使用PCA建模US股票收益的共同变动

# US equities panel: the few movements the whole panel shares


# US equities panel: the few movements the whole panel shares

Most of what three thousand stocks do on a given day is not three thousand different things.
One movement runs through nearly all of them, a few more run through overlapping groups, and
what is left over is specific to each name. **Principal component analysis** finds those shared
movements without being told anything about the stocks: it looks only at how the returns moved
together and extracts the directions that account for the most of that common variation, in
order.

Each extracted direction is a **factor**. Each stock gets a **loading** on each factor, saying
how much of that movement it takes. A prediction for a stock is then rebuilt from the factors
and that stock's loadings, so what the model can say about a stock is exactly what the stock has
in common with the rest of the panel - and nothing that is specific to it.

**That is a compression, and the discarding is the point.** A handful of factors stand in for the
whole cross-section, so this model is deliberately blind to what separates two stocks that
load the same way. The notebooks before it are where stock-specific information lives; this
one asks how much is left once you keep only what is shared.

**Everything is fitted on training rows only.** Factors and loadings are both estimated inside
each fold's training window and then applied to the validation rows, because a factor extracted
from the whole sample would have been computed partly from the returns it is later asked to
predict.

**Learning objectives.** By the end of this notebook you will be able to:

- Say what a factor and a loading are in terms of a panel of returns, without reference to an
  algorithm.
- Explain what a few-factor reconstruction of a stock's return can and cannot contain.
- Say why the factors have to be extracted inside a fold's training window, and what a
  whole-sample extraction would have used.
- State what the declared factor count does and does not establish about the right number.

**Book reference**: Chapter 13.

**Prerequisites**: [`03_financial_features`](03_financial_features.ipynb) and
[`04_model_based_features`](04_model_based_features.ipynb) have written the feature matrices,
[`02_labels`](02_labels.ipynb) the labels, and [`05_evaluation`](05_evaluation.ipynb) has
established the walk-forward folds.

**What it writes**: one training run and one complete validation prediction set per label, in
`run_log/registry.db` and under `run_log/training/` and `run_log/predictions/`, frozen under a
name per label. [`13_latent_factors`](13_latent_factors.ipynb) indexes them,
[`15_model_analysis`](15_model_analysis.ipynb) compares them against the other families, and
[`16_backtest`](16_backtest.ipynb) backtests every one. **Selection happens there, not here.**

```python
"""Generate PCA validation predictions through the shared research interface."""

import matplotlib.pyplot as plt
import polars as pl
import yaml

from case_studies.research import (
    candidate_set_supersedes,
    open_study,
    plan_models,
    run_model_population,
    supersedes_for_run,
)
from utils.modeling import ConfigError, load_configs
from utils.paths import get_case_study_dir
from utils.style import FIGSIZE, add_message_title, ml4t_palette, show_with_alt, zero_line
```

```python
CASE_STUDY_ID = "us_equities_panel"
LABELS = []
OVERRIDES = {}
POPULATION_NAME = ""
SUPERSEDES_POPULATION = ""
SUPERSEDES_SETS: dict = {}
EXECUTION_TIER = "canonical"
WORKSPACE = ""
PREVIEW_MAX_SYMBOLS = 0
PREVIEW_FOLD_IDS = []
PREVIEW_N_FACTORS = 0
```

## 1. Which labels, and how many factors

What each setting a run may pass decides:

- **`LABELS`** empty fits every label whose menu at `config/training/{label}.yaml` declares a
  latent-factor model, which in this case study is the primary label alone. A subset fits only
  those.
- **The factor count** is how many common movements are extracted. It is read from the preset at
  `case_studies/config/pca/pca.yaml` rather than set here, so there is one place to change it
  and one place to look. It is declared rather than searched, and that is a design choice with
  consequences: too few and distinct common movements are forced into one direction, too many
  and the later ones are fitting noise that will not repeat out of sample. Nothing in this case
  study tunes it, so no result here is evidence that the declared count is right - only evidence
  of what it gives.
- **`OVERRIDES`** changes a resolved model parameter. An override moves the training identity, so
  an overridden run registers beside the published one rather than replacing it.
- **`EXECUTION_TIER`** is `canonical` or `preview`. A canonical run fits the whole panel on every
  fold. A preview run declares its reductions and carries them in the identity, so its results
  can never be compared against canonical ones or reach a holdout decision.
- **`PREVIEW_FOLD_IDS`** and **`PREVIEW_MAX_SYMBOLS`** are the reductions a preview declares.

```python
case_dir = get_case_study_dir(CASE_STUDY_ID)
setup = yaml.safe_load((case_dir / "config" / "setup.yaml").read_text())
# "Published" here is the labels that declare a latent-factor model, not every label the case
# study declares. The two differ: `config/training/{label}.yaml` declares `latent_factors` on the
# primary label alone, so a canonical run fits that label and the population it publishes holds
# exactly what was declared. Reading the wider label list here would make every canonical run
# narrow its own scope and forfeit the published population under the guard below.


def declares_latent_factors(label: str) -> bool:
    # load_configs raises rather than returning an empty list when a label's menu omits the family,
    # so absence is read from the exception.
    try:
        return bool(load_configs(CASE_STUDY_ID, label, family="latent_factors"))
    except ConfigError:
        return False


published_labels = [
    label
    for label in [setup["labels"]["primary"], *setup["labels"].get("variants", [])]
    if declares_latent_factors(label)
]
if not published_labels:
    raise ValueError("no label in this case study declares a latent_factors model")
selected_labels = list(LABELS) if LABELS else published_labels
unknown_labels = sorted(set(selected_labels) - set(published_labels))
if unknown_labels:
    raise ValueError(f"Unknown labels: {unknown_labels}")
if len(selected_labels) != len(set(selected_labels)):
    raise ValueError("LABELS contains duplicates")

declared_factors = set()
for label in selected_labels:
    configured = {
        config["config_name"]: config
        for config in load_configs(CASE_STUDY_ID, label, family="latent_factors")
    }
    if "pca" not in configured:
        raise ValueError(f"PCA is not configured for {label}")
    declared_factors.add(int(configured["pca"]["params"]["n_factors"]))
# One declaration, read rather than restated. A count typed here as well would be a second
# declaration free to disagree with the preset, and the preset would then decide nothing.
if len(declared_factors) != 1:
    raise ValueError(f"the selected labels declare different factor counts: {declared_factors}")
N_FACTORS = declared_factors.pop()

label_menu = pl.DataFrame(
    {
        "label": published_labels,
        "selected": [label in selected_labels for label in published_labels],
        "n_factors": [N_FACTORS] * len(published_labels),
    }
)
label_menu
```

A run that narrows the labels or overrides a parameter produces a different set of predictions
from the one the canonical name stands for. Publishing it under that name would leave the name
meaning two different member sets at two different times, so the guard below requires such a run
to say what to call its own population, and the frozen set names in Section 6 are withheld from
it for the same reason.

```python
is_published_population = (
    EXECUTION_TIER == "canonical" and selected_labels == published_labels and not OVERRIDES
)
if EXECUTION_TIER == "canonical" and not is_published_population and not POPULATION_NAME:
    raise ValueError(
        "this run narrows the declared labels or overrides a parameter, so it cannot publish the "
        "canonical population; pass POPULATION_NAME to give it its own"
    )
```

Both tiers resolve the study through `open_study`. It reads the labels and features in place
and redirects only writes, so a preview run scores the same inputs a canonical one does and
cannot publish over it. A preview must be given a workspace to write into; a canonical run
leaves `WORKSPACE` empty and regenerates the case study's own artifacts in place.

```python
preview_reductions = {}
if PREVIEW_MAX_SYMBOLS:
    preview_reductions["max_symbols"] = int(PREVIEW_MAX_SYMBOLS)
if PREVIEW_FOLD_IDS:
    preview_reductions["folds"] = [int(fold) for fold in PREVIEW_FOLD_IDS]
if PREVIEW_N_FACTORS:
    preview_reductions["n_factors"] = int(PREVIEW_N_FACTORS)

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

## 2. Binding the declarations to the data

One request per label. A **request** is the declaration bound to a label and an execution
tier, with its overrides resolved; it holds no data, so the table below can be read before
anything is loaded.

The labels get separate requests rather than one shared fit because a label defines which
rows are scorable and over what horizon. Fitting once and scoring several ways would give every
label a common estimate built partly from rows that only one of them can see. Only `fwd_ret_1d`
declares a latent-factor model here, so this run builds one request.

