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| # -*- coding: utf-8 -*-
# 导入相关的库和模块
from iFinDPy import *
from datetime import datetime
import pandas as pd
import time as _time
import json
from threading import Thread, Lock, Semaphore
import requests
# 创建一个信号量，用于控制最大并发数为5
sem = Semaphore(5)
# 创建一个锁，用于控制实时行情推送中数据写入到本地的过程
dllock = Lock()
# 登录函数
# 定义登录函数
def thslogindemo():
# 使用给定的用户名和密码进行登录
# thsLogin = THS_iFinDLogin('账号', '密码')
print(thsLogin)
# 根据登录结果，判断是否登录成功
if thsLogin != 0:
print('登录失败')
else:
print('登录成功')
# 定义函数，提取沪深300的全部股票在2020-11-16日的日不复权收盘价
def datepool_basicdata_demo():
# 通过数据池的板块成分函数和基础数据函数，提取沪深300的全部股票在2020-11-16日的日不复权收盘价
data_hs300 = THS_DP('block', '2020-11-16;001005290', 'date:Y,thscode:Y,security_name:Y')
if data_hs300.errorcode != 0:
print('error:{}'.format(data_hs300.errmsg))
else:
seccode_hs300_list = data_hs300.data['THSCODE'].tolist()
data_result = THS_BD(seccode_hs300_list, 'ths_close_price_stock', '2020-11-16,100')
if data_result.errorcode != 0:
print('error:{}'.format(data_result.errmsg))
else:
data_df = data_result.data
print(data_df)
# 定义函数，提取上证50的全部股票的最新价数据,并将其导出为csv文件
def datapool_realtime_demo():
# 通过数据池的板块成分函数和实时行情函数，提取上证50的全部股票的最新价数据,并将其导出为csv文件
today_str = datetime.today().strftime('%Y-%m-%d')
print('today:{}'.format(today_str))
data_sz50 = THS_DP('block', '{};001005260'.format(today_str), 'date:Y,thscode:Y,security_name:Y')
if data_sz50.errorcode != 0:
print('error:{}'.format(data_sz50.errmsg))
else:
seccode_sz50_list = data_sz50.data['THSCODE'].tolist()
data_result = THS_RQ(seccode_sz50_list, 'latest')
if data_result.errorcode != 0:
print('error:{}'.format(data_result.errmsg))
else:
data_df = data_result.data
print(data_df)
data_df.to_csv('realtimedata_{}.csv'.format(today_str))
# 定义函数，演示如何通过不消耗流量的自然语言语句调用常用数据
def iwencai_demo():
print('输出资金流向数据')
data_wencai_zjlx = THS_WC('主力资金流向', 'stock')
if data_wencai_zjlx.errorcode != 0:
print('error:{}'.format(data_wencai_zjlx.errmsg))
else:
print(data_wencai_zjlx.data)
print('输出股性评分数据')
data_wencai_xny = THS_WC('股性评分', 'stock')
if data_wencai_xny.errorcode != 0:
print('error:{}'.format(data_wencai_xny.errmsg))
else:
print(data_wencai_xny.data)
# 定义函数，处理实时行情推送数据
def dlwork(tick_data):
# 本函数为实时行情订阅新启线程的任务函数
dllock.acquire()
with open('dlwork.txt', 'a') as f:
for stock_data in tick_data['tables']:
if 'time' in stock_data:
timestr = _time.strftime('%Y-%m-%d %H:%M:%S', _time.localtime(stock_data['time'][0]))
print(timestr)
f.write(timestr + str(stock_data) + '\n')
else:
pass
dllock.release()
# 定义函数，提取数据并写入到文件
def work(codestr, lock, indilist):
sem.acquire()
stockdata = THS_HF(codestr, ';'.join(indilist), '', '2020-08-11 09:15:00', '2020-08-11 15:30:00', 'format:json')
if stockdata.errorcode != 0:
print('error:{}'.format(stockdata.errmsg))
sem.release()
else:
print(stockdata.data)
lock.acquire()
with open('test1.txt', 'a') as f:
f.write(str(stockdata.data) + '\n')
lock.release()
sem.release()
# 定义函数，演示如何使用多线程加速数据提取
def multiThread_demo():
# 本函数为通过高频序列函数,演示如何使用多线程加速数据提取的示例,本例中通过将所有A股分100组,最大线程数量sem进行提取
# 用户可以根据自身场景进行修改
today_str = datetime.today().strftime('%Y-%m-%d')
print('today:{}'.format(today_str))
data_alla = THS_DP('block', '{};001005010'.format(today_str), 'date:Y,thscode:Y,security_name:Y')
if data_alla.errorcode != 0:
print('error:{}'.format(data_alla.errmsg))
else:
stock_list = data_alla.data['THSCODE'].tolist()
indi_list = ['close', 'high', 'low', 'volume']
lock = Lock()
btime = datetime.now()
l = []
for eachlist in [stock_list[i:i + int(len(stock_list) / 10)] for i in
range(0, len(stock_list), int(len(stock_list) / 10))]:
nowstr = ','.join(eachlist)
p = Thread(target=work, args=(nowstr, lock, indi_list))
l.append(p)
for p in l:
p.start()
for p in l:
p.join()
etime = datetime.now()
print(etime - btime)
# 设置pandas的显示选项
pd.options.display.width = 320
pd.options.display.max_columns = None
# 定义函数，下载满足条件的公告的pdf
def reportDownload():
df = THS_ReportQuery('300033.SZ', 'beginrDate:2021-08-01;endrDate:2021-08-31;reportType:901',
'reportDate:Y,thscode:Y,secName:Y,ctime:Y,reportTitle:Y,pdfURL:Y,seq:Y').data
print(df)
for i in range(len(df)):
pdfName = df.iloc[i, 4] + str(df.iloc[i, 6]) + '.pdf'
pdfURL = df.iloc[i, 5]
r = requests.get(pdfURL)
with open(pdfName, 'wb+') as f:
f.write(r.content)
def main():
# 本脚本为数据接口通用场景的实例,可以通过取消注释下列示例函数来观察效果
# 登录函数
thslogindemo()
# 通过数据池的板块成分函数和基础数据函数，提取沪深300的全部股票在2020-11-16日的日不复权收盘价
datepool_basicdata_demo()
# 通过数据池的板块成分函数和实时行情函数，提取上证50的全部股票的最新价数据,并将其导出为csv文件
# datapool_realtime_demo()
# 演示如何通过不消耗流量的自然语言语句调用常用数据
# iwencai_demo()
# 本函数为通过高频序列函数,演示如何使用多线程加速数据提取的示例,本例中通过将所有A股分100组,最大线程数量sem进行提取
# multiThread_demo()
# 本函数演示如何使用公告函数提取满足条件的公告，并下载其pdf
# reportDownload()
if __name__ == '__main__':
main()
|
