پیش‌بینی‌های مجموعه نگه‌داشته‌شده با کنترل نشت برای اختیارهای اس‌اندپی 500

# 16_holdout_predictions.py


```py
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# %% [markdown]
# # S&P 500 Options: 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
# straddles to write each week, how to size them, what to charge for the option spread and
# the hedge. 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. [`17_holdout_backtest`](17_holdout_backtest.ipynb) turns those predictions into
# short straddles and a return series, with the concentration, the allocator and the cost
# assumption the case study settled on, and
# [`18_strategy_analysis`](18_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.
#
# The label makes the gap between training and the window unusually long here. `ret_to_expiry`
# resolves when a contract expires, not a fixed number of sessions after the decision, so the
# buffer is declared in `setup.yaml` as `labels.buffer` and is a full option cycle rather than
# a formality.
#
# **Prerequisites:** [`15_costs`](15_costs.ipynb), which is the last stage that selects.
#
# **Scope:** one training run and one prediction set. No backtest, no straddles, no
# selection, no comparison - those are 17 and 18.

# %%
"""S&P 500 Options: 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.registry.maintenance import delete_prediction_generation
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 = "sp500_options"
EXECUTION_TIER = "canonical"
WORKSPACE: str = ""
# Replace a holdout evaluation this window already carries, rather than adding a second one.
# Off by default, because the default should be the methodology: one configuration is measured
# on the holdout, and the number means what it says only while that stays true. It is a
# parameter and not a prohibition because a holdout does get re-derived - a corrected input
# upstream moves every training identity, and the evaluation that was right for the old
# geometry is then an evaluation of a configuration this case study no longer selects.
# Refusing that outright would mean a correction could never be carried through to the end.
REPLACE_HOLDOUT = False

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


def _delete_holdout_generation(case_dir, prediction_hash):
    """Remove one holdout prediction set and everything registered against it.

    Reached only when ``REPLACE_HOLDOUT`` says a generation is superseded. The rows go rather
    than being marked, because a superseded holdout evaluation that is still readable is still
    a number someone can quote, and the point of replacing it is that it should not be one.

    The cascade lives in `case_studies/utils/registry/maintenance.py` and derives the child
    tables from the schema, because the version of this that listed them by hand missed
    `cohort_metrics.leader_hash` - which with foreign keys enabled aborts the delete rather
    than orphaning a row.
    """
    deleted = delete_prediction_generation(case_dir / "run_log" / "registry.db", prediction_hash)
    for table, n in sorted(deleted.items()):
        print(f"  deleted {n:>3} from {table}")


# %% [markdown]
# ## 1. Which configuration the holdout runs
#
# The holdout runs the configuration the case study reports, resolved through the same
# `resolve_solvent_carrier` [`15_costs`](15_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, and it was fixed before this notebook ran.
#
# **Its validation Sharpe is negative.** That is this case study's result, not a reason to look for
# a different configuration: writing straddles on a signal this weak does not pay for the option
# spread and the hedge, and the holdout is being used to see whether that reading holds on a window
# nothing was chosen on. Picking a configuration for its sign would be the selection the holdout
# exists to make honest.

# %%
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 - which is where this case study's configuration
# sits - 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 the label
# buffer. **For this case study the buffer is doing real work.** `ret_to_expiry` is realised
# at expiration rather than a fixed horizon after the decision, so a row dated `t` records an
# outcome that is not known until its contract expires - and a training set running to the day
# the window opens would be fitted on labels that resolve inside it. The buffer is therefore an
# option cycle's worth, 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. **That is
# not a hypothetical here.** Two holdout prediction sets were registered against this case
# study under the validation training identity, by a driver that refits the model and then
# registers the result under the identity it started from; their split said `holdout` and
# their model had been fitted on folds ending inside the window. They were removed, and the
# check below is what makes their shape impossible to reintroduce.
#
# **The window carries one configuration at a time.** The check below is on the selected
# configuration rather than on the notebook, and it has three 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 stops and names both configurations. With `REPLACE_HOLDOUT` set it replaces the
# earlier generation instead of standing beside it, so the registry never holds two refits of the
# same window.
#
# What the replacement does not do is undo having observed the earlier result, and that is the
# part worth understanding rather than enforcing. A holdout number is out-of-sample because
# nothing about the model was chosen after seeing it. Evaluate a second configuration on the
# same window and the second number is no longer that, however the registry is arranged: the
# selection may have been informed by the first. Deleting rows removes the evidence, not the
# knowledge.
#
# So the honest use of this switch is narrow: an input upstream was corrected, every training
# identity moved with it, and the evaluation being replaced belongs to a configuration this
# case study no longer selects. That is maintenance, and it has to be possible or a correction
# could never reach the end of the pipeline. Reaching for it because the first holdout
# disappointed is the thing that makes an out-of-sample claim false, and no parameter default
# can tell those two apart - the researcher can, and it is their call.

# %%
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 and not REPLACE_HOLDOUT:
    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. Establish which, then set REPLACE_HOLDOUT="
        "True to remove it, or leave it and resolve the identity instead."
    )
for row in retire.not_out_of_sample:
    print(
        f"REMOVING {row['prediction_hash']} ({row['config_name']}, training "
        f"{row['training_hash']}): its training run declares a non-holdout CV, so it is not "
        "reportable as a holdout evaluation under the identity it carries"
    )
    _delete_holdout_generation(CASE_DIR, row["prediction_hash"])
superseded = list(retire.superseded)
if superseded and not REPLACE_HOLDOUT:
    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 is a second configuration measured on a period this case study "
        "reports as unseen. Set REPLACE_HOLDOUT=True to replace the earlier generation - "
        "correct when an upstream fix moved every training identity and the evaluation being "
        "replaced belongs to a configuration no longer selected - or leave the selection "
        "where it was."
    )
for row in superseded:
    print(f"REPLACING holdout generation {row['prediction_hash']} ({row['config_name']})")
    _delete_holdout_generation(CASE_DIR, row["prediction_hash"])

# %% 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. The unit here is a contract on a
# date, not a name on a date: one underlying carries many strikes and expirations at once, so
# the row count is far larger than the session count and the symbol count is underlyings
# rather than tradeable instruments.

# %% 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():,} underlyings"
)

# %% [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 option cycle 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, turned
# into straddles, 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 - which, for this case study, is
# not an abstract risk: it had already happened once.
#
# 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:** [`17_holdout_backtest`](17_holdout_backtest.ipynb).

```