def calRSVRank(stk_code, Mdays, history_length=400):
df = get_k_data_JQ(stk_code,
count=history_length,
end_date=get_current_date_str())
# 移动平均线+RSV（未成熟随机值）
M = Mdays
df['low_M' + str(M)] = df['low'].rolling(window=M).mean()
df['high_M' + str(M)] = df['high'].rolling(window=M).mean()
df['close_M' + str(M)] = df['close'].rolling(window=M).mean()
# df['low_M'+str(M)+'_min'] = df['low_M'+str(M)].rolling(window=M).min()
# df['high_M'+str(M)+'_max'] = df['high_M'+str(M)].rolling(window=M).max()
df['RSV'] = df.apply(lambda x:
(x['close_M' + str(M)] - x['low_M' + str(M)]) /
(x['high_M' + str(M)] - x['low_M' + str(M)]),
axis=1)
# df['RSV_abs'] = df.apply(lambda x: (x['close_M'+str(M)] - x['low_M'+str(M)+'_min']), axis=1)
# df['RSV_Rank'] = df.apply(lambda x: relativeRank(df['RSV'], x['RSV']), axis=1)
return df.tail(1)['RSV'].values[0]
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 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()
net_amount_xl 超大单净额(万) 超大单：大于等于50万股或者100万元的成交单
net_pct_xl 超大单净占比(%) 超大单净占比 = 超大单净额 / 成交额
net_amount_l 大单净额(万) 大单：大于等于10万股或者20万元且小于50万股或者100万元的成交单
net_pct_l 大单净占比(%) 大单净占比 = 大单净额 / 成交额
net_amount_m 中单净额(万) 中单：大于等于2万股或者4万元且小于10万股或者20万元的成交单
net_pct_m 中单净占比(%) 中单净占比 = 中单净额 / 成交额
net_amount_s 小单净额(万) 小单：小于2万股或者4万元的成交单
net_pct_s 小单净占比(%) 小单净占比 = 小单净额 / 成交额
"""
df_m_f = get_money_flow('300508.XSHE',
start_date='2019-04-01',
end_date=None,
fields=None,
count=None)
df = get_k_data_JQ('300508', start_date='2019-04-01')
# 获取300508所在的通信行业的估值
df = finance.run_query(
query(finance.SW1_DAILY_VALUATION).filter(
finance.SW1_DAILY_VALUATION.code == '801770'))
df = df.set_index('date').sort_index(ascending=True)
"""
表名: valuation
列名 列的含义 解释 公式
code stk代码 带后缀.XSHE/.XSHG
day 日期 取数据的日期
capitalization 总股本(万股) 公司已发行的普通股股份总数(包含A股，B股和H股的总股本)
circulating_cap 流通股本(万股) 公司已发行的境内上市流通、以人民币兑换的股份总数(A股市场的流通股本)
market_cap 总市值(亿元) A股收盘价*已发行stk总股本（A股+B股+H股）
# encoding=utf-8
import tushare as ts
from RelativeRank.Sub import relativeRank, get_k_data_JQ
from SDK.MyTimeOPT import add_date_str, get_current_date_str
"""
测试单个stk的相对排名
"""
stk_code = '600487'
df = get_k_data_JQ(stk_code=stk_code, count=400)
# 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']) / x['close'], axis=1)
df['rank'] = df.apply(lambda x: relativeRank(df['diff_m9'], x['diff_m9']),
axis=1)
"""
df.plot('date', ['close', 'diff_m9', 'rank'], subplots=True)
"""
end = 0
def week_macd_stray_judge(stk_code, win_qq_name):
# 获取今天的大盘走势，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)
# 取均值
# df_floor['close_m5'] = df_floor['close'].rolling(window=5).mean()
# 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)):
df_floor_slice_5.plot('date', ['close', 'MACD'], subplots=True, style=['--', '*'])
plt.title(stk_code + 'week-stray'+date_last)
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_data(win_qq_name, data)
# 打印月线图
df_floor_slice_20.plot('date', ['close', 'MACD'], subplots=True, style=['--', '*'])
plt.title(stk_code + 'month-stray' + date_last)
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_data(win_qq_name, data)
return True
else:
return False
if __name__ == '__main__':
r = calendar.weekday(2019, 12, 5)
from JQData_Test.auth_info import *
# week_macd_stray_judge('300183', '影子')
checkWeekStrayForAll()
stk_code = '600707'
df = get_k_data_JQ(stk_code, count=600, end_date=get_current_date_str()).reset_index()
# 规整
df_floor = df.tail(math.floor(len(df)/20)*20-19)
# 取均值
df_floor['close_m5'] = df_floor['close'].rolling(window=5).mean()
# 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
# df_floor_slice_5 = df_floor.loc[::5, :]
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: