Vollständige Modellpopulationen und Prognosediagnostik für S&P-500-Optionen vergleichen

# 11_model_analysis.py


```py
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# %% [markdown]
# # S&P 500 Options: Model Analysis
#
# This notebook describes the complete official validation prediction populations produced by the
# model execution notebooks. Model identity always includes family, configuration, and checkpoint.
# Every comparison requires identical expected prediction keys and the same cross-validation
# identity. Information coefficient and related diagnostics do not select a model or checkpoint.
#
# Causal DML is reported separately because it estimates a treatment effect rather than a
# cross-sectional prediction configuration.
#
# **Why completeness is established before any number is read.** Every table here is a comparison,
# and a comparison across populations that do not cover the same rows is not a comparison at all:
# a model scored on an easier subset of the panel looks better for a reason that has nothing to do
# with the model. So the notebook refuses before it reports. It requires each prediction to be
# complete against its own registered eligibility contract, and requires the four populations to
# share one cross-validation identity, because two models cut on different folds have seen
# different training data and their diagnostics are not on one scale.
#
# **What a reader gets from this page.** A description of what was fitted and how the resulting
# predictions behave, at the grain the pipeline actually decides on, which is the configuration
# and its checkpoint together. What a reader does not get is a winner. Nothing here is ranked and
# nothing here is chosen; that happens downstream, on backtests, and the separation is deliberate.

# %%
"""Analyze complete S&P 500 options model populations."""

import plotly.graph_objects as go
import polars as pl

from case_studies.research import CausalResult, PredictionResult, Result
from case_studies.sp500_options.research_workflow import (
    official_prediction_catalog,
    open_study,
)
from case_studies.utils.registry.completeness import (
    key_digest_value,
    require_comparable_key_digests,
)

MODEL_POPULATIONS = (
    "sp500-options-linear-validation-v1",
    "sp500-options-gbm-validation-v1",
    "sp500-options-tabular-dl-validation-v1",
    "sp500-options-sequence-validation-v1",
)

# %% tags=["parameters"]
EXECUTION_TIER = "canonical"
WORKSPACE: str = ""

# %% [markdown]
# ## Complete population and its eligibility groups
#
# Named immutable populations replace hash lists and registry-presence filters. The coverage table
# comes from each prediction result's registered eligibility contract.
#
# The four populations share one CV identity but not one eligibility set. A sequence model scores a
# symbol only after its lookback window is available, so it is eligible on fewer symbols and fewer
# rows than the cross-sectional families reading the same panel. The audit below reports one row per
# eligibility group rather than asserting a single one, and the diagnostics that follow are read
# within a group.

# %% tags=["results"]
study = open_study(execution_tier=EXECUTION_TIER, workspace=WORKSPACE or None)
catalog = official_prediction_catalog(study, MODEL_POPULATIONS)

coverage_rows = []
for prediction_hash in catalog.get_column("prediction_hash"):
    result = Result.open(study, prediction_hash)
    if not isinstance(result, PredictionResult):
        raise TypeError(f"{prediction_hash} is not a prediction result")
    coverage = result.coverage()
    if coverage is None or coverage["status"] != "complete":
        raise RuntimeError(f"prediction {prediction_hash} has incomplete coverage")
    coverage_rows.append(
        {
            "prediction_hash": prediction_hash,
            "expected_key_digest": coverage["expected_key_digest"],
            "n_expected": coverage["n_expected"],
            "n_actual": coverage["n_actual"],
            "n_folds": coverage["n_folds_actual"],
        }
    )
coverage = pl.DataFrame(coverage_rows)

cv_identities = catalog.get_column("cv_identity").drop_nulls()
if cv_identities.is_empty() or cv_identities.n_unique() != 1:
    raise RuntimeError("official model populations do not share one CV identity")

