def KlinePlotting(target_file):
global trend_punishment
res = pickle.load(open(target_file, 'rb'))
c = (Line().add_xaxis([item[0] for item in res]).add_yaxis(
"打分",
[item[1] for item in res],
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
).set_global_opts(
title_opts=opts.TitleOpts(title="打分"),
xaxis_opts=opts.AxisOpts(type_="category"),
datazoom_opts=[
opts.DataZoomOpts(
type_="slider",
xaxis_index=[0],
range_start=80,
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"),
),
))
gridChart = Grid(init_opts=opts.InitOpts(width="1000px",
height="500px",
animation_opts=opts.AnimationOpts(
animation=False)))
gridChart.add(c, grid_opts=opts.GridOpts(pos_left="10%", pos_right="10%"))
gridChart.render("score_modified_plot_new_0.002.html")
def test_liquid_grid(fake_writer):
l1 = (
Liquid()
.add("lq", [0.6, 0.7], center=["60%", "50%"])
.set_global_opts(title_opts=opts.TitleOpts(title="多个 Liquid 显示"))
)
l2 = Liquid().add(
"lq",
[0.3254],
center=["25%", "50%"],
label_opts=opts.LabelOpts(
font_size=50,
formatter=JsCode(
"""function (param) {
return (Math.floor(param.value * 10000) / 100) + '%';
}"""
),
position="inside",
),
)
c = Grid().add(l1, grid_opts=opts.GridOpts()).add(l2, grid_opts=opts.GridOpts())
c.render()
_, content = fake_writer.call_args[0]
assert_in("center", content)
def draw_charts(fn1, fn2):
df1 = pd.read_csv(fn1)
df1['datetime'] = df1['date'] + ' ' + df1['time']
#print(df1.head())
kline1 = gen_kline_one(df1)
df2 = pd.read_csv(fn2)
df2['datetime'] = df2['date'] + ' ' + df2['time']
#print(df1.head())
kline2 = gen_kline_two(df2)
grid_chart = Grid(
init_opts=opts.InitOpts(
width="1300px",
height="700px",
#animation_opts=opts.AnimationOpts(animation=False),
)
)
grid_chart.add(
kline1,
grid_opts=opts.GridOpts(pos_left="5%", pos_right="3%", height="39%"),
)
grid_chart.add(
kline2,
grid_opts=opts.GridOpts(
pos_left="3%", pos_right="5%", pos_top="53%", height="39%" ),
)
fn = get_dss( ) + 'backtest/render/bar2.html'
grid_chart.render(fn)
def html_output(self, order, view, mode):
"""
output function of html
Args:
order(int): diversified_ranking use different order
view(View): view object
mode(str): single or multiple
Returns:
None
"""
instance = self.instance
data = {}
data['order'] = order
data['chartname'] = instance.table_name
data['describe'] = view.table.describe
data['x_name'] = view.fx.name
data['y_name'] = view.fy.name
data['chart'] = Chart.chart[view.chart]
data['classify'] = [v[0] for v in view.table.classes]
data['x_data'] = view.X
data['y_data'] = view.Y
data['title_top'] = 5
[chart, filename] = self.html_handle(data)
grid = Grid()
grid.add(chart,
grid_opts=opts.GridOpts(pos_bottom='20%', pos_top='20%'))
if mode == 'single':
self.page.add(grid) #the grid is added in the same page
elif mode == 'multiple':
grid.render('./html/' + self.table_name + '/' +
filename) #the grid is added in a new file
def draw(self, symbol, fn_render):
fn = get_dss() + 'fut/bar/day_' + symbol + '.csv'
df1 = pd.read_csv(fn)
# print(df1.head())
price_min = int(df1.close.min() * 0.99)
price_max = df1.close.max()
kline = self.gen_kline(df1, symbol)
line_cci = self.gen_cci(df1, 100)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1390px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
height="60%"),
)
grid_chart.add(
line_cci,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="75%",
height="17%"),
)
grid_chart.render(fn_render)
def draw(self, symbol, fn_render):
fn = get_dss() +'fut/bar/day_' + symbol + '.csv'
df = pd.read_csv(fn)
df1 = df.loc[:,['date','time','close']]
n = 30
close_list = df.apply(lambda record: float(record['close']), axis=1).tolist()
close_list = np.array(close_list)
ma_arr = talib.SMA(close_list, n)
df['ma'] = ma_arr
df['close'] = df['close'] - df['ma']
df2 = df.loc[:,['date','time','close']]
line1 = self.gen_line_one(df1, symbol)
line2 = self.gen_line_two(df2)
grid_chart = Grid(
init_opts=opts.InitOpts(
width="1300px",
height="700px",
#animation_opts=opts.AnimationOpts(animation=False),
)
)
grid_chart.add(
line1,
grid_opts=opts.GridOpts(pos_left="5%", pos_right="3%", height="39%"),
)
grid_chart.add(
line2,
grid_opts=opts.GridOpts(
pos_left="5%", pos_right="3%", pos_top="53%", height="39%" ),
)
grid_chart.render(fn_render)
def test_grid_options(fake_writer):
bar = _chart_for_grid()
gc = Grid().add(
bar,
opts.GridOpts(pos_left="5%", pos_right="20%", is_contain_label=True))
gc.render()
_, content = fake_writer.call_args[0]
assert_in("containLabel", content)
def draw_charts():
pz = 'CF'
vtSymbol = 'CF'
fn = get_dss() + 'backtest/fut/' + pz + '/day_' + vtSymbol + '.csv'
df1 = pd.read_csv(fn)
# df1 = df1[df1.date >= '2019-01-20']
#df1['datetime'] = df1['date'] + ' ' + df1['time']
df1['datetime'] = df1['date']
price_min = int(df1.close.min() * 0.99)
price_max = df1.close.max()
#fn = get_dss( )+ 'fut/engine/aberration_raw/signal_aberration_raw_duo_deal_CF.csv'
#fn = get_dss( )+ 'fut/engine/aberration_raw/signal_aberration_raw_kong_deal_CF.csv'
fn = get_dss(
) + 'fut/engine/aberration_enhance/signal_aberration_enhance_duo_deal_CF.csv'
#fn = get_dss( )+ 'fut/engine/aberration_enhance/signal_aberration_enhance_kong_deal_CF.csv'
df2 = pd.read_csv(fn)
dt_list = df2['datetime'].tolist()
dt_list = [dt[:10] for dt in dt_list]
# print(dt_list)
df2['datetime'] = dt_list
line = gen_line(df1, vtSymbol, price_min, price_max)
line_atr = gen_atr(df1, 10)
line_boll = gen_boll(df1, 10, 2)
line = line.overlap(line_boll)
scatter_open = gen_poit_open(df2)
scatter_close = gen_poit_close(df2)
line = line.overlap(scatter_open)
line = line.overlap(scatter_close)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1300px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="60%"),
)
grid_chart.add(
line_atr,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="76%",
height="17%"),
)
fn = get_dss() + 'backtest/render/k_deal_aberration_' + vtSymbol + '.html'
grid_chart.render(fn)
def draw_charts():
# #fn = get_dss() +'backtest/fut/m/' + 'm_01_05.csv'
# fn = get_dss() +'backtest/fut/m/' + 'day_m.csv'
# df1 = pd.read_csv(fn)
# df1['datetime'] = df1['date'] + ' ' + df1['time']
# #print(df1.head())
# kline1 = gen_kline_one(df1)
#
# fn = get_dss() +'backtest/fut/y/' + 'day_y.csv'
# df2 = pd.read_csv(fn)
# df2['datetime'] = df2['date'] + ' ' + df2['time']
# #print(df1.head())
# kline2 = gen_kline_two(df2)
#--------------------------------------------------------------------------
fn = 'bar_kamaresid_raw_duo_CF.csv'
fn = 'bar_kamaresid_raw_duo_m.csv'
df = pd.read_csv(fn)
df.columns = ['date','time','close','q1','q2','resid']
df1 = df.loc[:,['date','time','close']]
df2 = df.loc[:,['date','time','resid']]
df2['close'] = df2['resid']
print(df1.head(3))
print(df2.head(3))
s1 = 'close'
s2 = 'resid'
line1 = gen_line_one(df1)
line2 = gen_line_two(df2)
grid_chart = Grid(
init_opts=opts.InitOpts(
width="1300px",
height="700px",
#animation_opts=opts.AnimationOpts(animation=False),
)
)
grid_chart.add(
line1,
grid_opts=opts.GridOpts(pos_left="5%", pos_right="3%", height="39%"),
)
grid_chart.add(
line2,
grid_opts=opts.GridOpts(
