def dashboard(request):
tasks = Task.objects.all()[0:5]
my_tasks = Task.objects.filter(tester=request.user)[0:5]
attr = Results.objects.show_task_id()
script_list = []
line = line_time_kf(attr)
bar = bar_mp_kf(attr)
# myechart = bar.render_embed()
# myechart = line.render_embed()
script_list.extend(line.get_js_dependencies())
# script_list = bar.get_js_dependencies()
script_list.extend(bar.get_js_dependencies())
grid = Grid(width="auto")
grid.add(line, grid_bottom="60%")
grid.add(bar, grid_top="60%")
myechart = grid.render_embed()
script_list.extend(grid.get_js_dependencies())
# run_rtv_numbers = Results.objects.order_by('-id').values_list('id').first()[0]
# seconds = sum([int(i[0]) for i in Results.objects.values_list('time')])
# m, s = divmod(seconds, 60)
# h, m = divmod(m, 60)
# d, h = divmod(h, 24)
# time_cost = "%02dd:%02dh:%02dm:%02ds" % (d, h, m, s)
return render(request, 'dashboard.html', {
# 'run_rtv_numbers': run_rtv_numbers,
# 'time_cost': time_cost,
'tasks': tasks,
'my_tasks': my_tasks,
'if_dashboard_active': 'active',
'myechart': myechart,
'script_list': script_list
})
def echart1(request):
template = loader.get_template('ewpyecharts.html')
line = Line("压力分析", width=1200, height=700)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高压力", attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"], mark_line=["average"])
line.add("最低压力", attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"], legend_top="50%", mark_line=["average"],
# 设置 dataZoom 控制索引为 0,1 的 x 轴，即第一个和第二个
is_datazoom_show=True, datazoom_xaxis_index=[0, 1])
v1 = [[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("考核表分析", title_top="50%")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)],
v1, is_datazoom_show=True)
grid = Grid()
grid.add(line, grid_top="60%")
grid.add(kline, grid_bottom="60%")
# grid.render()
context = dict(
myechart=grid.render_embed(),
host=REMOTE_HOST,
script_list=grid.get_js_dependencies()
)
return HttpResponse(template.render(context, request))
def get_realtime_data():
# 历史数据优先从hdf5中获取 获取不到则实时从接口获取并添加到hdf5中
store = pd.HDFStore(app.config['STOCKS_PRICE_HDF5'], mode='r+')
code = request.args.get("code")
# sh_index_data = ts.get_k_data('000001', index=True)
real_data = ts.get_realtime_quotes(code)
if 'sh_index_data' in store:
sh_index_data = pd.read_hdf(app.config['STOCKS_PRICE_HDF5'],
'sh_index_data',
mode='r+')
else:
sh_index_data = ts.get_k_data('000001', index=True)
sh_index_data.to_hdf(app.config['STOCKS_PRICE_HDF5'],
'sh_index_data',
format='table',
data_columns=True)
if code in store:
hist_data = pd.read_hdf(app.config['STOCKS_PRICE_HDF5'],
code,
mode='r+')
else:
hist_data = ts.get_k_data(code)
hist_data.to_hdf(app.config['STOCKS_PRICE_HDF5'],
code,
format='table',
data_columns=True)
store.close()
kline = kline_chart(code, hist_data)
# 这时还没转dict
kline_sh = kline_chart_sh(sh_index_data)
grid = Grid(width="100%")
grid.add(kline, grid_bottom="60%")
grid.add(kline_sh, grid_top="60%")
real_data['delta'] = (real_data['price'].astype('float') -
real_data['pre_close'].astype('float'))
real_data['delta'] = real_data['delta'].apply(lambda x: round(x, 4))
real_data['delta%'] = real_data['delta'] / real_data['pre_close'].astype(
'float')
real_data['delta%'] = real_data['delta%'].apply(
lambda x: round(x * 100, 2))
real_data = real_data.to_dict('record')
hist_data = hist_data.sort_values('date', ascending=False)
hist_data = hist_data.to_dict('record')
return render_template(
"table_with_chart.html",
basic_data=url_for('get_stock_basics_json', code=code),
basic_data_columns=stock_basics_columns,
real_data=url_for('get_realtime_data_json', code=code),
real_data_columns=real_data_columns,
hist_data_columns=hist_data_columns,
hist_data=hist_data,
title=('{0}:({1})股票实时价格'.format(real_data[0]['name'], code)),
grid=grid.render_embed(),
host=app.config['REMOTE_HOST'],
grid_script_list=grid.get_js_dependencies(),