# %% [markdown]
# Eligibility is grouped, not assumed identical. A sequence model needs a lookback window before it
# can score a symbol, so it is eligible on strictly fewer rows than a cross-sectional model reading
# the same panel - which is a property of the model class, not a defect. Requiring one eligibility
# digest across all four populations would fail here for a correct run. What must hold is that every
# prediction sharing an eligibility contract agrees on its dimensions exactly.
#
# **The grouping is what makes the diagnostics readable rather than misleading.** Two checkpoints
# in the same group were scored on identical rows, so the difference between their numbers is the
# models. Two checkpoints in different groups were not, and the difference between those numbers
# is the models and the rows together, with no way to separate them from this table. The audit
# below prints one row per group so that a reader can see which comparisons are available before
# making one, rather than discovering afterwards that a sequence model was scored on the subset
# of the panel where a lookback window existed.

# %%
coverage = coverage.join(
    catalog.select("prediction_hash", "family"), on="prediction_hash", how="left"
)
# The grouping below is only meaningful within one key rendering. Two digests taken under
# different renderings are unequal whatever their key sets, so a mixed population reports more
# distinct eligibility contracts than exist and tells a reader that two checkpoints scored on
# identical rows are not comparable - which the dimension check on each group cannot catch,
# because the split halves agree on every dimension.
require_comparable_key_digests(
    coverage.get_column("expected_key_digest"), what="this notebook's model populations"
)
# Grouped on the digest value rather than the stored string. A row written before the rendering
# was stamped and a row written after it carry the same key set as `<value>` and `k2:<value>`,
# and grouping the strings would split one contract in two for no reason a reader could see -
# the same mis-grouping the check above refuses, arriving through the prefix instead.
coverage = coverage.with_columns(
    pl.col("expected_key_digest")
    .map_elements(key_digest_value, return_dtype=pl.String)
    .alias("eligibility")
)
for digest, group in coverage.group_by("eligibility"):
    if group.select("n_expected", "n_actual", "n_folds").n_unique() != 1:
        raise RuntimeError(
            f"predictions sharing eligibility {digest[0]} disagree on coverage dimensions"
        )
    if group.filter(pl.col("n_actual") != pl.col("n_expected")).height:
        raise RuntimeError(f"eligibility {digest[0]} has predictions short of their declaration")

population_audit = (
    coverage.group_by("eligibility")
    .agg(
        pl.col("family").unique().sort().str.join(", ").alias("families"),
        pl.len().alias("predictions"),
        pl.col("n_expected").first().alias("rows_per_prediction"),
        pl.col("n_folds").first().alias("folds"),
    )
    .with_columns(pl.lit(cv_identities[0]).alias("cv_identity"))
    .sort("rows_per_prediction", descending=True)
)
if population_audit.get_column("predictions").sum() != catalog.height:
    raise RuntimeError("the eligibility audit does not account for every declared prediction")
population_audit

