Comparación de modelos predictivos de ETF entre familias y particiones

# 13_model_analysis.py


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# %% [markdown]
# # ETF models compared: which of these signals is real
#
# Seven notebooks have fitted models to this panel, across five families and two label horizons.
# Each reported its own results against its own declared menu. **None of them could say how it
# compares to the others**, because none of them had the others in front of it.
#
# That is what this notebook is for. It reads every registered prediction set for this case study,
# puts the families on one axis with their uncertainty, and asks three questions of them: how
# strong is the signal, how consistent is it across the walk-forward folds, and are the families
# finding the same thing or different things.
#
# **This is a cross-asset panel, and that changes what a signal means here.** The universe mixes
# sector equity funds with country funds, bond funds, commodity funds and currency funds, so the
# thing a model has to rank is not which company is mispriced but which macro exposure is currently
# being rewarded. The instruments are already diversified, which is why cross-sectional information
# coefficients on this panel are small by the standards of a single-stock cross-section - there is
# less idiosyncratic variation left in them to find.
#
# **It selects nothing.** Selection is best validation backtest Sharpe and it happens in
# [`14_backtest`](14_backtest.ipynb), over the population this notebook describes. Every number
# here is measured on validation folds that have been read repeatedly by the time a case study
# reaches this point; the holdout is opened once, in
# [`20_strategy_analysis`](20_strategy_analysis.ipynb).
#
# **Learning objectives**
#
# - Read a family comparison from its confidence intervals rather than from its ordering.
# - Separate a mean information coefficient from the fold-level consistency behind it.
# - Say whether two model families are finding the same signal or different ones, and what follows.
# - Say why structural and causal evidence are read apart from a predictive ranking.
#
# **Book reference**: this notebook bridges Part III (models, chapters 11 to 15) and Part IV
# (strategy implementation, chapters 16 to 20). The chapter-insight notebooks compare one family
# across case studies; this compares every family within one.
#
# **Prerequisites**: [`06_linear`](06_linear.ipynb) through
# [`12_causal_dml`](12_causal_dml.ipynb), whose registered results are what this reads, and
# [`05_evaluation`](05_evaluation.ipynb) for the walk-forward folds every number is measured on.
#
# **What it writes**: nothing. It fits no model, registers no run and opens no holdout.

# %%
"""Compare every registered ETF model family on one panel, without selecting among them."""

import numpy as np
import plotly.graph_objects as go
import polars as pl
import yaml
from plotly.subplots import make_subplots

from case_studies.research import CausalResult, open_study, split_unpublished_members
from case_studies.utils.latent_factors import load_fold_extras
from case_studies.utils.model_analysis import (
    best_model_per_family_fast,
    fold_performance_matrix,
    load_all_metrics,
    load_gbm_feature_importance,
    load_predictions,
    prediction_bucket_monotonicity,
    prediction_correlation_matrix,
    regime_conditional_ic,
)
from case_studies.utils.model_viz import (
    plot_bucket_monotonicity,
    plot_correlation_matrix,
    plot_cv_timeline,
    plot_feature_importance_heatmap,
    plot_fold_boxplot,
    plot_label_horizon_forest,
    plot_learning_curves,
    plot_regime_bars,
)
from case_studies.utils.notebook_render import (
    conformal_coverage_diagnostic,
    fold_heatmap_with_ci,
    headline_forest_plot,
    holdout_decay_table,
    selection_adjusted_leader_table,
)
from case_studies.utils.registry import load_prediction_index
from case_studies.utils.warning_policy import apply_notebook_warning_policy
from utils.paths import get_case_study_dir
from utils.style import COLORS, show_plotly_with_alt, show_with_alt

apply_notebook_warning_policy()

# %% tags=["parameters"]
CASE_STUDY = "etfs"
PRIMARY_LABEL = "fwd_ret_21d"
DATE_COL = "timestamp"
ENTITY_COL = "symbol"
N_BUCKETS = 10
TOP_N_FEATURES = 15
REGIME_WINDOW = 63
# Both names stay bound here although nothing below reads them: that is what makes the harness
# force preview and supply a workspace - `_declares_tier_and_workspace` in `tests/pm_helpers.py`
# looks for exactly this pair. Without them the canonical
# branch regenerates in place, which needs symlinks a CI checkout does not have.
EXECUTION_TIER = "canonical"
WORKSPACE: str = ""

# %% [markdown]
# The study is opened before anything resolves a path or reads the registry. Under the preview
# tier, opening it activates a workspace and rewrites `ML4T_OUTPUT_DIR` process-wide, and every
# later `get_case_study_dir` call resolves against that. A `CASE_DIR` or a metrics table built
# first would point at the released registry while everything after it reads the preview one.

# %%
study = open_study(CASE_STUDY, execution_tier=EXECUTION_TIER, workspace=WORKSPACE or None)

CASE_DIR = get_case_study_dir(CASE_STUDY)

with open(CASE_DIR / "config" / "setup.yaml") as f:
    setup = yaml.safe_load(f)

n_splits = setup["evaluation"]["n_splits"]
train_size = setup["evaluation"]["train_size"]
val_size = setup["evaluation"]["val_size"]
holdout_start = setup["evaluation"].get("holdout_start")
n_assets = setup["universe"]["n_assets"]
costs = setup["costs"]
per_share_usd = costs["per_share"]
default_half_spread_usd = costs["default_half_spread_usd"]
# The bucket-monotonicity overlay wants a basis-point band, and this case study prices in cents
# per share; these two anchor the liquid and the sector ends of the universe.
COST_RANGE_BPS = [5, 15]

print(f"Case Study: {CASE_STUDY}")
print(f"  Universe: {n_assets} ETFs (equities, bonds, commodities, FX, sectors)")
print(f"  Label: {PRIMARY_LABEL} (monthly rebalancing)")
print(f"  CV: {n_splits} rolling-window folds, train={train_size}, val={val_size}")
print(f"  Holdout: {holdout_start} onwards")
print(
    f"  Trading costs: ${per_share_usd:.4f}/share + tiered half-spread "
    f"(mega 0.5¢, sector 1¢, default {default_half_spread_usd * 100:.0f}¢)"
)

# %% [markdown]
# ## 1. The prediction problem, and the shape of the evidence
#
# **The target is `fwd_ret_21d`**: the total return over the 21 trading days after the decision
# date, predicted for every fund, then ranked cross-sectionally at each rebalance. The strategy the
# backtest will build holds the top of that ranking and rebalances monthly, so what a model has to
# get right is the ordering within a date rather than the level of any one forecast. The metric is
# therefore an information coefficient - the rank correlation between the predicted and the
# realized cross-section, averaged over dates.
#
# The panel's proportions are unusual for this book. There are many more dates than funds, which is
# the opposite of a single-stock cross-section, and it is what makes latent-factor methods
# available here: a small number of common movements can plausibly explain a hundred funds whose
# categories - equity sectors, bonds, commodities, currencies - are themselves an economically
# meaningful low-rank structure.
#
# The features are entirely price-derived. Momentum at horizons from a week to a year, momentum
# adjusted for its own volatility, volatility itself, technical indicators and the yield-curve
# slope. There is no fundamental or alternative data, so anything a model finds here is a statement
# about price history and nothing else.
#
# The counts printed above come from `config/setup.yaml` and the artifacts, so they describe the
# run rather than restating a number that was true when this was written.

