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lof3.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Jun 15 21:06:30 2024
@author: polly
"""
import akshare as ak
import pandas as pd
import numpy as np
from flask import Flask, render_template_string
from concurrent.futures import ThreadPoolExecutor
def fetch_fund_data():
"""Fetch and process data from Akshare."""
def fetch_lof_spot():
return ak.fund_lof_spot_em()
def fetch_value_estimation():
return ak.fund_value_estimation_em()
def fetch_purchase_info():
return ak.fund_purchase_em().drop(columns=["序号", "基金简称"])
with ThreadPoolExecutor() as executor:
lof_future = executor.submit(fetch_lof_spot)
value_estimation_future = executor.submit(fetch_value_estimation)
purchase_info_future = executor.submit(fetch_purchase_info)
fund_lof_spot_df = lof_future.result()
fund_value_estimation_df = value_estimation_future.result()
fund_purchase_df = purchase_info_future.result()
fund_lof_spot_df = fund_lof_spot_df.reset_index()[['代码', '最新价', '成交额', '涨跌幅', '换手率']]
lof_list = fund_lof_spot_df["代码"].values
fund_value_estimation_df = fund_value_estimation_df[fund_value_estimation_df['基金代码'].isin(lof_list)]
fund_value_estimation_df = fund_value_estimation_df.drop(fund_value_estimation_df.columns[[0, 4, 5, 6, 7, 8]], axis=1)
fund_value_estimation_df = fund_value_estimation_df.rename(columns={fund_value_estimation_df.columns[2]: '估值'})
result_df = pd.merge(fund_value_estimation_df, fund_lof_spot_df, left_on='基金代码', right_on='代码', how='left')
result_df = result_df.drop(columns=['代码'])
result_df = pd.merge(result_df, fund_purchase_df, on='基金代码', how='left')
return result_df
def preprocess_data(df):
"""Preprocess the fetched data."""
df = df.replace('---', np.nan)
df['估值'] = df['估值'].astype(float)
df['最新价'] = df['最新价'].astype(float)
df['成交额'] = df['成交额'].fillna(0).astype(int)
df['涨跌幅'] = df['涨跌幅'].astype(float)
df['换手率'] = df['换手率'].astype(float)
df['最新净值'] = df['最新净值/万份收益'].astype(float)
df['购买起点'] = df['购买起点'].astype(int)
df['日累计限定金额'] = df['日累计限定金额'].astype('int64')
df['手续费'] = df['手续费'].astype(float)
return df
def calculate_premium_rate(df):
"""Calculate the premium rate for the funds."""
#计算基金溢价率。尽量用最新估值计算，如无法估值则用最新净值（QDII）
def calc_rate(row):
if pd.notnull(row['估值']):
return row['最新价'] / row['估值'] - 1
else:
return row['最新价'] / row['最新净值'] - 1
df['溢价率'] = df.apply(calc_rate, axis=1) * 100
df['溢价率'] = df['溢价率'].round(2)
df['溢价率abs'] = abs(df['溢价率'])
return df
def filter_funds(df):
"""Filter funds based on trading volume and premium rate criteria."""
#筛选成交额较高、折价或溢价明显、有潜在套利机会的基金
df = df[df["成交额"] >= 500000]
df = df[((df["溢价率"] >= 0.3) & (df["申购状态"] != "暂停申购")) |
((df["溢价率"] <= -0.7) & (df["赎回状态"] != "暂停赎回"))]
return df
def format_dataframe(df):
"""Format the dataframe columns and order."""
df = df.sort_values(by='溢价率abs', ascending=False)
new_column_order = [
'基金代码', '基金名称', '溢价率', '成交额', '日累计限定金额', '换手率',
'手续费', '申购状态', '赎回状态', '最新价', '最新净值/万份收益', '估值',
'购买起点', '涨跌幅', '基金类型', '最新净值/万份收益-报告时间',
'最新净值', '下一开放日', '溢价率abs'
]
df = df.reindex(columns=new_column_order)
df = df.drop(columns=['购买起点', '下一开放日', '溢价率abs', '最新净值/万份收益'])
df = df.rename(columns={'最新净值/万份收益-报告时间': '净值日期', '日累计限定金额': '限额'})
return df
def main():
"""Main function to fetch, process, and display fund data."""
# Display data using Flask
app = Flask(__name__)
@app.route('/')
def home():
# Fetch and process data
result_df = fetch_fund_data()
result_df = preprocess_data(result_df)
result_df = calculate_premium_rate(result_df)
result_df = filter_funds(result_df)
result_df = format_dataframe(result_df)
return render_template_string(
"""
<!doctype html>
<html lang="zh-cn">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<title>基金信息</title>
</head>
<body>
<h1>LOF套利分析工具 by Polly</h1>
<table border="1">
<thead>
<tr>
{% for col in df.columns %}
<th>{{ col }}</th>
{% endfor %}
</tr>
</thead>
<tbody>
{% for row in df.iterrows() %}
<tr>
{% for cell in row[1] %}
<td>{{ cell }}</td>
{% endfor %}
</tr>
{% endfor %}
</tbody>
</table>
</body>
</html>
""",
df=result_df
)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000)
# Run the main function
main()