dennisthiessen/signal-platform
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

research: prepare effective risk floor ab

Browse Source
This commit is contained in:
dennisthiessen
2026-08-05 22:49:50 +02:00
parent aa6cd5cac4
commit 6f1ee450f1
5 changed files with 573 additions and 51 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/research/README.md
+3 -3
View File
@@ -146,7 +146,7 @@ knobs.
| **Broader universe** | Composition changes factor signs (fip tug-of-war); vol-tilt on breadth is only a **directional hypothesis** (auth. −0.048 / t −1.36) | Any prod broaden must re-validate 80/20 tilt; offline research only; research.sqlite requires completion manifest |
| **Forward paper-trade record** | The only true out-of-sample evidence the snapshot cannot give | Time; mark entries at actual near-close fill once ops ships |
| **Better target model for clear-air names** | The return is demonstrably there (#2 wins on raw CAGR in *both* train and test); it's the *flat* 3× ATR target that makes it too expensive in risk | Needs a per-name model, not a constant k×ATR |
| **Minimum effective-risk floor** | In cap-never-bound paths, the confounded 0.5% floor arm removed about 8% of fills while EV rose from 0.328 to 0.399 R and PF from 1.60 to 1.75, with exposure nearly unchanged | Run a single-variable A/B: cap 10 control versus cap 10 plus `min_initial_risk_fraction=0.005`. [Capacity findings](portfolio-capacity-bracket-findings.md) |
| **Minimum effective-risk floor** | In cap-never-bound paths, the confounded 0.5% floor arm removed about 8% of fills while EV rose from 0.328 to 0.399 R and PF from 1.60 to 1.75, with exposure nearly unchanged | Run the frozen single-variable cap-10 A/B. [Specification](effective-risk-floor-ab.md) / [capacity findings](portfolio-capacity-bracket-findings.md) |
---
@@ -206,7 +206,7 @@ only 0.0018 R/trade in paths where cap 10 bound. Weekly current-rank replacement
reduced mean EV and created substantial churn. Keep cap 10 and do not build the
replacement policy. See the [frozen specification](portfolio-capacity-bracket.md)
and the separate [capacity findings](portfolio-capacity-bracket-findings.md).
The only open follow-up from that run is the confound-free 0.5% minimum
effective-risk-floor A/B.
The only open follow-up from that run is the
[frozen confound-free 0.5% minimum effective-risk-floor A/B](effective-risk-floor-ab.md).
The next real evidence is **forward**, not backward: the live paper-trade record.
docs/research/effective-risk-floor-ab.md
+124
View File
@@ -0,0 +1,124 @@
# Effective initial-risk floor A/B - frozen specification
Date frozen: 2026-08-05
Branch: research/portfolio-capacity-rebalancing
Runner: scripts/run_portfolio_construction_matrix.py
Study ID: risk-floor-ab
## Question
Does rejecting an otherwise qualified cap-10 entry when its actual initial
stop-risk after cash and notional sizing is below 0.5% of marked equity improve
trade selection?
The completed capacity bracket cannot answer this. Its cash_unbounded arm
removed the count cap and applied the 0.5% floor simultaneously. In the 70 paths
where the control cap never bound, that arm still raised mean EV from 0.328 to
0.399 R and profit factor from 1.60 to 1.75 while trades fell about 8% and
exposure stayed nearly flat. Capacity was a no-op in those paths, so the floor
is the plausible cause, but the prior arm remains confounded.
This A/B changes only the floor. It has no formal promotion gate and does not
automatically change production.
## Frozen arms
1. cap10_incumbent: current production-style cap-10 control, with no minimum
effective-risk floor.
2. cap10_min_risk_005: the same cap-10 strategy, rejecting an entry only when
actual initial stop-risk after cash/notional sizing is below 0.5% of marked
equity.
Both arms have max_positions=10, weekly replacement disabled, 1% target risk
per trade, and identical admission ordering. The only differing simulator
argument is min_initial_risk_fraction: None versus 0.005.
All other settings remain the frozen daily Phase A control: current production
construction universe, full-universe residual-momentum/low-volatility 80/20
rank, threshold 80, normal gate-reset re-entry, close fills, 3x ATR trail,
30-session maximum hold, 20% per-position notional ceiling, no leverage, and
costs of 0.10% and 0.20% per fill.
Every priced symbol contributes to the daily cross-sectional rank. Rank-only
symbols cannot submit trades. Validation retains the 450-600-symbol production
construction guardrail and the legacy-snapshot column-scoped loader.
