टर्नओवर और लागत नियंत्रण के साथ NASDAQ-100 संकेतों का बैकटेस्ट

# 14_backtest.py


```py
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# %% [markdown]
# # NASDAQ-100 Microstructure: Backtest & Signal Evaluation
#
# **Chapter 16 — Strategy Simulation**
#
# This notebook translates Ch11–15 model outputs into backtested strategies for
# the NASDAQ-100 microstructure case study: 15-minute decisions across the 113
# stocks of the declared 115 that carry prices.
# The backtest runs through the **ml4t-backtest engine** using 15-minute OHLCV
# bars constructed from AlgoSeek TAQ trade prices — the same data that supports
# position-level risk controls, realistic execution simulation, and proper cost
# accounting in downstream chapters (Ch17–19).
#
# 1. **Plumbing test** — verify the engine pipeline produces no spurious alpha
# 2. **Parametric sweep** — test all (prediction × signal method) combinations
# 3. **Statistical analysis** — DSR, family comparison, IC-to-Sharpe relationship
#
# Sections 1–2 generate new backtest results (write to registry). Section 3
# is read-only — it queries the registry via `BacktestExplorer` and can be
# re-run independently without re-running the sweep.
#
# **Book Reference:** Chapter 16, Sections 16.4–16.8
#
# **Prerequisites:** Completed model training (Ch11–15) for this case study.

# %%
"""Ch16 Backtest & Signal Evaluation — NASDAQ-100 Microstructure case study."""

import json
import sqlite3
import time

import polars as pl

from case_studies.research import (
    SUPERSEDES_LIVE,
    OfficialPopulation,
    open_study,
    population_supersedes,
    prediction_rows_at,
    published_population_names_at,
    reuse_disclosure,
    superseded_members_at,
)
from case_studies.utils.backtest_loaders import get_backtest_config, load_backtest_prices_for
from case_studies.utils.backtest_presets import (
    build_backtest_spec,
    serializable_backtest_spec,
    traded_universe_declaration,
)
from case_studies.utils.backtest_runner import (
    normalize_prediction_columns,
    run_backtest,
    run_plumbing_test,
)
from case_studies.utils.ensemble import (
    ensemble_training_spec,
    load_ensemble_declaration,
    mean_forecast,
    resolve_members,
)
from case_studies.utils.notebook_contracts import (
    degenerate_prediction_hashes,
    excluded_families,
    prediction_members_in_force,
)
from case_studies.utils.registry import (
    backtest_hash_from_parts,
    load_existing_backtest_hashes,
    load_prediction_index,
    read_predictions,
)
from case_studies.utils.registry.registration import (
    register_prediction_set,
    register_training_run,
)
from case_studies.utils.sweep_config import (
    get_entry_schemes_for,
    get_signal_passes_for,
    get_top_k_values_for,
    get_top_n_predictions,
    get_universe_filters_for,
)
from utils.paths import get_case_study_dir
from utils.style import show_with_alt

# %% tags=["parameters"]
CASE_STUDY_ID = "nasdaq100_microstructure"
LABEL = ""
SPLIT = "validation"
# Zero means the smallest top_k from setup.yaml backtest.sweep.top_k_grid.
TOP_K = 0
MAX_SYMBOLS = 0
FORCE_REBACKTEST = False  # Set True to re-backtest even if a complete backtest_hash exists
TOP_N_PREDICTIONS = None
# Zero means every feasible entry scheme. A positive value keeps the first N, which is what makes
# a reduced run of this notebook finish in minutes: `backtest.sweep.signal_nasdaq100` crosses two
# selection methods, three quantiles, two directions, three slot counts, two hold windows and two
# signal-exit thresholds, and 42 of those combinations are feasible on this panel. A backtest of
# this panel was measured at 31.8 s on 2026-09-11, so the full 45 arms is about 34 minutes for a
# single prediction set. The same lever as `MAX_RISK_VARIANTS` in 16 and `MAX_COST_POINTS` in the
# cost notebooks. Truncating the list keeps the canonical equal-weight arms, which are what
# `signal_passes.baseline_schemes` names, so a reduced run still completes pass 1.
MAX_ENTRY_SCHEMES = 0
# Both names stay bound here although nothing below reads them: that is what makes the harness
# force preview and supply a workspace - `_declares_tier_and_workspace` in `tests/pm_helpers.py`
# looks for exactly this pair. Without them the canonical branch regenerates in place, which
# needs generated-artifact symlinks a CI checkout does not have.
EXECUTION_TIER = "canonical"
WORKSPACE: str = ""

# %% [markdown]
# ## 1. Setup & Plumbing Test
#
# Before the sweep, check the machinery on a signal that carries no information.
# Replacing the predictions with random scores and running the same engine should
# not produce a profitable strategy. If it does, the profit came from the
# pipeline rather than the signal - a lookahead in the fill timing, a misaligned
# join, a cost model that never charges.
#
# The check is one-sided. Random trading pays the spread on every rebalance
# without any compensating edge, so a clearly negative result is the expected
# outcome and not a failure. What would fail is profit that persists after
# costs.

# %% [markdown]
# The study is opened before anything resolves a path or reads the registry. Opening it
# activates a root and rewrites `ML4T_OUTPUT_DIR` process-wide, and every later
# `get_case_study_dir`, prediction index and registry write resolves against that variable. A
# `CASE_DIR` bound before this line points at the released registry while the sweep writes to
# the workspace, and the two never meet: the sweep finds nothing registered and every reader
# scoped to hashes from the other root comes back empty.

# %%
study = open_study(CASE_STUDY_ID, execution_tier=EXECUTION_TIER, workspace=WORKSPACE or None)

CASE_DIR = get_case_study_dir(CASE_STUDY_ID)
bt_config = get_backtest_config(CASE_STUDY_ID)
if TOP_N_PREDICTIONS is None:
    TOP_N_PREDICTIONS = get_top_n_predictions(CASE_STUDY_ID, "signal")

if not LABEL:
    LABEL = bt_config.primary_label

print(
    f"""=== Protocol Term Sheet ===
  Case study:    {CASE_STUDY_ID}
  Label:         {LABEL}
  Calendar:      {bt_config.calendar}
  Cadence:       {bt_config.cadence}
  Commission:    {bt_config.commission_bps:.1f} bps
  Slippage:      {bt_config.slippage_bps:.1f} bps
  Total cost:    {bt_config.commission_bps + bt_config.slippage_bps:.1f} bps/leg
  Long/short:    {bt_config.long_short}
""",
    flush=True,
)
if excluded_families(CASE_STUDY_ID):
    print(
        "Active-model filter: excluding "
        f"{', '.join(sorted(excluded_families(CASE_STUDY_ID)))} pending corrected reruns",
        flush=True,
    )

# %%
prices = load_backtest_prices_for(CASE_STUDY_ID, LABEL, split="validation", max_symbols=MAX_SYMBOLS)

