import streamlit as st
import pywencai
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import akshare as ak
# Setting up pandas display options
pd.set_option('display.unicode.ambiguous_as_wide', True)
pd.set_option('display.unicode.east_asian_width', True)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.max_colwidth', 100)
def get_limit_up_data(date):
param = f"非ST,{date.strftime('%Y%m%d')}涨停"
df = pywencai.get(query=param, sort_key='成交金额'
, sort_order='desc', loop=True)
return df
def get_yesterday_zhangting_data(previous_date , date):
param = f"非ST,{previous_date.strftime('%Y%m%d')}涨停"
df = pywencai.get(query=param, sort_key='成交金额', sort_order='desc', loop=True)
return df
def get_poban(date):
param = f"非ST,{date.strftime('%Y%m%d')}曾涨停"
df = pywencai.get(query=param, sort_key='成交金额', sort_order='desc', loop=True)
return df
def get_limit_down_data(date):
param = f"非ST,{date.strftime('%Y%m%d')}跌停"
df = pywencai.get(query=param, sort_key='成交金额', sort_order='desc', loop=True)
return df
def analyze_continuous_limit_up(df, date):
# 提取连续涨停天数列和涨停原因类别列
continuous_days_col = f'连续涨停天数[{date.strftime("%Y%m%d")}]'
reason_col = f'涨停原因类别[{date.strftime("%Y%m%d")}]'
first_zhangting =f'首次涨停时间[{date.strftime("%Y%m%d")}]'
final_zhangting =f'最终涨停时间[{date.strftime("%Y%m%d")}]'
fengdanliang = f'涨停封单量[{date.strftime("%Y%m%d")}]'
fengdane = f'涨停封单额[{date.strftime("%Y%m%d")}]'
zhangting_type = f'涨停类型[{date.strftime("%Y%m%d")}]'
# 确保涨停原因类别列存在
if reason_col not in df.columns:
df[reason_col] = '未知'
# 按连续涨停天数降序排序，然后按涨停原因类别排序
df_sorted = df.sort_values([continuous_days_col, reason_col], ascending=[False, True])
print(df_sorted)
# 创建结果DataFrame
result = pd.DataFrame(columns=['连续涨停天数', '股票代码', '股票简称', '涨停原因类别', '最新价','首次涨停时间','最终涨停时间','涨停封单量','涨停封单额','涨停类型'])
# 遍历排序后的DataFrame，为每只股票创建一行
for _, row in df_sorted.iterrows():
new_row = pd.DataFrame({
'连续涨停天数': [row[continuous_days_col]],
'股票代码': [row['股票代码']],
'股票简称': [row['股票简称']],
'涨停原因类别': [row[reason_col]],
'最新价': [row['最新价']],
'首次涨停时间': [row[first_zhangting]],
'最终涨停时间': [row[final_zhangting]],
'涨停封单量': [row[fengdanliang]],
'涨停封单额': [row[fengdane]],
'涨停类型': [row[zhangting_type]],
})
result = pd.concat([result, new_row], ignore_index=True)
return result
def get_concept_counts(df, date):
concepts = df[f'涨停原因类别[{date.strftime("%Y%m%d")}]'].str.split('+').explode().reset_index(drop=True)
#concepts = df[f'涨停原因类别[{date.strftime("%Y%m%d")}]'].str.split('+', n=1).str[0].reset_index(drop=True)
concept_counts = concepts.value_counts().reset_index()
concept_counts.columns = ['概念', '出现次数']
return concept_counts
def calculate_promotion_rates(current_df, previous_df, current_date, previous_date):
"""Calculate promotion rates between consecutive days"""
current_days_col = f'连续涨停天数[{current_date.strftime("%Y%m%d")}]'
previous_days_col = f'连续涨停天数[{previous_date.strftime("%Y%m%d")}]'
promotion_data = []
# Calculate for each level (from 1 to max consecutive days)
max_days = max(current_df[current_days_col].max(), previous_df[previous_days_col].max())
for days in range(1, int(max_days)):
# Previous day count for current level
prev_count = len(previous_df[previous_df[previous_days_col] == days])
# Current day count for next level
curr_count = len(current_df[current_df[current_days_col] == days + 1])
