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Como projetar características financeiras a partir de hipóteses econômicas e do tempo dos dados

Código Machine Learning for Trading

Resumo

Este capítulo apresenta uma estrutura para transformar uma ideia de trading em uma especificação documentada de características. Recomenda alinhar os horizontes das características à decisão pretendida, formular uma hipótese sobre os fatores determinantes e distinguir sinais preditivos de variáveis de estado contextuais. As escolhas de referência, representação e agregação devem acompanhar a alegação econômica; transformações que alteram a hipótese devem ser diferenciadas daquelas que principalmente controlam o ruído. O capítulo aborda características de preço e volume, microestrutura, relações entre instrumentos, medidas implícitas em derivativos, fundamentos, dados macroeconômicos e variáveis de calendário.

O capítulo enfatiza a combinação de sinais mais rápidos com estados mais lentos por meio de filtros, escalas ou variantes condicionais, verificando se as interações acrescentam informação. A disponibilidade no ponto no tempo é essencial para dados fundamentais e macroeconômicos, pois atrasos na divulgação e revisões podem criar evidências falsas. A busca por características também exige disciplina, com variações controladas, eliminação de duplicatas dentro de cada família e verificações de robustez. O capítulo apresenta métodos e modos de falha em vez de uma estratégia ou resultado de desempenho único; suas famílias de características ainda exigem dados, premissas temporais, validação e avaliação adequada a custos.

Ideias principais

  • O desenho das características deve começar pelo alinhamento de horizontes, uma hipótese sobre os fatores determinantes e uma função clara para cada característica.
  • Escolhas de referência, representação e agregação podem mudar o significado econômico de uma característica.
  • Fundamentos e variáveis macroeconômicas de movimento lento exigem alinhamento no ponto no tempo que respeite atrasos de publicação e revisões.
  • Interações entre sinal e estado podem acrescentar informação condicional, mas aumentam o número de escolhas testadas.
  • Eliminação de duplicatas, variações controladas, estudos de eventos e verificações de robustez ajudam a manter confiável a seleção de características.

