def get_line_ba(self,df, init_account, stock_info):
trade_dates = np.array(df.trade_date).tolist()
balances = np.array((df['balance'] - init_account) / init_account).tolist()
c = (
Line()
.add_xaxis(trade_dates)
.add_yaxis("收益率"
, balances
, is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
title_opts=opts.TitleOpts(title=stock_info['name']),
xaxis_opts=opts.AxisOpts(
is_scale=True,
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0],
type_="slider",
pos_top="90%",
range_start=90,
range_end=100,
),
# opts.DataZoomOpts(
# is_show=True,
# yaxis_index=[0],
# type_="slider",
# orient = 'vertical',
# # pos_top="90%",
# pos_right="0%",
# range_start=0,
# range_end=100,
# ),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
)
)
return c
def drawer2(stage_name, data):
c_schema = [
{
"name": "乐",
"max": 0.55,
"min": 0
},
{
"name": "好",
"max": 0.55,
"min": 0
},
{
"name": "哀",
"max": 0.55,
"min": 0
},
{
"name": '恶',
"max": 0.55,
"min": 0
},
{
"name": "惧",
"max": 0.55,
"min": 0
},
]
c = (Radar().set_colors(["#CC3300"]).add_schema(
schema=c_schema,
shape="circle",
center=["50%", "50%"],
radius="80%",
angleaxis_opts=opts.AngleAxisOpts(
min_=0,
max_=360,
is_clockwise=False,
interval=5,
axistick_opts=opts.AxisTickOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
radiusaxis_opts=opts.RadiusAxisOpts(
min_=-4,
max_=4,
interval=2,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
polar_opts=opts.PolarOpts(),
splitarea_opt=opts.SplitAreaOpts(is_show=False),
splitline_opt=opts.SplitLineOpts(is_show=False),
).add(
series_name=stage_name,
data=data,
areastyle_opts=opts.AreaStyleOpts(opacity=0.1),
linestyle_opts=opts.LineStyleOpts(width=3),
).render("res\\output\\multi-emotion\\image\\" + stage_name + ".html"))
def draw_Kline(self):
"""
画股票的K线图，需要传入四个数据
分别是["open", "close", "high", "low"]
:return: 画出图像
"""
Df_s1 = Read_One_Stock(self.SC).select_col("open", "high", "low",
"close", "vol", "amount")
length = Df_s1.shape[0]
Df_s1.sort_values("trade_date", inplace=True)
Df_s1.index = list(range(length))
price = np.array(Df_s1[["open", "close", "high", "low"]]).tolist()
date = np.array(Df_s1["trade_date"], dtype=np.string_).tolist()
ma_value_5 = calculate_ma_n(list(Df_s1['close']), 5)
ma_value_10 = calculate_ma_n(list(Df_s1['close']), 10)
ma_value = np.array([ma_value_5, ma_value_10]).tolist()
kline = Kline()
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()],
title_opts=opts.TitleOpts(title="K-Line of {}".format(self.SC)),
)
kline.add_xaxis(date)
kline.add_yaxis('K-Line', price)
line = Line()
line.add_xaxis(date)
line.add_yaxis(series_name="ma5",
y_axis=ma_value[0],
label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis(series_name="ma10",
y_axis=ma_value[1],
label_opts=opts.LabelOpts(is_show=False))
line.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()],
title_opts=opts.TitleOpts(title=""))
kline.overlap(line)
kline.render("./Plots/{} Candle Plot.html".format(self.SC))
def heat_plot(data):
n, m = data.shape
x, y = np.arange(0, n, 1), np.arange(0, m, 1)
x, y = np.meshgrid(x, y, indexing='ij')
z = data.values.flatten()
x = m - 1 - x
_data = list(zip(y.flatten(), x.flatten(), z))
_data = [[int(d[0]), int(d[1]), np.round(d[2], 4)] for d in _data]
h = (
HeatMap(