pos_left="5%", pos_right="3%", pos_top="53%", height="39%" ),
)
fn = get_dss( )+ 'backtest/render/k_k_' + s1 + '_' + s2 + '.html'
grid_chart.render(fn)
def draw_charts():
vtSymbol = 'CF005'
fn = get_dss() + 'fut/bar/min5_' + vtSymbol + '.csv'
df1 = pd.read_csv(fn)
df1 = df1[df1.date >= '2019-11-20']
df1['datetime'] = df1['date'] + ' ' + df1['time']
# print(df1.head())
fn = get_dss() + 'fut/engine/rsiboll/signal_rsiboll_duo_deal_CF.csv'
df2 = pd.read_csv(fn)
kline = gen_kline(df1)
line_rsi = gen_rsi(df1)
line_atr = gen_atr(df1)
scatter_deal_one = gen_poit_one(df2)
scatter_deal_two = gen_poit_two(df2)
scatter_deal_three = gen_poit_three(df2)
kline_scatter = kline.overlap(scatter_deal_one)
kline_scatter = kline_scatter.overlap(scatter_deal_two)
kline_scatter = kline_scatter.overlap(scatter_deal_three)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline_scatter,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="45%"),
)
grid_chart.add(
line_rsi,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="57%",
height="17%"),
)
grid_chart.add(
line_atr,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="76%",
height="17%"),
)
grid_chart.render("k_deal_rsi_atr.html")
def draw_charts():
vtSymbol = 'm'
fn = get_dss() + 'backtest/fut/m/day_' + vtSymbol + '.csv'
df1 = pd.read_csv(fn)
#df1 = df1[df1.date >= '2019-10-12']
df1['datetime'] = df1['date'] + ' ' + df1['time']
# print(df1.head())
kline = gen_kline(df1)
line_rsi = gen_rsi(df1)
line_atr = gen_atr(df1)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1500px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="8%", height="45%"),
)
grid_chart.add(
line_rsi,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="8%",
pos_top="57%",
height="17%"),
)
grid_chart.add(
line_atr,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="8%",
pos_top="76%",
height="17%"),
)
fn = get_dss() + 'backtest/render/bar_ti_ti.html'
grid_chart.render(fn)
def gen_grid_chart(
overlap_kline_line: Kline,
width: str,
height: str,
bar_plot_list: typing.List[Bar],
filename: str,
) -> Grid:
index = 0
grid_chart = Grid(init_opts=opts.InitOpts(
width=width,
height=height,
animation_opts=opts.AnimationOpts(animation=False),
))
pos_top_list, height_list = gen_pos_top_height(plot_list=bar_plot_list)
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top=f"{pos_top_list[index]}%",
height=f"{height_list[index]}%",
),
)
index += 1
bar_plot_list = filter_bar_plot(bar_plot_list)
for bar_plot in bar_plot_list:
grid_chart.add(
bar_plot,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top=f"{pos_top_list[index]}%",
height=f"{height_list[index]}%",
),
)
index += 1
grid_chart.render(filename)
return grid_chart
def generate_kchart(self,MA=[5,10,21],renderfile=False) -> Grid:
if self.exists == False:
return None
# reassign cursor tick to trigger populate attributes like self.histBar and etc.
self.cursorTick = self._cursorTick
df_toPlot = self.allBars.loc[self.allBars['unit_intdate'] <= self.cursorTick['unit_intdate']]
# 输入时间序列中带有未来日期，则取当下能取到的最新online数据模拟
# if len(self.allBars.loc[self.allBars['unit_intdate'] == self.cursorTick['unit_intdate']]) == 0:
# df_liveQuote = self.get_livebar(assign_timestamp=self.cursorTick['timeAxis'])
# if len(df_liveQuote) > 0:
# df_toPlot = df_toPlot.append(df_liveQuote, ignore_index = True, sort=False)
str_secname = self.sec_name
str_plotname = '{} - {}'.format(self.ts_code,str_secname)
'''-----------------画图部分---------------------'''
lst_ohlcv=df_toPlot.loc[:,['open','close','low','high']].values.tolist()
lst_vol=list(df_toPlot['vol'].values)
lst_amount=list(df_toPlot['amount'].values)
# 需从处理过的histbar中获得，由于长度比allbar短1，需将值list添加一个空值补齐
lst_peaks=list(abs(self.histBars['peaks'].values))
# lst_pivotdown=[*(float('nan') if i<0 else i for i in list(self.histBars['pivots'].values)),float('nan')]
lst_pivotdown=[*(float('nan') if i<0 else i for i in list(self.histBars['valid_pivots'].values)),float('nan')]
# lst_pivotup=[*(float('nan') if i>0 else abs(i) for i in list(self.histBars['pivots'].values)),float('nan')]
lst_pivotup=[*(float('nan') if i>0 else abs(i) for i in list(self.histBars['valid_pivots'].values)),float('nan')]
lst_xaxis=list(df_toPlot['unit_intdate'].astype(str)) # xaxis values has to be STRING type, int will not work
def calculate_ma(day_count: int, d):
result: List[Union[float, str]] = []
for i in range(len(d)):
if i < day_count:
result.append("-")
continue
sum_total = 0.0
for j in range(day_count):
sum_total += float(d[i - j][1])
result.append(abs(float("%.3f" % (sum_total / day_count))))
return result
kline = (Kline()
.add_xaxis(lst_xaxis)
.add_yaxis(series_name=str_secname, y_axis=lst_ohlcv,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="min",value_dim="close"),
opts.MarkPointItem(type_="max",value_dim="close")],
symbol_size = [20, 20], #表示标记宽为 20，高为 10
),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#ec0000",
border_color0="#00da3c",
),)
.set_global_opts(
title_opts=opts.TitleOpts(
title=str_plotname,
subtitle='MA='+str(MA),
),
xaxis_opts=opts.AxisOpts(type_="category"),
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_top='10%', pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0,1,2],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0,1,2],
type_="slider",
pos_top="90%",
range_start=75,
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",font_size=10),
),
# 阴量绿阳量红
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=list(map(lambda x: x+len(MA),[4,5])), #动态算出vol和amt柱状图的series index
is_piecewise=True,
pieces=[
{"value": 1, "color": "#ec0000"},
{"value": -1, "color": "#00da3c"},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
)
trendline = (
Line()
.add_xaxis(lst_xaxis)
.add_yaxis('高低点', lst_peaks,
itemstyle_opts=opts.ItemStyleOpts(color="green"))
)
for i in MA:
if i is not None:
trendline.add_yaxis(
series_name='MA'+str(i),
y_axis=calculate_ma(day_count=i, d=lst_ohlcv),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
trendline.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
trendline.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
keyPoints = (
EffectScatter()
.add_xaxis(lst_xaxis)
.add_yaxis("末跌高", lst_pivotdown,symbol=SymbolType.ARROW,symbol_rotate=180,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color="purple"))
.add_yaxis("末升低", lst_pivotup,symbol=SymbolType.ARROW,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color="blue"))
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
vol_bar = (
Bar()
.add_xaxis(lst_xaxis)
.add_yaxis(
series_name="交易量",
y_axis=[
[i, lst_vol[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]
for i in range(len(lst_vol))
],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="最低",
max_="最高",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
# .add_yaxis("交易量", lst_vol,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
amnt_bar = (
Bar()
.add_xaxis(lst_xaxis)
.add_yaxis(
series_name="交易额",
y_axis=[
[i, lst_amount[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]
for i in range(len(lst_amount))
],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=2,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="最低",
