def query(self, qname, tscode_str='', **kwargs):
exchangeid = self.parseCode(tscode_str, seg='exch')
cryptoSymbol = self.parseCode(tscode_str, seg='pair')
if qname == 'ohlcv':
return self.dct_exchanges[exchangeid].fetch_ohlcv(
symbol=cryptoSymbol, limit=5000, timeframe='1d', **kwargs)
elif qname == 'tickers':
return self.dct_exchanges[exchangeid].fetch_tickers(
symbols=cryptoSymbol)
elif qname == 'load_alld': # 读取本地已存储的csv格式历史信息
fname = '{}{}.csv'.format(cfg.PATH_CPTFILE, tscode_str)
if os.path.isfile(fname):
df_result = pd.read_csv(fname,
encoding=cfg.FILE_ENCODE).astype(
dtype={'trade_date': 'int64'})
df_result = df_result if kwargs[
'tpl_dateindex'] is None else df_result[
df_result['trade_date'].isin(kwargs['tpl_dateindex'])]
return df_result
else:
libs.log_csv(
str_cat='WARNING',
str_op='query',
str_desc='WARNING! no csv file found for {}'.format(
tscode_str))
return False
else:
return False
def __init__(self,ts_code,heartbeat=None,tags=[]):
self.ts_code = ts_code
self.tags = tags # can label a sec with a chain of tags at run time so that they can be filtered
self.cursorBar = None
self.histBars = None
self.allBars = None
self.mchart = None
self.tradeunit = 0 # 0代表最低可买一股，-2代表100（1手）-3代表1000
self.commission = Decimal('0')
self.basicCols = ['ts_code','timeAxis','unit_intdate','trade_date','open','high','low','close','vol',
'sec_type','sec_market','candle_size','box_size','upper_stick','lower_stick','MA_S','MA_M','MA_L',
'MA_Y','VolMA_S','VolMA_M','VolMA_L']
self._dataagent = DataAgent_Factory.singleton() # expecting dataagent object to be a singleton
self._heartbeat = heartbeat
self._cursorTick = None # should be a str or a row in calendar table
self.anchorObj = None # to hold underlying anchor security object
targeted = self._dataagent.registry.loc[self._dataagent.registry['ts_code']==ts_code]
if self.ts_code=='cash':
self.exists = False
self.sec_name = 'cash'
self.sec_type = 'cash'
self.ts_market = ''
elif len(targeted)==0:
self.exists = False
libs.log_csv(str_cat='WARNING', str_op='Security initiation', str_desc='{} does not exist in registry'.format(self.ts_code))
raise Exception('{} does not exist in registry'.format(self.ts_code))
else:
self.exists = True
self.sec_name = targeted['name'].values[0]
self.sec_type = targeted['sec_type'].values[0]
self.ts_market = targeted['sec_market'].values[0]
# used to, e.g. capture the underlying index of an ETF fund
self.anchor_tscode = None if targeted['anchor'].isnull().values.any() else targeted['anchor'].values[0]
def get_livebar(self, assign_timestamp, assign_unitdate=None):
df_liveQuote = self._dataagent.query('quote_now', [
self.ts_code,
])
if len(df_liveQuote) > 0:
df_liveQuote.rename(columns={
'open': 'open',
'high': 'high',
'low': 'low',
'price': 'close',
'volume': 'vol',
'amount': 'amount'
},
inplace=True)
df_liveQuote = df_liveQuote.astype(
dtype={
'open': 'float',
'high': 'float',
'low': 'float',
'close': 'float',
'vol': 'float',
'amount': 'float'
})
# all self.basicCols need to be normalised here, extendCols to be normalised in child class
df_liveQuote['timeAxis'] = assign_timestamp
# unit_intdate 和timestamp有可能不是同一日，如周六时timeAxis是周六的某一时间而unit_intdate应是下一交易日即下周一日期，因此此处应对liveQuote设置之后再append allBar
df_liveQuote['unit_intdate'] = int(
