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Copy path北向资金持仓分析.py
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北向资金持仓分析.py
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import requests as req #web请求相关
import re,json,sys,datetime
from dateutil.relativedelta import relativedelta
import prettytable as pt #格式化成表格输出到html文件
from pyecharts.charts import Bar, Page,Line #画图
from pyecharts import options as opts
import pandas as pd #数据读取
import webbrowser #打开浏览器
'''手动安装 talib 去https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib 下载对应的版本“TA_Lib‑0.4.19‑cp37‑cp37m‑win_amd64.whl” 然后 pip3 install TA_Lib‑0.4.19‑cp37‑cp37m‑win_amd64.whl'''
import talib #Technical Analysis Library”, 即技术分析库 是Python金融量化的高级库，涵盖了150多种股票、期货交易软件中常用的技术分析指标，如MACD、RSI、KDJ、动量指标、布林带等等。
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec#分割子图
import mpl_finance as mpf # python中可以用来画出蜡烛图、线图的分析工具，目前已经从matplotlib中独立出来，非常适合用来画K线
import tushare as ts
pro = ts.pro_api('d0bf482fc51bedbefa41bb38877e169a43d00bd9ebfa1f21d28151c7')
import warnings
warnings.filterwarnings('ignore') #控制台不输出warning 信息
outfile=''
###获取股票代码
def get_stockcode(stockname):
if stockname.isdigit(): #如果输入的是代码
return stockname
else:
stockdata =pd.DataFrame(pro.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date'))
#print(stockdata)
for stock in stockdata.iterrows():
#print(stock)
if stockname == stock[1]['name']:
#print(stock[1]['name'])
#print(str(stock[1]['ts_code'])[0:6])
return str(stock[1]['ts_code'])[0:6]
else:
continue
# stocklist='./个股信息列表.txt'
# readata=pd.read_csv(stocklist,sep=',',header=0,names=['代码','名称'])
# for row in readata.iterrows():
# #print(row)
# if row[1]['名称']==stockname:
# return str(row[1]['代码']).rjust(6,'0')
# else:
# continue
def get_stockname(stockcode):
if stockcode.isdigit(): #如果输入的是代码
stockdata =pd.DataFrame(pro.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date'))
#print(stockdata)
for stock in stockdata.iterrows():
#print(stock)
if stockcode in stock[1]['ts_code']:
print(stock[1]['name'])
#print(str(stock[1]['ts_code'])[0:6])
return str(stock[1]['name'])
else:
continue
else:
return stockcode
#获个股日线数据
def get_stock_dateData(stockcode,start_date,end_date):
if stockcode[0:2] =='60' or stockcode[0:2]=='68':
stockcode=stockcode+'.SH'
else:
stockcode = stockcode+'.SZ'
#从tushare 获取日线数据
#print(stockcode,start_date,end_date)
df = pro.daily(ts_code=stockcode, start_date=start_date,end_date=end_date)
df=df.sort_values(by=['trade_date'],ascending=True) #按日期升序
return df
def format_tohtml(listdata):
if len(listdata)==0:
return
header = ['日期', '股票代码 ', '股票名称 ', '持股数亿', '占比', '收盘价 ', '当日涨跌幅 ', '持股市值亿 ', '一日市值变化亿', '五日市值变化亿', '十日市值变化亿']
tb = pt.PrettyTable()
tb.field_names = header # 设置表头
tb.align = 'l' # 对齐方式（c:居中，l居左，r:居右）
c = Line()
c1 = Line()
page = Page()
