def sendConcernedStkPicToSelf_V2():
"""
将自己关心的数据打印出图，发送到qq
相较于之前版本，本次改进为：
1、使用json文件中的code列表代替数据库中的列表
2、进行判断，出发发送条件再予以发送
:return:
"""
towho = '影子2'
send_qq(towho, '以下是已入的stk的形势图：')
code_list = read_config()['buy_stk']
for x in code_list + ['sh', 'sz', 'cyb']:
df = get_k_data_JQ(x, 400)
fig, _ = genStkPicForQQ(df)
plt.title(str(x))
send_pic_qq(towho, fig)
# plt.show()
plt.close()
fig, _ = genStkIdxPicForQQ(df)
plt.title(str(x))
send_pic_qq(towho, fig)
# plt.show()
plt.close()
send_W_M_MACD(x, towho)
def sendConcernedStkPicToSelf_T():
"""
将自己关心的数据打印出图，发送到qq
:return:
"""
towho = '影子2'
send_qq(towho, '以下是已入的stk的形势图：')
stk_buy = read_config()['buy_stk']
for x in stk_buy + ['sh', 'sz', 'cyb']:
df = get_k_data_JQ(x, 400)
fig, _, attention = genStkPicForQQ(df, x)
if attention:
send_pic_qq(towho, fig)
send_W_M_MACD(x, towho)
plt.close()
# 打印第二张套图
fig, _, attention = genStkIdxPicForQQ(df, x)
if attention:
send_pic_qq(towho, fig)
plt.close()
def printConcernedPredict2Self():
towho = u'影子2'
for stk in ['000001', '000333', '300508']:
close_today = ts.get_k_data(
stk, start=add_date_str(get_current_date_str(),
-5)).tail(1)['close'].values[0]
r = [(label, '%0.2f' % predict_tomorrow(stk,
label,
N_STEPS=N_STEPS,
feature_cols=feature_cols,
HIDDEN_SIZE=HIDDEN_SIZE,
NUM_LAYERS=NUM_LAYERS))
for label in ['high', 'low', 'close']]
# 增加与今天收盘价的对比
r_contrast = [
(x[0], x[1],
'%0.2f' % ((float(x[1]) - close_today) / close_today * 100) + '%')
for x in r
]
# stk2name = {
# 'sh': '上证',
# 'sz': '深证',
# 'cyb': '创业板'
# }
print(stk + ':\n' + str(r_contrast))
send_qq(towho, stk + ':\n' + str(r_contrast))
def updatePotInfo():
"""
:param df_H_L_Pot:
:return:
"""
# h_l_pot_info_url = '.\InfoRestore\df_H_L_Pot.pkl'
if os.path.exists(h_l_pot_info_url):
with open(h_l_pot_info_url, 'rb') as f:
try:
h_l_pot_info = pickle.load(f)
except:
h_l_pot_info = pd.DataFrame()
else:
h_l_pot_info = pd.DataFrame()
if h_l_pot_info.empty:
h_l_pot_info = initPotInfo(get_h_l_pot(stk_list))
else:
new_pot_info = get_h_l_pot(stk_list)
h_l_pot_info = pd.concat([
h_l_pot_info.drop([
'half_year_high', 'half_year_low', 'month_high', 'month_low',
'year_high', 'year_low'
], 1),
new_pot_info.drop('stk', 1)
],
axis=1)
# 将结果序列化
with open(h_l_pot_info_url, 'wb') as f:
pickle.dump(h_l_pot_info, f)
send_qq(u'影子', '高底线信息更新成功！')
def judgeAndSendMsg():
"""
按频率调用，
:return:
"""
if os.path.exists(h_l_pot_info_url):
with open(h_l_pot_info_url, 'rb') as f:
h_l_pot_info = pickle.load(f)
else:
print('函数 judgeAndSendMsg: 加载高低信息失败！')
return
df_H_L_Pot = h_l_pot_info
for stk in df_H_L_Pot.index:
# 获取该stk的实时价格
current_price = float(
ts.get_realtime_quotes(df_H_L_Pot.loc[stk,
'stk'])['price'].values[0])
# 将当前价格保存，用于验证计算准确性
df_H_L_Pot.loc[stk, 'current_price'] = current_price
""" 年线判断 """
df_H_L_Pot = lineJudge(df_H_L_Pot_index=stk,
current_price=current_price,
df_info=df_H_L_Pot,
line_str='year')
""" ----------------- 半年线判断 -----------------"""
df_H_L_Pot = lineJudge(df_H_L_Pot_index=stk,
current_price=current_price,
df_info=df_H_L_Pot,
line_str='half_year')
""" ----------------- 月线判断 ----------------"""