# `MAX_SYMBOLS` reduces the price panel, and until the run says so in its own specification
# that reduction did not reach `backtest_hash`: a reduced run and the full run over the same
# predictions hashed alike, so the second was served the first's result and the reduction
# bought nothing (ml4t/agent-workspace#911). Declaring it here, before anything is hashed,
# gives a reduced run an identity of its own; `run_backtest` checks the panel against the
# declaration and narrows the predictions to it, so the sweep ranks the cross-section this
# says it ranks and `n_assets` above describes that same set. A full run declares nothing and
# is byte-identical to before.
# A reduced run is a preview run. Refused on the canonical tier so a narrowed result can
# never land in the registry the book's numbers come from, and so the two can never sit in
# one registry to be ranked against each other: `resolve_best_predictions` takes MAX(sharpe)
# over every backtest of a prediction, and a Sharpe earned over a handful of names would
# advance a configuration ahead of one earned over the whole panel. `us_equities_panel` 16
# through 19 already refuse the parameter this way, and `canonically_refused_parameters`
# reads the refusal out of the source, so the canonical fixture path drops the name rather
# than handing the notebook something its first cell raises on.
if EXECUTION_TIER == "canonical" and MAX_SYMBOLS:
    raise ValueError(
        "MAX_SYMBOLS narrows the universe this run trades, which makes it a different "
        "portfolio from the declared one and gives it its own backtest identity "
        "(ml4t/agent-workspace#911). A canonical run trades the declared universe: set "
        "MAX_SYMBOLS=0, or run under EXECUTION_TIER='preview' with a WORKSPACE."
    )
TRADED_UNIVERSE = traded_universe_declaration(prices) if MAX_SYMBOLS else None

n_assets = prices["symbol"].n_unique()
if TOP_K == 0:
    _feasible_top_k = get_top_k_values_for(CASE_STUDY_ID, LABEL, n_assets)
    if not _feasible_top_k:
        raise ValueError(
            f"top_k_grid for {LABEL!r} in {CASE_STUDY_ID} has no value < "
            f"n_assets={n_assets}; declare a feasible k in setup.yaml"
        )
    TOP_K = _feasible_top_k[0]
print(f"Prices: {len(prices):,} rows, {n_assets} assets; plumbing-test TOP_K={TOP_K}", flush=True)

# %%
strategy_spec = build_backtest_spec(
    CASE_STUDY_ID,
    bt_config,
    prices=prices,
    traded_universe=TRADED_UNIVERSE,
    prediction_hash="plumbing_test",
    initial_cash=bt_config.initial_cash,
    chapter="ch16",
    # The cadence is per-label here (setup.yaml::decision.cadence_by_label), so a spec
    # built without one would be built against a default no run uses.
    label=LABEL,
    signal={
        "method": "score_weighted_top_k",
        "top_k": TOP_K,
        "long_short": bt_config.long_short,
    },
)

try:
    random_sharpe = run_plumbing_test(
        CASE_STUDY_ID,
        prices,
        strategy_spec,
        top_k=TOP_K,
        initial_cash=bt_config.initial_cash,
        calendar=bt_config.calendar,
    )

    status = "FAIL" if random_sharpe > 1.5 else "PASS"
    print(f"Random signal Sharpe: {random_sharpe:.3f}  [{status}]", flush=True)

    if random_sharpe > 1.5:
        print("WARNING: Random signal produces positive Sharpe — investigate pipeline", flush=True)
    elif random_sharpe < -1.5:
        print(
            "NOTE: Strongly negative random Sharpe reflects turnover drag under quote-aware costs",
            flush=True,
        )
except ValueError as e:
    if "zero variance" in str(e).lower():
        print(f"Plumbing test skipped: {e} (too few assets for meaningful test)", flush=True)
        random_sharpe = 0.0
    else:
        raise

# %% [markdown]
# ## 2. The Signal Sweep
#
# Every combination of prediction and entry scheme below runs through the same
# `run_backtest()` call as a one-off backtest would, so the sweep and a single
# backtest cannot diverge.
#
# The sweep runs in two passes. The first trades every prediction three ways -
# equal weight over the top 5, 10 and 20 names - on the cost-feasible universe,
# which is the universe the strategy this chapter ends on actually trades. The
# second takes the predictions that scored highest in the first and asks what the
# entry mechanism does to them, across the slot grid declared in
# `backtest.sweep.signal_nasdaq100`. The same three equal-weight arms are also run
# without the cost screen, on those same predictions: that unscreened pair is what
# Section 3 reads.
#
# Ranking over the whole universe is the naive approach the feasibility analysis
# warned against, and it is kept here as the comparison rather than as the main
# grid. The ordering will often place its strongest views on the least liquid
# names in the panel, and at this rebalancing frequency each of those positions is
# entered and exited repeatedly.
#
# The sweep also separates two things that are easily conflated: how well a
# prediction orders the cross-section, and how much trading that ordering
# provokes. A prediction whose scores change sharply from bar to bar produces
# more position changes than one whose scores move smoothly, at the same
# ordering quality. The cost of those changes is charged here and not in any
# score.

# %% [markdown]
# A grid cell is skipped when its identity is already registered or already
# queued by this sweep. Two schemes can resolve to one identity - a backtest is
# defined by what it computes, not by the label its scheme carries - and without
# the queued half of that test the first computes the result while the second
# reuses its cache and is still counted as work done, so the sweep reports more
# backtests than the registry holds.

# %%
pred_index = load_prediction_index(
    CASE_STUDY_ID,
    label=LABEL,
    split=SPLIT,
)
if not pred_index.is_empty():
    # Exclude causal_dml (not a trading signal) and the synthetic ensemble
    # forecast — the ensemble is a cost-feasible-universe construct introduced
    # in Section 4 (Act 2), not part of the full-universe baseline sweep.
    pred_index = pred_index.filter(~pl.col("family").is_in(["causal_dml", "ensemble"]))

if pred_index.is_empty():
    msg = f"No predictions found for {CASE_STUDY_ID}/{LABEL}/{SPLIT}"
    raise RuntimeError(msg)