Tags

Texto completo
# artifact_audit.py


```py
#!/usr/bin/env python3
"""Report registered fits whose named feature artifact is not on disk.

A training identity carries ``computation.feature_artifacts.<role>.sha256``, the hash of
the artifact the fit read. Nothing else checks the file is still there, so a fit whose
input is gone resolves, reads as healthy, and is a record of a result rather than a
reproducible one.

**The scope this walks is printed, and that is not decoration.** An earlier run of an
uncommitted version of this audit reported ``crypto_perps_funding/financial`` as missing
for 144 fits while the file sat at the canonical path under its registered hash, untouched
for two months (ml4t/agent-workspace#1176). A case study's ``features/`` is a symlink into
the artifacts root in every checkout that has one, so an audit run from a throwaway
worktree - or one that does not resolve the link - hashes a different tree than the fits
read and reports absence with full confidence. Every path below is resolved and printed,
and a resolved path outside the artifacts root is a hard error rather than a quiet
mis-scope.
"""

from __future__ import annotations

import argparse
import hashlib
import json
import sqlite3
import sys
from collections import Counter
from pathlib import Path

REPO_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO_ROOT))

from utils.paths import get_case_study_dir  # noqa: E402

CASE_STUDIES = (
    "cme_futures",
    "crypto_perps_funding",
    "etfs",
    "fx_pairs",
    "nasdaq100_microstructure",
    "sp500_equity_option_analytics",
    "sp500_options",
    "us_equities_panel",
    "us_firm_characteristics",
)
ARTIFACT_DIRS = ("features", "labels")


def sha256_of(path: Path) -> str:
    digest = hashlib.sha256()
    with path.open("rb") as handle:
        for block in iter(lambda: handle.read(1 << 20), b""):
            digest.update(block)
    return digest.hexdigest()


def registered_artifacts(spec_json: str) -> list[tuple[str, str, int | None]]:
    """``(role, sha256, size)`` for one spec, across both shapes the registry holds.

    A dict keyed by role, and a list of ``{role, sha256}`` whose hashes may carry a
    ``sha256:`` prefix. Matching on the hash alone reaches both, but the role and size are
    what make a report readable, so both shapes are parsed rather than regex-scraped.
    """
    artifacts = json.loads(spec_json).get("computation", {}).get("feature_artifacts")
    out: list[tuple[str, str, int | None]] = []
    if isinstance(artifacts, dict):
        for role, entry in artifacts.items():
            if isinstance(entry, dict) and entry.get("sha256"):
                out.append((role, str(entry["sha256"]).removeprefix("sha256:"), entry.get("size")))
    elif isinstance(artifacts, list):
        for entry in artifacts:
            if isinstance(entry, dict) and entry.get("sha256"):
                out.append(
                    (
                        str(entry.get("role", "?")),
                        str(entry["sha256"]).removeprefix("sha256:"),
                        entry.get("size"),
                    )
                )
    return out


def walk(
    case_study: str, artifacts_root: Path | None
) -> tuple[dict[str, Path], list[str], list[str]]:
    """Hash every parquet the case study's artifact directories hold, after resolving them.

    The third return is the directories that were not there. An absent one is not a small
    gap in an otherwise good answer: every hash it would have contributed is absent from
    ``on_disk``, so every fit that read it is counted as naming a missing artifact. The
    caller has to know, because the two readings are opposite and the output is identical.
    """
    case_dir = get_case_study_dir(case_study)
    on_disk: dict[str, Path] = {}
    scope: list[str] = []
    absent: list[str] = []
    for name in ARTIFACT_DIRS:
        declared = case_dir / name
        if not declared.exists():
            scope.append(f"    {name}/  ABSENT at {declared}")
            absent.append(f"{case_study}/{name} at {declared}")
            continue
        resolved = declared.resolve()
        scope.append(f"    {name}/  -> {resolved}")
        if artifacts_root is not None and artifacts_root not in resolved.parents:
            raise SystemExit(
                f"{case_study}/{name} resolves to {resolved}, which is not under "
                f"{artifacts_root}. Hashing it would compare the registry's fits against a "
                f"tree they never read. Run from a checkout whose artifact directories link "
                f"into the artifacts root, or pass --artifacts-root to name the tree you mean."
            )
        for parquet in sorted(resolved.rglob("*.parquet")):
            on_disk.setdefault(sha256_of(parquet), parquet)
    return on_disk, scope, absent


def default_artifacts_root(case_studies: tuple[str, ...]) -> Path | None:
    """The tree the artifact directories actually live in, taken from the first that exists.

    Derived from a RESOLVED artifact directory, never from the case-study directory: in a
    checkout those are two different trees, because ``case_studies/<cs>/features`` is a
    symlink into the artifacts root and its unresolved parent is the repository.
    """
    for case_study in case_studies:
        case_dir = get_case_study_dir(case_study)
        for name in ARTIFACT_DIRS:
            declared = case_dir / name
            if declared.exists():
