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 bs_judge(self):
if (self.current_price - self.b_p_min > self.thh_sale) & (
(self.current_price - self.b_p_min) / self.b_p_min >= self.pcr):
str_temp = "\n触发卖出网格！可以考虑卖出！ \n" + self.stk_code + code2name(self.stk_code) + \
'\n当前价格:' + str(self.current_price) + \
'\n上次买入价格:' + str(self.b_p_min) + \
'\n买入网格大小:' + '%0.3f' % self.thh_buy + \
'\n卖出网格大小:' + '%0.3f' % self.thh_sale + \
'\n最小操作幅度:' + '%0.3f' % self.pcr + \
'\n上次闪动价格:' + str(self.opt_record.get_config_value('last_prompt_point'))[:4]
self.bs_note(str_temp)
elif (self.current_price - self.last_p < -self.thh_buy) & (
(self.current_price - self.last_p) / self.b_p_min <= -self.pcr):
str_temp = "\n触发买入网格！可以考虑买入！\n" + self.stk_code + code2name(self.stk_code) + \
'\n当前价格:' + str(self.current_price) + \
'\n上次价格:' + str(self.last_p) + \
'\n买入网格大小:' + '%0.2f' % self.thh_buy + \
'\n卖出网格大小:' + '%0.2f' % self.thh_sale + \
'\n最小操作幅度:' + '%0.3f' % self.pcr + \
'\n上次闪动价格:' + str(self.opt_record.get_config_value('last_prompt_point'))[:4]
self.bs_note(str_temp)
else:
str_ = self.stk_code + ':未触发任何警戒线！\n'
self.add_msg(str_)
debug_print_txt('stk_judge', self.stk_code, str_, self.debug)
def hour_analysis(pipe_master):
"""
小时监测（闲置）
:return:
"""
debug_print_txt('main_log', '', 'begin hour_analysis \n', True)
debug_print_txt('hour_analysis', '', '\n' + '进入小时判断' + '\n')
for stk in list(
set(read_config()['buy_stk'] + read_config()['concerned_stk'] +
read_config()['index_stk'])):
# 阈值条件不满足，该stk直接pass
if not read_opt_json(stk, opt_record_file_url)['has_flashed_flag']:
pipe_master.send(
(MSG_UPDATE_NUM_A, code2name(stk) + '阈值条件不满足，不进行拐点检测\n'))
debug_print_txt('hour_analysis', '',
'\n' + code2name(stk) + '阈值条件不满足，不进行拐点检测\n')
continue
hour_idx_str = check_single_stk_hour_idx_wx(stk,
source='jq',
debug=True)
if len(hour_idx_str):
for str_tmp in hour_idx_str:
pipe_master.send(
(NOTE_UPDATE_NUM_A, change_font_color(str_tmp)))
# 打印日志
debug_print_txt('hour_analysis', '', '\n' + str(hour_idx_str))
# flash
pipe_master.send((FLASH_WINDOW_NUM, None))
def fluctuate_judge(self):
l_pp = self.opt_record.get_config_value('last_prompt_point')
c_p = self.current_price
thh_b = self.thh_buy
thh_s = self.thh_sale
str_ = '\n\n------------------------------------------------------------\n' \
'last_pp:%s current_price:%s b_thh:%s s_thh:%s' % (
str(l_pp)[:4], str(c_p)[:4], str(thh_b)[:4], str(thh_s)[:4])
debug_print_txt('fluctuate_judge', self.stk_code, str_ + '\n', self.debug)
# 若lpp为None，则将当前p设置为lpp，并返回
if pd.isnull(self.opt_record.get_config_value('last_prompt_point')) | \
(self.opt_record.get_config_value('last_prompt_point') == -1):
self.opt_record.set_config_value('last_prompt_point', self.current_price)
debug_print_txt('fluctuate_judge', self.stk_code, 'Result: Last_prompt_opint is null or -1' + '\n',
self.debug)
return
elif (self.current_price - self.opt_record.get_config_value('last_prompt_point') > self.thh_sale) & \
(self.opt_record.get_config_value('last_prompt_point') != -1):
str_temp = "当前价格距离上次提示的价格的上涨幅度超过卖出网格！\n " + self.stk_code + code2name(self.stk_code) + \
'\n当前价格:' + str(self.current_price) + \
'\n上次买入价格:' + str(self.b_p_min) + \
'\n买入网格大小:' + '%0.3f' % self.thh_buy + \
'\n卖出网格大小:' + '%0.3f' % self.thh_sale + \
'\n最小操作幅度:' + '%0.3f' % self.pcr + \
'\n上次闪动价格:' + str(self.opt_record.get_config_value('last_prompt_point'))[:4]
self.add_note(str_temp)
debug_print_txt('fluctuate_judge', self.stk_code, 'Result: fluctuate is triggered for sale!' + '\n',