## Frozen cohorts
Reuse the completed bracket's point-in-time daily candidate/rank cache and
cohort manifest:
- Empty book: first eligible session of each month in 2019-2025, with 504 prior
scoring sessions and 252 measurement sessions. This is the primary start-date
evidence.
- Warm book: weekly seeds 63-126 sessions before each 2019-2025 annual anchor,
with state carried into the same 252-session measurement window. This is a
state-carrying replication, not independent evidence.
The expected realization is 78 empty-book paths, 97 warm paths, seven annual
clusters in each protocol, two costs, two arms, and 700 cells.
Do not use warm-seed IQR as evidence. Six of seven completed-bracket anchors
were structurally degenerate because fractional sizing is scale invariant and
the 30-session maximum hold washed out books before anchors. The 2023 exception
shows that state carrying itself works.
## Reporting and interpretation
For every protocol and cost, pair identical paths. Report:
- mean, median, P25, and P75 paired net-EV changes in R;
- positive-path and bit-identical-path fractions;
- the median paired delta within each year and the median across seven years;
- simple 90% cluster-bootstrap context for EV and Calmar, with no CI gate;
- mean paired PF, Gain-to-Pain, Sortino, Calmar/MAR, CAGR, maximum drawdown,
total return, and Sharpe changes;
- trades, floor rejections, holding time, cash, gross exposure, average/peak
positions, turnover, and costs.
Means and identical-path fractions must appear beside medians so inert cohorts
cannot turn a left- or right-skewed treatment into a misleading zero headline.
For these 252-session windows, the implementation's full-window Calmar is CAGR
divided by maximum drawdown, the same numeric definition commonly called MAR;
do not present the duplicate label as a second independent metric.
Today's production membership is projected backward. Use paired differences
for the treatment conclusion; absolute profitability remains descriptive and
survivorship-biased. Empty and warm protocols cover the same seven market years
and must not be interpreted as independent replications.
Interpretation is deliberately simple:
- a positive result means the isolated floor improves the paired EV
distribution without an economically important loss of total-return or
drawdown quality;
- a negative result closes the floor;
- mixed EV/portfolio-quality results are reported as a trade-off, not forced
through a composite score.
## Reproducibility and macOS execution
The authoritative run refuses a dirty worktree. Its fingerprint includes the
implementation commit, this specification hash, snapshot hash, candidate-cache
key, construction view, cohort manifest, arm definitions, costs, and study
version. Cells checkpoint atomically and --resume verifies the fingerprint.
From the repository root on macOS:
python3 -m venv .venv
./.venv/bin/python -m pip install -e '.[dev]'
Preflight, reusing the completed bracket's candidate/rank cache:
./.venv/bin/python scripts/run_portfolio_construction_matrix.py + backtest_snapshots/research.sqlite + --study risk-floor-ab + --run-id prod505-effective-risk-floor-ab-daily-v1 + --candidate-cache reports/.cache/prod505-capacity-bracket-daily-v1-candidates.pkl + --workers 8 + --resume + --validate-only
Authoritative run:
./.venv/bin/python scripts/run_portfolio_construction_matrix.py + backtest_snapshots/research.sqlite + --study risk-floor-ab + --run-id prod505-effective-risk-floor-ab-daily-v1 + --candidate-cache reports/.cache/prod505-capacity-bracket-daily-v1-candidates.pkl + --workers 8 + --resume
On an M2 Pro, eight workers is the explicit high-utilization setting. Use six
instead on a memory-constrained machine; auto intentionally caps itself at six.
Changing worker count does not change the fingerprint or results.
Commit only the compact final JSON and Markdown reports. Candidate caches,
checkpoints, raw curves, and trade ledgers remain ignored.