datetime.strftime(
assign_timestamp,
'%Y%m%d')) if assign_unitdate is None else assign_unitdate
df_liveQuote['trade_date'] = df_liveQuote['date'].str.replace(
'-', '', regex=False).astype(int)
df_liveQuote['ts_code'] = self.ts_code
df_liveQuote['vol'] = df_liveQuote[
'vol'] / 100 if self.sec_type == 'stk' or self.ts_code[:3] == '399' else df_liveQuote[
'vol']
df_liveQuote['amount'] = df_liveQuote['amount'] / 1000
df_liveQuote['sec_type'] = self.sec_type
df_liveQuote['sec_market'] = self.ts_market
# 凡是需要前一bar数据配合计算的此处不做处理
df_liveQuote['candle_size'] = np.nan
df_liveQuote['box_size'] = np.nan
df_liveQuote['upper_stick'] = np.nan
df_liveQuote['lower_stick'] = np.nan
df_liveQuote['MA_S'] = np.nan
df_liveQuote['MA_M'] = np.nan
df_liveQuote['MA_L'] = np.nan
df_liveQuote['MA_Y'] = np.nan
df_liveQuote['VolMA_S'] = np.nan
df_liveQuote['VolMA_M'] = np.nan
df_liveQuote['VolMA_L'] = np.nan
return df_liveQuote
else:
libs.log_csv(str_cat='WARNING',
str_op='get_livebar method',
str_desc='{} query returned none result'.format(
self.ts_code))
return None
def __init__(self,ts_code,heartbeat=None,tags=[]):
super().__init__(ts_code,heartbeat=heartbeat,tags=tags)
self.kchart = None
self.exists = False if self.sec_type != 'idx' else self.exists
self.tradeunit = 0
self.commission = Decimal('0')
if self.exists == False:
libs.log_csv(str_cat='WARNING', str_op='Security initiation', str_desc='{} does not exist in registry'.format(self.ts_code))
raise Exception('{} no such index in registry'.format(self.ts_code))
else:
self.extendCols = [*self.basicCols,'pre_close','amount']
self.ts_market = self.ts_market
self.allBars = self._get_all_bars()
def sync_repo(self):
libs.log_csv(str_cat='INFO',
str_op='sync',
str_desc='Tsaisendo crypto data sync job started...')
''' 遍历给定code列表并同步本地存储数据 '''
for str_tscrypto in self.symbol_list:
str_paircode = self.parseCode(str_tscrypto, seg='pair')
fname = '{}{}.csv'.format(cfg.PATH_CPTFILE, str_tscrypto)
writeMode = 'a'
if os.path.isfile(fname):
int_lastRecDate = int(
pd.read_csv(
fname,
encoding=cfg.FILE_ENCODE)[-1:].iloc[0]['exch_timeid'])
result = self.query(qname='ohlcv',
tscode_str=str_tscrypto,
since=int_lastRecDate + 1)
else:
result = self.query(qname='ohlcv',
tscode_str=str_tscrypto,
since=0)
writeMode = 'w'
if len(result) > 0:
df_result = pd.DataFrame(result,
columns=[
'exch_timeid', 'open', 'high',
'low', 'close', 'volume'
])
df_result['ts_code'] = str_tscrypto
# df_result['trade_date'] = (df_result['exch_timeid']/1000).astype('int').astype('datetime64[s]').dt.strftime("%Y-%m-%d %H:%M:%S")
df_result['trade_date'] = (
df_result['exch_timeid'] / 1000).astype('int').astype(
'datetime64[s]').dt.strftime("%Y%m%d%H%M%S")
libs.df_csv(fname, (df_result, ), writeMode)
libs.log_csv(str_cat='INFO',
str_op='sync',
str_desc='{} crypto file syncronized...'.format(
str_tscrypto))
else:
libs.log_csv(
str_cat='INFO',
str_op='sync',
str_desc='{} crypto file skipped...'.format(str_tscrypto))
libs.log_csv(str_cat='INFO',
str_op='sync',
str_desc='Tsaisendo crypto data sync job ended...')
def pollPulse(self):
libs.log_csv(str_cat='INFO',
str_op='poll',
str_desc='Tsaisendo polling job started...')
