LSTM FX: прогнозирование с последовательностями без пропусков и проверкой контрольных точек

# LSTM - FX Pairs


# LSTM - FX Pairs

An LSTM carries a hidden state forward across the lookback window and updates it at each
observation, so what it can use from the history is not fixed in advance the way NLinear's
subtraction of the last level is, nor bounded by a receptive field the way TCN's dilated stack
is. It is the recurrent member of the three architectures this case study's `deep_learning` menu
declares. This notebook constructs only the LSTM request; comparisons with NLinear, TCN, TabM,
trees, and linear models are deferred to `12_model_analysis`, where the complete registered
population is available.

**Learning objectives**

- Resolve the LSTM's lookback, hidden size, depth, and checkpoint schedule before fitting.
- Use the shared gap-safe sequence eligibility instead of positional row windows.
- Prove weight reload and catalog handoff for every declared epoch.

**Book reference**: Chapter 13, Section 13.4

**Prerequisites**: `02_labels`, `03_financial_features`, and `04_model_based_features`.

```python
"""Fit and catalog the published LSTM FX configuration."""

import json

import polars as pl
import torch

from case_studies.research import (
    ExecutionTier,
    declared_labels,
    open_study,
    plan_models,
    population_supersedes,
    sweep_labels,
)
from utils.modeling import load_configs
from utils.reproducibility import set_global_seeds
```

```python
CASE_STUDY_ID = "fx_pairs"
PRIMARY_LABEL = ""
MAX_SYMBOLS = 0
MAX_FOLDS = 0
FORCE_RETRAIN = False
PREDICTION_SPLIT = "validation"
N_EPOCHS = 0
LOOKBACK = 0
BATCH_SIZE = 0
DEVICE = ""
SEED = 42
POPULATION_NAME = ""
SUPERSEDES_POPULATION: str = "2f5810edd6cd"
# The tier is a parameter, not something inferred from whether a reduction happens to be set.
# Inferring it meant a run could be reduced and still open the case study's own artifacts in
# place, which is the production path; a reader under test then wrote where the published run
# writes. WORKSPACE is the other half: a preview has nowhere else to put its results.
EXECUTION_TIER = "canonical"
WORKSPACE: str | None = None
```

