Ajuster les arbres de gradient pour classer les rendements des contrats à terme

# 07_gbm.py


```py
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# %% [markdown]
# # CME futures: capacity, loss function, and when to stop adding trees
#
# [`06_linear`](06_linear.ipynb) fitted a penalty grid to these 69 features and found almost
# nothing above zero at the five-day horizon the strategy chapters trade. A linear model can only
# represent a relationship it is given
# in the right shape: it sees an interaction between carry and momentum only because someone
# multiplied those columns together and named the product. Gradient boosting has no such
# restriction. It builds a sequence of shallow trees, each fitted to what the ones before it got
# wrong, and a tree splits on one feature inside a region defined by others - so an interaction is
# something it can find rather than something it must be handed.
#
# That freedom is the whole question. A model that can represent anything can also fit noise, and
# with 30 products and 69 features there is plenty to fit. Three dials control how far it goes,
# and this notebook varies all three:
#
# - **Capacity**, set by `num_leaves`: how many regions one tree may carve the feature space into.
#   Seven leaves can express a handful of conditions; 63 can express a fine partition that may
#   describe the training window and nothing beyond it.
# - **The loss function**, which decides what "got wrong" means. This matters more here than in an
#   equity universe. Commodity returns are heavy-tailed, and squared error weights an observation
#   by the square of its error, so a single extreme move can carry hundreds of times the weight of
#   a typical one. Absolute error and Huber loss do not, which makes the comparison between the
#   three objectives a comparison of how much the tails are allowed to steer the fit.
# - **When to stop**, set by the number of trees. Unlike a linear fit, a boosted model has a
#   meaningful state at every iteration, so this notebook scores each configuration at ten points
#   along its own training run rather than only at the end.
#
# The third dial is the one that changes how results must be read. **A checkpoint is part of a
# configuration, not a detail of how it was fitted.** Scoring 15 declared configurations at ten
# checkpoints each produces 150 candidate models per label, 300 across the two the case study
# declares, and treating that as 15 candidates while quietly keeping each one's best iteration
# would be reporting the maximum of ten numbers as though it were one.
#
# **Learning objectives.** By the end of this notebook you will be able to:
#
# - Read a declared gradient boosting grid and say what each configuration varies.
# - Explain why a boosted model produces one result per checkpoint while a linear model produces
#   one result in total, and what that implies for counting candidates.
# - Read a learning curve of out-of-sample information coefficient against tree count, and tell
#   apart a model still learning from one that has begun fitting the training window.
# - Say why the choice of loss function is a statement about the label's tails, and relate that to
#   what a rank-based metric rewards.
# - Recognise that picking each configuration's best checkpoint after seeing the results is a
#   selection decision, and locate where selection is actually made.
# - Compare the same grid across two prediction horizons and say what a reversal between them
#   rules out.
#
# **Book reference**: Chapter 12, Section 12.2 (GBM libraries) and Section 12.3 (how to tune a
# boosted model). Chapter 6, Section 6.7 (Search accounting and run logging) introduces the run
# log this notebook writes to.
#
# **Prerequisites**: [`03_financial_features`](03_financial_features.ipynb) and
# [`04_model_based_features`](04_model_based_features.ipynb) have written the feature matrices,
# [`05_evaluation`](05_evaluation.ipynb) has established the walk-forward folds, and
# [`06_linear`](06_linear.ipynb) fitted the linear population this one is compared against.
#
# **What it writes**: one training run per configuration and one complete validation prediction
# set per configuration and checkpoint, in `run_log/registry.db` and under `run_log/training/` and
# `run_log/predictions/`, grouped under a named population.
# [`13_backtest`](13_backtest.ipynb) reads that population and selects on validation backtest
# Sharpe. **Selection happens there, not here.**

# %%
"""Fit the declared CME futures gradient boosting population on the walk-forward folds."""