scripts/portfolio_capacity_research.py
+102 -10
View File
@@ -43,6 +43,16 @@ ARMS: tuple[dict[str, Any], ...] = (
)
ARM_BY_ID = {arm['id']: arm for arm in ARMS}
RISK_FLOOR_ARMS: tuple[dict[str, Any], ...] = (
ARMS[0],
{
'id': 'cap10_min_risk_005',
'label': 'Cap 10, 0.5% minimum effective initial risk',
'max_positions': 10,
'min_initial_risk_fraction': 0.005,
'weekly_top_n_rebalance': False,
},
)
COSTS_PER_SIDE_PCT = (0.1, 0.2)
ANCHOR_YEARS = tuple(range(2019, 2026))
SCORING_SESSIONS = 504
@@ -190,12 +200,22 @@ def validate_cohort_manifest(manifest: dict[str, Any]) -> list[str]:
return errors
def build_cells(manifest: dict[str, Any]) -> list[dict[str, Any]]:
paths = [*manifest['empty_book'], *manifest['warm_book']]
def build_cells(
manifest: dict[str, Any],
*,
arms: tuple[dict[str, Any], ...] = ARMS,
protocols: tuple[str, ...] = ('empty_book', 'warm_book'),
costs: tuple[float, ...] = COSTS_PER_SIDE_PCT,
) -> list[dict[str, Any]]:
paths = [
path
for protocol in protocols
for path in manifest[protocol]
]
cells: list[dict[str, Any]] = []
for cost in COSTS_PER_SIDE_PCT:
for cost in costs:
for path in paths:
for arm in ARMS:
for arm in arms:
cell_id = (
f'{arm["id"]}|{path["protocol"]}|{path["path_id"]}'
f'|cost={cost:.1f}'
@@ -531,11 +551,26 @@ def _cluster_rows(
return summaries
def aggregate_results(cells: list[dict[str, Any]]) -> dict[str, Any]:
def aggregate_results(
cells: list[dict[str, Any]],
*,
arms: tuple[dict[str, Any], ...] = ARMS,
protocols: tuple[str, ...] = ('empty_book', 'warm_book'),
costs: tuple[float, ...] = COSTS_PER_SIDE_PCT,
include_warm_dispersion: bool = True,
) -> dict[str, Any]:
paired: list[dict[str, Any]] = []
for cost in COSTS_PER_SIDE_PCT:
for protocol in ('empty_book', 'warm_book'):
for arm in ARMS:
path_distributions: list[dict[str, Any]] = []
for cost in costs:
for protocol in protocols:
control_by_path = {
row['path_id']: row
for row in cells
if row['arm_id'] == 'cap10_incumbent'
and row['protocol'] == protocol
and float(row['cost_per_side_pct']) == float(cost)
}
for arm in arms:
arm_id = str(arm['id'])
clusters = _cluster_rows(
cells,
@@ -588,13 +623,66 @@ def aggregate_results(cells: list[dict[str, Any]]) -> dict[str, Any]:
'clusters': clusters,
'headline': headline,
})
treatment_by_path = {
row['path_id']: row
for row in cells
if row['arm_id'] == arm_id
and row['protocol'] == protocol
and float(row['cost_per_side_pct']) == float(cost)
}
shared_paths = sorted(
set(treatment_by_path) & set(control_by_path)
)
path_metrics: dict[str, Any] = {}
for metric in PAIRED_METRICS:
deltas = [
float(treatment_by_path[path_id]['metrics'][metric])
- float(control_by_path[path_id]['metrics'][metric])
for path_id in shared_paths
if treatment_by_path[path_id]['metrics'].get(metric)
is not None
and control_by_path[path_id]['metrics'].get(metric)
is not None
and math.isfinite(
float(treatment_by_path[path_id]['metrics'][metric])
)
and math.isfinite(
float(control_by_path[path_id]['metrics'][metric])
)
]
path_metrics[metric] = {
'paired_paths': len(deltas),
'paired_delta_mean': (
statistics.fmean(deltas) if deltas else None
),
'paired_delta_median': median(deltas),
'paired_delta_p25': percentile(deltas, 0.25),
'paired_delta_p75': percentile(deltas, 0.75),
'positive_fraction': (
sum(delta > 0.0 for delta in deltas) / len(deltas)
if deltas
else None
),
'identical_fraction': (
sum(abs(delta) <= 1e-12 for delta in deltas)
/ len(deltas)
if deltas
else None
),
}
path_distributions.append({
'arm_id': arm_id,
'protocol': protocol,
'cost_per_side_pct': cost,
'metrics': path_metrics,
})
warm_rows = [
row for row in cells if row['protocol'] == 'warm_book'
]
warm_dispersion: list[dict[str, Any]] = []
for cost in COSTS_PER_SIDE_PCT:
for arm in ARMS:
for cost in costs:
for arm in arms:
arm_id = str(arm['id'])
anchor_rows: list[dict[str, Any]] = []
for cluster in ANCHOR_YEARS:
@@ -660,8 +748,12 @@ def aggregate_results(cells: list[dict[str, Any]]) -> dict[str, Any]:
'headline': headline,
})
if not include_warm_dispersion:
warm_dispersion = []
return {
'paired_per_year': paired,
'paired_path_distributions': path_distributions,
'warm_seed_dispersion': warm_dispersion,
'bootstrap': {
'replicates': BOOTSTRAP_REPLICATES,
scripts/run_portfolio_construction_matrix.py
+269 -38
View File
@@ -1,8 +1,8 @@
'''Run the focused four-arm daily portfolio-capacity research matrix.
'''Run focused daily portfolio-construction research matrices.
The expensive point-in-time daily replay and full-universe ranks are cached
once. Empty-book monthly paths and warm-book weekly seeds are then evaluated
under cap 10, cap 15, cash-only unbounded, and weekly current-rank top 10.
once and reused by the frozen capacity bracket and its focused effective-risk
floor follow-up.
'''
from __future__ import annotations
@@ -37,6 +37,7 @@ from scripts.portfolio_capacity_research import ( # noqa: E402
BOOTSTRAP_REPLICATES,
BOOTSTRAP_SEED,
COSTS_PER_SIDE_PCT,
RISK_FLOOR_ARMS,
aggregate_results,
build_cells,
build_cohort_manifest,
@@ -54,13 +55,76 @@ MIN_PRODUCTION_UNIVERSE = 450
MAX_PRODUCTION_UNIVERSE = 600
SPEC_PATH = ROOT / 'docs' / 'research' / 'portfolio-capacity-bracket.md'
DEFAULT_RUN_ID = 'prod505-capacity-bracket-daily-v1'
RISK_FLOOR_SPEC_PATH = (
ROOT / 'docs' / 'research' / 'effective-risk-floor-ab.md'
)
STUDIES: dict[str, dict[str, Any]] = {
'capacity-bracket': {
'arms': ARMS,
'protocols': ('empty_book', 'warm_book'),
'include_warm_dispersion': True,
'runner_version': RUNNER_VERSION,
'spec_path': SPEC_PATH,
'default_run_id': DEFAULT_RUN_ID,
'research_question': (
'Bracket the economic cost of the binding ten-position cap and '
'test whether weekly current-rank selection beats arrival order.'
),
'decision_rule': (
'No formal promotion gate. Report paired annual medians, warm-seed '
'EV/Calmar IQR ratios, and simple bootstrap intervals as context.'
),
'motivation': {
'source': 'reports/research-matrix-phase-a.json a0_control full window',
'trades': 472,
'skipped_book_full': 519,
'blocked_fraction': 519 / (519 + 472),
'stale_claim_corrected': (
'The older weekly pre-gate-reset claim that cap 10 never bound '
'does not apply to the current daily configuration.'
),
},
},
'risk-floor-ab': {
'arms': RISK_FLOOR_ARMS,
'protocols': ('empty_book', 'warm_book'),
'include_warm_dispersion': False,
'runner_version': 'effective-risk-floor-ab-v1',
'spec_path': RISK_FLOOR_SPEC_PATH,
'default_run_id': 'prod505-effective-risk-floor-ab-daily-v1',
'research_question': (
'Estimate the isolated effect of rejecting entries whose effective '
'initial stop risk is below 0.5% of marked equity.'
),
'decision_rule': (
'No formal promotion gate. Attribute paired differences causally to '
'the floor, headline means and identical-path fractions beside '
'annual medians, and decide paper adoption after interpretation.'
),
'motivation': {
'source': (
'portfolio-construction-prod505-capacity-bracket-daily-v1 '
'post-run decomposition'
),
'finding': (
'The confounded floor arm improved EV most where the position '
'cap never bound; isolate the 0.5% floor at cap 10.'