## Resolve one forecasting request

The shared runner derives fold boundaries from the finalized label timeline. A missing daily
observation invalidates every lookback window that crosses it, so validation coverage can be
smaller than the raw validation panel while still being exact.

```python
set_global_seeds(SEED)
# The reductions are read before the study is opened, because which study to open is decided by
# the tier and the two have to agree: a preview that reduces nothing is a canonical run wearing
# the wrong tier, and a canonical run carrying reductions would publish a narrowed population
# under the canonical name.
REDUCTION_PARAMETERS = {
    "folds": list(range(MAX_FOLDS)) if MAX_FOLDS else None,
    "max_symbols": MAX_SYMBOLS or None,
}
reductions = {key: value for key, value in REDUCTION_PARAMETERS.items() if value is not None}
tier = ExecutionTier(EXECUTION_TIER)
if tier is ExecutionTier.PREVIEW and not reductions:
    raise ValueError("preview execution must declare at least one reduction")
if tier is ExecutionTier.CANONICAL and reductions:
    raise ValueError(f"canonical execution cannot carry reductions: {sorted(reductions)}")
study = open_study(CASE_STUDY_ID, execution_tier=tier, workspace=WORKSPACE or None)

# Which labels this notebook fits is a question for the training menus, not for the sweep list:
# `setup.yaml` says which labels the case study carries, a menu says what to fit for one of them,
# and a sweep label whose menu declares no `deep_learning:` section owes nothing here. The two
# agree in this case study today, so restating the sweep list produced the right answer by
# coincidence and would have kept producing it silently after a menu changed. The order stays
# `setup.yaml`'s rather than `declared_labels`' menu-file order because the population is named
# after its labels and hashed over its members as an ordered list, so re-ordering would give the
# published population a new identity and demand a supersedes for a run that fits the same models.
declared = declared_labels(study, "deep_learning")
labels = (
    [PRIMARY_LABEL]
    if PRIMARY_LABEL
    else [label for label in sweep_labels(study) if label in set(declared)]
)

# A run that fits fewer labels than the menus declare is not the canonical population, and the
# architecture is fixed below, so the label set is the only knob that narrows it. Such a run must
# publish under its own name rather than register a partial snapshot under the canonical one.
if set(labels) != set(declared) and not POPULATION_NAME:
    raise ValueError(
        f"this run fits {len(labels)} of the {len(declared)} declared labels, so it cannot "
        "publish the canonical population; pass POPULATION_NAME to give it its own"
    )

if PREDICTION_SPLIT != "validation":
    raise ValueError("model selection uses validation predictions; holdout runs start from a lock")
if FORCE_RETRAIN:
    raise ValueError("valid checkpoints are reloaded by identity; change the request to refit")

# An empty DEVICE resolves to what the machine has. The runners refuse "cuda" on a host without
# it rather than falling back silently - which is the right contract for a run whose results get
# registered - so a hardcoded "cuda" default made the notebook unrunnable for any reader without
# an NVIDIA card, and unrunnable on a CPU CI runner. Resolving here keeps the refusal for anyone
# who asks for "cuda" explicitly; the resolved value is printed with the rest of the numerics
# below, so a run never leaves it implicit.
device = DEVICE or ("cuda" if torch.cuda.is_available() else "cpu")
overrides = {
    "device": device,
    **({"n_epochs": N_EPOCHS} if N_EPOCHS else {}),
    **({"batch_size": BATCH_SIZE} if BATCH_SIZE else {}),
    **({"lookback": LOOKBACK} if LOOKBACK else {}),
}
ARCHITECTURE = "lstm_h64"
menu = {
    label: [
        config["config_name"]
        for config in load_configs(CASE_STUDY_ID, label, family="deep_learning")
    ]
    for label in labels
}
uncovered = {label: sorted(set(names) - {ARCHITECTURE}) for label, names in menu.items()}
for label, names in menu.items():
    if ARCHITECTURE not in names:
        raise RuntimeError(
            f"{ARCHITECTURE} is not in the configured deep_learning menu for {label}: {names}"
        )

requests = [
    study.model(
        family="deep_learning",
        label=label,
        config_name=ARCHITECTURE,
        execution_tier=tier,
        preview_reductions=reductions,
        overrides=overrides,
    )
    for label in labels
]
plan = plan_models(study, requests=requests)

# This notebook owes one architecture on every configured label. The rest of the family menu is
# named here rather than left implicit, because a population that is short a configured model is
# otherwise indistinguishable from a complete one.
configured = {(label, ARCHITECTURE) for label in labels}
planned = {(member.label, member.config_name) for member in plan.members}
if planned != configured:
    raise RuntimeError(
        f"the plan does not match this notebook's declared coverage; "
        f"missing {sorted(configured - planned)}, unexpected {sorted(planned - configured)}"
    )
specs = {member.label: json.loads(member.spec_json) for member in plan.members}
computations = {label: spec.get("computation", spec) for label, spec in specs.items()}
computation = computations[labels[0]]

print(f"Labels: {', '.join(labels)}")
print(f"Execution tier: {tier.value}")
print(f"Device: {computation['numerics']['device']}")
print(f"Lookback: {computation['preprocessing']['lookback']} consecutive daily observations")
for horizon, values in computations.items():
    print(f"Eligible validation rows, {horizon}: {values['expected_prediction_keys']['n_rows']:,}")
for horizon, names in uncovered.items():
    print(
        f"Configured deep_learning models this notebook does not run, {horizon}: {names or 'none'}"
    )
```