import plotly.graph_objects as go
import polars as pl
from plotly.subplots import make_subplots

from case_studies.cme_futures.research_workflow import supersedes_declaration
from case_studies.research import (
    declared_labels,
    load_model_configs,
    model_requests,
    narrows_declared_catalog,
    open_study,
    population_supersedes,
    primary_label,
    resolved_model_plan,
    run_model_population,
)
from utils.style import COLORS, show_plotly_with_alt

# %% tags=["parameters"]
LABELS: list[str] = []
EXECUTION_TIER = "canonical"
WORKSPACE: str = ""
PREVIEW_REDUCTIONS: dict = {}
CONFIG_NAMES: list[str] = []
POPULATION_NAME = ""
SUPERSEDES_POPULATION: str = "9f26b89f9310"

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

# %% [markdown]
# ## 1. Which labels, and which models
#
# The labels are the same two the linear notebook fitted: `fwd_ret_5d`, the total return over the
# five trading days after the decision date, and `fwd_ret_21d` over 21 days. Keeping the set fixed
# is what makes the two populations comparable - the families differ, the targets do not. It also
# carries the horizon question forward: `06_linear` found the 21-day label reaching fifteen times
# what the traded five-day label reached, and a grid fitted on one label could not say whether
# that is a property of the horizon or of the linear family. `LABELS` restricts the run to a
# subset when you want one, and defaults to everything the menus declare.

# %%
declared_labels(study, "gbm")

# %% [markdown]
# Each label's menu at `config/training/{label}.yaml` lists 15 named configurations under `gbm:`,
# and each resolves to a preset in `case_studies/config/lgb/`. The two menus declare the same
# fifteen, so the grid is fitted twice over, once per horizon. The grid is a product of two axes:
#
# - **Five capacity profiles.** `default` uses the library's own leaf count; the rest fix it at 7,
#   15, 31 and 63. Leaf count is the direct control on how finely one tree may partition the
#   feature space, and it is the dial that decides whether sector-specific behaviour can be
#   expressed at all.
# - **Three objectives.** `mse` minimizes squared error, `mae` absolute error, and `huber` behaves
#   like squared error for small residuals and like absolute error beyond a threshold derived from
#   each fold's own label spread.
#
# Every configuration runs the same number of boosting iterations with the same learning rate, so
# the grid isolates capacity and loss rather than confounding them with training length.

# %%
configs = load_model_configs(
    study,
    "gbm",
    labels=LABELS or None,
    config_names=CONFIG_NAMES or None,
)
configs

# %% [markdown]
# `LABELS` and `CONFIG_NAMES` both narrow what is fitted, and a narrowed run declares a different
# set of members than the canonical population does. A population is immutable once written, so
# such a run must publish under its own name. Comparing the loaded rows against the complete
# declared catalog catches either knob, and says so here rather than several cells later in a
# message about hashes.

# %%
if narrows_declared_catalog(study, "gbm", configs) and not POPULATION_NAME:
    raise ValueError(
        f"this run declares {configs.height} label-configuration pairs, which is not the "
        "complete declared catalog, so it cannot publish the canonical population; pass "
        "POPULATION_NAME to give it its own"
    )

# %% [markdown]
# ## 2. Binding the declarations to the data
#
# Resolving reads the label and feature files, computes the fold boundaries, works out the exact
# rows each fit must predict, and turns any data-dependent parameter into the number it will use.
# Huber's threshold is one of those: it is a fraction of the training labels' standard deviation,
# so it is a different number on every fold and is resolved from that fold's own data.
#
# Nothing is fitted here, so the plan can be inspected first. Four things to check:
#
# **Read the checks within a label, not across the whole frame.** Each label has its own purge
# buffer in `config/setup.yaml` - `5D` for the primary and `21D` for the variant - so the two
# horizons resolve to different fold boundaries and different eligible samples, and `label` is the
# first column for that reason.
#
# - **`feature_count`, `eligible_entities` and `eligible_rows` agree across every row of the same
#   label.** A row that differs from its own label's neighbours is a configuration measured on a
#   different sample from theirs. Between labels they differ by construction.
# - **`folds` is the same everywhere**, and equals the number of walk-forward splits. This one
#   does hold across labels.
# - **`validation_start` and `validation_end` bracket the development sample**, with none of the
#   held-out tail visible. `validation_end` falls earlier for the 21-day label, because a longer
#   forward window has to stop earlier to keep its outcome inside the development period.
# - **`checkpoints` is where this differs from the linear plan.** It is the number of training
#   states each configuration will publish predictions for. Multiply it by the number of rows to
#   get the number of candidate models this notebook is about to create.

# %%
requests = model_requests(
    study,
    configs,
    execution_tier=EXECUTION_TIER,
    preview_reductions=PREVIEW_REDUCTIONS,
)
resolved = tuple(request.resolve() for request in requests)

plan = resolved_model_plan(resolved)
plan.select(
    "label",
    "config_name",
    "feature_count",
    "eligible_entities",
    "eligible_rows",
    "folds",
    "checkpoints",
    "validation_start",
    "validation_end",
)

