def prepare_data(self):
"""
准备数据
:return:
"""
if self.debug:
print('开始准备数据...')
jq_login()
# 准备数据
df_m = get_k_data_JQ(self.stk_code,
start_date=self.start_date,
end_date=self.end_date,
freq=str(self.retest_span) + 'm')
df_m['date'] = df_m.apply(lambda x: str(x['datetime'])[:10], axis=1)
df_day = get_k_data_JQ(
self.stk_code, start_date=self.start_date,
end_date=self.end_date).sort_index(ascending=True)
if len(df_day) < self.max_days:
if self.debug:
print('max_days:%d day_data_length:%d minute_data_length:%d' %
(self.max_days, len(df_day), len(df_m)))
return False
# 向day data 中 增加必要index
self.data_day = add_stk_index_to_df(df_day).reset_index().reset_index()
# 增加必要index
self.data_minute = add_stk_index_to_df(
df_m).reset_index().reset_index()
logout()
return True
def getSigleStkReseau(stk_code):
"""
计算单只stk的当前网格
:return:
"""
# df = my_pro_bar(stk_code=stk_code, start=add_date_str(get_current_date_str(), -10))
df = get_k_data_JQ(stk_code=stk_code,
start_date=add_date_str(get_current_date_str(), -10),
end_date=get_current_date_str())
if len(df) < 7:
# df = my_pro_bar(stk_code=stk_code, start=add_date_str(get_current_date_str(), -30))
df = get_k_data_JQ(stk_code=stk_code,
start_date=add_date_str(get_current_date_str(),
-30),
end_date=get_current_date_str())
df = df.reset_index()
df = df_win_std(df, 3)
df = df_win_std(df, 6)
df['std_m'] = df.apply(lambda x: np.mean([x['std_3'], x['std_6']]), axis=1)
return df.tail(1)['std_m'].values[0]
def gen_stk_sea_select_pic_sub(stk_code):
try:
jq_login()
# 保存路径
save_dir = sea_select_pic_dir + get_current_date_str() + '/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# 准备 小时 和 日线 数据
df_hour = gen_hour_macd_values(stk_code)
df_day = get_k_data_JQ(stk_code, 800)
# 定义图片名称
file_name = stk_code + '.png'
# 生成小时图片
gen_hour_macd_pic_local(df_hour, stk_code, 'jq', '',
save_dir + 'h_' + file_name)
gen_hour_index_pic_local(df_hour[0], stk_code,
save_dir + 'h_idx_' + file_name)
gen_day_pic_local(df_day, stk_code, save_dir + 'd_' + file_name)
gen_w_m_macd_pic_local(df_day, stk_code,
save_dir + 'wm_' + file_name)
gen_idx_pic_local(df_day, stk_code,
save_dir + 'd_idx_' + file_name)
except Exception as e:
print('生成股票走势图失败！原因：\n' + str(e))
finally:
logout()
def day_analysis_dict_pipe():
"""
day数据分析, 放在数据处理线程中
:return:
"""
# 判断结果list
result_analysis_list = []
jq_login()
for tab in dict_stk_list.keys():
stk_list = dict_stk_list[tab]
for stk_info in stk_list:
stk = stk_info[1]
df = get_k_data_JQ(stk, 400)
# 其他指标
r_tuple_index_pic = gen_idx_pic_wx(df, stk_code=stk)
result_analysis_list = result_analysis_list + r_tuple_index_pic[1]
# 日线分析结果汇总
r_tuple_day_pic = gen_day_pic_wx(df, stk_code=stk)
result_analysis_list = result_analysis_list + r_tuple_day_pic[1]
jq.logout()
debug_print_txt('hour_analysis', 'total_stk',
str(result_analysis_list) + '\n')
return result_analysis_list
def get_pic_dict():
"""
获取图片字典
:return:
"""
dict_stk_list = {
'index': ['sh', 'sz', 'cyb'],
'buy': readConfig()['buy_stk'],
'concerned': readConfig()['concerned_stk']
}
pic_dict = {}
for tab in dict_stk_list.keys():
stk_list = dict_stk_list[tab]
