def sample_415():
"""
4.1.5 重采样数据
:return
"""
days = pd.date_range('2017-1-1',
periods=stock_day_change.shape[1], freq='1d')
stock_symbols = ['股票 ' + str(x) for x in
xrange(stock_day_change.shape[0])]
df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days)
df = df.T
df_stock0 = df['股票 0']
# 以5天为周期重采样（周k）
df_stock0_5 = pd_resample(df_stock0.cumsum(), '5D', how='ohlc')
# 以21天为周期重采样（月k），
# noinspection PyUnusedLocal
df_stock0_20 = pd_resample(df_stock0.cumsum(), '21D', how='ohlc')
# 打印5天重采样，如下输出2017-01-01, 2017-01-06, 2017-01-11, 表4-6所示
print('df_stock0_5.head():\n', df_stock0_5.head())
from abupy import ABuMarketDrawing
# 图4-2所示
ABuMarketDrawing.plot_candle_stick(df_stock0_5.index,
df_stock0_5['open'].values,
df_stock0_5['high'].values,
df_stock0_5['low'].values,
df_stock0_5['close'].values,
np.random.random(len(df_stock0_5)),
None, 'stock', day_sum=False,
html_bk=False, save=False)
print('type(df_stock0_5.open.values):', type(df_stock0_5['open'].values))
print('df_stock0_5.open.index:\n', df_stock0_5['open'].index)
print('df_stock0_5.columns:\n', df_stock0_5.columns)
def sample_415():
"""
4.1.5 重采样数据
:return
"""
days = pd.date_range('2017-1-1',
periods=stock_day_change.shape[1],
freq='1d')
stock_symbols = ['股票 ' + str(x) for x in xrange(stock_day_change.shape[0])]
df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days)
df = df.T
df_stock0 = df['股票 0']
# 以5天为周期重采样（周k）
df_stock0_5 = pd_resample(df_stock0.cumsum(), '5D', how='ohlc')
# 以21天为周期重采样（月k），
# noinspection PyUnusedLocal
df_stock0_20 = pd_resample(df_stock0.cumsum(), '21D', how='ohlc')
# 打印5天重采样，如下输出2017-01-01, 2017-01-06, 2017-01-11, 表4-6所示
print('df_stock0_5.head():\n', df_stock0_5.head())
from abupy import ABuMarketDrawing
# 图4-2所示
ABuMarketDrawing.plot_candle_stick(df_stock0_5.index,
df_stock0_5['open'].values,
df_stock0_5['high'].values,
df_stock0_5['low'].values,
df_stock0_5['close'].values,
np.random.random(len(df_stock0_5)),
None,
'stock',
day_sum=False,
html_bk=False,
save=False)
print('type(df_stock0_5.open.values):', type(df_stock0_5['open'].values))
print('df_stock0_5.open.index:\n', df_stock0_5['open'].index)
print('df_stock0_5.columns:\n', df_stock0_5.columns)
# df_stock0=Trydataframe6['股票0']
# print(df_stock0)
# dt_stock0_draw = df_stock0.cumsum().plot()
# plt.show()
Trydataframe6_5 = Trydataframe6.resample('21D', how='ohlc')['股票0']
# print(Trydataframe6_5)
# print(Trydataframe6_5['open'])
print(Trydataframe6_5.columns)
ABuMarketDrawing.plot_candle_stick(Trydataframe6_5.index,
Trydataframe6_5['open'].values,
Trydataframe6_5['high'].values,
Trydataframe6_5['low'].values,
Trydataframe6_5['close'].values,
np.random.random(len(Trydataframe6_5)),
None,
'stock',
day_sum=False,
html_bk=False,
save=False)
# print(stock_day_change)
# print(stock_day_change[0:2,:5])
# tmp=stock_day_change[0:1,:5].copy()
# stock_day_change[0:2,0:5]=stock_day_change[-2:,-5:]
# print(stock_day_change[0:2,0:5],stock_day_change[-2:,-5:])
# mask = stock_day_change[0:2,0:5]>0.5
# print(mask)
# tmp_test = stock_day_change[0:2,0:5].copy()
# aa = tmp_test[mask]
# print(mask,aa)
# 2018-01-16 -6.890551 -6.091618 -6.890551 -6.827189
# 2018-01-21 -5.682790 -5.682790 -8.021937 -7.980051
"""
下面的代码使用abu量化系统中的ABuMarketDrawing.plot_candle_stick()方法，将数据的高开低收K线图绘制出来，
从而得到从日线级别的数据重新构建出轴线的数据，并且可视化
*注意下面K线线图的volum成交量通过np.random.random(len(df_stock5))生成的随机数据进行填充
*k线图的绘制将在数据可视化章节中详细的说明
"""
from abupy import ABuMarketDrawing
ABuMarketDrawing.plot_candle_stick(df_stock5.index,
df_stock5['open'].values,
df_stock5['high'].values,
df_stock5['low'].values,
df_stock5['close'].values,
np.random.random(len(df_stock5)),
None,
'stock',
day_sum=False,
html_bk=False,
save=False)
#结果如图pandas-2所示，类似的可以得到月k线
ABuMarketDrawing.plot_candle_stick(df_stock20.index,
df_stock20['open'].values,
df_stock20['high'].values,
df_stock20['low'].values,
df_stock20['close'].values,
np.random.random(len(df_stock20)),
None,
'stock',
day_sum=False,