# %% [markdown]
# **Which generation of each family is being described.** `prediction_metrics` is a catalog, and a
# catalog carries no lineage: when a model notebook refits, it publishes a second generation under
# the same population name and the generation it replaced stays behind, complete and current under
# a schema version that has not moved. Reading the catalog alone therefore lists a family twice -
# once as it is published and once as it was - and the representative chosen to stand for the
# family in every comparison below can be the retired one.
#
# `split_unpublished_members` asks the population lineage instead, and the excluded side is
# printed rather than dropped silently, so the count is auditable against the registry.
#
# It asks **membership** and not retirement, which is the stronger of the two questions and the
# one [`14_backtest`](14_backtest.ipynb) already scopes its sweep with. The two differ by the
# identities no population ever listed: a row written before its notebook declared a population
# is retired by nobody, so a retirement split admits it, and it can then stand for its family in
# every comparison below while being invisible to the selection rule. Measured on this registry:
# 60 such rows, all of them written by notebooks that have since moved onto the research
# boundary and republished under a real identity.

# %% [markdown]
# **Present in the metrics is not the same as eligible for selection.** This section reads
# `prediction_metrics`, which lists every prediction set that was scored.
# [`14_backtest`](14_backtest.ipynb) sweeps `load_prediction_index`, which drops rows for reasons
# a metrics table cannot show: a superseded identity generation, a fold whose predictions
# collapsed to a constant, a missing artifact. A family that appears in the first and not the
# second is reported here and never traded - so the comparison a reader takes from this notebook
# would be over more families than the selection rule ever ran on. Both sets are printed below,
# and the difference is named rather than left to be inferred from two lists.

# %%
# Phase 1: Load pre-computed metrics for ALL labels (coverage + multi-label analysis)
all_labels_metrics = load_all_metrics(CASE_STUDY, label=None).filter(pl.col("label").is_not_null())

_generations = split_unpublished_members(study, all_labels_metrics)
all_labels_metrics = _generations.live
print(f"Registered metric rows: {_generations.live.height + _generations.retired.height:,}")
if _generations.retired.is_empty():
    print("Not published by any current population: none")
else:
    print(f"Not published by any current population: {_generations.retired.height:,}")
    print(
        _generations.retired.group_by("family", "config_name")
        .agg(n=pl.len())
        .sort("n", descending=True)
    )

all_metrics = all_labels_metrics.filter(pl.col("label") == PRIMARY_LABEL)

if all_metrics.height == 0:
    raise RuntimeError(f"No metrics found for {CASE_STUDY} / {PRIMARY_LABEL}")

families_present = sorted(all_metrics["family"].unique().to_list())
print(f"Pre-computed metrics: {all_metrics.height} entries across {len(families_present)} families")

for fam in families_present:
    sub = all_metrics.filter(pl.col("family") == fam)
    configs = sub["config_name"].n_unique()
    checkpoints = sub["checkpoint_value"].drop_nulls().n_unique()
    best_ic = sub["ic_mean"].max()
    best_ic_text = f"{best_ic:+.4f}" if best_ic is not None else "n/a"
    print(
        f"  {fam:20s}  {configs:3d} configs  {checkpoints:3d} checkpoints  best IC={best_ic_text}"
    )

# Coverage completeness check. `causal_dml` is not in this set: it writes to `causal_runs` and
# never to `prediction_metrics`, for the reason the Causal DML section below gives, so listing it
# here would report a shortfall on every run that no amount of fitting could close.
EXPECTED_FAMILIES = {"linear", "gbm", "tabular_dl", "deep_learning", "latent_factors"}
missing = EXPECTED_FAMILIES - set(families_present)
# Scored is not the same as eligible for selection. A family the candidate index cannot reach is
# reported here and never traded.
SELECTABLE_FAMILIES = set(
    load_prediction_index(CASE_STUDY, label=PRIMARY_LABEL, split="validation")["family"]
    .unique()
    .to_list()
)
reported_only = sorted(set(families_present) - SELECTABLE_FAMILIES)

if missing:
    print(
        f"\nSCORED: {len(families_present)}/{len(EXPECTED_FAMILIES)} forecasting families. "
        f"Missing: {', '.join(sorted(missing))}"
    )
    print("  Recommendations below may change when missing families are added.")
else:
    print(f"\nAll {len(EXPECTED_FAMILIES)} forecasting families are scored below.")
print(f"Selectable by the backtest stages: {', '.join(sorted(SELECTABLE_FAMILIES))}")
if reported_only:
    print(
        f"SCORED BUT NOT SELECTABLE: {', '.join(reported_only)}. Registered, scored, and "
        "unreachable from every backtest stage, so the traded comparison is over "
        f"{len(SELECTABLE_FAMILIES)} families rather than {len(families_present)}."
    )

# %%
# Best model per family
best_per_family = best_model_per_family_fast(all_metrics)

print("\nBest model per family:")
print(best_per_family.select(["family", "config_name", "checkpoint_value", "ic_mean", "ic_std"]))

# %%
# Phase 2: Load raw predictions ONLY for the ~5 best models (not all 47M+)
representative_preds = []

for row in best_per_family.filter(pl.col("family") != "causal_dml").iter_rows(named=True):
    family = row["family"]
    config = row["config_name"]
    checkpoint = row.get("checkpoint_value")

    # By hash, not by attributes: family, config and checkpoint do not identify a generation,
    # so a refit's retired rows share all three with the live ones and would concatenate here.
    preds = load_predictions(
        CASE_STUDY,
        prediction_hash=row["prediction_hash"],
        family=family,
        label=PRIMARY_LABEL,
        config_name=config,
        checkpoint_value=checkpoint,
    )
    if preds.height > 0:
        representative_preds.append(preds)
        print(f"  Loaded {family}/{config}: {preds.height:,} predictions")

if representative_preds:
    best_preds = pl.concat(representative_preds, how="diagonal_relaxed")
    # Normalize DATE_COL to Datetime (parquets may store as Date or String)
    if best_preds.height > 0 and best_preds[DATE_COL].dtype == pl.String:
        best_preds = best_preds.with_columns(pl.col(DATE_COL).str.to_datetime())
    elif best_preds.height > 0 and best_preds[DATE_COL].dtype == pl.Date:
        best_preds = best_preds.with_columns(pl.col(DATE_COL).cast(pl.Datetime("ms")))
    print(f"\nTotal representative predictions: {best_preds.height:,}")
else:
    best_preds = pl.DataFrame()
    print("WARNING: No raw predictions could be loaded")