# `load_prediction_index` answers "what predictions exist in this registry" and leaves
# admissibility to the caller. A backtest is asking a different question - "what should
# I trade" - and the difference is exactly the conditions the catalog computes. Without
# this filter the sweep consumes rows the official population cannot contain: a run that
# reports every backtest completed and zero failed, off a population that resolves empty.
# That is not a late failure, it is a loud success on the wrong rows, and it costs the
# full sweep to discover.
# `complete` is the whole test: the catalog already requires a current identity before a row
# can be complete, and the tier is decided by which registry the rows were read from, not by
# a column comparison. Re-asserting either here would reject a preview run's own rows.
# The catalog is read off `CASE_DIR`, the directory `load_prediction_index` just read, and
# not by opening a `Study`: every `Study.open` branch ends in `activate()`, which would both
# answer for a different registry than the one being filtered and re-point the rest of the
# notebook - including where `run_backtest(register=True)` writes - at the activated root.
_catalog = prediction_rows_at(CASE_DIR)
# `complete` does not answer supersession, and the two look like the same question. A row is
# complete when its artifacts and fold metrics are present, and `identity_status == "current"`
# only says the registry still understands the schema it was written under. Neither moves when
# a model notebook refits: the retired generation's rows stay complete and current, and a sweep
# selecting on the catalog alone runs over both generations at once. It does not fail - it
# succeeds over twice the population and freezes the mixture into every backtest downstream.
#
# Measured against this registry on 2026-09-11: it records one supersedes edge, on
# `nasdaq100_microstructure-linear-validation-v1`. That name's retired generation lists 61
# prediction identities and its generation in force shares none of them, because a refit moves
# every hash. None of the 61 is in the catalog, so this join removes nothing today, and every
# one of the catalog's rows belongs to some declared population.
#
# A no-op is what this filter looks like whenever a refit takes the rows it retired out of the
# catalog with it, which is what the linear refit did. It stops being one the first time a
# generation is retired while its rows stay - and nothing here guarantees which of the two a
# given refit will be, which is the reason to hold the join rather than to decide per refit.
_retired = superseded_members_at(CASE_DIR)
_admissible = _catalog.filter(
    pl.col("complete") & ~pl.col("prediction_hash").is_in(list(_retired))
).select("prediction_hash")
_offered = len(pred_index)
_offered_hashes = set(pred_index["prediction_hash"])
pred_index = pred_index.join(_admissible, on="prediction_hash", how="inner")
if pred_index.is_empty():
    msg = (
        f"{_offered} prediction sets exist for {CASE_STUDY_ID}/{LABEL}/{SPLIT} but none is "
        "admissible. Either every row is missing an artifact or a fold metric, or carries an "
        "identity this schema version no longer recognises; or every row belongs to a "
        f"generation its own name has moved past ({len(_retired)} identities in this registry "
        "are retired that way). Backtesting them would produce a full sweep over a population "
        "that cannot be official. Re-run the model notebooks on the research interface first."
    )
    raise RuntimeError(msg)
if len(pred_index) < _offered:
    # Two conditions were tested and they call for different work, so they are counted apart: an
    # incomplete row needs its own fit finished, a superseded one needs nothing - it is a
    # retired generation and the sweep is right to leave it. Supersession is named first because
    # it decides the row on its own; completing a retired row would not readmit it.
    _dropped = _offered_hashes - set(pred_index["prediction_hash"])
    _dropped_superseded = len(_dropped & _retired)
    print(
        f"  Excluded {len(_dropped)} of {_offered} prediction sets: "
        f"{len(_dropped) - _dropped_superseded} not complete, "
        f"{_dropped_superseded} superseded by a later generation of their own population",
        flush=True,
    )

# Membership, and it runs here rather than beside the exclusion above for two reasons
# that are easy to get wrong. The admissibility refusal has to stay reachable: a pool
# emptied by the catalog filter must raise the error naming incompleteness and
# supersession, not this one, which would report an empty pool as unpublished. And the
# _dropped accounting above splits its exclusions into two counts by subtracting what
# survives, so a membership drop landing before it would be counted as not complete.
#
# Membership is a different set from the exclusion above, and the weaker one is what
# this sweep had (ml4t/agent-workspace#1088). "Not retired" admits a
# prediction no population ever listed - an experiment its case study never published is
# retired by nobody - while "published" does not. `published_members_at` subtracts the
# retired set itself, so this is never the looser test; it is the same shape
# `sp500_equity_option_analytics/14_backtest:292` already applies, and running the nine
# on two different rules is the thing worth removing even where the answer agrees.
#
# The two rules agree on the publication question here - the registry publishes 784
# prediction sets and holds 784 - but do not read that as the filter being inert. Measured
# on the 2026-09-13 canonical run, it narrowed the pool in two steps and both bit:
#
#     220 of 784 members dropped for covering less of the cross-section than their
#         feature panels offered them
#     564 prediction sets in the populations in force
#     80 further sets excluded on this label as complete, not retired, and published
#         by no population in force
#     162 predictions swept
#
# The first named drop was a `deep_learning/lstm_h64` set carrying 81.2% of the (entity,
# session) pairs its input panel offered it. Without this filter the previous sweep's
# pass-2 eight was four `nlinear` checkpoints at 67.4% coverage and four L1-linear sets
# that are constants - two distinct forecast values across 8,006,995 rows - so 180 of its
# 360 mechanism backtests priced a 45-arm entry grid over predictions that rank nothing.
#
# `None` means the registry declares no populations at all - a fixture, or a clean clone -
# and there is then nothing to filter against. Left unscoped in that case rather than
# narrowed to nothing, which is what `prediction_members_in_force` returns `None` to say.
#
# It is slow, and silently so: it opens every published member's artifact to check symbol
# coverage, which on this registry is 784 parquet files totalling 94.3 GB. Measured on the
# 2026-09-13 canonical run at **8,608 s - 2 h 23 m, 11.0 s per member** - before the
# sweep's first backtest, with nothing printed while it runs because every print in this
# cell comes after the call returns. That is the cost of the check rather than a hang, and
# it is said here because the next reader watching a quiet log is the one who needs to
# know. It is also label-agnostic: it coverage-checks every published member whatever the
# caller is sweeping, so two thirds of those reads are for labels this run never touches.
# Scoping it to the candidates the caller can act on is a change to the shared helper and
# not to this notebook, which is why it is described here rather than made here.
_members, _population_notes = prediction_members_in_force(study, CASE_DIR)
for _note in _population_notes:
    print(f"  {_note}", flush=True)
if _members is not None:
    print(f"  {len(_members):,} prediction sets in the populations in force", flush=True)
    _before_membership = set(pred_index["prediction_hash"])
    pred_index = pred_index.filter(pl.col("prediction_hash").is_in(list(_members)))
    _unpublished = _before_membership - set(pred_index["prediction_hash"])
    if _unpublished:
        print(
            f"  Excluded {len(_unpublished)} further prediction sets that are complete and "
            "not retired but that no population in force publishes",
            flush=True,
        )
    if pred_index.is_empty():
        msg = (
            f"{len(_before_membership)} prediction sets for {CASE_STUDY_ID}/{LABEL}/{SPLIT} "
            "are complete and not retired, and no population in force publishes any of "
            f"them. The registry publishes {len(_members):,} prediction identities under "
            f"{len(published_population_names_at(CASE_DIR))} population names; this label's "
            "rows are in none of them, so the model notebook that wrote them registered "
            "them outside the population mechanism. Re-run it before sweeping."
