オプションシグナル向け株式リターンラベルの作成

# 02_labels.py


```py
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# %% [markdown]
# # S&P 500 Equity + Option Analytics: Label Engineering
#
# The strategy reads a signal off a name's listed options and holds the share, so what every
# model here predicts is a forward return on the share. This notebook builds the price series
# that return is measured on, states which two prices a return is taken between, checks that
# every labelled row has a complete forward window inside one security, measures how much
# independent information those rows carry once consecutive windows overlap, measures what the
# simplest available signal already earns against the label, and writes one file per label.
#
# ## Learning objectives
#
# - Turn raw daily bars into a series a return can be taken on, using the adjustment factor
#   that carries splits and dividends, and the identifier that stays with a company when its
#   ticker changes
# - State a forward return as the pair of prices a strategy could have traded at, then check in
#   code that every window is complete and stays inside one security
# - Derive an up-or-down label from a return without inventing a class for the rows the return
#   itself could not fill
# - Cut a diagnostic off at the date a label's outcome is known rather than the date its signal
#   is read, so that nothing measured here has seen the holdout
# - Measure what one unmodified signal earns against the label, under a standard error that
#   allows for consecutive labels sharing most of their forward window
#
# ## Book reference, prerequisites and artifacts
#
# Chapter 7, Section 7.2. Reads the daily share bars and the option surface through
# `load_sp500_daily_bars()` and `load_sp500_options_surface()`, whose coverage
# [`01_feasibility_analysis`](01_feasibility_analysis.ipynb) establishes, and
# `config/setup.yaml`, which declares the label set, each label's horizon and buffer, and the
# holdout boundary. Writes one parquet per declared label into `labels/`, each with a
# `.digest.json` sidecar beside it. [`05_evaluation`](05_evaluation.ipynb) reads the primary
# label and scores the engineered features against it, the model stages read whichever label
# they train on, and `case_studies/utils/cv_window.py` reads each file's own timeline to place
# the folds those models are trained and validated on.

# %%
"""S&P 500 Equity + Option Analytics - Label Engineering."""

from datetime import date

import matplotlib.pyplot as plt
import numpy as np
import polars as pl
import yaml
from ml4t.diagnostic.metrics import compute_ic_hac_stats, cross_sectional_ic_series

from case_studies.utils.artifact_digest import value_digest, write_artifact
from case_studies.utils.artifact_quality import quality_report, render_quality_report
from case_studies.utils.label_diagnostics import effective_sample_size, panel_autocorrelation
from case_studies.utils.warning_policy import apply_notebook_warning_policy
from data import load_sp500_daily_bars, load_sp500_options_surface
from utils.artifact_specs import resolve_label_buffer, resolve_label_horizon
from utils.paths import get_case_study_dir
from utils.style import COLORS, FIGSIZE, add_message_title, show_with_alt

apply_notebook_warning_policy()

# %% [markdown]
# The share bars and the option surface are licensed extracts covering these five years, so the
# defaults span the whole of the data. Narrowing the window shortens a run, at the cost of less
# history behind the trailing volatility in Section C and fewer years in the development window
# that everything from Section E on is computed over.

# %% tags=["parameters"]
START_DATE = "2017-01-01"
END_DATE = "2021-12-31"

# %% [markdown]
# ## Configuration
#
# Everything that defines a label is declared in `config/setup.yaml` and bound here. A horizon
# or a boundary typed into a cell is a second copy of a value the rest of the pipeline reads
# from the file, and the two drift apart the first time either is edited.
#
# Two quantities are declared under separate keys here, and most case studies hold them equal.
# The **outcome horizon** is how long a position is held, and it decides which two prices a
# label is measured between. The **buffer** is the gap the walk-forward split leaves between the
# end of a training fold and the start of the validation fold after it, counted in trading
# sessions because the evaluation calendar is NYSE. A buffer shorter than the horizon would put
# a training row whose outcome is still unresolved next to the validation window that resolves
# it. The primary label carries ten sessions of buffer against a five-session horizon, twice
# what independence requires; each variant carries exactly its own horizon.
# `resolve_label_horizon` and `resolve_label_buffer` read the two from their separate keys, and
# Section H records both against every label.

# %%
CASE_STUDY_ID = "sp500_equity_option_analytics"
CASE_DIR = get_case_study_dir(CASE_STUDY_ID)
LABELS_DIR = CASE_DIR / "labels"
setup = yaml.safe_load((CASE_DIR / "config" / "setup.yaml").read_text())

PRIMARY_LABEL = setup["labels"]["primary"]
LABEL_NAMES = [PRIMARY_LABEL, *setup["labels"]["variants"]]
HORIZONS = {
    n: int(resolve_label_horizon(CASE_STUDY_ID, n, setup).rstrip("Dd")) for n in LABEL_NAMES
}
BUFFERS = {n: resolve_label_buffer(CASE_STUDY_ID, n, setup) for n in LABEL_NAMES}
DIRECTION_SOURCE = setup["labels"]["classification_eval_label"]
SCALED_LABEL = next(n for n in LABEL_NAMES if "risk_adj" in n)
PLAIN_RETURNS = [n for n in LABEL_NAMES if n.startswith("fwd_ret_") and n != SCALED_LABEL]
PRIMARY_HORIZON = HORIZONS[PRIMARY_LABEL]
HOLDOUT_START = date.fromisoformat(setup["evaluation"]["holdout_start"])
PERIODS_PER_YEAR = setup["evaluation"]["periods_per_year"]
ENTITY = "sec_id"
IV_COL = "iv_30_atm"
IV_LAG = int(setup["decision"]["iv_feature_lag"].split("_")[0])
RV_WINDOW = 20

print(f"{PRIMARY_LABEL} is the primary label, and every later stage defaults to it. Each label:")
for name in LABEL_NAMES:
    print(
        f"  {name} is held {HORIZONS[name]} trading sessions, and a training fold ends "
        f"{int(BUFFERS[name].rstrip('Dd'))} sessions before its validation fold begins"
    )
print(
    f"The holdout opens {HOLDOUT_START}; each development window ends a label's own horizon of "
    f"sessions earlier, so nothing measured below resolves inside the holdout"
)

