feat: grade gate-ablation variants under the hold-to-horizon exit too
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The ablation judged floors under the target/stop model, but the exit
sweeps point at replacing that exit with a fixed hold — under which the
R:R floor's rationale (bigger payoff at the target) may not apply. Each
ablation row now also carries hold_avg_r / hold_net_avg_r / hold_total_r
(30d hold, initial stop only), so the Phase 3 gate decision can be read
under the exit policy that would actually be used.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
2026-07-02 11:34:41 +02:00
parent 8750aac6d9
commit 942a22ce65
4 changed files with 47 additions and 11 deletions
+22 -8
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@@ -911,16 +911,27 @@ def _gate_ablation(candidates: list[dict], activation: dict, threshold: float) -
("no_neutral_exclusion", [rr_ok, conf_ok, tighteners_ok]),
("momentum_only", []),
]
return [
{
"variant": name,
**_bucket_stats([
# Grade each variant under BOTH exit models: the target/stop outcome
# (_bucket_stats) and the hold-to-horizon time exit. A floor that pays under
# the target model may be meaningless once the exit is a fixed hold — the
# hold_* columns are what a time-exit gate decision should read.
hold_days = max(TIME_EXIT_DAYS)
rows: list[dict] = []
for name, checks in variants:
matching = [
c for c in candidates
if momentum_ok(c) and all(check(c) for check in checks)
]),
}
for name, checks in variants
]
hold = _time_exit_bucket(matching, hold_days)
rows.append({
"variant": name,
**_bucket_stats(matching),
"hold_days": hold_days,
"hold_avg_r": hold["avg_r"],
"hold_net_avg_r": hold["net_avg_r"],
"hold_total_r": hold["total_r"],
})
return rows
async def run_backtest(
@@ -1051,7 +1062,10 @@ async def run_backtest(
"Each row re-qualifies the same candidates at the current momentum "
f"cutoff ({current_min_pct:.0f}) with one floor removed (long-only "
"while the momentum gate is active). If dropping a floor doesn't "
"hurt net expectancy, that floor isn't pulling its weight."
"hurt net expectancy, that floor isn't pulling its weight. The Hold "
"columns grade the same variants under the hold-to-horizon time exit "
"instead of the S/R target — the view that matters if the exit "
"policy moves to a fixed hold."
),
"take_profit_sweep": [_take_profit_bucket(qualified, tp) for tp in TP_LEVELS],
"trailing_sweep": [_trailing_bucket(qualified, round(f * 100)) for f in TRAIL_LEVELS],
@@ -285,6 +285,8 @@ export function BacktestPanel() {
<th className="px-4 py-2.5 text-right">Avg R</th>
<th className="px-4 py-2.5 text-right">Net Avg R</th>
<th className="px-4 py-2.5 text-right">Total R</th>
<th className="px-4 py-2.5 text-right">Hold Net Avg R</th>
<th className="px-4 py-2.5 text-right">Hold Total R</th>
</tr>
</thead>
<tbody>
@@ -303,6 +305,12 @@ export function BacktestPanel() {
{fmtR(row.net_avg_r ?? null)}
</td>
<td className={`num px-4 py-2.5 text-right ${rColor(row.total_r)}`}>{fmtR(row.total_r)}</td>
<td className={`num px-4 py-2.5 text-right font-semibold ${rColor(row.hold_net_avg_r ?? null)}`}>
{fmtR(row.hold_net_avg_r ?? null)}
</td>
<td className={`num px-4 py-2.5 text-right ${rColor(row.hold_total_r ?? null)}`}>
{fmtR(row.hold_total_r ?? null)}
</td>
</tr>
))}
</tbody>
+5
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@@ -280,6 +280,11 @@ export interface BacktestTimeExitRow {
export interface BacktestGateAblationRow extends BacktestBucket {
variant: string;
// The same variant graded under the hold-to-horizon time exit.
hold_days?: number;
hold_avg_r?: number | null;
hold_net_avg_r?: number | null;
hold_total_r?: number | null;
}
export interface BacktestSignalEvalRow {
+10 -1
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@@ -221,6 +221,7 @@ def _acand(
"outcome": OUTCOME_TARGET_HIT,
"realized_r": rr,
"risk_pct": 0.05,
"time_r": {d: 0.5 for d in bt.TIME_EXIT_DAYS},
}
@@ -249,6 +250,11 @@ class TestGateAblation:
assert rows["no_neutral_exclusion"]["total"] == 2
assert rows["momentum_only"]["total"] == 4
assert rows["all_floors"]["net_avg_r"] is not None
# Every variant is also graded under the hold-to-horizon exit.
assert rows["all_floors"]["hold_days"] == max(bt.TIME_EXIT_DAYS)
assert rows["all_floors"]["hold_avg_r"] == pytest.approx(0.5)
assert rows["all_floors"]["hold_net_avg_r"] is not None
assert rows["momentum_only"]["hold_total_r"] == pytest.approx(4 * 0.5, abs=0.01)
def test_threshold_zero_disables_momentum_gate(self):
# Floors only: the short and the low-momentum long both pass all_floors.
@@ -378,9 +384,12 @@ async def test_run_backtest_smoke(session):
assert report["params"]["cost_per_side_pct"] == pytest.approx(bt.COST_PER_SIDE * 100)
assert "net_avg_r" in report["overall_all"]
# ablation baseline reproduces the qualified set exactly
# ablation baseline reproduces the qualified set exactly, and every row
# carries the hold-to-horizon grading alongside the target model
ablation = {r["variant"]: r for r in report["gate_ablation"]}
assert ablation["all_floors"]["total"] == report["overall_qualified"]["total"]
for row in report["gate_ablation"]:
assert "hold_net_avg_r" in row
# time-exit sweep covers the configured hold lengths
assert [r["hold_days"] for r in report["time_exit_sweep"]] == list(bt.TIME_EXIT_DAYS)