def get_data():
df = data_processor.merge({
index_code: get_index_daily(),
stock_code: get_stock_daily()
})
df.fillna(method='ffill', inplace=True)
df.dropna(inplace=True)
return df
def test_merge_doc():
def app(df, **kwargs):
name = kwargs.pop('name', 'o')
return pd.Series(
['{0}_{1}'.format(name, i) for i in range(df.shape[0])],
index=df.index)
def drop(df, **kwargs):
return df.drop(**kwargs)
df = pd.DataFrame({
'key': ['K0', 'K1', 'K2', 'K3'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['A4', 'A5', 'A6', 'A7']
}).set_index('key')
other = pd.DataFrame({
'key': ['K0', 'K1', 'K2'],
'A': ['B0', 'B1', 'B2'],
'B': ['B3', 'B4', 'B5']
}).set_index('key')
print(data_processor.merge({'df': df, '_other': other}))
print(
data_processor.merge({
'df': df,
'_other': other
},
append_funcs={
'drop_column': (drop, {
'columns': ['B'],
'replace': True
})
}))
merge_df = data_processor.merge({
'df': df,
'_other': other
},
append_funcs={'C': [app, {
'name': 'N'
}]})
print(merge_df)
def test_skew():
logging.debug("测试偏度")
for i in range(2010, 2018):
s = '{0}-01-01'.format(i)
e = '{0}-12-31'.format(i)
df = data_processor.merge({
test.index_code: get_index_daily().loc[s:e],
test.stock_code: get_stock_daily().loc[s:e]
})
v = calcs.skew(df['close'].dropna())
print('{0}:{1}'.format(i, v))
def test_is_trade_suspension():
"""测试是否停牌的计算"""
df = data_processor.merge(
{
test.stock_code: get_stock_daily(),
test.index_code: get_index_daily()
},
how='right')
is_sus = calcs.is_trade_suspension(df)
assert not is_sus['2012-02-22']
assert is_sus['2012-02-23']
assert not is_sus['2012-02-24']
print(is_sus['2012-02-23'])
def test_merge_dropcolumn():
columns = ['up_count', 'down_count']
df = data_processor.merge(
{
test.index_code: get_index_daily(),
test.stock_code: get_stock_daily()
},
append_funcs={
'drop_columns': (calcs.drop_column, {
'columns': columns,
'replace': True
})
})
for col in columns:
assert col not in df.columns
print(df.dtypes)
def test_sharpe_ratio():
rate = get_deposit_rate().sort_index()
df_s = get_stock_daily().index[0].date()
df_e = get_stock_daily().index[-1].date()
for i in range(len(rate.index)):
s = str2date(rate.index[i]).date()
e = str2date(rate.index[i + 1]).date() if rate[i] != rate[-1] else df_e
if s >= df_s and e <= df_e:
df = data_processor.merge({
test.index_code:
get_index_daily().loc[s:e],
test.stock_code:
get_stock_daily().loc[s:e]
})
k = '{0}~{1}'.format(s, e)
v = calcs.sharpe_ratio(df['close'].dropna(), rate[i])
print('{0}:{1}'.format(k, v))
def merged_dataframe() -> dict:
return data_processor.merge({
index_code: get_index_daily(),
stock_code: get_stock_daily()
})