max_="最高",
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
# .add_yaxis("交易额", lst_amount,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
priceChart = kline.overlap(trendline).overlap(keyPoints)
gridChart = Grid()
gridChart.add(
priceChart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_bottom='40%'),
)
gridChart.add(
vol_bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="60%", height='15%'),
)
gridChart.add(
amnt_bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="75%"),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'kline')
gridChart.render(fname) if renderfile else None
self.kchart = gridChart # 将结果输出到analytics对象属性中用于function以外的调用
return self
def draw_chart():
kline = (
Kline().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(position="middle",
color="blue",
font_size=15),
data=split_data_part(),
symbol=["circle", "none"],
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True, data=split_data_part())).
set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(is_show=False,
type_="inside",
xaxis_index=[0,
0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0,
1],
pos_top="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0,
2],
range_end=100),
],
# 三个图的 axis 连在一块
# axispointer_opts=opts.AxisPointerOpts(
# is_show=True,
# link=[{"xAxisIndex": "all"}],
# label=opts.LabelOpts(background_color="#777"),
# ),
))
kline_line = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
))
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
# Bar-1
bar_1 = (
Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="Volumn",
y_axis=data["vols"],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
# 根据 echarts demo 的原版是这么写的
# itemstyle_opts=opts.ItemStyleOpts(
# color=JsCode("""
# function(params) {
# var colorList;
# if (data.datas[params.dataIndex][1]>data.datas[params.dataIndex][0]) {
# colorList = '#ef232a';
# } else {
# colorList = '#14b143';
# }
# return colorList;
# }
# """)
# )
# 改进后在 grid 中 add_js_funcs 后变成如下
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),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MACD",
y_axis=data["macds"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
line_2 = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="DIF",
y_axis=data["difs"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DIF",
y_axis=data["deas"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
# 最下面的柱状图和折线图
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(init_opts=opts.InitOpts(width="1400px", height="800px"))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(data["datas"]))
# K线图和 MA5 的折线图
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
# Volumn 柱状图
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="71%",
height="10%"),
)
# MACD DIFS DEAS
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="82%",
height="14%"),
)
grid_chart.render("Professional_kline_chart.html")
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[opts.DataZoomOpts(type_="inside")],
title_opts=opts.TitleOpts(title="股票价格增长对比",
pos_left='10%',
pos_top="40%"),
# 把所有的x轴连接在一起
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
grid_chart = Grid(init_opts=opts.InitOpts(
width="1400px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
line,
grid_opts=opts.GridOpts(pos_left="25%", pos_right="15%",
pos_top="10%"),
)
grid_chart.render('16个股票和医药生物指数增长对比.html')
wb.open('16个股票和医药生物指数增长对比.html')
print(data['300832.SZ'].tolist())
def show_xd_kline(kline_raw, show=True):
freq = '5min'
start_dt = kline_raw.iloc[0]["dt"]
end_dt = kline_raw.iloc[-1]["dt"]
x_data = kline_raw.dt.values.tolist()
oclh = kline_raw[['open', 'close', 'low', 'high']].values.tolist()
symbol = kline_raw.iloc[0]['symbol']
kline = (
Kline(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name=symbol,
y_axis=oclh,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="缠论分析结果：%s-%s" % (symbol, freq),
subtitle="时间区间：%s 至 %s" % (start_dt, end_dt)
),
xaxis_opts=opts.AxisOpts(type_="category"),
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_top=10, pos_left="center"
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
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"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{"value": 1, "color": "#ec0000"},
{"value": -1, "color": "#00da3c"},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
)
chan_fx = (
Scatter(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis("分型标记", kline_raw.fx.values.tolist(),
label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
)
)
chan_bi = (
Scatter(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis("笔标记", kline_raw.bi.values.tolist(),
label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
)
)
chan_xd = (
Scatter(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis("线段标记", kline_raw.xd.values.tolist(),
label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
)
)
bar = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="Volume",
yaxis_data=kline_raw.vol.values.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
# Kline And Line
kline = kline.overlap(chan_fx)
kline = kline.overlap(chan_bi)
kline = kline.overlap(chan_xd)
# Grid Overlap + Bar
grid_chart = Grid(opts.InitOpts(width="1400px", height="800px", theme=ThemeType.WHITE))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(
pos_left="8%", pos_right="8%", height="60%"
),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(
pos_left="8%", pos_right="8%", pos_top="70%", height="16%"
),
)
graph_path = os.path.join(cache_path, "%s_kline_%s.html" % (symbol, "fx"))
grid_chart.render(path=graph_path)
# 调用浏览器打开可视化结果
if show:
webbrowser.open(graph_path)
def plot_kline(ka, bs=None, file_html="kline.html", width="1400px", height="680px"):
"""
:param ka: KlineAnalyze
:param bs: pd.DataFrame
买卖点，包含三个字段 ["操作提示", "交易时间", "交易价格"]
:param file_html: str
:param width: str
:param height: str
:return: None
"""
df = ka.to_df(use_macd=True, ma_params=(5, 20,))
x = df.dt.to_list()
title = "%s | %s 至 %s" % (ka.symbol, ka.start_dt, ka.end_dt)
kline = (
Kline()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="",
y_axis=df[['open', 'close', 'low', 'high']].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True)
)
.set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside")
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
),
opts.DataZoomOpts(
is_show=True, xaxis_index=[0, 1], pos_top="96%", range_end=100
),
opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
],
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
)
)
kline_line = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="笔",
y_axis=df.bi.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='diamond',
symbol_size=8,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='dotted', width=2),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="线段",
y_axis=df.xd.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='triangle',
symbol_size=12,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='solid', width=2),
label_opts=opts.LabelOpts(is_show=True, position='right'),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
