Audit des prévisions de modèles FX : stabilité et diagnostics de prédiction conforme

# 12_model_analysis.py


```py
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# %% [markdown]
# # Model Analysis - FX Pairs
#
# This notebook reads the complete registered validation-prediction population. It compares model
# families, checkpoints, fold stability, prediction agreement, and uncertainty without choosing a
# model for deployment. Every exact model configuration continues to the equal-weight backtest;
# validation backtest Sharpe performs selection later.
#
# **Learning objectives**
#
# - Audit the complete prediction population by label, family, configuration, and checkpoint.
# - Compare predictive diagnostics without using them as selection criteria.
# - Evaluate stability and chronological conformal coverage from canonical prediction artifacts.
# - Keep causal estimates separate from predictive-family evidence.
#
# **Book reference**: Chapters 11-15
#
# **Prerequisites**: `06_linear`, `07_gbm`, `08_tabular_dl`, `09_dl_tcn`,
# `10_dl_nlinear`, and `11_causal_dml`.

# %%
"""Analyze the complete FX validation-prediction population."""

import plotly.express as px
import polars as pl
import yaml
from IPython.display import display
from ml4t.diagnostic.metrics import cross_sectional_ic

import utils.style  # noqa: F401
from case_studies.research import CausalResult, Result, open_study, superseded_members
from case_studies.research.results import PredictionResult
from case_studies.utils.conformal import (
    sizing_conformal_lag,
    walk_forward_conformal_coverage,
)
from utils.modeling import load_configs
from utils.paths import get_case_study_dir

# %% tags=["parameters"]
CASE_STUDY = "fx_pairs"
N_BUCKETS = 5
# This notebook reads; it fits nothing and registers nothing, so it has no preview form - a
# preview population is not the thing whose assembly it checks. It still takes the pair,
# because WORKSPACE is what lets a run read an isolated registry instead of the published one.
# Study.open(CASE_STUDY) resolved through the repo case directory, which holds a registry only
# where a maintainer worktree has linked one there; anywhere else it read nothing at all.
EXECUTION_TIER = "canonical"
WORKSPACE: str | None = None

# %% [markdown]
# ## Load the canonical population
#
# A downstream-selectable row must use the current identity schema, carry exact coverage and fold
# metrics, and have its prediction artifact available. Causal DML is deliberately absent because it
# estimates a treatment effect rather than a cross-sectional score.

# %%
case_dir = get_case_study_dir(CASE_STUDY)
setup = yaml.safe_load((case_dir / "config" / "setup.yaml").read_text())
primary_label = setup["labels"]["primary"]
configured_labels = [primary_label, *setup["labels"].get("variants", [])]
study = open_study(CASE_STUDY, execution_tier=EXECUTION_TIER, workspace=WORKSPACE or None)
catalog = study.predictions.table().filter(
    (pl.col("identity_status") == "current")
    & (pl.col("execution_tier") == "canonical")
    & (pl.col("split") == "validation")
)
# `identity_status` names the schema version a row was written under. It says nothing about
# which generation the row's producer still publishes: a model notebook that refits leaves the
# generation it replaced in the registry, complete and current under that column, so the filter
# above carries retired prediction sets into the analysis. The lineage is what answers it, and
# `superseded_members` reads that - the same exclusion `13_backtest` applies before it freezes
# the baseline population, so the analysed catalog and the backtested one describe one set of
# models rather than two.
retired = superseded_members(study, member_kind="prediction")
if retired:
    catalog = catalog.filter(~pl.col("prediction_hash").is_in(list(retired)))

if catalog.is_empty():
    raise RuntimeError("no current canonical validation predictions are registered")
if catalog.filter(~pl.col("complete")).height:
    raise RuntimeError("model analysis requires complete prediction sets")
if catalog.filter(~pl.col("artifact_available")).height:
    raise RuntimeError("model analysis requires every prediction artifact")
if "causal_dml" in set(catalog.get_column("family")):
    raise RuntimeError("causal DML must not enter the predictive catalog")

identity_columns = [
    "label",
    "family",
    "config_name",
    "checkpoint_kind",
    "checkpoint_value",
    "training_hash",
    "prediction_hash",
]
if catalog.select(identity_columns).n_unique() != catalog.height:
    raise RuntimeError("prediction rows do not retain one complete model identity")

