Python-Based Fund Market Data Extraction and Analysis

The article begins with a background explaining that the goal is to extract and analyze fund data from the Chinese fund market to help beginners assess whether a fund is worth buying.

Data acquisition : Required packages such as pandas, requests, BeautifulSoup, numpy, and matplotlib are imported, and the Eastmoney JavaScript file is fetched to obtain all fund codes, names, and types, which are then saved to an Excel file.

import pandas as pd
import re
import numpy as np
from bs4 import BeautifulSoup
import requests
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['font.sans-serif']='SimHei'
plt.rcParams['axes.unicode_minus']=False

After loading the data into a DataFrame, a bar chart visualizes the distribution of fund types, showing that mixed, bond, open‑ended bond, money‑market, and stock‑index funds dominate the market.

Fund‑specific analysis : A function get_fund_data is defined to request historical net‑value data for a given fund code and date range, handling pagination and missing values, and returning a cleaned DataFrame.

def get_fund_data(code, per=10, sdate='', edate='', proxies=None):
    url = 'http://fund.eastmoney.com/f10/F10DataApi.aspx'
    params = {'type':'lsjz','code':code,'page':1,'per':per,'sdate':sdate,'edate':edate}
    # ... pagination loop ...
    return data

The script fetches data for the "招商中证白酒" fund (code 161725) from 2015‑01‑01 to 2020‑12‑22, then converts columns to appropriate types, removes the percentage sign from the daily growth rate, and adds a benchmark column.

data['净值日期'] = pd.to_datetime(data['净值日期'], format='%Y/%m/%d')
data['单位净值'] = data['单位净值'].astype(float)
# ... other conversions ...

Two sub‑plots are created: one showing unit and cumulative net values over time, and another showing daily growth rate together with a benchmark line.

fig = plt.figure(figsize=(16,10), dpi=240)
ax1 = fig.add_subplot(211)
ax1.plot(net_value_date, net_asset_value, label='基金净值')
# ... other plotting commands ...
plt.show()

Further analysis adds a "year" column, counts positive and negative daily growth days per year, calculates the proportion of positive days, and computes the average daily growth rate for each year, revealing that most years have more positive than negative days and that the fund generally trends upward.

data1['年'] = data1['净值日期'].dt.year
# ... groupby and aggregation ...

The conclusion notes that while the fund shows a long‑term upward trend and relatively stable daily growth, investors should still consider other factors and risk management before making a purchase.