stk_list_pic_dict = {}
for stk in stk_list:
df = get_k_data_JQ(stk, 400)
stk_pic_dict = {
'hour': gen_Hour_MACD_Pic(stk),
'day': gen_Day_Pic(df, stk_code=stk)[0],
'wm': gen_W_M_MACD_Pic(stk),
'index': gen_Idx_Pic(df, stk_code='')[0]
}
stk_list_pic_dict[stk] = stk_pic_dict
# 将page中的stk pic存入字典
pic_dict[tab] = stk_list_pic_dict
return pic_dict
def check_single_stk_middle_level(stk_code, dict):
"""
输入代码，返回level
:param stk_code:
:return:
"""
# 获取当前价格
current_price = get_RT_price(stk_code, source='jq')
if stk_code in dict.keys():
l = relativeRank(dict[stk_code], current_price)
else:
df_hour = get_k_data_JQ(stk_code,
count=None,
start_date=add_date_str(
get_current_date_str(), -60),
freq='60m')
dict[stk_code] = list(df_hour['close'].values)
l = relativeRank(list(df_hour['close'].values), current_price)
with open(data_dir + 'middlePeriodHourData.json', 'w') as f:
json.dump(dict, f)
return l
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()
for idx in df.index:
if (df.loc[idx, 'high_M' + str(M)] - df.loc[idx, 'low_M' + str(M)]
== 0) | (df.loc[idx, 'close_M' + str(M)] -
df.loc[idx, 'low_M' + str(M)] == 0):
df.loc[idx, 'RSV'] = 0.5
else:
df.loc[idx, 'RSV'] = (df.loc[idx, 'close_M' + str(M)] -
df.loc[idx, 'low_M' + str(M)]) / (
df.loc[idx, 'high_M' + str(M)] -
df.loc[idx, 'low_M' + str(M)])
# 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)
return df.tail(1)['RSV'].values[0]
def check_single_stk_hour_idx_wx(stk_code, source='jq', debug=False):
"""
打印常用指标
"""
stk_df = get_k_data_JQ(stk_code,
count=120,
end_date=add_date_str(get_current_date_str(), 1),
freq='30m')
# 按升序排序
stk_df = stk_df.sort_values(by='datetime', ascending=True)
"""
增加指标
'RSI5', 'RSI12', 'RSI30'
'SAR'
'slowk', 'slowd'
'upper', 'middle', 'lower'
'MOM'
"""
# 删除volume为空值的情况！
stk_df = stk_df.loc[
stk_df.apply(lambda x: not (int(x['volume']) == 0), axis=1), :]
# 计算index
stk_df = add_stk_index_to_df(stk_df).tail(60)
result_analysis = []
# 检查SAR
sar_tail_origin = stk_df.tail(2)
sar_tail = sar_tail_origin.copy()
sar_tail['compare'] = sar_tail_origin.apply(
lambda x: x['SAR'] - x['close'], axis=1)
if sar_tail.head(1)['compare'].values[0] * sar_tail.tail(
1)['compare'].values[0] < 0:
if sar_tail.tail(1)['SAR'].values[0] < sar_tail.tail(
1)['close'].values[0]:
title_tmp = stk_code + ' ' + code2name(
stk_code) + ' 注意 SAR 指标翻转，后续数小时可能上涨！'
result_analysis.append(title_tmp)
GenPic.set_background_color(bc='b_r')
else:
title_tmp = stk_code + ' ' + code2name(
stk_code) + ' 注意 SAR 指标翻转，后续数小时可能下跌！'
result_analysis.append(title_tmp)
# 打印过程日志
if debug:
txt_name = 'hour_index'
# 打印原始数据
debug_print_txt(txt_name, stk_code, stk_df.to_string() + '\n\n')
# 打印结果
debug_print_txt(txt_name, stk_code,
'结果：\n' + str(result_analysis) + '\n\n')
return result_analysis
def cal_rt_bias(self):
"""
实时计算bias
:return:
"""
df = get_k_data_JQ(stk=self.stk_code, freq=self.freq, count=self.span_s + 2)
df_bias = self.add_bias(df)
return df_bias.tail(1)['bias'].values[0]
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 get_single_stk_reseau(self, stk_code):
"""
计算单只stk的当前网格
:return:
"""