),
},
},
}
_WORKER_CONTEXT: dict[str, Any] | None = None
def _parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('snapshot', help='SQLite backtest snapshot')
parser.add_argument('--run-id', default=DEFAULT_RUN_ID)
parser.add_argument(
'--study',
choices=tuple(STUDIES),
default='capacity-bracket',
)
parser.add_argument('--run-id', default=None)
parser.add_argument(
'--workers',
default='auto',
@@ -235,7 +299,7 @@ def _worker_run_cell(cell: dict[str, Any]) -> dict[str, Any]:
from app.services import backtest_service as bt
context = _WORKER_CONTEXT
arm = ARM_BY_ID[str(cell['arm_id'])]
arm = context.get('arm_by_id', ARM_BY_ID)[str(cell['arm_id'])]
measurement_start = date.fromisoformat(str(cell['measurement_start']))
hard_end = date.fromisoformat(str(cell['hard_end_exclusive']))
sim = bt._simulate_portfolio(
@@ -600,6 +664,7 @@ def _checkpoint_state(
fingerprint: str,
*,
resume: bool,
runner_version: str = RUNNER_VERSION,
) -> dict[str, dict[str, Any]]:
manifest_path = checkpoint_dir / 'manifest.json'
if checkpoint_dir.exists() and not resume:
@@ -620,7 +685,7 @@ def _checkpoint_state(
_atomic_json(
manifest_path,
{
'runner_version': RUNNER_VERSION,
'runner_version': runner_version,
'fingerprint': fingerprint,
'created_at': datetime.now(timezone.utc).isoformat(),
},
@@ -650,9 +715,13 @@ def _fmt(value: Any, digits: int = 3) -> str:
return str(value)
def _operational_summary(cells: list[dict[str, Any]]) -> list[dict[str, Any]]:
def _operational_summary(
cells: list[dict[str, Any]],
*,
arms: tuple[dict[str, Any], ...] = ARMS,
) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
for arm in ARMS:
for arm in arms:
arm_cells = [
row
for row in cells
@@ -676,6 +745,16 @@ def _operational_summary(cells: list[dict[str, Any]]) -> list[dict[str, Any]]:
'median_avg_positions': median(
row['metrics'].get('avg_positions') for row in arm_cells
),
'median_avg_hold_days': median(
row['metrics'].get('avg_hold_days') for row in arm_cells
),
'median_avg_cash_pct': median(
row['metrics'].get('avg_cash_pct') for row in arm_cells
),
'median_avg_gross_exposure_pct': median(
row['metrics'].get('avg_gross_exposure_pct')
for row in arm_cells
),
'peak_positions': max(
(
int(row['metrics'].get('peak_positions') or 0)
@@ -865,19 +944,158 @@ def _markdown(report: dict[str, Any]) -> str:
return '\n'.join(lines)
def _risk_floor_markdown(report: dict[str, Any]) -> str:
treatment_id = str(report['arms'][1]['id'])
lines = [
'# Effective initial-risk floor A/B',
'',
'Generated: ' + str(report['generated_at']),
'',
'## Question',
'',
'Does rejecting an otherwise qualified cap-10 entry when actual initial '
'stop risk is below 0.5% of marked equity improve trade selection?',
'',
'The arms differ only by min_initial_risk_fraction=0.005. There is no '
'position-cap, ranking, exit, sizing-target, or execution confound.',
'',
'Use paired differences, not absolute profitability, because current '
'production membership is projected backward.',
'',
]
paired = report['analysis']['paired_per_year']
distributions = report['analysis']['paired_path_distributions']
for cost in report['costs_per_side_pct']:
for protocol in report['protocols']:
annual = next(
row for row in paired
if row['arm_id'] == treatment_id
and row['protocol'] == protocol
and float(row['cost_per_side_pct']) == float(cost)
)
path = next(
row for row in distributions
if row['arm_id'] == treatment_id
and row['protocol'] == protocol
and float(row['cost_per_side_pct']) == float(cost)
)
metrics = path['metrics']
ev = metrics['ev_net_r']
annual_ev = annual['headline']['ev_net_r']
ev_ci = annual_ev['bootstrap_90']
separator = chr(124)
lines.append(
'## ' + protocol.replace('_', ' ').title()
+ f' at {float(cost):.2f}% per fill'
)
lines.extend([
'',
separator + ' Paths ' + separator + ' Mean dEV '
+ separator + ' Median dEV ' + separator + ' P25 '
+ separator + ' P75 ' + separator + ' Positive '
+ separator + ' Identical ' + separator + ' Annual median '
+ separator + ' 90% context ' + separator,