self.debug)
# 设置上次闪动价格
self.opt_record.set_config_value('last_prompt_point', self.current_price)
elif (self.current_price - self.opt_record.get_config_value('last_prompt_point') < -self.thh_buy) & \
(self.opt_record.get_config_value('last_prompt_point') != -1):
str_temp = "当前价格距离上次提示的价格的下跌幅度超过买入网格！\n" + self.stk_code + code2name(self.stk_code) + \
'\n当前价格:' + str(self.current_price) + \
'\n上次价格:' + str(self.last_p) + \
'\n买入网格大小:' + '%0.2f' % self.thh_buy + \
'\n卖出网格大小:' + '%0.2f' % self.thh_sale + \
'\n最小操作幅度:' + '%0.3f' % self.pcr + \
'\n上次闪动价格:' + str(self.opt_record.get_config_value('last_prompt_point'))[:4]
self.add_note(str_temp)
debug_print_txt('fluctuate_judge', self.stk_code, 'Result: fluctuate is triggered for buy!' + '\n',
self.debug)
# 设置上次闪动价格
self.opt_record.set_config_value('last_prompt_point', self.current_price)
def print_k_to_pdf(c, stk_code, date):
"""
将一只股票的走势图打印到pdf中的一页
:param c:
:param stk_code:
:param date:
:return:
"""
""" --------------------------------- 打印小时线图片 ------------------------------------ """
c.setFont("song", 10)
c.drawString(20, letter[1] - 20, stk_code + ' ' + code2name(stk_code) + ' ' + '小时走势图')
c.setLineWidth(3)
c.line(10, h - 24, w - 10, h - 24)
# 插入小时macd图
image_file = PIL.Image.open(sea_select_pic_dir + date + '/' + 'h_' + stk_code + '.png')
c.drawImage(ImageReader(image_file), x=w * 0.05, y=h * 0.65, height=0.3 * h, width=0.45 * w,
preserveAspectRatio=True)
# 插入小时指标图
image_file = PIL.Image.open(sea_select_pic_dir + date + '/' + 'h_idx_' + stk_code + '.png')
c.drawImage(ImageReader(image_file), x=w * 0.5, y=h * 0.65, height=0.3 * h, width=0.45 * w,
preserveAspectRatio=True)
""" --------------------------------- 打印日线图片 ------------------------------------ """
c.setFont("song", 10)
c.drawString(20, h * 0.65, stk_code + ' ' + code2name(stk_code) + ' ' + '日线走势图')
c.setLineWidth(3)
c.line(10, h * 0.65 - 4, w - 10, h * 0.65 - 4)
# 插入日线macd图
image_file = PIL.Image.open(sea_select_pic_dir + date + '/' + 'd_' + stk_code + '.png')
c.drawImage(ImageReader(image_file), x=w * 0.05, y=h * 0.35, height=0.3 * h, width=0.45 * w,
preserveAspectRatio=True)
# 插入日线指标图
image_file = PIL.Image.open(sea_select_pic_dir + date + '/' + 'd_idx_' + stk_code + '.png')
c.drawImage(ImageReader(image_file), x=w * 0.5, y=h * 0.35, height=0.3 * h, width=0.45 * w,
preserveAspectRatio=True)
""" --------------------------------- 打印周月线图片 ------------------------------------ """
c.setFont("song", 10)
c.drawString(20, h * 0.35, stk_code + ' ' + code2name(stk_code) + ' ' + '周/月走势图')
c.setLineWidth(3)
c.line(10, h * 0.35 - 4, w - 10, h * 0.35 - 4)
# 插入周线图
image_file = PIL.Image.open(sea_select_pic_dir + date + '/' + 'wm_' + stk_code + '.png')
c.drawImage(ImageReader(image_file), x=w * 0.05, y=h * 0.05, height=0.3 * h, width=0.45 * w,
preserveAspectRatio=True)
c.showPage()
return c
def check_single_stk_hour_idx_sub(stk_df, stk_code, debug=False):
"""
打印常用指标
"""
# 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'
"""
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 check_stk_list_middle_level(stk_list):
"""
检测一系列stk的中期水平
:param stk_list:
:param threshold:
:return:
"""
if not os.path.exists(data_dir + 'middlePeriodHourData.json'):
update_middle_period_hour_data()
# 读取历史小时数据
with open(data_dir + 'middlePeriodHourData.json', 'r') as f:
dict = json.load(f)
r = [(x, (1 - check_single_stk_middle_level(x, dict) / 100) * 100)
for x in list(set(stk_list))]