# %%
# Fold date ranges for timeline
if best_preds.height > 0:
    fold_ranges = (
        best_preds.filter(pl.col("fold_id").is_not_null())
        .group_by("fold_id")
        .agg(
            pl.col(DATE_COL).min().cast(pl.Date).alias("val_start"),
            pl.col(DATE_COL).max().cast(pl.Date).alias("val_end"),
        )
        .sort("fold_id")
    )

# %% [markdown]
# ### Figure 1: Cross-Validation Timeline

# %%
if best_preds.height > 0 and fold_ranges.height > 0:
    plot_cv_timeline(fold_ranges, n_splits, holdout_start)

# %% [markdown]
# Each fold trains on a fixed 10-year rolling window and validates on the
# year that follows it. The folds are numbered chronologically: fold 0
# validates the earliest year (2016) and fold 7 the most recent
# pre-holdout year (2023), with the 10-year training window sliding
# forward accordingly. The holdout period (2024 onwards) is never used for
# model selection.
#
# Because the window is a fixed 10 years rather than expanding, every fold
# sees the same amount of training data; what differs across folds is the
# market regime each validation year falls in (the 2020 COVID shock, the
# 2022 rate shock). A model that works only in a handful of folds may be
# learning regime-specific patterns rather than persistent structure.

# %% [markdown]
# ## 2. What Was Actually Run?
#
# Before comparing results, we map what is actually comparable. Not all
# model families were trained on all labels, and the five modeling
# chapters contribute different kinds of evidence: chapters 11 to 13 produce predictive
# forecasts, chapter 14 extracts latent structure, and chapter 15 estimates causal effects. One
# ranking over all three would be a ranking over three different questions.

# %%
# Coverage map: family × label × evidence type
EVIDENCE_TYPE = {
    "linear": "predictive",
    "gbm": "predictive",
    "tabular_dl": "predictive",
    "deep_learning": "predictive",
    "latent_factors": "structural",
    "causal_dml": "causal",
}
FAMILY_CHAPTER = {
    "linear": "Ch11",
    "gbm": "Ch12",
    "tabular_dl": "Ch12",
    "deep_learning": "Ch13",
    "latent_factors": "Ch14",
    "causal_dml": "Ch15",
}

coverage = (
    all_labels_metrics.group_by(["family", "label"])
    .agg(
        pl.col("config_name").n_unique().alias("n_configs"),
        pl.col("ic_mean").max().alias("best_ic"),
    )
    .with_columns(
        chapter=pl.col("family").replace(FAMILY_CHAPTER),
        evidence=pl.col("family").replace(EVIDENCE_TYPE),
    )
    .sort(["family", "label"])
)

print("Coverage Map: Families × Labels")
print(coverage.select(["chapter", "family", "label", "evidence", "n_configs", "best_ic"]))

# %%
# Primary label coverage summary
primary_coverage = coverage.filter(pl.col("label") == PRIMARY_LABEL)
predictive_families = primary_coverage.filter(pl.col("evidence") == "predictive")[
    "family"
].to_list()
structural_families = primary_coverage.filter(pl.col("evidence") == "structural")[
    "family"
].to_list()
causal_families = primary_coverage.filter(pl.col("evidence") == "causal")["family"].to_list()
all_labels = sorted(coverage["label"].unique().to_list())

print(f"\nPrimary label ({PRIMARY_LABEL}):")
print(f"  Predictive families: {predictive_families}")
print(f"  Structural families: {structural_families or 'none'}")
print(f"  Causal families: {causal_families or 'none'}")
print(f"\nAll labels trained: {all_labels}")

# %% [markdown]
# The coverage map is the first result, before any comparison. It says which families were fitted
# on which label, and it is read rather than asserted because a family that was never fitted and a
# family that was fitted and did nothing look identical in a table of results.
#
# The three evidence types are not three grades of the same thing. **Predictive** families forecast
# the return from the feature row and are compared on how well they order the cross-section.
# **Structural** ones estimate a low-dimensional factor structure and produce a forecast as a
# consequence; they are compared with the predictive families on IC because that is the quantity a
# strategy uses, but the object they estimate is different. **Causal** evidence answers a question
# about the world rather than about a ranking and is reported in section 7, outside the comparison.
# One ranking over all three would be a ranking over three different questions.
#
# Two labels are declared - `fwd_ret_21d` at the monthly rebalancing horizon and `fwd_ret_5d` at
# the weekly one - and the coverage above shows they are not equally covered. Every cross-family
# comparison in this notebook uses the primary label unless it says otherwise; section 6 is where
# the two horizons are read against each other.

# %% [markdown]
# ## 3. What each family's leading configuration achieved
#
# The comparison starts from a baseline rather than from the leader. If ordinary least squares on
# the feature matrix - the simplest thing anyone would try, with no regularization and no
# non-linearity - produces no information coefficient at all, then the elaborate models are being
# compared on a problem that has nothing in it, and the right conclusion is about the panel rather
# than about them.

# %%
# Linear baseline
linear_metrics = all_metrics.filter(pl.col("family") == "linear")
if linear_metrics.height > 0:
    for name in ["ols", "ridge_a0.001", "ridge_a0.01", "ridge"]:
        baseline = linear_metrics.filter(pl.col("config_name") == name)
        if baseline.height > 0:
            ic = baseline["ic_mean"][0]
            std = baseline["ic_std"][0] if baseline["ic_std"][0] is not None else 0
            print(f"Linear baseline ({name}):")
            print(f"  IC mean:  {ic:+.4f}" if ic is not None else "  IC mean:  n/a")
            if std > 0:
                print(f"  IC std:   {std:.4f}")
                t_stat = (ic if ic is not None else 0) / (std / np.sqrt(n_splits))
                print(f"  t-stat:   {t_stat:.1f} (across {n_splits} folds)")
            break

# %% [markdown]
# Only full-coverage configurations are shown. One that degenerates to a constant prediction on
# some folds has an undefined daily IC there and a short `ic_n_days`, so its average is taken over
# fewer folds than the others and is not the same quantity.