## Inspect identity-bearing settings

The model request records its architecture parameters, exact folds, expected prediction-key
digest, and every epoch that must remain reproducible from stored weights.

```python
checkpoint_schedule = pl.DataFrame(computation["checkpoint_schedule"])
pl.DataFrame(
    {
        "label": list(computations),
        "architecture": [c["model"]["class"] for c in computations.values()],
        "gap_policy": [c["preprocessing"]["gap_policy"] for c in computations.values()],
        "validation_folds": [
            c["expected_prediction_keys"]["n_folds"] for c in computations.values()
        ],
        "key_digest": [c["expected_prediction_keys"]["digest"] for c in computations.values()],
    }
)
checkpoint_schedule
```

## Record the official population, then fit or reload the LSTM

The runner validates every fold separately before any checkpoint becomes downstream-selectable.
Checkpoint rank correlation is retained as a diagnostic and does not remove other epochs.

`SUPERSEDES_POPULATION` names the population hash this run replaces. A population is the set of
prediction identities it publishes, so anything that moves a training identity produces a
different population under the same name, and the registry refuses to write it without being
told which snapshot it supersedes. That lineage is the only record of which generation is which,
and what moved the identities here was a change to the family's own source file rather than to
anything the notebook declares.

`population_supersedes` decides whether the declared hash may be offered. It is offered when the
name already carries the generation this declaration produced, so a re-run resolves to the
population it published, and when the declaration names the generation in force, so a refit
publishes the next one. It is withheld everywhere else - on a reader's clean clone, where
`run_log/` is gitignored and the registry has no generation at all; under a caller's own
`POPULATION_NAME`; and in a preview, whose isolated registry holds nothing under this name.

```python
if len(plan.expected_prediction_hashes) != checkpoint_schedule.height * len(labels):
    raise RuntimeError("the plan does not cover every declared epoch checkpoint on every label")
population_name = POPULATION_NAME or f"{CASE_STUDY_ID}:{'+'.join(labels)}:lstm_h64"
population = (
    plan.create_population(
        name=population_name,
        supersedes=population_supersedes(
            study, name=population_name, declared=SUPERSEDES_POPULATION
        ),
    )
    if tier is ExecutionTier.CANONICAL
    else None
)

execution = plan.run()
catalog = execution.catalog_rows.sort("label", "checkpoint_value")
if set(catalog.get_column("prediction_hash")) != set(plan.expected_prediction_hashes):
    raise RuntimeError("the published catalog differs from the population planned before fitting")
if catalog.filter(~pl.col("complete")).height:
    raise RuntimeError("partial LSTM checkpoints cannot pass to backtesting")
for label in labels:
    published = catalog.filter(pl.col("label") == label).get_column("checkpoint_value").to_list()
    if published != checkpoint_schedule["value"].to_list():
        raise RuntimeError(f"catalog checkpoints for {label} differ from the resolved request")

catalog.select(
    "label",
    "config_name",
    "checkpoint_kind",
    "checkpoint_value",
    "complete",
    "ic_mean",
    "ic_t",
    "training_hash",
    "prediction_hash",
)
```

## Verify checkpoint reload

Repeating the request validates the fitted-state digests and returns the same prediction
identities. The notebook never reconstructs another family from an empty cache path.

```python
replayed = plan.run()
if set(replayed.catalog_rows.get_column("prediction_hash")) != set(
    catalog.get_column("prediction_hash")
):
    raise RuntimeError("LSTM checkpoint reload changed the prediction population")

if population is not None:
    population.require_complete()
    print(f"Official prediction population: {population.hash}")
else:
    print("Preview sequence checkpoints remain outside official comparisons.")
```

## Key takeaways

- The LSTM, NLinear and TCN use the same sequence eligibility contract but keep separate model
  identities, so each is scored on the rows its own lookback leaves eligible.
- Gaps remove affected windows instead of being hidden by positional indexing.
- Stored weights reproduce every declared checkpoint without retraining.