```python
requests = tuple(
    study.model(
        family="latent_factors",
        label=label,
        config_name="pca",
        # The preset supplies the factor count; a value passed here would win over it, which is
        # what OVERRIDES is for and what makes such a run unpublishable.
        overrides=dict(OVERRIDES),
        execution_tier=EXECUTION_TIER,
        preview_reductions=preview_reductions,
        notebook="13a_pca",
    )
    for label in selected_labels
)

request_table = pl.DataFrame(
    {
        "family": [request.family for request in requests],
        "label": [request.label for request in requests],
        "config_name": [request.config_name for request in requests],
        "overrides": [str(request.overrides) for request in requests],
        "execution_tier": [request.execution_tier.value for request in requests],
        "preview_reductions": [str(request.preview_reductions) for request in requests],
    }
)
request_table
```

## 3. Planning, then fitting

**Planning resolves every identity before any fitting starts**, and the list of them is written
down as a population the run then has to fill completely - so a run that came out short reads as
a failure rather than as a smaller experiment.

The planner resolves every label-specific training and checkpoint identity before fitting and
writes the canonical checkpoint population first. Each fold then fits PCA on its training return
panel only. The runner persists the fitted components
and reconstructs each registered prediction set from those artifacts before accepting cached
work.

```python
plan = plan_models(study, requests=requests)

planned_population = pl.DataFrame(
    {
        "label": [member.label for member in plan.members],
        "config_name": [member.config_name for member in plan.members],
        "checkpoint_kind": [member.checkpoint_kind for member in plan.members],
        "checkpoint_value": [member.checkpoint_value for member in plan.members],
        "training_hash": [member.training_hash for member in plan.members],
        "prediction_hash": [member.prediction_hash for member in plan.members],
    }
)
planned_population
```

`run_model_population` takes the plan, writes the population down, fits every member and then
checks that what came out is what was declared. The same call serves both tiers: a canonical run
registers an immutable population that the later notebooks bind to, and a preview run gets a
declaration that is verified and then discarded with its workspace, so no notebook here has to
branch on the tier to decide what to publish.

`SUPERSEDES_POPULATION` names the population hash this run replaces. A population is a set of
prediction identities, so anything that moves a training identity - a changed preset as much as a
changed menu - produces a different population under the same name, and the registry refuses to
write it without being told which snapshot it supersedes. Leaving it empty is right for a first
run and for a reader's clean clone, and `supersedes_for_run` withholds a declared hash wherever
offering it would be refused.

```python
population_name = POPULATION_NAME or "us-equities-pca-checkpoints-v1"
execution, official_population = run_model_population(
    study,
    plan,
    population_name=population_name,
    supersedes=supersedes_for_run(
        study,
        population_name=population_name,
        declared=SUPERSEDES_POPULATION,
        execution_tier=EXECUTION_TIER,
    ),
)

print(f"population {official_population.name}: {len(official_population.members)} prediction sets")
```

## 4. What was actually fitted

The fully resolved specification, including every default nothing above restated: the factor
count, the feature count, the fold count, and the cross-validation identity. This is the
record a result is checked against, and it is what the training hash is computed from - so
two rows with the same hash were fitted under the same declaration, and two with different
hashes were not.