# %%
_full_days = all_metrics["ic_n_days"].max()
forest_df = (
    all_metrics.filter(
        pl.col("ic_mean_daily").is_not_null(),
        pl.col("ic_n_days") == _full_days,
    )
    .sort("ic_mean_daily", descending=True, nulls_last=True)
    .group_by("family")
    .first()
    .sort("ic_mean_daily", descending=True, nulls_last=True)
    .select(
        "family",
        "config_name",
        "ic_mean_daily",
        "ic_ci_lo",
        "ic_ci_hi",
        "ic_t_hac",
        "ic_n_days",
    )
)
print("Validation IC with HAC 95% CI (highest-IC config per family):")
print(
    forest_df.select(
        "family",
        "config_name",
        pl.col("ic_mean_daily").round(4).alias("ic"),
        pl.col("ic_ci_lo").round(4).alias("ci_lo"),
        pl.col("ic_ci_hi").round(4).alias("ci_hi"),
        pl.col("ic_t_hac").round(2).alias("t_hac"),
        pl.col("ic_n_days").cast(pl.Int64).alias("n_days"),
    )
)

# %%
forest_fig = headline_forest_plot(
    forest_df,
    ic_col="ic_mean_daily",
    ci_lo_col="ic_ci_lo",
    ci_hi_col="ic_ci_hi",
    label_col="config_name",
    family_col="family",
    title=f"ETFs / {PRIMARY_LABEL}: daily-pooled IC with HAC 95% intervals",
)
_ic = forest_df["ic_mean_daily"]
show_with_alt(
    forest_fig,
    "Forest plot of each model family's highest daily-pooled information coefficient with its "
    "HAC 95% confidence interval, one row per family. Counted from the frame: "
    f"{forest_df.height} families, IC from {_ic.min():+.4f} to {_ic.max():+.4f}, "
    f"{int((forest_df['ic_ci_lo'] > 0).sum())} with an interval entirely above zero.",
)

# %% [markdown]
# **The interval is the result; the point estimate is a summary of it.** Each row above is one
# family's highest-IC configuration with a Driscoll-Kraay ninety-five percent interval around it,
# taken that way because these ICs are daily correlations on an overlapping label, so consecutive
# days are dependent and an ordinary standard error would be far too small.
#
# Three things to read off it, in order:
#
# - **Which intervals exclude zero.** Those are the families this panel supports a claim about.
#   A family whose interval crosses zero is directionally positive and not distinguishable from
#   having no signal, whatever its rank in the table.
# - **Which intervals overlap each other.** Where two overlap heavily, the ordering between them is
#   not something the data decided, and reading a table sorted by point estimate as a ranking puts
#   a claim on that ordering the interval does not support.
# - **Whether any family stands clear of the others.** One that does is the panel's strongest
#   single piece of evidence and is worth asking what it does differently. Section 7 is where the
#   latent-factor members are separated by exactly that question.
#
# **Only full-coverage configurations appear.** A configuration that degenerates to a constant
# prediction on some folds - a heavily L1-penalised linear model whose coefficients all reach zero
# is the usual case - has an undefined daily IC on those folds, so its average is taken over fewer
# of them. Ranking it against one measured on all of them compares two different quantities, so it
# is excluded rather than shown with a footnote.

# %% [markdown]
# ### Fold by fold, not on average
#
# The comparison below takes each family's leading configuration and looks at what it did in every
# fold rather than at the average over them. A configuration whose highest average IC comes from
# one or two exceptional windows is not the more credible choice, and the average alone cannot say
# which case it is.

# %%
# Build fold × family IC matrix from raw predictions
fold_ic = (
    fold_performance_matrix(best_preds, date_col=DATE_COL)
    if best_preds.height > 0
    else pl.DataFrame()
)

# %% [markdown]
# ### Fold by family, with the uncertain cells muted
#
# A cell is muted where the within-fold information coefficient is not credibly different from zero
# - a two-sided normal approximation on the fold's own IC against its within-fold dispersion, at
# the five percent level. What is left in colour is where the IC is large relative to how much it
# varied inside that fold, which is not the same set of cells as where the mean IC was highest.

# %%
fold_heatmap_fig = fold_heatmap_with_ci(
    CASE_STUDY,
    label=PRIMARY_LABEL,
    title=f"ETFs / {PRIMARY_LABEL}: fold IC by family, muted where not credibly nonzero",
)
show_with_alt(
    fold_heatmap_fig,
    "Heatmap of the within-fold information coefficient, one row per model family and one column "
    "per walk-forward fold, with cells muted to grey where the within-fold IC is not credibly "
    "different from zero at the five percent level.",
)

# %%
# Summary statistics per family
if fold_ic.height > 0:
    family_stats = (
        fold_ic.group_by("model_label")
        .agg(
            pl.col("ic_mean").mean().alias("mean_ic"),
            pl.col("ic_mean").median().alias("median_ic"),
            pl.col("ic_mean").std().alias("std_ic"),
            pl.col("ic_mean").min().alias("worst_fold"),
            pl.col("ic_mean").max().alias("best_fold"),
            (pl.col("ic_mean") > 0).mean().alias("pct_positive"),
            pl.col("ic_mean").count().alias("n_folds"),
        )
        .sort("mean_ic", descending=True)
    )
    print("Family performance summary:")
    print(family_stats)

# %% [markdown]
# **The heatmap answers a question the average cannot.** A family's mean IC is compatible with two
# very different pictures: a modest positive result repeated in every fold, or a large one in two
# folds and nothing in the rest. The first is a property of the panel; the second is a property of
# two years. Reading across a row tells you which.
#
# The muting is what keeps that reading honest. A cell is greyed when its within-fold IC is not
# credibly different from zero given the dispersion inside that fold, so a row that looks warm on
# average but is mostly grey is a row whose average is carried by the few cells that are not.
#
# The per-family table beneath it puts numbers on the same thing: `pct_positive` is how often the
# family was on the right side, `worst_fold` is what it did in its worst year, and the gap between
# `mean_ic` and `median_ic` says whether the average is being pulled by one fold.

# %% [markdown]
# ## 4. Stability over time
#
# A mean IC carried by two exceptional windows and a mean IC repeated in every window are the same
# number describing two different things. For a strategy that runs for years and cannot choose
# which regime it meets, the second is what can be sized and the first is a bet on the calendar.
# This section separates them.

# %% [markdown]
# ### The spread of each family's fold results

# %%
plot_fold_boxplot(fold_ic)

# %% [markdown]
# The box plots put each family's eight fold ICs on one axis. The box is the interquartile range,
# so a narrow one is a family that says roughly the same thing in every window, and the lower
# whisker is the year that would have been hardest to hold through.
#
# **For a strategy that has to be deployed before its regime is known, the lower whisker is the
# number that decides.** A family with a high average and a bad worst fold is offering a bet on
# which years arrive. A family with a modest average and a tight box is offering the same thing
# every year, which is the thing that can actually be sized. Neither is better in the abstract, and
# the reason to look at both is that the mean IC alone hides the distinction completely.

# %% [markdown]
# ## 5. What Are the Models Learning?
#
# Beyond aggregate IC, we examine the *structure* of predictions. Two
# diagnostic questions matter:
#
# 1. **Monotonicity**: do higher predicted scores correspond to higher
#    realized returns? A monotonic relationship confirms ranking ability.
# 2. **Diversity**: do different model families produce similar or
#    different rankings? Low correlation between families means ensemble
#    value; high correlation means diminishing returns from complexity.