)
)
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
c = (
Scatter()
.add_xaxis(bs['交易时间'].to_list())
.add_yaxis(
"买卖点",
bs['交易价格'].to_list(),
label_opts=opts.LabelOpts(
is_show=True,
position="left",
formatter=JsCode(
"function(params){return bsName[params.dataIndex][0];}"
)
),
))
overlap_kline_line = overlap_kline_line.overlap(c)
# draw volume
bar_1 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="Volumn",
yaxis_data=df.vol.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),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=True, position='inside')
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
# Bar-2 (Overlap Bar + Line)
bar_2 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="MACD",
yaxis_data=df.macd.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
line_2 = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="DIF",
y_axis=df['diff'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="DEA",
y_axis=df['dea'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
)
# draw MACD
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(init_opts=opts.InitOpts(width=width, height=height, page_title=title))
grid_chart.add_js_funcs("var barData = {}".format(df[['open', 'close', 'low', 'high']].values.tolist()))
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
grid_chart.add_js_funcs("var bsName = {}".format(bs[["操作提示", "交易价格"]].values.tolist()))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="71%", height="10%"),
)
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="82%", height="14%"),
)
grid_chart.render(path=file_html)
xaxis_opts=opts.AxisOpts(name='商品'),
yaxis_opts=opts.AxisOpts(name='单位:件')))
bar.render('柱状图.html')
# html转图片
from pyecharts.charts import Bar, Grid, Pie
from pyecharts.render import make_snapshot
from snapshot_selenium import snapshot
init_opts = opts.InitOpts(width="600px", height="360px")
path_html = '柱状图.html'
path_png = "E:/GZ/Django/Django_API-1/Django_baogao/DjangoWord/Python_docx/柱状图.png"
grid = Grid(init_opts=init_opts)
grid.add(bar, grid_opts=opts.GridOpts(pos_top='5')) # 仅使用pos_top修改相对顶部的位置
grid.render(path_html)
make_snapshot(snapshot,
grid.render(path_html),
path_png,
delay=2,
pixel_ratio=2)
#直接生成图片
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.render import make_snapshot
from snapshot_selenium import snapshot
def draw_grow_chart(data):
for i in ts_codes:
data[i] = data[i].values
# print(data[i])
data[i] = data[i] / (data[i][data[i].notnull()].iloc[0]) #除以每一列的第一个非空值
print(data)
x = data.index.values.tolist()
line = (
Line().add_xaxis(x).add_yaxis(
'医药生物指数',
data['000808.SH'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'天宇股份',
data['300702.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'凯普生物',
data['300639.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'普利制药',
data['300630.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'贝达药业',
data['300558.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'健帆生物',
data['300529.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'富祥药业',
data['300497.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'万孚生物',
data['300482.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'泰格医药',
data['300347.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'智飞生物',
data['300122.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'安科生物',
data['300009.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'凯莱英',
data['002821.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'长春高新',
data['000661.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'硕世生物',
data['688399.SH'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'健友股份',
data['603707.SH'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'安图生物',
data['603658.SH'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
'新产业',
data['300832.SZ'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).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="股票价格增长对比",
pos_left='10%',
pos_top="40%"),
# 把所有的x轴连接在一起
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
grid_chart = Grid(init_opts=opts.InitOpts(
width="1400px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
line,
grid_opts=opts.GridOpts(pos_left="25%", pos_right="15%",
pos_top="10%"),
)
grid_chart.render('股票增长对比.html')
def draw(df, file_png="test.png"):
x = df.dt.to_list()
kline = (
Kline()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="",
y_axis=df[['open', 'close', 'low', 'high']].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True)
)
.set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside")
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
),
opts.DataZoomOpts(
is_show=True, xaxis_index=[0, 1], pos_top="97%", range_end=100
),
opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
],
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
)
)
kline_line = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="笔",
y_axis=df.bi.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='diamond',
symbol_size=8,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='dotted', width=1.5),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="线段",
y_axis=df.xd.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='triangle',
symbol_size=12,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='solid', width=1.5),
label_opts=opts.LabelOpts(is_show=True, position='right'),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
)
)
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
df1 = df[df['基准价格'] > 0]
c = (
Scatter()
.add_xaxis(df1['出现时间'].to_list())
.add_yaxis(
"买卖点",
df1['基准价格'].to_list(),
label_opts=opts.LabelOpts(
is_show=True,
position="left",
formatter=JsCode(
"function(params){return bsName[params.dataIndex][0];}"
)
),
))
overlap_kline_line = overlap_kline_line.overlap(c)
# draw volume
bar_1 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="Volumn",
yaxis_data=df.vol.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),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=True, position='inside')
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
# Bar-2 (Overlap Bar + Line)
bar_2 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="MACD",
yaxis_data=df.macd.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
line_2 = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="DIF",
y_axis=df['diff'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="DEA",
y_axis=df['dea'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
)
# draw MACD
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(init_opts=opts.InitOpts(width="1400px", height="680px"))
grid_chart.add_js_funcs("var barData = {}".format(df[['open', 'close', 'low', 'high']].values.tolist()))
grid_chart.add_js_funcs("var bsName = {}".format(df1[["操作提示", "基准价格"]].values.tolist()))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="71%", height="10%"),
)
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="82%", height="14%"),
)
file_html = file_png.replace(".png", '.html')
make_snapshot(snapshot, grid_chart.render(path=file_html), file_png)
