Échantillonnage hebdomadaire pour prévoir les rendements boursiers à un pas

# 12_dl_weekly.py


```py
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# %% [markdown]
# # US equities panel: predicting a week ahead instead of five days ahead
#
# The three sequence notebooks before this one - [`09_dl_nlinear`](09_dl_nlinear.ipynb),
# [`10_dl_lstm`](10_dl_lstm.ipynb) and [`11_dl_tsmixer`](11_dl_tsmixer.ipynb), the last of which
# is offered rather than run - read a window of daily rows and predict a return five sessions
# ahead. That is a **multi-step** problem: the
# quantity being predicted spans five periods of the grid the model reads. This notebook changes
# the grid rather than the models, and asks whether that alone is worth anything.
#
# **Sampling on Fridays makes the same label a one-step problem.** `fwd_ret_5d` is the return over
# the next five *sessions*, and a full trading week holds exactly five, so on a Friday-only grid
# each row's label lands on about the next row. Nothing about the label changed; what changed is
# how many steps of the model's own grid it spans. Two things follow, and they pull in opposite
# directions. A one-step target avoids the error compounding that makes a multi-step forecast
# progressively vaguer, and the reader should expect that to help. Against it, subsampling to one
# day in five throws away four fifths of the rows, and a sequence model on a smaller sample is a
# weaker fit. The experiment is worth running because neither effect is obviously larger.
#
# **Two formulations of the same task are compared.** Both read a **lookback** window of the
# twelve most recent weekly feature vectors for a stock - about three months - and both are scored
# on the same rows over the same horizon.
#
# - **Direct regression** treats the window as fixed-length input to a model whose output is one
#   number, the return. `lstm_h64` is a two-layer recurrent network that consumes the window one
#   week at a time and carries a hidden state forward; `nlinear` normalizes the window by its last
#   value and applies a single linear map. Neither has any notion that its output is a future
#   value of one of its inputs.
# - **One-step forecasting** treats the window as the history of a time series and asks for its
#   next value. `NBEATSModel`, from the Darts library, is built for that formulation: a stack of
#   blocks that each fit a piece of the signal and pass the remainder to the next.
#   `darts_output_chunk_length=1` is what makes it predict a single week rather than a sequence.
#
# The comparison is therefore between two ways of writing down the same prediction, at a frequency
# where the second is well posed. On a daily grid it would not be: N-BEATS asked for five days
# would compound its own output four times.
#
# **What the reader should carry away is the reframing, not this panel's numbers.** Whether a
# weekly grid helps depends on how much history the panel has and how strongly the signal decays,
# and both differ by market.
#
# **Learning objectives.** By the end of this notebook you will be able to:
#
# - Say what makes a prediction one-step rather than multi-step, and why that is a property of the
#   sampling grid rather than of the label.
# - State the two costs of subsampling a daily panel to weekly and say which one a longer history
#   would relieve.
# - Distinguish direct regression from forecasting as two formulations of one prediction, and say
#   what each assumes about the relationship between input and output.
# - Explain why a walk-forward fold has to be resolved from the label file rather than written into
#   the notebook, and what a hand-written window would be free to do.
# - Say what a training identity has to cover before a run may be skipped as already complete, and
#   what goes wrong when the feature set is outside it.
#
# **Book reference**: Chapter 13, Section 13.9. Table 13.5 reports the results this notebook
# produces.
#
# **Prerequisites**: [`03_financial_features`](03_financial_features.ipynb) and
# [`04_model_based_features`](04_model_based_features.ipynb) have written the feature matrices,
# and [`02_labels`](02_labels.ipynb) has written `fwd_ret_5d`.
#
# **What it writes**: one training run and one validation prediction set per configuration, in
# `run_log/registry.db` and under `run_log/training/`. These predictions sit on a Friday grid and
# are deliberately **not** entered into the canonical backtest pool that
# [`16_backtest`](16_backtest.ipynb) ranks: the daily models it ranks are scored on every session,
# and a Friday-only series competing against them on the same label would be compared on a
# different set of decision dates rather than on a different model.

# %%
"""Weekly-frequency sequence models: direct regression against one-step forecasting."""

# %%
import gc
import shutil

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import polars as pl
import torch
from IPython.display import display

from case_studies.utils.cv_window import modeling_fold_boundaries
from case_studies.utils.deep_learning import run_dl_cv
from case_studies.utils.registry import (
    load_prediction_metrics,
    load_prediction_sets,
    load_training_runs,
)
from utils.artifact_specs import load_setup_config, resolve_label_buffer
from utils.modeling import (
    RANDOM_SEED,
    build_modeling_input_lineage,
    reduce_to_top_entities,
    seed_everything,
)
from utils.paths import get_case_study_dir
from utils.style import (
    COLORS,
    FIGSIZE,
    add_message_title,
    ml4t_palette,
    show_with_alt,
)

# %% [markdown]
# ## The values a run can be given
#
# What each one decides:
#
# - **`LOOKBACK`** is how many weekly observations a model sees before predicting. Twelve is about
#   three months, which is long enough to carry a quarter's momentum and short enough that a stock
#   needs only twelve weeks of history before it contributes any training window at all.
# - **`N_EPOCHS`** is how many passes the fit makes over its training rows. Fifty rather than the
#   daily notebooks' longer schedules, because a Friday grid holds a fifth of the rows and a pass
#   over it is a fifth of the gradient steps.
# - **`MAX_FOLDS`** takes that many of the case study's walk-forward folds, evenly spaced and
#   always including the earliest and the most recent. Four rather than all sixteen: this notebook
#   fits four sequence models and the reframing it tests shows up across the sample's span rather
#   than in the density of folds along it.
# - **`MAX_TRAIN_SEQUENCES`** caps how many lookback windows one fold contributes. A cap is what
#   keeps a fold's memory bounded on a three-thousand-name panel; it is lower here than in the
#   daily notebooks because a weekly grid yields fewer windows to begin with.
# - **`BATCH_SIZE`** is how many windows a gradient step averages over. It affects speed and the
#   noise in each step, not what the model can represent.
# - **`MAX_SYMBOLS`** caps the universe, taking the stocks with the most rows. Zero, the default,
#   keeps all of them.
# - **`FORCE_RETRAIN`** discards fitted state and refits configurations the registry already holds
#   as complete. Leave it off unless the identity below has changed in a way the registry cannot
#   see.

# %%
CASE_STUDY_ID = "us_equities_panel"
PRIMARY_LABEL = "fwd_ret_5d"
NOTEBOOK = "12_dl_weekly"

MAX_TRAIN_SEQUENCES = 200_000
# Sessions the label looks ahead, which is what makes a Friday grid one-step. Read from the
# label's own name rather than typed, so a different horizon cannot leave this behind.
LABEL_HORIZON_SESSIONS = int(PRIMARY_LABEL.rsplit("_", 1)[1].rstrip("d"))

# %% tags=["parameters"]
BATCH_SIZE = 2048
LOOKBACK = 12
MAX_SYMBOLS = 0
FORCE_RETRAIN = False
PREDICTION_SPLIT = "validation"
N_EPOCHS = 50
MAX_FOLDS = 4

# %%
seed_everything(RANDOM_SEED)
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Device: {device}")

CASE_DIR = get_case_study_dir(CASE_STUDY_ID)
SAVE_ROOT = CASE_DIR / "run_log" / "training" / "deep_learning" / NOTEBOOK
PYTORCH_SAVE_DIR = SAVE_ROOT / "pytorch"
DARTS_SAVE_DIR = SAVE_ROOT / "darts"
if FORCE_RETRAIN and SAVE_ROOT.exists():
    shutil.rmtree(SAVE_ROOT)
PYTORCH_SAVE_DIR.mkdir(parents=True, exist_ok=True)
DARTS_SAVE_DIR.mkdir(parents=True, exist_ok=True)

# %% [markdown]
# ## Subsampling the panel to a Friday grid
#
# The daily features and labels are subsampled to Fridays, so `fwd_ret_5d` becomes a
# one-step-ahead target: each row's window runs to about the next row's date.
#
# **The windows are mostly, not entirely, non-overlapping, and the label is why.**
# `02_labels` resolves `fwd_ret_5d` five *sessions* ahead. A full trading week holds exactly
# five sessions, so a Friday's window closes on the next Friday. A week carrying a market
# holiday holds four, so the fifth session falls on the Monday after that Friday and the
# window overlaps the next observation's by a session. The cell below counts how often each
# case occurs, on the label file's own session index rather than on a figure written down
# here - the rate is a property of the exchange calendar, so it moves if the span does.
#
# An overlap is usually one session and is not always: a week with one closure pushes the fifth
# session to the Monday after the next Friday, and a span with two closures can push it further.
# The printed distribution is what says how far, and it is worth reading rather than assuming,
# because the length of an overlap is the size of the dependence it introduces.
#
# The overlap is described rather than removed because the alternative that closes it exactly -
# sampling every fifth session - drifts across weekdays and produces a cadence nobody trades. A
# Friday grid is a week as a trader keeps it, and the price of that is the overlap counted above.
#
# What it costs is the mechanical autocorrelation an overlapping window induces, on the share of
# weeks the cell reports. That share is small enough to leave the one-step formulation intact and
# too large to describe the windows as non-overlapping.
#
# Non-overlap would not buy independence in any case. Returns cluster in volatility and share
# a market factor across the cross-section, so what non-overlap removes is the correlation the
# construction itself imposes, not the dependence in the data.
#
# The cell below counts how often each case occurs. Each Friday's window closes on the fifth
# session after that Friday, and the question is where that session falls relative to the next
# one. Zero is an exact fit;
# a positive distance is an overlap of that many sessions; a negative one means the window closed
# before the next Friday, which happens when that Friday is itself a holiday and the label file
# carries no row for it. The whole distribution is reported rather than a single count, because
# the size of an overlap is the size of the dependence it introduces. The session index comes
# from the label file, so this is a statement about the data this notebook reads.

# %% tags=["results"]
_sessions = (
    pl.scan_parquet(CASE_DIR / "labels" / f"{PRIMARY_LABEL}.parquet")
    .select("timestamp")
    .unique()
    .sort("timestamp")
    .collect()
    .get_column("timestamp")
    .to_list()
)
_position = {session: index for index, session in enumerate(_sessions)}
_fridays = [session for session in _sessions if session.weekday() == 4]
_gaps: dict[int, int] = {}
for _this_friday, _next_friday in zip(_fridays, _fridays[1:]):
    _close_index = _position[_this_friday] + LABEL_HORIZON_SESSIONS
    if _close_index >= len(_sessions):
        continue
    _gap = _close_index - _position[_next_friday]
    _gaps[_gap] = _gaps.get(_gap, 0) + 1
_pairs = sum(_gaps.values())
_exact = _gaps.get(0, 0)
_after = sum(count for gap, count in _gaps.items() if gap > 0)
_before = sum(count for gap, count in _gaps.items() if gap < 0)
print(
    f"{_sessions[0]} to {_sessions[-1]}, {len(_sessions):,} sessions, {_pairs:,} consecutive "
    "Friday pairs:"
)
print(f"  {_exact:,} ({_exact / _pairs:.1%}) close exactly on the next Friday")
print(f"  {_after:,} ({_after / _pairs:.1%}) close after it, overlapping the next observation")
print(f"  {_before:,} ({_before / _pairs:.1%}) close before it, the next Friday carrying no row")
print("  distance in sessions from the close to the next Friday, and how often:")
for _gap in sorted(_gaps):
    print(f"    {_gap:+d}: {_gaps[_gap]:,}")

# %% [markdown]
# The three parquet files are filtered to Fridays before anything is joined, because the full
# daily join does not fit in memory on this panel.
#
# **`model_based.parquet` is keyed on `(symbol, timestamp)` alone.** Every estimate behind it is
# bounded by a refit schedule rather than by a fold, so a stock-session carries one value whichever
# fold reads it and the join multiplies nothing. Both properties are asserted rather than assumed:
# a repeated key would fan every weekly observation out to one row per duplicate and leave the
# sequence builder no fold it could form, which surfaces as "No valid folds created" several cells
# later, a long way from the join that caused it.
#
# **The temporal columns are named in `feature_names`.** `prepare_fold_sequence_stores` builds its
# `use_cols` from that list, so a column joined here and left out of it would be dropped again and
# the model would train on the financial features alone without saying so.
#
# **A capped universe takes the stocks with the most rows**, ties broken by name. A sequence model
# needs history - a stock that lists part-way through a fold contributes no complete lookback
# window to it - and this is the rule every reduced notebook in the case study applies, so a capped
# run here selects the same universe as a capped run elsewhere.

# %%
print("Loading weekly features...")
weekly_filter = pl.col("timestamp").dt.weekday() == 5

feat = (
    pl.scan_parquet(CASE_DIR / "features" / "financial.parquet")
    .filter(weekly_filter)
    .collect(streaming=True)
)
print(f"  Weekly financial features: {feat.shape[0]:,} rows, {feat.shape[1]} cols")

mb = (
    pl.scan_parquet(CASE_DIR / "features" / "model_based.parquet")
    .filter(weekly_filter)
    .collect(streaming=True)
)
print(f"  Weekly model-based features: {mb.shape[0]:,} rows, {mb.shape[1]} cols")

assert mb.select("symbol", "timestamp").is_duplicated().sum() == 0, (
    "model_based.parquet repeats a symbol and timestamp; the sequence builder would see "
    "duplicate dates per symbol and create no folds"
)
assert "fold" not in mb.columns, (
    "model_based.parquet carries a fold column, which this stage has no key to read it by: "
    "a stock-session is expected to carry one value"
)

feat_cols = [c for c in feat.columns if c not in ("symbol", "timestamp")]
temporal_feature_names = [c for c in mb.columns if c not in ("symbol", "timestamp")]
temporal_by_fold = None
features = feat.join(mb, on=["symbol", "timestamp"], how="left")
feature_names = feat_cols + temporal_feature_names
print(
    f"  Base features: {features.shape[0]:,} rows, {len(feat_cols)} financial "
    f"+ {len(temporal_feature_names)} temporal = {len(feature_names)} features"
)

labels = (
    pl.scan_parquet(CASE_DIR / "labels" / f"{PRIMARY_LABEL}.parquet")
    .filter(weekly_filter)
    .collect(streaming=True)
)
print(f"  Weekly labels: {labels.shape[0]:,} rows")

dataset = features.join(labels, on=["symbol", "timestamp"], how="inner")
del feat, mb, features, labels
gc.collect()

if MAX_SYMBOLS > 0:
    dataset = reduce_to_top_entities(dataset, "symbol", MAX_SYMBOLS)
    print(f"  Filtered to {MAX_SYMBOLS} symbols: {dataset.shape[0]:,} rows")

# Convert to pandas (pipeline expects pandas)
n_symbols = dataset["symbol"].n_unique()
print(f"  Weekly (Friday): {dataset.shape[0]:,} rows, {n_symbols} symbols")
dataset_pd = dataset.to_pandas()
dataset_pd["timestamp"] = pd.to_datetime(dataset_pd["timestamp"])
del dataset
gc.collect()

# %% [markdown]
# ## The walk-forward folds this run is scored on
#
# The folds are the case study's own, resolved from the label file through
# `modeling_fold_boundaries` - the call [`04_model_based_features`](04_model_based_features.ipynb)
# and `load_modeling_dataset` both resolve them with, so fold *k* here is fold *k* everywhere
# else. `MAX_FOLDS` of them are taken, evenly spaced across the sequence and always including
# the earliest and the most recent, because this notebook fits four sequence models on a weekly
# grid and the full sixteen would cost four times what the comparison needs.
#
# They are resolved rather than written out. A window typed into this notebook is a second
# declaration of the fold design, free to disagree with the one every other stage reads and
# free to reach past the holdout without anything noticing. A window derived from the label
# file cannot, and the assertion below is what establishes it rather than the prose.

# %%
SETUP = load_setup_config(CASE_STUDY_ID)
LABEL_BUFFER = resolve_label_buffer(CASE_STUDY_ID, PRIMARY_LABEL, SETUP)
HOLDOUT_START = pd.Timestamp(str(SETUP["evaluation"]["holdout_start"]))

canonical = sorted(
    modeling_fold_boundaries(CASE_STUDY_ID, PRIMARY_LABEL), key=lambda f: f["val_start"]
)
chosen = sorted(
    {round(i) for i in np.linspace(0, len(canonical) - 1, min(MAX_FOLDS, len(canonical)))}
)
splits = [
    {
        "fold": canonical[i]["fold"],
        "train_start": pd.Timestamp(canonical[i]["train_start"]),
        "train_end": pd.Timestamp(canonical[i]["train_end"]),
        "val_start": pd.Timestamp(canonical[i]["val_start"]),
        "val_end": pd.Timestamp(canonical[i]["val_end"]),
    }
    for i in chosen
]

for split in splits:
    assert split["val_end"] < HOLDOUT_START, (
        f"fold {split['fold']} is scored through {split['val_end'].date()}, inside the holdout "
        f"opening {HOLDOUT_START.date()}"
    )

print(f"CV splits: {len(splits)} of the case study's {len(canonical)} folds, evenly spaced")
for s in splits:
    n_train = dataset_pd[
        (dataset_pd["timestamp"] >= s["train_start"]) & (dataset_pd["timestamp"] <= s["train_end"])
    ].shape[0]
    n_val = dataset_pd[
        (dataset_pd["timestamp"] >= s["val_start"]) & (dataset_pd["timestamp"] <= s["val_end"])
    ].shape[0]
    print(
        f"  Fold {s['fold']}: trained on {s['train_start'].date()} to {s['train_end'].date()} "
        f"({n_train:,} weekly rows), scored over {s['val_start'].date()} to "
        f"{s['val_end'].date()} ({n_val:,})"
    )

# %% [markdown]
# ## The identity these runs register under
#
# `run_dl_cv` skips a configuration whose training hash is already complete, so whatever the
# hash is built from is what a re-run is able to notice. Without an input lineage the hash
# covers the family, the configuration, the label, the fold count, the epochs and the feature
# *names* - and a stage-04 artifact regenerated under a different estimation schedule keeps
# every one of its column names. The corrected values would then never reach a model, and the
# notebook would report the previous run's numbers under them, which a clean registry hides
# because everything retrains.
#
# `build_modeling_input_lineage` is the same payload `load_modeling_dataset` builds for the
# sibling notebooks. It digests the three parquet files this one reads and the fold windows it
# runs, so a changed artifact or a changed window is a changed identity.

# %%
INPUT_LINEAGE = build_modeling_input_lineage(
    artifacts={
        "financial": CASE_DIR / "features" / "financial.parquet",
        "model_based": CASE_DIR / "features" / "model_based.parquet",
        "label": CASE_DIR / "labels" / f"{PRIMARY_LABEL}.parquet",
    },
    feature_names=feature_names,
    splits=splits,
    label_buffer=LABEL_BUFFER,
    task_type="regression",
    eval_label_col=None,
    max_symbols=MAX_SYMBOLS,
    symbols=None,
)
print(f"Input lineage fingerprint {INPUT_LINEAGE['fingerprint'][:12]} over")
for name, record in INPUT_LINEAGE["artifacts"].items():
    print(f"  {name}: {record['sha256'][:12]}, {record['size'] / 1e9:.2f} GB")

# %% [markdown]
# ## The direct-regression arm
#
# Both configurations read the same twelve-week window and emit one number, the return. Neither
# has any notion that the number is a future value of one of its inputs. `lstm_h64` steps through
# the window one week at a time, carrying a hidden state that summarises everything it has seen.
# `nlinear` works one feature at a time: it subtracts that feature's last value, maps the twelve
# weeks to a single number with a linear layer, and adds the last value back, so each feature is
# summarised on its own; a final linear layer then combines those per-feature numbers into the
# prediction. The subtract-and-restore is a normalisation of each input column, not a claim about
# the output.
#
# What comes out is used as a ranking signal across stocks on a date, not as a return forecast to
# be believed at face value, which is why the scoring below is a rank correlation.
#
# The two architectures are the ones [`09_dl_nlinear`](09_dl_nlinear.ipynb) and
# [`10_dl_lstm`](10_dl_lstm.ipynb) fit, under a weekly schedule: twelve weekly observations rather
# than sixty daily ones, and fifty epochs rather than a hundred. They keep the daily presets' names
# because they are the same architectures, and the registry keeps the two apart anyway - the
# lookback, the epoch count and the input lineage all sit inside the training identity, so a weekly
# `lstm_h64` and a daily one are different rows.

# %%
pytorch_configs = []
for name, arch in [("lstm_h64", "lstm"), ("nlinear", "nlinear")]:
    cfg = {
        "config_name": name,
        "family": "deep_learning",
        "library": "pytorch",
        "n_epochs": N_EPOCHS,
        "batch_size": BATCH_SIZE,
        "checkpoint_interval": 5,
        "params": {
            "architecture": arch,
            "lookback": LOOKBACK,
            "dropout": 0.1,
        },
    }
    if arch == "lstm":
        cfg["params"]["hidden_size"] = 64
        cfg["params"]["n_layers"] = 2
    pytorch_configs.append(cfg)

print(f"Running {len(pytorch_configs)} PyTorch configs on {device}...")

# %%
pytorch_result = run_dl_cv(
    dataset_pd,
    splits,
    configs=pytorch_configs,
    n_features=len(feature_names),
    feature_names=feature_names,
    temporal_by_fold=temporal_by_fold,
    temporal_keys=["symbol", "timestamp"],
    temporal_feature_names=temporal_feature_names,
    label_col=PRIMARY_LABEL,
    date_col="timestamp",
    entity_col="symbol",
    device=device,
    save_dir=PYTORCH_SAVE_DIR,
    max_train_sequences=MAX_TRAIN_SEQUENCES,
    register=True,
    force_retrain=FORCE_RETRAIN,
    prediction_split=PREDICTION_SPLIT,
    # The feature set belongs in the identity, not only in the training call. A run is skipped
    # when the registry already holds its training hash as complete, so anything the hash does
    # not cover is something a re-run cannot notice has changed.
    identity_params={"feature_names": feature_names},
    input_data_spec=INPUT_LINEAGE,
    case_study=CASE_STUDY_ID,
    notebook=NOTEBOOK,
)

# %%
print("\nDirect regression, highest-IC checkpoint per configuration:")
print(f"  configuration: {pytorch_result['best_config_name']}")
print(f"  epoch: {pytorch_result['best_epoch']}")
print(f"  mean validation IC: {pytorch_result['best_ic']:.4f}")

# %% [markdown]
# ## The forecasting arm
#
# `NBEATSModel` treats the window as the history of a series and predicts its next value. Its
# blocks each fit part of the signal and hand the remainder to the next block, so the fit is built
# up as a sum of pieces rather than as one map from window to output.
#
# `darts_output_chunk_length=1` is the setting that makes this one-step. Asked for five steps on a
# daily grid the same model would feed its own output back in four times, and each of those
# passes carries the previous error forward - which is the compounding a weekly grid removes by
# construction rather than by choosing a better model.
#
# Both arms read the same rows over the same horizon with the same lookback, so the difference
# between them is the formulation and nothing else.

# %%
darts_configs = [
    {
        "config_name": "nbeats",
        "family": "deep_learning",
        "library": "darts",
        "n_epochs": N_EPOCHS,
        "batch_size": BATCH_SIZE,
        "checkpoint_interval": 5,
        "params": {
            "architecture": "nbeats",
            "lookback": LOOKBACK,
            "hidden_size": 128,
            "n_blocks": 3,
            "n_layers": 4,
            "dropout": 0.1,
            # Weekly-specific: predict 1 step (1 week) instead of default 5 days
            "darts_output_chunk_length": 1,
            "darts_input_chunk_length": LOOKBACK,
        },
    }
]

print(f"Running DARTS N-BEATS (1-step weekly forecasting) on {device}...")

# %%
darts_result = run_dl_cv(
    dataset_pd,
    splits,
    configs=darts_configs,
    n_features=len(feature_names),
    feature_names=feature_names,
    temporal_by_fold=temporal_by_fold,
    temporal_keys=["symbol", "timestamp"],
    temporal_feature_names=temporal_feature_names,
    label_col=PRIMARY_LABEL,
    date_col="timestamp",
    entity_col="symbol",
    device=device,
    save_dir=DARTS_SAVE_DIR,
    max_train_sequences=MAX_TRAIN_SEQUENCES,
    register=True,
    force_retrain=FORCE_RETRAIN,
    # The feature set belongs in the identity, not only in the training call. A run is skipped
    # when the registry already holds its training hash as complete, so anything the hash does
    # not cover is something a re-run cannot notice has changed.
    identity_params={"feature_names": feature_names},
    input_data_spec=INPUT_LINEAGE,
    case_study=CASE_STUDY_ID,
    notebook=NOTEBOOK,
    prediction_split=PREDICTION_SPLIT,
)

# %%
print("\nOne-step forecasting, highest-IC checkpoint:")
print(f"  epoch: {darts_result['best_epoch']}")
print(f"  mean validation IC: {darts_result['best_ic']:.4f}")

# %% [markdown]
# ### What more training does to each formulation
#
# One line per configuration, tracing out-of-sample information coefficient against the number of
# training epochs. This is the comparison the notebook exists for, and a single end-of-training
# number cannot show it.
#
# 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
# around zero without trend never had anything to learn, and its highest point is wherever the
# noise happened to peak. Both produce a respectable-looking maximum, which is why the curve
# rather than the maximum is what to read.
#
# The two direct-regression lines and the forecasting line are drawn together because the
# formulation is the axis under test. Where they separate, and whether they separate more as
# training goes on, is what says the reframing did something.

# %%
curves = pl.concat(
    [
        frame.select("config", "epoch", "ic_mean")
        for frame in (
            pytorch_result.get("all_learning_curves"),
            darts_result.get("all_learning_curves"),
        )
        if frame is not None and frame.height > 0
    ],
    how="vertical",
).sort("config", "epoch")

fig, ax = plt.subplots(figsize=FIGSIZE["single"])
config_names = curves.get_column("config").unique(maintain_order=True).to_list()
# `ml4t_palette` returns a list of that many colours, so it is called once and indexed.
palette = ml4t_palette(len(config_names), categorical=True)
for index, config_name in enumerate(config_names):
    series = curves.filter(pl.col("config") == config_name)
    ax.plot(
        series.get_column("epoch").to_list(),
        series.get_column("ic_mean").to_list(),
        lw=1.4,
        color=palette[index],
        label=config_name,
    )
ax.axhline(0.0, color=COLORS["neutral"], ls="--", lw=1.0)
ax.set_xlabel("Training epochs")
ax.set_ylabel("Mean validation IC")
ax.legend(frameon=False, fontsize=7)
add_message_title(
    ax,
    "Mean validation IC against training epoch, weekly models",
    subtitle="Out-of-sample information coefficient against training epoch, one line per model",
)
# The alt text reads the frame rather than asserting a shape, so a panel described as turning
# over when it does not is a claim the data refutes.
_peaks = (
    curves.group_by("config")
    .agg(
        peak_epoch=pl.col("epoch").sort_by("ic_mean", descending=True).first(),
        last_epoch=pl.col("epoch").max(),
        first_epoch=pl.col("epoch").min(),
    )
    .with_columns(
        interior=pl.col("peak_epoch").is_between(
            pl.col("first_epoch"), pl.col("last_epoch"), closed="none"
        )
    )
)
_n_interior = int(_peaks.get_column("interior").sum())
show_with_alt(
    fig,
    "Line chart of mean validation information coefficient against training epoch, one line per "
    "weekly configuration, with a dashed line at zero. Counted from the underlying frame, "
    f"{_n_interior} of {_peaks.height} configurations reach their highest information "
    "coefficient at an epoch that is neither the first nor the last, which is what an interior "
    "optimum looks like.",
)

# %% [markdown]
# ## Reading the summary table
#
# **Every number in it is a maximum.** Each fit is scored at ten checkpoints, and the row below
# carries that configuration's best one - which is the same quantity the daily notebooks refuse to
# report as a single result, because the maximum of ten draws is larger than any one of them
# whether or not the model learned anything. The curve above is what the maximum was taken from,
# and it is the honest object: read the row to see where a configuration got to, and the curve to
# see whether getting there meant anything.
#
# Two comparisons follow, and they answer different questions.
#
# **Within the weekly grid**, direct regression against one-step forecasting is the comparison
# this notebook was built for: same rows, same horizon, same lookback, two ways of writing the
# prediction down. What the table cannot do is settle it. Each row is a maximum over that
# configuration's own ten checkpoints, both sides are, and applying the same operation to both is
# not the same as the bias cancelling - how far a maximum of ten sits above the quantity
# underneath depends on how much those ten vary and how correlated they are, and a recurrent fit,
# a linear map over a normalised window and a stack of forecasting blocks have no reason to agree
# on either. So an ordering here is not evidence that the formulation did anything. Establishing
# that would take a paired comparison of the two on the same decision dates with an interval
# around the difference, which is what `15_model_analysis` does for the daily families and which
# nothing in this notebook computes.
#
# **Against the daily families**, the comparison is looser twice over. Those models are scored on
# every session and these on Fridays only, so the two are measured over different sets of decision
# dates; and the daily figure below is a mean over the validation period rather than a maximum
# over checkpoints, so it is not the same statistic. A difference between the two blocks is a
# difference in the experiment before it is a difference in the model, which is also why these
# predictions stay out of the canonical backtest pool.
#
# **What generalizes is the reframing.** Whether sampling to a coarser grid pays depends on how
# much history the panel holds and how fast the signal decays, and a reader applying this to their
# own data should expect the balance between the two costs to land differently.

# %%
print("\n" + "=" * 60)
print("Weekly sequence models: validation IC by formulation")
print("=" * 60)

# Collect DL results
all_results = []
for r in pytorch_result["grid_results"]:
    all_results.append(
        {
            "model": r["config_name"],
            "approach": "direct regression",
            "ic": r["best_ic"],
            "epoch": r["best_epoch"],
        }
    )
all_results.append(
    {
        "model": "nbeats (DARTS)",
        "approach": "1-step forecasting",
        "ic": darts_result["best_ic"],
        "epoch": darts_result["best_epoch"],
    }
)

results_df = pl.DataFrame(all_results).sort("ic", descending=True).rename({"ic": "best_ic"})
display(results_df)

# The daily families are read through the registry's own accessors. The three tables this needs
# are exactly what `load_training_runs`, `load_prediction_sets` and `load_prediction_metrics`
# return, and `ic_mean_daily` is the mean the metrics table already holds.

# %%
daily_runs = [
    frame
    for family in ("linear", "gbm", "tabular_dl")
    if (frame := load_training_runs(CASE_STUDY_ID, family=family, label=PRIMARY_LABEL)).height
]
if daily_runs:
    runs = pl.concat(daily_runs, how="vertical_relaxed")
    sets = load_prediction_sets(CASE_STUDY_ID, split="validation")
    metrics = load_prediction_metrics(CASE_STUDY_ID)
    if sets.height and metrics.height and "ic_mean_daily" in metrics.columns:
        baselines = (
            runs.select("training_hash", "family", "config_name", "label")
            .join(sets.select("training_hash", "prediction_hash"), on="training_hash", how="inner")
            .join(
                metrics.select("prediction_hash", "ic_mean_daily"),
                on="prediction_hash",
                how="inner",
            )
            .drop_nulls("ic_mean_daily")
            .sort("ic_mean_daily", descending=True)
            .head(5)
            .select("family", "config_name", "label", "ic_mean_daily")
        )
    else:
        baselines = pl.DataFrame()
else:
    baselines = pl.DataFrame()

if baselines.height:
    print("\nDaily families on the same label, mean validation IC over the period:")
    display(baselines)
else:
    # Said rather than skipped. The paragraph above promises this comparison, and a cell that
    # prints nothing when the daily families have not been registered yet leaves the reader
    # looking for a table that was never going to appear.
    print(
        "\nNo daily linear, gbm or tabular_dl runs are registered for "
        f"{PRIMARY_LABEL} in this run log, so the second comparison above has nothing to make. "
        "Run 06_linear, 07_gbm and 08_tabular_dl first."
    )

```