        )
        raise RuntimeError(msg)

# The pool the sweep is entitled to draw on, captured HERE: after the membership and
# coverage filter above, and before the preview truncation below. The ensemble reads it
# rather than the catalog-level `_admissible`, which is only "complete and not retired"
# and so predates both. Captured before the truncation because `TOP_N_PREDICTIONS` narrows
# what a preview run *sweeps*, not what the case study is allowed to average.
SWEPT_POOL = set(pred_index["prediction_hash"])

if TOP_N_PREDICTIONS > 0:
    pred_index = pred_index.head(TOP_N_PREDICTIONS)

n_predictions = len(pred_index)
print(f"Predictions to sweep: {n_predictions}", flush=True)
ic_min, ic_max = pred_index["ic_mean"].min(), pred_index["ic_mean"].max()
print(
    f"  IC range: {ic_min:.4f} — {ic_max:.4f}"
    if ic_min is not None
    else "  IC range: not yet computed",
    flush=True,
)

# %%
# Passed as the cross-section width when the question is what setup.yaml declares rather than
# what this panel can trade: it is above every concentration any grid here declares, so
# `get_entry_schemes_for` drops nothing for feasibility and returns the declared names.
NO_FEASIBILITY_LIMIT = 1_000_000

entry_schemes = get_entry_schemes_for(
    CASE_STUDY_ID, LABEL, n_assets, long_short=bt_config.long_short
)
if MAX_ENTRY_SCHEMES:
    entry_schemes = entry_schemes[:MAX_ENTRY_SCHEMES]

# The universe axis, and which arms are crossed with which of its values.
#
# `backtest.sweep.universe_filter` declares `cost_feasible` here, and `universe.cost_feasible`
# names the 50 validation and 50 holdout symbols `apply_universe_filter` restricts to. Nothing
# in this notebook read either: every registered spec omitted `signal.universe_filter`, so
# `BacktestExplorer` defaulted all of them to "full". Act 1 worked by that accident, and the
# two readers that ask for the declared value got nothing - Section 4 below printed an empty
# table and exited 0, `17_costs` priced nothing and exited 0, and only `20_strategy_analysis`
# failed. Measured 2026-09-09: 202 signal backtests registered, 0 carrying the key.
_declared_universes = [u for u in get_universe_filters_for(CASE_STUDY_ID) if u]
universe_filters: list[str | None] = [None, *_declared_universes]

# The grid is walked in two passes, declared under `backtest.sweep.signal_passes`.
#
# Crossing every arm with every universe over every prediction is what this cell used to
# build, and on this case study that is 741 prediction sets × 117 arms × 2 universes =
# 173,394 backtests. One backtest of this panel was measured at 31.8 s on 2026-09-11, which
# puts the cross-product at about 1,500 hours. No other case study in the book declares more
# than four entry schemes or more than one universe.
#
# Pass 1 asks which predictions are worth studying, on three equal-weight concentrations and
# on the universe the carrier trades. Pass 2 asks what the entry mechanism does to them, and
# asks it only of the predictions pass 1 ranked highest. The two questions have different
# widths, and one cross-product answered both at the width of the wider one.
_passes = get_signal_passes_for(CASE_STUDY_ID)
_by_name = {s["name"]: s for s in entry_schemes}

if _passes is None:
    # No plan declared: the full cross-product, which is what every other case study runs.
    baseline_arms = [(s, u) for u in universe_filters for s in entry_schemes]
    mechanism_arms: list[tuple[dict, str | None]] = []
    mechanism_top_n = 0
else:
    # A name in `baseline_schemes` goes missing for two reasons and only one of them is a
    # defect. `get_entry_schemes_for` drops a concentration the cross-section cannot realize,
    # so any run narrower than the declared grid loses names by construction. The CI fixture
    # trades twelve symbols long-short, and a long-short selection needs 2k distinct names,
    # so `ew_top5` is realizable there and neither `ew_top10` nor `ew_top20` is - measured by
    # calling this function at n_assets=12, which reproduces that run's arm list exactly. A
    # name no declared grid produces at any width is a typo in setup.yaml.
    #
    # Asking the same function again with the feasibility rule lifted separates the two without
    # restating anywhere how a scheme is named. `MAX_ENTRY_SCHEMES` was the previous test and it
    # answers a different question - it is one of several reductions, and the fixture reaches
    # here with it unset - so a narrowed panel raised the typo error against a correct config.
    _declared_names = {
        s["name"]
        for s in get_entry_schemes_for(
            CASE_STUDY_ID,
            LABEL,
            NO_FEASIBILITY_LIMIT,
            long_short=bt_config.long_short,
            ranked_width=NO_FEASIBILITY_LIMIT,
        )
    }
    _undeclared = [n for n in _passes["baseline_schemes"] if n not in _declared_names]
    if _undeclared:
        msg = (
            f"backtest.sweep.signal_passes.baseline_schemes names {_undeclared}, which "
            f"get_entry_schemes_for does not produce for {LABEL} at any cross-section width. "
            f"Names the declared grids do produce: {sorted(_declared_names)[:8]}"
        )
        raise KeyError(msg)
    _baseline_names = [n for n in _passes["baseline_schemes"] if n in _by_name]
    if not _baseline_names:
        # Every declared baseline concentration is above what this run can rank. Pass 1 would
        # sweep nothing, rank nothing and report a completed run, which is the failure the
        # check above used to catch by accident.
        msg = (
            f"none of backtest.sweep.signal_passes.baseline_schemes "
            f"{_passes['baseline_schemes']} is feasible for {LABEL} on this run: "
            f"{n_assets} assets in the price panel, long_short={bt_config.long_short}, and "
            f"every declared concentration needs more names than that leaves. Declare a "
            f"smaller concentration, or widen the panel and the fitting stages together."
        )
        raise RuntimeError(msg)
    baseline_arms = [(_by_name[n], _passes["baseline_universe"]) for n in _baseline_names]
    mechanism_arms = [
        (s, _passes["baseline_universe"]) for s in entry_schemes if s["name"] not in _baseline_names
    ]
    # The reference universe carries the canonical concentrations only. It is what `17_costs`
    # reads for its full-vs-screened comparison and what Act 1 below reads for the unscreened
    # baseline. It is not a second copy of the sweep.
    mechanism_arms += [
        (_by_name[n], _passes["reference_universe"])
        for n in _passes["reference_schemes"]
        if n in _by_name
    ]
    mechanism_top_n = _passes["mechanism_top_n"]

print(f"\nEntry schemes ({len(entry_schemes)}):", flush=True)
for es in entry_schemes:
    print(f"  {es['name']}: {es['method']} (top_k={es.get('top_k', '-')})", flush=True)
print(
    "Universes: " + ", ".join("full" if u is None else str(u) for u in universe_filters),
    flush=True,
)

n_pass2_predictions = min(mechanism_top_n, n_predictions) if mechanism_arms else 0
n_pass1 = n_predictions * len(baseline_arms)
n_pass2 = n_pass2_predictions * len(mechanism_arms)
total_backtests = n_pass1 + n_pass2
print(
    f"\nPass 1 (baseline): {n_predictions} predictions × {len(baseline_arms)} arms "
    f"= {n_pass1} backtests",
    flush=True,
)
print(
    f"Pass 2 (mechanism): top {n_pass2_predictions} by pass-1 Sharpe × "
    f"{len(mechanism_arms)} arms = {n_pass2} backtests",
    flush=True,
)
print(f"Total grid: {total_backtests} backtests", flush=True)

# %%
t0 = time.time()
tally = {"completed": 0, "skipped": 0, "failed": 0}
existing_hashes = load_existing_backtest_hashes(CASE_STUDY_ID, stage="signal")
# Identities already registered, plus the ones this sweep has queued. Both mean
# "running this grid cell would add nothing", which is what the skip test needs.
planned = set(existing_hashes)
print(f"Existing signal-stage hashes in registry: {len(existing_hashes):,}", flush=True)


def run_arms(rows, arms, pass_name):
    """Run one pass: every arm in `arms` against every prediction in `rows`.