data=markareadata,
itemstyle_opts=opts.ItemStyleOpts(opacity=0.5, )))
kline.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True),
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(type_="inside", range_start=-100),
opts.DataZoomOpts(pos_bottom="0%"),
],
# title_opts=opts.TitleOpts(title=stock_code),
)
grid_chart = Grid(init_opts=opts.InitOpts(
width="100%",
height="950px",
theme=ThemeType.ESSOS,
page_title=stock_code,
))
grid_chart.add_js_funcs("var areaData={}".format(markareadata))
grid_chart.add_js_funcs("console.log('hello world')")
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="85%"),
)
grid_chart.render('pyecharts.html')
def generate_mchart(self,lst_tscode=[],lst_macro=['shibor.1w','pimon.nt_yoy','pimon.ppi_yoy','pimon.m1_yoy','pimon.m2_yoy','pimon.allstock_circ_mv_sum'],
lst_findmedian=['pe',()],renderfile=False) -> Grid:
if self.exists == False:
return None
''' testing findmedian with a given index '''
# df=self._dataagent.query('index_weight',index_code='000300.SH',start_date='20200630', end_date='20200630')
# lst_findmedian=['pb',tuple(df['con_code'].values)]
''' ----------test end--------------------'''
lst_tscode=list(set([self.ts_code,*lst_tscode]))
# lst_findmedian[1] = tuple(self._dataagent.registry.loc[self._dataagent.registry['sec_type']=='stk']['ts_code']) if lst_findmedian[1] is None or len(lst_findmedian[1])==0 else lst_findmedian[1]
dat_cursor = self.cursorTick['timeAxis']
int_latestViewDate = self._dataagent.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )
df_baseCal = self._dataagent.df_calTbl.loc[self._dataagent.df_calTbl['cal_date']<=int_latestViewDate]['cal_date'].copy()
lst_xaxis = ['{}/{}/{}'.format(i[:4],i[4:6],i[6:]) for i in df_baseCal.values.astype('str')] # xaxis values has to be STRING type, int will not work
linechart = (
Line()
.add_xaxis(lst_xaxis)
)
# 生成主线，即目标证券/指数，及比较用的指数
for str_tscode in lst_tscode:
# str_tscode = DataAgent.formatCode(code)
df_target = self._dataagent.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)
if len(df_target)>0:
df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')
else:
continue
'''=====主图画图部分包括主标的和副标的价格曲线，在主图中续把主图及后面叠加图的option，y轴等设置好==='''
linename = '{} - {}'.format(str_tscode,self.sec_name)
lst_values = list(df_toPlot['close'])
lst_values = list(libs.normByRange(lst_values))
linechart.add_yaxis(linename, lst_values,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.3),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color="#00da3c",
),
)
linechart.extend_axis( # 所有保持原比例的折线都放在linechart2里，使用右侧y轴
yaxis=opts.AxisOpts(
name="倍数",
type_="value",
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="black")
),
axislabel_opts=opts.LabelOpts(formatter="{value}倍"),
)
)
linechart.set_global_opts(
title_opts=opts.TitleOpts(
title='宏观比较',
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(name="等比例",
is_scale=True,
),
legend_opts=opts.LegendOpts(is_show=True, pos_top='5%', pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0],
type_="slider",
pos_top="90%",
range_start=75,
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",font_size=10),
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
'''=============================主图画图部分结束================='''
'''=========叠加图1 尝试读取pe等股票才有的属性，如失败则说明不是股票，循环继续============='''
try:
lst_pe = df_toPlot['pe'].values.tolist()
lst_pb = df_toPlot['pb'].values.tolist()
lst_pepb = [round(lst_pe[index]/value,2) for index, value in enumerate(lst_pb) if value!=0]
linechart.add_yaxis(str_tscode+"_PE", lst_pe, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
linechart.add_yaxis(str_tscode+"_PE/PB", lst_pepb, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
except:
continue
'''==========================叠加图1结束================================'''
df_shibor = self._dataagent.query('load_shibor')
# print('Supported shibor columns: {}'.format(df_shibor.columns))
df_aggDay = self._dataagent.query('load_aggDayKPI')
# print('Supported macro eco columns: {}'.format(df_aggDay.columns))
df_pimon = self._dataagent.query('load_piMon')
df_pimon = pd.merge(df_pimon, df_aggDay, how='left', left_on='date', right_on='trade_date') #与aggday按每月末日期合并以获得如“流通市值”等kpi
# print('Supported macro eco columns: {}'.format(df_pimon.columns))
'''=================叠加图2 所有股票的倍数类KPI，使用倍数类y轴============='''
lst_aggDayKPIloop = ['allstock_pe_median','allstock_pe_ttm_median','allstock_pb_median','allstock_pepb_median']
for kpi in lst_aggDayKPIloop:
lst_values = df_aggDay[kpi].values.tolist()
linechart.add_yaxis(kpi, lst_values, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
'''==========================叠加图2结束================================'''
'''===========叠加图3 宏观数据如shibor，cpi，m1等，按“。”分割类别进行图表生成，使用等比类y轴 ========'''
for item in lst_macro:
try:
topic,colname = item.split('.')
except:
print('WARNING! input code {} not recognized...'.format(item))
if topic=='shibor':
df_target = df_shibor
maxarr = df_target.max(axis=0, skipna=True).values[1:]
minarr = df_target.min(axis=0, skipna=True).values[1:]
deltaRng = max(maxarr)-min(minarr) # overwrite the normalization function delta to put all shibor rates comparable
elif topic=='pimon':
df_target = df_pimon
deltaRng = None
else:
print('WARNING! subject code {} is not recognized...'.format(topic))
if len(df_target)>0:
df_toPlot = pd.merge(df_baseCal, df_target, how='left', left_on='cal_date', right_on='date')
df_toPlot[colname] = df_toPlot[colname].bfill()
else:
continue
linename = item
lst_values = list(df_toPlot[colname])
lst_values = list(libs.normByRange(lst_values,delta=deltaRng))
linechart.add_yaxis(linename, lst_values, # 所有等比例缩放的折线都放在linechart里，使用左侧y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1,opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
'''==========================叠加图3结束================================'''
'''========叠加图4 如参数给出需要寻找中位数的股票列表则读取相应所有本地文件后计算并生成叠加图标========'''
if lst_findmedian[1] is not None and len(lst_findmedian[1])>0 :
str_cat = lst_findmedian[0]
df_result = pd.DataFrame()
for str_tscode in lst_findmedian[1]:
try:
df = self._dataagent.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)[['ts_code','trade_date',str_cat]]
print('{} csv file loaded for {} trend analysis...'.format(str_tscode,str_cat),end='\r')
except Exception as e:
print('WARNING! {} csv file not found...'.format(str_tscode))
continue
df_result = pd.concat([df_result,df])
df_result.dropna(subset=[str_cat],inplace=True)
df_result = df_result.loc[df_result['trade_date']>19940301]
# libs.df_csv(cfg.PATH_BUGFILE,(df_result.groupby('trade_date')['ts_code'].count(),))
# print(df_result.groupby('trade_date')['ts_code'].count())
df_target = df_result.groupby('trade_date')[str_cat].median()
if len(df_target)>0:
df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')
linename = '{}-median'.format(str_cat)
lst_values = list(df_toPlot[str_cat])
lst_values = list(lst_values)
linechart.add_yaxis(linename, lst_values, yaxis_index=1,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
else:
pass
'''==========================叠加图4结束================================'''
