Как интерпретировать метрики прогнозных моделей для финансирования бессрочных фьючерсов

# 12_model_analysis.py


```py
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# %% [markdown]
# # What the fitted models can and cannot tell you about funding
#
# Notebooks 06 through 11 each fitted a family and published its validation predictions. This one
# reads all of them together and asks what they say - and, as importantly, what they are not
# entitled to say.
#
# **Three metrics appear here and none of them selects anything.**
#
# The **information coefficient** is the rank correlation, on each settlement, between the
# predictions and the returns that followed, averaged over settlements. It asks whether the model
# ordered the perpetuals correctly. It is silent on how far apart they were: a model that ranks
# perfectly and separates the top from the bottom by a basis point scores the same as one that
# separates them by a percent.
#
# **AUC** applies to the two direction labels rather than to returns. It is the probability that a
# randomly chosen settlement the model scored higher went on to rise, against one it scored lower.
# Half is a coin. It measures ordering too, so it is silent about calibration - a model whose
# probabilities are all near one half in the right order scores well.
#
# **Log loss** is the one that reads the probabilities as probabilities. It punishes confidence in
# the wrong direction far more than hesitation, so a model can improve its AUC and worsen its log
# loss at the same time by ordering better while becoming overconfident.
#
# **Why none of them chooses the strategy.** Every one measures the signal in isolation, on the
# settlements where it happened to be scored, before any position is sized, any perpetual is
# funded, or any cost is paid. A model with the highest IC can lose to a lower-ranked one after
# funding and turnover, and on this case study it can lose for a reason specific to perpetuals:
# funding is paid by whoever holds the position at settlement, so a signal that flips often pays
# it repeatedly. Selection therefore happens in [`13_backtest`](13_backtest.ipynb) and after, on
# validation backtest Sharpe. The numbers here are for understanding what was fitted.
#
# **Every checkpoint stays a row.** The deep-learning families save weights every 25 epochs, so a
# configuration arrives here as eight scoreable models rather than one. They are not collapsed to
# a per-configuration best: taking the best epoch of each family and comparing those is choosing
# after seeing the answer, and the epoch that wins on validation is not the one a reader could
# have picked in advance.
#
# **Learning objectives.** By the end of this notebook you will be able to:
#
# - Say what IC, AUC and log loss each measure, and name a model that would score well on one
#   while scoring badly on another.
# - Explain why a ranking metric cannot decide a trading configuration, and what specifically
#   about perpetual funding widens that gap.
# - Read a distribution that keeps every checkpoint, and say why collapsing it to a per-family
#   best would flatter whichever family saves the most.
# - Say why the causal estimate is reported beside these tables rather than among them.
#
# **Book reference:** Chapters 11 to 19, model comparison and diagnostics.
#
# **Prerequisites:** complete canonical outputs from [`06_linear`](06_linear.ipynb) through
# [`10_dl_tcn`](10_dl_tcn.ipynb), and the causal result from
# [`11_causal_dml`](11_causal_dml.ipynb).
#
# **What it writes:** nothing to the registry. This notebook is read-only over what the model
# notebooks published; every table below is a description of rows that already exist, and no
# decision taken here propagates anywhere.

# %%
import os

import plotly.express as px
import polars as pl
from IPython.display import Markdown

from case_studies.crypto_perps_funding.research_workflow import (
    OFFICIAL_POPULATION,
    open_study,
    plan_official_models,
)
from case_studies.research import CausalResult, OfficialPopulation, superseded_members
from utils.style import COLORS, ml4t_palette, show_plotly_with_alt

# %% tags=["parameters"]
EXECUTION_TIER = "canonical"
WORKSPACE = os.environ.get("ML4T_OUTPUT_DIR", "")

# %% [markdown]
# ## 1. Assemble the complete predictive catalog
#
# Diagnostics on a partial catalog are worse than no diagnostics, because they look the same. If
# one family's checkpoints are missing, every cross-family comparison below silently becomes a
# comparison over a different set of models, and nothing in the resulting tables says so. So the
# catalog is assembled and checked for completeness first, and the analysis refuses to begin
# otherwise.

# %% tags=["results"]
study = open_study(execution_tier=EXECUTION_TIER, workspace=WORKSPACE or None)
catalog = study.predictions.table(include_preview=EXECUTION_TIER == "preview").filter(
    (pl.col("identity_status") == "current")
    & (pl.col("execution_tier") == EXECUTION_TIER)
    & (pl.col("split") == "validation")
    & pl.col("complete")
)
# `identity_status` names the schema version a row was written under, not the generation its
# producer still publishes. A model notebook that refits leaves the generation it replaced in
# the registry - complete, and current under a column that has not moved - so the filter above
# carries retired prediction sets into the analysis. The population lineage is what answers it,
# and `superseded_members` reads that, so the analysed catalog and the frozen population
# 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() or catalog["prediction_hash"].n_unique() != catalog.height:
    raise RuntimeError("the canonical model catalog is empty or has duplicate identities")

