
second_modified_text, \
`second_modified_text.1` as second_modified_text_1, \
best_matches
FROM future_events
WHERE data_id = :item_id \
"""
@@ -5932,6 +6073,13 @@ def api_future_event_detail(item_id):
'data': None
}), 404
# 使用新字段回退机制获取 former 和 forecast
# second_modified_text -> former
former_value = get_future_event_field(event, 'second_modified_text', 'former')
# second_modified_text.1 -> forecast
forecast_new = getattr(event, 'second_modified_text_1', None)
forecast_value = forecast_new if (forecast_new and str(forecast_new).strip()) else getattr(event, 'forecast', None)
extracted_concepts = extract_concepts_from_concepts_field(event.concepts)
# 解析相关股票
@@ -5975,136 +6123,150 @@ def api_future_event_detail(item_id):
'环保': '公共产业板块', '综合': '公共产业板块'
}
# 处理相关股票
# 处理相关股票 - 优先使用 best_matches，回退到 related_stocks
related_avg_chg = 0
related_max_chg = 0
related_week_chg = 0
if event.related_stocks:
# 优先使用 best_matches（新结构，含研报引用）
best_matches = getattr(event, 'best_matches', None)
if best_matches and str(best_matches).strip():
# 使用新的 parse_best_matches 函数解析
parsed_stocks = parse_best_matches(best_matches)
else:
# 回退到旧的 related_stocks 处理
parsed_stocks = []
if event.related_stocks:
try:
import ast
if isinstance(event.related_stocks, str):
try:
stock_data = json.loads(event.related_stocks)
except:
stock_data = ast.literal_eval(event.related_stocks)
else:
stock_data = event.related_stocks
if stock_data:
for stock_info in stock_data:
if isinstance(stock_info, list) and len(stock_info) >= 2:
parsed_stocks.append({
'code': stock_info[0],
'name': stock_info[1],
'description': stock_info[2] if len(stock_info) > 2 else '',
'score': stock_info[3] if len(stock_info) > 3 else 0,
'report': None
})
except Exception as e:
print(f"Error parsing related_stocks for event {event.data_id}: {e}")
# 处理解析后的股票数据
if parsed_stocks:
try:
import json
import ast
daily_changes = []
week_changes = []
# **修正：正确解析related_stocks数据结构**
if isinstance(event.related_stocks, str):
try:
# 先尝试JSON解析
stock_data = json.loads(event.related_stocks)
except:
# 如果JSON解析失败，尝试ast.literal_eval解析
stock_data = ast.literal_eval(event.related_stocks)
else:
stock_data = event.related_stocks
for stock_info in parsed_stocks:
stock_code = stock_info.get('code', '')
stock_name = stock_info.get('name', '')
description = stock_info.get('description', '')
score = stock_info.get('score', 0)
report = stock_info.get('report', None)
print(f"Parsed stock_data: {stock_data}") # 调试输出
if stock_code:
# 规范化股票代码，移除后缀
clean_code = stock_code.replace('.SZ', '').replace('.SH', '').replace('.BJ', '')
if stock_data:
daily_changes = []
week_changes = []
print(f"Processing stock: {clean_code} - {stock_name}") # 调试输出
# **修正：处理正确的数据格式 [股票代码, 股票名称, 描述, 分数]**
for stock_info in stock_data:
if isinstance(stock_info, list) and len(stock_info) >= 2:
stock_code = stock_info[0] # 第一个元素是股票代码
stock_name = stock_info[1] # 第二个元素是股票名称
description = stock_info[2] if len(stock_info) > 2 else ''
score = stock_info[3] if len(stock_info) > 3 else 0
else:
continue # 跳过格式不正确的数据
# 使用模糊匹配LIKE查询申万一级行业F004V
sector_query = """
SELECT F004V as sw_primary_sector
FROM ea_sector
WHERE SECCODE LIKE :stock_code_pattern
AND F002V = '申银万国行业分类' LIMIT 1 \
"""
sector_result = db.session.execute(text(sector_query),
{'stock_code_pattern': f'{clean_code}%'})
sector_row = sector_result.fetchone()
if stock_code:
# 规范化股票代码，移除后缀
clean_code = stock_code.replace('.SZ', '').replace('.SH', '').replace('.BJ', '')
# 根据申万一级行业（F004V）映射到主板块
sw_primary_sector = sector_row.sw_primary_sector if sector_row else None
primary_sector = sector_map.get(sw_primary_sector, '其他') if sw_primary_sector else '其他'
print(f"Processing stock: {clean_code} - {stock_name}") # 调试输出
print(
f"Stock: {clean_code}, SW Primary: {sw_primary_sector}, Primary Sector: {primary_sector}")
# 使用模糊匹配LIKE查询申万一级行业F004V
sector_query = """
SELECT F004V as sw_primary_sector
FROM ea_sector
WHERE SECCODE LIKE :stock_code_pattern