df_H_L_Pot = lineJudge(df_H_L_Pot_index=stk,
current_price=current_price,
df_info=df_H_L_Pot,
line_str='month')
""" 检查并发送消息 """
for idx in df_H_L_Pot.index:
# 遍历年线、半年线和月线
for sts in ['year_status', 'half_year_status', 'month_status']:
if (df_H_L_Pot.loc[idx, sts] != u'正常') & (df_H_L_Pot.loc[
idx, sts] != df_H_L_Pot.loc[idx, sts + '_last']):
send_qq(
u'影子', 'stk:' + get_name_by_stk_code(
g_total_stk_info_mysql, df_H_L_Pot.loc[idx, 'stk']) +
'\n' + '当前价格：' +
str(df_H_L_Pot.loc[idx, 'current_price']) + '\n' +
'事件： “' + df_H_L_Pot.loc[idx, sts + '_last'] + '” --> “' +
df_H_L_Pot.loc[idx, sts] + '”' + '\n\n')
df_H_L_Pot.loc[idx, sts + '_last'] = df_H_L_Pot.loc[idx, sts]
with open(h_l_pot_info_url, 'wb') as f:
pickle.dump(h_l_pot_info, f)
print('函数 judgeAndSendMsg: 完成本次判断！')
def printMainRank():
"""
打印重要的index
"""
r = [(x, '%.2f' % (calRealtimeRankWithGlobal(stk_code=x)[0]))
for x in ['sh', 'sz', 'cyb']]
send_qq('影子', "Main_Index:\n" + str(r))
def checkWeekStrayForAll():
towho = '影子2'
send_qq(towho, '以下是今晚海选结果：')
df_total = ts.get_stock_basics()
# 过滤掉年龄小于四岁的
df_age_filter = df_total[df_total.apply(lambda x: int(
str(x['timeToMarket'])[:4]) <= int(get_current_date_str()[:4]) - 4,
axis=1)]
# 根据week反转情况进行过滤，保留有反转的单位
df_age_filter_stray = list(df_age_filter.reset_index().apply(
lambda x: (x['code'], week_MACD_stray_judge(x['code'], towho)),
axis=1))
# 过滤掉非反转的情况
df_age_f2 = list(filter(lambda x: x[1][0], df_age_filter_stray))
# 增加stk的水平信息
df_level = [(x[0], x[1][1], cal_stk_p_level(x[1][1]['close'].values))
for x in df_age_f2]
# 按总体水平排序，筛选优异者
df_level.sort(key=lambda x: x[2]['total_last'])
df_level = df_level[:math.floor(len(df_level) / 3 * 2)]
# 按照近30的波动率进行排序,筛选优异者
df_level.sort(key=lambda x: x[2]['std'], reverse=True)
df_level = df_level[:math.floor(len(df_level) / 3 * 2)]
# 按照近30的水平排序，留下最后8只
df_level.sort(key=lambda x: x[2]['t30_last'], reverse=False)
df_level = df_level[:np.min([math.floor(len(df_level) / 3 * 2), 15])]
# 打印信息
stk_list = [k[0] for k in df_level]
for stk in stk_list:
# 打印周与月信息
send_W_M_MACD(stk_code=stk, towho=towho)
# 打印日线信息
df = get_k_data_JQ(stk, count=400, end_date=get_current_date_str())
fig, _, _ = genStkPicForQQ(df)
plt.title(str(stk))
send_pic_qq(towho, fig)
plt.close()
fig, _, _ = genStkIdxPicForQQ(df)
plt.title(str(stk))
send_pic_qq(towho, fig)
plt.close()
def updateRSVRecord():
try:
code_list = read_config()['buy_stk']
# global RSV_Record
for stk in code_list:
RSV_Record[stk] = cal_rsv_rank(stk, 5)
except Exception as e:
send_qq('影子2', 'RSV数据更新失败！\n' + str(e))
def update_price_ratio_info():
"""
:param df_H_L_Pot:
:return:
"""
# 将结果序列化
with open(price_ratio_info_url, 'wb') as f:
pickle.dump(h_l_pot_info, f)
send_qq(u'影子', '高底线信息更新成功！')
def printMainRankForPublic():
"""
打印重要的index
"""
r = [(x, '%.2f' % (calRealtimeRankWithGlobal(stk_code=x)[0]),
'%.2f' % (calRealtimeRankWithGlobal(stk_code=x)[2]))
for x in ['sh', 'sz', 'cyb']]
note = str([x[0]+'\n'+'上涨概率：'+str(x[1])+'%\n'+'当前值：'+str(x[2])+'\n' for x in r])\
.replace('sh', '上证')\
.replace('sz', '深证')\
.replace('cyb', '创业板')\