```python
resolved_rows = []
for run in execution.runs:
    spec = run.training.spec()
    computation = spec["computation"]
    resolved_rows.append(
        {
            "label": spec["label"],
            "features": len(computation["feature_names"]),
            "folds": computation["expected_prediction_keys"]["n_folds"],
            "eligible_rows": computation["expected_prediction_keys"]["n_rows"],
            "n_factors": computation["model"]["n_factors"],
            "device": computation["runtime"]["device"],
            "training_hash": run.training.hash,
        }
    )

resolved_table = pl.DataFrame(resolved_rows).sort("label")
resolved_table
```

## 5. What came out

One row per label, each a complete set of validation predictions carrying the hash of the
training run behind it and of the predictions themselves, so any row traces back to the
fitted state that produced it.

Coverage is checked exactly rather than approximately: a factor model reconstructs a
prediction for every stock-date its loadings cover, so a shortfall means a stock or a
session the reconstruction could not reach, and that is a fact about the fit rather than a
rounding difference.

```python
catalog_columns = [
    "family",
    "config_name",
    "label",
    "split",
    "checkpoint_kind",
    "checkpoint_value",
    "execution_tier",
    "complete",
    "ic_mean",
    "training_hash",
    "prediction_hash",
]
catalog_rows = execution.catalog_rows.select(
    column for column in catalog_columns if column in execution.catalog_rows.columns
).sort("label", "checkpoint_value", "prediction_hash")
catalog_rows
```

```python
coverage_rows = []
for run in execution.runs:
    if not run.training.complete:
        raise RuntimeError(f"Incomplete training result: {run.training.hash}")
    for prediction in run.predictions:
        coverage = prediction.coverage()
        if not prediction.complete or coverage is None or coverage["status"] != "complete":
            raise RuntimeError(f"Incomplete prediction result: {prediction.hash}")
        coverage_rows.append(
            {
                "label": run.training.spec()["label"],
                "training_hash": run.training.hash,
                "prediction_hash": prediction.hash,
                "coverage_status": coverage["status"],
                "expected_rows": coverage["n_expected"],
                "actual_rows": coverage["n_actual"],
                "training_artifacts": len(run.training.artifacts()),
                "prediction_artifacts": len(prediction.artifacts()),
            }
        )

coverage_table = pl.DataFrame(coverage_rows).sort("label", "prediction_hash")
coverage_table
```

```python
execution_diagnostics = pl.DataFrame(execution.diagnostics)
execution_diagnostics
```

### How the ranking held across the walk-forward folds

The headline information coefficient above is an average over the folds, and an average says
nothing about whether the folds agreed. Each fold is a different year of the market with a
different set of names quoting in it, so a factor model can rank the cross-section well in a few
of them and not at all in the rest and still show a respectable mean.

The per-fold values come from the registry rather than from the raw predictions: each fold's
information coefficient is registered alongside the prediction set, so reading it back costs a
query rather than a seven-million-row load. A model built only from what a stock shares with
the panel has no name-specific information to fall back on, so a fold where the panel moved
together for reasons the factors did not capture is where this one has least to say.

```python
fold_rows = []
for run in execution.runs:
    run_label = run.training.spec()["label"]
    for prediction in run.predictions:
        fold_rows.append(prediction.folds().with_columns(label=pl.lit(run_label)))
folds_frame = pl.concat(fold_rows, how="vertical_relaxed").sort("label", "fold_id")

fold_labels = folds_frame.get_column("label").unique(maintain_order=True).to_list()
# `ml4t_palette` returns a list of that many colours, so it is called once and indexed.
palette = ml4t_palette(len(fold_labels), categorical=True)