init_opts=opts.InitOpts(width="1000px", height="300px")).add_xaxis(
xaxis_data=data.columns.astype(str).tolist()).add_yaxis(
series_name="",
yaxis_data=data.index.astype(str).tolist()[::-1],
value=_data,
label_opts=opts.LabelOpts(position='inside',
is_show=True,
font_size=15),
).set_series_opts().set_global_opts(
# toolbox_opts=opts.ToolboxOpts(is_show=True),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, ),
xaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
yaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
visualmap_opts=opts.VisualMapOpts(min_=z.min(),
max_=z.max(),
is_calculable=True,
orient="vertical",
pos_left="left"),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=2,
name="pic",
), )),
))
return h
def draw(self,freq=""):
kdata=[]
ktime=[]
kvol=[]
for i in range(len(self.klines_merge)):
current_kline = self.klines_merge[i]
onedata=[]
onedata.append(current_kline['open'])
onedata.append(current_kline['close'])
onedata.append(current_kline['low'])
onedata.append(current_kline['high'])
kdata.append(onedata)
ktime.append(current_kline['date'])
kvol.append(current_kline['volume'])
kline=(Kline().add_xaxis(ktime)
.add_yaxis(series_name= self.klines_merge[0]['code'],y_axis= kdata,
markline_opts=opts.MarkLineOpts(label_opts=opts.LabelOpts(position="middle", color="blue", font_size=15),
data=self.split_data_part(),
symbol=["circle", "none"],),
)
.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()],
title_opts=opts.TitleOpts(title=self.klines_merge[0]['code']) ,
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True, data=self.split_data_part())
)
)
bar = (Bar().add_xaxis(ktime).add_yaxis("",kvol))
path =self.klines_merge[0]['code']+"-"+freq+".html";
kline.render(path)
def draw_amount_bar(self):
"""
画出股票的成交量和
需要传入的数据是股票的["amount"
:return:
"""
# SC = "000021.SZ"
Df_s1 = Read_One_Stock(self.SC).select_col("vol", "amount")
length = Df_s1.shape[0]
Df_s1.sort_values("trade_date", inplace=True)
Df_s1.index = list(range(length))
amount = np.array(Df_s1["amount"]).tolist()
date = np.array(Df_s1["trade_date"], dtype=np.string_).tolist()
bar = Bar()
bar.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()],
title_opts=opts.TitleOpts(title="K-Line of {}".format(self.SC)))
bar.add_xaxis(date)
bar.add_yaxis("Amounts",
amount,
label_opts=opts.LabelOpts(is_show=False))
bar.render("./Plots/{} Amount Bar Plot.html".format(self.SC))
def plot(self):
# 资金曲线
value_array,date_array,max_retrace,max_retrace_date,max_return,min_return = self.calculate()
date_list = [i.strftime('%Y-%m-%d') for i in date_array]
subtitle = "最大回撤 {} {:.2%}, 最高收益率 {:.2%} 最低收益率 {:.2%}, 目前收益率 {:.2%}".format(max_retrace_date,max_retrace,max_return,min_return,value_array[-1])
data_dict = glovar.query.history(security_list=self.betaStock, field_list=['close'], limit=len(date_list), unit='1D')
beta = data_dict[self.betaStock][:,0]
beta = beta/beta[0]-1
alpha = value_array - beta
security_name = self.betaStock
save_name = glovar.context.run_params['start_date'][:10]+'---'+glovar.context.run_params['end_date'][:10]
(
Line()
.add_xaxis(date_list)
.add_yaxis("策略", value_array.round(3).tolist())
.add_yaxis(security_name, beta.round(3).tolist())
.add_yaxis("Alpha", alpha.round(3).tolist(), is_symbol_show=False)
.set_global_opts(title_opts=opts.TitleOpts(title='资金曲线',subtitle=subtitle),
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()])
.render(sys.path[0]+'/img/{}/{}/{}.html'.format(self.run_params['mode'], self.run_params['strategy'], save_name))
)
def gen_line(df1, symbol, price_min, price_max):
#df1['datetime'] = df1['date'] + ' ' + df1['time']
df1['datetime'] = df1['date']
dt_list = list(df1['datetime'])
close_list = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list = np.array(close_list)
line1 = Line(init_opts=opts.InitOpts(height='700px', width='1300px'))
line1.set_global_opts(
yaxis_opts=opts.AxisOpts(
min_=price_min,
#min_=999,
max_=price_max,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
range_start=0,
range_end=100,
),
],
)
line1.add_xaxis(xaxis_data=dt_list)
line1.add_yaxis(
symbol,
y_axis=close_list,
)
line1.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return line1
def year_store_order_base():
# 不同门店的年营业额数据查询
dataX, dataY1, dataY2 = year_store_query()
# 对象声明
radar = Radar(init_opts=opts.InitOpts(
width="1280px", height="720px", bg_color="#CCCCCC"))
radar.add_schema(
schema=[
opts.RadarIndicatorItem(name="门店订单金额", max_=34000),
opts.RadarIndicatorItem(name="门店顾客数", max_=10000),
opts.RadarIndicatorItem(name="门店商品类型", max_=10000),
opts.RadarIndicatorItem(name="门店订单量", max_=10000)
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
radar.add(
series_name="Store 1",
data=[list(dataY1)],
linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
)
radar.add(
series_name="Store 2",
data=[list(dataY2)],
linestyle_opts=opts.LineStyleOpts(color="#5CACEE"),
)
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
radar.set_global_opts(title_opts=opts.TitleOpts(title="2005年门店竞争优势多维分析"),
legend_opts=opts.LegendOpts())
return radar
def gen_line_two(self, df1):
df1['datetime'] = df1['date'] + ' ' + df1['time']
dt_list1 = list(df1['datetime'])
# print( len(dt_list1) )
# dt_list1 = [s[5:10] for s in dt_list1]
close_list1 = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list1 = np.array(close_list1)
kline = Line()
kline.add_xaxis(dt_list1)
kline.add_yaxis('resid',
close_list1,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False))
kline.set_global_opts(yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1))),
legend_opts=opts.LegendOpts(is_show=True,
pos_right="40%")
#xaxis_opts=opts.AxisOpts(type_='time'))
)
return kline
def draw_net_value(result):
timeline = result['portfolio'].index
timeline = [i.strftime("%Y/%m/%d") for i in timeline]
# print(timeline)
total_value = result['portfolio']['total_value'].tolist()
total_value_b = result['benchmark_portfolio']['total_value'].tolist()
rtn_line = (Line(init_opts=opts.InitOpts(
width="1400px", height="500px")).add_xaxis(timeline).add_yaxis(
"回测总资产",
total_value,
).add_yaxis(
"基准总资产",
total_value_b,
).set_global_opts(
title_opts=opts.TitleOpts(title="总资产"),
xaxis_opts=opts.AxisOpts(is_scale=True, type_='time'),
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="-1%")],
toolbox_opts=opts.ToolboxOpts(is_show=True),
).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))
return rtn_line
def radar_product_rating_base():
"""
此函数用于获取产品多维评价的雷达图的参数。
Returns
-------
tl : TYPE-echarts parameters
return echarts parameters.
