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 = readConfig()['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 = readConfig()['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 sendM305Pic(win_qq_name, stk_code):
df = ts.get_k_data(stk_code, start=add_date_str(get_current_date_str(), -400))
# 测试相对均值偏移度
df['m5'] = df['close'].rolling(window=5).mean()
df['m30'] = df['close'].rolling(window=30).mean()
df['diff_m305'] = df.apply(lambda x: x['m5'] - x['m30'], axis=1)
df['rank'] = df.apply(lambda x: relativeRank(df['diff_m305'], x['diff_m305']), axis=1)
df = df.dropna(axis=0)
df.plot('date', ['close', 'diff_m305', 'rank'], subplots=True)
plt.title(stk_code+'M5-M30 分数')
output = BytesIO() # BytesIO实现了在内存中读写byte
buf_save = BytesIO()
plt.savefig(output)
output.seek(0)
img = Image.open(output) # Image.open可以打开网络图片与本地图片。
img.convert("RGB").save(buf_save, "BMP") # 以RGB模式保存图像
data = buf_save.getvalue()[14:]
buf_save.close()
output.close()
plt.close()
send_pic_qq(win_qq_name, data)
def checkWeekStrayForAll():
win_qq_name = '影子'
df_total = ts.get_stock_basics()
for stk in df_total.index:
if int(str(df_total.loc[stk, 'timeToMarket'])[:4]) >= int(get_current_date_str()[:4]) - 4:
print('函数 checkWeekStrayForAll：'+stk+'年龄不够4岁！')
continue
try:
stray_flag = week_macd_stray_judge(stk, win_qq_name)
except:
print('函数 checkWeekStrayForAll：' + stk + '判断翻转出错！')
stray_flag = False
if stray_flag:
df = get_k_data_JQ(stk, count=400, end_date=get_current_date_str())
fig, _ = genStkPicForQQ(df)
plt.title(str(stk))
send_pic_qq(win_qq_name, fig)
plt.close()
fig, _ = genStkIdxPicForQQ(df)
plt.title(str(stk))
send_pic_qq(win_qq_name, fig)
plt.close()
else:
print('No weed-stray in '+stk)
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], calStkPlevel(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, win_qq_name, 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(win_qq_name, fig)
plt.close()
fig, _ = genStkIdxPicForQQ(df)
plt.title(str(x) + title_str)
send_pic_qq(win_qq_name, 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 sendConcernedStkPicToSelf_T():
"""
将自己关心的数据打印出图，发送到qq
:return:
"""
for x in stk_list:
df = get_k_data_JQ(x, 400)
fig, _ = genStkPicForQQ(df)
plt.title(str(x))
send_pic_qq('影子2', fig)
plt.close()
fig, _ = genStkIdxPicForQQ(df)
plt.title(str(x))
send_pic_qq('影子2', fig)
plt.close()
def sendMainIndexPicToPublic():
"""
不准，因为历史数据获取的不够
:return:
"""
for x in ['sh', 'sz', 'cyb']:
# df = ts.get_k_data(x, start=add_date_str(get_current_date_str(), -400))
stk_code_normal = {
'sh': '000001.XSHG',
'sz': '399001.XSHE',
'cyb': '399006.XSHE'
}[x]
df = jqdatasdk.get_price(stk_code_normal,
frequency='daily',
count=100,
end_date=get_current_date_str())
df['datetime'] = df.index
df['date'] = df.apply(lambda x: str(x['datetime'])[:10], axis=1)
# 计算MACD
df['MACD'], df['MACDsignal'], df['MACDhist'] = talib.MACD(
df.close, fastperiod=12, slowperiod=26, signalperiod=9)
# 测试相对均值偏移度
df['m9'] = df['close'].rolling(window=9).mean()
df['diff_m9'] = df.apply(lambda x: x['close'] - x['m9'], axis=1)
df['rank'] = df.apply(
lambda x: relativeRank(df['diff_m9'], x['diff_m9']), axis=1)
df_plot = df.tail(50)
# df.tail(50).plot('date', ['close', 'rank', 'MACD'],
# subplots=True,
# title=['历史收盘价', '历史分数', 'MACD指标'],
# legend=True)
fig, ax = subplots(ncols=1, nrows=3)