fig, ax = plt.subplots(figsize=FIGSIZE["single"])
for index, fold_label in enumerate(fold_labels):
    series = folds_frame.filter(pl.col("label") == fold_label)
    ax.plot(
        series.get_column("fold_id"),
        series.get_column("ic"),
        marker="o",
        markersize=4,
        lw=1.4,
        color=palette[index],
        label=fold_label,
    )
zero_line(ax)
ax.set_xlabel("Walk-forward fold")
ax.set_ylabel("Mean validation IC")
ax.legend(fontsize=8, frameon=False)
add_message_title(
    ax,
    "Mean validation IC by walk-forward fold",
    subtitle="One line per declared label, over the folds the evaluation stage established",
)
# The alt text counts rather than asserts: how many folds land above zero is a fact about the
# frame, and a line described as steady when it is not is a claim the data refutes.
_signs = (
    folds_frame.group_by("label").agg(above=(pl.col("ic") > 0).sum(), total=pl.len()).sort("label")
)
_sign_text = " and ".join(
    f"{row['above']} of {row['total']} for {row['label']}" for row in _signs.iter_rows(named=True)
)
show_with_alt(
    fig,
    "A line chart of mean validation information coefficient against walk-forward fold, one line "
    "per declared label, with a dashed line at zero. Counted from the underlying frame, the folds "
    f"whose information coefficient is above zero are {_sign_text}.",
)
```

## 6. Naming the sets the later notebooks open

One frozen set per label, under a name the later notebooks open by. Only an unnarrowed
canonical run publishes one: a name must not mean two different member sets at two different
times, so a run that overrode a parameter, narrowed the labels or ran under the preview tier
keeps its rows and publishes no name.

Each label gets two names, the same pair every other model notebook publishes: a **full set**
that [`16_backtest`](16_backtest.ipynb) backtests member by member, and a **bounded diagnostic
set** that [`15_model_analysis`](15_model_analysis.ipynb) loads raw predictions for. Here the
two hold the same single member, because this family declares one configuration and fits it
once rather than checkpointing it, so there is nothing to bound away. The two names still exist
so that every family is opened the same way downstream; the registry stores one set and binds
both names to it.

```python
set_rows = []
if is_published_population:
    for selected_label in selected_labels:
        label_name = selected_label.replace("_", "-")
        label_rows = execution.catalog_rows.filter(pl.col("label") == selected_label)
        full_set_name = f"us-equities-{label_name}-pca-v1"
        full_set = study.predictions.freeze(
            label_rows,
            name=full_set_name,
            supersedes=candidate_set_supersedes(
                study, name=full_set_name, declared=SUPERSEDES_SETS.get(full_set_name, "")
            ),
        )
        diagnostic_set_name = f"us-equities-{label_name}-pca-diagnostics-v1"
        diagnostic_set = study.predictions.freeze(
            label_rows,
            name=diagnostic_set_name,
            supersedes=candidate_set_supersedes(
                study,
                name=diagnostic_set_name,
                declared=SUPERSEDES_SETS.get(diagnostic_set_name, ""),
            ),
        )
        set_rows.extend(
            [
                {
                    "role": "backtest population",
                    "set_name": full_set.name,
                    "members": len(full_set.members),
                },
                {
                    "role": "bounded diagnostics",
                    "set_name": diagnostic_set.name,
                    "members": len(diagnostic_set.members),
                },
            ]
        )
compatible_sets = pl.DataFrame(
    set_rows,
    schema={"role": pl.String, "set_name": pl.String, "members": pl.Int64},
)
compatible_sets
```

`15_model_analysis` reopens both names per label: the full set to confirm the run filled every
member it promised, and the diagnostic set to read raw predictions. `16_backtest` passes every
full-set catalog row to the shared backtest runner. Neither the metrics here nor the ones there
choose a configuration or a checkpoint; selection is on validation backtest Sharpe in
`16_backtest`.

## What to notice

**A factor has no name.** It is a direction in the returns, ordered by how much common variation
it accounts for. The first one usually looks like the market because the market is what most
stocks share, but nothing in the method labels it, and reading an economic story into the second
or third is an interpretation this notebook does not support.

**What this model cannot say is as informative as what it can.** A prediction is built only from
what a stock shares with the panel, so where it ranks the cross-section well, the ranking is
coming from common movement rather than from anything specific to a name.

**A loading is fitted per stock, on the stocks a fold had.** A stock with no training history in
a fold gets none, and a stock whose character changes over a decade keeps the loading it was
fitted with. [`13b_ipca`](13b_ipca.ipynb) is the answer to both, and the
comparison between the two is what the pair is for.

**Known limitations.** The factor count is declared rather than searched, so nothing here says
the declared one is right. Everything is measured on validation folds read many times over by the
time a case study reaches this notebook, and ranking accuracy is not strategy performance.

**Next**: [`13b_ipca`](13b_ipca.ipynb) makes a stock's loading a function of what the stock is.
![notebook output](figures/p1_1.png)