# put everything in a grid for easy resizing and positioning
gridChart = Grid()
gridChart.add(
linechart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top='25%'),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'macro')
gridChart.render(fname) if renderfile else None
self.mchart = gridChart #输出至可访问对象属性中
return self
"lq",
[0.3254],
center=["25%", "50%"],
label_opts=opts.LabelOpts(
font_size=50,
formatter=JsCode("""function (param){
return (Math.floor(param.value * 10000)/100) + '%';
}"""),
position="inside",
),
)
grid = Grid().add(l1, grid_opts=opts.GridOpts()).add(l2,
grid_opts=opts.GridOpts())
grid.render("multiple_liquid.html")
# Section2 平行坐标系
from pyecharts.charts import Parallel
data = [
[1, 91, 45, 125, 0.82, 34, 23, "良"],
[2, 65, 27, 78, 0.86, 45, 29, "良"],
[3, 83, 60, 84, 1.09, 73, 27, "良"],
[4, 109, 81, 121, 1.28, 68, 51, "轻度污染"],
[5, 106, 77, 114, 1.07, 55, 51, "轻度污染"],
[6, 109, 81, 121, 1.28, 68, 51, "轻度污染"],
[7, 106, 77, 114, 1.07, 55, 51, "轻度污染"],
[8, 89, 65, 78, 0.86, 51, 26, "良"],
[9, 53, 33, 47, 0.64, 50, 17, "良"],
[10, 80, 55, 80, 1.01, 75, 24, "良"],
def createView(self, Clt, city_name): # Clt客户端程序
# self.day_datas, self.time_datas = SearchSQL().airDatas(city_name)
air_datas = Clt.sendData(f"3 {city_name}")
self.day_datas = air_datas["day"]
self.time_datas = air_datas["time"]
timepoint = [[], []]
aqi = [[], []]
so2 = [[], []]
no2 = [[], []]
co = [[], []]
o3 = [[], []]
pm2_5 = [[], []]
pm10 = [[], []]
for datas in self.day_datas:
# 分割日期
day = re.sub("^2.*?-", '', datas[2].split(' ')[0]) # 取出日期，将年份去掉
timepoint[0].append(day)
aqi[0].append(int(datas[4]))
so2[0].append(int(datas[5]))
no2[0].append(int(datas[6]))
co[0].append(float(datas[7]))
o3[0].append(int(datas[8]))
pm2_5[0].append(int(datas[9]))
pm10[0].append(int(datas[10]))
# print(timepoint[0], aqi[0], so2[0], no2[0], co[0], o3[0], pm2_5[0], pm10[0])
day_line = Line()
day_line.add_xaxis(timepoint[0]) # 增加x轴
day_line.add_yaxis(
"AQI",
aqi[0],
is_smooth=True, # 线条平滑
areastyle_opts=opts.AreaStyleOpts(opacity=0), # 曲线填充面积的透明度
color="#4169E1")
day_line.add_yaxis("SO2",
so2[0],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#FF0033")
day_line.add_yaxis("NO2",
no2[0],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#4169E1")
day_line.add_yaxis("CO",
co[0],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#FF0033")
day_line.add_yaxis("O3",
o3[0],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#FF0033")
day_line.add_yaxis("PM2.5",
pm2_5[0],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#4169E1")
day_line.add_yaxis("PM10",
pm10[0],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#FF0033")
day_line.set_global_opts(
title_opts=opts.TitleOpts(title=city_name,
subtitle="日报",
padding=1,
pos_left="center",
item_gap=7),
# 设置标题居中
legend_opts=opts.LegendOpts(
pos_right="right", # 将标签设置为水平居右
pos_top="center", # 将标签设置为垂直居中
padding=1, # 标签与边界距离
item_gap=16, # 标签之间的间距
item_height=12, # 标签的高度
),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False), # 图像贴近y轴
yaxis_opts=opts.AxisOpts(
splitline_opts=opts.SplitLineOpts(is_show=True)) # 增加y轴分割线
)
day_line.set_series_opts(
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(
type_="max",
itemstyle_opts=opts.ItemStyleOpts("#e61b14")),
opts.MarkPointItem(
type_="min",
itemstyle_opts=opts.ItemStyleOpts("#14e66f")),
], # 标注最大值最小值
symbol_size=[30, 34], # 标注样式的宽，高
label_opts=opts.LabelOpts(
position="inside",
color="#fff", # 标注内的字体颜色
font_size=9)), # 标注内字体的大小
label_opts=opts.LabelOpts(is_show=True) # 显示每个坐标点的值
# markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]) # 标注平均线
) # 系列配置项
# 设置图表在页面居中：Anaconda3\Lib\site - packages\pyecharts\render\templates\macro
# 添加margin:auto;top:8px;
# 框架
day_grid = Grid(init_opts=opts.InitOpts(width="800px", height="380px"))
day_grid.add(day_line, grid_opts=opts.GridOpts(pos_top="70"))
day_grid.render("./web/day_line.html")
for datas in self.time_datas:
temp_time = datas[2].split(' ')[1] # 取出时间
time = re.sub(":00$", '', temp_time) # 删除秒
timepoint[1].append(time)
aqi[1].append(int(datas[3]))
so2[1].append(int(datas[4]))
no2[1].append(int(datas[5]))
co[1].append(float(datas[6]))
o3[1].append(int(datas[7]))
pm2_5[1].append(int(datas[8]))
pm10[1].append(int(datas[9]))
# print(timepoint[1], aqi[1], so2[1], no2[1], co[1], o3[1], pm2_5[1], pm10[1])
time_line = Line(
init_opts=opts.InitOpts(width="800px", height="300px"))
time_line.add_xaxis(timepoint[1]) # 增加x轴
time_line.add_yaxis(
"AQI",
aqi[1],
is_smooth=True, # 线条平滑
areastyle_opts=opts.AreaStyleOpts(opacity=0), # 曲线填充面积的透明度
color="#4169E1")
time_line.add_yaxis("SO2",
so2[1],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#FF0033")
time_line.add_yaxis("NO2",
no2[1],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#4169E1")
time_line.add_yaxis("CO",
co[1],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#FF0033")
time_line.add_yaxis("O3",
o3[1],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#FF0033")
time_line.add_yaxis("PM2.5",
pm2_5[1],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#4169E1")
time_line.add_yaxis("PM10",
pm10[1],
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0),
color="#FF0033")
time_line.set_global_opts(
title_opts=opts.TitleOpts(title=city_name,
subtitle="时报",
padding=1,
pos_left="center",
item_gap=7),
legend_opts=opts.LegendOpts(
pos_right="right", # 将标签设置为水平居右
pos_top="center", # 将标签设置为垂直居中
padding=1, # 标签与边界距离
item_gap=16, # 标签之间的间距
item_height=12, # 标签的高度
),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False), # 图像贴近y轴
yaxis_opts=opts.AxisOpts(
splitline_opts=opts.SplitLineOpts(is_show=True)) # 增加y轴分割线
)
time_line.set_series_opts(
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(
type_="max",
itemstyle_opts=opts.ItemStyleOpts("#e61b14")),
opts.MarkPointItem(
type_="min",
itemstyle_opts=opts.ItemStyleOpts("#87CEFA"))
], # 标注最大值最小值
symbol_size=[30, 34], # 标注样式的宽，高
label_opts=opts.LabelOpts(position="inside",
color="#fff",
font_size=9)), # 标注内字体的大小
label_opts=opts.LabelOpts(is_show=True) # 显示每个坐标点的值
# markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]) # 标注平均线
) # 系列配置项
time_grid = Grid(
init_opts=opts.InitOpts(width="800px", height="380px"))
time_grid.add(time_line, grid_opts=opts.GridOpts(pos_top="70"))
time_grid.render("./web/time_line.html")
def draw_kine(stock_data):
'''
pyecharts V1 版本开始支持链式调用
文档地址 https://pyecharts.org/#/zh-cn/
'''
stock_data.index = pd.to_datetime(stock_data['TIME'], format="%Y/%m/%d")
stock_data = stock_data[["TIME", "OPEN", "CLOSE", "LOW", "HIGH", "VOLUME"]]
# stock_data = stock_data.sort_index(ascending=True) # 倒序，看时间顺序是否正常
# k线图
kline = (
Kline().add_xaxis(stock_data[["TIME"]].values.tolist()).add_yaxis(
"K线图",
stock_data.iloc[:, 1:5].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True, is_show=False),
# axis_opts=opts.AxisOpts(is_scale=True,min_=0), #y轴起始坐标可以设为0
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
), # y轴起始坐标可自动调整
title_opts=opts.TitleOpts(title="价格",
subtitle=ts_code,
pos_top="20%"),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
datazoom_opts=[ # 设置zoom参数后即可缩放
opts.DataZoomOpts(