"""
# data query
dataX, products, list_dataY = product_rating_query()
c_schema = []
for i in range(len(dataX)):
c_schema.append({"name":dataX[i],"max":5,"min":0})
# Declare objects, render pictures
tl = Timeline()
for i in range(len(products)):
c = (
Radar(init_opts=opts.InitOpts(width="1280px", height="720px"))
.add_schema(schema=c_schema,
splitarea_opt=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
.add(series_name=products[i], data=[list(list_dataY[i])],
linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(title_opts=opts.TitleOpts(title="Multi-dimensional analysis of product performance"),
legend_opts=opts.LegendOpts(pos_left="80%", pos_top="50%"))
)
tl.add(c, "{}".format(products[i]))
return tl
def radar_base(data, email_max_number, copy_max_number, copied_max_number, receiver_max_num, mail_server_max_number) -> Radar:
radar = (
Radar(init_opts=opts.InitOpts(width="1280px", height="720px"))
.add_schema(
schema=[
opts.RadarIndicatorItem(name="邮件数量", max_=email_max_number),
opts.RadarIndicatorItem(name="含有抄送的邮件数量", max_=copy_max_number),
opts.RadarIndicatorItem(name="被抄送的邮件数量", max_=copied_max_number),
opts.RadarIndicatorItem(name="联系人数量", max_=receiver_max_num),
opts.RadarIndicatorItem(name="服务器个数", max_=mail_server_max_number),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True
),
textstyle_opts=opts.TextStyleOpts(color="#000000"),
)
)
for mailbox in data:
tot = 0
for num in data[mailbox][0]:
tot += num
if num is not 0:
radar.add(
series_name=mailbox,
data=data[mailbox],
linestyle_opts=opts.LineStyleOpts(color=utils.randomColor(), width=1),
)
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
legend_opts=opts.LegendOpts()
)
return radar
def line_smooth(index, data, name) -> Line:
c = (
Line(init_opts=opts.InitOpts(
theme=ThemeType.ROMANTIC,
# width="100%",
height="500px",
)).add_xaxis(index).add_yaxis(
"Sentiment value",
data,
is_smooth=True,
is_connect_nones=True,
yaxis_index=0).extend_axis(yaxis=opts.AxisOpts(
name="Stock K-line",
is_scale=True,
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#675bba")),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(
opacity=1)),
)).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
name="Sentiment Value",
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="Sentiment Analysis : " +
name),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
))
return c
def line(self, data, xaxis=None, position=0):
if not xaxis: xaxis = self.xaxis
line = Line(init_opts=opts.InitOpts(**self.chart_opt)).add_xaxis(xaxis_data=xaxis)
for d in data:
line.add_yaxis(
series_name=d["label"],
y_axis=d["data"],
is_smooth=True,
is_selected=True,
is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=1),
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=position,
is_scale=True,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
axistick_opts=opts.AxisTickOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
)
return line
def user_ability_radar(v1, v2) -> Radar:
radar = (Radar(init_opts=opts.InitOpts()).add_schema(
schema=[
opts.RadarIndicatorItem(name="字符串", max_=100),
opts.RadarIndicatorItem(name="线性表", max_=100),
opts.RadarIndicatorItem(name="数组", max_=100),
opts.RadarIndicatorItem(name="查找算法", max_=100),
opts.RadarIndicatorItem(name="排序算法", max_=100),
opts.RadarIndicatorItem(name="数字操作", max_=100),
opts.RadarIndicatorItem(name="树结构", max_=100),
opts.RadarIndicatorItem(name="图结构", max_=100),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#000"),
).add(
series_name="学生平均得分",
data=v2,
linestyle_opts=opts.LineStyleOpts(color="#5CACEE",
width=3,
opacity=0.7),
).add(
series_name="学生能力评估",
data=v1,
linestyle_opts=opts.LineStyleOpts(color="#CD0000",
width=3,