# %% [markdown]
# ## A. The learning task
#
# The hypothesis is cross-sectional, and it is about a disagreement between two views of the
# same company. The option market prices a distribution of outcomes for a name over the coming
# month; the share price says what the equity market pays for it today. The claim is that names
# whose options are priced richly against what their shares go on to do can be ranked against
# each other, and that the ranking pays over the following week. The label is therefore a
# forward return on the **share** and not on any option: the strategy reads options and holds
# equity.
#
# The decision cadence comes from `setup.yaml`. A Friday close is observed, the option
# quantities are lagged one session behind it - the surface summary for a session is stamped at
# that session's close and cannot be acted on until the next one - and the resulting portfolio is
# entered at Monday's open. That fixes the primary horizon at one trading week. The two-week
# variant asks whether the same signal still pays when the portfolio turns over half as often, which
# is a question about cost and turnover rather than a second hypothesis. The volatility-scaled
# variant asks the same question of a target made comparable across names of very different
# volatility. The two direction variants reframe the weekly and the two-weekly return as
# classification, which is what the logistic configurations in the model stages train on.
#
# Labels are sampled every session rather than only on Fridays: that buys five times the rows at
# the price of overlap between consecutive windows, and Section F measures what those rows are
# worth.

# %% [markdown]
# ## B. Preparation before the label
#
# **These are raw traded prices, and a forward return taken on them is not a return.** Each row
# carries `open` and `close` as they printed on the day, and `adj_factor`, the cumulative factor
# that puts a price on a comparable footing with the rest of that security's history. Splits,
# reverse splits, demergers and cash dividends all move it. Multiplying gives a series in which a
# four-for-one split is not a three-quarter loss and a dividend is money the holder received
# rather than a fall in the price. Because the factor carries dividends as well as splits, the
# label below is a **total return** to a holder of the share.
#
# The entity a label may not cross is the **security**, and the column that identifies it is
# `sec_id` rather than the ticker. A ticker gets reassigned after a merger or a spin-off and
# `adj_factor` restarts with the new security, so a window that steps across the change reads a
# price from one company against a price from another. How much of that this extract carries is
# counted below, and most of it arrives with no gap in the series to notice it by.
#
# `session` numbers the market's own trading sessions, and Sections C, D and F count in it rather
# than in rows: a name that stops trading for a fortnight and returns has consecutive rows
# spanning a hole, and only a market-wide session index shows that. No eligibility filter runs
# before the shift, because once rows are dropped from inside a series a shift counts survivors,
# the horizon stops being measured in trading sessions, and the window silently spans whatever
# was removed. Point-in-time eligibility is applied downstream in `03_financial_features`.
#
# **The universe is bounded before any label is built, and the bound is the one
# `setup.yaml::universe` declares.** `eligibility_rule` is `sp500_with_options`, and what makes a
# name satisfy it is having a row in the option-surface summary: this strategy reads an option to
# rank a share, so a share with no listed options is not a candidate however liquid it is. The
# share-bar extract is wider than that, and the loader takes a `symbols=` argument that nothing
# was passing, so every count below and every label file would otherwise have covered names the
# strategy can never hold.
#
# The roster is derived from the surface rather than typed out, and every roster name is then
# checked against the share bars: a name that can be ranked must have a price to trade at.
#
# **The roster is read off the whole extract, not off the requested window**, because which names
# have listed options is a property of the dataset. Deriving it after `START_DATE` would give a
# roster missing every name not yet listed, and the bound would then vary with a parameter the
# notebook documents as free to change.
#
# **How many names that comes to is a fact about the extract, so it is reported rather than
# asserted.** `universe.n_assets` describes the production dataset, and the reduced extract CI
# runs on carries a handful of names by design; an assertion on the count fails there for being
# small rather than for being wrong. `tests/test_eoa_universe_roster.py` holds the declaration to
# the production data, which is the only place the comparison means anything.
#
# The session grid is taken from the **unbounded** extract, because a session is a property of the
# market rather than of the universe, and the horizon is counted on it.

# %%
full_surface = load_sp500_options_surface()
full_bars = load_sp500_daily_bars()
ROSTER = sorted(full_surface["symbol"].unique().to_list())

assert ROSTER, "the option-surface extract carries no names to rank"
priced = set(full_bars["symbol"].unique().to_list())
assert not set(ROSTER) - priced, f"no share bars for {sorted(set(ROSTER) - priced)}"
outside = sorted(priced - set(ROSTER))

window = pl.col("timestamp").is_between(
    pl.lit(START_DATE).str.to_date(), pl.lit(END_DATE).str.to_date()
)
surface = full_surface.filter(window)
extract = full_bars.filter(window)

sessions = extract.select("timestamp").unique().sort("timestamp").with_row_index("session")
sessions = sessions.with_columns(pl.col("session").cast(pl.Int64))
bars = extract.filter(pl.col("symbol").is_in(ROSTER))
prices = (
    bars.join(sessions, on="timestamp")
    .with_columns(
        (pl.col("open") * pl.col("adj_factor")).alias("adj_open"),
        (pl.col("close") * pl.col("adj_factor")).alias("adj_close"),
    )
    .sort([ENTITY, "session"])
)

# Every label's `inputs`: without it a re-run against a refreshed download is invisible. It is
# taken over the bounded panel, which is what the labels are actually built from.
PRICE_COLS = ["symbol", "sec_id", "timestamp", "open", "close", "adj_factor"]
MARKET_DATA_DIGEST = value_digest(bars, PRICE_COLS)
print(f"market_data digest: {MARKET_DATA_DIGEST}")
DECLARED = setup["universe"]["n_assets"]
against = (
    "as universe.n_assets declares"
    if len(ROSTER) == DECLARED
    else f"a reduced extract; universe.n_assets declares {DECLARED}"
)
print(
    f"Universe {len(ROSTER)} names with an option surface ({against}); "
    f"{len(outside)} priced names carry no surface and are excluded ({', '.join(outside)})"
)

pairs = prices.select("symbol", ENTITY).unique()
shared = {
    key: pairs.group_by(key).len().filter(pl.col("len") > 1).height for key in ("symbol", ENTITY)
}
moves = (
    prices.sort(["symbol", "session"])
    .with_columns(
        (pl.col(ENTITY) != pl.col(ENTITY).shift(1).over("symbol")).alias("_moved"),
        pl.col("session").diff().over("symbol").alias("_gap"),
    )
    .filter(pl.col("_moved") & pl.col("_gap").is_not_null())
)

print(f"{prices['symbol'].n_unique()} tickers over {sessions.height:,} sessions")
print(f"{prices.height:,} name-sessions in {prices[ENTITY].n_unique()} securities")
print(
    f"{shared['symbol']} tickers cover more than one security and {shared[ENTITY]} securities "
    f"trade under more than one ticker; {moves.filter(pl.col('_gap') == 1).height} of "
    f"{moves.height} changeovers land on the very next session"
)

# %% [markdown]
# ## C. Label construction
#
# One execution convention, written once and applied at both horizons:
#
# $$r^{(h)}_{i,t} = \frac{C_{i,t+h}}{O_{i,t+1}} - 1$$
#
# where $O$ and $C$ are the adjusted open and close of security $i$, and $t+h$ counts $h$
# **trading sessions**. The signal is read at the close of $t$, the position is entered at the
# next open, and it is exited $h$ sessions later at the close. That is the convention
# `setup.yaml` declares under `decision`, and it is deliberately not Chapter 7.2's close-to-close
# form: a label anchored on the close of $t$ credits the strategy with the overnight move between
# the signal and the first price it can trade at.
#
# The library's `fixed_time_horizon_labels` cannot express this convention: it divides by the
# price at $t$ in the same column it takes the numerator from, and here the numerator is a close
# and the denominator an open. The construction is written out below instead, under the rule the
# library would have applied - a window counts only if every price in it exists and its two ends
# are exactly $h$ sessions apart.
#
# Three working columns are computed on the complete series, because each is only meaningful
# before a row is dropped: `from_end`, how far back a row sits from the last session its security
# trades; `session`, the market's own session index; and the one-session return the scaled
# label's trailing volatility is built from.


# %%
def forward_return(df: pl.DataFrame, horizon: int, name: str) -> pl.DataFrame:
    """Next-open-to-close return over `horizon` sessions, null unless the window is complete."""
    entry = pl.col("adj_open").shift(-1).over(ENTITY)
    exit_price = pl.col("adj_close").shift(-horizon).over(ENTITY)
    spans_horizon = pl.col("session").shift(-horizon).over(ENTITY) - pl.col("session") == horizon
    return df.with_columns(
        pl.when(spans_horizon & (entry > 0) & (exit_price > 0))
        .then(exit_price / entry - 1)
        .alias(name)
    )


ONE_SESSION = pl.col("session").diff().over(ENTITY) == 1
labels_df = prices.with_columns(
    (pl.len().over(ENTITY) - 1 - pl.int_range(pl.len()).over(ENTITY)).alias("from_end"),
    pl.when(ONE_SESSION).then(pl.col("adj_close").pct_change().over(ENTITY)).alias("_daily_ret"),
)
for label_name in PLAIN_RETURNS:
    labels_df = forward_return(labels_df, HORIZONS[label_name], label_name)

# %% [markdown]
# The scaled variant divides the weekly return by the volatility realized over the previous
# twenty sessions, about a month of trading, annualized on the sessions per year `setup.yaml`
# declares, so that a five percent week
# in a quiet utility and a five percent week in a semiconductor are not the same target value.
# The denominator is guarded twice. A daily return is only a daily return between two adjacent
# sessions, so it is null wherever the security missed the session before, and the rolling window
# stays null until it holds a full month of consecutive returns rather than closing over the gap
# as though nothing had been missed. And a security that did not move at all
# over the lookback has an undefined ratio, which is left undefined rather than floored at a
# constant that would answer with a very large number instead.
#
# The direction variants are the sign of the return they come from, and
# `setup.yaml::labels.classification_eval_label` says which return that is. **The guard on the
# outer `when` is the whole point of the cell.** In Polars a null predicate is not true, so
# `when(ret > 0).then(1).otherwise(0)` writes a confident zero wherever the return is null - in
# every row of the last week of every security, which have no forward window at all. Those rows
# cannot be removed afterwards, because the value is not null, it is wrong.

# %%
trailing_rv = pl.col("_daily_ret").rolling_std(RV_WINDOW).over(ENTITY) * PERIODS_PER_YEAR**0.5
labels_df = labels_df.with_columns(trailing_rv.alias("_trailing_rv"))
labels_df = labels_df.with_columns(
    pl.when(pl.col("_trailing_rv") > 0)
    .then(pl.col(PRIMARY_LABEL) / pl.col("_trailing_rv"))
    .alias(SCALED_LABEL)
)
for label_name, source in DIRECTION_SOURCE.items():
    sign = pl.when(pl.col(source).is_not_null()).then((pl.col(source) > 0).cast(pl.Int32))
    labels_df = labels_df.with_columns(sign.alias(label_name))

# %% [markdown]
# ## D. Window validity
#
# A shift always returns something; the question is whether what it returns is the quantity the
# label claims. Each property below fails silently and leaves plausible numbers behind, so each
# is asserted rather than described.
#
# The second assertion is a full reconciliation rather than a bound. Every row carrying no label
# is attributed to exactly one cause - the tail of its security's series, a hole between the two
# ends of the window, or a missing price at one end - and the counts have to sum to the height of
# the frame. A label crossing a security boundary, or a short label masked by a longer one's null
# set, breaks that identity.
#
# The third assertion bounds the calendar span a labelled window may cover, and it is what tests
# the session count against the calendar instead of against itself: $h$ trading sessions span
# about $7h/5$ calendar days on a five-session week, plus a week for exchange holidays. A
# security that stops trading for a year and returns would produce windows far wider than any
# holiday pattern accounts for.

# %%
for label_name in [*PLAIN_RETURNS, SCALED_LABEL]:
    horizon = HORIZONS[label_name]
    checked = labels_df.with_columns(
        (pl.col("timestamp").shift(-horizon).over(ENTITY) - pl.col("timestamp"))
        .dt.total_days()
        .alias("_span")
    )
    tail = pl.col("from_end") < horizon
    holed = pl.col("session").shift(-horizon).over(ENTITY) - pl.col("session") != horizon
    unfilled = ~tail & ~holed & pl.col(label_name).is_null()
    warm_up = unfilled & pl.col(PRIMARY_LABEL).is_not_null() & (label_name == SCALED_LABEL)
    causes = {"no forward window": tail, "a hole in the window": ~tail & holed}
    causes |= {"no trailing volatility yet": warm_up, "a missing price": unfilled & ~warm_up}
    labelled = checked.drop_nulls(label_name)

    # 1. An incomplete forward window is null, never a value.
    assert checked.filter(tail)[label_name].null_count() == checked.filter(tail).height
    # 2. Labelled rows plus the causes account for every row, each cause once.
    counts = {cause: checked.filter(cond).height for cause, cond in causes.items()}
    assert labelled.height + sum(counts.values()) == checked.height, (label_name, counts)
    # 3. No labelled window spans more calendar days than holidays alone can explain.
    tolerance = -(-horizon * 7 // 5) + 7
    assert labelled.filter(pl.col("_span") > tolerance).height == 0, label_name
    unlabelled = ", ".join(f"{n:,} with {cause}" for cause, n in counts.items() if n)
    print(
        f"{label_name}: {labelled.height:,} labelled, spans up to {labelled['_span'].max()}d "
        f"against a {tolerance}d tolerance; unlabelled {unlabelled}"
    )

# 4. No direction label is derived from a null return.
for label_name, source in DIRECTION_SOURCE.items():
    fabricated = labels_df.filter(pl.col(source).is_null() & pl.col(label_name).is_not_null())
    assert fabricated.height == 0, (label_name, fabricated.height)
    print(f"{label_name}: the sign of {source}, and null wherever {source} is")

# %% [markdown]
# Position zero below is each security's last session. The non-null rate has to fall to zero over
# exactly the last `horizon` positions and sit flat beyond them, and each direction label has to
# fall exactly where the return it comes from does. A scalar count of valid rows shows neither
# failure this catches: a tail written as a confident zero reads as fully valid, and a short
# label masked by a longer one's null set reads as the longer one's count. The figure reads only
# which rows are null and never a label value, so it covers the whole extract rather than the
# development window that Sections E to G are restricted to.

# %%
profile = (
    labels_df.filter(pl.col("from_end") <= max(HORIZONS.values()) + 3)
    .group_by("from_end")
    .agg([pl.col(n).is_not_null().mean().alias(n) for n in LABEL_NAMES])
    .sort("from_end")
)
family = {5: COLORS["blue"], 10: COLORS["amber"]}
styles = {n: dict(lw=1.8, color=family[HORIZONS[n]]) for n in LABEL_NAMES}
styles |= {n: dict(marker="o", ms=7, mfc="none", ls="none", **styles[n]) for n in DIRECTION_SOURCE}
styles[SCALED_LABEL] = dict(lw=1.4, ls="--", color=COLORS["copper"])

fig, ax = plt.subplots(figsize=FIGSIZE["single"])
for label_name in LABEL_NAMES:
    ax.plot(profile["from_end"], profile[label_name], label=label_name, **styles[label_name])
for horizon, colour in family.items():
    ax.axvline(horizon - 0.5, color=colour, linestyle=":", lw=1)
ax.set_xlabel("Sessions from the end of each security's own series")
ax.set_ylabel("Share of securities with a non-null label")
ax.set_ylim(-0.05, 1.15)
add_message_title(
    ax,
    "Every label nulls exactly its own horizon of trailing sessions",
    subtitle="Dotted lines mark each horizon; a fabricated tail would sit flat across it",
)
ax.legend(loc="center left", frameon=False, fontsize=8)
show_with_alt(fig, "Non-null rate of each label by position from the end of a security's series.")

# %% [markdown]
# ## E. Distribution and base rate
#
# What scale is each label, and does it mean the same thing across names and across regimes?
# Everything from here through Section G is computed on the development window only, and that
# window is cut at the date each label's outcome is **known** rather than at the date its signal
# is read. A row observed shortly before the holdout still resolves inside it, so a filter on the
# observation date leaves holdout outcomes in the diagnostic. Each label is cut at its own
# endpoint, because the horizons differ. The label files still carry every row: the cut governs
# what this notebook looks at, not what it writes.

# %%
dev = {
    name: labels_df.with_columns(
        pl.col("timestamp").shift(-horizon).over(ENTITY).alias("_label_end")
    )
    .filter(pl.col("_label_end") < HOLDOUT_START)
    .drop_nulls(name)
    for name, horizon in HORIZONS.items()
}
for label_name, frame in dev.items():
    print(f"{label_name}: {frame.height:,} development rows through {frame['timestamp'].max()}")

