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| import pandas as pd
import numpy as np
import os,re,random,csv,shutil
from datetime import date,time,timedelta
from pyecharts.charts import Kline, Line, Bar, Grid,Map,Pie,Timeline,Geo
from pyecharts.commons.utils import JsCode
from pyecharts import options as opts
from pyecharts.globals import CurrentConfig, NotebookType,ThemeType,ChartType, SymbolType
from pytdx.reader import TdxDailyBarReader, TdxFileNotFoundException
import baostock as bs
from chinese_calendar import is_workday, is_holiday
reader = TdxDailyBarReader()
def getfullcode(code):
if code.startswith('6',0,1):
code = 'sh' + code
elif code.startswith('0',0,1) or code.startswith('3',0,1):
code = 'sz' + code
return code
def get_filepath(code):
"""
根据6位数字的股票代码获取完整的日k线数据路径
"""
code = getfullcode(code)
fd1 = r'D:\new_tdx\vipdoc'
fd2 = f"{re.match(r'[a-z]+',code).group()}\lday\{code}.day"
return os.path.join(fd1,fd2)
def get_nameidu(code):
"""
根据股票代码获取对应的股票名称和所属行业
返回list [name,idu]
code: '600000' str格式
"""
dn = pd.read_csv(r"D:\stock_data\allstock.csv",dtype={'代码':str})
dn['代码'] = dn['代码'].map(lambda x: x.rjust(6,'0'))
names = dict(zip(dn['代码'].tolist(),dn['名称'].tolist()))
idus = dict(zip(dn['代码'].tolist(),dn['细分行业'].tolist()))
return [names.get(code),idus.get(code)]
def add_ma(data):
'''添加列数据,均线数据'''
data['ma5'] = data['close'].rolling(5).mean()
data['ma10'] = data['close'].rolling(10).mean()
data['ma20'] = data['close'].rolling(20).mean()
data['ma30'] = data['close'].rolling(30).mean()
data['ma60'] = data['close'].rolling(60).mean()
data['ma90'] = data['close'].rolling(90).mean()
data['ma120'] = data['close'].rolling(120).mean()
data['ma250'] = data['close'].rolling(250).mean()
data.dropna(inplace=True)
data = data.applymap(lambda x: round(x,2) if isinstance(x,float) else x)
return data
def get_tdxklinedata(code):
"""
根据股票代码,读取行情数据。为DF添加均线数据列,并对数值取小数点2位数。
"""
df = reader.get_df(get_filepath(getfullcode(code)))
return add_ma(df)
def pelevel(arr):
"""
计算arr中最后一个数在数组的分位点
返回: value ---float
参数：
arr: numpy的一维数组
"""
target = arr[-1]
level = 1 - np.count_nonzero(target <= arr) / arr.size
return level
def get_pestartdate():
x= 1050
dates = pd.date_range(end=date.today(),periods=x,freq='B')
tradedates = [i for i in dates if is_workday(i)]
while len(tradedates)<1000:
x+=1
dates = pd.date_range(end=date.today(),periods=x,freq='B')
tradedates = [i for i in dates if is_workday(i)]
return [f"{tradedates[0]:%Y-%d-%d}",f"{tradedates[-1]:%Y-%m-%d}"]
pedates = get_pestartdate()
selfdate = pedates[0]
idudate = pedates[1]
def get_selfpes(code):
bs.login()
if code.startswith('6'):
code = 'sh.' + code
else:
code = 'sz.' + code
data = bs.query_history_k_data_plus(
code=code,
fields="date,code,turn,peTTM",
frequency="d",
start_date= selfdate,
adjustflag="2").get_data()
bs.logout()
truns = [round(float(i),2) for i in data['turn'].tolist()[-5:]]
pes = [f"{float(i):.2f}" for i in data['peTTM'].tolist() if i!='']
return [truns,pes]
def get_iduavgpe(code):
bs.login()
idu = get_nameidu(code)[1]
df_idu = pd.read_csv(r"D:\stock_data\allstock.csv",dtype={'代码':str})
codes = df_idu.query('细分行业 == @idu')['代码'].tolist()
dfpes = pd.DataFrame()
for code in codes:
if code.startswith('6'):
code = 'sh.' + code
else:
code = 'sz.' + code