)
def get(self, request, *args, **kwargs):
template_name = 'echart/echart.html'
bar = Bar()
bar.add('服装', ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"],
[5, 20, 36, 10, 75, 90])
line = Line()
line.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"],
[5, 20, 36, 10, 75, 90])
echart = Grid(page_title='组合图', width=1200, height=250)
echart.add(bar, grid_left='50%')
echart.add(line, grid_right='55%')
context = dict(myechart=echart.render_embed(),
host=REMOTE_HOST,
script_list=echart.get_js_dependencies())
return render(request, template_name, context)
def get_bars(trade):
_id = trade.exec_id[0]
_match_trade = []
_match = []
_match_vol = 0
for i, v in trade.iterrows():
_match.append(v)
_match_vol += (v.last_quantity
if v.side == "BUY" else -v.last_quantity)
if _match_vol == 0:
_match_trade.append(_match)
_match = []
else:
if _match_vol != 0:
_match_trade.append(_match)
for m in _match_trade:
for t in m:
if t.exec_id == int(_id):
trade_points_raw = m
break
profit = 0
for tpr in trade_points_raw:
profit += tpr.last_price * (tpr.last_quantity if tpr.side == 'SELL'
else -tpr.last_quantity)
trade_lines_raw = []
pos = 0
p_pos = 0
for i in range(len(trade_points_raw)):
tpr1 = trade_points_raw[i]
pos += (tpr1.last_quantity
if tpr1.side == 'BUY' else -tpr1.last_quantity)
for j in range(i + 1, len(trade_points_raw)):
tpr2 = trade_points_raw[j]
if tpr1.side != tpr2.side:
p_pos += (tpr2.last_quantity
if tpr2.side == 'BUY' else -tpr2.last_quantity)
if abs(pos) > abs(p_pos):
continue
trade_lines_raw.append([tpr1, tpr2])
p_pos = 0
break
trade_points = [{
'coord': [str(tpr.datetime), tpr.last_price, tpr.last_quantity],
'name':
'trade',
'label': {
'show': True,
'formatter': label_tooltip,
'offset': [0, -30],
'fontSize': 15,
'fontFamily': 'monospace',
'color': 'black'
},
'symbol':
'triangle',
'symbolSize':
8 + 3 * tpr.last_quantity,
'symbolKeepAspect':
False,
'itemStyle': {
'color': 'blue' if tpr.side == 'BUY' else 'green'
},
'emphasis': {
'label': {
'show': True,
'formatter': label_tooltip,
'offset': [0, -30],
'fontSize': 30,
'fontFamily': 'monospace',
'color': 'black',
'fontWeight': 'bolder'
},
'itemStyle': {
'color': 'black'
}
},
'symbolRotate':
0 if tpr.side == 'BUY' else 180
} for tpr in trade_points_raw]
trade_lines = [[{
'coord': [str(tpr1.datetime), tpr1.last_price],
'lineStyle': {
'type':
'dashed',
'color':
'red' if
((tpr2.last_price - tpr1.last_price) if tpr2.side == 'SELL' else
(tpr1.last_price - tpr2.last_price)) >= 0 else 'green'
},
'emphasis': {
'lineStyle': {
'type':
'dashed',
'color':
'red' if
((tpr2.last_price -
tpr1.last_price) if tpr2.side == 'SELL' else
(tpr1.last_price - tpr2.last_price)) >= 0 else 'green',
'width':
2 + 0.1 * abs(tpr2.last_price - tpr1.last_price) *
abs(tpr1.last_quantity)
},
'label': {
'show': True,
'formatter':
f'{abs(tpr2.last_price - tpr1.last_price)*abs(tpr1.last_quantity)}',
'position': 'middle',
'fontSize': 25,
}
},
}, {
'coord': [str(tpr2.datetime), tpr2.last_price],
'lineStyle': {
'type':
'dashed',
'color':
'red' if
((tpr2.last_price - tpr1.last_price) if tpr2.side == 'SELL' else
(tpr1.last_price - tpr2.last_price)) >= 0 else 'green',
},
'emphasis': {
'lineStyle': {
'type':
'dashed',
'color':
'red' if
((tpr2.last_price -
tpr1.last_price) if tpr2.side == 'SELL' else
(tpr1.last_price - tpr2.last_price)) >= 0 else 'green',
'width':
2 + 0.1 * abs(tpr2.last_price - tpr1.last_price) *
abs(tpr1.last_quantity)
},
'label': {
'show': True,
'formatter':
f'{abs(tpr2.last_price - tpr1.last_price)*abs(tpr1.last_quantity)}',
'position': 'middle',
'fontSize': 25,
}
},
}] for tpr1, tpr2 in trade_lines_raw]
_from = trade_points_raw[0].datetime - dt.timedelta(minutes=60)
_to = trade_points_raw[-1].datetime + dt.timedelta(minutes=60)
code = trade.order_book_id[0]
f = Future(user='******', pwd='kairuitouzi') if code[:3] not in [
'HSI', 'MHI', 'HHI'
