# encoding=utf-8
# encoding=utf-8
def gen_Hour_MACD_Pic(stk_code, source='jq', title=''):
# 生成小时macd数据
df_30, df_60 = gen_hour_macd_values(stk_code, source=source, title=title)
# 根据情况设置背景色
attention = False
m30 = df_30.tail(3)['MACD'].values
m60 = df_60.tail(3)['MACD'].values
if (m30[1] == np.min(m30)) | (m60[1] == np.min(m60)):
# 设置背景红
set_background_color('b_r')
elif (m30[1] == np.max(m30)) | (m60[1] == np.max(m60)):
# 设置背景绿
set_background_color('b_g')
else:
set_background_color()
fig, ax = plt.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')
# 设置下标
ax[1] = addXticklabel_list(
ax[1],
list([str(x)[-11:-3] for x in df_30['datetime']]),
30, rotation=45)
ax[3] = addXticklabel_list(
ax[3],
list([str(x)[-11:-3] for x in df_60['datetime']]),
30, rotation=45)
# ax[3].set_xticks(list(range(0, len(df_60['datetime']))))
# ax[3].set_xticklabels(list([str(x)[-11:-3] for x in df_60['datetime']]), rotation=45)
for ax_sig in ax:
ax_sig.legend(loc='best')
# 设置标题
if m30[1] == np.min(m30):
ax[0].set_title(stk_code + '半小时MACD低点！')
elif m60[1] == np.min(m60):
ax[0].set_title(stk_code + '小时MACD低点！')
elif m30[1] == np.max(m30):
ax[0].set_title(stk_code + '半小时MACD高点！')
elif m60[1] == np.max(m60):
ax[0].set_title(stk_code + '小时MACD高点！')
else:
ax[0].set_title(stk_code)
fig.tight_layout()
plt.subplots_adjust(wspace=0, hspace=1) # 调整子图间距
# plt.close()
return fig
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_hour_macd_pic(stk_data, debug=False, stk_code=''):
if debug:
print('进入GenPic.gen_hour_macd_pic函数！')
debug_print_txt('macd_hour_pic',
stk_code,
'\n----------------------\n\n',
enable=debug)
# 生成小时macd数据
# df_30, df_60 = gen_hour_macd_values(stk_code, source=source, title=title)
df_30, df_60 = stk_data
if debug:
debug_print_txt('macd_hour_pic',
stk_code,
'df_30原始数据:\n' + str(df_30) + '\n\n' +
'df_60原始数据:\n' + str(df_60) + '\n\n',
enable=debug)
# 根据情况设置背景色
m30 = df_30.tail(3)['MACD'].values
m60 = df_60.tail(3)['MACD'].values
if debug:
debug_print_txt(
'macd_hour_pic', stk_code, 'm30原始数据:\n' + str(m30) + '\n\n' +
'm60原始数据:\n' + str(m60) + '\n\n')
if (m30[1] == np.min(m30)) | (m60[1] == np.min(m60)):
# 设置背景红
GenPic.set_background_color('b_r')
elif (m30[1] == np.max(m30)) | (m60[1] == np.max(m60)):
# 设置背景绿
GenPic.set_background_color('b_g')
else:
GenPic.set_background_color()
# 调整显示长度
df_30 = df_30.tail(40)
df_60 = df_60.tail(40)
fig, ax = plt.subplots(ncols=1, nrows=4)
ax[0].plot(range(0, len(df_30)),
df_30['close'],
'g*--',
label='close_30min')
ax[1] = GenPic.plot_macd(ax[1], df_30, '30min')
ax[2].plot(range(0, len(df_60)),
df_60['close'],
'g*--',
label='close_60min')
ax[3] = GenPic.plot_macd(ax[3], df_60, '60min')
# 设置下标
ax[1] = addXticklabel_list(
ax[1],
list([str(x)[-9:-3] for x in df_30['datetime']]),
15,
rotation=0,
fontsize=6)
ax[3] = addXticklabel_list(
ax[3],
list([str(x)[-9:-3] for x in df_60['datetime']]),
15,
rotation=0,
fontsize=6)
for ax_sig in ax:
ax_sig.legend(loc='best')
# 设置默认标题
title = stk_code + ' ' + code2name(stk_code) + '/上次更新时间：%s' % str(
get_current_datetime_str())
# 设置标题
if m30[1] == np.min(m30):
title = stk_code + '半小时MACD低点！'
elif m60[1] == np.min(m60):
title = stk_code + '小时MACD低点！'
elif m30[1] == np.max(m30):
title = stk_code + '半小时MACD高点！'
elif m60[1] == np.max(m60):
title = stk_code + '小时MACD高点！'
ax[0].set_title(title)
if debug:
debug_print_txt('macd_hour_pic', stk_code, '结论:' + title + '\n\n')
fig.tight_layout()
plt.subplots_adjust(wspace=0, hspace=0.15) # 调整子图间距
return fig
def gen_Hour_MACD_Pic(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')
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()
fig, ax = plt.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')
# 设置下标
ax[1] = addXticklabel_list(
ax[1],
list([str(x)[-11:-3] for x in df_30['datetime']]),
30,
rotation=45)
ax[3].set_xticks(list(range(0, len(df_60['datetime']))))
ax[3].set_xticklabels(list([str(x)[-11:-3] for x in df_60['datetime']]),
rotation=45)
for ax_sig in ax:
ax_sig.legend(loc='best')
plt.title(stk_code + title)
fig.tight_layout()
plt.subplots_adjust(wspace=0, hspace=1) # 调整子图间距
# plt.close()
return fig