if prev_count > 0:
promotion_rate = f"{curr_count}/{prev_count}={round(curr_count / prev_count * 100 if prev_count > 0 else 0)}%"
else:
promotion_rate = "N/A"
# Get stocks that promoted
promoted_stocks = current_df[current_df[current_days_col] == days + 1][
['股票简称', f'涨停原因类别[{current_date.strftime("%Y%m%d")}]']]
promotion_data.append({
'连板数': f"{days}板{days + 1}",
'晋级率': promotion_rate,
'股票列表': promoted_stocks
})
return pd.DataFrame(promotion_data)
def app():
st.title("A股涨停概念分析")
# Date selection
max_date = datetime.now().date()
selected_date = st.date_input("选择分析日期", max_value=max_date, value=max_date)
trade_date_range = ak.tool_trade_date_hist_sina()
trade_date_range['trade_date'] = pd.to_datetime(trade_date_range['trade_date']).dt.date
if selected_date not in trade_date_range['trade_date'].values:
st.write("所选日期不是A股交易日，请选择其他日期。")
return
target_date = selected_date
previous_dates = trade_date_range[trade_date_range['trade_date'] < target_date]
if previous_dates.empty:
raise ValueError("No previous trading day found before the given date")
# 获取最近的交易日
previous_date = previous_dates['trade_date'].max()
st.write(f"分析日期: {selected_date} 和 {previous_date} (前一交易日)")
# 获取关键数据
selected_df = get_limit_up_data(selected_date) # 今日涨停
previous_df = get_limit_up_data(previous_date) # 昨日涨停
poban_df = get_poban(selected_date) # 今日曾涨停
yesterdayZhangting = get_yesterday_zhangting_data(previous_date, selected_date) # 昨日涨停股票
# 计算关键指标 ----------------------------------------------------------
# 昨日涨停股票列表
yesterday_zt_stocks = yesterdayZhangting['股票代码'].tolist() if not yesterdayZhangting.empty else []
# 今日涨停股票列表
today_zt_stocks = selected_df['股票代码'].tolist() if not selected_df.empty else []
# 连板率计算（昨日涨停今日继续涨停）
lianban_molecule = len(set(yesterday_zt_stocks) & set(today_zt_stocks))
lianban_denominator = len(yesterday_zt_stocks)
lianban_rate = (lianban_molecule / lianban_denominator * 100) if lianban_denominator > 0 else 0
# 破板率计算（曾涨停但未封板）
poban_stocks = poban_df['股票代码'].tolist() if not poban_df.empty else []
poban_molecule = len(poban_stocks)
poban_denominator = len(poban_stocks) + len(today_zt_stocks)
poban_rate = (poban_molecule / poban_denominator * 100) if poban_denominator > 0 else 0
# 昨日涨停今日涨幅（获取实际涨幅数据）
yesterday_today_pct = get_yesterday_zhangting_data(previous_date,selected_date)
# 计算上涨比例
yesterday_today_pct['最新涨跌幅'] = pd.to_numeric(yesterday_today_pct['最新涨跌幅'], errors='coerce')
# Calculate up_count, ignoring NaN values
up_count = np.sum(yesterday_today_pct['最新涨跌幅'] > 0)
# Calculate total_count, excluding NaN values
total_count = yesterday_today_pct['最新涨跌幅'].count()
# Calculate up_rate
up_rate = (up_count / total_count * 100) if total_count > 0 else 0
# 展示关键指标 ----------------------------------------------------------
st.subheader("情绪指标")
col1, col2, col3 = st.columns(3)
# 昨日涨停今日上涨率
col1.metric(
"昨日涨停今日上涨率",
f"{up_count}/{total_count}={up_rate:.1f}%",
help="昨日涨停的股票中今日上涨的比例"
)
# 连板率
col2.metric(
"连板晋级率",
f"{lianban_molecule}/{lianban_denominator}={lianban_rate:.1f}%",
help="昨日涨停股票今日继续涨停的比例"
)
# 破板率
col3.metric(
"涨停破板率",
f"{poban_molecule}/{poban_denominator}={poban_rate:.1f}%",
help="今日曾触及涨停但收盘未封板的比例"
)
# Fetch data for both days
selected_df = get_limit_up_data(selected_date)
