get_day_all_stock_infov4.py

#
# exec script
# 计算股票昨日涨跌 前200，和100日之前的涨跌对比


import os
import sys
import signal
import threading,time
import queue
import pandas as pd
from datetime import datetime
import baostock as bs
import json
import argparse
import requests
from common.framework import init_chouma,get_chouma

# 判断是否 是显示，还是重新下载数据计算
# 数据每天只需要下载一次

parser = argparse.ArgumentParser()
parser.add_argument("--display", help="显示本地数据",default='0')
parser.add_argument("--ishtml", help="生成html格式",default='1')
parser.add_argument("--save_db", help="存储到远程数据库",default='0')
parser.add_argument("--file", help="存文件",default='0')
parser.add_argument("--endday", help="日期",default='0')
args = parser.parse_args()

display = args.display
ishtml = args.ishtml
filename = args.file
save_db = args.save_db
endday = args.endday

requests.adapters.DEFAULT_RETRIES = 5
####################
#1. 获取股票数据
####################
#hostname = "http://klang.org.cn"
hostname = "http://klang.zhanluejia.net.cn"

today = datetime.now()
if endday== '0':
	endday = str(today.year) + str(today.month) + str(today.day)

if filename == '0':
    filename = './datas/stock_'+endday+'.html'
	
# print 打印color 表
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'

#所有的股票表计算后的数据表
all_up_down_list=[]

# 处理异常，在出现异常的时候存盘
def handler(signum, frame):
	print("是不是想让我退出啊")
	make_save_data()	
	sys.exit()

#存盘并且打印
def make_save_data():
	df = pd.DataFrame(all_up_down_list, columns = ['名称','date','代码','当日收盘','前日收盘','21日收盘','百日收盘','当日涨跌','21日涨跌','百日涨跌','流通股值','行业','筹码'])
	df.to_csv("./datas/stock_up_down_{0}.csv".format(endday),float_format='%.2f',index_label="序号")


def create_clickable_code(code):
    code = code.replace(".","")
    url_template= '''<a href="https://gu.qq.com/{code}" target="_blank"><font color="blue">{code}</font></a>'''.format(code=code)
    return url_template
def create_clickable_name(name):
    url_template= '''<a href="http://so.eastmoney.com/News/s?keyword={name}" target="_blank"><font color="blue">{name}</font></a>'''.format(name=name)
    return url_template

def create_color_rise1(rise):
    url_template= '''<font color="#ef4136">{rise}</font></a>'''.format(rise=rise)
    return url_template

def create_color_hqltgz(hqltsz):
    if hqltsz >= 300.0:
        url_template= '''<font color="#ef4136">{hqltsz}</font></a>'''.format(hqltsz=hqltsz)
    else:
        url_template = '''{hqltsz}'''.format(hqltsz=hqltsz)
    return url_template

"""
    name: String,
    code: String,
    date: String,
    industry: String,
    close: Number,
    close1: Number,
    close21: Number,
    close100: Number,
    rise1: Number,
    rise21: Number,
    rise100: Number,
"""

#仅仅显示
def display_save_data():
    df= pd.read_csv("./datas/stock_up_down_{0}.csv".format(endday))
	
    del df['序号']
    #df = df.set_index('date')
    print("当日涨幅榜单:")
    df = df.sort_values(by="当日涨跌",ascending=False).reset_index()
    del df['index']
    if ishtml == "1":
        df['代码'] = df['代码'].apply(create_clickable_code)
        #df['名称'] = df['名称'].apply(create_clickable_name)
        df['当日涨跌'] = df['当日涨跌'].apply(create_color_rise1)
        df['流通股值'] = df['流通股值'].apply(create_color_hqltgz)
        #print(df.iloc[0:200]
        #    .reset_index(drop=True)
        #    .style.set_table_attributes('border="1" class="table"').render())
        from common.common import save_file
        content = "当日涨幅榜单:\n"
        content += df.iloc[0:200].to_html(escape=False)
        content += "当日涨幅榜，之前超跌榜\n"
        print("当日涨幅榜，之前超跌榜")
        
        df = df.iloc[0:200].sort_values(by="百日涨跌",ascending=True)
        print(df.to_html(escape=False))
        print("save to ", 'file://'+os.getcwd()+ '/' +filename)
        save_file(filename,content + df.to_html(escape=False))
    else:
        print(df.iloc[0:200])
    print("注：当日涨跌是date日期和他前一个交易日比较,百日涨跌是date日期和100天的股价比较")

def get_day_data(code,name):