r_df = pd.DataFrame(data=r, columns=['code', 'level_value'])
r_df['name'] = r_df.apply(lambda x: code2name(x['code']), axis=1)
r_df_sort = r_df.sort_values(by='level_value', ascending=True).head(12)
r_df_sort['level'] = r_df_sort.apply(
lambda x: '%0.2f' % x['level_value'] + '%', axis=1)
r_df_sort = r_df_sort.loc[:, ['name', 'level']].reset_index(drop=True)
return r_df_sort
def __init__(self, stk_code, opt_record_, debug=False):
self.opt_record = opt_record_
self.stk_code = stk_code
self.debug = debug
self.str_gui = {
'note': '',
'msg': ''
}
self.add_msg('\n\n\n------------------------------------------\n' + code2name(stk_code) + ':开始进入本周期判断!\n')
self.opt_record_stk = {}
self.current_price = -1
self.last_p = -1
self.b_p_min = -1
# self.money_each_opt = 5000
# self.buy_amount = math.floor((self.money_each_opt / self.current_price) / 100) * 100
self.thh_sale = 0
self.thh_buy = 0
self.has_flashed_flag = False
self.pcr = 0
self.opt_record_stk = opt_record_.opt_record_stk
def cal_reseau(self, rsv):
""" 调节 buy 和 sale 的threshold """
""" 实时计算网格大小 """
reseau = Reseau()
earn_threshold_unit = reseau.get_single_stk_reseau(self.stk_code)
stk_name = code2name(self.stk_code)
rsv.msg = ''
rsv_stk = rsv.get_stk_rsv(self.stk_code)
if len(rsv.msg) > 0:
self.add_msg(rsv.msg + '\n')
self.thh_sale = earn_threshold_unit * 2 * rsv_stk
self.thh_buy = earn_threshold_unit * 2 * (1 - rsv_stk)
self.buy_price =self.current_price - self.thh_buy
self.sell_price = self.thh_sale + self.current_price
str1_ = '\nearn_threshold_unit:' + '%0.3f' % earn_threshold_unit + '\nrsv_stk:' + '%0.3f' % rsv_stk + '\nthh_buy = earn_threshold_unit * 2 * (1 - rsv_stk)' +'\n'
self.add_msg(str1_)
str2_ = '\nstk_code:' + self.stk_code +'\ncurrent_price:' + '%0.3f' % self.current_price + '\n' + stk_name + ' 真卖 5000 ' + '%0.3f' % self.sell_price + '\n' + stk_name + ' 真买 5000 ' + '%0.3f' % self.buy_price + '\n'
self.add_msg(str2_)
#self.add_note(str2_)
str_ = '\n卖出网格大小:' + '%0.3f' % self.thh_sale + '\n买入网格大小:' + '%0.3f' % self.thh_buy + '\n'
debug_print_txt('stk_judge', self.stk_code, str_, self.debug)
self.add_msg(str_)
def get_filter_result(self):
"""
获取当前 self.stk_list_ss
:return:
"""
if len(self.stk_list_ss) == 0:
return '\n已无符合条件的标的！\n'
else:
return '\n' + str([(x.stk_code, code2name(x.stk_code)) for x in self.stk_list_ss]) + '\n'
def add_row_name(self, stk_code_list):
"""
添加行名称
:param stk_code_list:
:return:
"""
for info in stk_code_list:
self.my_grid4.SetRowLabelValue(info[0], code2name(info[1]))
def cat_stk(kind, tc):
"""
查看相关stk列表
:param kind:
:return:
"""
stk_list = read_config()[kind]
stk_name = str([code2name(x) for x in stk_list])
text_append_color(tc, stk_name.replace('[', '').replace(']', '').replace(',', '\n'))
def plot_opt(stk_code, opt_record, tc):
if len(opt_record) == 0:
text_append_color(tc, code2name(stk_code) + '：没有操作记录！')
return
df = pd.DataFrame(opt_record).set_index('date_time')
# 筛选
df = df[df['stk_code'] == stk_code]
if df.empty:
text_append_color(tc, code2name(stk_code) + '：没有操作记录！')
# 计算上下限
df['sale_pot'] = df.apply(lambda x: x['p_last'] + x['sale_reseau'], axis=1)
df['buy_pot'] = df.apply(lambda x: x['p_last'] + x['buy_reseau'], axis=1)
df.loc[:, ['p_last', 'p_now', 'buy_pot', 'sale_pot']].plot(style=['*', '*', '^--', '^--'])
plt.show()
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 get_stk_rsv(self, stk_code):
"""
获取一只股票的RSV
:return:
"""
if stk_code in self.rsv.keys():
return self.rsv[stk_code]
else:
self.msg = stk_code + code2name(stk_code) + 'rsv值不存在，临时下载！'
rsv = self.cal_stk_rsv_rank(stk_code, 5)
self.rsv[stk_code] = rsv
return rsv
def get_opt_record_json(self):
"""
判断该股票的opt_record配置是否正常
:return:
"""
# 如果没有相应的json文件，不进行判断，直接返回
if pd.isnull(self.opt_record_stk) | (not bool(self.opt_record_stk)):
str_ = code2name(self.stk_code) + '没有历史操作记录，不进行阈值判断！\n'
debug_print_txt('stk_judge', self.stk_code, '函数 judge_single_stk：' + str_,
self.debug)
self.add_msg(str_)
return False
elif len(self.opt_record_stk['b_opt']) == 0:
str_ = code2name(self.stk_code) + '没有历史操作记录，不进行阈值判断！\n'
debug_print_txt('stk_judge', self.stk_code, '函数 judge_single_stk：' + str_,
self.debug)
self.add_msg(str_)
return False
else:
return True
def on_timer_pic(win, pool, debug=False):
"""
图片定时器响应函数（闲置）
:return:
"""
global last_upt_t
upt_flag, last_upt_t = is_time_h_macd_update(last_upt_t)
wx.PostEvent(win, ResultEvent(id=LAST_TIME_UPDATE_ID, data=last_upt_t))
if not upt_flag:
wx.PostEvent(win, ResultEvent(id=MSG_UPDATE_ID_A, data='图片更新定时器：“小时图片”更新时间点未到！\n'))
return
# 清屏
wx.PostEvent(win, ResultEvent(id=NOTE_UPDATE_ID_S, data='检测时间：' + get_current_datetime_str() + '\n\n'))
# 生成更新的图片
wx.PostEvent(win, ResultEvent(id=MSG_UPDATE_ID_A, data='开始更新小时图片...\n'))
pic_dict = {'h_macd': gen_kind_pic('h', pool), 'h_idx': gen_kind_pic('h_idx', pool)}
wx.PostEvent(win, ResultEvent(id=HOUR_UPDATE_ID, data=pic_dict))
wx.PostEvent(win, ResultEvent(id=MSG_UPDATE_ID_A, data='小时图片更新完成！\n'))
# 拐点检测
window_flash_flag = False
for stk in list(set(read_config()['buy_stk'] + read_config()['concerned_stk'] + read_config()['index_stk'])):
# 阈值条件不满足，该stk直接pass
if not read_opt_json(stk, opt_record_file_url)['threshold_satisfied_flag']:
wx.PostEvent(win, ResultEvent(id=MSG_UPDATE_ID_A, data=code2name(stk) + '阈值条件不满足，不进行拐点检测\n'))
continue
hour_idx_str = check_single_stk_hour_idx_wx(stk, source='jq', debug=True)
if len(hour_idx_str):
window_flash_flag = True
for str_tmp in hour_idx_str:
wx.PostEvent(win, ResultEvent(id=NOTE_UPDATE_ID_A, data=change_font_color(str_tmp)))
# 窗口闪烁
if window_flash_flag:
wx.PostEvent(win, ResultEvent(id=FLASH_WINDOW_ID, data=None))
wx.PostEvent(win, ResultEvent(id=MSG_UPDATE_ID_A, data=note_sar_inflection_point))
def checkSingleStkHourMACD_wx(stk_code, source='jq'):
df_30, df_60 = gen_hour_macd_values(stk_code, source=source, title='')
l_60 = df_60.tail(3)['MACD'].values
l_30 = df_30.tail(3)['MACD'].values
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
if send_pic & (sts != 0):
return code2name(stk_code) + '-' + title_str + '\n'
else:
return ''
# encoding=utf-8
# encoding=utf-8
def prepare_trade_record(self, input_str, json_file_url_):
# 返回字符串
return_str = []
# 已有文件，打开读取
if os.path.exists(json_file_url_):
with open(json_file_url_, 'r') as _f:
_opt_record = json.load(_f)
else:
_opt_record = {}
# 解析输入
stk_code, opt, amount, p = input_str.split(' ')
#stk_code = name2code(stk_name)
stk_name = code2name(stk_code)
p, amount = float(p), float(amount)
# 对输入格式进行检查
#debug_print_txt('main_log', '', input_str +' ' + json_file_url_ + 'inside prepare_trade_record \n',True)
if amount % 100 != 0:
#tc.AppendText('格式错误！参考格式：\n美的集团 卖出 400 51.3')
return
if stk_code in _opt_record.keys():
opt_r_stk = _opt_record[stk_code]
else:
opt_r_stk = {
'b_opt': [],
's_opt': [],
'b_suggest': None,
's_suggest': None,
'p_last': None,
'has_flashed_flag': False,
'total_earn': 0,
'last_prompt_point': -1
}
# 初步只支持每日一次操作
if opt_r_stk['has_flashed_flag'] == True:
return