previous_df = get_limit_up_data(previous_date)
selected_limit_down_df = get_limit_down_data(selected_date)
previous_limit_down_df = get_limit_down_data(previous_date)
# Analyze continuous limit-up for both days
selected_continuous = analyze_continuous_limit_up(selected_df, selected_date)
previous_continuous = analyze_continuous_limit_up(previous_df, previous_date)
# Get concept counts for both days
selected_concepts = get_concept_counts(selected_df, selected_date)
previous_concepts = get_concept_counts(previous_df, previous_date)
# Merge concept counts
merged_concepts = pd.merge(selected_concepts, previous_concepts, on='概念', how='outer',
suffixes=('_selected', '_previous'))
merged_concepts = merged_concepts.fillna(0)
# Calculate change
merged_concepts['变化'] = merged_concepts['出现次数_selected'] - merged_concepts['出现次数_previous']
# Sort by '出现次数_selected' in descending order
sorted_concepts = merged_concepts.sort_values('出现次数_selected', ascending=False)
# Display total limit-up and limit-down stocks for both days
st.subheader("涨停和跌停股票数量变化")
# 计算涨停和跌停数量
selected_total = len(selected_continuous) if selected_continuous is not None else 0
previous_total = len(previous_continuous) if previous_continuous is not None else 0
change = selected_total - previous_total
def get_safe_limit_down_total(limit_down_df):
"""安全地获取跌停股票数量，如果数据为 None 则返回 0"""
return len(limit_down_df) if limit_down_df is not None else 0
selected_limit_down_total = len(selected_limit_down_df) if selected_limit_down_df is not None else 0
previous_limit_down_total = len(previous_limit_down_df) if previous_limit_down_df is not None else 0
limit_down_change = selected_limit_down_total - previous_limit_down_total
# 计算涨停环比百分比变化
if previous_total != 0:
percentage_change_limit_up = (change / previous_total) * 100
else:
percentage_change_limit_up = 0
# 计算跌停环比百分比变化
if previous_limit_down_total != 0:
percentage_change_limit_down = (limit_down_change / previous_limit_down_total) * 100
else:
percentage_change_limit_down = 0
# 显示合并后的涨停和跌停数量
col1, col2, col3 = st.columns(3)
col1.metric("上交易日涨跌停数", f"{previous_total} : {previous_limit_down_total}")
col2.metric("选定日期涨跌停数", f"{selected_total} : {selected_limit_down_total}")
col3.metric("涨停变化 ： 跌停变化"
, f"{change:+d} : {limit_down_change:+d}")
# Display concept changes
st.subheader("涨停概念变化")
st.dataframe(sorted_concepts)
# Create a bar chart for top 10 concepts
top_10_concepts = sorted_concepts.head(10)
# Display continuous limit-up analysis
st.subheader("连续涨停天数分析")
st.dataframe(selected_continuous)
st.subheader("连板晋级率分析")
promotion_rates = calculate_promotion_rates(selected_df, previous_df, selected_date, previous_date)
# 将DataFrame转换为字典列表
promotion_list = promotion_rates.to_dict('records')
# 每行显示3个
for i in range(0, len(promotion_list), 3):
cols = st.columns(3)
group = promotion_list[i:i + 3]
for j in range(len(group)):
with cols[j]:
item = group[j]
# 使用卡片式布局
st.markdown(f"""
{item['连板数']}
晋级率: {item['晋级率']}
""", unsafe_allow_html=True)
# 显示股票列表
if not item['股票列表'].empty:
for _, stock in item['股票列表'].iterrows():
concept = stock[f'涨停原因类别[{selected_date.strftime("%Y%m%d")}]']
st.markdown(f"""
{stock['股票简称']}
{concept}
""", unsafe_allow_html=True)
st.markdown("
if __name__ == "__main__":
#st.set_page_config(layout="wide")
app()
