def saveStkMRankHistoryData(stk_code, history_days, m_days, save_dir):
"""
保存stk的历史数据，用来实时计算均线离心度分数，需要存历史数据，尽量不要用！
:param stk_code:
:param history_days:
:param save_dir: './M_data/'
:return:
"""
df = get_k_data_JQ(stk_code, 400)
if len(df) < history_days*0.8:
print('函数 calSingleStkRank: 该stk历史数据不足！')
return -1
# 测试相对均值偏移度
df['m9'] = df['close'].rolling(window=m_days).mean()
df['diff_m9'] = df.apply(lambda x: (x['close'] - x['m9'])/x['close'], axis=1)
df = df.dropna()
dict_restore = {
'stk_code': stk_code,
'history_M_diverge_data': list(df['diff_m9'].values),
'latest_data': list(df.tail(m_days-1)['close'].values),
'update_date': df.tail(1)['date'].values[0]
}
shelveP(
data=dict_restore,
saveLocation=save_dir,
fileName=stk_code+'_M'+str(m_days))
def initScale(stk_list, list_name):
"""
:param stk_list:
:return:
"""
shelveP(dict([(x, -1) for x in stk_list]), LastScale, list_name)
def checkDivergeLowLevel_Sub(stk_list, stk_list_name, scale_threshold, hist_data_dir, qq_win_name, desk=2, logic=True):
"""
:param stk_list:
:param stk_list_name:
:param scale_threshold:
:param hist_data_dir:
:param desk:
:param logic: 为真时，大于阈值会触发，为假时，小于阈值会触发
:return:
"""
# 判断是否存在上次分数，存在则加载，否则初始化一个
if os.path.exists(LastScale+stk_list_name+'.dat'):
lastscale_stk_pool = loadLastScale(stk_list_name)
else:
initScale(stk_list, stk_list_name)
lastscale_stk_pool = loadLastScale(stk_list_name)
for stk in stk_list:
r, history_data, p_now, update_date = calRealtimeRankWithGlobal(stk_code=stk)
# 生成语言描述
if (r-lastscale_stk_pool[stk]) > 0:
note = '分数上涨'+'%0.1f' % (r-lastscale_stk_pool[stk])
else:
note = '分数下落'+'%0.1f' % (r - lastscale_stk_pool[stk])
if logic:
if considerMainIndex(stk, r):
if math.fabs(r-lastscale_stk_pool[stk]) > desk:
# 更新上次分数
lastscale_stk_pool[stk] = r
send_qq(qq_win_name,
'Attention：\n' + stk + note +
'\nscore：'+str('%0.2f') % r +
'\np_now:' + str(p_now) +
'\nhistory:' + str(history_data) +
'\nupdate_date:' + str(update_date))
shelveP(lastscale_stk_pool, LastScale, stk_list_name)
else:
print('与上次命令相同！')
else:
print(stk+'分数处于正常状态！分数为：'+str('%0.2f') % r)
else:
if r < scale_threshold:
if math.fabs(r-lastscale_stk_pool[stk]) > desk:
# 更新上次分数
lastscale_stk_pool[stk] = r
send_qq(qq_win_name,
'Attention：\n' + stk + note +
'\nscore：' + str('%0.2f') % r +
'\np_now:' + str(p_now) +
'\nhistory:' + str(history_data) +
'\nupdate_date:' + str(update_date))
shelveP(lastscale_stk_pool, LastScale, stk_list_name)
else:
print('与上次命令相同！')
else:
print(stk + '分数处于正常状态！分数为：' + str('%0.2f') % r)
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