# 定义历史数据留多久
str_oldestViewDateTime = str(
self.da.int_findOffsetDate(self.int_pollPulseDate, -21)) + '000000'
df_indexpoll = pd.DataFrame()
df_stockpoll = pd.DataFrame()
''' 遍历所有时间点并请求最新数据'''
for beat in self.pollPulseRange:
while True:
timecheck = datetime.now().astimezone(
timezone(cfg.STR_TIMEZONE)).replace(tzinfo=None)
if timecheck.replace(second=0, microsecond=0) > beat:
libs.log_csv(
str_cat='INFO',
str_op='poll',
str_desc=
'WARNING! {}/{} - {}:{} heartbeat dropped at {} due to happening too late...'
.format(
beat.month, beat.day, beat.hour, beat.minute,
datetime.now().astimezone(
timezone(cfg.STR_TIMEZONE))))
break
elif timecheck.replace(second=0, microsecond=0) == beat:
libs.log_csv(str_cat='INFO',
str_op='poll',
str_desc='poll time...{}'.format(timecheck))
df_index = self.da.query('allindex_now')
df_stock = self.da.query('allstock_now')
# data_store = libs.tryOpenH5(cfg.H5_FILE_POLL)
if len(df_index) > 0:
df_index['time_index'] = int(
datetime.strftime(beat, '%Y%m%d%H%M%S'))
df_index['candle_size'] = round(
(df_index['high'] - df_index['low']) /
df_index['pre_close'], 3)
df_index['box_size'] = round(
(df_index['close'] - df_index['open']) /
df_index['pre_close'], 3)
df_index['upper_stick'] = round(
(df_index['high'] -
df_index[['open', 'close']].max(axis=1)) /
df_index['pre_close'], 3)
df_index['lower_stick'] = round(
(df_index[['open', 'close']].min(axis=1) -
df_index['low']) / df_index['pre_close'], 3)
binRange0 = [0, 0.005, 0.010, 0.016,
float('inf')] # 指数类的尺寸划分标准
df_index['klineSML'] = ''
df_index.loc[df_index['sec_type'] == 0,
['klineSML']] = DataAgent.classifyKline(
df_index, DataAgent.binRange0, 0)
df_index.to_sql('index_poll',
DataAgent.dbsession.bind,
if_exists='append',
index=False)
# data_store.append('indexPoll', df_index,format='t')
if len(df_stock) > 0:
df_stock['time_index'] = int(
datetime.strftime(beat, '%Y%m%d%H%M%S'))
df_stock['candle_size'] = round(
(df_stock['high'] - df_stock['low']) /
df_stock['pre_close'], 3)
df_stock['box_size'] = round(
(df_stock['close'] - df_stock['open']) /
df_stock['pre_close'], 3)
df_stock['upper_stick'] = round(
(df_stock['high'] -
df_stock[['open', 'close']].max(axis=1)) /
df_stock['pre_close'], 3)
df_stock['lower_stick'] = round(
(df_stock[['open', 'close']].min(axis=1) -
df_stock['low']) / df_stock['pre_close'], 3)
binRange1 = [0, 0.01, 0.033, 0.066,
float('inf')] # 股票类的尺寸划分标准
df_stock['klineSML'] = ''
df_stock.loc[df_stock['sec_type'] == 1,
['klineSML']] = DataAgent.classifyKline(
df_stock, DataAgent.binRange1, 1)
'''df_stock.to_sql('stock_poll', DataAgent.dbsession.bind, if_exists='append',index=False)'''
# data_store.append('stockPoll', df_stock,format='t')
# data_store.close()
libs.log_csv(
str_cat='INFO',
str_op='poll',
str_desc='[{}] index and [{}] stock data acquired'.