separator.join(
['', '---:', '---:', '---:', '---:', '---:', '---:',
'---:', '---:', '---:', '']
),
separator + ' ' + str(ev['paired_paths']) + ' '
+ separator + ' ' + _fmt(ev['paired_delta_mean']) + ' '
+ separator + ' ' + _fmt(ev['paired_delta_median']) + ' '
+ separator + ' ' + _fmt(ev['paired_delta_p25']) + ' '
+ separator + ' ' + _fmt(ev['paired_delta_p75']) + ' '
+ separator + ' '
+ _fmt(ev['positive_fraction'] * 100.0, 1) + '% '
+ separator + ' '
+ _fmt(ev['identical_fraction'] * 100.0, 1) + '% '
+ separator + ' '
+ _fmt(annual_ev['paired_delta_median']) + ' '
+ separator + ' [' + _fmt(ev_ci['p05']) + ', '
+ _fmt(ev_ci['p95']) + '] '
+ separator,
'',
separator + ' Mean dPF ' + separator + ' Mean dGtP '
+ separator + ' Mean dSortino ' + separator
+ ' Mean dCalmar/MAR '
+ separator + ' Mean dCAGR pp ' + separator
+ ' Mean dMaxDD pp ' + separator,
separator.join(
['', '---:', '---:', '---:', '---:', '---:', '---:', '']
),
separator
+ ' ' + _fmt(
metrics['profit_factor']['paired_delta_mean']
) + ' '
+ separator
+ ' ' + _fmt(
metrics['gain_to_pain']['paired_delta_mean']
) + ' '
+ separator
+ ' ' + _fmt(metrics['sortino']['paired_delta_mean']) + ' '
+ separator
+ ' ' + _fmt(metrics['calmar']['paired_delta_mean']) + ' '
+ separator
+ ' ' + _fmt(metrics['cagr_pct']['paired_delta_mean']) + ' '
+ separator
+ ' ' + _fmt(
metrics['max_drawdown_pct']['paired_delta_mean']
) + ' '
+ separator,
'',
])
lines.extend([
'## Operations at 0.10% per fill',
'',
separator + ' Arm ' + separator + ' Trades ' + separator + ' Hold '
+ separator + ' Cash ' + separator + ' Gross ' + separator
+ ' Positions ' + separator + ' Floor rejects ' + separator,
separator.join(
['', '---', '---:', '---:', '---:', '---:', '---:', '---:', '']
),
])
for row in report['operational_summary']:
lines.append(
separator + ' ' + str(row['arm_id']) + ' '
+ separator + ' ' + _fmt(row['median_trades'], 1) + ' '
+ separator + ' ' + _fmt(row['median_avg_hold_days'], 1) + ' '
+ separator + ' ' + _fmt(row['median_avg_cash_pct'], 1) + '% '
+ separator
+ ' ' + _fmt(row['median_avg_gross_exposure_pct'], 1) + '% '
+ separator + ' ' + _fmt(row['median_avg_positions'], 2) + ' '
+ separator + ' ' + str(row['min_risk_rejections']) + ' '
+ separator
)
lines.extend([
'',
'Empty-book starts are primary. Warm-book paths are a state-carrying '
'replication over the same seven years, not independent evidence or an '
'initialization-dispersion test.',
'',
'The 90% intervals resample seven annual paired summaries. They are '
'descriptive context, not gates or population-confidence claims.',
])
return '\n'.join(lines)
async def _main() -> None:
args = _parse_args()
study = STUDIES[str(args.study)]
args.run_id = args.run_id or study['default_run_id']
arms = tuple(study['arms'])
protocols = tuple(study['protocols'])
runner_version = str(study['runner_version'])
spec_path = Path(study['spec_path'])
snapshot = Path(args.snapshot)
if not snapshot.exists():
raise SystemExit(f'Snapshot does not exist: {snapshot}')
if not SPEC_PATH.exists():
raise SystemExit(f'Frozen specification is missing: {SPEC_PATH}')
if not spec_path.exists():
raise SystemExit(f'Frozen specification is missing: {spec_path}')
os.environ['BACKTEST_SNAPSHOT_OFFLINE'] = '1'
os.environ['BACKTEST_ALLOW_SPAWN'] = '1'
workers = _worker_count(str(args.workers))
snapshot_sha256 = _sha256_file(snapshot)
specification_sha256 = _sha256_file(SPEC_PATH)
specification_sha256 = _sha256_file(spec_path)
snapshot_data = await _load_snapshot(snapshot, quiet=bool(args.quiet))
cache_path = (
@@ -906,9 +1124,14 @@ async def _main() -> None:
snapshot_data['construction_universe_manifest']
)
validation_errors = [*universe_errors, *cohort_errors]
cells = build_cells(cohort_manifest)
cells = build_cells(
cohort_manifest,
arms=arms,
protocols=protocols,
)
validation_payload = {
'runner_version': RUNNER_VERSION,
'study': args.study,
'runner_version': runner_version,
'snapshot': str(snapshot.resolve()),
'snapshot_sha256': snapshot_sha256,