if opt == '买入':
opt_r_stk['b_opt'].clear()
opt_r_stk['b_opt'].append(dict(time=get_current_datetime_str(), p=p, amount=amount, status='PRICE', entrust_no=0))
opt_r_stk['b_suggest'] = stk_name + ' 真买 ' + '%0.0f' % amount + ' ' + str(p)
if opt == '卖出':
#if len(opt_r_stk['b_opt']) > 0:
opt_r_stk['s_opt'].clear()
opt_r_stk['s_opt'].append(dict(time=get_current_datetime_str(), p=p, amount=amount, status='PRICE', entrust_no=0))
opt_r_stk['s_suggest'] = stk_name + ' 真卖 ' + '%0.0f' % amount + ' ' + str(p)
#opt_r_stk, earn_this = sale_stk_sub(opt_r_stk, amount, p, tc)
#return_str.append('earn：' + str(earn_this) + '\n')
opt_r_stk['p_last'] = p
opt_r_stk['has_flashed_flag'] = False
# 保存数据
_opt_record[stk_code] = opt_r_stk
with open(json_file_url_, 'w') as _f:
#json.dump(_opt_record, _f)
json.dump(_opt_record, _f, ensure_ascii=False, indent = 2, separators=(',', ': '))
# 返回
#debug_print_txt('main_log', '', stk_code +' end of prepare_trade_record \n',True)
return return_str
# encoding=utf-8
# coding=utf-8
def JudgeSingleStk(stk_code, stk_amount_last, qq, debug=False, gui=False):
str_gui = {'note': '', 'msg': ''}
# 获取该stk的实时价格,如果是大盘指数，使用聚宽数据，否则有限使用tushare
if stk_code in ['sh', 'sz', 'cyb']:
current_price = get_current_price_JQ(stk_code)
else:
try:
current_price = get_RT_price(stk_code, source='ts')
except:
str_gui = myPrint(str_gui,
stk_code + '获取实时price失败！',
method={
True: 'gm',
False: 'n'
}[gui])
return str_gui
# 获取上次price
stk_price_last = readLastP(stk_code)
if stk_price_last < 0:
saveLastP(stk_code, current_price)
stk_price_last = current_price
# 实时计算价差
price_diff = current_price - stk_price_last
price_diff_ratio = price_diff / stk_price_last
if debug:
str_gui = myPrint(str_gui,
'\n\n' + stk_code + ':\np_now:' +
str(current_price) + '\np_last:' +
str(stk_price_last) + '\np_change_ratio:' +
str(price_diff_ratio),
method={
True: 'gm',
False: 'n'
}[gui])
if current_price == 0.0:
str_gui = myPrint(str_gui,
stk_code + 'price==0.0! 返回！',
method={
True: 'gm',
False: 'n'
}[gui])
return str_gui
buy_amount = math.floor((money_each_opt / current_price) / 100) * 100
# 实时计算网格大小
earn_threshold_unit = getSigleStkReseau(stk_code)
# 调节 buy 和 sale 的threshold
if stk_code in RSV_Record.keys():
thh_sale = earn_threshold_unit * 2 * RSV_Record[stk_code]
thh_buy = earn_threshold_unit * 2 * (1 - RSV_Record[stk_code])
else:
RSV_Record[stk_code] = calRSVRank(stk_code, 5) / 100
thh_sale = earn_threshold_unit * 2 * RSV_Record[stk_code]
thh_buy = earn_threshold_unit * 2 * (1 - RSV_Record[stk_code])
# 计算其离心度分数
try:
# rank9, _, _, _ = calRealtimeRankWithGlobal(stk_code=stk_code)
rank9 = -1
except:
rank9 = -1
if debug:
str_gui = myPrint(str_gui,
stk_code + ':\np_change:' +
str(price_diff * stk_amount_last) + '\nthreshold:' +
str(earn_threshold_unit) + '\nthh_sale:' +
str(thh_sale) + '\nthh_buy:' + str(thh_buy),
method={
True: 'gm',
False: 'n'
}[gui])
if price_diff > thh_sale:
# if JudgePChangeRatio(stk_code, price_diff_ratio):
str_temp = "触发卖出网格！可以考虑卖出！ "+stk_code + code2name(stk_code) +\
'\nAmount:' + str(stk_amount_last) +\
'\n当前价格:' + str(current_price) +\
'\n上次价格:' + str(stk_price_last) +\
'\n买入网格大小:' + '%0.2f' % thh_buy +\
'\n卖出网格大小:' + '%0.2f' % thh_sale
# '\nM9_rank:' + str('%0.2f' % rank9)
str_gui = myPrint(str_gui,
str_temp,
method={
True: 'gn',
False: 'qq'
}[gui],
towho=qq)
if not gui:
sendHourMACDToQQ(stk_code, qq, source='jq')
# 更新本地价格
saveLastP(stk_code, current_price)
elif price_diff < -thh_buy:
str_temp = "触发买入网格！可以考虑买入！" + stk_code + code2name(stk_code) +\
'\nAmount:' + str(buy_amount) +\
'\n当前价格:' + str(current_price) +\
'\n上次价格:' + str(stk_price_last) +\
'\n买入网格大小:' + '%0.2f' % thh_buy +\
'\n卖出网格大小:' + '%0.2f' % thh_sale
# '\nM9_rank:' + str('%0.2f' % rank9)
str_gui = myPrint(str_gui,
str_temp,
method={
True: 'gn',
False: 'qq'
}[gui],
towho=qq)
if not gui:
sendHourMACDToQQ(stk_code, qq, source='jq')
saveLastP(stk_code, current_price)
else:
str_gui = myPrint(str_gui,
stk_code + ':未触发任何警戒线！',
method={
True: 'gm',
False: 'n'
}[gui])
# 波动检测
change_flag, str_gui = JudgePChangeRatio(stk_code,
price_diff_ratio,
str_gui=str_gui,
gui=gui)
if change_flag:
str_temp = "波动推送! " + stk_code + code2name(stk_code) +\
'\nAmount:' + str(buy_amount) +\
'\n当前价格:' + str(current_price) +\
'\n上次价格:' + str(stk_price_last) +\
'\n买入网格大小:' + '%0.2f' % thh_buy +\
'\n卖出网格大小:' + '%0.2f' % thh_sale
# '\nM9_rank:' + str('%0.2f' % rank9)
str_gui = myPrint(str_gui,
str_temp,
method={
True: 'gn',
False: 'qq'
}[gui],
towho=qq)
if not gui:
sendHourMACDToQQ(stk_code, qq, source='jq')
return str_gui
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 gen_Day_Pic(stk_df, stk_code=''):
"""
函数功能：给定stk的df，已经确定stk当前处于拐点状态，需要将当前stk的信息打印成图片，便于人工判断！
:param stk_df 从tushare下载下来的原生df
:param root_save_dir 配置文件中定义的存储路径
:return: 返回生成图片的路径
"""
"""
规划一下都画哪些图
1、该stk整体走势，包括60日均线、20日均线和收盘价
2、stk近几天的MACD走势
"""
"""
在原数据的基础上增加均线和MACD
"""
# 按升序排序
stk_df = stk_df.sort_values(by='date', ascending=True)
stk_df['M20'] = stk_df['close'].rolling(window=20).mean()
stk_df['M60'] = stk_df['close'].rolling(window=60).mean()
stk_df['MACD'], stk_df['MACDsignal'], stk_df['MACDhist'] = talib.MACD(stk_df.close,
fastperiod=12, slowperiod=26,
signalperiod=9)
# 检查日级别的MACD是否有异常
attention = False
MACD_list = stk_df.tail(3)['MACD'].values
if MACD_list[1] == np.min(MACD_list):
attention = True
# 设置背景红
set_background_color('b_r')
elif MACD_list[1] == np.max(MACD_list):
attention = True
# 设置背景绿
set_background_color('b_g')
else:
set_background_color()
fig, ax = plt.subplots(nrows=4, ncols=1)
ax[0].plot(range(0, len(stk_df['date'])), stk_df['M20'], 'b--', label='20日均线', linewidth=1)
ax[0].plot(range(0, len(stk_df['date'])), stk_df['M60'], 'r--', label='60日均线', linewidth=1)
ax[0].plot(range(0, len(stk_df['date'])), stk_df['close'], 'g*--', label='收盘价', linewidth=0.5, markersize=1)
ax[1].bar(range(0, len(stk_df['date'])), stk_df['MACD'], label='MACD')
# 准备下标
xticklabels_all_list = list(stk_df['date'].sort_values(ascending=True))
xticklabels_all_list = [x.replace('-', '')[2:] for x in xticklabels_all_list]
for ax_sig in ax[0:2]:
ax_sig = addXticklabel_list(ax_sig, xticklabels_all_list, 30, rotation=45)
ax_sig.legend(loc='best', fontsize=5)
# 画出最近几天的情况（均线以及MACD）
stk_df_current = stk_df.tail(plot_current_days_amount)
ax[2].plot(range(0, len(stk_df_current['date'])), stk_df_current['M20'], 'b--', label='20日均线', linewidth=2)
ax[2].plot(range(0, len(stk_df_current['date'])), stk_df_current['M60'], 'r--', label='60日均线', linewidth=2)
ax[2].plot(range(0, len(stk_df_current['date'])), stk_df_current['close'], 'g*-', label='收盘价', linewidth=1,
markersize=5)
ax[3].bar(range(0, len(stk_df_current['date'])), stk_df_current['MACD'], label='MACD')
# 设置标题并返回分析结果
result_analysis = []
if MACD_list[1] == np.min(MACD_list):
title_tmp = stk_code + ' ' + code2name(stk_code) + ' 日级别 MACD 低点！后续数天可能上涨！'
ax[0].set_title(title_tmp)
result_analysis.append(title_tmp)
elif MACD_list[1] == np.max(MACD_list):
