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 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 sendPLevel2QQ(df, towho):
"""
将stk的价格水平发送到qq
:df: code, level
:return:
"""
r_df = df
# 按level从低到高进行排序
r_df_sort = r_df.sort_values(by='level', ascending=True).head(12)
fig, ax = plt.subplots(ncols=1, nrows=1)
ax.bar(range(0, len(r_df_sort)), r_df_sort['level'])
# ax.plot(range(0, len(r_df_sort)), [0.1 for x in r_df_sort['level']], 'r--')
# ax.plot(range(0, len(r_df_sort)), [0.5 for x in r_df_sort['level']], 'r--')
# 获取code2name字典
c2n = code2name_dict()
ax.set_xticks(range(0, len(r_df_sort)))
ax.set_xticklabels([c2n[x] for x in r_df_sort['code']], rotation=45)
plt.ylim((0, 1))
plt.grid()
plt.title('注意低位囤货！')
send_pic_qq(towho, fig)
plt.close()
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 printStkListPic2QQ(code_list, towho, title=None):
if title is not None:
title_str = title
else:
title_str = ''
for x in code_list:
df = get_k_data_JQ(x, 400)
fig, _ = genStkPicForQQ(df)
plt.title(str(x) + title_str)
send_pic_qq(towho, fig)
plt.close()
fig, _ = genStkIdxPicForQQ(df)
plt.title(str(x) + title_str)
send_pic_qq(towho, fig)
plt.close()
def sendMainIndexStkPic2Public():
"""
将自己关心的数据打印出图，发送到qq
:return:
"""
for x in ['sh', 'sz', 'cyb']:
df = get_k_data_JQ(x, 400)
fig, _ = genStkPicForQQ(df)
plt.title(str(x))
send_pic_qq('大盘上涨概率公示', fig)
plt.close()
fig, _ = genStkIdxPicForQQ(df)
plt.title(str(x))
send_pic_qq('大盘上涨概率公示', fig)
plt.close()
def send_W_M_MACD(stk_code, towho):
"""
:param stk_code:
:param towho:
:return:
"""
# 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] < 0.03:
# print('函数week_MACD_stray_judge：' + stk_code + '涨幅不够！')
# return False
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
# 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]
# 获取最后的日期
date_last = df_floor_slice_5.tail(1)['date'].values[0]
# 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)
""" --------------------------------------- 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()
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()
# encoding=utf-8
# encoding=utf-8