ax[0].plot(range(0, len(df_plot)),
df_plot['close'],
'g--',
label='收盘价')
ax[1].plot(range(0, len(df_plot)),
df_plot['rank'],
'r--',
label='上涨概率')
ax[2].bar(range(0, len(df_plot)), df_plot['MACD'])
for ax_sig in ax:
ax_sig.set_xticks(range(0, len(df_plot)))
ax_sig.set_xticklabels(
[x[-5:] for x in list(df_plot['date'].values)], rotation=90)
ax_sig.legend(loc='best')
ax[0].set_title({'sh': "上证", 'sz': '深证', 'cyb': '创业板'}[x])
# ----------------------- 将图片发到qq -----------------------------------
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
"""
将原先使用email发送的图转为使用qq发送
"""
from CornerDetectAndAutoEmail.Sub import genStkPicForQQ, genStkIdxPicForQQ
from SDK import plot_agg
from General.AutoStkConfig import stk_list
import tushare as ts
from pylab import *
from SendMsgByQQ.SendPicByQQ import send_pic_qq
for stk in stk_list[:3]:
df = ts.get_k_data(stk)
fig, _ = genStkPicForQQ(df)
plt.title(str(stk))
send_pic_qq('影子2', fig)
plt.close()
fig, _ = genStkIdxPicForQQ(df)
plt.title(str(stk))
send_pic_qq('影子2', fig)
plt.close()
def checkSingleStkHourMACD(stk_code, source='jq'):
if source == 'jq':
df_30 = get_k_data_JQ(stk_code, start_date=add_date_str(get_current_date_str(), -20), freq='30m')
df_60 = get_k_data_JQ(stk_code, start_date=add_date_str(get_current_date_str(), -20), freq='60m')
elif source == 'ts':
df_30 = my_pro_bar(stk_code, start=add_date_str(get_current_date_str(), -20), freq='30min')
df_60 = my_pro_bar(stk_code, start=add_date_str(get_current_date_str(), -20), freq='60min')
# 去掉volume为空的行
df_30 = df_30.loc[df_30.apply(lambda x: not (x['volume'] == 0), axis=1), :]
df_60 = df_60.loc[df_60.apply(lambda x: not (x['volume'] == 0), axis=1), :]
df_30['MACD'], _, _ = talib.MACD(df_30.close,
fastperiod=12, slowperiod=26,
signalperiod=9)
df_60['MACD'], _, _ = talib.MACD(df_60.close,
fastperiod=12, slowperiod=26,
signalperiod=9)
l_60 = df_60.tail(3)['MACD'].values
l_30 = df_30.tail(3)['MACD'].values
print('函数 checkSingleStkHourMACD：'+stk_code+':\n30min:'+str(l_30)+'\n60min:'+str(l_60)+'\n')
if l_60[1] == np.min(l_60):
title_str = '60分钟开始上涨'
sts = 1
elif l_60[1] == np.max(l_60):
title_str = '60分钟开始下跌'
sts = 2
elif l_30[1] == np.max(l_30):
title_str = '30分钟开始下跌'
sts = 3
elif l_30[1] == np.min(l_30):
title_str = '30分钟开始上涨'
sts = 4
else:
title_str = '当前无拐点'
sts = 0
# 避免重复发图！
if stk_code in MACD_min_last.keys():
if MACD_min_last[stk_code] != sts:
send_pic = True
MACD_min_last[stk_code] = sts
else:
send_pic = False
else:
send_pic = True
MACD_min_last[stk_code] = sts
print('函数 checkSingleStkHourMACD：' + stk_code + ':\nsend_pic标志位:' + str(send_pic) + '\nsts标志位:' + str(sts) + '\n')
# 生成图片
df_30 = df_30.dropna()
df_60 = df_60.dropna()
fig, ax = subplots(ncols=1, nrows=4)
ax[0].plot(range(0, len(df_30)), df_30['close'], 'g*--', label='close_30min')
ax[1].bar(range(0, len(df_30)), df_30['MACD'], label='macd_30min')
ax[2].plot(range(0, len(df_60)), df_60['close'], 'g*--', label='close_60min')
ax[3].bar(range(0, len(df_60)), df_60['MACD'], label='macd_60min')
for ax_sig in ax:
ax_sig.legend(loc='best')
plt.title(stk_code + '-' + title_str)
if send_pic & (sts != 0):
send_pic_qq('影子', fig)
# send_pic_qq('影子', fig)
plt.close()
# encoding=utf-8