title_tmp = stk_code + ' ' + code2name(stk_code) + ' 日级别 MACD 高点！后续数天可能下跌！'
ax[0].set_title(title)
result_analysis.append(title_tmp)
# 准备下标
xticklabels_all_list = list(stk_df_current['date'].sort_values(ascending=True))
xticklabels_all_list = [x.replace('-', '')[2:] for x in xticklabels_all_list]
for ax_sig in ax[2:4]:
ax_sig = addXticklabel_list(ax_sig, xticklabels_all_list, 30, rotation=45)
ax_sig.legend(loc='best', fontsize=5)
fig.tight_layout() # 调整整体空白
plt.subplots_adjust(wspace=0, hspace=1) # 调整子图间距
# plt.close()
return fig, ax, attention, result_analysis
# encoding=utf-8
def gen_Idx_Pic(stk_df, stk_code=''):
"""
打印常用指标
"""
# 按升序排序
stk_df = stk_df.sort_values(by='date', ascending=True)
"""
增加指标
'RSI5', 'RSI12', 'RSI30'
'SAR'
'slowk', 'slowd'
'upper', 'middle', 'lower'
'MOM'
"""
stk_df = addStkIndexToDf(stk_df).tail(60)
set_background_color(bc='w')
result_analysis = []
# 检查SAR
attention = False
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)
title_tmp = stk_code + ' ' + code2name(stk_code)
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，后续数天可能上涨！'
result_analysis.append(title_tmp)
set_background_color(bc='b_r')
else:
title_tmp = stk_code + ' ' + code2name(stk_code) + ' 注意 SAR 指标翻转，后续数天可能下跌！'
result_analysis.append(title_tmp)
set_background_color(bc='b_g')
attention = True
fig, ax = plt.subplots(nrows=5, ncols=1)
ax[0].plot(range(0, len(stk_df['date'])), stk_df['RSI5'], 'b--', label='RSI5线', linewidth=1)
ax[0].plot(range(0, len(stk_df['date'])), stk_df['RSI12'], 'r--', label='RSI12线', linewidth=1)
ax[0].plot(range(0, len(stk_df['date'])), stk_df['RSI30'], 'g*--', label='RSI30', linewidth=0.5, markersize=1)
ax[0].plot(range(0, len(stk_df['date'])), [20 for a in range(len(stk_df['date']))], 'b--', linewidth=0.3)
ax[0].plot(range(0, len(stk_df['date'])), [80 for a in range(len(stk_df['date']))], 'b--', linewidth=0.3)
ax[0].set_ylim(0, 100)
ax[1].plot(range(0, len(stk_df['date'])), stk_df['SAR'], 'r--', label='SAR', linewidth=0.5, markersize=1)
ax[1].plot(range(0, len(stk_df['date'])), stk_df['close'], 'g*--', label='close', linewidth=0.5, markersize=1)
ax[2].plot(range(0, len(stk_df['date'])), stk_df['slowk'], 'g*--', label='slowk', linewidth=0.5, markersize=1)
ax[2].plot(range(0, len(stk_df['date'])), stk_df['slowd'], 'r*--', label='slowd', linewidth=0.5, markersize=1)
ax[2].plot(range(0, len(stk_df['date'])), [20 for a in range(len(stk_df['date']))], 'b--', linewidth=0.3)
ax[2].plot(range(0, len(stk_df['date'])), [80 for a in range(len(stk_df['date']))], 'b--', linewidth=0.3)
ax[2].set_ylim(0, 100)
ax[3].plot(range(0, len(stk_df['date'])), stk_df['upper'], 'r*--', label='布林上线', linewidth=0.5, markersize=1)
ax[3].plot(range(0, len(stk_df['date'])), stk_df['middle'], 'b*--', label='布林均线', linewidth=0.5, markersize=1)
ax[3].plot(range(0, len(stk_df['date'])), stk_df['lower'], 'g*--', label='布林下线', linewidth=0.5, markersize=1)
ax[4].plot(range(0, len(stk_df['date'])), stk_df['MOM'], 'g*--', label='MOM', linewidth=0.5, markersize=1)
# 准备下标
xlabel_series = stk_df.apply(lambda x: x['date'][2:].replace('-', ''), axis=1)
ax[0] = addXticklabel(ax[0], xlabel_series, 40, rotation=45)
ax[1] = addXticklabel(ax[1], xlabel_series, 40, rotation=45)
ax[2] = addXticklabel(ax[2], xlabel_series, 40, rotation=45)
ax[3] = addXticklabel(ax[3], xlabel_series, 40, rotation=45)
ax[4] = addXticklabel(ax[4], xlabel_series, 40, rotation=45)
for ax_sig in ax:
ax_sig.legend(loc='best', fontsize=5)
fig.tight_layout() # 调整整体空白