.replace('[', '')\
.replace(']', '')\
.replace("'", '')\
.replace('\\n', "\n")\
.replace(',', '--------------------\n')
send_qq('大盘上涨概率公示', '\n\n--------------------\n' + note)
def myPrint(str_gui, str_temp, method='n', towho=''):
"""
:param gui:
:param str_gui:
:param method:
:param towho:
:return:
"""
if method is 'n':
print(str_temp)
elif method is 'gm':
str_gui['msg'] = str_gui['msg'] + str_temp + '\n\n'
elif method is 'gn':
str_gui['note'] = str_gui['note'] + str_temp + '\n\n'
elif method is 'qq':
send_qq(towho, str_temp)
return str_gui
def week_MACD_stray_judge(stk_code, towho, debug_plot=False):
try:
# 获取今天的情况，涨幅没有超过3%的不考虑
df_now = get_k_data_JQ(stk_code,
count=2,
end_date=get_current_date_str()).reset_index()
if (df_now.tail(1)['close'].values[0] - df_now.head(1)
['close'].values[0]) / df_now.head(1)['close'].values[0] < -0.05:
print('函数week_MACD_stray_judge：' + stk_code + '涨幅不够！')
return False, pd.DataFrame()
df = get_k_data_JQ(stk_code,
count=400,
end_date=get_current_date_str()).reset_index()
if len(df) < 350:
print('函数week_MACD_stray_judge：' + stk_code + '数据不足！')
return False, pd.DataFrame()
# 规整
df_floor = df.tail(math.floor(len(df) / 20) * 20 - 19)
# 增加每周的星期几
df_floor['day'] = df_floor.apply(lambda x: calendar.weekday(
int(x['date'].split('-')[0]), int(x['date'].split('-')[1]),
int(x['date'].split('-')[2])),
axis=1)
# 增加每周的星期几
df_floor['day'] = df_floor.apply(lambda x: calendar.weekday(
int(x['date'].split('-')[0]), int(x['date'].split('-')[1]),
int(x['date'].split('-')[2])),
axis=1)
# 隔着5个取一个
if df_floor.tail(1)['day'].values[0] != 4:
df_floor_slice_5 = pd.concat(
[df_floor[df_floor.day == 4],
df_floor.tail(1)], axis=0)
else:
df_floor_slice_5 = df_floor[df_floor.day == 4]
# 计算指标
df_floor_slice_5['MACD'], df_floor_slice_5[
'MACDsignal'], df_floor_slice_5['MACDhist'] = talib.MACD(
df_floor_slice_5.close,
fastperiod=6,
slowperiod=12,
signalperiod=9)
# 隔着20个取一个（月线）
df_floor_slice_20 = df_floor.loc[::20, :]
# 计算指标
df_floor_slice_20['MACD'], df_floor_slice_20[
'MACDsignal'], df_floor_slice_20['MACDhist'] = talib.MACD(
df_floor_slice_20.close,
fastperiod=4,
slowperiod=8,
signalperiod=9)
# 获取最后的日期
date_last = df_floor_slice_5.tail(1)['date'].values[0]
# 判断背离
MACD_5 = df_floor_slice_5.tail(3)['MACD'].values
MACD_20 = df_floor_slice_20.tail(4)['MACD'].values
if (MACD_5[1] == np.min(MACD_5)) & (MACD_20[1] != np.max(MACD_20)) & (
MACD_20[2] != np.max(MACD_20)):
if debug_plot:
""" --------------------------------------- 生成图片 -------------------------------------"""
fig, ax = plot_W_M(df_floor_slice_5, df_floor_slice_20)
# 增加标题
plt.title(stk_code + 'month-stray' + date_last)
# 发送图片
send_pic_qq(towho, fig)
# 关闭图片
plt.close()
return True, df
else:
return False, pd.DataFrame()
except Exception as e:
send_qq(towho, stk_code + '出错：\n' + str(e))
return False, pd.DataFrame()
def autoShutdown():
send_qq('影子', '120秒后将自动关机！')
os.system('shutdown -s -f -t 120')
# encoding=utf-8
def sendRelaLevel2QQ():
send_qq(towho='影子2', msge='注意低位囤货：')
calRelaPLevel(readConfig()['safe_stk'], -720, '影子2')
# encoding=utf-8