    Both passes walk the same loop, so what separates them is only which
    predictions and which arms they are handed. A pass that is given no arms
    returns nothing rather than raising: a preview run truncates the scheme list
    and can legitimately leave pass 2 empty.
    """
    out = []
    n_cells = len(rows) * len(arms)
    if not n_cells:
        print(f"\n{pass_name}: no grid cells", flush=True)
        return out
    print(
        f"\n{pass_name}: {len(rows)} predictions × {len(arms)} arms = {n_cells} cells", flush=True
    )

    for i, pred_row in enumerate(rows.iter_rows(named=True)):
        pred_hash = pred_row["prediction_hash"]
        source = pred_row["source"]
        ic_mean = pred_row["ic_mean"]

        pending_schemes = []

        for j, (scheme, universe) in enumerate(arms):
            idx = i * len(arms) + j + 1

            signal = {
                "method": scheme["method"],
                "top_k": scheme.get("top_k", 20),
                "long_short": bt_config.long_short,
            }
            signal.update({k: v for k, v in scheme.items() if k not in ("name", "method")})
            # Only when a filter is declared. A `None` written into the spec is not the same as
            # an absent key: it would move `backtest_hash` for every row registered before this
            # axis existed, and `get_universe_filters_for` normalizes "full" and "none" to None
            # for that reason.
            if universe is not None:
                signal["universe_filter"] = universe
            spec = build_backtest_spec(
                CASE_STUDY_ID,
                bt_config,
                prices=prices,
                traded_universe=TRADED_UNIVERSE,
                prediction_hash=pred_hash,
                initial_cash=bt_config.initial_cash,
                chapter="ch16",
                label=LABEL,
                signal=signal,
            )
            backtest_hash = backtest_hash_from_parts(pred_hash, serializable_backtest_spec(spec))

            if backtest_hash in planned:
                # Recorded, not just counted. A skipped cell is a cell of this pass whose
                # result is already in the registry, and pass 2 ranks on the pass-1 cells
                # rather than on the subset this invocation happened to compute.
                tally["skipped"] += 1
                out.append(
                    {
                        "prediction_hash": pred_hash,
                        "source": source,
                        "family": pred_row["family"],
                        "config_name": pred_row["config_name"],
                        "ic_mean": ic_mean,
                        "signal_method": scheme["name"],
                        "universe": "full" if universe is None else universe,
                        "pass": pass_name,
                        "backtest_hash": backtest_hash,
                        "ran": False,
                        # The metrics of a skipped cell are in the registry, not here, and
                        # the two halves of `out` have to share a schema for polars to
                        # build one frame from them.
                        "sharpe": None,
                        "total_return": None,
                        "max_drawdown": None,
                        "cagr": None,
                        "volatility": None,
                        "num_trades": None,
                        "failure": None,
                    }
                )
                continue
            planned.add(backtest_hash)
            pending_schemes.append((idx, scheme, universe, spec))

        if not pending_schemes:
            continue

        predictions = normalize_prediction_columns(read_predictions(CASE_STUDY_ID, pred_hash))

        for idx, scheme, universe, spec in pending_schemes:
            record = {
                "prediction_hash": pred_hash,
                "source": source,
                "ic_mean": ic_mean,
                "family": pred_row["family"],
                "config_name": pred_row["config_name"],
                "signal_method": scheme["name"],
                "universe": "full" if universe is None else universe,
                "pass": pass_name,
                "ran": True,
            }
            try:
                result = run_backtest(
                    CASE_STUDY_ID,
                    pred_hash,
                    spec,
                    prices=prices,
                    predictions=predictions,
                    label=LABEL,
                    register=True,
                    force_rebacktest=FORCE_REBACKTEST,
                    initial_cash=bt_config.initial_cash,
                    calendar=bt_config.calendar,
                )
                record.update(
                    backtest_hash=result.backtest_hash,
                    sharpe=result.metrics["sharpe"],
                    total_return=result.metrics["total_return"],
                    max_drawdown=result.metrics["max_drawdown"],
                    cagr=result.metrics.get("cagr", 0.0),
                    volatility=result.metrics.get("volatility", 0.0),
                    num_trades=result.metrics["num_trades"],
                    failure=None,
                )
                tally["completed"] += 1
                if result.backtest_hash:
                    existing_hashes.add(result.backtest_hash)
                    planned.add(result.backtest_hash)
            except Exception as exc:  # noqa: BLE001 - one cell must not end the sweep
                # Say what threw and which cell it was. A counter the log never
                # expands is how 144 of 360 pass-2 cells failed on `fwd_ret_5m`
                # while the run exited 0 and label coverage reported ok: every
                # one of them was a constant-forecast prediction meeting a slot
                # arm that cannot rank it, and nothing on the way out said so.
                # `record` already carries the family, config and arm, so the
                # message identifies the cell rather than only the exception.
                tally["failed"] += 1
                print(
                    f"  FAILED [{idx}/{n_cells}] {record['family']}/{record['config_name']} "
                    f"{record['signal_method']} on {record['universe']} "
                    f"(pred {pred_hash[:12]}): {type(exc).__name__}: {exc}",
                    flush=True,
                )
                record.update(
                    backtest_hash=None,
                    sharpe=None,
                    total_return=None,
                    max_drawdown=None,
                    cagr=None,
                    volatility=None,
                    num_trades=None,
                    failure=f"{type(exc).__name__}: {exc}",
                )
            out.append(record)

            if idx % 20 == 0 or idx == n_cells:
                elapsed = time.time() - t0
                rate = idx / elapsed if elapsed > 0 else 0
                print(
                    f"  [{idx}/{n_cells}] {elapsed:.0f}s ({rate:.1f} bt/s) | "
                    f"completed: {tally['completed']} skipped: {tally['skipped']} "
                    f"failed: {tally['failed']}",
                    flush=True,
                )
    return out


baseline_results = run_arms(pred_index, baseline_arms, "Pass 1 (baseline)")