# df = my_pro_bar(stk_code=stk_code, start=add_date_str(get_current_date_str(), -10))
df_ = get_k_data_JQ(stk=stk_code,
start_date=add_date_str(get_current_date_str(),
-10),
end_date=get_current_date_str())
if len(df_) < 7:
# df = my_pro_bar(stk_code=stk_code, start=add_date_str(get_current_date_str(), -30))
df_ = get_k_data_JQ(stk=stk_code,
start_date=add_date_str(
get_current_date_str(), -30),
end_date=get_current_date_str())
return self.get_single_stk_reseau_sub(df_)
def cal_stk_rsv_rank(self, stk_code, m_days, history_length=400):
df = get_k_data_JQ(stk_code, count=history_length, end_date=get_current_date_str())
debug_print_txt('rsv_cal', '', code2name(stk_code) + '开始计算rsv:', True)
rsv = self.cal_rsv_rank_sub(df, m_days)
debug_print_txt('rsv_cal', '', '最终rsv：' + '%0.3f' % rsv, True)
return rsv
def prepare_data(self, period='5m', count=48 * 100):
"""
准备数据
:return:
"""
jq_login()
# 准备数据
df_5m = get_k_data_JQ(self.stk_code, count=count, freq=period)
df_5m['date'] = df_5m.apply(lambda x: str(x['datetime'])[:10], axis=1)
df_day = get_k_data_JQ(self.stk_code, count=8 * 100,
freq='30m').sort_index(ascending=True)
# 想day data 中 增加必要index
self.data_day = add_stk_index_to_df(df_day)
# 增加必要index
self.data_minute = add_stk_index_to_df(df_5m)
logout()
def update_middle_period_hour_data():
stk_list = read_config()['buy_stk'] + read_config()['concerned_stk']
# stk_list = readConfig()['buy_stk']
# 获取stk的小时数据
result = {}
for stk in stk_list:
df_hour = get_k_data_JQ(stk, count=None, start_date=add_date_str(get_current_date_str(), -60), freq='60m')
result[stk] = list(df_hour['close'].values)
with open(data_dir+'middlePeriodHourData.json', 'w') as f:
json.dump(result, f)
def plot_test(self):
"""
用以测试效果
:return:
"""
df = get_k_data_JQ(stk=self.stk_code, freq=self.freq, count=self.hist_count)
df_bias = self.add_bias(df)
# 增加rank数据
df_bias['rank'] = df_bias.apply(lambda x: self.cal_rank_now(x['bias']), axis=1)
df_bias.reset_index().reset_index().plot('level_0', ['close', 'rank'], subplots=True, style='*--')
def download_stk_list_day_data(stk_list, days=None):
"""
给定stk_list,下载日期数据,以tuple的形式返回
:return:
"""
jq_login()
stk_list_data = [(x, get_k_data_JQ(x, count=days)) for x in stk_list]
stk_list_data = list(filter(lambda x: not x[1].empty, stk_list_data))
logout()
return stk_list_data
def gen_hour_macd_values(stk_code, source='jq', title=''):
if source == 'jq':
# df_30 = get_k_data_JQ(stk_code, start_date=add_date_str(get_current_date_str(), -20),
# end_date=add_date_str(get_current_date_str(), 1), freq='30m')
# df_60 = get_k_data_JQ(stk_code, start_date=add_date_str(get_current_date_str(), -20),
# end_date=add_date_str(get_current_date_str(), 1), freq='60m')
df_30 = get_k_data_JQ(stk_code, count=120,
end_date=add_date_str(get_current_date_str(), 1), freq='30m')
df_60 = get_k_data_JQ(stk_code, count=120,
end_date=add_date_str(get_current_date_str(), 1), 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)
# 生成图片
df_30 = df_30.dropna()
df_60 = df_60.dropna()
if str(df_60.index[-1]) > get_current_datetime_str():
df_30 = df_30[:-1]
df_60 = df_60[:-1]