AND F002V = '申银万国行业分类' LIMIT 1 \
"""
sector_result = db.session.execute(text(sector_query),
{'stock_code_pattern': f'{clean_code}%'})
sector_row = sector_result.fetchone()
# 通过SQL查询获取真实的日涨跌幅和周涨跌幅
trade_query = """
SELECT F007N as close_price, F010N as change_pct, TRADEDATE
FROM ea_trade
WHERE SECCODE LIKE :stock_code_pattern
ORDER BY TRADEDATE DESC LIMIT 7 \
"""
trade_result = db.session.execute(text(trade_query),
{'stock_code_pattern': f'{clean_code}%'})
trade_data = trade_result.fetchall()
# 根据申万一级行业（F004V）映射到主板块
sw_primary_sector = sector_row.sw_primary_sector if sector_row else None
primary_sector = sector_map.get(sw_primary_sector, '其他') if sw_primary_sector else '其他'
daily_chg = 0
week_chg = 0
print(
f"Stock: {clean_code}, SW Primary: {sw_primary_sector}, Primary Sector: {primary_sector}")
if trade_data:
# 日涨跌幅（当日）
daily_chg = float(trade_data[0].change_pct or 0)
# 通过SQL查询获取真实的日涨跌幅和周涨跌幅
trade_query = """
SELECT F007N as close_price, F010N as change_pct, TRADEDATE
FROM ea_trade
WHERE SECCODE LIKE :stock_code_pattern
ORDER BY TRADEDATE DESC LIMIT 7 \
"""
trade_result = db.session.execute(text(trade_query),
{'stock_code_pattern': f'{clean_code}%'})
trade_data = trade_result.fetchall()
# 周涨跌幅（5个交易日）
if len(trade_data) >= 5:
current_price = float(trade_data[0].close_price or 0)
week_ago_price = float(trade_data[4].close_price or 0)
if week_ago_price > 0:
week_chg = ((current_price - week_ago_price) / week_ago_price) * 100
daily_chg = 0
week_chg = 0
print(
f"Trade data found: {len(trade_data) if trade_data else 0} records, daily_chg: {daily_chg}")
if trade_data:
# 日涨跌幅（当日）
daily_chg = float(trade_data[0].change_pct or 0)
# 统计各分类数量
sector_stats['全部股票'] += 1
sector_stats[primary_sector] += 1
# 周涨跌幅（5个交易日）
if len(trade_data) >= 5:
current_price = float(trade_data[0].close_price or 0)
week_ago_price = float(trade_data[4].close_price or 0)
if week_ago_price > 0:
week_chg = ((current_price - week_ago_price) / week_ago_price) * 100
# 收集涨跌幅数据
daily_changes.append(daily_chg)
week_changes.append(week_chg)
print(
f"Trade data found: {len(trade_data) if trade_data else 0} records, daily_chg: {daily_chg}")
related_stocks_list.append({
'code': stock_code, # 原始股票代码
'name': stock_name, # 股票名称
'description': description, # 关联描述
'score': score, # 关联分数
'sw_primary_sector': sw_primary_sector, # 申万一级行业（F004V）
'primary_sector': primary_sector, # 主板块分类
'daily_change': daily_chg, # 真实的日涨跌幅
'week_change': week_chg, # 真实的周涨跌幅
'report': report # 研报引用信息（新字段）
})
# 统计各分类数量
sector_stats['全部股票'] += 1
sector_stats[primary_sector] += 1
# 计算平均收益率
if daily_changes:
related_avg_chg = sum(daily_changes) / len(daily_changes)
related_max_chg = max(daily_changes)
# 收集涨跌幅数据
daily_changes.append(daily_chg)
week_changes.append(week_chg)
related_stocks_list.append({
'code': stock_code, # 原始股票代码
'name': stock_name, # 股票名称
'description': description, # 关联描述
'score': score, # 关联分数
'sw_primary_sector': sw_primary_sector, # 申万一级行业（F004V）
'primary_sector': primary_sector, # 主板块分类
'daily_change': daily_chg, # 真实的日涨跌幅
'week_change': week_chg # 真实的周涨跌幅
})
# 计算平均收益率
if daily_changes:
related_avg_chg = sum(daily_changes) / len(daily_changes)
related_max_chg = max(daily_changes)
if week_changes:
related_week_chg = sum(week_changes) / len(week_changes)
if week_changes:
related_week_chg = sum(week_changes) / len(week_changes)
except Exception as e:
print(f"Error processing related stocks: {e}")
import traceback
traceback.print_exc()
# 构建返回数据
# 构建返回数据，使用新字段回退后的值
detail_data = {
'id': event.data_id,
'title': event.title,
'type': event.type,
'star': event.star,
'calendar_time': event.calendar_time.isoformat() if event.calendar_time else None,
'former': event.former,
'forecast': event.forecast,
'former': former_value, # 使用回退后的值（优先 second_modified_text）
'forecast': forecast_value, # 使用回退后的值（优先 second_modified_text.1）
'fact': event.fact,
'concepts': event.concepts,
'extracted_concepts': extracted_concepts,