【Python技术】同花顺wencai涨停分析基础上增加连板分析

周末，有读者加我， 说 之前的涨停分析 是否可以增加连板分析。 这个可以加上。

先看效果

这里附上完整代码：

import streamlit as st
import pywencai
import pandas as pd
from datetime import datetime, timedelta
import plotly.graph_objects as go
from chinese_calendar import is_workday, is_holiday

# 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_previous_trading_day(date):
    previous_date = date - timedelta(days=1)
    while not is_workday(previous_date) or is_holiday(previous_date):
        previous_date -= timedelta(days=1)
    return previous_date


def get_limit_up_data(date):
    param = f"{date.strftime('%Y%m%d')}涨停，成交金额排序"
    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")}]'

    # 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])

    # 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'])

    # 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]]
        })
        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)
    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)

    if not is_workday(selected_date) or is_holiday(selected_date):
        st.write("所选日期不是A股交易日，请选择其他日期。")
        return

    previous_date = get_previous_trading_day(selected_date)

    st.write(f"分析日期: {selected_date} 和 {previous_date} (前一交易日)")

    # Fetch data for both days
    selected_df = get_limit_up_data(selected_date)
    previous_df = get_limit_up_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 stocks for both days
    st.subheader("涨停股票数量变化")
    selected_total = len(selected_continuous)
    previous_total = len(previous_continuous)
    change = selected_total - previous_total

    col1, col2, col3 = st.columns(3)
    col1.metric("前一交易日涨停数", previous_total)
    col2.metric("选定日期涨停数", selected_total)
    col3.metric("变化", change, f"{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)

    fig = go.Figure(data=[
        go.Bar(name='选定日期', x=top_10_concepts['概念'], y=top_10_concepts['出现次数_selected']),
        go.Bar(name='前一交易日', x=top_10_concepts['概念'], y=top_10_concepts['出现次数_previous'])
    ])

    fig.update_layout(barmode='group', title='Top 10 涨停概念对比')
    st.plotly_chart(fig)

    # Display continuous limit-up analysis
    st.subheader("连续涨停天数分析")
    st.dataframe(selected_continuous)

    # Create a bar chart for continuous limit-up days distribution
    continuous_days_count = selected_continuous['连续涨停天数'].value_counts().sort_index()

    fig_continuous = go.Figure(data=[
        go.Bar(x=continuous_days_count.index, y=continuous_days_count.values)
    ])

    fig_continuous.update_layout(
        title='连续涨停天数分布',
        xaxis_title='连续涨停天数',
        yaxis_title='股票数量',
        xaxis=dict(tickmode='linear')
    )

    st.plotly_chart(fig_continuous)

    # Display raw data
    st.subheader("选定日期涨停股票详情")
    st.dataframe(selected_df)

    st.subheader("连板晋级率分析")
    promotion_rates = calculate_promotion_rates(selected_df, previous_df, selected_date, previous_date)

    # Display promotion rates in a custom format
    for _, row in promotion_rates.iterrows():
        col1, col2 = st.columns([1, 3])
        with col1:
            st.write(f"**{row['连板数']}**")
            st.write(f"晋级率: {row['晋级率']}")

        with col2:
            if not row['股票列表'].empty:
                for _, stock in row['股票列表'].iterrows():
                    concept = stock[f'涨停原因类别[{selected_date.strftime("%Y%m%d")}]']
                    st.write(f"{stock['股票简称']} ({concept})")

        st.markdown("---")

    # Create visualization for promotion rates
    promotion_rates_fig = go.Figure()

    # Extract numeric values from promotion rates
    rates = []
    labels = []
    for _, row in promotion_rates.iterrows():
        if row['晋级率'] != 'N/A':
            rate = int(row['晋级率'].split('=')[1].replace('%', ''))
            rates.append(rate)
            labels.append(row['连板数'])

    promotion_rates_fig.add_trace(go.Bar(
        x=labels,
        y=rates,
        text=[f"{rate}%" for rate in rates],
        textposition='auto',
    ))

    promotion_rates_fig.update_layout(
        title='连板晋级率分布',
        xaxis_title='连板数',
        yaxis_title='晋级率 (%)',
        yaxis_range=[0, 100]
    )

    st.plotly_chart(promotion_rates_fig)


if __name__ == "__main__":
    app()

在原来代码基础上增加了 根据连板天数排序， 每支个股的涨停原因分析。 这个我之前没加， 是因为我很少关注连续涨停股， 毕竟我不是龙头选手。

另外增加了连板晋级率， 比如1进2,2进3 可以分析涨停板晋级概率。对于龙头选手有一定的辅助效果。

另外交易日判断，我之前肤浅了，主要是脑子短路了。根据读者提醒，改为引入日历控件chinese_calendar的is_workday, is_holiday 判断工作日、节假日。

原文链接：

【Python技术】同花顺wencai涨停分析基础上增加连板分析