def test_sharpe_ratio():
df = sharpe_ratio()
print(df)
df_describe = df.describe()
print(df_describe)
print(df_describe.T.sort_values('mean', ascending=False))
print(df_describe.T.sort_values('std'))
for code in df.columns:
stock = StockCN(code)
print('{0}:{1}.'.format(
code,
stock.get_cum_returns(start='2005-01-01', end='2018-12-31')[-1]))
def sharpe_ratio(**kwargs):
"""计算指定行业的夏普比率
Examples:
>>> sharpe_ratio()
. 601988 601398 600036 ... 600000 000001 600015
2005 NaN NaN 7.862071 ... 0.065330 5.211949 2.752302
2006 5.423067 3.269645 2.508599 ... 1.674587 1.714323 2.387494
2007 5.520200 3.744622 2.646470 ... 2.711249 2.535364 2.678983
2008 2.678578 3.136494 2.646753 ... 2.194612 2.218519 2.306832
2009 4.409181 4.293783 4.531678 ... 3.496624 3.730579 6.084146
2010 7.478264 10.313356 12.802671 ... 9.754310 6.521051 12.330933
2011 13.690974 22.193504 11.786194 ... 10.525052 16.241037 10.762463
2012 20.270051 15.875993 10.438670 ... 9.520852 9.550869 6.414574
2013 18.892670 25.002890 11.693822 ... 8.823424 7.255290 9.936727
2014 4.025824 6.332456 6.430317 ... 5.487892 5.728920 5.893492
2015 8.052619 10.798641 9.794130 ... 8.867595 6.183538 8.009029
2016 14.122428 14.663031 11.673594 ... 25.512644 19.262027 13.585451
2017 9.030946 6.170423 5.266668 ... 22.523839 5.819815 21.213616
2018 11.248927 9.429071 16.268102 ... 9.185897 7.025021 9.874763
Args:
start: 开始日期。默认为 '2005-01-01'
end: 结束日期。默认为 '2018-12-31'
block: 板块名称。默认为 '银行'。
值参考 :func:stock_ai.data_processor.load_stock_block` 返回值中的 'blockname' 列。
n: 最少统计 n 年的数据。数据少于 n 年，不包含在统计结果中。默认为 end-start 中取年数。
Returns:
:class:`~pandas.DataFrame`: 板块夏普比率报表。
"""
start = kwargs.pop('start', '2005-01-01')
end = kwargs.pop('end', '2018-12-31')
block = kwargs.pop('block', '银行')
blocks = load_stock_block()
blocks = blocks[blocks['blockname'] == block]
logging.debug('show_all_sharpe_ratio - ' + block)
result = pd.DataFrame()
n = kwargs.pop('n', (str2date(end).year - str2date(start).year))
for stock in blocks['code'].unique():
stock = StockCN(code=stock)
logging.debug(stock)
df = stock.get_sharpe_ratio(start=start,
end=end).rename(columns={0: stock.code})
if df.shape[0] >= n:
result = result.join(df, how='outer')
return result
def test_stockcn_construct():
"""测试构造函数"""
s = StockCN('601398')
_test_info(s)
assert not s.getblock_online
assert not s.getdaily_online
assert not s.getinfo_online
def test_getdaily_online():
s = StockCN('601398', getdaily_online=True)
d1 = s.get_daily()
assert not d1.empty
d2 = s.get_daily(start='2010-01-04')
assert d1.iloc[-1].equals(d2.iloc[-1])
assert not d2.empty
d3 = s.get_daily(end='2010-01-04')
assert not d3.empty
assert d1.iloc[0].equals(d3.iloc[0])
assert d2.iloc[0].equals(d3.iloc[-1])
d4 = s.get_daily(start='2010-01-04', end='2010-01-04')
assert not d4.empty
d4 = s.get_daily(start='2010-01-01', end='2010-01-01')
assert d4.empty
def test_get_sharpe_ratio():
print(StockCN('601398', getdaily_online=is_travis).get_sharpe_ratio())
def test_getrelated_codes():
s = StockCN('601398', getblock_online=is_travis)
print(s.related_codes)
assert len(s.related_codes) > 0
assert '601398' not in s.related_codes
assert '601939' in s.related_codes
def test_getblock_online():
s = StockCN('601398', getblock_online=is_travis)
print(s.block)
assert not s.block.empty
def test_get_info_online():
s = StockCN('601398', getinfo_online=True)
_test_info(s)
def test_get_info_mongodb():
"""测试默认数据"""
s = StockCN('601398')
_test_info(s)