پیش‌بینی‌های داده نگه‌داشته‌شدهِ ایمن از نشت برای مدل‌های ETF

# 18_holdout_predictions.py


```py
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# %% [markdown]
# # ETFs: Holdout Predictions
#
# **Chapter 20 - Out-of-sample evaluation**
#
# Every number in this case study so far was measured on the validation folds, and every
# choice was made by looking at them: which model family, which configuration, how many
# funds to hold, how to size them, which risk control to overlay, what to charge. A result
# selected that way cannot also be evidence that the selection was sound - the ranking and
# the evidence would be the same measurement.
#
# The holdout is the window nothing has been selected on. This notebook fits the selected
# configuration on the history available before that window opens and writes its
# predictions over it. [`19_holdout_backtest`](19_holdout_backtest.ipynb) turns those
# predictions into a return series with the sizing and the overlay the case study settled
# on, and [`20_strategy_analysis`](20_strategy_analysis.ipynb) reads both back.
#
# **What this notebook is careful about**
#
# A holdout prediction is not the validation model scored on a later window. Section 2
# fits again, over a training interval that ends before the window opens, and the new
# training identity is what makes the refit visible rather than asserted: the identity
# covers the CV interval, so a run that came back with the validation training hash would
# mean no refit happened. The check is in section 3 and it raises.
#
# **Prerequisites:** [`17_costs`](17_costs.ipynb), which is the last stage that selects.
#
# **Scope:** one training run and one prediction set. No backtest, no selection, no
# comparison - those are 19 and 20.

# %%
"""ETFs: Holdout Predictions."""

import polars as pl

from case_studies.research import open_study
from case_studies.research.holdout import build_holdout_training_spec
from case_studies.research.models import reconstruct_locked_model_request
from case_studies.utils.registry import training_hash_from_spec
from case_studies.utils.strategy_analysis import (
    holdout_generations_to_retire,
    registered_holdout_generations,
    resolve_solvent_carrier,
)
from utils.paths import get_case_study_dir

# %% tags=["parameters"]
CASE_STUDY_ID = "etfs"
EXECUTION_TIER = "canonical"
WORKSPACE: str = ""

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


# %% [markdown]
# ## 1. Which configuration the holdout runs
#
# The holdout runs the configuration the case study reports, resolved through the same
# `resolve_solvent_carrier` [`17_costs`](17_costs.ipynb) prices. Resolving it again here
# rather than passing it along is deliberate: the two notebooks must agree by construction,
# and a hash written down in one and read in the other agrees only until the sweep is
# rebuilt.
#
# Nothing about the holdout enters this choice. The selected configuration is the cross-stage
# validation rank-1 - which for this case study is a risk-overlay run, not the allocation leader -
# and it was fixed before this notebook ran.

# %%
carrier = resolve_solvent_carrier(CASE_STUDY_ID)
print(
    f"Selected configuration: {carrier['val_backtest_hash']}  stage={carrier['val_stage']}  "
    f"family={carrier['family']}  config={carrier['config_name']}  "
    f"label={carrier['label']}"
)
print(
    f"  validation Sharpe {carrier['val_sharpe']:.3f}, max drawdown {carrier['max_drawdown']:.3f}"
)
print(f"  fitted by training run {carrier['training_hash']}")

# %% [markdown]
# The checkpoint is part of the configuration. Where a family publishes a prediction set per
# checkpoint on a declared schedule, the selected configuration's prediction set names one of them,
# and refitting without it would produce a model at the end of training rather than the one that
# was ranked. A family with no checkpoint dimension stores NULL in both columns and carries that
# NULL through unchanged.

# %%
validation_prediction = study.results.open(carrier["val_prediction_hash"])
prediction_record = validation_prediction.registry_record()
CHECKPOINT_KIND = prediction_record["checkpoint_kind"]
CHECKPOINT_VALUE = prediction_record["checkpoint_value"]
print(f"Checkpoint: {CHECKPOINT_KIND}={CHECKPOINT_VALUE}")

