| """Expanding-window, annually refitted out-of-sample backtest. |
| |
| For each test year Y, models are trained on rows whose TARGET month falls before |
| January of Y (so every training label was already realised) and evaluated on rows |
| whose target month falls in Y. |
| |
| Training scopes, all evaluated on the same test region: |
| own train on the test region only (EM -> EM) |
| dm_only train on developed markets only, test on EM (the transfer test) |
| pooled train on DM and EM together |
| """ |
|
|
| from __future__ import annotations |
|
|
| import logging |
|
|
| import pandas as pd |
|
|
| from .features import FEATURES |
| from .models import LEARNED, RULES, Forecaster |
|
|
| log = logging.getLogger(__name__) |
|
|
| SCOPES = ("own", "dm_only", "pooled") |
|
|
|
|
| def run_backtest( |
| data: pd.DataFrame, |
| test_region: str = "EM", |
| models: tuple[str, ...] = LEARNED + RULES, |
| scopes: tuple[str, ...] = SCOPES, |
| oos_start: int = 2000, |
| min_train_months: int = 120, |
| seed: int = 0, |
| features: tuple[str, ...] = tuple(FEATURES), |
| ) -> pd.DataFrame: |
| """Return one row per (test observation, model, scope) with the forecast.""" |
| last_year = int(data["target_month"].max().year) |
| out: list[pd.DataFrame] = [] |
| first_month = data["target_month"].min() |
|
|
| for year in range(oos_start, last_year + 1): |
| cutoff = pd.Period(f"{year}-01", freq="M") |
| if (cutoff - first_month).n < min_train_months: |
| continue |
| test = data[(data["target_month"].dt.year == year) & (data["region"] == test_region)] |
| if test.empty: |
| continue |
| past = data[data["target_month"] < cutoff] |
| train_sets = { |
| "own": past[past["region"] == test_region], |
| "dm_only": past[past["region"] == "DM"], |
| "pooled": past[past["region"].isin(["DM", "EM"])], |
| } |
| for scope in scopes: |
| train = train_sets[scope] |
| if train.empty: |
| continue |
| for name in models: |
| if name in RULES and scope != "own": |
| continue |
| f = Forecaster(name, seed=seed, features=features).fit(train) |
| pred = test[["month", "target_month", "country", "factor", "region", "target", "hist_mean", "shrink_mean", "own_m12"]].copy() |
| pred["model"] = name |
| pred["scope"] = scope if name not in RULES else "rule" |
| pred["pred"] = f.predict(test) |
| pred["scale"] = f.scale |
| out.append(pred) |
| log.info("year %d: trained on %d rows, tested on %d", year, len(train_sets["own"]), len(test)) |
|
|
| if not out: |
| raise ValueError("No out-of-sample years: check oos_start and sample length") |
| return pd.concat(out, ignore_index=True) |
|
|