plt.subplots_adjust(wspace=0, hspace=0) # 调整子图间距
ax[0].set_title(title_tmp)
return fig, ax, attention, result_analysis
# encoding=utf-8
def rpl_stk_hour_page(canvas_para, stk_code):
"""
函数功能：在pdf中增加bk信息，篇幅为一整页，或者更多，以页为单位
:param stk_code:
:param days: 用于指示近期的期限，比如近30天
:return:
"""
# 插入字符串，用以表明stk代码及名称
canvas_para.setFont("song", 10)
if stk_code in ['sh', 'sz', 'cyb']:
stk_name = stk_code
else:
stk_name = code2name(stk_code)
# 打印stk代码和名字
canvas_para.drawString(20, letter[1] - 10,
stk_code + ' ' + stk_name + ' ' + '小时数据')
# 准备数据
df_stk = get_k_data_JQ(stk_code,
start_date=add_date_str(get_current_date_str(),
-200),
freq='30m')
df_stk['date'] = df_stk.reset_index().index
df_stk = df_stk.reset_index(drop=True)
close = extract_point_from_df_date_x(df_stk,
'date',
'close',
timeAxis='year')
data = [tuple(close)]
data_name = ['close']
drawing_ave = gen_lp_drawing(data=data,
data_note=data_name,
height=letter[1] * 0.1,
line_width=0.5,
marker_size=2,
time_axis='year')
renderPDF.draw(drawing=drawing_ave,
canvas=canvas_para,
x=10,
y=letter[1] * 0.85)
# 按时间降序排序，方便计算MACD
df_stk = df_stk.sort_values(by='date', ascending=True)
# 加入各种指标
df_stk = add_stk_index_to_df(df_stk)
# 去除空值，截取最新数据
df_stk = df_stk.dropna(axis=0, how='any').tail(40)
close = extract_point_from_df_date_x(df_stk,
'date',
'close',
timeAxis='year')
sar = extract_point_from_df_date_x(df_stk, 'date', 'SAR', timeAxis='year')
MACD = extract_point_from_df_date_x(df_stk,
'date',
'MACD',
timeAxis='year')
data = [tuple(close), tuple(sar)]
data_name = ['close', 'sar']
drawing_ave = gen_lp_drawing(data=data,
data_note=data_name,
height=letter[1] * 0.1,
line_width=1.5,
marker_size=5,
time_axis='year')
renderPDF.draw(drawing=drawing_ave,
canvas=canvas_para,
x=10,
y=letter[1] * 0.7)
drawing_MACD = gen_bar_drawing(data=MACD,
data_note=['MACD'],
height=letter[1] * 0.1)
renderPDF.draw(drawing=drawing_MACD,
canvas=canvas_para,
x=10,
y=letter[1] * 0.5)
# 整理kdj信息
slowk = extract_point_from_df_date_x(df_stk,
'date',
'slowk',
timeAxis='year')
slowd = extract_point_from_df_date_x(df_stk,
'date',
'slowd',
timeAxis='year')
data_kdj = [tuple(slowk), tuple(slowd)]
data_kdj_note = ['k', 'd']
drawing_kdj = gen_lp_drawing(data=data_kdj,
data_note=data_kdj_note,
height=letter[1] * 0.1,
time_axis='year')
renderPDF.draw(drawing=drawing_kdj,
canvas=canvas_para,
x=10,
y=letter[1] * 0.4)
# 画图RSI信息
RSI5 = extract_point_from_df_date_x(df_stk,
'date',
'RSI5',
timeAxis='year')
RSI12 = extract_point_from_df_date_x(df_stk,
'date',
'RSI12',
timeAxis='year')
RSI30 = extract_point_from_df_date_x(df_stk,
'date',
'RSI30',
timeAxis='year')
data_RSI = [tuple(RSI5), tuple(RSI12), tuple(RSI30)]
data_RSI_note = ['RSI5', 'RSI12', 'RSI30']
drawing_RSI = gen_lp_drawing(data=data_RSI,
data_note=data_RSI_note,
height=letter[1] * 0.1,
time_axis='year')
renderPDF.draw(drawing=drawing_RSI,
canvas=canvas_para,
x=10,
y=letter[1] * 0.25)
# 画图布林线
upper = extract_point_from_df_date_x(df_stk,
'date',
'upper',
timeAxis='year')
middle = extract_point_from_df_date_x(df_stk,
'date',
'middle',
timeAxis='year')
lower = extract_point_from_df_date_x(df_stk,
'date',
'lower',
timeAxis='year')
data_BOLL = [tuple(upper), tuple(middle), tuple(lower)]
data_BOLL_note = ['上线', '中线', '下线']
drawing_BOLL = gen_lp_drawing(data=data_BOLL,
data_note=data_BOLL_note,
height=letter[1] * 0.1,
time_axis='year')
renderPDF.draw(drawing=drawing_BOLL,
canvas=canvas_para,
x=10,
y=letter[1] * 0.1)
canvas_para.showPage()
return canvas_para