'snapshot_sessions': {
@@ -939,6 +1162,8 @@ async def _main() -> None:
'empty_cluster_count': cohort_manifest['empty_cluster_count'],
'warm_cluster_count': cohort_manifest['warm_cluster_count'],
'expected_clusters': list(ANCHOR_YEARS),
'protocols': list(protocols),
'arms': [arm['id'] for arm in arms],
'matrix_cells': len(cells),
'cache_path': str(cache_path.resolve()),
'cache_key_hash': _json_hash(candidate_cache['key']),
@@ -958,7 +1183,8 @@ async def _main() -> None:
_assert_clean_worktree()
git_commit = _git_output('rev-parse', 'HEAD')
fingerprint_payload = {
'runner_version': RUNNER_VERSION,
'study': args.study,
'runner_version': runner_version,
'git_commit': git_commit,
'snapshot_sha256': snapshot_sha256,
'specification_sha256': specification_sha256,
@@ -969,7 +1195,9 @@ async def _main() -> None:
snapshot_data['construction_universe_manifest']
),
'cohort_manifest': cohort_manifest,
'arms': list(ARMS),
'arms': list(arms),
'protocols': list(protocols),
'include_warm_dispersion': bool(study['include_warm_dispersion']),
'costs_per_side_pct': list(COSTS_PER_SIDE_PCT),
'bootstrap': {
'replicates': BOOTSTRAP_REPLICATES,
@@ -983,12 +1211,13 @@ async def _main() -> None:
else ROOT
/ 'reports'
/ '.cache'
/ f'{args.run_id}-{RUNNER_VERSION}-checkpoint'
/ f'{args.run_id}-{runner_version}-checkpoint'
)
completed = _checkpoint_state(
checkpoint_dir,
fingerprint,
resume=bool(args.resume),
runner_version=runner_version,
)
expected_ids = {str(cell['cell_id']) for cell in cells}
unknown = set(completed) - expected_ids
@@ -1006,6 +1235,7 @@ async def _main() -> None:
)
worker_context = {
'arm_by_id': {arm['id']: arm for arm in arms},
'qualified_candidates': candidate_cache['qualified_candidates'],
'daily_rank_map': candidate_cache['daily_rank_map'],
'prices': snapshot_data['prices'],
@@ -1060,34 +1290,25 @@ async def _main() -> None:
completed.values(),
key=lambda row: str(row['cell_id']),
)
analysis = aggregate_results(result_cells)
analysis = aggregate_results(
result_cells,
arms=arms,
protocols=protocols,
include_warm_dispersion=bool(study['include_warm_dispersion']),
)
dependency_manifest = ROOT / 'pyproject.toml'
report: dict[str, Any] = {
'run_id': args.run_id,
'study': args.study,
'status': 'complete',
'generated_at': datetime.now(timezone.utc).isoformat(),
'research_question': (
'Bracket the economic cost of the binding ten-position cap and '
'test whether weekly current-rank selection beats arrival order.'
),
'decision_rule': (
'No formal promotion gate. Report paired annual medians, warm-seed '
'EV/Calmar IQR ratios, and simple bootstrap intervals as context.'
),
'research_question': study['research_question'],
'decision_rule': study['decision_rule'],
'survivorship_bias_caveat': (
'The current production universe is projected backward; construction '
'conclusions rely on paired relative comparisons, not absolute levels.'
),
'motivation': {
'source': 'reports/research-matrix-phase-a.json a0_control full window',
'trades': 472,
'skipped_book_full': 519,
'blocked_fraction': 519 / (519 + 472),
'stale_claim_corrected': (
'The older weekly pre-gate-reset claim that cap 10 never bound '
'does not apply to the current daily configuration.'
),
},
'motivation': dict(study['motivation']),
'fingerprint': fingerprint,
'fingerprint_payload': fingerprint_payload,
'environment': {
@@ -1117,12 +1338,17 @@ async def _main() -> None:
}
},
'cohort_manifest': cohort_manifest,
'arms': list(ARMS),
'arms': list(arms),
'protocols': list(protocols),
'include_warm_dispersion': bool(study['include_warm_dispersion']),
'costs_per_side_pct': list(COSTS_PER_SIDE_PCT),
'cell_count': len(result_cells),
'cells': result_cells,
'analysis': analysis,
'operational_summary': _operational_summary(result_cells),
'operational_summary': _operational_summary(
result_cells,
arms=arms,
),
}
out_path = (
Path(args.out)
@@ -1132,7 +1358,12 @@ async def _main() -> None:
/ f'portfolio-construction-{args.run_id}.json'
)
_atomic_json(out_path, report)