is_show=True,
type_="inside",
xaxis_index=[0, 1], # 设置第0轴和第1轴同时缩放
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=0,
range_end=100,
),
],
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
# 成交量柱形图
x = stock_data[["TIME"]].values.tolist()
y = stock_data[["VOLUME"]].values[:, 0].tolist()
bar = (Bar().add_xaxis(x).add_yaxis(
"成交量",
y,
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(
title_opts=opts.TitleOpts(title="成交量", pos_top="70%"),
legend_opts=opts.LegendOpts(is_show=False),
))
# 使用网格将多张图标组合到一起显示
grid_chart = Grid(init_opts=opts.InitOpts(
width="1200px",
height="600px",
animation_opts=opts.AnimationOpts(animation=False),
))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(
stock_data.iloc[:, 1:5].values.tolist()))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="15%", pos_right="15%", height="55%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="15%",
pos_right="15%",
pos_top="70%",
height="20%"),
)
grid_chart.render('stock_{}.html'.format(ts_code)) #保存成用股票代码命名的文档
def kline_viewer(ts_code, freq, end_date, asset='E', show=True):
"""
:param show:
:param ts_code:
:param freq:
:param end_date:
:param asset:
:return:
example:
>>> from chan.web.vis_kline import kline_viewer
>>> kline_viewer(ts_code='002739.SZ', freq='1min', end_date="20190809", asset='E')
"""
kline_raw = get_kline(ts_code,
freq=freq,
end_date=end_date,
asset=asset,
indicators=('ma', 'macd'))
for col in ['open', 'close', 'high', 'low']:
kline_raw[col] = kline_raw[col].apply(round, args=(2, ))
kline_chan = find_xd(find_bi(find_fx(preprocess(kline_raw))))
kline_chan = kline_chan[[
'dt', 'fx', 'fx_mark', 'bi', 'bi_mark', 'xd', 'xd_mark'
]]
kline_raw = kline_raw.merge(kline_chan, how='left', on='dt')
start_dt = kline_raw.iloc[0]["dt"]
end_dt = kline_raw.iloc[-1]["dt"]
x_data = kline_raw.dt.values.tolist()
oclh = kline_raw[['open', 'close', 'low', 'high']].values.tolist()
symbol = kline_raw.iloc[0]['symbol']
kline = (Kline(init_opts=opts.InitOpts(theme=ThemeType.WHITE)).add_xaxis(
xaxis_data=x_data).add_yaxis(
series_name=symbol,
y_axis=oclh,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
title_opts=opts.TitleOpts(title="缠论分析结果：%s-%s" % (symbol, freq),
subtitle="时间区间：%s 至 %s" %
(start_dt, end_dt)),
xaxis_opts=opts.AxisOpts(type_="category"),
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_top=5,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="60%",
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"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#ec0000"
},
{
"value": -1,
"color": "#00da3c"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
line = (Line(init_opts=opts.InitOpts(theme=ThemeType.WHITE)).add_xaxis(
xaxis_data=x_data).add_yaxis(
series_name="MA5",
y_axis=kline_raw.ma5.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA10",
y_axis=kline_raw.ma10.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA20",
y_axis=kline_raw.ma20.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA60",
y_axis=kline_raw.ma60.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA120",
y_axis=kline_raw.ma120.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA250",
y_axis=kline_raw.ma250.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
chan_fx = (Scatter(init_opts=opts.InitOpts(
theme=ThemeType.WHITE)).add_xaxis(xaxis_data=x_data).add_yaxis(
"分型标记", kline_raw.fx.values.tolist()).set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size",
max_=150,
min_=20), ))
chan_bi = (Scatter(init_opts=opts.InitOpts(
theme=ThemeType.WHITE)).add_xaxis(xaxis_data=x_data).add_yaxis(
"笔标记", kline_raw.bi.values.tolist()).set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size",
max_=150,
min_=20), ))
chan_xd = (Scatter(init_opts=opts.InitOpts(
theme=ThemeType.WHITE)).add_xaxis(xaxis_data=x_data).add_yaxis(
"线段标记", kline_raw.xd.values.tolist()).set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size",
max_=150,
min_=20), ))
# Kline And Line
kline = kline.overlap(line)
kline = kline.overlap(chan_fx)
kline = kline.overlap(chan_bi)
kline = kline.overlap(chan_xd)
# ==========================================================
macd_line = (Line(init_opts=opts.InitOpts(
theme=ThemeType.WHITE)).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="DEA",
y_axis=kline_raw['dea'].values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DIFF",
y_axis=kline_raw['diff'].values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
macd_bar = (Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_xaxis(
xaxis_data=x_data).add_yaxis(
series_name="Volume",
yaxis_data=kline_raw.macd.values.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
macd = macd_line.overlap(macd_bar)
# ==========================================================
bar = (Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_xaxis(
xaxis_data=x_data).add_yaxis(
series_name="Volume",
yaxis_data=kline_raw.vol.values.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Grid Overlap + Bar
grid_chart = Grid(
opts.InitOpts(width="1500px", height="800px", theme=ThemeType.WHITE))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="8%", pos_right="8%", height="50%"),
)
grid_chart.add(
macd,
grid_opts=opts.GridOpts(pos_left="8%",
pos_right="8%",
pos_top="60%",
height="16%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="8%",
pos_right="8%",
pos_top="70%",
height="16%"),
)
# 调用浏览器打开可视化结果
if show:
graph_path = os.path.join(cache_path,
"%s_kline_%s.html" % (symbol, freq))
grid_chart.render(path=graph_path)
webbrowser.open(graph_path)
return grid_chart
def draw_chart():
kline = (Kline().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143"),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(position="middle",
color="blue",
font_size=15),
data=split_data_part(),
symbol=["circle", "none"],
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True, data=split_data_part())).set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(is_show=True,
type_="inside",
xaxis_index=[0, 0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0, 1],
pos_top="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0, 2],
range_end=100),
],
))
kline_line = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
))
overlap_kline_line = kline.overlap(kline_line)
bar_1 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="Volumn",
y_axis=data["vols"],
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",
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
bar_2 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MACD",
y_axis=data["macds"],
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params){
var colorList;
if(params.data >= 0){
colorList = '#ef232a';
}else{
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
line_2 = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="DIF",
y_axis=data["difs"],
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
overlap_bar_line = bar_2.overlap(line_2)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1400px", height="800px", theme=ThemeType.ESSOS))
grid_chart.add_js_funcs("var barData={}".format(data["datas"]))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="71%",
