def test_translate_currency_real(self, ticker):
columns = ['equity','eps','revenue','netinccmn',
'cashneq','debt','ebit','ebitda']
data_loader = SF1Data(config['sf1_data_path'])
quarterly_df = data_loader.load_quarterly_data(ticker, 10)
trans_df = SF1Data.translate_currency(quarterly_df, columns)
for col in columns:
diff = trans_df['{}usd'.format(col)] - trans_df[col]
diff = np.abs(diff.values / trans_df['{}usd'.format(col)].values)
diff = diff[~np.isnan(diff)]
assert diff.max() < 0.1
def test_translate_currency_synthetic(self, cnt):
np.random.seed(0)
currency_arr = np.array(range(1, cnt + 1))
df = pd.DataFrame()
df['debtusd'] = np.random.uniform(-1e5, 1e5, cnt)
df['debt'] = df['debtusd'] * currency_arr
df['ebitusd'] = np.random.uniform(-10, 10, cnt)
noise = np.random.uniform(-0.1, 0.1, cnt)
df['ebit'] = df['ebitusd'] * (currency_arr + noise)
del_proba = np.random.uniform(0, 0.3)
drop_mask = np.random.choice([True, False], cnt,
p=[del_proba, 1 - del_proba])
df.loc[drop_mask, 'ebitusd'] = None
trans_df = SF1Data.translate_currency(df, ['debt', 'ebit'])
for col in ['debt', 'ebit']:
diff = trans_df['{}usd'.format(col)] - trans_df[col]
diff = np.abs(diff.values / trans_df['{}usd'.format(col)].values)
diff = diff[~np.isnan(diff)]
assert diff.max() < 0.1