opacity=0.7),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(title_opts=opts.TitleOpts(),
legend_opts=opts.LegendOpts()))
return radar
def getRadar(self, sheet: xlrd.sheet) -> Radar:
namelist = sheet.col_values(0, 1)
maxvaluelist = sheet.col_values(1, 1)
maxlist = [
opts.RadarIndicatorItem(name=namelist[i], max_=maxvaluelist[i])
for i in range(len(namelist))
]
valuelists = dict()
for i in range(2, sheet.ncols):
valuelists[sheet.cell(0, i).value] = sheet.col_values(i, 1)
r = Radar()
r.add_schema(
schema=maxlist,
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
#设置颜色的列表
colorstr = ["#CD0000", "#5CACEE", "#7B68EE", "#FFFF00"]
colorth = 0
for name, values in valuelists.items():
r.add(
series_name=name,
data=values,
linestyle_opts=opts.LineStyleOpts(color=colorstr[colorth]),
)
colorth = (colorth + 1) % len(colorstr) #颜色循环
r.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
r.set_global_opts(title_opts=opts.TitleOpts(
title=sheet.name,
title_textstyle_opts=opts.TextStyleOpts(color="#fff")),
legend_opts=opts.LegendOpts())
return r
def kline_base() -> Kline:
chinaDayList, worlddata = get_chinaDayList()
#每日确诊增加数
a = []
c = [x['confirm'] for x in chinaDayList]
for i in range(len(c)):
if i == 0:
a.append(0)
else:
a.append(int(c[i]) - int(c[i - 1]))
b = []
for i in range(len(a)):
if i == 0:
b.append([0, 0, 0, a[i]])
elif i == 1:
b.append([0, 0, a[i - 1], a[i]])
elif i == 2:
b.append([0, a[i - 2], a[i - 1], a[i]])
else:
b.append([a[i - 3], a[i - 2], a[i - 1], a[i]])
c = (Kline().add_xaxis([x['date'] for x in chinaDayList]).add_yaxis(
"kline", b).set_global_opts(
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
xaxis_opts=opts.AxisOpts(is_scale=True),
title_opts=opts.TitleOpts(title="2019-nCov K线图"),
datazoom_opts=[opts.DataZoomOpts(pos_bottom="-2%")],
))
return c
def kline_itemstyle() -> Kline:
c = (
Kline()
.add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)])
.add_yaxis(
"kline",
data,
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
),
)
.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(type_="inside")],
title_opts=opts.TitleOpts(title="Kline-ItemStyle"),
)
)
return c
def kline_base(mydate, data, name) -> Kline:
kline = (
Kline()
.add_xaxis(mydate)
.add_yaxis("%s" % name, data, markpoint_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="max", value_dim="close")]
), markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="max", value_dim="close")]
),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
),
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
xaxis_opts=opts.AxisOpts(is_scale=True,
axislabel_opts=opts.LabelOpts(rotate=-30)),
title_opts=opts.TitleOpts(title="股票走势"),
datazoom_opts=[opts.DataZoomOpts()],
toolbox_opts=opts.ToolboxOpts(is_show=True),
)
)
return kline
def bar_base() -> Kline:
exchane = ccxt.okex()
kline_BTC = exchane.fetch_ohlcv('BTC/USDT', timeframe=time_change)
df_BTC = pd.DataFrame(kline_BTC, dtype='float')
df_BTC[0] = pd.to_datetime(df_BTC[0], unit='ms') # 转换为想要的时间
date = df_BTC[0].tolist()
list_BTC_USDT = np.array(df_BTC[[1, 4, 3, 2]]).tolist()
c = (
Kline()
.add_xaxis(date)
.add_yaxis('BTC', list_BTC_USDT)
.set_global_opts(
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
xaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[opts.DataZoomOpts(type_="inside")],
title_opts=opts.TitleOpts(title=""))
.dump_options()
)
return c
def get_transactions_of_ticker(ticker):
"""
返回某个股票的全年数据，用于前端绘制图形
:param ticker:
:return:
"""
global transaction_data
global ticker_to_transaction_index
if ticker not in ticker_to_transaction_index:
return utils.error_response('no such ticker')
idx = ticker_to_transaction_index[ticker]
data = transaction_data.values[idx]
date, list1, list2 = data[:, 0], data[:, 3:5], data[:, 5:7]