return df_30, df_60
def gen_hist_data(self):
"""
将历史bias数据保存到本地
:return:
"""
df = get_k_data_JQ(stk=self.stk_code,
freq=self.freq,
count=self.hist_count)
bias_values = self.add_bias(df)['bias'].values
bias_p = list(filter(lambda x: x >= 0, bias_values))
bias_n = list(filter(lambda x: x < 0, bias_values))
self.bias_dict = {'bias_p': bias_p, 'bias_n': bias_n}
def day_analysis_dict():
"""
day数据分析
:return:
"""
# 判断结果list
result_analysis_list = []
pic_dict = {}
for tab in dict_stk_list.keys():
stk_list = dict_stk_list[tab]
stk_list_pic_dict = {}
for stk_info in stk_list:
stk = stk_info[1]
df = get_k_data_JQ(stk, 400)
# 其他指标
r_tuple_index_pic = gen_idx_pic_wx(df, stk_code=stk)
result_analysis_list = result_analysis_list + r_tuple_index_pic[1]
# 日线分析结果汇总
r_tuple_day_pic = gen_day_pic_wx(df, stk_code=stk)
result_analysis_list = result_analysis_list + r_tuple_day_pic[1]
# stk_pic_dict = {
# 'hour': gen_hour_macd_pic_wx(stk),
# 'hour_index': gen_hour_index_pic_wx(stk),
# 'day': r_tuple_day_pic[0],
# 'wm': gen_w_m_macd_pic_wx(stk),
# 'index': r_tuple_index_pic[0]
# }
#
# stk_list_pic_dict[stk] = (stk_info[0], stk_pic_dict)
# 将page中的stk pic存入字典
# pic_dict[tab] = stk_list_pic_dict
debug_print_txt('day_analysis', 'total_stk',
str(result_analysis_list) + '\n')
return pic_dict, result_analysis_list
def download_stk_list_hour_data(stk_list):
"""
给定stk_list,下载半小时数据,以tuple的形式返回
:return:
"""
jq_login()
stk_list_data = [
(x,
get_k_data_JQ(x,
count=120,
end_date=add_date_str(get_current_date_str(), 1),
freq='30m')) for x in stk_list
]
# 清除空值
stk_list_data = list(filter(lambda x: not x[1].empty, stk_list_data))
logout()
return stk_list_data
def down_minute_data(stk_code, freq):
try:
df = get_k_data_JQ(stk_code,
count=300,
end_date=add_date_str(get_current_date_str(),
1),
freq=freq)
# 去掉volume为空的行
df = df.loc[df.apply(lambda x: not (x['volume'] == 0), axis=1), :]
# 增加指标计算
df = add_stk_index_to_df(df)
if str(df.index[-1]) > get_current_datetime_str():
df = df[:-1]
return df
except Exception as e_:
# self.log = self.log + '函数down_minute_data：\n %s\n' % str(e_)
print('函数down_minute_data：\n %s\n' % str(e_))
return pd.DataFrame()
def get_pic_dict():
"""
获取图片字典
:return:
"""
# 判断结果list
result_analysis_list = []
pic_dict = {}
for tab in dict_stk_list.keys():
stk_list = dict_stk_list[tab]
stk_list_pic_dict = {}
for stk_info in stk_list:
stk = stk_info[1]
df = get_k_data_JQ(stk, 400)
# 其他指标
r_tuple_index_pic = gen_Idx_Pic_wx(df, stk_code=stk)
result_analysis_list = result_analysis_list + r_tuple_index_pic[1]
# 日线分析结果汇总
r_tuple_day_pic = gen_Day_Pic_wx(df, stk_code=stk)
result_analysis_list = result_analysis_list + r_tuple_day_pic[1]
stk_pic_dict = {
'hour': gen_Hour_MACD_Pic_wx(stk),
'day': r_tuple_day_pic[0],
'wm': gen_W_M_MACD_Pic_wx(stk),
'index': r_tuple_index_pic[0]
}
stk_list_pic_dict[stk] = (stk_info[0], stk_pic_dict)
# 将page中的stk pic存入字典
pic_dict[tab] = stk_list_pic_dict
return pic_dict, result_analysis_list
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 stk_sea_select(stk_code, towho, tc, 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()