# %% [markdown]
# ## Compare the assembled population against the configured menu
#
# Each notebook checks that it produced what it requested. Nothing checks that the requests covered
# what the case study configures, and a population that is internally consistent but short a
# configured model reads exactly like a complete one. This compares the assembled population to the
# menu itself.
#
# `deep_learning` is the one family split across several notebooks - `09_dl_tcn`,
# `10_dl_nlinear` and `10a_dl_lstm` - so a configured architecture outside that set would have
# no notebook to produce it. Such members are excluded by name and reason rather than passing
# unnoticed, and the exclusions are derived from the menu per label rather than listed, so a
# label that never declares a member does not gain a phantom exclusion for it. The set is empty
# today: `lstm_h64` was the one configured architecture nothing fitted, and `10a_dl_lstm` closed
# it. The derivation stays because a menu that grows again should say so here rather than
# silently publish a population short of what it declares.

# %% tags=["results"]
SEQUENCE_NOTEBOOK_ARCHITECTURES = {"tcn", "nlinear", "lstm_h64"}
EXCLUSION_REASON = "configured but no scoped fx_pairs notebook runs it; coverage decision open"

configured_members = {
    (label, family, config["config_name"])
    for label in configured_labels
    for family in ("linear", "gbm", "tabular_dl", "deep_learning")
    for config in load_configs(CASE_STUDY, label, family=family)
}
excluded_members = {
    (label, family, config_name)
    for label, family, config_name in configured_members
    if family == "deep_learning" and config_name not in SEQUENCE_NOTEBOOK_ARCHITECTURES
}
expected_members = configured_members - excluded_members
present_members = set(catalog.select("label", "family", "config_name").unique().iter_rows())

if excluded_members:
    print(f"Declared exclusions ({EXCLUSION_REASON}):")
    for member in sorted(excluded_members):
        print(f"  {member[0]} / {member[1]} / {member[2]}")

missing_members = sorted(expected_members - present_members)
unexpected_members = sorted(present_members - expected_members)
if missing_members or unexpected_members:
    raise RuntimeError(
        "the assembled population does not match the configured menu; "
        f"missing {missing_members}, unexpected {unexpected_members}"
    )
if set(catalog.get_column("label")) != set(configured_labels):
    raise RuntimeError("the canonical prediction population does not cover every configured label")

# %% tags=["results"]
population_summary = (
    catalog.group_by("label", "family")
    .agg(
        pl.col("config_name").n_unique().alias("configurations"),
        pl.len().alias("checkpoints"),
        pl.col("complete").all().alias("complete"),
        pl.col("ic_mean").is_not_null().sum().alias("diagnosed_checkpoints"),
    )
    .sort("label", "family")
)
population_summary

# %% [markdown]
# ## Compare predictive diagnostics
#
# Rank correlation is computed across pairs at each decision date and then averaged through time.
# The rows shown below are descriptive representatives for plots. They do not filter the official
# prediction population and do not choose checkpoints for backtesting.

# %% tags=["results"]
diagnosed = catalog.filter(pl.col("ic_mean").is_not_null())
representatives = (
    diagnosed.sort(
        ["label", "family", "ic_mean", "config_name", "checkpoint_value", "prediction_hash"],
        descending=[False, False, True, False, False, False],
        nulls_last=True,
    )
    .group_by("label", "family", maintain_order=True)
    .first()
    .sort("label", "family")
)
representatives.select(
    "label",
    "family",
    "config_name",
    "checkpoint_kind",
    "checkpoint_value",
    "ic_mean",
    "ic_t",
    "prediction_hash",
)