list0 = np.concatenate([list2, list1], axis=1)
kline = (Kline().add_xaxis(date.tolist()).add_yaxis(
"k line chart", list0.tolist()).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()],
title_opts=opts.TitleOpts(title=""),
))
return kline.dump_options_with_quotes()
def candle(self, data, label="", xaxis=None, zoom_setting=[], position=0, show_rgb=False, mark_point=[], mark_line=[]):
zoomopt = []
for setting in zoom_setting:
zoomopt.append(
opts.DataZoomOpts(**setting)
)
if not xaxis: xaxis = self.xaxis
rgb_color = {
"itemstyle_opts": opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
}
if not show_rgb: rgb_color = {}
mark_point_item = []
for point in mark_point:
mark_point_item.append(
opts.MarkPointItem(**point),
)
return (Kline(init_opts=opts.InitOpts(**self.chart_opt))
.add_xaxis(xaxis_data=xaxis)
.add_yaxis(
series_name=label,
y_axis=data,
markpoint_opts=opts.MarkPointOpts(data=mark_point_item),
markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(
position="middle", color="blue", font_size=15
),
data=mark_line,
symbol=["circle", "none"],
),
**rgb_color
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True, data=[])
)
.set_global_opts(
title_opts=opts.TitleOpts(title="Candle Chart", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=position,
is_scale=True,
boundary_gap=False,
axislabel_opts=opts.LabelOpts(is_show=True),
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
datazoom_opts=zoomopt,
)
)
def draw_centrality():
degree_centrality = nx.degree_centrality(G)
closeness_centrality = nx.closeness_centrality(G)
betweenness_centrality = nx.betweenness_centrality(G)
degree_centrality_sort = sorted(degree_centrality, key=lambda x: degree_centrality[x], reverse=True)
closeness_centrality_sort = sorted(closeness_centrality, key=lambda x: closeness_centrality[x], reverse=True)
betweenness_centrality_sort = sorted(betweenness_centrality, key=lambda x: betweenness_centrality[x], reverse=True)
top_authors = sorted(
[(x, degree_centrality[x] + closeness_centrality[x] + betweenness_centrality[x]) for x in set.intersection(
set(betweenness_centrality_sort[:30]), set(closeness_centrality_sort[:30]),
set(degree_centrality_sort[:30]))], key=lambda x: x[1], reverse=True)
table = prettytable.PrettyTable(
['Rank', 'Name', 'Degree centrality', 'Closeness centrality', 'Betweenness centrality', 'Sum centrality'])
[table.add_row(
[index + 1, value[0], round(degree_centrality[value[0]], 3), round(closeness_centrality[value[0]], 3),
round(betweenness_centrality[value[0]], 3), round(value[1], 3)]) for index, value in enumerate(top_authors)]
print(table)
h_authors = set.intersection(
set(betweenness_centrality_sort[:15]),
set(closeness_centrality_sort[:15]),
set(degree_centrality_sort[:15]))
data = [[G.nodes[x]["name"], [betweenness_centrality[x], closeness_centrality[x], degree_centrality[x]]] for x in
h_authors[:10]]
c = (
Radar()
.add_schema(
schema=[
opts.RadarIndicatorItem(name="Betweenness centrality [0,0.15]", max_=0.15, min_=0),
opts.RadarIndicatorItem(name="Closeness centrality [0,0.6]", max_=0.6, min_=0),
opts.RadarIndicatorItem(name="Degree centrality [0,0.35]", max_=0.35, min_=0),
],
shape="circle",
center=["50%", "50%"],
radius="80%",
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
textstyle_opts=opts.TextStyleOpts(color="#000"),
).set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
for x in data:
color = randomcolor()
c.add(
series_name=x[0],
data=[x[1]],
areastyle_opts=opts.AreaStyleOpts(opacity=0.1, color=color),
linestyle_opts=opts.LineStyleOpts(width=1, color=color),
label_opts=opts.LabelOpts(is_show=False)
)
return c
def abc():
ts_code='002713.SZ'
csv_file = '{0}.csv'.format(ts_code)
render_html = '{0}.html'.format(ts_code)