height="10%"),
)
grid_chart.add(overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="82%",
height="14%"))
grid_chart.render("professional_kline_chart.html")
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1))
)).set_global_opts(
yaxis_opts=opts.AxisOpts(
name='Game numbers',
min_=0,
max_=1500,
position='right',
offset=135,
# axisline_opts=opts.AxisLineOpts(
# linestyle_opts=opts.LineStyleOpts(color='#d14a61')
# )
),
title_opts=opts.TitleOpts(title='Why?'),
tooltip_opts=opts.TooltipOpts(trigger='axis',
axis_pointer_type='cross'),
datazoom_opts=opts.DataZoomOpts(),
).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))
bar2 = (Bar().add_xaxis(year_list).add_yaxis(
'Mean Sales',
mean_list,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False)))
bar.overlap(bar2)
grid = Grid()
grid.add(bar,
opts.GridOpts(pos_left='5%', pos_right='20%'),
is_control_axis_index=True)
grid.render('Why total sales are growing when mean sales are reducing.html')
def draw_charts(x_axis_list, oclh_data_list, volume_list, tech_line_dict):
upColor = '#ec0000'
downColor = '#00da3c'
upBorderColor = '#8A0000'
downBorderColor = '#008F2'
kline = (Kline().add_xaxis(x_axis_list).add_yaxis(
series_name="kline",
y_axis=oclh_data_list,
markpoint_opts=opts.MarkPointOpts(
data=gen_mark_points_data_list())).set_series_opts(
itemstyle_opts=opts.ItemStyleOpts(
color=upColor,
color0=downColor,
border_color=upBorderColor,
border_color0=downBorderColor), ).set_global_opts(
legend_opts=opts.LegendOpts(pos_bottom=10,
pos_left="center"),
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
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')),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": downColor
},
{
"value": -1,
"color": upColor
},
],
),
datazoom_opts=[
opts.DataZoomOpts(
type_="inside",
xaxis_index=[0, 1],
range_start=98,
range_end=100,
),
opts.DataZoomOpts(
type_="slider",
xaxis_index=[0, 1],
range_start=98,
range_end=100,
pos_top='85%',
)
],
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
title_opts=opts.TitleOpts(title="Kline")))
if tech_line_dict:
line = (Line().add_xaxis(xaxis_data=x_axis_list).set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category")))
[
line.add_yaxis(
series_name=name,
y_axis=value_list,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
) for name, value_list in tech_line_dict.items()
]
kline = kline.overlap(line)
bar = (Bar().add_xaxis(x_axis_list).add_yaxis(
series_name="成交量",
yaxis_data=volume_list,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
grid_chart = Grid(init_opts=opts.InitOpts(
width="2000px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(kline,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
height="50%"))
grid_chart.add(bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="63%",
height="16%"))
folder_path = get_folder_path("backtesting_result_pyecharts")
file_path = f"{folder_path}/render.html"
grid_chart.render(path=file_path)
return file_path
def draw_chart(df,
extend_field,
save_path="",
trade_list=None,
point_list=None,
area_list=None,
kline_title="买卖点K线图",
is_smooth=True,
is_connect_nones=False):
datetime_array = df.datetime.tolist()
volume_array = df.volume.tolist()
# open close low high
kline_data = df[["open", "close", "low", "high"]].values.tolist()
# 绘制买卖点
_point_list = []
# 绘制标志点
if point_list is not None and len(point_list) > 1:
for point in point_list:
if type(point["datetime"]) == str:
point_datetime = point["datetime"]
else:
point_datetime = point["datetime"].strftime(
"%Y-%m-%d %H:%M:%S.%f")
point_name = point.get("name", "")
_point_list.append(
opts.MarkPointItem(
name=point_name,
coord=[point_datetime, point["y"]],
value=point_name if point_name else point["y"],
itemstyle_opts=opts.ItemStyleOpts(
color=point.get("color", "#ef232a"))))
else:
if trade_list is not None and len(trade_list) > 1:
for trade in trade_list:
# 开多
if type(trade[0]) == str:
trade_datetime = trade[0]
else:
trade_datetime = trade[0].strftime(
"%Y-%m-%d %H:%M:%S.%f")
if trade_datetime < datetime_array[0]:
continue
if trade[1] == "多" and trade[4] == "开":
_point_list.append(
opts.MarkPointItem(
name="开多",
coord=[trade_datetime, trade[6]],
value="开多",
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a")))
# 开空
elif trade[1] == "空" and trade[4] == "开":
_point_list.append(
opts.MarkPointItem(
name="开空",
coord=[trade_datetime, trade[6]],
value="开空",
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a")))
# 平多
elif trade[1] == "多" and (trade[4] == "平" or trade[4]
== "平今" or trade[4] == "平昨"):
_point_list.append(
opts.MarkPointItem(
name="平空",
coord=[trade_datetime, trade[6]],
value="平空",
itemstyle_opts=opts.ItemStyleOpts(
color="#14b143")))
# 平空
elif trade[1] == "空" and (trade[4] == "平" or trade[4]
== "平今" or trade[4] == "平昨"):
_point_list.append(
opts.MarkPointItem(
name="平多",
coord=[trade_datetime, trade[6]],
value="平多",
itemstyle_opts=opts.ItemStyleOpts(
color="#14b143")))
# 绘制矩形
_area_list = []
if area_list is not None and len(area_list) > 1:
for item in area_list:
_area_list.append(
opts.MarkAreaItem(
x=item["x"],
y=item["y"],
itemstyle_opts=opts.ItemStyleOpts(
color="transparent",
border_color=item.get("border_color", "#4D0099"),
border_width=3,
border_type="dashed"),
))
kline = (Kline().add_xaxis(xaxis_data=datetime_array).add_yaxis(
series_name="Kline",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
markpoint_opts=opts.MarkPointOpts(data=_point_list),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
data=_area_list, )).set_global_opts(
title_opts=opts.TitleOpts(title=kline_title,
pos_left="2%",
pos_top="1%"),
legend_opts=opts.LegendOpts(is_show=True,
pos_top=10,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="88%",
range_start=0,
range_end=100,
),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
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"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#00da3c"
},
{
"value": -1,
"color": "#ec0000"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
bar = (Bar().add_xaxis(xaxis_data=datetime_array).add_yaxis(
series_name="Volume",
y_axis=volume_array,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
if len(extend_field) > 0:
line = (Line().add_xaxis(
xaxis_data=datetime_array).set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category"),
legend_opts=opts.LegendOpts(is_show=True),
))
for field in extend_field:
field_value_array = df[field].tolist()
line.add_yaxis(
series_name=field,
y_axis=field_value_array,
is_smooth=is_smooth,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.8),
label_opts=opts.LabelOpts(is_show=False),
is_connect_nones=is_connect_nones,
)
# Kline And Line
kline = kline.overlap(line)
# Grid Overlap + Bar
grid_chart = Grid(init_opts=opts.InitOpts(
width="100%",
height="760px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="3.5%",
pos_right="3.5%",
height="60%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="3.5%",
pos_right="3.5%",
pos_top="70%",
height="16%"),
)
grid_chart.render(os.path.join(save_path, "KLineChart.html"))