# %% [markdown]
# ## 3. Fitting the population
#
# `run_model_population` fits every resolved request. For one request it walks the folds, and on
# each one:
#
# 1. takes the rows inside that fold's training window,
# 2. casts the design matrix to the precision LightGBM works in and leaves missing values in
#    place - a tree routes a missing value down its own branch, so imputing a median here would
#    hand the model an observation nobody made,
# 3. fits the declared number of boosting iterations,
# 4. predicts the fold's validation rows at each checkpoint, using only the trees built up to that
#    iteration.
#
# Step 4 is what makes one fit produce many results. The fold predictions are concatenated into
# one series per checkpoint covering the whole validation period, and each becomes its own
# registered prediction set with its own identity.
#
# Each configuration prepares its own folds here, and that is a consequence of resolving the
# requests above before running them. A resolved request goes straight to its own fit; only an
# unresolved one reaches the family's batch runner, which walks folds on the outside and
# configurations on the inside so that one prepared fold set serves every configuration.
#
# This notebook resolves first, and pays for it, because the plan table above is the point: a
# resolved request knows its real feature count, eligible row count and fold boundaries, and can
# show them before anything is fitted. Planning from unresolved requests computes the same
# identities from placeholder folds, so the table would name configurations without being able to
# say what each will actually read. On this panel the repeated preparation is affordable and the
# visible plan is worth more. On a panel where it is not - `us_equities_panel` is the case the
# runner was written for - pass the requests unresolved and let the batch runner hold one fold set
# at a time.
#
# **What the call publishes is a population**: a named, immutable list of the prediction sets it
# will produce, written down before the first fit. Afterwards every member must exist and be
# complete, which is what makes the downstream comparison well defined.
#
# **One population covers both labels**, because one run fits both and the population is what
# that run declares. A population is immutable once written, so a notebook that fitted one label
# per run under a single name would publish the first label and be refused for the second, which
# is what happened before this notebook fitted them together.
#
# `SUPERSEDES_POPULATION` names the population hash this run replaces. A population is the set of
# prediction identities, so anything that moves a training identity - a changed estimator
# parameter as much as a changed configuration menu - produces a different population under the
# same name, and the registry refuses to write it without being told which snapshot it supersedes.
# That lineage is the only record of which generation is which. It has advanced twice: first for
# the refit onto LightGBM's CPU `max_bin` default, and now for the refit onto the corrected ARIMA
# feature, since `04` walking each product over its own history moved `model_based.parquet`'s
# digest and with it every training identity fitted on it. The value here is the generation this
# run replaces, not the first one.
#
# It defaults to the hash the published population actually superseded, not to empty. The hash is
# part of what the snapshot is hashed over, so a run that left it empty would compute a different
# population and be refused against the one on record. Carrying the value the published run used
# is what lets this notebook re-run and resolve to the population it published rather than to a
# new one.
#
# **A default that is a real hash rather than empty cannot be passed unconditionally**, and
# `population_supersedes` is where the three cases that must drop it are decided. A preview population
# is discarded with its workspace, so it has no lineage to extend and `run_model_population`
# refuses one that carries a hash - passing it would fail every preview of this notebook before it
# reached a fit, which is how the reduced-scale preview that gates the production run would go red
# for a reason that has nothing to do with the model. The other two are runs where the population
# does not exist yet: `run_log/` is not in the repository, so a reader running this from a fresh
# checkout is writing a first version, and so is anyone narrowing a run under a `POPULATION_NAME`
# of their own. `OfficialPopulation.create` refuses a first version that supersedes anything, so
# without this the default would break both.
#
# Where a generation exists and the declaration names neither it nor the one it superseded, the
# hash is withheld and `OfficialPopulation.create` refuses the changed population, naming the
# snapshot it requires. The refusal belongs to the registry; withholding is how it is left to it.
#
# **The preview case is decided here, not inside the helper.** `population_supersedes` reads
# `study.root`, and a preview in a maintainer worktree does not get its own root - `open_study`
# reads the symlinked generated directories in place and redirects only the writes. The lookup
# therefore finds the canonical generation and returns a real hash, which `run_model_population`
# refuses for a preview, killing the run before the first fit. Withholding the declaration outside
# the tier that can act on it is what keeps the preview runnable.

# %%
population_name = POPULATION_NAME or "cme_futures-gbm-validation-v1"
supersedes = population_supersedes(
    study,
    name=population_name,
    declared=supersedes_declaration(EXECUTION_TIER, SUPERSEDES_POPULATION),
)
execution, population = run_model_population(
    study,
    resolved,
    population_name=population_name,
    supersedes=supersedes,
)

fitted = sum(len(item["fitted_folds"]) for item in execution.diagnostics)
reused = sum(len(item["reused_folds"]) for item in execution.diagnostics)
print(f"{len(execution.runs)} configurations: {fitted} folds fitted, {reused} reused")
print(f"population {population.name}: {len(population.members)} prediction sets")

# %% [markdown]
# Re-running this notebook unchanged costs the time it takes to read the data. Every identity is
# re-derived from the inputs, the registry already holds the matching rows, and the runner returns
# the stored result rather than fitting again.
#
# ### Running configurations of your own
#
# The published run log is read-only. To add runs, open the study against a workspace, which holds
# its own registry and artifacts and reads the same labels and features:
#
# ```python
# study = open_study("cme_futures", workspace="~/ml4t-experiments")
# configs = load_model_configs(
#     study, "gbm", labels=["fwd_ret_5d"], config_names=["leaves_15_huber", "leaves_31_huber"]
# )
# requests = model_requests(study, configs)
# resolved = tuple(request.resolve() for request in requests)
# execution, population = run_model_population(study, resolved, population_name="my-gbm-v1")
# ```
#
# `CONFIG_NAMES` fits a subset of what the menu declares. To fit something new, add a preset at
# `case_studies/config/lgb/leaves_127_huber.yaml` and list `leaves_127_huber` under `gbm:` in the
# label's menu. Editing an existing preset changes that configuration's identity, so its result
# registers as a new row beside the old one rather than replacing it.
# [`RUN_LOG.md`](../RUN_LOG.md#running-your-own-configurations) covers the rest.

# %% [markdown]
# ## 4. What came out
#
# One row per configuration and checkpoint. `ic_mean` is the **information coefficient**: on each
# validation date, rank the products by the model's prediction, rank them by the return they went
# on to earn, correlate the two rankings, and average that daily correlation over the validation
# period.
#
# The table is sorted by label and then by IC, and the top of each label's block is the trap this
# notebook exists to describe. The leading row for a label is the maximum of 150 numbers - fifteen
# configurations at ten checkpoints each. Reading it as the result of one experiment would
# attribute to the model whatever the stopping point contributed, and the section below measures
# how large that contribution is before anything is concluded from the ranking.
#
# Coverage is judged against each label's own maximum number of scorable validation dates. The
# horizons do not offer the same number to begin with, since a longer forward window runs out
# earlier, so one global maximum would mark a whole label incomplete for a reason unrelated to any
# model.

# %% tags=["results"]
catalog = execution.catalog_rows.select(
    "config_name",
    "label",
    "complete",
    "checkpoint_value",
    "ic_mean",
    "ic_std",
    "ic_n_days",
    "n_folds",
    "training_hash",
    "prediction_hash",
).sort(["label", "ic_mean"], descending=[False, True])

if catalog.filter(~pl.col("complete")).height:
    raise RuntimeError("gbm execution returned a partial prediction set")

catalog = catalog.with_columns(
    full_coverage=pl.col("ic_n_days") == pl.col("ic_n_days").max().over("label")
)

primary = primary_label(study)
present = sorted(set(catalog.get_column("label")))
# The primary label leads when it was fitted. A subset run that leaves it out orders the panels
# by whichever label it did fit rather than by one that is not there.
panel_labels = [label for label in [primary] if label in present] + [
    label for label in present if label != primary
]
order_label = panel_labels[0]
print(f"{catalog.height} candidate models: {catalog.n_unique('config_name')} configurations")
print(f"at {catalog.n_unique('checkpoint_value')} checkpoints each, on {len(panel_labels)} labels")
catalog.select(
    "label",
    "config_name",
    "checkpoint_value",
    "ic_mean",
    "ic_std",
    "ic_n_days",
    "full_coverage",
).head(15)

# %% [markdown]
# ### What more trees do
#
# Each line traces one configuration's out-of-sample IC as trees are added to it. This is the
# figure the checkpoint dimension exists to produce, and it separates two things a single
# end-of-training number cannot.
#
# A line that rises and then falls has an interior optimum: the model was still learning, then
# began fitting the training window at the expense of the validation folds. A line that wanders
# without trend around zero never had anything to learn in the first place, and its highest point
# is wherever the noise happened to peak. The difference matters, because both produce a
# respectable-looking maximum.

# %%
curves = catalog.filter("full_coverage").sort("label", "config_name", "checkpoint_value")
objectives = {"mse": COLORS["blue"], "mae": COLORS["amber"], "huber": COLORS["copper"]}


def objective_of(name: str) -> str:
    """Read the loss function out of a declared configuration name."""