# %% [markdown]
# Both return labels go on one axis with identical bins and a logarithmic count axis. The claim
# the figure has to support is about shape rather than width - two dispersion scalars carry the
# width - and the shape is that the longer horizon adds mass across the whole body rather than
# only in the tails. The axis is symmetric and narrower than either label's range, so rows
# outside it are counted below rather than drawn.

# %%
bins = np.linspace(-0.15, 0.15, 81)
fig, ax = plt.subplots(figsize=FIGSIZE["single"])
hist_styles = (dict(color=COLORS["amber"]), dict(color=COLORS["blue"], histtype="step", lw=2))
for label_name, style in zip(PLAIN_RETURNS[::-1], hist_styles, strict=True):
    series = dev[label_name][label_name]
    ax.hist(series.to_numpy(), bins=bins, label=f"{label_name}, std {series.std():.3f}", **style)
ax.axvline(0, color=COLORS["neutral"], linestyle="--", lw=0.8)
ax.set_yscale("log")
ax.set_xlabel("Forward total return on the adjusted share price")
ax.set_ylabel("Rows per bin, log scale")
add_message_title(
    ax,
    "The longer horizon adds mass across the whole body, not only the tails",
    subtitle="Identical bins, development window; rows beyond the axis are counted below",
)
ax.legend(loc="upper left", frameon=False)
show_with_alt(fig, "Histograms of the two forward-return labels on identical bins, log counts.")

for label_name in [*PLAIN_RETURNS, SCALED_LABEL]:
    series, on_axis = dev[label_name][label_name], label_name != SCALED_LABEL
    beyond = f", {int((series.abs() > bins[-1]).sum()):,} rows beyond the axis" if on_axis else ""
    print(f"{label_name}: std {series.std():.5f}, kurtosis {series.kurtosis():.2f}{beyond}")
short, long_ = (dev[n][n].std() for n in PLAIN_RETURNS)
root_h = (HORIZONS[PLAIN_RETURNS[1]] / PRIMARY_HORIZON) ** 0.5
print(f"width ratio {long_ / short:.2f} against {root_h:.2f} under square-root-of-horizon scaling")

# %% [markdown]
# The scaled variant lives on a different axis, a multiple of the name's own volatility rather
# than a return, so it is not drawn on the return axis above and its width is printed instead.
# What the division does is visible in the kurtosis: a five percent week is an ordinary week in
# one name and an extreme one in another, and pooling them unscaled puts the ordinary weeks of
# the most volatile names where a model reads them as the extremes of the target.

# %% [markdown]
# Chapter 7.2 asks for the base rate to be tracked through time. For a continuous label ranked
# across a cross-section, the quantity that has to be stable is the spread the model ranks
# within: where it is not, the same rank correlation buys a different amount of return. The
# spread is taken across names on each session first and only then averaged over the year.
# Pooling every name-session in a year into one standard deviation instead adds the movement of
# the market's own mean from session to session to the spread across names on a session, and a
# ranking model is scored on the second alone. For a direction label the same question is the
# share of rows in the positive class, which is what a classifier's threshold sits against.


# %%
def annual_profile(name: str) -> pl.DataFrame:
    """Per year: the spread across names on a session for a return, the positive share for a sign."""
    frame = dev[name].with_columns(pl.col("timestamp").dt.year().alias("year"))
    if name in DIRECTION_SOURCE:
        return frame.group_by("year").agg(pl.col(name).mean().alias("value")).sort("year")
    return (
        frame.group_by("timestamp", "year")
        .agg(pl.col(name).std().alias("value"))
        .group_by("year")
        .agg(pl.col("value").mean())
        .sort("year")
    )


annual = {n: annual_profile(n) for n in [*PLAIN_RETURNS, *DIRECTION_SOURCE]}

# %% [markdown]
# The two panels share a year axis so the same regime can be found in both. The dashed line on the
# right marks an even split, which is where a direction label carries no information at all before
# a model sees it. A share that sits that close to its reference needs an axis that does not start
# at zero, so it is drawn as a series against the reference rather than as a bar whose baseline
# would have to carry the comparison.

# %%
fig, axes = plt.subplots(1, 2, figsize=FIGSIZE["dual_h_tall"], layout="tight")
for offset, name in zip((-0.2, 0.2), PLAIN_RETURNS, strict=True):
    table = annual[name]
    axes[0].bar(
        table["year"] + offset, table["value"], 0.4, color=family[HORIZONS[name]], label=name
    )
for name in DIRECTION_SOURCE:
    table = annual[name]
    axes[1].plot(
        table["year"], table["value"], "o-", ms=4, color=family[HORIZONS[name]], label=name
    )
axes[1].axhline(0.5, color=COLORS["neutral"], linestyle="--", lw=0.8)
axes[1].set_ylim(0.45, 0.65)
axes[0].set_ylabel("Cross-name spread, mean over sessions")
axes[1].set_ylabel("Share of rows in the positive class")
for ax, loc in zip(axes, ("upper left", "lower left"), strict=True):
    ax.set_xticks(annual[PRIMARY_LABEL]["year"].to_list())
    ax.tick_params(axis="x", labelsize=8)
    ax.legend(loc=loc, frameon=False, fontsize=8)
add_message_title(
    axes[0],
    "Neither the spread nor the positive-class share holds still",
    subtitle="Development window; the dashed line marks an even split",
)
fig.tight_layout()
show_with_alt(fig, "Annual cross-name dispersion and annual positive-class share, by label.")

