def test_linear_model_time_series(data):
mod = TradedFactorModel(data.portfolios, data.factors)
mod.fit()
res = mod.fit()
get_all(res)
all_params = []
all_tstats = []
nobs, nport = data.portfolios.shape
nf = data.factors.shape[1]
e = np.zeros((nobs, (nport * (nf + 1))))
x = np.zeros((nobs, (nport * (nf + 1))))
factors = sm.add_constant(data.factors)
loc = 0
for i in range(data.portfolios.shape[1]):
if isinstance(data.portfolios, pd.DataFrame):
p = data.portfolios.iloc[:, i:(i + 1)]
else:
p = data.portfolios[:, i:(i + 1)]
ols_res = _OLS(p, factors).fit(cov_type='robust', debiased=True)
all_params.extend(list(ols_res.params))
all_tstats.extend(list(ols_res.tstats))
x[:, loc:(loc + nf + 1)] = factors
e[:, loc:(loc + nf + 1)] = ols_res.resids.values[:, None]
loc += nf + 1
cov = res.cov.values[(nf + 1) * i:(nf + 1) * (i + 1),
(nf + 1) * i:(nf + 1) * (i + 1)]
ols_cov = ols_res.cov.values
assert_allclose(cov, ols_cov)
assert_allclose(res.params.values.ravel(), np.array(all_params))
assert_allclose(res.tstats.values.ravel(), np.array(all_tstats))
assert_allclose(res.risk_premia, np.asarray(factors).mean(0)[1:])
xpxi_direct = np.eye((nf + 1) * nport + nf)
f = np.asarray(factors)
fpfi = np.linalg.inv(f.T @ f / nobs)
nfp1 = nf + 1
for i in range(nport):
st, en = i * nfp1, (i + 1) * nfp1
xpxi_direct[st:en, st:en] = fpfi
f = np.asarray(factors)[:, 1:]
xe = np.c_[x * e, f - f.mean(0)[None, :]]
xeex_direct = xe.T @ xe / nobs
cov = xpxi_direct @ xeex_direct @ xpxi_direct / (nobs - nfp1)
assert_allclose(cov, res.cov.values)
alphas = np.array(all_params)[0::nfp1][:, None]
alpha_cov = cov[0:(nfp1 * nport):nfp1, 0:(nfp1 * nport):nfp1]
stat_direct = float(alphas.T @ np.linalg.inv(alpha_cov) @ alphas)
assert_allclose(res.j_statistic.stat, stat_direct)
assert_allclose(1.0 - stats.chi2.cdf(stat_direct, nport),
res.j_statistic.pval)
def test_drop_missing(data):
p = data.portfolios
if isinstance(p, pd.DataFrame):
p.iloc[::33] = np.nan
else:
p[::33] = np.nan
res = TradedFactorModel(p, data.factors).fit()
p = IVData(p)
f = IVData(data.factors)
isnull = p.isnull | f.isnull
p.drop(isnull)
f.drop(isnull)
res2 = TradedFactorModel(p, f).fit()
assert_equal(np.asarray(res.params), np.asarray(res2.params))
def test_repr(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors)
assert "LinearFactorModelGMM" in mod.__repr__()
assert str(data.portfolios.shape[1]) + " test portfolios" in mod.__repr__()
assert str(data.factors.shape[1]) + " factors" in mod.__repr__()
mod = LinearFactorModel(data.portfolios, data.factors, risk_free=True)
assert "LinearFactorModel" in mod.__repr__()
assert "Estimated risk-free" in mod.__repr__()
assert "True" in mod.__repr__()
mod = TradedFactorModel(data.portfolios, data.factors)
assert "TradedFactorModel" in mod.__repr__()
assert str(hex(id(mod))) in mod.__repr__()
def test_repr(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors)
assert 'LinearFactorModelGMM' in mod.__repr__()
assert str(data.portfolios.shape[1]) + ' test portfolios' in mod.__repr__()
assert str(data.factors.shape[1]) + ' factors' in mod.__repr__()
mod = LinearFactorModel(data.portfolios, data.factors, risk_free=True)
assert 'LinearFactorModel' in mod.__repr__()
assert 'Estimated risk-free' in mod.__repr__()
assert 'True' in mod.__repr__()
mod = TradedFactorModel(data.portfolios, data.factors)
assert 'TradedFactorModel' in mod.__repr__()
assert str(hex(id(mod))) in mod.__repr__()
def test_errors(data):
p = data.portfolios.copy()
f = data.factors.copy()
if isinstance(p, pd.DataFrame):
p2 = p.copy()
p3 = p.copy().iloc[:-1]
p4 = p.copy()
p5 = p.copy().iloc[:f.shape[1] - 1, :1]
p4 = p4.iloc[:, :(f.shape[1] - 1)]
p2["dupe"] = p.iloc[:, 0]
p["const"] = 1.0
f5 = f.copy()
f5 = f5.iloc[:p5.shape[0]]
f2 = f.copy()
f2["dupe"] = f.iloc[:, 0]
f["const"] = 1.0
else:
p2 = np.c_[p, p[:, [0]]]
p3 = p.copy()[:-1]
p4 = p.copy()
p5 = p.copy()[:f.shape[1] - 1, :1]
p4 = p4[:, :(f.shape[1] - 1)]
p = np.c_[np.ones((p.shape[0], 1)), p]
f5 = f.copy()
f5 = f5[:p5.shape[0]]
f2 = np.c_[f, f[:, [0]]]
f = np.c_[np.ones((f.shape[0], 1)), f]
with pytest.raises(ValueError):
TradedFactorModel(p, data.factors)
with pytest.raises(ValueError):
TradedFactorModel(p2, data.factors)
with pytest.raises(ValueError):
TradedFactorModel(p3, data.factors)
with pytest.raises(ValueError):
TradedFactorModel(data.portfolios, f)
with pytest.raises(ValueError):
TradedFactorModel(data.portfolios, f2)
with pytest.raises(ValueError):
TradedFactorModel(p5, f5)
with pytest.raises(ValueError):
LinearFactorModel(p4, data.factors)
def test_all_missing():
p = np.nan * np.ones((1000, 10))
f = np.nan * np.ones((1000, 3))
with pytest.raises(ValueError):
TradedFactorModel(p, f)
def test_linear_model_time_series_error(data):
mod = TradedFactorModel(data.portfolios, data.factors)
with pytest.raises(ValueError):
mod.fit(cov_type="unknown")
def test_linear_model_time_series_kernel_smoke(data):
mod = TradedFactorModel(data.portfolios, data.factors)
mod.fit(cov_type="kernel")