    match = next((key for key in objectives if name.endswith(key)), None)
    if match is None:
        raise ValueError(f"{name!r} does not end in a declared objective: {sorted(objectives)}")
    return match


fig_curves = make_subplots(
    rows=len(panel_labels),
    cols=1,
    shared_xaxes=True,
    vertical_spacing=0.05,
    subplot_titles=[
        f"{label} ({'primary' if label == primary else 'variant'})" for label in panel_labels
    ],
)
for row, label in enumerate(panel_labels, start=1):
    panel = curves.filter(pl.col("label") == label)
    for objective, color in objectives.items():
        members = [
            name
            for name in panel.get_column("config_name").unique(maintain_order=True)
            if objective_of(name) == objective
        ]
        for index, config_name in enumerate(members):
            series = panel.filter(pl.col("config_name") == config_name)
            fig_curves.add_trace(
                go.Scatter(
                    x=series.get_column("checkpoint_value").to_list(),
                    y=series.get_column("ic_mean").to_list(),
                    mode="lines",
                    name=objective,
                    legendgroup=objective,
                    # One legend entry per loss function, not per configuration: the colour is
                    # the claim, and thirty named lines would bury it.
                    showlegend=row == 1 and index == 0,
                    line=dict(color=color, width=1.5),
                    opacity=0.75,
                ),
                row=row,
                col=1,
            )
    fig_curves.add_hline(
        y=0, line_width=1, line_dash="dash", line_color=COLORS["neutral"], row=row, col=1
    )
    fig_curves.update_yaxes(title_text="Mean IC (validation)", row=row, col=1)
fig_curves.update_xaxes(title_text="Boosting iterations (trees kept)", row=len(panel_labels), col=1)
fig_curves.update_layout(
    title="Validation IC against boosting iteration, by loss function and label",
    height=330 * len(panel_labels),
    width=1000,
    margin=dict(t=90),
    legend=dict(title_text="Loss function"),
)
# How many lines dip below zero at some checkpoint is a fact about the frame, so the alt text
# reads it rather than asserting it: two five-day configurations touch a fraction of a thousandth
# below zero at their earliest checkpoints, and describing the panel as uniformly positive would
# be a claim the data refutes.
dip_text = " and ".join(
    f"{row['below']} of {row['total']} in the {row['label']} panel"
    for row in curves.group_by("label")
    .agg(
        total=pl.col("config_name").n_unique(),
        below=pl.col("config_name").filter(pl.col("ic_mean") < 0).n_unique(),
    )
    .sort("label")
    .iter_rows(named=True)
)
show_plotly_with_alt(
    fig_curves,
    "Line charts of mean validation information coefficient against boosting iteration, one line "
    "per configuration, coloured by loss function: dark navy for squared error, gold for absolute "
    "error, copper for Huber. Each panel carries a dashed zero line. The five-day panel sits "
    "almost entirely above that line, with the copper Huber lines running highest and the navy "
    "squared-error lines lowest; the 21-day panel sits below it, and its two lowest navy lines "
    "run near -0.04 throughout. Counted from the underlying frame, the lines that dip below zero "
    f"at some checkpoint are {dip_text}. The lines wander "
    "up and down rather than rising to a common peak and falling away, so no single stopping "
    "point stands out in either panel.",
)

