| """Command line: python -m emft run --data <path> --out <dir> (or --synthetic).""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import logging |
| from pathlib import Path |
|
|
| import pandas as pd |
|
|
| from . import backtest, data, evaluate, features, report |
| from .models import LEARNED, RULES, SCALED |
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|
|
| def main(argv: list[str] | None = None) -> None: |
| ap = argparse.ArgumentParser(prog="emft") |
| sub = ap.add_subparsers(dest="cmd", required=True) |
|
|
| r = sub.add_parser("run", help="build features, backtest, evaluate, report") |
| src = r.add_mutually_exclusive_group(required=True) |
| src.add_argument("--data", help="JKP CSV file, directory, glob or .zip") |
| src.add_argument("--synthetic", action="store_true", help="known-truth synthetic panel") |
| r.add_argument("--out", default="reports/latest") |
| r.add_argument("--oos-start", type=int, default=2000) |
| r.add_argument("--test-region", default="EM", choices=["EM", "DM"]) |
| r.add_argument("--models", default="ols,enet,gbrt,hist_mean,shrink_mean,fmom", |
| help="comma list; add nn for the neural-net ensemble (slow on the full panel)") |
| r.add_argument("--cost-bps", type=float, default=20.0) |
| r.add_argument("--min-stocks", type=int, default=5) |
| r.add_argument("--weighting", default="vw_cap", choices=["vw_cap", "vw", "ew"]) |
| r.add_argument("--kappa", type=float, default=120.0, help="prior weight (months) in the shrinkage mean") |
| r.add_argument("--market-history", action="store_true", |
| help="time-varying MSCI classification from config/market_history.yaml") |
| r.add_argument("--macro", help="directory of FRED CSVs; adds U.S. state variables as predictors") |
| r.add_argument("--sample-start", help="drop feature months before YYYY-MM (to match --macro samples)") |
| r.add_argument("--seed", type=int, default=0) |
|
|
| c = sub.add_parser("coverage", help="print country coverage of a JKP file") |
| c.add_argument("--data", required=True) |
|
|
| args = ap.parse_args(argv) |
| logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s") |
|
|
| if args.cmd == "coverage": |
| panel = data.load_jkp(args.data) |
| print(data.coverage(panel).to_string(index=False)) |
| return |
|
|
| if args.synthetic: |
| panel, _ = data.synthetic_panel(seed=args.seed) |
| provenance = "SYNTHETIC" |
| oos_start = 2005 if args.oos_start < 2005 else args.oos_start |
| else: |
| panel = data.load_jkp(args.data, weighting=args.weighting, min_stocks=args.min_stocks) |
| if args.market_history: |
| panel = data.apply_market_history(panel) |
| provenance = "JKP" |
| oos_start = args.oos_start |
|
|
| built = features.build_features(panel, kappa=args.kappa) |
| predictors = tuple(features.FEATURES) |
| if args.macro: |
| built = built.merge(data.load_macro(args.macro), left_on="month", right_index=True, how="left") |
| predictors += tuple(data.MACRO_FEATURES) |
| if args.sample_start: |
| built = built[built["month"] >= pd.Period(args.sample_start, freq="M")] |
| feats = features.model_ready(built, extra=predictors[len(features.FEATURES):]) |
| models = tuple(m.strip() for m in args.models.split(",") if m.strip()) |
| preds = backtest.run_backtest(feats, test_region=args.test_region, models=models, |
| oos_start=oos_start, seed=args.seed, features=predictors) |
| out = Path(args.out) |
| out.mkdir(parents=True, exist_ok=True) |
| preds.to_parquet(out / "predictions.parquet", index=False) |
|
|
| r2 = evaluate.r2_table(preds) |
| learned = r2[r2["model"].isin(LEARNED + SCALED)] |
| top = learned.iloc[0] if not learned.empty else r2.iloc[0] |
| r2c = evaluate.r2_by_country(preds, top["model"], top["scope"]) |
| monthly = evaluate.portfolio_returns(preds, cost_bps=args.cost_bps) |
| port = evaluate.portfolio_table(monthly) |
| report.write_report( |
| out, provenance, data.coverage(panel), r2, r2c, port, monthly, |
| {"test_region": args.test_region, "oos_start": oos_start, "cost_bps": args.cost_bps, |
| "models": models, "weighting": args.weighting, "min_stocks": args.min_stocks, "kappa": args.kappa, |
| "market_history": args.market_history, |
| "macro": args.macro, "sample_start": args.sample_start, |
| "data": args.data or "synthetic", "seed": args.seed}, |
| ) |
| print(r2.to_string(index=False)) |
| print() |
| print(port.to_string(index=False)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
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|