    #json = requests.get("http://127.0.0.1:1337/dayks",
    #    	params={"code":code,"end":endday,"limit":150},timeout=1000).json()
    try:
        json = requests.get(hostname+"/dayks",
        	params={"code":code,"end":endday,"limit":150},timeout=1000).json()
    except:
        time.sleep(2)
        json = requests.get(hostname+"/dayks",
        	params={"code":code,"end":endday,"limit":150},timeout=1000).json()
	
    df = pd.io.json.json_normalize(json)
	
    if len(df) < 2:
       return df
    df = df.drop(columns=['_id','codedate'])
    df = df.sort_values(by="date",ascending=True)


    return df

def upordown(code,date,name,industry,lastday,lastday1,lastday21,lastday100,hqltsz):
	lastday = float(lastday)
	lastday1 = float(lastday1)
	lastday21 = float(lastday21)
	lastday100 = float(lastday100)
	delta1 = (lastday-lastday1)/lastday1 * 100.0

	delta21 = 0
	delta100 = 0
	chouma = get_chouma(code)

	if lastday21 > 0:
		delta21 = (lastday-lastday21)/lastday21 * 100.0
	if lastday100 > 0:
		delta100 = (lastday-lastday100)/lastday100 * 100.0
	code = code.replace(".","")
	
	print(OKBLUE)
	print("%.2f %.2f %.2f %.2f %.2f %.2f %.2f %s %s" %(lastday,lastday1,
		lastday21,
		lastday100,
		delta1,
		delta21,
		delta100,
		name,code)
		) 
	print(ENDC)

	all_up_down_list.append([
        name,date,code,
		lastday,lastday1,
		lastday21,
		lastday100,
		delta1,
		delta21,
		delta100,
		hqltsz*lastday,
        industry,
        chouma
	])	

		
#获取股票的名字和代码号
def getstockinfo(stock):
	#2019-12-09,sz.002094,青岛金王,化工,申万一级行业
	# 时间，股票代码，名称，类别
	d,code,name,industry,skip2,tdxbk,tdxgn= stock.split(',')
	return code,name,tdxbk,tdxgn

#获取所有的股票并下载数据
def handler_data_thread(n):
    while True:
        code,date,name,industry,lastday,lastday1,lastday21,lastday100,hqltsz = q.get()
        print('正在分析',name,'代码',code,hqltsz)
        upordown(code,date,name,industry,lastday,lastday1,lastday21,lastday100,hqltsz)
        q.task_done() #每次做完任务就通知 join，jion收到最后一条通知就主程序退出

def get_data():
	for stock in stocklist:
		code ,name,industry,tdxgn = getstockinfo(stock)
		print('正在获取',name,'代码',code)
		df = get_day_data(code,name)
		#print(df)
		if len(df) > 2:
			try:
			    date = df.date[df.index[-1]]
			    turn = df.turn[df.index[-1]] 
			    volume = df.volume[df.index[-1]]
			    hqltsz = volume / turn / 1000000 
			    lastday  = df.close[df.index[-1]]
			    lastday1 = df.close[df.index[-2]]
			except:
			    continue
		else:
			continue
		lastday21 = 0
		if len(df) > 21:
			lastday21 = df.close[df.index[-21]] 
		lastday100 = 0
		if len(df) > 99:
			lastday100 = df.close[df.index[-100]] 
		q.put((code,date,name,industry,lastday,lastday1,lastday21,lastday100,hqltsz))

#
# 程序开始，监听信号
#
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGHUP, handler)
signal.signal(signal.SIGTERM, handler)
q = queue.Queue()

# 判断是否已经下载了股票分类代码
filename_sl = os.path.expanduser("~/.klang_stock_list.csv")

if not os.path.exists(filename_sl):
	print('正在下载股票库列表....')
	os.system('python3 bs_get_industry_check.py')

stocklist = open(filename_sl).readlines()
stocklist = stocklist[1:] #删除第一行

init_chouma()

# 判断是仅仅显示，还是需要下载数据计算
if display == '1':
    display_save_data()
else:
    t = threading.Thread(target=handler_data_thread,args=(1,))
    t.start()

    get_data()

    q.join() #等待任务完成
    make_save_data()	
    display_save_data()
    os._exit(os.EX_OK)