for label_name, table in annual.items():
    peak, low = (table.sort("value", descending=d).row(0, named=True) for d in (True, False))
    print(
        f"{label_name}: {low['value']:.4f} in {low['year']} rising to {peak['value']:.4f} in {peak['year']}"
    )

# %% [markdown] tags=["results"]
# On the development window the weekly label has a standard deviation of 0.04697 and the
# two-weekly one 0.06540, a ratio of 1.39 against the 1.41 that square-root-of-horizon scaling
# implies; dividing the weekly label by each name's trailing volatility cuts its kurtosis from
# 16.48 to 7.16. Neither the spread nor the base rate is stable: the daily cross-name spread of
# the weekly label runs from 0.0285 in 2017 to 0.0476 in 2020, and the share of weeks that end
# higher from 0.5070 in 2018 to 0.5783 in 2019.

# %% [markdown]
# ## F. Overlap and effective sample size
#
# Sampling a multi-session label at every session makes consecutive rows share most of their
# forward window, so the row count overstates the evidence. Two measurements answer that in
# different units: how fast the overlap decays, and what the rows are worth once it is priced in.
# `effective_sample_size` applies Chapter 7.2's average-uniqueness weighting per security,
# because concurrency is a property of one security's own overlapping windows.
#
# Both are counted on `session`, the grid the label was built on, rather than on position among
# the rows that survive into the development frame. Closing over a security's own trading gap
# would pair windows that share nothing and report the overlap as larger than it is.

# %%
max_lag = max(HORIZONS.values()) + 4
acf = {
    name: panel_autocorrelation(
        dev[name], name, max_lag=max_lag, bar_col="session", entity_col=ENTITY
    )
    for name in PLAIN_RETURNS
}

fig, ax = plt.subplots(figsize=FIGSIZE["single"])
lags = np.arange(1, max_lag + 1)
for label_name in PLAIN_RETURNS:
    colour = family[HORIZONS[label_name]]
    ax.plot(lags, acf[label_name], "o-", ms=3, c=colour, lw=1.8, label=label_name)
    ax.axvline(HORIZONS[label_name], color=colour, linestyle=":", lw=1.5)
ax.axhline(0, color=COLORS["neutral"], lw=0.8)
ax.set_xlabel("Lag in trading sessions")
ax.set_ylabel("Panel autocorrelation")
add_message_title(
    ax,
    "The overlap in each label decays to zero at its own horizon",
    subtitle="Dotted lines mark each horizon; what remains past one is not overlap",
)
ax.legend(loc="upper right", frameon=False)
show_with_alt(fig, "Panel autocorrelation of both return labels against lag in trading sessions.")

# %% [markdown]
# The second measurement turns the same overlap into a row count. Average uniqueness weights each
# row by the share of its forward window no concurrent label also spans, and summing those weights
# gives the number of independent observations the frame is worth. A horizon-h label consumes the
# h returns realized over its window and its neighbour one session later shares h-1 of them, so
# average uniqueness converges to 1/h and the ratio below is what it converges to.

# %%
for label_name, horizon in HORIZONS.items():
    n_rows, n_eff = effective_sample_size(
        dev[label_name], horizon=horizon, bar_col="session", entity_col=ENTITY
    )
    print(
        f"{label_name}: N={n_rows:,}, N_eff={n_eff:,.0f}, ratio {n_eff / n_rows:.4f} against "
        f"{1 / horizon:.4f} for windows overlapping this fully"
    )
for label_name in PLAIN_RETURNS:
    decay, horizon = acf[label_name], HORIZONS[label_name]
    print(
        f"{label_name}: autocorrelation {decay[0]:.3f} at lag one, {decay[horizon - 1]:.3f} at lag {horizon}"
    )

# %% [markdown] tags=["results"]
# The weekly label's 503,079 development rows carry 101,099 effective observations, a ratio of
# 0.2010 against the 0.2000 a fully overlapped five-session window implies; the two-weekly
# label's 500,062 rows carry 50,548, a ratio of 0.1011 against 0.1000. Both sit just above the
# reference value, because a security that stops trading ends an overlap early. Panel
# autocorrelation falls from 0.772 at lag one to -0.051 at lag five for the weekly label, and
# from 0.891 to 0.005 at lag ten for the two-weekly one. The purge gap a fold needs is set by the
# forward window itself, not by these counts.