# %% [markdown]
# ### Which predictions pass 2 studies
#
# The mechanism grid runs on the predictions that scored highest in pass 1, and
# on nothing else. The ranking is by pass-1 validation Sharpe.
#
# Not by information coefficient. `load_prediction_index` returns its rows
# ordered by `ic_mean` descending, so taking the head of it - which is what
# `top_n_predictions.signal` would do - would let a rank correlation decide which
# models are backtested at all. A rank correlation and a traded result disagree
# whenever turnover differs between two predictions of the same ordering quality,
# which is the whole subject of this chapter. Every case study in the book
# declares `signal: 0` for that reason.
#
# What this does inherit from pass 1 is pass 1's own arbitrariness: the three
# equal-weight concentrations are one way to trade a prediction, and a prediction
# that suits the slot mechanism but not equal weight will not reach pass 2. That
# is a property of a two-pass design and not of this particular grid, and it is
# the price of not running the cross-product.

# %%
# The Sharpe comes from the registry and not from `baseline_results`, although pass 1 just
# produced both. A cell whose identity was already registered is skipped rather than re-run -
# that is what makes this sweep resumable - and a skipped cell computes no metrics for this
# invocation to hold. Ranking on what this invocation computed would therefore rank on
# whatever pass 1 had left to do: the whole pass on a first run, a fragment of it after an
# interruption, and nothing at all on a re-run of a finished sweep, which would silently move
# the selection or empty it. The registry holds every pass-1 cell either way.
#
# Built from the two hash fields under an explicit schema rather than from the records
# whole. `pl.DataFrame` infers a schema from the first 100 rows, the metric columns of a
# skipped record are all null, and a run that skips its first hundred cells and then
# computes one would hand a float to a column inferred as Null and fail at construction -
# before the `.select` that discards those columns ever runs. Resuming a long sweep is
# exactly the case that orders the records that way.
_cells = pl.DataFrame(
    [
        {"prediction_hash": r["prediction_hash"], "backtest_hash": r["backtest_hash"]}
        for r in baseline_results
    ],
    schema={"prediction_hash": pl.String, "backtest_hash": pl.String},
    orient="row",
).drop_nulls()
pass2_index = pred_index.head(0)
if mechanism_arms and not _cells.is_empty():
    _conn = sqlite3.connect(str(CASE_DIR / "run_log" / "registry.db"))
    _metrics = pl.read_database(
        "SELECT backtest_hash, sharpe FROM backtest_metrics",
        connection=_conn,
        schema_overrides={"sharpe": pl.Float64},
    )
    _conn.close()
    # `drop_nulls` is the complete test here, and only because the Sharpe crosses SQLite.
    # A ruined path writes `float("nan")` for every ranking metric
    # (`backtest_runner.RUIN_UNRANKABLE_METRICS`), and a NaN read back into polars would
    # survive `drop_nulls` and then sort AHEAD of every finite Sharpe under
    # `descending=True`, so a bankrupt strategy would take a pass-2 slot. SQLite has no
    # NaN: it stores one as NULL, which is what makes the null test sufficient. Read the
    # scores from anywhere but the registry and this line needs `is_finite` as well.
    _scored = _cells.join(_metrics, on="backtest_hash", how="inner").drop_nulls("sharpe")
    if not _scored.is_empty():
        _ranked = (
            _scored.group_by("prediction_hash")
            .agg(best_sharpe=pl.col("sharpe").max())
            .sort("best_sharpe", descending=True)
            .head(mechanism_top_n)
        )
        pass2_index = pred_index.join(
            _ranked.select("prediction_hash"), on="prediction_hash", how="inner"
        )
        print(
            _ranked.join(
                pred_index.select("prediction_hash", "source", "family"),
                on="prediction_hash",
                how="left",
            ).select("source", "family", "best_sharpe")
        )

if mechanism_arms and pass2_index.is_empty():
    # Pass 1 registered no metric for any of its cells, so pass 2 has nothing to select and
    # would run over an empty frame while the sweep reported success.
    msg = (
        f"pass 1 ran {len(_cells)} grid cells for {LABEL} and the registry holds a Sharpe "
        "for none of them, so the mechanism grid has nothing to select. Either every "
        "pass-1 backtest failed, or the metrics were written to a different registry than "
        f"{CASE_DIR / 'run_log' / 'registry.db'}."
    )
    raise RuntimeError(msg)

# %%
mechanism_results = run_arms(pass2_index, mechanism_arms, "Pass 2 (mechanism)")
results = baseline_results + mechanism_results

elapsed = time.time() - t0
completed, skipped, failed = tally["completed"], tally["skipped"], tally["failed"]
print(
    f"\nSweep complete in {elapsed:.0f}s: {reuse_disclosure(completed, skipped, failed)}",
    flush=True,
)

# %% [markdown]
# ## 3. Full-Universe Signal Evaluation (Act 1)
#
# This section is **read-only** — it queries the registry via `BacktestExplorer`
# and reads the rows that carry no cost-feasibility screen. The cost-feasible
# carrier is Section 4.
#
# What it reads is the unscreened arm of pass 2: equal weight over the top 5, 10
# and 20 names, on the predictions pass 1 ranked highest, with every name in the
# panel eligible. The slot mechanism is not in these rows - it is run on the
# screened universe only - so the comparison this section draws is between
# concentrations and between model families at one entry rule, and not between
# entry rules. The entry-rule comparison is Section 4's.
#
# Reading the unscreened arm against the screened one is what makes the cost
# screen a measured decision rather than a declared one: the same predictions and
# the same three ways of trading them, with and without the constraint.

# %%
from case_studies.utils.backtest_explorer import BacktestExplorer

explorer = BacktestExplorer(CASE_STUDY_ID)
print(repr(explorer))

# Scope Act 1 to the full universe. The cost-feasible carrier rows
# (universe_filter == "cost_feasible") are analyzed in Section 4.
all_signal = explorer.best(stage="signal", top_n=99999)
full_signal = all_signal.filter(pl.col("universe_filter") == "full")
print(
    f"Full-universe signal backtests: {full_signal.height:,} "
    f"(of {all_signal.height:,} total signal-stage rows)"
)

# %% [markdown]
# ### What turnover does to the same ordering
#
# The two entry schemes differ in how much trading they provoke from the same
# ordering. Equal-weight top-k re-ranks and rebalances at every decision time, so
# any name that drifts across the cut-off is sold and another bought. The slot
# mechanism holds a fixed number of positions and replaces one only when a
# candidate scores above the position it would displace, which leaves a position
# alone while it stays competitive.
#
# Comparing the two on the same predictions isolates the cost of turnover from
# the quality of the ordering, because only the trading rule differs. The
# per-method summary below reports the spread of outcomes rather than one figure
# per method: on a grid this size the extremes are the configurations most likely
# to be reading noise, and the median says more about the method itself.

# %%
method_split = (
    full_signal.group_by("signal_method")
    .agg(
        n=pl.len(),
        pos_frac=(pl.col("sharpe") > 0).mean().round(3),
        sharpe_max=pl.col("sharpe").max().round(2),
        sharpe_median=pl.col("sharpe").median().round(2),
    )
    .sort("n", descending=True)
)
print(method_split)

