| """Break-even trading cost of each timing portfolio against a benchmark portfolio. |
| |
| Monthly net return at cost c (per unit of turnover) is gross - c * turnover, averaged over |
| countries, so it is linear in c and can be recomputed from portfolio_monthly.csv without |
| rerunning anything. The break-even cost is the c at which the timing portfolio's net |
| Sharpe ratio equals the benchmark's. "never" means the timing portfolio's Sharpe ratio is |
| below the benchmark's even at zero cost. |
| |
| python scripts/breakeven.py --run Baseline=reports/jkp_em --run "Equal-weighted factors=reports/jkp_em_ew" |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
|
|
| CELLS = [("enet:own", "Elastic net, EM"), ("enet:dm_only", "Elastic net, DM"), |
| ("gbrt:own", "GBRT, EM"), ("gbrt:dm_only", "GBRT, DM")] |
| MAX_BP = 2000.0 |
|
|
|
|
| def sharpe(g: pd.DataFrame, c_bp: float) -> float: |
| net = g["gross"].to_numpy() - c_bp / 1e4 * g["turnover"].to_numpy() |
| return float(np.sqrt(12) * net.mean() / net.std(ddof=1)) |
|
|
|
|
| def breakeven(timing: pd.DataFrame, bench: pd.DataFrame) -> float: |
| """Cost in bp at which the two net Sharpe ratios are equal; NaN if timing never wins.""" |
| diff = lambda c: sharpe(timing, c) - sharpe(bench, c) |
| if diff(0.0) <= 0: |
| return np.nan |
| if diff(MAX_BP) > 0: |
| return np.inf |
| lo, hi = 0.0, MAX_BP |
| for _ in range(60): |
| mid = (lo + hi) / 2 |
| lo, hi = (mid, hi) if diff(mid) > 0 else (lo, mid) |
| return (lo + hi) / 2 |
|
|
|
|
| def _f(x: float) -> str: |
| return "never" if np.isnan(x) else (f"$>${MAX_BP:.0f}" if np.isinf(x) else f"{x:.0f}") |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--run", action="append", required=True, help="label=run_dir, repeatable") |
| ap.add_argument("--out", default="reports/robustness/breakeven.csv") |
| ap.add_argument("--tex", default="paper/tables/breakeven.tex") |
| a = ap.parse_args() |
| rows = [] |
| for spec in a.run: |
| label, d = spec.rsplit("=", 1) |
| if json.loads((Path(d) / "run_manifest.json").read_text()).get("provenance") != "JKP": |
| raise SystemExit(f"{d} is not a real-data run") |
| m = pd.read_csv(Path(d) / "portfolio_monthly.csv") |
| by = {s: g.sort_values("target_month") for s, g in m.groupby("strategy")} |
| row = {"spec": label} |
| for s, _ in CELLS: |
| row[f"vs_static_{s}"] = breakeven(by[s], by["static_ew"]) |
| row[f"vs_shrink_{s}"] = breakeven(by[s], by["shrink_mean:rule"]) |
| rows.append(row) |
| df = pd.DataFrame(rows) |
| Path(a.out).parent.mkdir(parents=True, exist_ok=True) |
| df.to_csv(a.out, index=False) |
|
|
| lines = [r"\begin{table}[!ht]", r"\centering\small", |
| r"\caption{Break-even trading cost of timing portfolios (basis points per unit of turnover)}", |
| r"\label{tab:breakeven}", r"\begin{tabular}{lrrrr}", r"\toprule", |
| "Specification & " + " & ".join(lab for _, lab in CELLS) + r" \\", r"\midrule"] |
| for _, r in df.iterrows(): |
| lines.append(r["spec"] + " & " + " & ".join(_f(r[f"vs_static_{s}"]) for s, _ in CELLS) + r" \\") |
| pos = [(r["spec"], lab, r[f"vs_shrink_{s}"]) for _, r in df.iterrows() for s, lab in CELLS |
| if not np.isnan(r[f"vs_shrink_{s}"])] |
| if not pos: |
| shrink_note = "Against the shrinkage-mean portfolio no timing portfolio has a positive break-even cost." |
| else: |
| shrink_note = ("Against the shrinkage-mean portfolio the break-even cost is positive only for " |
| + "; ".join(f"{lab} ({spec.lower()}, {_f(v)} bp)" for spec, lab, v in pos) + ".") |
| lines += [r"\bottomrule", r"\end{tabular}", "", r"\vspace{4pt}", |
| r"\begin{minipage}{0.95\linewidth}\footnotesize", |
| r"\textit{Notes:} Cost per unit of monthly timing turnover at which the timing portfolio's net " |
| r"Sharpe ratio equals that of the static equal-weight factor portfolio, which is charged the same " |
| r"cost on its own turnover. ``never'': the timing portfolio's Sharpe ratio is lower even at zero " |
| r"cost. EM / DM = trained on emerging / developed markets only. " + shrink_note, |
| r"\end{minipage}", r"\end{table}"] |
| Path(a.tex).write_text("\n".join(lines) + "\n") |
| with pd.option_context("display.width", 250, "display.max_columns", 20): |
| print(df.round(1).to_string(index=False)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