# %%
# Compute prediction bucket monotonicity for best model per family
bucket_results = {}
for row in best_per_family.iter_rows(named=True):
    family = row["family"]
    config = row["config_name"]
    checkpoint = row.get("checkpoint_value")

    mask = (pl.col("family") == family) & (pl.col("config_name") == config)
    if checkpoint is not None:
        mask = mask & (pl.col("checkpoint_value") == checkpoint)

    model_preds = best_preds.filter(mask) if best_preds.height > 0 else pl.DataFrame()
    if model_preds.height == 0:
        continue

    buckets = prediction_bucket_monotonicity(model_preds, N_BUCKETS, DATE_COL)
    if buckets.height > 0:
        bucket_results[family] = buckets

# %% [markdown]
# ### Figure 4: Prediction Bucket Monotonicity

# %%
plot_bucket_monotonicity(
    bucket_results,
    N_BUCKETS,
    unconditional_mean=best_preds["y_true"].mean() if best_preds.height > 0 else None,
    label_name="21-Day Return",
    cost_range=COST_RANGE_BPS,
)

# %% [markdown]
# The monotonicity plot confirms genuine ranking ability across model
# families. The top-bottom bucket spreads and edge-to-cost ratios are
# computed above for each family. The practical implication: a top-N
# selection strategy with this signal needs to favor the liquid end
# of the ETF universe: the largest funds have costs
# of 2–5 bps per leg, while thematic and country ETFs cost 10–20 bps.
# Trading costs will consume the edge unless the portfolio tilts
# toward the more liquid instruments.

# %%
# Pairwise prediction correlations
corr_matrix, corr_labels = (
    prediction_correlation_matrix(best_preds, date_col=DATE_COL, entity_col=ENTITY_COL)
    if best_preds.height > 0
    else (np.array([]), [])
)

# %% [markdown]
# ### Figure 5: Prediction Correlation Across Models

# %%
plot_correlation_matrix(corr_matrix, corr_labels)

# %% [markdown]
# **A correlation matrix over predictions answers whether the families are doing different work.**
# Two models that produce nearly the same ranking are one model with two implementations, whatever
# their architectures: combining them adds nothing and the simpler of the two is the one to keep.
# Two that produce weakly related rankings are disagreeing about something, and a combination of
# them can be better than either.
#
# The matrix is the input to that decision rather than the decision. Ensembling happens in the
# synthesis chapter, over families that also survive the signal-stage backtest, and a pair that
# disagrees usefully here may still be a pair where one member has nothing.

# %% [markdown]
# ### How Much Does Additional Model Complexity Help?
#
# For models with checkpoint data, we observe how validation IC evolves
# with training. This reveals where diminishing returns begin and
# whether models overfit with additional epochs.

# %%
# Learning curves from pre-computed metrics (fast path)
cp_data = all_metrics.filter(pl.col("checkpoint_value").is_not_null())
cp_families = (
    cp_data.group_by("family")
    .agg(pl.col("checkpoint_value").n_unique().alias("n_cp"))
    .filter(pl.col("n_cp") > 1)["family"]
    .to_list()
    if cp_data.height > 0
    else []
)

print(f"Families with checkpoint data: {cp_families}")

# %% [markdown]
# ### Figure 6: Learning Curves

# %%
plot_learning_curves(cp_data, cp_families)

# %% [markdown]
# **A checkpoint is part of a configuration's identity, not a detail of how it was trained.** Each
# point on these curves is a separately registered prediction set with its own hash, so choosing a
# checkpoint is choosing a model, and it happens on the same evidence and under the same selection
# rule as choosing between architectures.
#
# What the curves are for is the shape. A curve that climbs and then plateaus says the extra
# training reached diminishing returns and the choice within the plateau matters little. One that
# climbs and then falls says the model started fitting the training window rather than the
# structure, and where it turns is the useful reading. One that never leaves zero says the
# configuration has nothing, regardless of how long it was trained.
#
# The confidence bands are the reason not to read a peak as a choice: where they are wide relative
# to the differences along the curve, the highest checkpoint and its neighbours are not
# distinguishable, and picking the argmax is picking noise.

# %% [markdown]
# ### Which features the forecasts rest on
#
# Feature importance from one fit is an anecdote about one fit. Importance that recurs across every
# walk-forward fold is evidence about the panel, and importance that appears in one fold and not
# the others is the model finding something local to that window. The heatmap below is arranged to
# show the difference: a feature is credible when its row is consistently dark, not when it is
# dark somewhere.

# %%
# Try GBM booster-based importance first, fall back to feature-prediction correlation
gbm_importance = load_gbm_feature_importance(CASE_STUDY, label=PRIMARY_LABEL, top_n=TOP_N_FEATURES)

if gbm_importance is None:
    # Fallback: compute feature-prediction IC (correlation between each feature and y_score)
    print("No GBM booster files available. Computing feature-prediction correlation as fallback...")

    features_path = CASE_DIR / "features" / "financial.parquet"
    if features_path.exists() and best_preds.height > 0:
        features_df = pl.read_parquet(features_path)
        feat_cols = [c for c in features_df.columns if c not in [DATE_COL, ENTITY_COL]]

        # Join best linear model predictions with features
        linear_preds = best_preds.filter(pl.col("family") == "linear")
        if linear_preds.height > 0:
            # Cast to the prediction frame's own timestamp type rather than to a named unit:
            # Polars refuses a join across two datetime units instead of widening one.
            timestamp_dtype = linear_preds.schema[DATE_COL]
            if features_df.schema[DATE_COL] == pl.String:
                features_df = features_df.with_columns(pl.col(DATE_COL).str.to_datetime())
            if features_df.schema[DATE_COL] != timestamp_dtype:
                features_df = features_df.with_columns(pl.col(DATE_COL).cast(timestamp_dtype))
            merged = linear_preds.join(features_df, on=[DATE_COL, ENTITY_COL], how="inner")

            # Compute correlation of each feature with y_score per fold
            importance_rows = []
            for fold in sorted(merged["fold_id"].unique().drop_nulls().to_list()):
                fold_data = merged.filter(pl.col("fold_id") == fold)
                for feat in feat_cols:
                    vals = fold_data[[feat, "y_score"]].drop_nulls()
                    if vals.height > 50:
                        from scipy.stats import spearmanr

                        corr, _ = spearmanr(vals[feat].to_numpy(), vals["y_score"].to_numpy())
                        importance_rows.append(
                            {
                                "config_name": "linear",
                                "fold_id": int(fold),
                                "feature": feat,
                                "importance": abs(float(corr)),
                            }
                        )

            if importance_rows:
                gbm_importance = pl.DataFrame(importance_rows)
                # Normalize per fold
                gbm_importance = gbm_importance.with_columns(
                    (
                        pl.col("importance")
                        / pl.col("importance").max().over(["config_name", "fold_id"])
                    ).alias("importance_norm")
                )
                # Filter to top features
                top_features = (
                    gbm_importance.group_by("feature")
                    .agg(pl.col("importance_norm").mean().alias("mean_imp"))
                    .sort("mean_imp", descending=True)
                    .head(TOP_N_FEATURES)["feature"]
                    .to_list()
                )
                gbm_importance = gbm_importance.filter(pl.col("feature").is_in(top_features))
                print(
                    f"Computed feature-prediction correlation for {len(top_features)} features across {merged['fold_id'].n_unique()} folds"
                )

if gbm_importance is not None and gbm_importance.height > 0:
    print(
        f"Feature importance: {gbm_importance['feature'].n_unique()} features × {gbm_importance['fold_id'].n_unique()} folds"
    )
else:
    print("Feature importance data not available.")