x = ['持股占比']
HDDATElist = []
SHAREHOLDSUMlist = [] # 持股数
SHARESRATElist = [] # 持股占比
# SHAREHOLDlist=[]#持股数量
CLOSEPRICElist = [] #收盘价
#数据分类格式化
for data in listdata:
#print(data+'\n----------------------------------------')
jsdata = json.loads(data)
#print(type(jsdata), jsdata)
HDDATE = str(jsdata['HDDATE'])[0:10]
HDDATE = datetime.datetime.strptime(HDDATE, '%Y-%m-%d').strftime('%Y%m%d')
HDDATElist.append(HDDATE)
SCODE = jsdata['SCODE']
SNAME = jsdata['SNAME']
SHAREHOLDSUM = format(jsdata['SHAREHOLDSUM'] / 100000000, '.3f')
SHAREHOLDSUMlist.append(SHAREHOLDSUM)
SHARESRATE = jsdata['SHARESRATE']
SHARESRATElist.append(SHARESRATE)
CLOSEPRICE = jsdata['CLOSEPRICE']
CLOSEPRICElist.append(CLOSEPRICE)
ZDF = jsdata['ZDF']
SHAREHOLDPRICE = format(jsdata['SHAREHOLDPRICE'] / 100000000, '.3f')
SHAREHOLDPRICEONE = format(jsdata['SHAREHOLDPRICEONE'] / 100000000, '.3f')
SHAREHOLDPRICEFIVE = format(jsdata['SHAREHOLDPRICEFIVE'] / 100000000, '.3f')
SHAREHOLDPRICETEN = format(jsdata['SHAREHOLDPRICETEN'] / 100000000, '.3f')
# # 打印结果
# print('%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t' % (
# date, code, name, kaipanhuanshuoz, kaipanjine, liangbi, xianliang, liutongsizhi, liutongguyi, xifenhangye))
tb.add_row(
[HDDATE, SCODE, SNAME, SHAREHOLDSUM, SHARESRATE, CLOSEPRICE, ZDF, SHAREHOLDPRICE, SHAREHOLDPRICEONE,
SHAREHOLDPRICEFIVE, SHAREHOLDPRICETEN])
##################图表输出
x1 = HDDATElist[::-1]
y1 = SHARESRATElist[::-1] # 将占比数据设置为y轴
y2 = SHAREHOLDSUMlist[::-1]
y3 = CLOSEPRICElist[::-1]
# y2 = [1000, 300, 500]
# bar = Bar()
# 设置x轴
c.add_xaxis(xaxis_data=x)
c.add_xaxis(xaxis_data=x1)
# 设置y轴
c.add_yaxis(series_name='持股百分比', y_axis=y1)
c.add_yaxis(series_name='持股数量亿', y_axis=y2)
c.set_global_opts(title_opts=opts.TitleOpts(title='北向资金持股分析: ' + SNAME))
# 生成html文件
outfile = '北向资金_' + SNAME + '.html'
# c.render(path=outfile)
#输出K线图
#先获取日线历史数据
date=datetime.date.today() - relativedelta(months=+4) #当前日期减2个月
date=datetime.datetime.strptime(str(date), '%Y-%m-%d').strftime('%Y%m%d')
#print(date)
getstockdata=get_stock_dateData(SCODE,str(date), x1[-1])
#getstockdata = pd.DataFrame(getstockdata)
#print(getstockdata)
getstockdata['trade_date'] = pd.to_datetime(getstockdata['trade_date']) #设置字段trade_date 为datetime
getstockdata = getstockdata.set_index('trade_date') #设置trade_date为索引
#getstockdata.sort_values(by=['trade_date','close'],ascending=False)
#设置四个绘图区域 包括 K线（均线），成交量，MACD
np.seterr(divide='ignore', invalid='ignore') # 忽略warning
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
fig,ax = plt.subplots(figsize=(9 , 6)) # 创建fig对象
# 画绘图区域
gs = gridspec.GridSpec(2, 1, left=0.08, bottom=0.15, right=0.99, top=0.96, wspace=None, hspace=0,height_ratios=[3.5, 1])
#添加指标
graph_KAV = fig.add_subplot(gs[0, :]) #K线图
graph_VOL = fig.add_subplot(gs[1, :])
#graph_MACD = fig.add_subplot(gs[2, :])