# %% tags=["results"]
# Families whose checkpoint is `final` carry no numeric checkpoint position, so they have no x
# coordinate on this axis and plotly would drop them without saying so. They are excluded here by
# name rather than silently, and the count is stated in the title.
numbered = diagnosed.filter(pl.col("checkpoint_value").is_not_null())
unnumbered = diagnosed.filter(pl.col("checkpoint_value").is_null())
excluded_families = sorted(set(unnumbered.get_column("family"))) if unnumbered.height else []
figure = px.scatter(
    numbered,
    x="checkpoint_value",
    y="ic_mean",
    color="family",
    facet_row="label",
    hover_data=["config_name", "prediction_hash"],
    title=(
        "Validation rank correlation across numbered model checkpoints"
        + (
            f" (excludes {unnumbered.height} rows from {', '.join(excluded_families)},"
            " whose checkpoint is `final` and has no numeric position)"
            if excluded_families
            else ""
        )
    ),
    labels={"checkpoint_value": "Checkpoint", "ic_mean": "Mean daily rank correlation"},
)
figure.show()

# %% [markdown]
# ## Load canonical prediction columns
#
# Producers use `symbol`, `timestamp`, `fold`, `prediction`, and `actual`. Adding the full catalog
# identity to each frame keeps two checkpoints from collapsing into one plot label or fold count.

# %%
prediction_frames = []
for row in representatives.iter_rows(named=True):
    result = Result.open(study, row["prediction_hash"])
    if not isinstance(result, PredictionResult):
        raise TypeError(f"{row['prediction_hash']} is not a prediction result")
    frame = result.load()
    required = {"symbol", "timestamp", "fold", "prediction", "actual"}
    missing = required - set(frame.columns)
    if missing:
        raise ValueError(f"prediction {result.hash} lacks canonical columns: {sorted(missing)}")
    if frame.select("symbol", "timestamp", "fold").n_unique() != frame.height:
        raise ValueError(f"prediction {result.hash} has duplicate eligible keys")
    prediction_frames.append(
        frame.with_columns(
            pl.lit(row["label"]).alias("label"),
            pl.lit(row["family"]).alias("family"),
            pl.lit(row["config_name"]).alias("config_name"),
            pl.lit(row["checkpoint_value"]).alias("checkpoint_value"),
            pl.lit(row["prediction_hash"]).alias("prediction_hash"),
        )
    )

representative_predictions = pl.concat(prediction_frames, how="diagonal_relaxed")

# %% [markdown]
# ## Fold stability
#
# Fold identifiers are labels, not dates. The table orders validation windows by their earliest
# timestamp and retains checkpoint identity in every row.
#
# **What the spread across folds is telling you.** An average IC computed over five validation
# windows can be produced two ways that mean opposite things. A configuration that scores a
# similar small positive number in every window has found something that persisted across five
# different market regimes. A configuration that scores strongly in one window and around zero or
# negative in the rest can reach the same average, and what it has found is one period.
#
# The second is the more common outcome and the easier one to mistake for the first, because the
# summary row is identical. It is also the one a walk-forward protocol is specifically designed to
# expose: each window is fitted on its own past, so a configuration that only works when a
# particular regime is in the training data reveals itself as soon as a window arrives where it
# is not.
#
# For currencies this matters more than it would elsewhere, because the regimes are long. Carry
# and momentum in FX go through multi-year stretches of working and multi-year stretches of not,
# and five validation windows drawn from a single decade can easily contain only one turn between
# them. A configuration that is stable across these five folds has cleared a lower bar than the
# phrase suggests, and one that is not stable across them has failed a very low one.

# %% tags=["results"]
fold_rows = []
for keys, frame in representative_predictions.group_by(
    "label", "family", "config_name", "checkpoint_value", "prediction_hash", "fold"
):
    stats = cross_sectional_ic(
        frame.select("symbol", "timestamp", "prediction"),
        frame.select("symbol", "timestamp", "actual"),
        pred_col="prediction",
        ret_col="actual",
        date_col="timestamp",
        entity_col="symbol",
        min_obs=5,
    )
    fold_rows.append(
        {
            "label": keys[0],
            "family": keys[1],
            "config_name": keys[2],
            "checkpoint_value": keys[3],
            "prediction_hash": keys[4],
            "fold": keys[5],
            "validation_start": frame.get_column("timestamp").min(),
            "ic_mean": stats["ic_mean"],
            "n_decision_times": stats["n_periods"],
        }
    )
fold_metrics = pl.DataFrame(fold_rows).sort("label", "validation_start", "family")
fold_metrics

# %% tags=["results"]
fold_figure = px.box(
    fold_metrics,
    x="family",
    y="ic_mean",
    color="family",
    facet_row="label",
    points="all",
    title="Validation rank correlation varies across chronological folds",
    labels={"family": "Model family", "ic_mean": "Fold mean rank correlation"},
)
fold_figure.show()