# %% [markdown]
# ## Predictive diagnostics
#
# The table retains each checkpoint as a separate row. It supports descriptive comparison only;
# strategy selection occurs after every row has an equal-weight validation backtest.
#
# **What the four columns are, and the grain they are aggregated at.** The information coefficient
# is the rank correlation between a model's predictions and the realised label across the symbols
# priced at one decision time. Those are averaged within a fold to give the fold's IC, and the
# columns here aggregate over *folds*, not over decision times: `ic_mean` is the mean of the fold
# ICs, `ic_std` their dispersion across folds, and `pct_positive` the share of folds whose IC came
# out above zero.
#
# **`ic_t` is a fold-level diagnostic and is not the significance test.** It divides `ic_mean` by
# the standard error implied by that fold-level dispersion, so with a handful of folds it rests on
# a handful of numbers and is easily moved by one of them. The inferential statistic is `ic_t_hac`,
# computed on the daily IC series with a HAC correction at the label's overlap, because overlapping
# labels make neighbouring days dependent and an uncorrected error is too small. Read `ic_t` as a
# description of how consistent the folds were, never as evidence that the mean is real.
#
# **Rank correlation is the point of the choice.** It is invariant to any increasing transform of
# the predictions, so a model whose values are badly scaled but correctly ordered scores the same
# as one that is calibrated, and a squared-error fit is not rewarded for matching the magnitude of
# a heavy tail it was never going to match. What that invariance costs is any information about
# the size of the move, which is the reason the number below cannot stand in for a return.
#
# **Why none of it selects.** A rank correlation says nothing about whether the ordering survives
# position sizing, turnover and cost, and those are what decide whether a strategy makes money.
# Selection is therefore by best validation backtest Sharpe, taken over configurations that each
# already have an equal-weight backtest, with the checkpoint part of the configuration's identity
# rather than a detail of how it was fitted. A high IC here is a reason to look, never a result.
#
# Each IC is computed on its own prediction's eligible rows, so two rows are directly comparable
# only when the same eligibility group above covers both. A sequence checkpoint and a linear
# checkpoint are scored on different populations, and the difference between their IC values
# therefore mixes model behaviour with the population each was scored on.

# %% tags=["results"]
analysis = (
    catalog.with_columns(
        pl.when(pl.col("checkpoint_value").is_null())
        .then(pl.col("checkpoint_kind").fill_null("final"))
        .otherwise(
            pl.concat_str(
                pl.col("checkpoint_kind"),
                pl.col("checkpoint_value").cast(pl.String),
                separator="=",
            )
        )
        .alias("checkpoint"),
    )
    .with_columns(
        pl.concat_str(
            "family",
            "config_name",
            "checkpoint",
            separator=" / ",
        ).alias("model_identity")
    )
    .select(
        "family",
        "config_name",
        "checkpoint",
        "model_identity",
        "ic_mean",
        "ic_std",
        "ic_t",
        "pct_positive",
        "prediction_hash",
    )
    .sort("family", "config_name", "checkpoint")
)
if analysis.select("ic_mean", "ic_std", "ic_t", "pct_positive").null_count().sum_horizontal().sum():
    raise RuntimeError("official model population has missing regression diagnostics")
analysis

# %% tags=["results"]
family_summary = (
    analysis.group_by("family")
    .agg(
        pl.len().alias("configuration_checkpoints"),
        pl.col("ic_mean").min().alias("ic_min"),
        pl.col("ic_mean").median().alias("ic_median"),
        pl.col("ic_mean").max().alias("ic_max"),
    )
    .sort("family")
)
family_summary

# %% tags=["results"]
fig = go.Figure()
for family in analysis.get_column("family").unique(maintain_order=True):
    rows = analysis.filter(pl.col("family") == family)
    fig.add_trace(
        go.Box(
            name=family,
            y=rows.get_column("ic_mean").to_list(),
            text=rows.get_column("model_identity").to_list(),
            boxpoints="all",
            jitter=0.35,
            pointpos=0,
            hovertemplate="%{text}<br>validation IC %{y:+.4f}<extra></extra>",
        )
    )
fig.add_hline(y=0, line_width=1, line_dash="dot", line_color="#666666")
fig.update_layout(
    title="Validation IC across declared configurations and checkpoints",
    xaxis_title="Model family",
    yaxis_title="Mean daily rank IC",
    showlegend=False,
)
fig.show()

# %% [markdown]
# ## Causal DML artifact
#
# The causal result is not mixed into the predictive population or its checkpoint summaries.
#
# **It answers a different question from everything above.** The models above rank symbols against
# each other at a decision time; the estimate below asks what happens to the outcome when the
# treatment moves, holding the controls fixed. Double machine learning gets there by fitting two
# nuisance models - one predicting the outcome from the controls, one predicting the treatment
# from them - and regressing the parts neither explains against each other, so that the effect is
# estimated on what is left after the controls are accounted for rather than on the raw series.
#
# **Two standard errors are reported and they are not interchangeable.** The HAC standard error
# corrects for the serial correlation that overlapping labels induce, which is what makes the
# conventional error too small on this data. The placebo p-value is a permutation test: the
# treatment is shuffled in blocks long enough to preserve that serial dependence, the estimate is
# recomputed, and the reported value is the share of shuffles whose HAC t-statistic reaches the
# observed one. The comparison is on the t-statistic rather than the effect because a permuted
# treatment is no longer predictable from the controls, so its residual keeps nearly all its
# variance - and that variance is the denominator of the second-stage effect. On the effect scale
# every placebo draw is divided by a larger number than the observed one, which narrows the null in
# the one direction that makes a refutation read as passed. The t-statistic carries the same
# denominator and cancels it. The first asks whether the estimate is distinguishable from zero given
# the dependence; the second asks whether the procedure would have produced it from a treatment that
# carries no signal.
#
# It is resolved as canonical whatever tier this notebook runs at, because the populations above
# are canonical whatever tier this notebook runs at. Asking a preview run for a preview causal
# artifact would make the notebook fail unless `10_causal_dml` happened to have run in the same
# workspace first, and would pair a preview estimate with canonical predictions if it had.

# %% tags=["results"]
causal = CausalResult.one(study, label="ret_to_expiry", execution_tier="canonical")
if not causal.complete:
    raise RuntimeError("the causal DML artifact is incomplete")
causal_summary = pl.DataFrame(
    {
        "causal_hash": [causal.hash],
        "treatment": [causal.spec["computation"]["estimand"]["treatment"]],
        "outcome": [causal.spec["computation"]["estimand"]["outcome"]],
        "observations": [causal.metrics["n_obs"]],
        "effect": [causal.metrics["dml_effect"]],
        "hac_standard_error": [causal.metrics["dml_se_hac"]],
        "hac_p_value": [causal.metrics["p_value_hac"]],
        "placebo_p_value": [causal.metrics["refutation_p"]],
    }
)
causal_summary

# %% [markdown]
# Predictive diagnostics and the causal estimate are now available for reader inspection. Neither
# table changes the complete prediction population or selects a strategy.
#
# **Reading the two tables together, and the trap in doing so.** They describe the same data from
# two directions, and neither confirms the other. A family can rank symbols well and carry no
# causal effect on the treatment studied here, because ranking exploits any stable association
# while the estimate is restricted to what survives the controls. The reverse also happens: a
# treatment effect that is real and small can be invisible to a rank correlation computed across
# a cross-section it barely moves. Agreement between the two is worth noticing and is not
# evidence, and disagreement is not a defect in either.
#
# **What carries forward.** Only the population itself. The diagnostics are read and left here;
# the next stage takes every configuration in the population, gives each an equal-weight backtest,
# and selects on that. Anything a reader concludes from the numbers above should be held until
# those backtests exist, because the ordering above and the ordering there routinely differ.

```