end_date = datetime.now().strftime('%Y%m%d')
df = None
if not os.path.exists(csv_file):
MY_TOKEN = '190040a13eb5b092ca76fa003f58d693c9121e0fc621f6d2ad221468'
ts.set_token(MY_TOKEN)
pro = ts.pro_api()
df1 = pro.daily(ts_code=ts_code, start_date='20140101', end_date=end_date)
df = df1.sort_values(by=['trade_date'])
df.reset_index(level=0, inplace=True)
df.drop(['index'], axis=1, inplace=True)
print(df)
df.to_csv(csv_file)
if not df is None:
df = pd.read_csv(csv_file)
date = df.trade_date.tolist()
data = []
for idx in df.index :
row=[df.iloc[idx]['open'],df.iloc[idx]['close'],df.iloc[idx]['low'],df.iloc[idx]['high']]
data.append(row)
WIDTH = 1100
HEIGHT = 550
chart_init = {
"width": WIDTH,
"height": HEIGHT,
}
kline = Kline()
kline.add_xaxis(date).add_yaxis('日K', data)
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()],
# title_opts=opts.TitleOpts(title='日K线图:{0}'.format(ts_code)),
)
# kline.add_yaxis(
# "日K",
# date,
# data,
# mark_point=["max"],
# is_datazoom_show=True,
# )
# 生成一个名为 render.html 的文件
kline.render(render_html)
kline.render_notebook()
# kline.render('a.html')
# 在jupyter中使用，只需要使用xxx.render_notebook() 方法即可在Jupyter中显示图
def _timeline(perf, name=""):
tl = Timeline(init_opts=opts.InitOpts(width="1000px", height="400px"))
series = perf.index.get_level_values(0).unique() if isinstance(
perf.index, pd.MultiIndex) else [name]
for i, _w in enumerate(series):
_data = perf.loc[_w] if isinstance(perf.index, pd.MultiIndex) else perf
n, m = _data.shape
x, y = np.arange(0, n, 1), np.arange(0, m, 1)
x, y = np.meshgrid(x, y, indexing='ij')
z = _data.values.flatten()
data = list(zip(y.flatten(), x.flatten(), z))
data = [[int(d[0]), int(d[1]), np.round(d[2], 4)] for d in data]
h = (HeatMap(
init_opts=opts.InitOpts(width="1000px", height="300px")).add_xaxis(
xaxis_data=_data.columns.astype(str).tolist()).add_yaxis(
series_name="",
yaxis_data=_data.index.astype(str).tolist(),
value=data,
label_opts=opts.LabelOpts(position='inside', is_show=True),
).set_series_opts().set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, ),
xaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
yaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
visualmap_opts=opts.VisualMapOpts(min_=z.min(),
max_=z.max(),
is_calculable=True,
orient="vertical",
pos_left="left"),
))
tl.add(h, "{}".format(_w))
if not isinstance(perf.index, pd.MultiIndex):
tl.add_schema(is_timeline_show=False)
return tl
def box_plot(data: dict,
title: str = "箱线图",
width: str = "900px",
height: str = "680px") -> Boxplot:
"""
:param data: 数据
样例：
data = {
"expr 0": [960, 850, 830, 880],
"expr 1": [960, 850, 830, 880],
}
:param title:
:param width:
:param height:
:return:
"""
x_data = []
y_data = []
for k, v in data.items():
x_data.append(k)
y_data.append(v)
init_opts = opts.InitOpts(page_title=title, width=width, height=height)
chart = Boxplot(init_opts=init_opts)
chart.add_xaxis(xaxis_data=x_data)
chart.add_yaxis(series_name="", y_axis=y_data)
chart.set_global_opts(title_opts=opts.TitleOpts(pos_left="center", title=title),
tooltip_opts=opts.TooltipOpts(trigger="item", axis_pointer_type="shadow"),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=True,
splitarea_opts=opts.SplitAreaOpts(is_show=False),
axislabel_opts=opts.LabelOpts(formatter="{value}"),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
type_="value",
name="",
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
)
))
return chart
def gen_kline(df1):
dt_list = list(df1['datetime'])
kline_data = df1.apply(lambda record: [
float(record['open']),
float(record['close']),
float(record['low']),
float(record['high'])
],
axis=1).tolist()
#kline = Kline(init_opts=opts.InitOpts(width='1000px'))
kline = Kline()
kline.add_xaxis(list(df1['datetime']))