format(len(df_index), len(df_stock)))
break
else:
libs.log_csv(
str_cat='INFO',
str_op='poll',
str_desc=
'waiting {} seconds until next poll heartbeat - {}'.
format(libs.datediff_insec(timecheck, beat), beat))
time.sleep(libs.datediff_insec(timecheck, beat) + 1)
''' 删除掉过旧的数据'''
# 创建储存对象，将 DataFrame 放进对象中，并设置 key
'''DataAgent.dbsession.query(Stock_poll).filter(Stock_poll.time_index<int(str_oldestViewDateTime)).delete()
DataAgent.dbsession.query(Index_poll).filter(Index_poll.time_index<int(str_oldestViewDateTime)).delete()'''
'''
data_store = libs.tryOpenH5(cfg.H5_FILE_POLL,mode='a')
try:
df_toRemove = data_store['indexPoll'][data_store['indexPoll']['time_index']<=int(str_oldestViewDateTime)]
if len(df_toRemove)>0:
df_toKeep = data_store['indexPoll'][data_store['indexPoll']['time_index']>int(str_oldestViewDateTime)]
data_store.put('indexPoll', df_toKeep,format='t')
libs.log_csv(str_cat='INFO',str_op='poll',str_desc='{} rows of old index data removed from offline data'.format(len(df_toRemove)))
except Exception as e:
data_store.put('indexPoll', df_indexpoll,format='t')
libs.log_csv(str_cat='INFO',str_op='poll',str_desc='{} ---- indexPoll dataset doesnot exist in datastore, new one created'.format(e.__class__.__name__))
try:
df_toRemove = data_store['stockPoll'][data_store['stockPoll']['time_index']<=int(str_oldestViewDateTime)]
if len(df_toRemove)>0:
df_toKeep = data_store['stockPoll'][data_store['stockPoll']['time_index']>int(str_oldestViewDateTime)]
data_store.put('stockPoll', df_toKeep,format='t')
libs.log_csv(str_cat='INFO',str_op='poll',str_desc='{} rows of old stock ata removed from offline data'.format(len(df_toRemove)))
except Exception as e:
data_store.put('stockPoll', df_indexpoll,format='t')
libs.log_csv(str_cat='INFO',str_op='poll',str_desc='{} ---- stockPoll dataset doesnot exist in datastore, new one created'.format(e.__class__.__name__))
print(data_store.info())
data_store.close()
'''
libs.log_csv(str_cat='INFO',
str_op='poll',
str_desc='Tsaisendo polling job ended...')
def candlechart(self,str_tscode=None,subset='masterCandle',dat_cursor=None,MA=[5,10,21], renderfile=False) -> Grid:
str_tscode = DataAgent.formatCode(str_tscode)
int_chartsize = 500
if str_tscode == False or str_tscode is None:
print('invalid sec code provided...')