# %% [markdown]
# ## Prediction agreement and ranking shape
#
# Correlation between model scores shows whether families rank pairs similarly. One correlation
# matrix covers one label: scores fitted against different targets are not comparable ranks of the
# same quantity, so the pivot takes the primary label the menu declares rather than all three.
# Return buckets and conformal coverage carry no such restriction and run over every label.
#
# **Why ask whether the families agree.** The sweep fits a linear model, gradient boosting, and
# three sequence architectures, and the implicit promise of doing that is that they bring
# different views of the same data. This is where that promise is checked rather than assumed.
#
# If the score correlations come back near one, the families are reproducing each other's
# rankings and the breadth of the sweep is nominal: the effective number of independent attempts
# is far smaller than the row count, whichever one wins the backtest wins by a margin that is
# mostly noise, and there is nothing for an ensemble to average away. If they come back low or
# mixed, the families are disagreeing about which pairs to hold, and the choice between them is a
# real one with real consequences for the backtest.
#
# It also bears on how the eventual winner should be read. Selecting the best of five
# near-identical configurations is close to selecting at random among them, and the deflated
# Sharpe machinery downstream exists precisely because the number of things tried inflates the
# best result. That correction divides by a count of trials, and this section is where a reader
# can see whether that count is measuring independent attempts or the same attempt five times.

# %% tags=["results"]
primary = representative_predictions.filter(pl.col("label") == primary_label)
print(f"Score agreement is computed on {primary_label}, the primary label in the menu")
# The pivot index is the canonical eligibility key alone. `actual` is the same realized return for
# every model at a given key, but the families do not agree on its float representation, so including
# it split the rows into disjoint groups - each score column populated on a different half - and every
# correlation, including the diagonal, came out NaN.
wide = primary.pivot(
    on="prediction_hash",
    index=["symbol", "timestamp", "fold"],
    values="prediction",
)
prediction_columns = [
    column for column in wide.columns if column not in {"symbol", "timestamp", "fold"}
]
if wide.height != primary.height // primary.get_column("prediction_hash").n_unique():
    raise RuntimeError("the agreement pivot did not align every representative on the same keys")
if any(wide.get_column(column).null_count() for column in prediction_columns):
    raise RuntimeError("a representative is missing scores at keys the others cover")
agreement = wide.select(prediction_columns).corr()
agreement

# %% tags=["results"]
bucket_rows = []
for keys, frame in representative_predictions.group_by(
    "label", "family", "config_name", "checkpoint_value", "prediction_hash", "timestamp"
):
    if frame.height < N_BUCKETS:
        continue
    ranked = frame.sort("prediction").with_columns(
        ((pl.int_range(pl.len()) * N_BUCKETS) // pl.len())
        .clip(upper_bound=N_BUCKETS - 1)
        .alias("bucket")
    )
    for bucket, values in ranked.group_by("bucket"):
        bucket_rows.append(
            {
                "label": keys[0],
                "family": keys[1],
                "config_name": keys[2],
                "checkpoint_value": keys[3],
                "prediction_hash": keys[4],
                "timestamp": keys[5],
                "bucket": bucket[0],
                "actual": values.get_column("actual").mean(),
            }
        )
bucket_summary = (
    pl.DataFrame(bucket_rows)
    .group_by("label", "family", "config_name", "checkpoint_value", "prediction_hash", "bucket")
    .agg(pl.col("actual").mean().alias("mean_realized_return"))
    .sort("label", "family", "bucket")
)
# Three labels sort as 1d, 21d, 5d, so the default row window hides the middle one entirely and
# a reader would see the same two-thirds coverage this section exists to remove.
with pl.Config(tbl_rows=bucket_summary.height):
    display(bucket_summary)

