def plot(self, m):
"""
图示m线概况
:param m:
:return:
"""
self.add_average_line(m)
self.add_average_pn(m)
self.add_idx()
df = self.data
df = df.reset_index().reset_index()
df_p = df[df.apply(lambda x: x['m_pn_' + str(m)], axis=1)]
df_n = df[df.apply(lambda x: not x['m_pn_' + str(m)], axis=1)]
fig, ax = subplots(ncols=1, nrows=2)
ax[0].plot(df['level_0'], df['close'], 'b*--', label='close')
ax[0].plot(df_p['level_0'], df_p['close'], 'r*', label='close_m' + str(m) + '_p')
ax[0].plot(df_n['level_0'], df_n['close'], 'g*', label='close_m' + str(m) + '_n')
ax[0].legend(loc='best')
ax[0] = add_axis(ax[0], df['datetime'], 60, fontsize=5, rotation=90)
ax[1].bar(df['level_0'], df['MACD'])
plt.show()
def plot_op_result(df):
"""
打印BS操作效果
列名字：
origin_money ：不使用策略时的效益
strategy_money ：使用策略时的效益
BS : 记录操作行为的列
money_remain : 剩余的资金
:param df:
:return:
"""
sh_index = df
fig, ax = plt.subplots(nrows=3, ncols=1)
ax[0].plot(range(len(sh_index['date'])), sh_index['close'], 'b--')
# 打印BS操作
tuple_bs = list(zip(range(len(sh_index['date'])), sh_index['BS'], sh_index['close']))
tuple_b = list(filter(lambda x: x[1] == 'buy', tuple_bs))
ax[0].plot(list(map(lambda x: x[0], tuple_b)), list(map(lambda x: x[2], tuple_b)), 'r*', label='buy')
tuple_s = list(filter(lambda x: x[1] == 'sale', tuple_bs))
ax[0].plot(list(map(lambda x: x[0], tuple_s)), list(map(lambda x: x[2], tuple_s)), 'g*', label='sale')
# 打印对比
ax[1].plot(range(len(sh_index['date'])), sh_index['origin_money'], 'b--', label='no_use_strategy')
ax[1].plot(range(len(sh_index['date'])), sh_index['strategy_money'], 'r--', label='use_strategy')
# 打印stk数量和剩余money
ax[2].plot(range(len(sh_index['date'])), normal01(sh_index['money_remain']), 'b--', label='剩余money')
# ax[2].plot(range(len(sh_index['date'])), normal01(sh_index['amount_remain']), 'r--', label='剩余stk数量')
# 整理x轴label
x_label = sh_index.apply(lambda x: str(x['date'])[2:].replace('-', ''), axis=1)
ax[0] = add_axis(ax[0], x_label, 40, rotation=45, fontsize=8)
ax[1] = add_axis(ax[1], x_label, 40, rotation=45, fontsize=8)
ax[2] = add_axis(ax[2], x_label, 40, rotation=45, fontsize=8)
# ax[3] = addXticklabel(ax[3], x_label, 40, rotation=45, fontsize=8)
for ax_sig in ax:
ax_sig.legend(loc='best')
plt.show()
def plot(self):
if self.debug:
print('开始可视化结果...')
df = self.data_minute.dropna().reset_index(drop=True)
df['stk_money_ratio'] = df.apply(
lambda x: x['stk_amount'] * x['close'] / (x['money_total']),
axis=1)
# 筛选买卖点
df_b = df[df['opt_result'] == 'buy']
df_s = df[df['opt_result'] == 'sale']
df_opt = df[df.apply(lambda x: (x['opt_result'] == 'buy') |
(x['opt_result'] == 'sale'),
axis=1)]
fig, ax = plt.subplots(ncols=1, nrows=4)
ax[0].plot(df.index, df['close'], 'k*--', label='收盘价')
ax[0].plot(df_opt.index,
df_opt['close'],
'y-',
label=u'买卖阶梯',
linewidth=2.5)
ax[0].plot(df_b.index, df_b['close'], 'g*', label='买入点', markersize=10)
ax[0].plot(df_s.index, df_s['close'], 'r*', label='卖出点', markersize=10)
# ax[0].legend(loc='best')
# 展示收益比
c_origin = np.array(df['close'])
ratio_origin = list((c_origin / c_origin[0]))
c_reseau = np.array(df['money_total'])
ratio_reseau = list((c_reseau / c_reseau[0]))
ax[1].plot(df.index, ratio_origin, 'y--', label=u'不操作收益曲线')
ax[1].plot(df.index, ratio_reseau, 'r--', label=u'网格收益曲线')
# ax[1].legend(loc='best')
ax[2].plot(df.index, df['stk_money_ratio'], 'b--', label=u'仓位')
# ax[2].legend(loc='best')
ax[3].plot(df.index, df['total_earn'], 'b--', label=u'网格收益')
# 增加横轴
ax[3] = add_axis(ax[3], df['date'], 20, 8)
for ax_ in ax:
ax_.legend(loc='best')
plt.show()
stk_code = '000001'
sh_index = ts.get_k_data(code=stk_code,start='2014-01-01')
sh_index['M10'] = sh_index['close'].rolling(window=10, center=False).mean()