data = bs.query_history_k_data_plus(
code=code,
fields="date,code,peTTM",
frequency="d",
start_date= idudate,
adjustflag="2").get_data()
dfpes = pd.concat([dfpes,data])
dfpes['peTTM'] = dfpes['peTTM'].astype(float)
bs.logout()
return dfpes['peTTM'].mean(skipna = True)
def kline(code):
"""
绘制K线图
参数: df (DataFrame)
df 必须包含 columns:['open','close','high','low','amount','date']
返回: K线图html
参数：
code: 股票代码 '60000'
"""
df = add_ma(get_tdxklinedata(code))
x_data = list(map(lambda x: x.strftime("%Y-%m-%d"), df.index.tolist()))
y_data = df[["open", "close", "low","high"]].values.tolist()
name = get_nameidu(code)[0]
idu = get_nameidu(code)[1]
trunpes = get_selfpes(code)
pe = trunpes[1][-1]
pes = np.array(trunpes[1])
turns = trunpes[0]
pes_250 = pes[-250:]
pes_500 = pes[-500:]
pes_750 = pes[-750:]
pes_1000 = pes[-1000:]
level1 = f"{pelevel(pes_250):.3f}"
level2 = f"{pelevel(pes_500):.3f}"
level3 = f"{pelevel(pes_750):.3f}"
level4 = f"{pelevel(pes_1000):.3f}"
idupeavg = get_iduavgpe(code)
k = (Kline(init_opts=opts.InitOpts(width="100%", height="1200px"))
.add_xaxis(x_data)
.add_yaxis("kline",y_data).set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(type_="inside",
range_start=95,
range_end=100),
opts.DataZoomOpts(type_="slider",
xaxis_index=[0,1],
range_start=int(100 - 150/len(df)*100),
range_end=100,
is_show=True),
],
title_opts=opts.TitleOpts(
subtitle=f"代码: {code} 名称: {name} 行业: {idu} \n\n当前pe: {pe} 行业pe: {idupeavg:.2f}\n\n近5天换手率: {turns}\n\npe分位点:\n\n {level1} / 250 days\n\n {level2} / 500 days\n\n {level3} / 750 days\n\n {level4} / 1000 days ",
pos_top = '1%',
pos_right ="10%",
),
)
)
l =(Line(init_opts=opts.InitOpts(width="100%", height="1200px")).add_xaxis(x_data)
.add_yaxis("ma5",
df['ma5'].values.tolist(),
symbol = None,
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
{"yAxis": 150},
opts.MarkPointItem(type_="min"),
opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="average")])
)
.add_yaxis("ma10",
df['ma10'].values.tolist(),
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis("ma20",
df['ma20'].values.tolist(),
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis("ma30",
df['ma30'].values.tolist(),
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis("ma60",
df['ma60'].values.tolist(),
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis("ma120",
df['ma120'].values.tolist(),
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis("ma250",
df['ma250'].values.tolist(),
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
)
)
v = (Bar()
.add_xaxis(xaxis_data=date).add_yaxis(
series_name="成交额",
y_axis=df["amount"].tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
ov1 = k.overlap(l)
ov1.render_notebook()
ov1.render('3.html')
ov = k.overlap(l)
g = (Grid(init_opts=opts.InitOpts(
width="100%",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
)
)
.add_js_funcs("var barData={}".format(df[["open", "close"]].values.tolist()))
.add(ov,
grid_opts=opts.GridOpts(
pos_top="2%",
height="70%",
),
)
.add(v,
grid_opts=opts.GridOpts(
pos_top="76%",
height="19%",
),)
)
outph = f'C:\\Users\\xiaoyx\\Desktop\\{date.today():%y%m%d}'
filename = f"{code}.html"
outfile = os.path.join(outph,filename)
if os.path.exists(outph):
pass
else:
os.makedirs(outph)
return g.render(outfile)
if __name__ == '__main__':
kline('000409')
|