kline.add_yaxis('日K', kline_data)
kline.set_global_opts(
title_opts=opts.TitleOpts(title='K线'),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
xaxis_index=[0, 1, 2],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1, 2],
range_start=0,
range_end=100,
),
],
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1))),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
)
return kline
def RadarChart(BookNumsOfPubs: list, NamesOfPubs: list, AvgPoints: list, AvgPrice: list):
'''
出版物数量前10的出版社的数量和评分\n
:param BookNumsOfPubs: 详见RankOfPublisher()函数
:param NamesOfPubs: 同上
:param AvgPoints: 同上
:param AvgPrice: 同上
:return: 画图函数，不知道能返回什么
'''
global dir_to_save
AvgPrice = [[x * 80 for x in AvgPrice]] # 价格放大到原来的80倍
AvgPoints = [[math.exp(x) for x in tqdm(AvgPoints)]] # 平均分取指数，放大差异
BookNumsOfPubs = [[5 * x for x in BookNumsOfPubs]] # 书籍数量放大5倍,与放大后的评分在同一数量级，便于可视化
# 以上数据处理方式没有什么理论依据，仅仅是调整到了一个数量级。有关的统计学知识后续学习
(
Radar(init_opts=opts.InitOpts(width="1000px", height="500px", bg_color="#CCCCCC"))
.add_schema(
schema=[
opts.RadarIndicatorItem(name=NamesOfPubs[0], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[1], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[2], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[3], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[4], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[5], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[6], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[7], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[8], max_=5500),
opts.RadarIndicatorItem(name=NamesOfPubs[9], max_=5500),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
textstyle_opts=opts.TextStyleOpts(color="#228B22"),
)
.add(
series_name="平均分(取指数后)",
data=AvgPoints,
linestyle_opts=opts.LineStyleOpts(color="#D9173B"),
)
.add(
series_name="出版物数量(扩大5倍后)",
data=BookNumsOfPubs,
linestyle_opts=opts.LineStyleOpts(color="#0000CD"),
)
.add(
series_name="出版物均价(扩大80倍后)",
data=AvgPrice,
linestyle_opts=opts.LineStyleOpts(color="#FFD700"),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="出版物数量前10的出版社"), legend_opts=opts.LegendOpts()
)
.render(dir_to_save + "Top10PubsInBookNums_radar.html")
)
print("Successfully drew Top10PubsInBookNums_radar")
def draw_centrality():
degree_centrality = nx.degree_centrality(G)
closeness_centrality = nx.closeness_centrality(G)
betweenness_centrality = nx.betweenness_centrality(G)
degree_centrality_sort = sorted(degree_centrality,
key=lambda x: degree_centrality[x],
reverse=True)
closeness_centrality_sort = sorted(closeness_centrality,
key=lambda x: closeness_centrality[x],
reverse=True)
betweenness_centrality_sort = sorted(
betweenness_centrality,
key=lambda x: betweenness_centrality[x],
reverse=True)
h_authors = set.intersection(set(betweenness_centrality_sort[:15]),
set(closeness_centrality_sort[:15]),
set(degree_centrality_sort[:15]))
data = [[
G.nodes[x]["name"],
[
betweenness_centrality[x], closeness_centrality[x],
degree_centrality[x]
]
] for x in h_authors]
c = (Radar().add_schema(
schema=[
opts.RadarIndicatorItem(name="Betweenness centrality [0,1]",
max_=1,
min_=0),
opts.RadarIndicatorItem(name="Closeness centrality [0,0.6]",
max_=0.6,
min_=0),
opts.RadarIndicatorItem(name="Degree centrality [0,0.1]",
max_=0.1,
min_=0),
],
shape="circle",
center=["50%", "50%"],
radius="80%",
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#000"),
).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))
for x in data:
color = randomcolor()
c.add(series_name=x[0],
data=[x[1]],
areastyle_opts=opts.AreaStyleOpts(opacity=0.1, color=color),
linestyle_opts=opts.LineStyleOpts(width=1, color=color),
label_opts=opts.LabelOpts(is_show=False))
c.render("radar_angle_radius_axis.html")