sh_index['C-M10'] = sh_index.apply(lambda x: x['close']-x['M10'], axis=1)
sh_index['M20'] = sh_index['close'].rolling(window=20, center=False).mean()
sh_index['C-M20'] = sh_index.apply(lambda x: x['close']-x['M20'], axis=1)
sh_index = sh_index.dropna(how='any')
# 展示C-M20的分布
count, bins = np.histogram(sh_index['C-M20'], 100, normed=True)
plt.bar(range(len(count)), count)
plt.show()
fig, ax = plt.subplots(nrows=2, ncols=1)
ax[0].plot(range(0, len(sh_index['date'])), sh_index['close'], 'r-', label='20日均线', linewidth=1)
ax[1].plot(range(0, len(sh_index['date'])), sh_index['C-M20'], 'g*', label='C-20日均线', linewidth=1)
ax[1].plot(range(0, len(sh_index['date'])), np.zeros(len(sh_index)), 'b', label='零线')
# 准备下标
ax[0] = add_axis(ax[0], sh_index['date'], 40)
ax[1] = add_axis(ax[1], sh_index['date'], 40)
for ax_sig in ax:
ax_sig.legend(loc='best')
plt.show()
sh_index['M20'] = sh_index['close'].rolling(window=180,
center=False).mean()
sh_index['M5'] = sh_index['close'].rolling(window=30, center=False).mean()
sh_index['M5-M20'] = sh_index.apply(lambda x: x['M5'] - x['M20'], axis=1)
sh_index['MM9'] = sh_index['M5-M20'].rolling(window=5, center=False).mean()
sh_index['MACD'] = sh_index.apply(lambda x: x['M5-M20'] - x['MM9'], axis=1)
sh_index = sh_index.dropna(how='any')
""" -------------------------------- 图示结果 ------------------------------------ """
fig, ax = plt.subplots(nrows=2, ncols=1)
ax[0].plot(range(len(sh_index['date'])), sh_index['close'], 'b--')
# ax[0].plot(range(len(sh_index['date'])), sh_index['M5-M20'], 'r--')
ax[1].plot(range(len(sh_index['date'])), sh_index['MACD'], 'r--')
# 整理x轴label
x_label = sh_index.apply(lambda x: str(x['date'])[2:].replace('-', ''),
axis=1)
ax[0] = add_axis(ax[0], x_label, 40, rotation=45, fontsize=8)
ax[1] = add_axis(ax[1], x_label, 40, rotation=45, fontsize=8)
# ax[2] = addXticklabel(ax[2], x_label, 40, rotation=45, fontsize=8)
# ax[3] = addXticklabel(ax[3], x_label, 40, rotation=45, fontsize=8)
for ax_sig in ax:
ax_sig.legend(loc='best')
plt.show()
end = 0
# 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_ = 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 genStkIdxPicForQQ(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)
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] = add_axis(ax[0], xlabel_series, 40, rotation=45)
ax[1] = add_axis(ax[1], xlabel_series, 40, rotation=45)
ax[2] = add_axis(ax[2], xlabel_series, 40, rotation=45)
ax[3] = add_axis(ax[3], xlabel_series, 40, rotation=45)
ax[4] = add_axis(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
# 检查SAR
attention = False
sar_tail = stk_df.tail(2)
sar_tail['compare'] = sar_tail.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:
plt.title(stk_code + ' 注意 SAR 指标异动！')
attention = True
return fig, ax, attention
def genStkIdxPic(stk_df,
stk_code,
current_date,
root_save_dir,
pic_name='stk_idx.png'):
"""
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)
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[1].plot(range(0, len(stk_df['date'])),
stk_df['SAR'],
'g*--',
label='SAR',
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[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] = add_axis(ax[0], xlabel_series, 40, rotation=45)
ax[1] = add_axis(ax[1], xlabel_series, 40, rotation=45)
ax[2] = add_axis(ax[2], xlabel_series, 40, rotation=45)
ax[3] = add_axis(ax[3], xlabel_series, 40, rotation=45)
ax[4] = add_axis(ax[4], xlabel_series, 40, rotation=45)
for ax_sig in ax:
ax_sig.legend(loc='best', fontsize=5)
# 保存图片
save_dir = root_save_dir + current_date + '/' + str(stk_code) + '/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
plt.tight_layout()
plt.savefig(save_dir + pic_name, dpi=300)
plt.close()
return save_dir + pic_name
