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 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 add_idx(self):
self.data = add_stk_index_to_df(self.data)
# 增加排名
self.data = cal_df_col_rank(self.data, 'MACD')
# 增加乖离度排名
self.data = BIAS.add_bias_rank_public(self.data, span_q=3, span_s=9)
self.data = BIAS.add_bias_rank_public(self.data, span_q=9, span_s=30)
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 add_index(self):
"""
向日线数据中增加常用指标
:return:
"""
self.data = add_stk_index_to_df(self.data)
# 增加其他指标
idx = Index(self.data)
idx.add_cci(5)
idx.add_cci(20)
self.data = idx.stk_df
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()
# 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
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_ = add_stk_index_to_df(stk_df).tail(60)
GenPic.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 +
RankNote.print_day_close_rank(stk_df))
GenPic.set_background_color(bc='b_r')
else:
title_tmp = stk_code + ' ' + code2name(
stk_code) + ' 注意 SAR 指标翻转，后续数天可能下跌！'
result_analysis.append(title_tmp +
RankNote.print_day_close_rank(stk_df))
GenPic.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[4] = add_axis(ax[4], xlabel_series, 40, rotation=45, fontsize=10)
# 设置默认标题
title_tmp = stk_code + ' ' + code2name(stk_code) + '/上次更新时间：%s' % str(
get_current_datetime_str())
ax[0].set_title(title_tmp)
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
def add_reseau(self):
"""
计算动态网格，并添加到数据当中
:return:
"""
if self.debug:
print('开始增加网格信息...')
t_s = time.time()
# 想day data 中 增加必要index
self.data_day = add_stk_index_to_df(self.data_day)
# 增加必要index
self.data_minute = add_stk_index_to_df(self.data_minute)
# 增加rsv数据
self.data_day = RSV.add_rsv(self.data_day, self.rsv_span)
# 最初的数天数据不进行回测，开始的部分数据因为数据量原因计算不出网格大小
max_days = self.max_days
i_start = self.i_start
i = len(self.data_minute) - i_start
data_day = self.data_day.set_index(keys='date')
# 大循环
for idx in self.data_minute[i_start:].index:
# 获取当天数据
df_today = self.cal_today_ochl(
self.data_minute.loc[idx -
int(60 * 4 / self.retest_span):idx, :])
# 获取时间
date = self.data_minute.loc[idx, 'date']
# 获取该日期之前数天的数据
df_day_data = self.data_day[
self.data_day['date'] < date][1 - self.reseau_slow:]
# 增加今天的数据
df_day_complete = df_day_data.append(df_today, ignore_index=True)
# 计算rsv和波动情况
# rsv = RSV.cal_rsv_rank_sub(df_day_complete, self.rsv_span)
try:
rsv = data_day.loc[date, 'RSV']
except:
print()
reseau_object = Reseau()
reseau = reseau_object.get_single_stk_reseau_sub(
df_=df_day_complete,
slow=self.reseau_slow,
quick=self.reseau_quick)
self.data_minute.loc[idx, 'rsv'] = rsv
self.data_minute.loc[idx, 'reseau'] = reseau
# 调节 buy 和 sale 的 threshold
self.data_minute.loc[idx, 'thh_sale'] = reseau * 2 * rsv
self.data_minute.loc[idx, 'thh_buy'] = reseau * 2 * (1 - rsv)
if self.debug:
i = i - 1
print('还剩%d行' % i)
minute_reckon_print(t_s, '增加网格')
def add_day_index(self):
"""
向日线数据中增加常用指标
:return:
"""
self.day_data = add_stk_index_to_df(self.day_data)