# %% [markdown]
# ### Figure 7: Feature Importance Stability Heatmap

# %%
plot_feature_importance_heatmap(gbm_importance, TOP_N_FEATURES)

# %% [markdown]
# The feature importance heatmap uses the fallback method (feature-prediction
# Spearman correlation) because stored GBM boosters lack named features.
# Values are normalized to [0, 1] per fold. Features that appear in the
# top 5 across 6+ of 8 folds are credible signal sources; those appearing
# only once or twice likely capture regime-specific noise.
#
# The features span momentum at horizons from a week to a year, momentum adjusted for its own
# volatility, volatility itself, technical indicators and the yield-curve slope. Where the
# fallback method is used, what it measures is how strongly each feature aligns with the linear
# model's predictions in that fold, which is not the same as how much the model relied on it -
# a feature correlated with one the model uses will score highly without being used at all.

# %% [markdown]
# ## 6. Heterogeneity: Labels, Horizons, and Regimes
#
# This section answers two questions: does the signal strengthen or
# weaken at shorter horizons? And does model ranking depend on the
# market regime? Both matter for strategy design.

# %% [markdown]
# ### The same families at a shorter horizon
#
# Two label horizons are declared: `fwd_ret_21d` at the monthly rebalancing cadence and
# `fwd_ret_5d` at the weekly one. The panels below put each family's highest-IC configuration for
# each horizon side by side with its HAC interval, and a tile marked "no run" is a family that was
# never fitted at that horizon rather than one that was fitted and produced nothing.
#
# Two questions come out of the comparison: whether the cross-sectional signal is stronger at one
# horizon than the other, and whether the ordering among families is the same at both. A family
# that leads at one horizon and not at the other is a family whose advantage is about the horizon
# rather than about the model.

# %%
multi_rows = []
for lbl in [PRIMARY_LABEL] + [l for l in all_labels if l != PRIMARY_LABEL]:
    lbl_metrics = all_labels_metrics.filter(pl.col("label") == lbl)
    # Full-coverage guard (see forest_df above): exclude configs whose daily IC is
    # defined on only a subset of folds so a family's per-horizon leader is a
    # full-coverage estimator, not a partial-coverage outlier.
    _lbl_full = lbl_metrics["ic_n_days"].max()
    lbl_metrics = lbl_metrics.filter(pl.col("ic_n_days") == _lbl_full)
    for fam in lbl_metrics["family"].unique().to_list():
        fam_data = lbl_metrics.filter(pl.col("family") == fam)
        rank1 = fam_data.sort("ic_mean_daily", descending=True, nulls_last=True).head(1)
        if rank1.height == 0:
            continue
        r = rank1.row(0, named=True)
        if r.get("ic_mean_daily") is None:
            continue
        multi_rows.append(
            {
                "label": lbl,
                "family": fam,
                "config_name": r["config_name"],
                "ic_mean_daily": r["ic_mean_daily"],
                "ic_ci_lo": r.get("ic_ci_lo"),
                "ic_ci_hi": r.get("ic_ci_hi"),
                "ic_t_hac": r.get("ic_t_hac"),
            }
        )

multi_label_df = pl.DataFrame(multi_rows)
multi_label_df

# %%
plot_label_horizon_forest(
    multi_label_df,
    families=["linear", "gbm", "tabular_dl", "deep_learning", "latent_factors", "causal_dml"],
    labels=[PRIMARY_LABEL] + [l for l in all_labels if l != PRIMARY_LABEL],
    label_display={
        "fwd_ret_21d": "fwd_ret_21d (monthly)",
        "fwd_ret_5d": "fwd_ret_5d (weekly)",
    },
    title="ETFs: highest IC per family and horizon, with HAC intervals",
)

# %% [markdown]
# **Coverage is the first thing the panel above says, before any comparison.** A family with no
# tile at a horizon was never fitted there, which is a fact about what was run rather than a result
# about the horizon - and reading an absence as a weak result is the mistake this layout exists to
# prevent.
#
# Where a family spans both horizons, the comparison is worth making carefully, because the two
# panels do not have the same amount of evidence behind them. A five-day label produces more
# scoreable validation dates than a twenty-one-day one over the same window, so its intervals are
# tighter for reasons that have nothing to do with signal. A family that looks more credible at the
# shorter horizon may simply have been measured more times there, and the point estimates are what
# to compare on strength while the intervals are what to compare on confidence.

# %% [markdown]
# ### Regime Conditioning
#
# Models do not have one universal performance level. The ETF cross-section
# is driven by macro regimes: risk-on/risk-off rotations, volatility spikes,
# and trend reversals can all shift which model family extracts signal. We
# condition performance on a volatility regime derived from cross-sectional
# return dispersion, which is a usable proxy for macro uncertainty.

# %%
# Compute regime-conditional IC
regime_results = []

for row in best_per_family.iter_rows(named=True):
    family = row["family"]
    config = row["config_name"]
    checkpoint = row.get("checkpoint_value")

    mask = (pl.col("family") == family) & (pl.col("config_name") == config)
    if checkpoint is not None:
        mask = mask & (pl.col("checkpoint_value") == checkpoint)

    model_preds = best_preds.filter(mask) if best_preds.height > 0 else pl.DataFrame()
    if model_preds.height == 0:
        continue

    regime_ic = regime_conditional_ic(model_preds, date_col=DATE_COL)
    if regime_ic.height > 0:
        regime_ic = regime_ic.with_columns(pl.lit(family).alias("family"))
        regime_results.append(regime_ic)

regime_df = pl.concat(regime_results) if regime_results else pl.DataFrame()

# %% [markdown]
# ### Figure 8: Conditional Performance by Volatility Regime

# %%
plot_regime_bars(regime_df)

# %% [markdown]
# Regime sensitivity is critical for ETF rotation strategies because the
# universe itself is a macro instrument. During high-volatility periods
# (risk-off), the cross-section separates more sharply, with bonds rallying
# while commodities and emerging markets sell off, creating larger
# cross-sectional spreads for models to exploit. During low-volatility
# periods (risk-on), ETF returns converge, and cross-sectional
# dispersion shrinks, making ranking harder.
#
# If one model family achieves the highest IC in high-vol but fails in
# low-vol, a regime-conditional strategy that switches models (or adjusts
# position sizing) based on realized volatility may produce a higher
# net IC than a static choice.

# %% [markdown]
# ## 7. Structural and Causal Evidence
#
# Not all model chapters produce comparable predictive scores. Ch14
# (latent factors) extracts structure; Ch15 (causal DML) estimates
# treatment effects. These require separate evidence blocks.

