Get the median premium rate of stock conversion of Tonghuashun.

Python Environment Configuration#

For detailed installation steps, please refer to Python Installation and Environment Configuration Detailed Tutorial

iFind Python Environment Configuration#

Click to Download Tonghuashun Data Interface
For first-time download users, please open the SuperCommand file in the folder and log in to your account
After logging in, the system will prompt you to repair the relevant programming language environment. Please follow the instructions to repair the related environment
Alternatively, after logging in, click on the environment settings option in the tools menu, select Python, and then click the OK button
Then select the installed Python path and click Continue

Install Pycharm#

For detailed installation steps, please refer to Pycharm Installation Tutorial

Running Python Programs#

"""
Author: binxin
Date: 2023/11/18 14:44
"""
import os
import numpy as np
import pandas as pd
from iFinDPy import *
import datetime


# Login function
def login(username, password):
    thsLogin = THS_iFinDLogin(username, password)
    if thsLogin != 0:
        print('Login failed')
    else:
        print('Login successful')


# Get data
def get_data(edate):
    get_str = 'edate=' + edate + ';zqlx=全部'
    data_p00868 = THS_DR('p00868', get_str, 'p00868_f027:Y,p00868_f022:Y', 'format:list')
    return data_p00868


# Save data to Excel
def save_to_excel(file_name, str_date, premium):
    if not os.path.exists(file_name):
        data = {"Date": [str_date], "Convertible Premium%": [premium]}
        df = pd.DataFrame(data)
    else:
        df = pd.read_excel(file_name)
        new_data = pd.DataFrame({"Date": [str_date], "Convertible Premium%": [premium]})
        df = pd.concat([df, new_data], ignore_index=True)

    df.to_excel(file_name, index=False)


# Calculate median
def calculate_median(data):
    # Convert value range
    max_value = 101
    min_value = 99
    float_values = []

    data_f022 = data['p00868_f022']
    data_f027 = data['p00868_f027']

    for f027, f022 in zip(data_f027, data_f022):
        if '--' in f027 or '--' in f022:
            continue

        f027_value = float(f027)
        f022_value = float(f022)

        if min_value <= f027_value <= max_value:
            float_values.append(f022_value)

    return np.median(float_values) if float_values else None


# Get data - single day
def get_today_data():
    today = datetime.date.today()
    edate = today.strftime("%Y%m%d")
    return get_data(edate)


# Get data - interval
def get_interval_data(start_date, end_date):
    delta = datetime.timedelta(days=1)
    data_list = []

    while start_date <= end_date:
        edate = start_date.strftime("%Y%m%d")
        data = get_data(edate)
        if data.data is not None:
            data_list.append((start_date.strftime("%Y/%m/%d"), data))
        start_date += delta

    return data_list

# Main function
def main():
    username = "username"
    password = "password"
    login(username, password)

    # Get data for today
    # today_data = get_today_data()
    # if today_data.data is not None:
    #     today_median = calculate_median(today_data.data[0]['table'])
    #     if today_median is not None:
    #         save_to_excel("Convertible Premium Rate Records (Conversion Value).xlsx", datetime.date.today().strftime("%Y/%m/%d"), today_median)

    # Get data for interval
    start_date = datetime.date(2023, 11, 1)
    end_date = datetime.date(2023, 11, 18)
    interval_data = get_interval_data(start_date, end_date)

    for date, data in interval_data:
        median_value = calculate_median(data.data[0]['table'])
        if median_value is not None:
            save_to_excel("Convertible Premium Rate Records (Conversion Value).xlsx", date, median_value)


if __name__ == '__main__':
    main()