def cvar_cubes():
res = []
for s in scenarios:
data = fetch_opt_data_test_file(f"portfolio-regret-{s}-cvar")
if data is None:
data = _derive_main_cube(s)
res.append(data)
return res
def cvar_data() -> OptData:
data = fetch_opt_data_test_file(f"active-cvar")
if data is None:
data = OptData(load_monte_carlo(total=True)) \
.cut_by_horizon(3) \
.calibrate_data(sd=adj.Vol) \
.alter_frequency('quarterly') \
.aggregate_assets(agg_weights)
return data
def cvar_cubes():
data = fetch_opt_data_test_file("active-cvar")
if data is not None:
return [data] * len(scenarios)
cube = load_monte_carlo(total=True)
return [
OptData(cube, 'monthly').cut_by_horizon(3).calibrate_data(sd=adj.Vol).
alter_frequency('quarterly').aggregate_assets(agg_weights)
] * len(scenarios)
def main_cubes():
res = []
for s in scenarios:
d = fetch_opt_data_test_file(f"active-{s}")
if d is None:
break
res.append(d)
else:
return res
with ProcessPoolExecutor(max(cpu_count() - 1, 1)) as P:
res = P.map(_derive_main_cube, scenarios)
return list(res)
def scenario_data_map() -> Dict[str, OptData]:
res = {}
for s in scenarios:
data = fetch_opt_data_test_file(f"active-{s}")
if data is None:
data = OptData(load_monte_carlo(total=True)) \
.calibrate_data(adj[s], adj.Vol) \
.alter_frequency('quarter') \
.aggregate_assets(agg_weights) \
.set_cov_mat(cov_mat)
res[s] = data
return res