# %% [markdown]
# ## 2. The window, and the model that is allowed to see it
#
# The holdout window is not a choice made here. It is `evaluation.holdout_start` and
# `evaluation.holdout_end` from this case study's own `setup.yaml`, read through the same
# `canonical_window` the fold derivation and the backtest slice both go through, so the three
# cannot disagree.
#
# The training interval is everything available before that window, bounded above by a label
# buffer. **For this case study the buffer is doing real work.** The label is a 21-day forward
# return, so a row dated `t` records an outcome that is not known until `t + 21` sessions - and
# a training set running to the day the window opens would be fitted on labels whose returns
# resolve inside it. The buffer is therefore a horizon's worth rather than a formality, and the
# derivation refuses to default it: a zero gap here would be a leak.
#
# Everything else about the configuration is carried across unchanged, and the fields that
# cannot be - the eligibility manifest, and any parameter this family resolves from a fold's
# own training rows - are recomputed against the holdout fold. Carrying those forward would fit
# a model keyed to the validation folds and call it a retrain.

# %%
observation_timeline = (
    pl.read_parquet(study.root / "labels" / f"{carrier['label']}.parquet")
    .get_column("timestamp")
    .unique()
    .sort()
    .to_list()
)
validation_spec = study.results.open(carrier["training_hash"]).spec()
holdout_spec = build_holdout_training_spec(
    study,
    validation_spec,
    timeline=observation_timeline,
    case_study=CASE_STUDY_ID,
)

fold = holdout_spec["computation"]["cv"]["folds"][0]
print(f"Holdout fold {fold['fold']}")
print(f"  trains  {fold['train_start']} -> {fold['train_end']}")
print(f"  predicts {fold['val_start']} -> {fold['val_end']}")
print(f"  label buffer: {holdout_spec['computation']['cv']['request']['label_buffer']}")

# The validation folds are what the buffer is measured against, and the last of them ends
# before the holdout opens. Printing both is what lets a reader check the gap rather than take
# it on the derivation's word.
validation_folds = validation_spec["computation"]["cv"]["folds"]
latest_validation_end = max(str(entry["val_end"]) for entry in validation_folds)
print(f"Validation folds: {len(validation_folds)}, latest evaluation end {latest_validation_end}")
print(f"Holdout training ends {fold['train_end']}, holdout opens {fold['val_start']}")

# %% [markdown]
# ## 3. Fit, and register the predictions
#
# `reconstruct_locked_model_request` builds the request from the spec above. Its name comes
# from a locked holdout path this case study does not use; it takes a training specification
# and a checkpoint, not a lock, and it is used here because it is the one call that refuses a
# request that is not exactly the spec it was handed - the training identity, the checkpoint
# schedule, the feature lineage and the runtime parameters are all checked before anything is
# fitted.
#
# The training identity below is new. It has to be: it covers the CV interval, and the holdout
# fold is not one of the validation folds. A run that came back with the validation training
# hash would mean the refit did not happen, so that is checked rather than assumed.
#
# **The window carries one configuration, and this notebook has no way past that.** The check below
# is on the selected configuration rather than on the notebook, and it has exactly two outcomes.
# With the selected configuration unchanged this is an idempotent replay: the derivation is
# deterministic and the training identity covers it, so the same identity comes back and the fit is
# served from the registry, which is why re-running the notebook is free and safe. With the
# selected configuration changed it refuses, names both configurations, and stops.
#
# It refuses rather than offering a replacement switch, and the reason is that a replacement
# would not be one. Deleting the earlier generation's rows does not undo having observed its
# result: the selection that produced the new configuration may have been informed by the old
# holdout number, and no deletion reaches that. A switch here would let the case study take a
# second look at the window while leaving a registry that shows only one, which is the
# specific thing that would make the out-of-sample claim false rather than merely weak.

# %%
holdout_training_hash = training_hash_from_spec(holdout_spec)
this_generation = (holdout_training_hash, (CHECKPOINT_KIND, CHECKPOINT_VALUE))
retire = holdout_generations_to_retire(CASE_DIR, this_generation=this_generation)
# A row whose training run records no CV split cannot be shown either way, and deleting on
# that would discard a result nothing has established is wrong. It stops the run instead.
if retire.unattributable:
    raise RuntimeError(
        "the holdout window carries prediction sets whose training runs record no CV split, "
        "so whether they were refitted for the holdout cannot be established: "
        + ", ".join(
            f"{row['prediction_hash']} (training {row['training_hash']})"
            for row in retire.unattributable
        )
        + ". Establish what produced them before registering another evaluation on the same "
        "window; this notebook will not delete a row it cannot show is not a holdout result."