# %% [markdown]
# ### Latent Factors (Ch14)
#
# ETFs is one of the five case studies in scope for the latent-factor
# pipeline. Five estimators were fitted on this cross-section: principal components, instrumented
# PCA, the conditional autoencoder, the stochastic discount factor and the supervised
# autoencoder. [`11_latent_factors`](11_latent_factors.ipynb) sets out what separates them. The
# diagnostics below read each one's persisted fold extras to look inside the fit rather than at
# its score.

# %% [markdown]
# The per-fold diagnostics are stored under the **training hash** of the run that produced them -
# `run_log/training/<training_hash>/fold_extras.json` - so the configuration name from the metrics
# table has to be resolved to that hash first. Passing the name straight through returns `None` for
# every estimator, and because the loader answers `None` for "no file" rather than raising, the
# figures below silently render nothing while the prose beside them describes what they show. The
# estimators that wrote no extras are named rather than left out, so an empty panel is
# distinguishable from a panel that was never asked for.

# %%
lf_runs = (
    all_labels_metrics.filter(
        pl.col("family") == "latent_factors", pl.col("label") == PRIMARY_LABEL
    )
    .sort("ic_mean_daily", descending=True, nulls_last=True)
    .group_by("config_name", maintain_order=True)
    .first()
    .select("config_name", "training_hash", "ic_mean_daily")
)
lf_extras = {}
for _row in lf_runs.iter_rows(named=True):
    _extras = load_fold_extras(CASE_STUDY, _row["training_hash"])
    if _extras:
        lf_extras[_row["config_name"]] = _extras
_missing_extras = sorted(set(lf_runs["config_name"]) - set(lf_extras))
if _missing_extras:
    print(f"no fold_extras.json written by: {', '.join(_missing_extras)}")

# Print IC summary from registry
lf_metrics = all_labels_metrics.filter(
    pl.col("family") == "latent_factors", pl.col("label") == PRIMARY_LABEL
)
if lf_metrics.height > 0:
    lf_best = (
        lf_metrics.group_by("config_name")
        .agg(ic=pl.col("ic_mean").max())
        .sort("ic", descending=True)
    )
    print(f"Latent factor IC on {PRIMARY_LABEL}:")
    for row in lf_best.iter_rows(named=True):
        ic_text = f"{row['ic']:+.4f}" if row["ic"] is not None else "n/a"
        print(f"  {row['config_name']:6s}: {ic_text}")

print(f"\nFold extras available: {list(lf_extras.keys())}")

# %% [markdown]
# #### How concentrated is the return panel's variance?
#
# PCA's loadings come from the covariance of the training returns, so how much of that covariance
# the leading components carry decides how much structure there is to load on. A steep drop after
# one or two components means the panel is dominated by a few common movements; a flat profile
# means the variance is spread thinly and a low-rank model has little to work with.
#
# Both panels average over the folds, because each fold refits the decomposition on its own
# training window and a single fold's profile is one draw of it.

# %%
if "pca" not in lf_extras:
    print("PCA fold extras are not available, so the decomposition cannot be read back")
else:
    var_ratios = [e["explained_variance_ratio"] for e in lf_extras["pca"]]
    mean_var = np.asarray(np.mean(var_ratios, axis=0))
    components = list(range(1, len(mean_var) + 1))
    cumulative = np.cumsum(mean_var)

    scree = make_subplots(
        rows=1,
        cols=2,
        subplot_titles=("Variance per component", "Cumulative variance"),
    )
    scree.add_trace(
        go.Bar(x=components, y=mean_var.tolist(), marker_color=COLORS["blue"], showlegend=False),
        row=1,
        col=1,
    )
    scree.add_trace(
        go.Scatter(
            x=components,
            y=cumulative.tolist(),
            mode="lines+markers",
            line=dict(color=COLORS["blue"]),
            showlegend=False,
        ),
        row=1,
        col=2,
    )
    scree.add_hline(
        y=0.5, line_width=1, line_dash="dash", line_color=COLORS["neutral"], row=1, col=2
    )
    scree.update_xaxes(title_text="Component", row=1, col=1)
    scree.update_xaxes(title_text="Components retained", row=1, col=2)
    scree.update_yaxes(title_text="Share of training variance", row=1, col=1)
    scree.update_yaxes(title_text="Cumulative share", row=1, col=2)
    scree.update_layout(
        title="Where the ETF return panel's variance sits",
        height=400,
        width=920,
        margin=dict(t=110),
    )
    show_plotly_with_alt(
        scree,
        "Two side-by-side charts of the ETF return panel's principal-component variance, averaged "
        "across walk-forward folds: a bar chart of the share each component carries, and a line "
        "chart of the cumulative share with a dashed line at half. Counted from the array: "
        f"{len(mean_var)} components, the leading one carrying {mean_var[0]:.1%}, "
        f"the whole set {cumulative[-1]:.1%}.",
    )

# %% [markdown]
# **What the decomposition does and does not settle.** A concentrated variance profile says the
# funds move together, which is what makes a factor model the right shape for this panel. It says
# nothing about whether those factors carry a **premium** - whether loading on them is rewarded.
# That is a separate question, and it is the one the latent-factor IC table above answers.
#
# The estimators in that table differ in exactly one thing: how much the fit is allowed to know
# about returns. PCA reads the return panel alone; IPCA lets the features set the exposures; the
# conditional autoencoder does the same with a network in place of a linear map; the stochastic
# discount factor and the supervised autoencoder each drop the two-stage split, one by pricing the
# cross-section directly and one by predicting the return directly. Where the ordering in the table
# sorts them by that axis rather than by model complexity, the panel is telling you that what
# helps is supervision, not capacity. [`11_latent_factors`](11_latent_factors.ipynb) sets out the
# family, and each member's own notebook reports its fit.

# %% [markdown]
# #### Did the autoencoders converge, and did they converge to the same place?
#
# One line per fold, per model. What matters is not the level - the loss scales with the fold's own
# training window - but the shape: a curve that descends and flattens has converged, and a set of
# curves that flatten at similar rates is a model finding reproducible structure. Curves that
# diverge from each other say the representation is a property of the fold rather than of the
# panel, which is a reason to distrust it downstream whatever its IC.