#graph_KDJ = fig.add_subplot(gs[3, :])
mpf.candlestick2_ochl(graph_KAV, getstockdata.open, getstockdata.close, getstockdata.high, getstockdata.low, width=0.5,colorup='r', colordown='g') # 绘制K线走势
#mpf.plot(getstockdata.iloc[:100],type='candle') # 绘制K线走势
# 绘制移动平均线图
getstockdata['Ma5'] = getstockdata.close.rolling(window=5).mean() # pd.rolling_mean(df_stockload.close,window=20)
getstockdata['Ma10'] = getstockdata.close.rolling(window=10).mean() # pd.rolling_mean(df_stockload.close,window=30)
getstockdata['Ma20'] = getstockdata.close.rolling(window=20).mean() # pd.rolling_mean(df_stockload.close,window=60)
# getstockdata['Ma30'] = getstockdata.close.rolling(window=30).mean() # pd.rolling_mean(df_stockload.close,window=60)
# getstockdata['Ma60'] = getstockdata.close.rolling(window=60).mean() # pd.rolling_mean(df_stockload.close,window=60)
graph_KAV.plot(np.arange(0, len(getstockdata.index)), getstockdata['Ma5'], 'black', label='M5', lw=1.0)
graph_KAV.plot(np.arange(0, len(getstockdata.index)), getstockdata['Ma10'], 'green', label='M10', lw=1.0)
graph_KAV.plot(np.arange(0, len(getstockdata.index)), getstockdata['Ma20'], 'blue', label='M20', lw=1.0)
# graph_KAV.plot(np.arange(0, len(getstockdata.index)), getstockdata['Ma30'], 'pink', label='M30', lw=1.0)
# graph_KAV.plot(np.arange(0, len(getstockdata.index)), getstockdata['Ma60'], 'yellow', label='M60', lw=1.0)
# 添加网格
graph_KAV.grid()
graph_KAV.legend(loc='best')
graph_KAV.set_title(SCODE + ' ' + SNAME+'(日线)')
graph_KAV.set_ylabel(u"价格")
graph_KAV.set_xlim(0, len(getstockdata.index)) # 设置一下x轴的范围
# 绘制成交量图
graph_VOL.bar(np.arange(0, len(getstockdata.index)), getstockdata.vol,
color=['g' if getstockdata.open[x] > getstockdata.close[x] else 'r' for x in
range(0, len(getstockdata.index))])
graph_VOL.set_ylabel(u"成交量")
graph_VOL.set_xlim(0, len(getstockdata.index)) # 设置一下x轴的范围
graph_VOL.set_xticks(range(0, len(getstockdata.index), 1)) # X轴刻度设定 每1天标一个日期
# 绘制MACD
# macd_dif, macd_dea, macd_bar = talib.MACD(getstockdata['close'].values, fastperiod=12, slowperiod=26,
# signalperiod=9)
# graph_MACD.plot(np.arange(0, len(getstockdata.index)), macd_dif, 'red', label='macd dif') # dif
# graph_MACD.plot(np.arange(0, len(getstockdata.index)), macd_dea, 'blue', label='macd dea') # dea
#
# bar_red = np.where(macd_bar > 0, 2 * macd_bar, 0) # 绘制BAR>0 柱状图
# bar_green = np.where(macd_bar < 0, 2 * macd_bar, 0) # 绘制BAR<0 柱状图
# graph_MACD.bar(np.arange(0, len(getstockdata.index)), bar_red, facecolor='red')
# graph_MACD.bar(np.arange(0, len(getstockdata.index)), bar_green, facecolor='green')
#
# graph_MACD.legend(loc='best', shadow=True, fontsize='10')
# graph_MACD.set_ylabel(u"MACD")
# graph_MACD.set_xlim(0, len(getstockdata.index)) # 设置一下x轴的范围
# graph_MACD.set_xticks(range(0, len(getstockdata.index), 2)) # X轴刻度设定 每15天标一个日期
# X-轴每个ticker标签都向右倾斜45度
for label in graph_KAV.xaxis.get_ticklabels():
