def down_minute_data(self, count=400, freq=None):
if pd.isnull(freq):
self.data = get_k_data_jq(self.stk_code,
count=count,
end_date=get_current_datetime_str(),
freq=self.freq)
else:
self.data = get_k_data_jq(self.stk_code,
count=count,
end_date=get_current_datetime_str(),
freq=freq)
def init_today_minute_data(self, count=200, debug_datetime=None):
"""
为了避免重复下载数据，有时需要维护最近一段时间的分钟数据
:return:
"""
try:
datetime_now = {
True: get_current_datetime_str(),
False: fgv.debug_datetime
}.get(isinstance(fgv.debug_datetime, type(None)))
self.rt_minute_data = get_k_data_jq(stk=self.stk_code,
freq=self.freq,
count=count,
end_date=datetime_now)
self.rt_minute_data.loc[:, 'datetime'] = self.rt_minute_data.index
self.rt_minute_data.loc[:, 'num'] = range(len(self.rt_minute_data))
self.rt_minute_data = self.rt_minute_data.set_index('num')
self.rt_minute_update_minute = self.get_minute_from_datetime(
self.rt_minute_data.tail(1)['datetime'].values[0])
return True
except Exception as e_:
logger_eml.exception('初始下载“rt minute数据”时出错！具体为：%s' % str(e_))
return False
def save_hist_data(self):
"""
在内存测试时，应该先运行此函数，此函数会向类的成员变量self.data中注入数据!
将历史bias数据保存到本地
:return:
"""
df = get_k_data_jq(stk=self.stk_code,
freq=self.freq,
count=self.hist_count)
self.data = self.add_bias_rank_public(df, self.span_q, self.span_s)
self.bias_history_data = self.data[self.bias_col].values
self.save_bias_to_json()
def get_today_df(self, debug_date=None):
df_ = get_k_data_jq(stk=self.stk_code,
count=50,
end_date={
True: get_current_datetime_str(),
False: debug_date
}.get(isinstance(debug_date, type(None))))
# 调整df，增加date列，增加正数index列
df_['datetime'] = df_.index
df_['datetime_str'] = df_.apply(lambda x: str(x['datetime']), axis=1)
# 重置索引
df_ = df_.reset_index(drop=True)
return df_
def get_single_trade_time_span(f_code):
df = get_k_data_jq(stk=f_code, count=60 * 20, freq='1m')
l_group = list(df.groupby(by='date'))
l_group.sort(key=lambda x: len(x[1]), reverse=True)
df = l_group[0][1]
# 获取该天数据
df.loc[:, 'datetime_dt'] = df.apply(
lambda x: convert_np_datetime64_to_datetime(x['datetime']), axis=1)
df.loc[:, 'num'] = range(0, len(df))
df.set_index('num')
df.loc[:, 'dt_next'] = df['datetime_dt'].shift(-1)
time_span = []
t_s: datetime.datetime = None
t_e: datetime.datetime = None
for idx in df.index:
t_now: datetime.datetime = df.loc[idx, 'datetime_dt']
t_next: datetime.datetime = df.loc[idx, 'dt_next']
if isinstance(t_s, type(None)):
t_s = t_now
if t_s.minute % 5 != 0:
t_s = t_now - datetime.timedelta(minutes=t_now.minute % 5)
if pd.isnull(t_next):
t_e = t_now
elif t_next.hour * 60 + t_next.minute - (t_now.hour * 60 +
t_now.minute) > 1:
t_e = t_now
if not isinstance(t_e, type(None)):
time_span.append((t_s, t_e))
t_s = None
t_e = None
return list(set(time_span))
# encoding=utf-8
def down_day_data(self, count=150, start_date=None, end_date=None):
self.data = get_k_data_jq(self.stk_code,
count=count,
start_date=start_date,
end_date=end_date,
freq=self.freq)
# encoding=utf-8
# encoding=utf-8