# %% [markdown]
# ## G. Baseline floor
#
# One signal against the primary label on the development window, with no feature
# engineering: the thirty-day at-the-money implied volatility whose persistence
# [`01_feasibility_analysis`](01_feasibility_analysis.ipynb) measured, lagged by the session
# `setup.yaml::decision.iv_feature_lag` declares, and read raw. Every option-derived feature in
# `03_financial_features` is built out of that quantity, so it is the floor those features have
# to clear, and measuring it before building them is what makes a later improvement mean
# something.
#
# The lag is a market session inside the security, not one row of the surface. Coverage of the
# surface swings on a monthly cycle, so on the narrow weeks a name's previous quoted row can be
# a fortnight back, and a shift taken over the surface's own rows would hand the model a
# fortnight-old volatility while calling it yesterday's. Joining the surface onto the dense
# price panel first makes the missing sessions visible as nulls, and the guard drops them.
#
# The information coefficient is the cross-sectional rank correlation on each session, averaged
# over sessions, which is the quantity a ranking model is scored on; pooling every name-session
# instead mixes a cross-sectional claim with a time-series one. The series is put back in time
# order before the standard error reads it, because that standard error is built out of the
# correlations between one session and the next.
#
# A correlation over a handful of names is noise, so a session is scored only where at least a
# minimum number of names are quoted. That minimum is half the median cross-section rather than
# a fixed count, so it means the same thing on a universe of another size, and the sessions that
# fall below it are dropped from the statistic - the count printed below is how many of the
# window's sessions were scored. The standard error is HAC-adjusted, because the series of
# per-session correlations inherits the label's overlap and a naive statistic would treat five
# consecutive sessions of one week's return as five separate pieces of evidence.

# %%
signal_panel = (
    prices.select("timestamp", "symbol", ENTITY, "session")
    .join(surface.select("timestamp", "symbol", IV_COL), on=["timestamp", "symbol"], how="left")
    .sort([ENTITY, "session"])
    .with_columns(
        pl.when(ONE_SESSION).then(pl.col(IV_COL).shift(IV_LAG).over(ENTITY)).alias("signal")
    )
    .drop_nulls("signal")
)
baseline = dev[PRIMARY_LABEL].join(
    signal_panel.select("timestamp", "symbol", "signal"), on=["timestamp", "symbol"], how="inner"
)
min_obs = int(baseline.group_by("timestamp").len()["len"].median() // 2)

ic = cross_sectional_ic_series(
    baseline,
    baseline,
    pred_col="signal",
    ret_col=PRIMARY_LABEL,
    date_col="timestamp",
    entity_col="symbol",
    min_obs=min_obs,
).sort("timestamp")  # a HAC autocovariance over a permutation of the time axis means nothing
stats = compute_ic_hac_stats(ic, ic_col="ic", label_horizon=PRIMARY_HORIZON)

print(
    f"Baseline: {IV_COL} lagged {IV_LAG} session against {PRIMARY_LABEL}, "
    f"{baseline.height:,} rows, minimum cross-section {min_obs} names\n"
    f"  sessions scored {stats['n_periods']:,} of the {ic.height:,} in the window, "
    f"mean IC {stats['mean_ic']:.4f}\n"
    f"  HAC t {stats['t_stat']:.2f} on {stats['effective_lags']} Bartlett lags, "
    f"naive t {stats['naive_t_stat']:.2f}, p {stats['p_value']:.3g}"
)

# %% [markdown] tags=["results"]
# The lagged at-the-money volatility earns a mean information coefficient of -0.0072 against the
# weekly label. Every one of the 1,001 sessions in the development window quotes at least the 130
# names the minimum cross-section asks for, so all 1,001 are scored. Under the naive standard
# error that is a t-statistic of -0.93; the Newey-West rule picks 6 lags here, above the four the
# horizon alone requires, and the HAC statistic is -0.50 with a p-value of 0.614. The floor a
# feature has to clear is a mean IC the data cannot separate from zero, so the sign of it carries
# nothing either.

# %% [markdown]
# ## H. Artifacts and the audit record
#
# Beside each label file the notebook writes a second, small JSON file, `<label>.digest.json`.
# It is there so that a later stage can tell whether the label it is reading still holds the
# values it was built against. It does that with a **digest**: a short hash of the values in the
# file, which changes if any value changes and stays the same if the same values are written
# again. The sidecar records that digest, the row count, the columns, the key columns, the
# notebook that wrote the file, and the digest of the price data the label was built from. The
# last of those is what ties a label to a data vintage - without it, a re-run against a refreshed
# download looks exactly like a re-run against the same one.
#
# The key is `timestamp` and `symbol`, the pair every file downstream joins on. `sec_id` is the
# entity the label was built inside and is not carried into the file.
#
# The folds that train models are derived per label by `case_studies/utils/cv_window.py` from
# `config/setup.yaml` and the timeline of the label parquet written here, so which rows land in
# these files is what sets where the fold boundaries fall.

# %%
KEYS = ["timestamp", "symbol"]
for label_name in LABEL_NAMES:
    record = write_artifact(
        labels_df.select([*KEYS, label_name]).drop_nulls(),
        LABELS_DIR / f"{label_name}.parquet",
        keys=KEYS,
        written_by="02_labels",
        inputs={"market_data": MARKET_DATA_DIGEST},
    )
    print(f"{label_name}.parquet: {record['n_rows']:,} rows, digest {record['digest']}")

# %% [markdown]
# The record Chapter 7.2 requires to close a label definition, one row per label, built from the
# values computed above rather than written by hand. The horizon and the buffer are printed
# separately, because this is the case study that separates them.

# %%
readers = {
    PRIMARY_LABEL: "05_evaluation.py, which scores the features against it, and the model stages"
}
print("\nLabel audit record")
for label_name, horizon in HORIZONS.items():
    frame, source = dev[label_name], DIRECTION_SOURCE.get(label_name)
    anchor = f"the sign of {source}" if source else "the adjusted open of the session after t"
    buffer_sessions = int(BUFFERS[label_name].rstrip("Dd"))
    print(
        f"\n{label_name}\n  anchor       {anchor}"
        f"\n  horizon      {horizon} trading sessions, against a {buffer_sessions}-session fold buffer"
        f"\n  resolution   fixed at the close of t+{horizon}; daily bars need no tie-break"
        f"\n  overlap      {horizon - 1} sessions shared by consecutive rows"
        f"\n  base rate    mean {frame[label_name].mean():+.5f}, std {frame[label_name].std():.5f}"
        f"\n  consumed by  {readers.get(label_name, 'the model stages, as a variant')}"
    )