# %% [markdown]
# Canonical analysis also reconstructs the complete request population. Exact hash equality makes a
# missing, extra, or differently resolved checkpoint a blocking error before diagnostics begin.

# %% tags=["results"]
if EXECUTION_TIER == "canonical":
    population = OfficialPopulation.one(study, name=OFFICIAL_POPULATION)
    population.require_complete()
    declared_hashes = set(plan_official_models(study).expected_prediction_hashes)
    frozen_hashes = set(population.members)
    if frozen_hashes != declared_hashes:
        raise RuntimeError("the frozen model population differs from the declared requests")
    if set(catalog["prediction_hash"]) != frozen_hashes:
        raise RuntimeError("the canonical model catalog differs from the declared population")

# %% tags=["results"]
catalog.select(
    "family",
    "label",
    "config_name",
    "checkpoint_kind",
    "checkpoint_value",
    "training_hash",
    "prediction_hash",
    "ic_mean",
    "auc_roc",
    "log_loss",
).sort("label", "family", "config_name", "checkpoint_value")

# %% [markdown]
# ## 2. Diagnostic summaries
#
# These tables describe the registered validation predictions without collapsing checkpoint
# identity or intersecting away missing keys. Any incomplete row was rejected before this analysis.
#
# Both of those conditions are doing work. **Not collapsing checkpoints** means a deep-learning
# configuration appears as its eight scored models rather than as one number chosen after the fact;
# what you are reading is a distribution per family, not a leaderboard. **Not intersecting keys**
# means each row is scored on the settlements its own fold declares, rather than on the settlements
# every family happens to share. Intersecting would make the numbers look more comparable while
# quietly changing the sample each was measured on - and on an unbalanced perpetual panel, where
# symbols list and delist mid-history, that intersection can be a good deal smaller than any
# individual family's coverage.
#
# Read these as descriptions of what was fitted. A family that scores poorly here may still win the
# backtest, and a family that scores well here may lose it to funding paid on a signal that flips.

# %% tags=["results"]
diagnostics = catalog.select(
    "label",
    "family",
    "config_name",
    "checkpoint_kind",
    "checkpoint_value",
    "training_hash",
    "prediction_hash",
    "n_folds",
    "ic_mean",
    "ic_std",
    "auc_roc",
    "log_loss",
).sort("label", "family", "config_name", "checkpoint_value")
diagnostics

# %% [markdown]
# The distribution below retains every checkpoint. A family with more configured checkpoints is
# therefore shown with more points rather than receiving extra weight in a collapsed ranking.

# %% tags=["results"]
ic_points = catalog.filter(pl.col("ic_mean").is_not_null()).sort(
    "label", "family", "config_name", "checkpoint_value"
)
fig = px.strip(
    ic_points,
    x="ic_mean",
    y="label",
    color="family",
    hover_data=["config_name", "checkpoint_kind", "checkpoint_value"],
    title="Validation IC for every complete model checkpoint",
    labels={"ic_mean": "Mean validation rank IC", "label": "Prediction target"},
    color_discrete_sequence=ml4t_palette(5, categorical=True),
)
fig.add_vline(x=0, line_width=1, line_color=COLORS["neutral"])
fig.update_layout(legend_title_text="Model family")
show_plotly_with_alt(
    fig,
    "Horizontal strip plot with one point per complete model checkpoint, grouped by prediction "
    "target and colored by model family. A vertical zero line separates positive and negative "
    "validation rank correlations.",
)