label.set_visible(False)
for label in graph_VOL.xaxis.get_ticklabels():
label.set_visible(True)
label.set_fontsize(10)
# for label in graph_MACD.xaxis.get_ticklabels():
# label.set_visible(True)
# label.set_fontsize(10)
#输出图片
plt.savefig('./Kline.jpg')
# for label in graph_KDJ.xaxis.get_ticklabels():
# label.set_rotation(45)
# label.set_fontsize(10) # 设置标签字体
# c1.add_xaxis(xaxis_data=x)
# c1.add_xaxis(xaxis_data=x1)
# c1.add_yaxis(series_name='收盘价', y_axis=y3)
# c1.set_global_opts(title_opts=opts.TitleOpts(title='北向资金持股分析: ' + SNAME))
# c1.render(path=outfile)
#page.add(c, c1)
page.add(c)
page.render(path=outfile)
# 如果要输出柱图
'''
bar = Bar()
然后将c 换成bar
'''
s = tb.get_html_string() #格式化成html文件
#将画的图片输出
kline='''<img src=./Kline.jpg />'''
fw = open(outfile, 'a+', encoding='utf-8')
fw.write(kline)
fw.write(s) # 输出到文件
fw.close()
#获取个股的所有北向资金数据
def getnorth(code):
url = 'http://dcfm.eastmoney.com//em_mutisvcexpandinterface/api/js/get'
northdatainfos=[]
headers = {
'Accept': '*/*',
'Accept-Encoding': 'gzip, deflate',
'Accept-Language': 'zh - CN, zh; q = 0.9, en; q = 0.8 ',
'Connection': 'keep-alive',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.111 Safari/537.36',
'Cookie': 'pgv_pvi=3794568192; _qddaz=QD.6ofmf2.j6jr4m.kat8wucp; ct=u_GCXp_V0BUfw6EE3hFHtqMglz3afgkppJcv5vbFImFCEcWBrdbJ1czxMgSRvdgdMHMxnKracqlOZgxC4VNfwrkiwCCnYCNVFUzHMie-NyeUGcc8-NdJwvaXLimNiEt9gsOQO3q161JU2fTSAHZYRo5byr67JKvMwuA_2qSbhls; ut=FobyicMgeV5ghfUPKWOH5wak5fe7PCdYa2maZFrymrOdfN-wAEFtpNp1MzH070EBSmKRLG6vmIcYwEk2SvuUDiGwHB7BHzpaN3m4xMthhPoNqi89FTByaNH4MkRCfEYW4JX960vY0ITlmRY-cPk1PQzTvxCYnVj0Ey0NtYOnUdj24K9O1_tKWeyEDf1k_bIV6hcX360Qn8yYsWTrETZTzGYR7tn62AgnDFAq58DbSa3StLkggc5c7wB94try8c_WEpaHHyl5rA7BBAJZkje3dZ7Q7pZSUWri; pi=3323115305075326%3bc3323115305075326%3b%e8%82%a1%e5%8f%8bjHWZa22110%3bAc4gMB%2bahzpZU8kVvDCm4%2f9QLFcpRepVrDlj4DSAFvQS9L41u5PjbhW1g0ATNFBs2U6jdaiAi0v97coryIUwYaBWyHAUTbi1GDBZdDmkrBugnCGTBDTgPjXURUbrtmze597viYIL2RjHQTBKDzTIQqxuco%2b4pIMvD3B%2f2gF3Z2HSKCRGXGX%2bMcFxewJmIXD8wOJYtqii%3bM4Rnsdjx0lNLDrlCNBv6VhW13wgvkjpsoKd52WM1JsrPCSqUd%2fySTvks6nwUjCNsGby4fYU2Y%2bbjGtRBVly22B%2bqdAhoqGh6XrZIWQGX4LDnpd4CKtckek2Rlq7r9qjcQSdzcprF%2bmmkr9EqKBQVnmt9ppYRhg%3d%3d; uidal=3323115305075326%e8%82%a1%e5%8f%8bjHWZa22110; sid=126018279; _ga=GA1.2.1363410539.1596117007; em_hq_fls=js; AUTH_FUND.EASTMONEY.COM_GSJZ=AUTH*TTJJ*TOKEN; emshistory=%5B%22%E4%BA%BA%E6%B0%94%E6%8E%92%E8%A1%8C%E6%A6%9C%22%2C%22%E6%AF%94%E4%BA%9A%E8%BF%AA%E4%BA%BA%E6%B0%94%E6%8E%92%E5%90%8D%22%2C%22%E5%9F%BA%E9%87%91%E6%8E%92%E8%A1%8C%22%2C%22%E8%BF%913%E4%B8%AA%E6%9C%88%E8%B7%8C%E5%B9%85%E6%9C%80%E5%A4%A7%E7%9A%84%E5%9F%BA%E9%87%91%22%2C%22%E5%85%BB%E8%80%81%E9%87%91%E6%8C%81%E8%82%A1%E5%8A%A8%E5%90%91%E6%9B%9D%E5%85%89%22%2C%22%E5%A4%96%E7%9B%98%E6%9C%9F%E8%B4%A7%22%2C%22A50%22%2C%22%E6%81%92%E7%94%9F%E6%B2%AA%E6%B7%B1%E6%B8%AF%E9%80%9A%E7%BB%86%E5%88%86%E8%A1%8C%E4%B8%9A%E9%BE%99%E5%A4%B4A%22%2C%22%E7%BB%86%E5%88%86%E8%A1%8C%E4%B8%9A%E9%BE%99%E5%A4%B4%22%5D; vtpst=%7c; HAList=d-hk-00288%2Cd-hk-00772%2Cf-0-399006-%u521B%u4E1A%u677F%u6307%2Ca-sz-002008-%u5927%u65CF%u6FC0%u5149%2Ca-sz-002739-%u4E07%u8FBE%u7535%u5F71%2Cf-0-000001-%u4E0A%u8BC1%u6307%u6570%2Cd-hk-00981%2Ca-sz-002082-%u4E07%u90A6%u5FB7%2Ca-sz-300511-%u96EA%u6995%u751F%u7269; st_si=85201197981579; cowCookie=true; waptgshowtime=2021121; qgqp_b_id=3a2c1ce1f45a81a3fa7cc2fbad8e2a24; st_asi=delete; intellpositionL=581px; st_pvi=03400063938128; st_sp=2020-05-23%2013%3A48%3A35; st_inirUrl=https%3A%2F%2Fwww.baidu.com%2Flink; st_sn=60; st_psi=2021012310245852-113300303605-1019447906; intellpositionT=2133.55px'
}
print(code)
params = {'type': 'HSGTHDSTA',
'token': '70f12f2f4f091e459a279469fe49eca5',
'filter':' (SCODE=\''+code+'\')',
'st': 'HDDATE',
'sr': -1,
'p': 1,
'ps': 50,
'js': 'var nLvHRzKi={pages:(tp),data:(x)}',
'rt': '53732197'}
#print(params)
response=req.get(url=url,headers=headers,params=params).text
#print(response.url)
#print(response.text+'\n----------------------------------------')
regex = r'data:\[({.*?)\]}'
jsondata=str(re.findall(regex,response))
#print((jsondata))
data=jsondata.replace('[\'','',-1).replace('\']','',-1).replace('},','}},',-1)
#print(len(data))
listdata=data.split('},',-1)
return listdata
#print(len(listdata))
#print(listdata)
# northdatainfos.append(listdata)
# return northdatainfos
#formatresults(listdata, header)#每一页写表
if __name__ == '__main__':
'002044'
'002179'
# code = get_stockcode('科大讯飞')
# listdata=getnorth(code)
# format_tohtml(listdata)
#format_tohtml(listdata)
#get_stock_dateData('SZ.002179','2021-01-27')
var = sys.argv # 可以接收从外部传入参数
if len(var)>1:
var1=str(var[1]).strip(' ')
code=get_stockcode(var1)
listdata=getnorth(code) #实时查询北向资金
format_tohtml(listdata)
if var1.isdigit():
stockname=get_stockname(var[1])
webbrowser.open('北向资金_' + stockname+ '.html')
else:
webbrowser.open('北向资金_' + var1 + '.html')
else:
name='三一重工'
code = get_stockcode(name)
#print(code)
listdata = getnorth(code) # 实时查询北向资金
format_tohtml(listdata)
if name.isdigit():
stockname = get_stockname(name)
#print(stockname)
outfile='北向资金_'+stockname + '.html'
webbrowser.open(outfile)
else:
webbrowser.open('北向资金_' + name + '.html')
print(outfile)
'''
data: [{
"HDDATE": "2020-12-30T00:00:00", 日期
"HKCODE": "1000145950",
"SCODE": "00700", 代码
"SNAME": "腾讯控股", 名称
"SHAREHOLDSUM": 425931727.0, 持股数
"SHARESRATE": 4.43,占比
"CLOSEPRICE": 559.5,收盘价
"ZDF": 5.4665,当日涨跌幅
"SHAREHOLDPRICE": 238308801256.5, 持股市值
"SHAREHOLDPRICEONE": 19102759903.0,一日市值变化
"SHAREHOLDPRICEFIVE": 2113479276.5,五日市值变化
"SHAREHOLDPRICETEN": 3843934536.5,十日市值变化 '''