# %% [markdown]
# Whether the lines trend, and which way, is not something to read off a page of overlapping
# curves. The frame below measures it: for each configuration, its IC at the last checkpoint
# minus its IC at the first, and whether its best checkpoint is an interior one rather than
# either end. A family that is still learning would show positive changes and interior peaks; a
# family that is overfitting would show negative changes; a family with nothing to learn would
# show changes centred on zero with peaks scattered anywhere.

# %% tags=["results"]
drift = (
    curves.group_by("label", "config_name")
    .agg(
        first_ic=pl.col("ic_mean").sort_by("checkpoint_value").first(),
        last_ic=pl.col("ic_mean").sort_by("checkpoint_value").last(),
        peak_checkpoint=pl.col("checkpoint_value").sort_by("ic_mean", descending=True).first(),
        first_checkpoint=pl.col("checkpoint_value").min(),
        last_checkpoint=pl.col("checkpoint_value").max(),
    )
    .with_columns(
        change=pl.col("last_ic") - pl.col("first_ic"),
        interior_peak=pl.col("peak_checkpoint").is_between(
            pl.col("first_checkpoint"), pl.col("last_checkpoint"), closed="none"
        ),
    )
)
trees_effect = (
    drift.group_by("label")
    .agg(
        configurations=pl.len(),
        median_change=pl.col("change").median(),
        ended_lower=(pl.col("change") < 0).sum(),
        interior_peaks=pl.col("interior_peak").sum(),
    )
    .sort("label")
)
trees_effect

# %% [markdown]
# ### Comparing every configuration at the same training length
#
# The curves are coloured by objective because that is the axis with a mechanism behind it. If
# heavy tails are steering the squared-error fits, the three colours should separate, and they
# should separate more as trees are added, since each additional tree is fitted to the residuals
# the previous ones left.
#
# The chart below drops the checkpoint dimension by taking each configuration's final state, so
# every configuration is compared at the same amount of training. That is the comparison that does
# not require choosing anything after the fact. The configurations are held in one order across
# the panels - their ranking on the primary label - so a panel that does not descend is a horizon
# that orders the grid differently.

# %%
final = (
    catalog.filter(pl.col("checkpoint_value") == pl.col("checkpoint_value").max().over("label"))
    .filter("full_coverage")
    .with_columns(objective=pl.col("config_name").map_elements(objective_of, return_dtype=pl.Utf8))
    .sort(["label", "ic_mean"], descending=[False, True])
)
final_iteration = int(final.get_column("checkpoint_value").max())
config_order = (
    final.filter(pl.col("label") == order_label)
    .sort("ic_mean", descending=True)
    .get_column("config_name")
    .to_list()
)

fig_obj = make_subplots(
    rows=len(panel_labels),
    cols=1,
    shared_xaxes=True,
    vertical_spacing=0.09,
    subplot_titles=[
        f"{label} ({'primary' if label == primary else 'variant'})" for label in panel_labels
    ],
)
for row, label in enumerate(panel_labels, start=1):
    panel = final.filter(pl.col("label") == label)
    fig_obj.add_trace(
        go.Bar(
            x=panel.get_column("config_name").to_list(),
            y=panel.get_column("ic_mean").to_list(),
            marker_color=[objectives[value] for value in panel.get_column("objective")],
        ),
        row=row,
        col=1,
    )
    fig_obj.add_hline(
        y=0, line_width=1, line_dash="dash", line_color=COLORS["neutral"], row=row, col=1
    )
    fig_obj.update_yaxes(title_text="Mean IC (validation)", row=row, col=1)
fig_obj.update_xaxes(
    categoryorder="array",
    categoryarray=config_order,
    tickangle=-45,
    title_text=f"Configuration (ordered by validation IC on {order_label})",
    row=len(panel_labels),
    col=1,
)
fig_obj.update_layout(
    title="Validation IC at the final iteration, by loss function and label",
    height=320 * len(panel_labels),
    width=1000,
    showlegend=False,
    margin=dict(t=90),
)
show_plotly_with_alt(
    fig_obj,
    "Stacked bar charts of mean validation information coefficient at the final boosting "
    "iteration, coloured by loss function: dark navy for squared error, gold for absolute error, "
    "copper for Huber. The configurations are in the same order in each panel, that order being "
    f"their ranking on {order_label}, and each panel carries a dashed zero line. Every bar in the "
    "five-day panel rises above the line, from 0.026 down to 0.006, led by four copper Huber bars "
    "and a gold one. Every bar in the 21-day panel hangs below it, and the two longest there are "
    "navy squared-error bars near -0.042 that sit seventh and thirteenth in the panel above.",
)

# %% [markdown]
# ### Whether the horizon does here what it did to the linear grid
#
# The question this notebook took from `06_linear`, answered at the same training length for
# every configuration. There the best full-coverage IC and the count above zero both rose sharply
# with the horizon, and every configuration that cleared zero was an L1 fit; the frame below says
# whether a tree ensemble reproduces that.

# %% tags=["results"]
horizons = (
    final.group_by("label")
    .agg(
        configurations=pl.len(),
        best_ic=pl.col("ic_mean").max(),
        worst_ic=pl.col("ic_mean").min(),
        above_zero=(pl.col("ic_mean") > 0).sum(),
    )
    .sort("label")
)
horizons

# %% [markdown]
# ### Whether the loss function separates them
#
# The colours in the chart above are the claim, so here is the claim as numbers: at the final
# iteration, each loss function's mean IC and how many of its configurations finished above zero,
# per label. The ordering the heavy-tailed case studies show is Huber highest, absolute error
# next, squared error lowest.

# %% tags=["results"]
objective_summary = (
    final.group_by("label", "objective")
    .agg(
        configurations=pl.len(),
        mean_ic=pl.col("ic_mean").mean(),
        best_ic=pl.col("ic_mean").max(),
        above_zero=(pl.col("ic_mean") > 0).sum(),
    )
    .sort(["label", "mean_ic"], descending=[False, True])
)
objective_summary

# %% [markdown]
# ### What the tails of each label look like
#
# The usual explanation for a loss function changing a rank measure is the tail of the label. A
# squared-error fit weights a residual by its square, so one extreme observation moves the fit
# more than many ordinary ones; absolute error and Huber do not, and a rank measure does not
# reward chasing the extreme. Where that mechanism is at work, the ordering Huber, absolute
# error, squared error should be clearest on the heavier-tailed label.
#
# Excess kurtosis is the direct measurement of that, so it is worth taking before the explanation
# is applied rather than after. Zero is the normal distribution; larger means more of the
# variance sits in rare large moves.
#
# The label artifact on disk runs to the end of the data, holdout included. Measuring across all
# of it would put a statistic computed partly on sealed outcomes into a validation-stage
# notebook, so each label's rows are cut at its own `validation_end` - the same development
# boundary its fits were resolved against, taken from the plan rather than re-derived.
#
# The artifact is asked for at the tier this notebook is running, rather than at the default.
# `LabelCatalog.get` activates the tier it is given before resolving, and a preview activates a
# workspace that carries `labels` as a link while its canonical root is empty - so a diagnostic
# cell that takes the default reads a path only a canonical run has, and fails in preview after
# the fits it is describing have already succeeded.

# %% tags=["results"]
# Each label has its own development boundary, because a longer forward window has to stop
# earlier to keep its outcome inside the development period. One global maximum would cut the
# 21-day label at the five-day label's date and leave decision dates whose forward window ends
# inside the holdout.
development_end = dict(plan.group_by("label").agg(pl.col("validation_end").max()).iter_rows())
tails = pl.DataFrame(
    [
        {
            "label": label,
            "development_end": development_end[label],
            "rows": measured.height,
            "excess_kurtosis": measured.get_column(label).kurtosis(),
        }
        for label in panel_labels
        for measured in [
            pl.read_parquet(study.labels.get(label, execution_tier=EXECUTION_TIER).path)
            .filter(pl.col("timestamp") <= development_end[label])
            .drop_nulls(label)
        ]
    ]
)
tails

# %% [markdown]
# ### How much the checkpoint moves a configuration
#
# One number per configuration and label: the range its IC covers across its own ten checkpoints.
# This is the quantity that decides whether choosing a stopping point is a decision worth making
# carefully or one being made by noise. A configuration whose IC varies more across its own
# training run than the configurations vary among themselves is one where the checkpoint, not the
# model, is doing the ranking.
#
# The frame below reduces that to one comparison per label: how far the fifteen configurations
# spread at a fixed training length, against how far the median configuration travels across its
# own checkpoints. A ratio near one means the two decisions are the same size, and choosing a
# checkpoint is as consequential as choosing a model. The five widest-ranging configurations at
# each label follow it.

# %%
spread = (
    curves.group_by("label", "config_name")
    .agg(
        ic_min=pl.col("ic_mean").min(),
        ic_max=pl.col("ic_mean").max(),
        ic_final=pl.col("ic_mean").filter(pl.col("checkpoint_value") == final_iteration).first(),
    )
    .with_columns(checkpoint_range=pl.col("ic_max") - pl.col("ic_min"))
    .sort(["label", "checkpoint_range"], descending=[False, True])
)
comparison = (
    final.group_by("label")
    .agg(across_configurations=pl.col("ic_mean").max() - pl.col("ic_mean").min())
    .join(
        spread.group_by("label").agg(within_one_configuration=pl.col("checkpoint_range").median()),
        on="label",
    )
    .with_columns(ratio=pl.col("across_configurations") / pl.col("within_one_configuration"))
    .sort("label")
)
comparison

# %% tags=["results"]
spread.group_by("label", maintain_order=True).head(5)

# %% [markdown]
# ## 5. What to notice
#
# **At the traded horizon every configuration ranks the cross-section the right way, which the
# linear grid did not.** Every five-day configuration is positive at the end of training - read
# `above_zero` against `configurations` in the horizons frame - and the weakest of them is a
# multiple of the leading full-coverage linear result at that horizon. Something in this data
# is available to a model that can split on one
# feature inside a region defined by another, and is not available to a weighted sum of the
# columns. The two hand-built interaction terms in the feature set, `carry_mom_composite` and
# `carry_mom_interaction`, exist because someone had to name that structure for the linear model;
# the trees are finding more of it than was named.
#
# **The horizons reverse between the two families, which is what fitting both labels here was
# for.** The linear grid did better at 21 days than at five - by how much depends on how the
# coverage filter is handled, and `06_linear` works that through - while this grid does the
# opposite outright: every five-day configuration above zero, and *nothing* above zero at 21,
# where `best_ic` and `worst_ic` in the horizons frame are both negative. The direction of the
# reversal
# does not depend on any of the qualifications the linear comparison needs, which is what makes
# it the durable finding here. What each family can represent decides which horizon it reads. So
# neither notebook's horizon ranking carries into the other, and the strong-L1 advantage at 21
# days in the linear notebook is a fact about that family on that label rather than about the
# label.
#
# It does not follow that five days is the better horizon. The two labels are fitted under
# different purge buffers, so they resolve different folds and different samples, and a
# comparison across them is between two label-specific protocols. What is being compared cleanly
# is the two *families* on each label, and there the answer is unambiguous at both.
#
# **Squared error is last at both horizons; the rest of the ordering is not stable.** At five
# days the objective frame gives the textbook ordering - Huber, then absolute error, then
# squared error - and at 21 days absolute error and Huber swap while squared error stays
# clearly last. The mechanism usually offered is the label's tails: squared error
# weights an observation by the square of its error, so the largest commodity moves dominate what
# each successive tree is fitted to, while a rank correlation cares about order rather than
# magnitude, and effort spent getting the extremes right buys nothing on this metric. The tails
# frame is consistent with that as far as it goes - the five-day label carries the heavier excess
# kurtosis of the two, and the clean ordering is on the heavier one - but the two kurtoses are
# close enough that this is one observation and not a demonstration. What the two labels do
# establish is the part
# that did not move: **an objective is a claim about which errors matter, and squared error is the
# wrong claim when the result is judged on ranks.**
#
# **The checkpoint moves the answer within a small factor of what the model does.** The
# `comparison` frame puts the spread across the configurations against the median
# configuration's own checkpoint range at each horizon, and the ratio is a small multiple at
# both. So the stopping point is not a detail: it is a second dial of the same order as the
# first. Adding trees does not settle it either - the median configuration ends near where it
# started at both horizons, a substantial share end lower, and the best checkpoint is an
# interior one for most configurations at 21 days. There is no common turning point to read
# off, which is why the comparison that holds is the fixed-iteration chart rather than each
# configuration's best moment. Reporting the leading row of the table would be reporting the
# maximum of every candidate as though it were one.
#
# **The capacity axis does not produce the textbook shape.** The usual reason to sweep leaf count
# is to find where out-of-sample performance turns over, and here it does not turn over cleanly:
# the deepest and shallowest settings both appear near the top and near the bottom depending on
# the objective they are paired with, at both horizons. Capacity and loss interact, and a
# one-dimensional reading of either axis on its own will find a pattern that the other axis
# explains.
#
# **None of this selects anything.** IC measures whether predictions rank products correctly, not
# whether a strategy trading them makes money after costs and turnover. A few hundredths of rank
# correlation is not by itself a tradeable edge, and turnover on a five-day horizon is exactly
# where a small ranking advantage is lost. Selection is on validation backtest Sharpe over the population just
# published, and it happens in [`13_backtest`](13_backtest.ipynb), where the checkpoint is part of
# what is selected.
#
# **Known limitations.** The IC is an average of daily rank correlations with no adjustment for
# the serial dependence overlapping returns create, so it is a diagnostic rather than a test, and
# it does not carry an interval that would say whether these configurations differ from each
# other. Thirty products is a narrow cross-section for a daily rank correlation. The grid varies
# capacity and loss at a fixed learning rate and a fixed tree count, so it says nothing about
# trading one against another. And every number here is measured on validation folds that have
# been read many times over by the time a case study reaches this notebook.
#
# **Next**: [`08_tabular_dl`](08_tabular_dl.ipynb) asks the same question of a neural network
# built for tabular data, which represents interactions in a third way again. The useful thing to
# watch there is whether it recovers the same structure the trees found, and which horizon it
# reads - a third family with a third answer would say the horizon ranking is a fact about
# representation rather than about the data.

```