_atomic_text(out_path.with_suffix('.md'), _markdown(report))
markdown = (
_risk_floor_markdown(report)
if args.study == 'risk-floor-ab'
else _markdown(report)
)
_atomic_text(out_path.with_suffix('.md'), markdown)
if not args.quiet:
print(f'wrote {out_path}', flush=True)
print(f'wrote {out_path.with_suffix(".md")}', flush=True)
tests/unit/test_portfolio_capacity_research.py
+75
View File
@@ -10,6 +10,7 @@ import pytest
from app.services import backtest_service as bt
from scripts.portfolio_capacity_research import (
ANCHOR_YEARS,
RISK_FLOOR_ARMS,
aggregate_results,
bootstrap_median_interval,
build_cells,
@@ -20,6 +21,7 @@ from scripts.portfolio_capacity_research import (
)
from scripts.run_portfolio_construction_matrix import (
CACHE_VERSION,
STUDIES,
_assert_clean_worktree,
_build_candidate_cache,
_checkpoint_state,
@@ -29,6 +31,7 @@ from scripts.run_portfolio_construction_matrix import (
_load_snapshot,
_markdown,
_operational_summary,
_risk_floor_markdown,
_worker_init,
_worker_run_cell,
_write_cell_checkpoint,
@@ -538,6 +541,29 @@ def test_cohort_manifest_realizes_seven_frozen_clusters():
assert len(cells) == (
len(manifest['empty_book']) + len(manifest['warm_book'])
) * 4 * 2
floor_cells = build_cells(manifest, arms=RISK_FLOOR_ARMS)
assert len(floor_cells) == (
len(manifest['empty_book']) + len(manifest['warm_book'])
) * 2 * 2
assert {row['arm_id'] for row in floor_cells} == {
'cap10_incumbent',
'cap10_min_risk_005',
}
def test_risk_floor_study_changes_only_the_effective_risk_floor():
control, treatment = RISK_FLOOR_ARMS
assert control['max_positions'] == treatment['max_positions'] == 10
assert (
control['weekly_top_n_rebalance']
== treatment['weekly_top_n_rebalance']
is False
)
assert control['min_initial_risk_fraction'] is None
assert treatment['min_initial_risk_fraction'] == 0.005
assert STUDIES['risk-floor-ab']['arms'] == RISK_FLOOR_ARMS
assert STUDIES['capacity-bracket']['arms'] != RISK_FLOOR_ARMS
def test_zero_outcome_horizon_extends_rank_replay_to_last_session(monkeypatch):
@@ -700,6 +726,18 @@ def test_aggregate_reports_paired_years_and_separate_warm_iqrs():
assert cash_empty['headline']['ev_net_r']['paired_delta_median'] == pytest.approx(
0.2
)
cash_paths = next(
row
for row in report['paired_path_distributions']
if row['arm_id'] == 'cash_unbounded'
and row['protocol'] == 'empty_book'
and row['cost_per_side_pct'] == 0.1
)
assert cash_paths['metrics']['ev_net_r']['paired_delta_mean'] == pytest.approx(
0.2
)
assert cash_paths['metrics']['ev_net_r']['positive_fraction'] == 1.0
assert cash_paths['metrics']['ev_net_r']['identical_fraction'] == 0.0
cash_warm = next(
row
for row in report['warm_seed_dispersion']
@@ -738,6 +776,43 @@ def test_aggregate_reports_paired_years_and_separate_warm_iqrs():
assert 'Rank-only qualified rows removed: 137000.' in markdown
assert 'formal promotion gate' in markdown
focused_cells = [
row
for row in cells
if row['arm_id'] == 'cap10_incumbent'
] + [
{
**row,
'arm_id': 'cap10_min_risk_005',
}
for row in cells
if row['arm_id'] == 'cash_unbounded'
]
focused_analysis = aggregate_results(
focused_cells,
arms=RISK_FLOOR_ARMS,
include_warm_dispersion=False,
)
assert focused_analysis['warm_seed_dispersion'] == []
focused_markdown = _risk_floor_markdown({
'generated_at': '2026-08-05T00:00:00Z',
'arms': list(RISK_FLOOR_ARMS),
'protocols': ['empty_book', 'warm_book'],
'costs_per_side_pct': [0.1, 0.2],
'analysis': focused_analysis,
'operational_summary': _operational_summary(
focused_cells,
arms=RISK_FLOOR_ARMS,
),
})
assert '# Effective initial-risk floor A/B' in focused_markdown
assert 'Mean dEV' in focused_markdown
assert 'Identical' in focused_markdown
assert 'Mean dGtP' in focused_markdown
assert 'Mean dCalmar/MAR' in focused_markdown
assert 'Floor rejects' in focused_markdown
assert 'not independent evidence' in focused_markdown
def test_synthetic_worker_matrix_covers_four_arms_protocols_and_costs(monkeypatch):
monkeypatch.setenv('BACKTEST_SNAPSHOT_OFFLINE', '0')