# %% [markdown]
# ## What the labels hold, and what they owe
#
# The sections above check each label against its own definition - that the sign matches the
# return, that no value is fabricated where the source is null, that the last sessions are
# withheld. This one asks the two questions that are about the file rather than the rule.
#
# The first is what each label's distribution looks like: how much is null, how much is exactly
# zero, how far the extreme values sit from the body. A concentration at zero is a defect in a
# return and is what a balanced direction label is supposed to look like, so the check reports the
# number and this notebook says which it is.
#
# The second is coverage, and it needs a denominator that is not the labels themselves. **The
# reference is `bars`** - every session a security in the roster actually traded, which is the set
# a forward return could in principle have been computed on. Comparing a label to the union of the
# other labels would hide any session where all five are absent together; comparing it to the
# price panel cannot.
#
# A percentage on its own decides nothing, so each label also declares *where* it is entitled to
# be short and by how much, and the report is read against that declaration rather than against
# the raw number. A forward return owes no value in the last `horizon` sessions of a security's
# history, because the price that would resolve it is past the end of the sample; the
# risk-adjusted variant additionally owes none until its trailing volatility window has filled.
# Both are counted per security, so a name that enters late pays its own burn-in on its own dates.
# What the sign-off then has to speak to is not the shortfall but the residual: keys missing
# inside a security's own span, where no window length explains them, and securities that spend
# more than the mechanism allows.

# %%
expected_keys = bars.select(KEYS).unique()
print(
    f"price panel: {expected_keys.height:,} traded (symbol, session) keys across "
    f"{expected_keys['symbol'].n_unique()} securities and "
    f"{expected_keys['timestamp'].n_unique()} sessions\n"
)
for label_name in LABEL_NAMES:
    written = labels_df.select([*KEYS, label_name]).drop_nulls()
    budget: dict[str, tuple[int | None, str]] = {
        "trailing": (
            HORIZONS[label_name],
            f"{HORIZONS[label_name]}-session forward window past the end of the sample",
        )
    }
    if label_name == SCALED_LABEL:
        budget["leading"] = (
            RV_WINDOW,
            f"{RV_WINDOW}-session trailing realized volatility in the denominator",
        )
    render_quality_report(
        quality_report(
            written,
            name=label_name,
            key_columns=KEYS,
            expected=expected_keys,
            keys=KEYS,
            entity="symbol",
            session="timestamp",
            expected_missing=budget,
        )
    )
    print()

# %% [markdown]
# ### Sign-off
#
# **The price panel offers 632,602 traded keys over 633 securities and 1,259 sessions, every label
# emits keys the panel has and nothing else, and no column crossed a distribution threshold.** The
# three coverage figures differ from each other by amounts each label's own definition predicts,
# which is the point of declaring the budget before printing the number: 97.53% and 99.50% are
# both complete, and a reader who only saw the percentages could not know that.
#
# - **`fwd_ret_5d` and `fwd_dir_5d` reach 99.50%.** Of the 3,159 keys they lack, 3,071 sit after a
#   security's last session, a median of 5 apiece against a declared horizon of 5. The forward
#   window runs past the end of the sample and there is no future price to return; withholding
#   them is the seal working, and a value there would have to be invented.
# - **`fwd_ret_10d` and `fwd_dir_10d` reach 99.01%**, and lose 6,087 keys the same way at a median
#   of 10 apiece against a horizon of 10. Twice the horizon costs twice the history, which is the
#   trade the horizon choice makes and is worth seeing rather than assuming.
# - **`fwd_ret_risk_adj_5d` reaches 97.53%**, and it is the only label that pays at *both* ends.
#   It loses 3,070 keys to its five-session horizon like the plain return, and a further 12,187 to
#   the front of each security's history at a median of exactly 20 sessions - the trailing
#   realized volatility in its denominator, undefined until its 20-session window fills. Only two
#   of 608 securities spend more than the 20, and 27 keys between them. **This is the label the
#   model stages select on**, so a reader should know it is measured on 2% fewer decisions than the
#   plain five-session return beside it, and that the decisions it lacks are the earliest ones.
#
# **What no mechanism accounts for is 350 keys at the worst label, and 88 at the primary.** Two
# shapes make it up, and neither reaches a scale that moves anything. A handful of securities -
# seven at the ten-session horizon, three at the five - carry no label at all, because their entire
# life in this panel is shorter than the horizon: a name with four traded sessions has no fifth to
# return to. The rest sit inside a security's own span, a median of one horizon's worth each, which
# is what a break in a security's traded sessions costs when the forward window steps across it.
# At 0.06% of the panel at the extreme, neither changes a fold, a fit or a ranking, and the reason
# to print them is that a residual nobody states is a residual nobody would notice growing.
#
# **The direction labels sit near half at each value and nothing flagged them.** A binary down/up
# indicator is supposed to; a share far from half would say the sign convention or the null
# handling had gone wrong, which is why the number is printed rather than assumed. It is a little
# below half in both, which is the equity drift showing through - more sessions up than down over
# this sample. The zero-share ceiling exists to catch a *return* that is mostly zero, and it is
# deliberately loose enough not to fire on a label that is meant to be half zeros.
#
# **The return labels carry heavy tails and are almost never exactly zero.** That is what a forward
# equity return looks like at daily frequency, and it is the reason the risk-adjusted variant
# exists at all. Nothing is winsorized here; how a model handles the tail is a modelling choice
# made downstream.
#
# **No label column is constant and none carries a non-finite value.** Both are flagged
# unconditionally above, and neither appears in any of the five.

# %% [markdown]
# ## Key takeaways
#
# 1. **On a raw equity panel, rebuild the tradable price series before writing any label.** A
#    split, a spin-off, or a ticker reassigned to another company all leave the raw prices intact
#    and the return between them meaningless, and none of them raises anything.
# 2. **Check the window in code, and account for every unlabelled row.** An incomplete window,
#    a hole in a security's series and a window crossing a change of security all fail without
#    raising, and a reconciliation that has to balance catches what a row count passes over.
# 3. **Derive a discrete label under an explicit null guard.** A comparison against a null return
#    is false rather than null, so the naive form writes a confident class into every row the
#    return could not fill, and no later `drop_nulls` can find it.
# 4. **Cut a diagnostic off at the label's endpoint, not at its observation date.** A row
#    observed before the holdout whose outcome resolves inside it is a holdout row, so each
#    label's usable boundary is the holdout date minus its own horizon, counted in trading
#    sessions.
# 5. **A row count overstates the evidence when forward windows overlap.** The effective count
#    says by how much, and it does not set the purge gap - the forward window does.
#
# **Known limitations.** The label is a total return to a holder of the share, because the
# adjustment factor carries dividends as well as splits; a strategy credited only with price
# returns would be measured against a slightly different target. The universe is every name
# carrying an option surface, which is what `universe.eligibility_rule` declares, and not a
# liquidity screen applied point in time: a name enters on having listed options, not on trading
# enough of them. Point-in-time eligibility is `03_financial_features`'. The hole rule tests each
# security's own position on the market calendar, so it finds an absent session and not a session
# on which the name barely traded. The baseline is one signal, read raw off the surface with no
# adjustment for the level of volatility a name usually carries.
#
# **Next**: `03_financial_features.py` builds the volatility-surface, variance-premium, skew and
# equity-momentum features from the same two sources. `05_evaluation.py` is where those features
# are first scored against the primary label written here.

```