# %% [markdown]
# ### The strongest validation configurations
#
# The highest-scoring configurations from the sweep, kept for the out-of-sample
# test later in the pipeline. Two cautions attach to this table.
#
# It is the top of a large grid, so the configurations in it are the ones that
# suited this particular validation window best, and part of what put them there
# is chance. The larger the grid, the more of the top is chance.
#
# Nothing here is a selection. The configuration carried forward is chosen once,
# under the rule in the strategy analysis notebook, and tested on the holdout
# exactly once.

# %%
top = full_signal.sort("sharpe", descending=True).head(10)
print(top.select("source", "signal_method", "sharpe", "cagr", "max_drawdown"))

# %% [markdown]
# ### Model family comparison across the universe
#
# Grouping the sweep by the model family that produced each prediction asks
# whether a family's ranking quality carries through to a traded result.
#
# The two need not agree, and the reason is turnover. A model can order the
# cross-section well and still produce a poor strategy if its scores jump between
# decision times, because each jump is a trade and each trade pays the spread. A
# discrete score, such as one derived from predicted class membership, changes in
# steps and provokes more of those than a continuous one at the same ordering
# quality.

# %%
families = (
    full_signal.group_by("family")
    .agg(
        n=pl.len(),
        sharpe_max=pl.col("sharpe").max(),
        sharpe_median=pl.col("sharpe").median(),
    )
    .sort("sharpe_max", descending=True)
)
print(families)

# %%
import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Sharpe distribution histogram (full universe)
if not full_signal.is_empty():
    axes[0].hist(full_signal["sharpe"].to_numpy(), bins=30, edgecolor="white")
    axes[0].axvline(0, color="red", linestyle="--", linewidth=1)
    axes[0].set_xlabel("Sharpe Ratio")
    axes[0].set_ylabel("Count")
    axes[0].set_title("Full-Universe Sweep: Turnover Drives the Sharpe Distribution")

    # IC vs Sharpe
    axes[1].scatter(
        full_signal["ic_mean"].fill_null(0).to_numpy(),
        full_signal["sharpe"].to_numpy(),
        alpha=0.4,
        s=20,
    )
    axes[1].set_xlabel("Prediction IC (mean)")
    axes[1].set_ylabel("Backtest Sharpe")
    axes[1].set_title("IC → Sharpe: Better Prediction = Better Trading?")

# No `tight_layout()`: `matplotlibrc` sets the layout engine for every figure, so a second
# layout pass writes a warning into the render rather than improving it.
show_with_alt(
    fig,
    "Two panels side by side from the full-universe baseline sweep. Left: a 30-bin "
    "histogram of backtest Sharpe ratios, counts on the vertical axis and Sharpe on the "
    "horizontal, with a dashed red vertical line at zero. Right: a scatter of backtest "
    "Sharpe on the vertical axis against mean prediction IC on the horizontal, one "
    "translucent point per backtested configuration. The two panels answer the same "
    "question from opposite directions: how much of the Sharpe spread the histogram shows "
    "is accounted for by prediction quality.",
)

# %% [markdown]
# ### Sharpe vs Trade Count Diagnostic
#
# At 15-minute cadence, the relationship between Sharpe and trade count
# reveals the cost dominance problem. Strategies with high trade counts
# (active rebalancing) have deeply negative Sharpe because cost drag scales
# linearly with the number of trades. The only configurations with positive
# Sharpe are those with very few trades — either degenerate strategies
# (near-constant predictions from DL models) or extreme concentration
# (top_k=5 with infrequent position changes).
#
# This diagnostic motivates the cadence × cost analysis in Ch18: reducing
# the rebalance frequency is the structural solution to cost dominance.

# %% [markdown]
# The query below reads the full-universe runs only. Rows produced under the
# screened universe are excluded so the relationship between trade count and
# outcome is read on the unscreened baseline rather than on a mixture of the two.

# %%
db_path = CASE_DIR / "run_log" / "registry.db"
conn = sqlite3.connect(str(db_path))

trade_df = pl.read_database(
    """
    SELECT
        br.backtest_hash,
        bm.sharpe,
        bm.num_trades
    FROM backtest_runs br
    JOIN backtest_metrics bm ON br.backtest_hash = bm.backtest_hash
    WHERE br.stage = 'signal'
      AND json_extract(br.spec_json, '$.strategy.signal.universe_filter') IS NULL
    """,
    connection=conn,
    schema_overrides={"num_trades": pl.Float64, "sharpe": pl.Float64},
)
conn.close()

trade_df = trade_df.drop_nulls("num_trades")
print(f"Signal-stage backtests with trade data: {len(trade_df)}")
print(f"Trade count range: {trade_df['num_trades'].min():.0f} — {trade_df['num_trades'].max():.0f}")
print(f"Median trades: {trade_df['num_trades'].median():.0f}")

# %%
if not trade_df.is_empty():
    fig, axes = plt.subplots(1, 2, figsize=(14, 5))

    # Scatter: Sharpe vs trades
    axes[0].scatter(
        trade_df["num_trades"].to_numpy(),
        trade_df["sharpe"].to_numpy(),
        alpha=0.15,
        s=10,
    )
    axes[0].axhline(0, color="red", linestyle="--", linewidth=1)
    axes[0].set_xlabel("Number of Trades")
    axes[0].set_ylabel("Sharpe Ratio")
    axes[0].set_title("Sharpe vs Trade Count (baseline stage)")

    # Highlight: positive Sharpe only
    positive = trade_df.filter(pl.col("sharpe") > 0)
    if not positive.is_empty():
        axes[0].scatter(
            positive["num_trades"].to_numpy(),
            positive["sharpe"].to_numpy(),
            color="green",
            alpha=0.6,
            s=20,
            label=f"Sharpe > 0 ({len(positive)})",
        )
        axes[0].legend()

    # Histogram: trade count distribution
    axes[1].hist(trade_df["num_trades"].to_numpy(), bins=50, edgecolor="white")
    axes[1].set_xlabel("Number of Trades")
    axes[1].set_ylabel("Count")
    axes[1].set_title("Distribution of Trade Counts")

    # See the note on the figure above: the layout engine is set repository-wide.
    show_with_alt(
        fig,
        "Two panels side by side. Left: a scatter of backtest Sharpe on the vertical axis "
        "against number of trades on the horizontal, one translucent grey point per "
        "backtested configuration, with a dashed red horizontal line at zero Sharpe and "
        "the configurations above zero redrawn in green and counted in the legend. Right: "
        "a 50-bin histogram of the same trade counts, counts on the vertical axis. Read "
        "the horizontal position of the green points against the bulk of the histogram.",
    )

    # Summary: positive-Sharpe strategies are low-trade
    if not positive.is_empty():
        print(
            f"\nPositive-Sharpe backtests: {len(positive)} / {len(trade_df)} "
            f"({len(positive) / len(trade_df):.1%})"
        )
        print(f"  Median trades (positive Sharpe): {positive['num_trades'].median():.0f}")
        print(f"  Median trades (all): {trade_df['num_trades'].median():.0f}")
        print(
            f"  → Positive Sharpe strategies trade "
            f"{trade_df['num_trades'].median() / max(positive['num_trades'].median(), 1):.1f}x less"
        )