# %% [markdown]
# ## Coverage of the widths that size positions
#
# **What a conformal width is, and why a portfolio wants one.** A point prediction says where a
# return is likely to land and says nothing about how confident that is. Two pairs can carry the
# same predicted return with entirely different dispersion around it, and a portfolio that sizes
# both the same is taking more risk in one than in the other without having decided to.
#
# Split conformal produces the missing quantity without assuming a distribution. Residuals from
# data the model was not fitted on are collected, and the width is a quantile of their absolute
# values: the interval is as wide as the model has recently been wrong. Nothing is assumed to be
# Gaussian and nothing is estimated from the evaluation window.
#
# The allocator downstream does not use it as an interval at all. It reads the width as a
# per-symbol volatility estimate and sizes inversely to it, so a pair the model has lately been
# wrong about gets a smaller position. That is why coverage is checked here even though no
# decision reads a coverage level: the width is only a sensible risk proxy if it tracks the
# dispersion it claims to, and a width that systematically under-covers is a position size that
# is systematically too large.
#
# The width reported here is the one `conformal_weighted` allocates with: calibrated per symbol on
# every residual known at `t - h`, where `h` is the label's horizon in data steps, with a pooled
# quantile where a symbol has too few of its own. A decision is covered when its absolute residual
# falls inside that half-width.
#
# Read it as a diagnostic of residual dispersion, not as a guarantee. Split conformal's
# finite-sample coverage needs the calibration and evaluation residuals to be exchangeable, and
# currency returns are heteroskedastic and regime-dependent. Nothing in the allocation path reads
# an interval or a coverage level - the width stands in for a volatility estimate, and `n_test`
# counts the decisions a width could be calibrated for.

# %% tags=["results"]
conformal_rows = []
for keys, frame in representative_predictions.group_by(
    "label", "family", "config_name", "checkpoint_value", "prediction_hash"
):
    embargo_steps = sizing_conformal_lag("fx_pairs", keys[0])
    for row in walk_forward_conformal_coverage(frame, embargo_steps=embargo_steps):
        conformal_rows.append(
            {
                "label": keys[0],
                "family": keys[1],
                "config_name": keys[2],
                "checkpoint_value": keys[3],
                "prediction_hash": keys[4],
                **row,
            }
        )
conformal = pl.DataFrame(conformal_rows).sort("label", "nominal_level", "family")
with pl.Config(tbl_rows=conformal.height, tbl_cols=conformal.width, tbl_width_chars=200):
    display(conformal)

# %% [markdown]
# ## Causal evidence remains separate
#
# A causal result answers whether the configured momentum treatment has an estimated effect after
# adjustment for its declared confounders. It does not count as predictive-family coverage and does
# not enter the model or backtest population. `11_causal_dml` registers one estimate per label the
# menu declares, so naming a single label here would leave the rest of them unreported.

# %% tags=["results"]
causal_rows = []
for label in configured_labels:
    causal = CausalResult.one(study, label=label)
    causal_rows.append({"label": label, "causal_hash": causal.hash, **causal.metrics})
causal_summary = (
    pl.DataFrame(causal_rows)
    .select(
        "label",
        "causal_hash",
        "n_obs",
        "dml_effect",
        "dml_se_hac",
        "p_value_hac",
        "naive_effect",
        "confounding_bias_pct",
        "refutation_p",
    )
    .sort("label")
)
causal_summary

# %% [markdown]
# ## Handoff to the equal-weight backtest
#
# Every complete catalog row, including every checkpoint, advances to `13_backtest`. The backtest
# receives these rows directly and records one result per row. Neither this notebook's descriptive
# representatives nor any IC or causal statistic changes that population.

# %%
handoff = catalog.select([*identity_columns, "cv_identity", "complete"]).sort(
    "label", "family", "config_name", "checkpoint_value"
)
handoff

# %% [markdown]
# ## Key takeaways
#
# - Model identity includes label, family, configuration, checkpoint, and validation protocol.
# - Daily rank correlation, fold stability, bucket shape, and conformal coverage are diagnostics.
# - The equal-weight validation backtest receives the complete prediction population.
# - Causal estimates remain a separate form of evidence.

```