                # <root>/<case study>/<features|labels>
                return declared.resolve().parents[1]
    return None


def audit(
    case_studies: tuple[str, ...], artifacts_root: Path | None
) -> tuple[int, list[str], list[str]]:
    """Returns the missing-fit count, the case studies skipped, and the directories unseen.

    The second and third are not details. A partial audit that prints "0 fits name an
    artifact that is not on disk" is indistinguishable from a clean one, and a checkout is
    missing a registry whenever its gitignored ``run_log`` symlink was never created -
    which is the normal state of most worktrees, not an exception.

    The third exists because the second was not enough. An absent *artifact directory*
    inside an audited case study used to print one ABSENT line in the middle of the scope
    block and then contribute every one of that case study's fits to a confident total.
    Measured 2026-09-14 on a checkout whose ``us_equities_panel`` had ``run_log`` but no
    ``features/``: the audit reported 393 fits naming missing artifacts and exited 1, with
    no PARTIAL, while ``financial.parquet`` sat on disk at exactly the 4,478,156,899 bytes
    the MISSING line quoted. A case study that could not be fully seen is now not counted
    at all, because there is no way to tell its real findings from its blind ones.
    """
    total_missing = 0
    unaudited: list[str] = []
    unseen: list[str] = []
    for case_study in case_studies:
        db = get_case_study_dir(case_study) / "run_log" / "registry.db"
        print(f"\n{case_study}")
        if not db.exists():
            print(f"    NOT AUDITED: no registry at {db}")
            unaudited.append(case_study)
            continue
        on_disk, scope, absent = walk(case_study, artifacts_root)
        for line in scope:
            print(line)
        if absent:
            print(
                f"    NOT AUDITED: {len(absent)} artifact directory(ies) absent, so every "
                f"fit here would read as missing. Nothing from this case study is counted."
            )
            unseen.extend(absent)
            continue
        print(f"    {len(on_disk)} distinct parquet hashed")

        connection = sqlite3.connect(f"file:{db}?mode=ro", uri=True)
        try:
            rows = connection.execute("SELECT training_hash, spec_json FROM training_runs")
            missing: Counter[tuple[str, str, int | None]] = Counter()
            fits = 0
            for _, spec_json in rows:
                fits += 1
                for role, sha, size in registered_artifacts(spec_json):
                    if sha not in on_disk:
                        missing[(role, sha, size)] += 1
        finally:
            connection.close()

        if not missing:
            print(f"    {fits} fits, every named artifact present")
            continue
        for (role, sha, size), count in sorted(missing.items(), key=lambda kv: -kv[1]):
            size_text = f"{size:,} bytes" if size else "size not recorded"
            print(f"    MISSING  {role:<14} {sha[:16]}…  {size_text}  named by {count} fits")
            total_missing += count
    return total_missing, unaudited, unseen


def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--case-study", action="append", choices=CASE_STUDIES)
    parser.add_argument(
        "--artifacts-root",
        type=Path,
        help=(
            "Tree every artifact directory must resolve under. Defaults to the parent of the "
            "first case study's resolved directory. Pass 'none' to disable the check, which "
            "is how you audit a deliberately isolated tree."
        ),
    )
    args = parser.parse_args()
    selected = tuple(args.case_study) if args.case_study else CASE_STUDIES

    if args.artifacts_root is not None and str(args.artifacts_root) == "none":
        artifacts_root = None
    elif args.artifacts_root is not None:
        artifacts_root = args.artifacts_root.resolve()
    else:
        artifacts_root = default_artifacts_root(selected)
        if artifacts_root is None:
            raise SystemExit(
                "no case study has a features/ or labels/ directory, so there is no tree to "
                "audit. Pass --artifacts-root to name one explicitly."
            )

    print(f"artifacts root: {artifacts_root if artifacts_root else 'UNCHECKED'}")
    total, unaudited, unseen = audit(selected, artifacts_root)
    if unaudited or unseen:
        # Deliberately no total. A number printed here is read as the answer however it is
        # qualified, and over an incomplete scope it is not one.
        print("\nPARTIAL: this audit did not see the whole tree, so it reports no count.")
        if unaudited:
            print(
                f"  {len(unaudited)} of {len(selected)} case studies had no registry: "
                f"{', '.join(unaudited)}"
            )
        if unseen:
            print(f"  {len(unseen)} artifact directory(ies) absent:")
            for line in unseen:
                print(f"    {line}")
        print("  Run from a checkout where every case study links into the artifacts root.")
        return 2
    print(f"\n{total} fits name an artifact that is not on disk")
    return 1 if total else 0


if __name__ == "__main__":
    raise SystemExit(main())

```

Exibido na íntegra, com atribuição conforme a licença da fonte. Licença: MIT

Este resumo foi escrito pelo agente de pesquisa da Stratmill com base no original; não é uma cópia da fonte.