# %% [markdown]
# A prediction that changes little from bar to bar triggers few position changes,
# and so pays little in spread, whatever its ordering quality. That is model
# smoothness standing in for a decision about how often to trade, and it is not a
# decision anyone made. `17_costs` makes it explicitly, by sweeping the rebalance
# cadence itself.

# %% [markdown]
# ## 4. The Cost-Feasible Carrier (Act 2)
#
# Act 1 established that ranking across all 113 names and rebalancing every bar
# is cost-defeated. Act 2 applies the **cost-feasibility screen** from the
# feasibility analysis (restrict to the cost-feasible universe — the
# cheapest-to-trade names, frozen per split) and replaces every-bar rebalancing
# with a turnover-controlled **slot mechanism**: a fixed number of concurrent
# positions (slots), each entered when its signal clears a rolling per-symbol
# percentile gate and held to a maximum horizon, displaced only by fresher
# signals. The book then trades on composition changes rather than re-ranking
# every bar. See `case_studies/utils/slot_strategy.py` for the mechanism.
#
# The design shown here fixes the number of concurrent slots, the maximum time a
# position may be held, the percentile a signal must clear to enter, and the exit
# rule. The grid those four were chosen from runs on the screened universe only,
# which is also the universe the chosen design is read on: a mechanism whose
# purpose is to cut the cost of trading a cost-expensive tail has nothing to
# choose between on a panel that still contains the tail. These backtests are
# registered under `universe_filter='cost_feasible'` and are queried directly.

# %% [markdown]
# ### The forecast the chapter carries
#
# The strategy this chapter ends on does not trade one model. It trades the mean
# of every regularized LightGBM forecast in the study - twelve configurations,
# three losses across four leaf counts - as a single ordering.
#
# That object has to be built before it can be traded, and it is built here. The
# members are resolved from what is registered rather than listed, each enters at
# its last checkpoint, and the average is registered as a prediction set of its
# own under `family='ensemble'`. Everything downstream then reads it exactly the
# way it reads a model: the backtest below, the holdout notebooks, and the
# cross-case-study comparison in `20_strategy_analysis`.
#
# Averaging is not a way of getting a better forecast, and normally it does not
# produce one. What it produces is a forecast nobody chose. The rule that picks
# it is fixed before the validation window is read, so the validation result is
# a measurement of that rule rather than the best of the twelve results it could
# have reported.
#
# Its last checkpoint and not its best one, for the same reason: a checkpoint
# chosen on validation is a choice made on the window the result is then read on.

# %%
_ens = load_ensemble_declaration(CASE_STUDY_ID)
ensemble_prediction_hash = None
# How many configurations the mean forecast averages. `_members` cannot answer that far
# down the notebook: the name holds the in-force prediction identities from the membership
# filter until the branch below rebinds it to `resolve_members`' frame, so a run that skips
# the ensemble leaves a frozenset where a later `.height` would be read.
ENSEMBLE_MEMBER_COUNT = None
if _ens is None:
    print("No ensemble declared for this case study.", flush=True)
elif EXECUTION_TIER != "canonical":
    # A preview training spec has to identity-cover its reductions, and this one has no
    # reductions of its own to cover - it is an average of whatever the members are. The
    # ensemble is a canonical-tier object; a preview run reads the canonical registry's
    # rows if they are there and otherwise leaves Section 4's ensemble line empty.
    print(f"Ensemble skipped: it is a canonical-tier object and this run is {EXECUTION_TIER}.")
else:
    # The pool the sweep actually ran on, so the two stages agree by construction rather
    # than by comment. This used to pass `_admissible`, which is the catalog filter -
    # complete and not retired - computed before `prediction_members_in_force` narrowed
    # the pool on publication and cross-sectional coverage. On the 2026-09-13 run that gap
    # was 784 catalog rows against 162 swept, so an unpublished or undercovered member
    # could have entered the ensemble while the sweep refused to backtest it, and
    # publishing the ensemble would have carried it downstream anyway.
    #
    # Degenerate sets are excluded on top, because the sweep has no degeneracy filter of
    # its own and a constant forecast averaged into a mean is not a weaker member - it is
    # a fixed offset. Four L1-linear sets in this registry hold two distinct forecast
    # values across 8,006,995 rows.
    _eligible = SWEPT_POOL - degenerate_prediction_hashes(CASE_DIR)
    _members = resolve_members(
        CASE_DIR,
        label=LABEL,
        family=str(_ens["member_family"]),
        split=SPLIT,
        max_num_leaves=int(_ens["max_num_leaves"]),
        admissible=_eligible,
    )
    _member_spec_json = sqlite3.connect(
        f"file:{CASE_DIR / 'run_log' / 'registry.db'}?mode=ro", uri=True
    )
    try:
        _row = _member_spec_json.execute(
            "SELECT spec_json FROM training_runs WHERE training_hash = ?",
            (_members["training_hash"][0],),
        ).fetchone()
    finally:
        _member_spec_json.close()
    _member_spec = json.loads(_row[0])
    _task = (_member_spec.get("computation", {}).get("task") or {}).get("type")

    if _task != "regression":
        # Averaging class scores is a different construction from averaging forecasts - it
        # needs the class values and the continuous column the classes were cut from, and
        # the registry asks for both. The featured carrier is a continuous label, so the
        # construction is not built rather than guessed at.
        print(
            f"Ensemble skipped for {LABEL}: its {_ens['member_family']} members are "
            f"{_task} models, and a mean forecast is defined here for regression only.",
            flush=True,
        )
    else:
        ENSEMBLE_MEMBER_COUNT = _members.height
        print(
            f"Ensemble members ({_members.height}, {_ens['member_family']} at "
            f"<= {_ens['max_num_leaves']} leaves, last checkpoint):",
            flush=True,
        )
        for _m in _members.iter_rows(named=True):
            print(
                f"  {_m['config_name']:22} leaves {_m['num_leaves']:>3}  "
                f"checkpoint {_m['checkpoint_value']}",
                flush=True,
            )

        _spec = ensemble_training_spec(
            _row[0],
            members=_members,
            method=str(_ens["method"]),
            config_name=str(_ens["config_name"]),
            provenance={
                "entry_point": "case_studies.utils.ensemble",
                "notebook_path": "14_backtest",
                "member_family": str(_ens["member_family"]),
            },
        )
        _ens_training_hash = register_training_run(
            CASE_STUDY_ID, _spec, case_dir=CASE_DIR, entry_point="14_backtest.py"
        )
        _averaged = mean_forecast(CASE_STUDY_ID, _members["prediction_hash"].to_list())
        ensemble_prediction_hash =