""" ------------------------ 下载原始数据 ------------------------------- """
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)
""" ------------------------ 判断周线是否达标 ------------------------------- """
# 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]
# 计算周线指标
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)
# 判断周线的走势,周线不是底部，直接返回
MACD_5 = df_floor_slice_5.tail(3)['MACD'].values
if not (MACD_5[1] == np.min(MACD_5)):
tc.AppendText(stk_code + code2name(stk_code) + '：“周线”不符合要求！')
return False
""" ------------------------ 判断月线是否达标 ------------------------------- """
# 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]
# 判断月线的走势，不符合条件直接返回
MACD_20 = df_floor_slice_20.tail(4)['MACD'].values
if not ((MACD_20[1] != np.max(MACD_20)) &
(MACD_20[2] != np.max(MACD_20))):
tc.AppendText(stk_code + code2name(stk_code) + '：“月线”不符合要求！')
return False
""" ------------------------ 判断日线SAR是否达标 ------------------------------- """
# 判断日线SAR指标
df_floor['SAR'] = talib.SAR(df_floor.high,
df_floor.low,
acceleration=0.05,
maximum=0.2)
if df_floor.tail(1)['SAR'].values[0] > df_floor.tail(
1)['SAR'].values[0]:
tc.AppendText(stk_code + code2name(stk_code) + '：“日线SAR指标”不符合要求！')
return False
""" ------------------------ 判断半小时SAR是否达标 ------------------------------- """
df_half_hour = get_k_data_JQ(stk_code,
count=120,
end_date=add_date_str(
get_current_date_str(), 1),
freq='30m')
# 判断日线SAR指标
df_half_hour['SAR'] = talib.SAR(df_half_hour.high,
df_half_hour.low,
acceleration=0.05,
maximum=0.2)
if df_half_hour.tail(1)['SAR'].values[0] > df_half_hour.tail(
1)['SAR'].values[0]:
tc.AppendText(stk_code + code2name(stk_code) + '：“半小时SAR指标”不符合要求！')
return False
# 符合要求，返回True
tc.AppendText(stk_code + code2name(stk_code) + '：符合要求！')
return True
except Exception as e:
tc.AppendText(stk_code + '出错：\n' + str(e))
return False
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()
if __name__ == '__main__':
from DataSource.auth_info import *
# week_MACD_stray_judge('603421', '影子')
checkWeekStrayForAll()
send_W_M_MACD('000001', '影子')
r = calendar.weekday(2019, 12, 5)
# week_MACD_stray_judge('300508', '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, :]
def gen_kind_pic(kind, pool):
"""
造图片，存本地
:param kind:
h:小时
h_idx:小时idx
d:天
wm:周、月
idx: 指数
:return:
返回的图片应该 执行page和行号，便于更新！
以多层字典的方式返回结果，第一层区分page，第二层区分行号！
"""
r_dic = {'Index': {}, 'Buy': {}, 'Concerned': {}}
dict_stk_hour = copy.deepcopy(dict_stk_list)
jq_login()
""" 在外部下载需要的数据，防止多进程中重复连接聚宽 """
for page in dict_stk_hour.keys():
for stk_info in dict_stk_list[page]:
stk = stk_info[1]
if kind is 'h':
r_dic[page][stk + '_d'] = GenPic.gen_hour_macd_values(stk)
elif kind is 'h_idx':
r_dic[page][stk + '_d'] = GenPic.gen_hour_macd_values(stk)[0]
elif kind is 'd':
r_dic[page][stk + '_d'] = get_k_data_JQ(stk, 400)
elif kind is 'wm':
r_dic[page][stk + '_d'] = get_k_data_JQ(
stk, count=400).reset_index()
elif kind is 'd_idx':
r_dic[page][stk + '_d'] = get_k_data_JQ(stk, 400)
jq.logout()
""" 生成pic """
for page in dict_stk_hour.keys():
for stk_info in dict_stk_list[page]:
stk = stk_info[1]
