def test_apply_split_SQL_dataloader():
engine = create_engine("sqlite://")
dl = DataLoader(directory="tests/data/NASDAQ/data",
mode="SQL",
engine=engine,
tablename="eod")
dl.load_data()
dl.apply_splits(directory="tests/data/NASDAQ/adjustments/")
df = pd.read_sql_table("eod", engine)
result = pd.read_csv("NASDAQ/nasdaq_results.csv", parse_dates=["date"])
splits = pd.read_csv("tests/data/NASDAQ/adjustments/splits.csv",
parse_dates=["date"])
for i, row in splits.iterrows():
sym = row.at["symbol"]
cond = 'symbol == "{}"'.format(sym)
frame1 = df.query(cond).sort_values(by="date").reset_index(drop=True)
frame2 = result.query(cond).sort_values(by="date").reset_index(
drop=True)
L = len(frame1)
cols = frame1.columns
for i in range(L):
for j in cols:
if j in ["open", "high", "low", "close", "volume"]:
a = frame1.loc[i, j]
b = frame2.loc[i, j]
assert isclose(a, b, abs_tol=0.015)
else:
assert frame1.loc[i, j] == frame2.loc[i, j]
def test_apply_split_HDF_dataloader():
with tempfile.NamedTemporaryFile() as fp:
engine = fp.name
dl = DataLoader(
directory="tests/data/NASDAQ/data",
mode="HDF",
engine=engine,
tablename="eod",
)
dl.load_data()
dl.apply_splits(directory="tests/data/NASDAQ/adjustments/")
df = pd.read_hdf(engine, "data/eod")
result = pd.read_csv("tests/data/NASDAQ/nasdaq_results.csv",
parse_dates=["date"])
splits = pd.read_csv("tests/data/NASDAQ/adjustments/splits.csv",
parse_dates=["date"])
for i, row in splits.iterrows():
sym = row.at["symbol"]
cond = 'symbol == "{}"'.format(sym)
frame1 = df.query(cond).sort_values(by="date").reset_index(
drop=True)
frame2 = result.query(cond).sort_values(by="date").reset_index(
drop=True)
L = len(frame1)
cols = frame1.columns
for i in range(L):
for j in cols:
if j in ["open", "high", "low", "close", "volume"]:
a = frame1.loc[i, j]
b = frame2.loc[i, j]
print(a, b, sym)
assert isclose(a, b, abs_tol=0.015)
else:
assert frame1.loc[i, j] == frame2.loc[i, j]
def test_apply_split_HDF_dataloader():
with tempfile.NamedTemporaryFile() as fp:
engine = fp.name
dl = DataLoader(directory='tests/data/NASDAQ/data',
mode='HDF',
engine=engine,
tablename='eod')
dl.load_data()
dl.apply_splits(directory='tests/data/NASDAQ/adjustments/')
df = pd.read_hdf(engine, 'data/eod')
result = pd.read_csv('tests/data/NASDAQ/nasdaq_results.csv',
parse_dates=['date'])
splits = pd.read_csv('tests/data/NASDAQ/adjustments/splits.csv',
parse_dates=['date'])
for i, row in splits.iterrows():
sym = row.at['symbol']
cond = 'symbol == "{}"'.format(sym)
frame1 = df.query(cond).sort_values(by='date').reset_index(
drop=True)
frame2 = result.query(cond).sort_values(by='date').reset_index(
drop=True)
L = len(frame1)
cols = frame1.columns
for i in range(L):
for j in cols:
if j in ['open', 'high', 'low', 'close', 'volume']:
a = frame1.loc[i, j]
b = frame2.loc[i, j]
print(a, b, sym)
assert isclose(a, b, abs_tol=0.015)
else:
assert frame1.loc[i, j] == frame2.loc[i, j]