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# -*- coding:utf-8 -*-
import tushare as ts
import matplotlib.pyplot as plt
#import matplotlib.finance as mpf
import mpl_finance as mpf
import matplotlib.ticker as ticker
import datetime
import numpy as np
import pandas as pd
import time
from pyecharts.charts import Kline
from pyecharts.charts import Line
from pyecharts.charts import Bar
from pyecharts.charts import Grid
from pandas import DataFrame, Series
from pyecharts import options as opts
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def n_days_ago(n):
today=datetime.date.today()
ndays_ago=today-datetime.timedelta(n)
return str(ndays_ago)
def hhv(s, n):
return Series.rolling(s, n).max()
def llv(s, n):
return Series.rolling(s, n).min()
#ichimoku
def ichimoku(s, n1=9, n2=26, n3=52):
#average of N-day high and N-day low
conv = (hhv(s, n1) + llv(s, n1)) / 2
#mid point of the latest 26 days
base = (hhv(s, n2) + llv(s, n2)) / 2
#mid-point between the first 2 lines, and plot 26 periods ahead
spana = (conv + base) / 2
#mid-point between the 52-period low and 52-period high, and plot 26 periods
spanb = (hhv(s, n3) + llv(s, n3)) / 2
k = s
#Lspan is closing price, and plot 26 periods in the past
return DataFrame(dict(k=k,conv=conv, base=base, spana=spana.shift(n2),
spanb=spanb.shift(n2), lspan=s.shift(-n2)))
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start_date = '2018-07-01'
now = int(time.time())
timeArray = time.localtime(now)
Time = time.strftime("%Y-%m-%d %H:%M:%S", timeArray)
end_date = Time
ndays = 360
stock_selected = '600487'
MAX_SMA=100
start_date = n_days_ago(ndays + MAX_SMA)
ktype = '60'
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df = ts.get_k_data(stock_selected,start_date,end_date)
#v_kline = np.array(df[['open','close','low','high']][MAX_SMA:])
date_kline = []
for i,j in enumerate(np.array(df[['date']][MAX_SMA:])):
#print(j)
temp_str = str(j[0]).replace('-0','/')
date_kline.append(temp_str.replace('-','/'))
v_kline= []
for i,j in enumerate(np.array(df[['open','close','low','high']][MAX_SMA:])):
#print(j)
v_kline.append(list(j))
SMA5= []
for i,j in enumerate(np.array(pd.Series.rolling(df[['close']],5).mean()[MAX_SMA:])):
#print(j)
SMA5.append(list(j))
SMA55= []
for i,j in enumerate(np.array(pd.Series.rolling(df[['close']],55).mean()[MAX_SMA:])):
#print(j)
SMA55.append(list(j))
SMA100= []
for i,j in enumerate(np.array(pd.Series.rolling(df[['close']],100).mean()[MAX_SMA:])):
#print(j)
SMA100.append(list(j))
df_ich = ichimoku(df['close'])
base= list(np.array(df_ich['base'])[MAX_SMA:])
conv= list(np.array(df_ich['conv'])[MAX_SMA:])
spana= list(np.array(df_ich['spana'])[MAX_SMA:])
spanb= list(np.array(df_ich['spanb'])[MAX_SMA:])
v_volume = np.array(df['volume'][MAX_SMA:])
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kline=Kline().add_xaxis(date_kline).add_yaxis('Kline',list(v_kline)).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
datazoom_opts=[opts.DataZoomOpts(pos_bottom="-2%")],
title_opts=opts.TitleOpts(title="Kline"),
)
#kline.render_notebook()
volume_bar = Bar().add_xaxis(date_kline).add_yaxis("", list(v_volume),label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="", pos_top="48%"),
legend_opts=opts.LegendOpts(pos_top="48%"),
)
#volume_bar.render_notebook()
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sma_line = Line().add_xaxis(date_kline).add_yaxis("SMA5",list(SMA5),is_smooth=True,label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(color="red", width=1))
sma_line.add_yaxis("base",list(base),is_smooth=True,label_opts=opts.LabelOpts(is_show=False))
sma_line.add_yaxis("conv",list(conv),is_smooth=True,label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(color="black", width=3))
sma_line.add_yaxis("spana",list(spana),is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(opacity=0.3),
linestyle_opts=opts.LineStyleOpts(color="red", width=3))
sma_line.add_yaxis("spanb",list(spanb),is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(opacity=0.3),
linestyle_opts=opts.LineStyleOpts(color="green", width=3))
kline.overlap(sma_line).render_notebook()
grid = (
Grid()
.add(kline, grid_opts=opts.GridOpts(pos_bottom="40%"))
.add(volume_bar, grid_opts=opts.GridOpts(pos_top="70%"))
)
grid.render_notebook()
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r_hex = '#dc2624' # red, RGB = 220,38,36
dt_hex = '#2b4750' # dark teal, RGB = 43,71,80
tl_hex = '#45a0a2' # teal, RGB = 69,160,162
r1_hex = '#e87a59' # red, RGB = 232,122,89
tl1_hex = '#7dcaa9' # teal, RGB = 125,202,169
g_hex = '#649E7D' # green, RGB = 100,158,125
o_hex = '#dc8018' # orange, RGB = 220,128,24
tn_hex = '#C89F91' # tan, RGB = 200,159,145
g50_hex = '#6c6d6c' # grey-50, RGB = 108,109,108
bg_hex = '#4f6268' # blue grey, RGB = 79,98,104
g25_hex = '#c7cccf' # grey-25, RGB = 199,204,207
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from pyecharts.commons.utils import JsCode
x_data = ["14", "15", "16", "17", "18", "19", "20", "21", "22", "23"]
y_data = [393, 438, 485, 631, 689, 824, 987, 1000, 1100, 1200]
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
c=Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js))).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="注册总量",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True, position="top", color="white"),
itemstyle_opts=opts.ItemStyleOpts(
color="red", border_color="#fff", border_width=3
),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1),
).set_global_opts(
title_opts=opts.TitleOpts(
title="OCTOBER 2015",
pos_bottom="5%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#fff", font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
),
),
legend_opts=opts.LegendOpts(is_show=False),
)
c.render_notebook()
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