| import zipfile |
|
|
| import pandas as pd |
| import pytest |
|
|
| from emft.data import load_jkp, normalise |
|
|
| SITE = pd.DataFrame( |
| { |
| "location": ["bra", "bra", "usa", "usa"], |
| "name": ["be_me", "be_me", "be_me", "be_me"], |
| "freq": "monthly", |
| "weighting": "vw_cap", |
| "direction": 1, |
| "n_stocks": [100, 100, 3000, 3000], |
| "n_stocks_min": [30, 3, 900, 900], |
| "date": ["2020-01-31", "2020-02-29", "2020-01-31", "2020-02-29"], |
| "ret": [0.01, -0.02, 0.005, 0.0], |
| } |
| ) |
|
|
|
|
| def test_site_layout_is_normalised_and_thin_cells_dropped(): |
| df = normalise(SITE.copy(), min_stocks=5) |
| assert list(df.columns) == ["country", "factor", "month", "ret", "region"] |
| assert len(df) == 3 |
| assert set(df["region"]) == {"EM", "DM"} |
| assert str(df["month"].iloc[0]) == "2020-01" |
|
|
|
|
| def test_internal_layout_uses_requested_weighting(): |
| raw = pd.DataFrame( |
| {"excntry": ["CHN"], "characteristic": ["ret_12_1"], "date": ["2021-06-30"], |
| "ret_ew": [0.03], "ret_vw": [0.02], "ret_vw_cap": [0.01]} |
| ) |
| assert normalise(raw, weighting="vw").loc[0, "ret"] == pytest.approx(0.02) |
| assert normalise(raw).loc[0, "country"] == "chn" |
|
|
|
|
| def test_percent_returns_are_rejected(): |
| bad = SITE.copy() |
| bad["ret"] = [1.5, -2.0, 0.8, 0.9] |
| with pytest.raises(ValueError, match="percent"): |
| normalise(bad, min_stocks=0) |
|
|
|
|
| def test_duplicates_are_rejected(): |
| dup = pd.concat([SITE, SITE.iloc[[0]]]) |
| with pytest.raises(ValueError, match="duplicate"): |
| normalise(dup, min_stocks=0) |
|
|
|
|
| def test_macro_state_variables(tmp_path): |
| import numpy as np |
|
|
| from emft.data import load_macro |
|
|
| days = pd.date_range("2004-01-01", "2007-12-31", freq="D") |
| pd.DataFrame({"observation_date": days, "VIXCLS": np.arange(len(days)) % 40 + 10.0}).to_csv( |
| tmp_path / "VIXCLS.csv", index=False) |
| pd.DataFrame({"observation_date": days, "T10Y3M": "."}).assign(T10Y3M=lambda d: d["T10Y3M"].where( |
| d["observation_date"].dt.day != 15, "1.5")).to_csv(tmp_path / "T10Y3M.csv", index=False) |
| months = pd.date_range("2004-01-01", "2007-12-01", freq="MS") |
| pd.DataFrame({"observation_date": months, "TWEXBMTH": 100 * 1.01 ** np.arange(len(months))}).to_csv( |
| tmp_path / "TWEXBMTH.csv", index=False) |
| new_days = pd.date_range("2006-01-01", "2007-12-31", freq="D") |
| |
| level = 50 * 1.01 ** ((new_days.year - 2006) * 12 + new_days.month - 1) |
| pd.DataFrame({"observation_date": new_days, "DTWEXBGS": level}).to_csv(tmp_path / "DTWEXBGS.csv", index=False) |
| m = load_macro(tmp_path) |
| assert m.loc[pd.Period("2004-02", freq="M"), "mac_vix"] == pytest.approx(np.log(10 + (59 % 40))) |
| assert m.loc[pd.Period("2006-06", freq="M"), "mac_term"] == pytest.approx(1.5) |
| |
| assert m.loc["2005-01":"2007-12", "mac_usd12"].to_numpy() == pytest.approx(12 * np.log(1.01)) |
|
|
|
|
| def test_directory_with_site_zip_is_read(tmp_path): |
| |
| with zipfile.ZipFile(tmp_path / "[all_countries]_[all_factors]_[monthly]_[vw_cap].zip", "w") as z: |
| z.writestr("factors.csv", SITE.to_csv(index=False)) |
| df = load_jkp(tmp_path, min_stocks=5) |
| assert len(df) == 3 |
|
|