    )
# A row whose training run declares a non-holdout CV may not be reported as a holdout
# result, and it is also not something to delete unattended: `generate_holdout` refits on a
# holdout fold and then registers the predictions under the VALIDATION training identity, so
# this record covers both a validation-fitted model published over the window and a real
# refit filed under the wrong identity. Nothing owned this before - the filter here was
# `row["refitted"]`, which made exactly these rows invisible to the refusal and to
# everything after it.
if retire.not_out_of_sample:
    raise RuntimeError(
        "the holdout window carries prediction sets whose training runs declare a CV split "
        "other than the holdout: "
        + ", ".join(
            f"{row['prediction_hash']} ({row['config_name']}, training {row['training_hash']})"
            for row in retire.not_out_of_sample
        )
        + ". Each is either a validation-fitted model published over the window, which is "
        "not an out-of-sample result, or a refit registered under its validation training "
        "identity, which the retired `20_strategy_synthesis/holdout.py::generate_holdout` "
        "wrote until it was deleted on 2026-09-12 - and "
        "the registry cannot tell those apart. This notebook has no way past that: "
        "establish which it is and resolve it through the registry's own lifecycle, which "
        "records that the row was retired."
    )
superseded = list(retire.superseded)
if superseded:
    raise RuntimeError(
        "the holdout window already carries a refit of a different configuration: "
        + ", ".join(
            f"{row['prediction_hash']} ({row['config_name']}, training {row['training_hash']})"
            for row in superseded
        )
        + f". This run would evaluate {carrier['config_name']} (training "
        f"{holdout_training_hash}, checkpoint {CHECKPOINT_KIND}={CHECKPOINT_VALUE}) on the "
        "same window, which would be a second configuration measured on a period this case "
        "study reports as unseen. This notebook has no way past that: deleting the earlier "
        "generation would not undo having observed it, and the selection bias it introduces "
        "is not removed by removing the rows. Either leave the selection where it was, or "
        "retire the earlier evaluation through the registry's own lifecycle, which records "
        "that a second look was taken."
    )

# %% tags=["results"]
request = reconstruct_locked_model_request(
    study,
    holdout_spec,
    checkpoint_kind=CHECKPOINT_KIND,
    checkpoint_value=CHECKPOINT_VALUE,
)
model_run = request.run()
holdout_prediction = model_run.predictions[0]

if model_run.training.hash == carrier["training_hash"]:
    raise RuntimeError(
        "the holdout refit produced the validation training identity "
        f"{carrier['training_hash']}, which means it did not refit"
    )
print(f"Holdout training run:  {model_run.training.hash}")
print(f"Holdout prediction set: {holdout_prediction.hash}")

# %% [markdown]
# What the prediction set covers, read back from the registry rather than from the request.
# The two agree only if the fit published what it declared, and the counts are what a reader
# checks the window against: this is a daily panel, so the date count is trading sessions in
# the window and the row count is those sessions times the funds eligible on each.

# %% tags=["results"]
record = holdout_prediction.registry_record()
predictions = holdout_prediction.load()
print(
    f"split={record['split']}  checkpoint={record['checkpoint_kind']}={record['checkpoint_value']}"
)
print(f"rows={predictions.height:,}  sessions={predictions['timestamp'].n_unique():,}")
print(
    f"  {predictions['timestamp'].min()} -> {predictions['timestamp'].max()}, "
    f"{predictions['symbol'].n_unique():,} funds"
)

# %% [markdown]
# Every holdout prediction set the registry holds, and whether the model behind it was
# refitted for the window. All of them are listed rather than one silently preferred, because
# the registry is immutable and a reader looking at it later will see whatever is there. A row
# marked VALIDATION-FITTED is not an out-of-sample result whatever its numbers say.

# %% tags=["results"]
for row in registered_holdout_generations(CASE_DIR):
    note = (
        "refitted for the holdout" if row["refitted"] else "VALIDATION-FITTED - not out of sample"
    )
    print(
        f"  {row['prediction_hash']}  training={row['training_hash']}  {row['config_name']}  {note}"
    )

# %% [markdown]
# ## What this notebook establishes, and what it does not
#
# It establishes one thing: a prediction set over the holdout window, produced by the
# configuration this case study selected, fitted on data that ends a full label horizon before
# the window opens. That is a precondition for an out-of-sample claim, not the claim itself.
# Nothing here says whether the predictions are any good - they have not been scored, sized or
# traded.
#
# It does not make the holdout a fresh test in the strict sense. The configuration reached this
# notebook through a selection made on the validation folds, and this window is being used once
# per configuration that gets here. What it does remove is the specific circularity of scoring
# a validation-fitted model on the period meant to judge it.
#
# Re-running this notebook is free: the same configuration re-derives the same training identity and
# the fit is served from the registry. Evaluating a DIFFERENT configuration is not, and is
# refused here. If a later pass finds the selection was wrong, that is a question for the
# registry's lifecycle, which records that a second look was taken - not something to settle by
# deleting rows until the registry agrees.
#
# **Next:** [`19_holdout_backtest`](19_holdout_backtest.ipynb).

```