# 保存路径
save_dir = hist_pic_dir + get_current_date_str(
) + '/' + stk + kind + '/'
file_name = get_current_datetime_str()[:-3].replace(
':', '').replace(' ', '').replace('-', '') + '.png'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if kind is 'h':
r_dic[page][stk + '_res'] = pool.apply_async(
GenPic.gen_hour_macd_pic_local,
(r_dic[page][stk + '_d'], stk, save_dir + file_name))
elif kind is 'h_idx':
r_dic[page][stk + '_res'] = pool.apply_async(
GenPic.gen_hour_index_pic_local,
(r_dic[page][stk + '_d'], stk, save_dir + file_name))
elif kind is 'd':
r_dic[page][stk + '_res'] = pool.apply_async(
GenPic.gen_day_pic_local,
(r_dic[page][stk + '_d'], stk, save_dir + file_name))
elif kind is 'wm':
r_dic[page][stk + '_res'] = pool.apply_async(
GenPic.gen_w_m_macd_pic_local,
(r_dic[page][stk + '_d'], stk, save_dir + file_name))
elif kind is 'd_idx':
r_dic[page][stk + '_res'] = pool.apply_async(
GenPic.gen_idx_pic_local,
(r_dic[page][stk + '_d'], stk, save_dir + file_name))
# if kind is 'h':
# r_dic[page][stk + '_res'] = pool.apply_async(gen_hour_macd_pic_local, (
# r_dic[page][stk + '_d'], stk, 'jq', '', save_dir + file_name))
#
# elif kind is 'h_idx':
# r_dic[page][stk + '_res'] = pool.apply_async(gen_hour_index_pic_local,
# (r_dic[page][stk + '_d'], stk, save_dir + file_name))
# elif kind is 'd':
# r_dic[page][stk + '_res'] = pool.apply_async(gen_day_pic_local,
# (r_dic[page][stk + '_d'], stk, save_dir + file_name))
# elif kind is 'wm':
# r_dic[page][stk + '_res'] = pool.apply_async(gen_w_m_macd_pic_local,
# (r_dic[page][stk + '_d'], stk, save_dir + file_name))
# elif kind is 'd_idx':
# r_dic[page][stk + '_res'] = pool.apply_async(gen_idx_pic_local,
# (r_dic[page][stk + '_d'], stk, save_dir + file_name))
# 在字典中保存图片路径
r_dic[page][stk + '_url'] = save_dir + file_name
return r_dic
# 获取pcr
reseau_judge.get_pcr()
# 打印日志
reseau_judge.bs_info_print()
# 进行bs判断
reseau_judge.bs_judge()
return reseau_judge.str_gui
if __name__ == '__main__':
jq_login()
df = get_k_data_JQ('000001', 400)
buy_stk_list = list(
set(read_config()['buy_stk'] + read_config()['index_stk']))
# 局部变量
note_list = []
# 对stk进行检查
for stk in buy_stk_list:
str_gui = judge_single_stk(stk_code=stk,
stk_amount_last=400,
gui=True,
debug=True)
logout()
app = wx.App()
# encoding=utf-8
stk_code = '000001'
if __name__ == '__main__':
jq_login()
""" --------------------- 生成相关图片 ------------------------ """
# 保存路径
save_dir = sea_select_pic_dir + get_current_date_str() + '/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# 准备 小时 和 日线 数据
df_hour = gen_hour_macd_values(stk_code)
df_day = get_k_data_JQ(stk_code, 800)
# 定义图片名称
file_name = stk_code + '.png'
# 生成小时图片
gen_hour_macd_pic_local(df_hour, stk_code, 'jq', '',
save_dir + 'h_' + file_name)
gen_hour_index_pic_local(df_hour[0], stk_code,
save_dir + 'h_idx_' + file_name)
gen_day_pic_local(df_day, stk_code, save_dir + 'd_' + file_name)
gen_w_m_macd_pic_local(df_day, stk_code, save_dir + 'wm_' + file_name)
gen_idx_pic_local(df_day, stk_code, save_dir + 'd_idx_' + file_name)
""" --------------------- 读取图片生成pdf ------------------------ """
end = 0