return False
dat_cursor = datetime.now().astimezone(timezone(cfg.STR_TIMEZONE)) if dat_cursor is None else dat_cursor
closestTradeDate = self.da.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )
tpl_dateindex = tuple(str(i) for i in self.da.df_calTbl.loc[self.da.df_calTbl['cal_date'] <= closestTradeDate]['cal_date'][-int_chartsize:] )
# 连接储存对象
if subset=='masterCandle':
data_store = libs.tryOpenH5(cfg.H5_FILE_PRE,mode='r')
else:
data_store = libs.tryOpenH5('{}{}.dat'.format(cfg.H5_FILE_PATH,subset),mode='r')
# 初始化交易日表格，作为标准交易日序列join个股交易数据
df_toPlot = data_store['masterCandle'].loc[data_store['masterCandle']['ts_code']==str_tscode]
df_dateindex = pd.DataFrame({'trade_caldate': tpl_dateindex}).astype(int)
data_store.close()
try:
str_secname = self.da.df_lookupTbl.loc[self.da.df_lookupTbl['ts_code']==str_tscode]['name'].values[0]
str_sectype = self.da.df_lookupTbl.loc[self.da.df_lookupTbl['ts_code']==str_tscode]['sec_type'].values[0]
except Exception as e:
return libs.log_csv(str_cat='WARNING',str_op='candlechart',str_desc='security code {} does not exist in basic info table...'.format(str_tscode))
str_plotname = '{} - {}'.format(str_tscode,str_secname)
# 交易日时间轴标准化，补全停牌日,当日live数据等k线
'''------ 获取实时交易数据部分--------------------'''
int_liveDate = self.da.int_findClosestTradeDate(datetime.strftime(datetime.now().astimezone(timezone(cfg.STR_TIMEZONE)),'%Y%m%d') )
if int_liveDate<=closestTradeDate and int_liveDate>df_toPlot['trade_date'].max():
df_liveQuote = self.da.query('quote_now',[self.da.formatCode(str_tscode,False),])
if len(df_liveQuote)>0:
df_liveQuote.rename(columns={'open':'adj_open',
'high':'adj_high',
'low':'adj_low',
'price':'adj_close',
'volume':'vol',
'amount':'amount'}, inplace=True)
df_liveQuote=df_liveQuote.astype(dtype= {'adj_open':'float','adj_high':'float','adj_low':'float','adj_close':'float','vol':'float','amount':'float'})
df_liveQuote['trade_date'] = int_liveDate
df_liveQuote['ts_code'] = str_tscode
df_liveQuote['vol'] = df_liveQuote['vol']/100 if str_sectype==1 or str_tscode[:3]=='399' else df_liveQuote['vol']
df_liveQuote['amount'] = df_liveQuote['amount']/1000
df_toPlot = df_toPlot.append(df_liveQuote[['ts_code','trade_date','adj_open','adj_high','adj_low','adj_close','vol','amount']],sort=False)
'''------ 结束 获取实时交易数据部分--------------------'''
df_toPlot = pd.merge(df_dateindex, df_toPlot, left_on='trade_caldate', right_on='trade_date', how='left')
df_toPlot['close'].fillna(method='ffill',inplace=True)
df_toPlot['ts_code'].fillna(method='ffill',inplace=True)
df_toPlot['klineSML'].fillna('NNNN',inplace=True)
df_toPlot['adj_close'].fillna(method='ffill',inplace=True)
df_toPlot['vol'].fillna(value=0,inplace=True)
df_toPlot['amount'].fillna(value=0,inplace=True)
df_toPlot['open'].fillna(df_toPlot['close'],inplace=True)
df_toPlot['high'].fillna(df_toPlot['close'],inplace=True)
df_toPlot['low'].fillna(df_toPlot['close'],inplace=True)
df_toPlot['adj_open'].fillna(df_toPlot['adj_close'],inplace=True)
df_toPlot['adj_high'].fillna(df_toPlot['adj_close'],inplace=True)
df_toPlot['adj_low'].fillna(df_toPlot['adj_close'],inplace=True)
self.kchartdf = df_toPlot #输出至可访问对象属性中
'''-----------------画图部分---------------------'''
lst_ohlcv=df_toPlot.loc[:,['adj_open','adj_close','adj_low','adj_high']].values.tolist()
lst_vol=list(df_toPlot['vol'].values)
lst_amount=list(df_toPlot['amount'].values)
lst_peaks=list(abs(df_toPlot['peaks'].values))
# lst_pivotdown=[float('nan') if i<0 else i for i in list(df_toPlot['pivots'].values)]
lst_pivotdown=[float('nan') if i<0 else i for i in list(df_toPlot['valid_pivots'].values)]
# lst_pivotup=[float('nan') if i>0 else abs(i) for i in list(df_toPlot['pivots'].values)]
lst_pivotup=[float('nan') if i>0 else abs(i) for i in list(df_toPlot['valid_pivots'].values)]
lst_xaxis=list(df_toPlot['trade_caldate'].astype(str))
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",
yaxis_data=[
[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",
yaxis_data=[
[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