# %%
for model_name in ("cae", "sae"):
    if model_name not in lf_extras:
        print(f"{model_name.upper()} fold extras are not available")
        continue
    curves = []
    for fold_index, fold in enumerate(lf_extras[model_name]):
        history = [h for h in fold.get("train_history", []) if "train_loss" in h]
        if history:
            curves.append(
                (fold_index, [h["epoch"] for h in history], [h["train_loss"] for h in history])
            )
    if not curves:
        print(f"{model_name.upper()} fold extras carry no training history")
        continue

    loss_fig = go.Figure()
    for fold_index, epochs, losses in curves:
        loss_fig.add_trace(
            go.Scatter(
                x=epochs,
                y=losses,
                mode="lines",
                name=f"fold {fold_index}",
                line=dict(color=COLORS["blue"], width=1.5),
                opacity=0.5,
            )
        )
    loss_fig.update_xaxes(title_text="Epoch")
    loss_fig.update_yaxes(title_text="Training loss")
    loss_fig.update_layout(
        title=f"{model_name.upper()} training loss, one line per fold",
        height=380,
        width=800,
        margin=dict(t=90),
    )
    _finals = [losses[-1] for _, _, losses in curves]
    show_plotly_with_alt(
        loss_fig,
        f"Line chart of the {model_name.upper()} autoencoder's training loss against epoch, one "
        "line per walk-forward fold. Counted from the histories: "
        f"{len(curves)} folds over up to {max(len(e) for _, e, _ in curves)} epochs, "
        f"final loss from {min(_finals):.4g} to {max(_finals):.4g}.",
    )

# %% [markdown]
# ### Causal DML (Ch15)
#
# [`12_causal_dml`](12_causal_dml.ipynb) writes to `causal_runs` rather than to
# `prediction_metrics`, because what it registers is not a forecast. It answers what would happen
# to a fund's next return if its momentum were different, holding the declared confounders fixed -
# a question about the world, not about a ranking. It has no prediction set, enters no population,
# and is not a candidate for [`14_backtest`](14_backtest.ipynb).
#
# The row is resolved by label rather than read out of the table by position. A label resolves to
# exactly one **current** causal identity, and a registry holding two undeclared ones raises rather
# than picking the first - which is the difference between reading a result and reading a row that
# happens to be there.
#
# The refutation p-value is nullable by contract: fewer than ten successful placebo draws leaves it
# NULL, and the classification derived from it None, because a p-value from that few draws cannot
# reject at any threshold. The cell prints "not run" rather than formatting a missing number, which would
# fail after half the block had already printed.

# %% tags=["results"]
causal_study = study
try:
    causal = CausalResult.one(causal_study, label=PRIMARY_LABEL)
except ValueError as error:
    causal = None
    print(f"No resolvable causal result for {PRIMARY_LABEL}: {error}")

if causal is not None:
    estimand = causal.spec["computation"]["estimand"]
    metrics = causal.metrics
    interval = 1.96 * metrics["dml_se_hac"]
    print(f"Causal DML on {PRIMARY_LABEL}, identity {causal.hash}")
    print(f"  Treatment:        {estimand['treatment']}")
    print(f"  Confounders:      {', '.join(estimand['confounders'])}")
    print(f"  Observations:     {metrics['n_obs']:,}")
    print(
        f"  Adjusted effect:  {metrics['dml_effect']:+.4f} "
        f"(Driscoll-Kraay SE {metrics['dml_se_hac']:.4f}, "
        f"95% interval {metrics['dml_effect'] - interval:+.4f} to "
        f"{metrics['dml_effect'] + interval:+.4f}, p {metrics['p_value_hac']:.4f})"
    )
    print(f"  Unadjusted (OLS): {metrics['naive_effect']:+.4f}")
    # NULL when fewer than ten placebo draws succeed; see the note above the cell.
    refutation_p = metrics["refutation_p"]
    print(
        f"  Refutation:       {metrics['refutation_class'] or 'not run'}"
        + ("" if refutation_p is None else f" (p {refutation_p:.4f})")
    )

# %% [markdown]
# **The causal estimate and the predictive IC answer different questions, and neither settles the
# other.** A positive information coefficient says the model orders the cross-section usefully. The
# causal estimate says how much of the momentum-return association is left once volatility, regime
# and the yield-curve slope are accounted for. A strategy that goes long the top of a ranking and
# short the bottom needs the first and does not need the second, which is why nothing here feeds
# selection.
#
# What the causal row does carry is a warning about **interpretation**. If the adjusted effect
# above is small relative to its standard error, or sits on the opposite side of zero from the
# unadjusted one, then the momentum feature is not a directional bet the data support - whatever
# the predictive families are extracting from it, "high momentum causes high returns" is not the
# mechanism, and describing the strategy that way would be describing something the estimate does
# not show. [`12_causal_dml`](12_causal_dml.ipynb) sets out why the Driscoll-Kraay interval and the
# block-permutation p-value can disagree, and which one to believe when they do.

# %% [markdown]
# ### Calibration: Are Prediction Intervals Honest?
#
# Point IC tells us whether the ranking is correct on average; it says
# nothing about whether the model's *uncertainty* is well calibrated. The
# width measured here is the one the `conformal_weighted` allocator sizes
# positions with: calibrated per symbol on every absolute residual known at
# `t - h`, where `h` is this label's horizon in data steps, falling back to a
# quantile pooled over every symbol where one has too few residuals of its
# own. A decision is covered when its absolute residual falls inside that
# half-width, and `n_uncalibrated` counts the decisions that cleared no
# warm-up and that no coverage figure describes.
#
# Empirical coverage materially below the nominal level signals overconfident
# residual scaling - the model is more wrong, more often, than its
# training-time spread suggests. Width is reported as a fraction of the
# standard deviation of the outcomes it was measured against, so families with
# different return scales are comparable; smaller width at matched coverage
# means tighter, more useful intervals.
#
# Read it as a diagnostic of residual dispersion rather than a guarantee.
# Split conformal's finite-sample coverage (Vovk et al., 2005; Lei et al.,
# 2018) requires the calibration and evaluation scores to be exchangeable and
# return residuals are not, and nothing in the allocation path reads an
# interval or a coverage level - the width stands in for a volatility
# estimate. See Ch12 §12.6 / `11_conformal_gbm` for the full conformal toolkit
# (CQR, ACI).
#
# Each row is the family's highest-IC configuration for the primary label.
# That is a model-level ranking and not the funnel's - every selection stage
# ranks on validation backtest Sharpe - and it is used here because this
# diagnostic runs before any backtest exists to rank.

# %%
conformal_etfs = conformal_coverage_diagnostic(
    CASE_STUDY,
    label=PRIMARY_LABEL,
)
conformal_etfs

# %%
if conformal_etfs.height > 0:
    pivot = conformal_etfs.pivot(
        on="nominal_level",
        index=["family", "config_name"],
        values=["empirical_coverage", "mean_interval_width_frac_std"],
    )
    print("Empirical coverage and width (× std of returns) at 80/90/95% nominal:")
    print(pivot)

# %% [markdown]
# Coverage at the three nominal levels tracks the targets reasonably
# closely across families, with most highest-IC configs landing within a few
# percentage points of nominal. Departures are informative: under-coverage
# (empirical < nominal) signals residuals heavier-tailed than the
# calibration window suggests, which is a known concern for daily ETF returns
# with episodic volatility shocks; over-coverage means intervals are
# wider than needed. Width-per-std is the more useful axis for
# distinguishing models: at matched coverage, a family whose intervals
# are narrower in std-units is producing tighter forecasts of the
# residual distribution. This calibration diagnostic feeds Ch19 risk
# management, where wider intervals scale down position size, and
# ACI extensions (Ch12 §12.6) update interval width online to track
# regime shifts in residual variance.

# %% [markdown]
# ## 8. What this analysis can and cannot settle
#
# Two tables close the notebook. The first is the validation recor