【Python技术】借助tushare、wencai完成前一交易日与最近交易日涨停变化分析

先上实际效果

看着上面的效果，你会想到什么？

1、涨停数变少，说明投机情绪在逐渐退潮

2、概念涨停变化，比如 风电、军工、光伏 涨停数多起来了，说明了资金开始切换到了相对低位。

3、越早涨停的，说明资金越认可。尽量观察 越早涨停的股，相对更强势。

程序我准备用streamlit构建应用界面， tushare获取交易日历数据， wencai获取涨停数分析。

另外程序运行时间可能要考虑的点：

比如今天是交易日，那么9点30之前执行， 基本是考虑10月22日、10月23日的涨停对比吧。 交易时间段执行 判断当天什么时候题材概念比较火热。

import streamlit as st
import pywencai
import pandas as pd
from datetime import datetime, timedelta
import tushare as ts
import plotly.graph_objects as go

# 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)

# Initialize Tushare with your token
ts.set_token('YOUR_TUSHARE_TOKEN')
pro = ts.pro_api()

def get_latest_trading_day():
    now = datetime.now()
    today = now.date()

    # Get the trading calendar for the past week
    cal_df = pro.trade_cal(exchange='', start_date=(today - timedelta(days=10)).strftime('%Y%m%d'), end_date=today.strftime('%Y%m%d'))

    # Filter for open trading days and sort in descending order
    open_days = cal_df[cal_df['is_open'] == 1]['cal_date'].sort_values(ascending=False)

    # Check if today is a trading day
    is_trading_day = cal_df[cal_df['cal_date'] == today.strftime('%Y%m%d')]['is_open'].iloc[0] == 1

    if is_trading_day:
        # If it's before 9：30 AM on a trading day, use the previous trading day
        if now.time() < datetime.strptime('09:30', '%H:%M').time():
            return open_days.iloc[1]
        else:
            return open_days.iloc[0]
    else:
        # If today is not a trading day, use the most recent trading day
        return open_days.iloc[0]

def get_previous_trading_day(date):
    # Get the trading calendar for the past week before the given date
    end_date = datetime.strptime(date, '%Y%m%d').date()
    start_date = end_date - timedelta(days=10)

    cal_df = pro.trade_cal(exchange='', start_date=start_date.strftime('%Y%m%d'), end_date=date)

    # Filter for open trading days and sort in descending order
    open_days = cal_df[cal_df['is_open'] == 1]['cal_date'].sort_values(ascending=False)

    # Return the previous trading day
    return open_days.iloc[1]  # iloc[1] because iloc[0] would be the input date itself

def get_limit_up_data(date):
    param = f"{date}涨停，非涉嫌信息披露违规且非立案调查且非ST，非科创板，非北交所"
    df = pywencai.get(query=param, sort_key='成交金额', sort_order='desc')
    selected_columns = ['股票代码', '股票简称', '最新价', '最新涨跌幅', f'首次涨停时间[{date}]', 
                        f'连续涨停天数[{date}]', f'涨停原因类别[{date}]', f'a股市值(不含限售股)[{date}]', 
                        f'涨停类型[{date}]']
    return df[selected_columns]

def get_concept_counts(df, date):
    concepts = df[f'涨停原因类别[{date}]'].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 main():
    st.title("A股涨停概念分析")

    today = get_latest_trading_day()
    yesterday = get_previous_trading_day(today)

    st.write(f"分析日期: {today} (最新交易日) 和 {yesterday} (前一交易日)")

    # Fetch data for both days
    today_df = get_limit_up_data(today)
    yesterday_df = get_limit_up_data(yesterday)

    # Get concept counts for both days
    today_concepts = get_concept_counts(today_df, today)
    yesterday_concepts = get_concept_counts(yesterday_df, yesterday)

    # Merge concept counts
    merged_concepts = pd.merge(today_concepts, yesterday_concepts, on='概念', how='outer', suffixes=('_today', '_yesterday'))
    merged_concepts = merged_concepts.fillna(0)

    # Calculate change
    merged_concepts['变化'] = merged_concepts['出现次数_today'] - merged_concepts['出现次数_yesterday']

    # Sort by '出现次数_today' in descending order
    sorted_concepts = merged_concepts.sort_values('出现次数_today', ascending=False)

    # Display total limit-up stocks for both days
    st.subheader("涨停股票数量变化")
    today_total = len(today_df)
    yesterday_total = len(yesterday_df)
    change = today_total - yesterday_total

    col1, col2, col3 = st.columns(3)
    col1.metric("昨日涨停数", yesterday_total)
    col2.metric("今日涨停数", today_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='今日', x=top_10_concepts['概念'], y=top_10_concepts['出现次数_today']),
        go.Bar(name='昨日', x=top_10_concepts['概念'], y=top_10_concepts['出现次数_yesterday'])
    ])

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

    # Display raw data
    st.subheader("今日涨停股票详情")
    st.dataframe(today_df)

if __name__ == "__main__":
    main()

原文链接： 【Python技术】利用tushare、wencai分析某天涨停股票次日涨跌幅情况

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