] else HKFuture(user='******', pwd='kairuitouzi')
_bars = f.get_bars(code,
fields=['datetime', 'open', 'close', 'low', 'high'],
start=_from,
end=_to)
x_axis = _bars[0].astype(str)
kline = Kline(f'{code}-1min KLine Profit:{profit}')
kline.add(
f'{code}',
x_axis,
_bars[1:].T,
mark_point_raw=trade_points,
mark_line_raw=trade_lines,
# label_formatter=label_tooltip,
# xaxis_type='category',
tooltip_trigger='axis',
tooltip_formatter=kline_tooltip,
is_datazoom_show=True,
datazoom_range=[0, 100],
datazoom_type='horizontal',
is_more_utils=True)
overlap_kline = Overlap('KLine', width='1500px', height='600px')
overlap_kline.add(kline)
_close = _bars[2].astype(float)
MA = {}
for w in [5, 10, 30, 60]:
ma = talib.MA(_close, timeperiod=w)
MA[w] = Line(f'MA{w}')
MA[w].add(f'MA{w}',
x_axis,
np.round(ma, 2),
is_symbol_show=False,
is_smooth=True)
overlap_kline.add(MA[w])
macdDIFF, macdDEA, macd = talib.MACDEXT(_close,
fastperiod=12,
fastmatype=1,
slowperiod=26,
slowmatype=1,
signalperiod=9,
signalmatype=1)
diff_line = Line('diff')
dea_line = Line('dea')
macd_bar = Bar('macd')
diff_line.add('diff',
x_axis,
macdDIFF,
line_color='yellow',
is_symbol_show=False,
is_smooth=True)
dea_line.add('diff',
x_axis,
macdDEA,
line_color='blue',
is_symbol_show=False,
is_smooth=True)
macd_bar.add('macd',
x_axis,
macd,
is_visualmap=True,
visual_type='color',
is_piecewise=True,
pieces=[{
max: 0
}, {
min: 0
}])
overlap_macd = Overlap('MACD', width='1500px', height='200px')
overlap_macd.add(diff_line)
overlap_macd.add(dea_line)
overlap_macd.add(macd_bar)
grid = Grid('Anlysis', width='1500px', height='800px')
grid.add(overlap_kline, grid_top='0%')
grid.add(overlap_macd, grid_top='80%')
return grid.render_embed()
def echart1(request):
template = loader.get_template('myfirstvis/pyecharts.html')
#
# # value = [20, 190, 253, 77, 65]
# # attr = ['汕头市', '汕尾市', '揭阳市', '阳江市', '肇庆市']
# # map = Map("广东地图示例", width=1200, height=600)
# # map.add("", attr, value, maptype='广东', is_visualmap=True,
# # visual_text_color='#000')
# #map.render()
# # context = dict(
# # myechart=map.render_embed(),
# # host=REMOTE_HOST,
# # script_list=map.get_js_dependencies()
# # )
#
# # gauge = Gauge("仪表盘示例")
# # gauge.add("业务指标", "完成率", 66.66)
# # gauge.show_config()
#
line = Line("折线图示例", width=1200, height=700)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"], legend_top="50%", mark_line=["average"],
# 设置 dataZoom 控制索引为 0,1 的 x 轴，即第一个和第二个
is_datazoom_show=True, datazoom_xaxis_index=[0, 1])
v1 = [[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("K 线图示例", title_top="50%")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)],
v1, is_datazoom_show=True)
grid = Grid()
grid.add(line, grid_top="60%")
grid.add(kline, grid_bottom="60%")
# grid.render()
context = dict(
myechart=grid.render_embed(),
host=REMOTE_HOST,
script_list=grid.get_js_dependencies()
)
# # value = [155, 10, 66, 78]
# # attr = ["福建", "山东", "北京", "上海"]
# # map = Map("全国地图示例", width=1200, height=600)
# # map.add("", attr, value, maptype='china',
# # is_visualmap=True, is_piecewise=True,
# # visual_text_color="#000",
# # visual_range_text=["", ""],
# # pieces=[
# # {"max": 160, "min": 70, "label": "高数值"},
# # {"max": 69, "min": 0, "label": "低数值"},
# # ])
# #
# # context = dict(
# # myechart=map.render_embed(),
# # host=REMOTE_HOST,
# # script_list=map.get_js_dependencies()
#
# style = Style(
# title_top="#fff",
# title_pos="center",
# width=1200,
# height=600,
# background_color="#404a59"
# )
#
# data_guangzhou = [
# ["广州", "上海"],
# ["广州", "北京"],
# ["广州", "南京"],
# ["广州", "重庆"],
# ["广州", "兰州"],
# ["广州", "杭州"]
# ]
# geolines = GeoLines("GeoLines 示例", **style.init_style)
# geolines.add("从广州出发", data_guangzhou, is_legend_show=False)
#
# context = dict(
# myechart=geolines.render_embed(),
# host=REMOTE_HOST,
# script_list=geolines.get_js_dependencies()
# )
return HttpResponse(template.render(context, request))