# %% [markdown]
# ## The causal estimate, reported beside the diagnostics and not among them
#
# The double machine learning estimate from [`11_causal_dml`](11_causal_dml.ipynb) answers a
# different question from every metric above. Those ask how well a model orders perpetuals it did
# not train on. This asks what would happen to the return if the treatment were changed, holding
# the confounders fixed - a claim about an intervention, not about a ranking.
#
# The two do not convert into one another in either direction. A model with a high IC has found
# something that predicts, which may be an effect, a proxy for one, or a regularity with no causal
# content at all. An effect estimate that is large and well identified need not predict well,
# because most of the variation in funding is not the treatment. Putting them in one table would
# invite exactly that arithmetic, so the estimate is reported on its own with its own uncertainty.
#
# `refutation_p` is the placebo check rather than a significance test: it asks how often an
# estimate this large appears once the treatment is disrupted. It is nullable by contract, and a
# run that did not reach a verdict is reported as not having one.

# %% tags=["results"]
causal = CausalResult.one(study, label="fwd_ret_8h", execution_tier=EXECUTION_TIER)
if not causal.complete:
    raise RuntimeError("causal result is incomplete")
causal_summary = pl.DataFrame(
    {
        "causal_hash": [causal.hash],
        "n_obs": [causal.metrics["n_obs"]],
        "effect": [causal.metrics["dml_effect"]],
        "hac_standard_error": [causal.metrics["dml_se_hac"]],
        "refutation_p": [causal.metrics["refutation_p"]],
    }
)
causal_summary

# %% tags=["results"]
# refutation_p is nullable by contract (`case_studies/utils/causal.py`), so it is reported as absent
# rather than formatted. A run whose refutation did not produce an empirical p-value is a
# weaker result, and saying so is more useful than omitting the sentence or failing to render.
#
# `refutation_n_successful` is the denominator the p-value was actually computed over, and it
# is not the requested count: `empirical_permutation_p` drops placebos whose second stage
# returned NaN. It arrived with a migration, so a row written before the column existed
# carries None, and `CausalResult.open` then publishes no `refutation_class` at all rather
# than classifying on the p-value alone. This run is one of those rows.
_refutation = causal.metrics["refutation_p"]
_n_successful = causal.metrics["refutation_n_successful"]
_refutation_class = causal.metrics["refutation_class"]
_n_placebo = causal.spec["computation"]["refutation"]["n_placebo"]
_floor = 1.0 / (_n_placebo + 1)
if _refutation is None:
    _refutation_text = "No temporal refutation p-value was registered for this estimate."
elif _refutation <= _floor:
    # The floor pins the denominator even when the column is absent. The p-value is
    # (1 + exceedances) / (1 + successful) with at least one in the numerator, and
    # successful <= requested, so p <= 1/(requested + 1) is only reachable when every
    # requested placebo succeeded and none was as extreme as the observed effect. So this
    # branch may state the count and the exceedances without reading either back.
    #
    # What the floor does NOT establish is that the underlying permutation tail probability
    # is at most 1/(n+1). That is a property of all permutations; this is a sample of n of
    # them, and zero exceedances in a sample bounds the sample, not the population.
    _refutation_text = (
        f"The temporal refutation p-value is **{_refutation:.3f}**, the smallest value "
        f"{_n_placebo} permutations can produce: every placebo fit succeeded and none was "
        f"as extreme as the observed effect. This is the Monte Carlo estimate sitting at "
        f"its resolution floor of 1/{_n_placebo + 1}, not a bound on the permutation tail "
        f"probability itself - resolving that finer needs more permutations, not a smaller "
        f"reported number."
    )
    # Being at the floor is not the same as being able to reject from there. Below 20
    # draws the floor itself sits above 5 %, so the run is simultaneously as extreme as
    # its permutation count allows and unable to reject at all - the verdict has to be
    # said out loud rather than left to the reassuring half of the sentence.
    if _refutation_class == "Underpowered":
        _refutation_text += (
            f" Even so, the registered verdict is **Underpowered**: with {_n_placebo} "
            "permutations the floor is above 5 %, so no outcome of this test could have "
            "rejected."
        )
elif _n_successful is None:
    # Off the floor the denominator matters and is not recoverable, so the count is not
    # asserted and no pass/fail verdict is published - the registry withholds one for
    # exactly this reason.
    _refutation_text = (
        f"The temporal refutation p-value is **{_refutation:.3f}**, a Monte Carlo estimate "
        f"over at most the {_n_placebo} requested permutations. How many of them actually "
        "produced a second-stage fit is not registered for this run, so the effective "
        "sample behind the estimate - and with it whether the draws could have rejected at "
        "all - cannot be read back, and no pass/fail verdict is published."
    )
else:
    _refutation_text = (
        f"The temporal refutation p-value is **{_refutation:.3f}**, a Monte Carlo estimate "
        f"over the **{_n_successful}** of {_n_placebo} requested permutations whose second "
        f"stage produced a fit, and no finer than that sample supports. The registered "
        f"verdict is **{_refutation_class}**"
        + (
            " - too few successful draws to reject at 5 % whatever the data showed. "
            if _refutation_class == "Underpowered"
            else ". "
        )
    )

_effect = causal.metrics["dml_effect"]
_se = causal.metrics["dml_se_hac"]
_t = _effect / _se if _se else float("nan")
# The block and the bandwidth are sized by different rules and the reader is being asked to
# weigh a ratio built from the second. The bandwidth is not statsmodels': it requires an
# explicit `maxlags` for both `HAC` and `hac-groupsum` and supplies no default. The
# cube-root-of-decision-times rule is this repository's own fallback, applied in
# `manual_dml_timeseries` in `case_studies/utils/causal.py` because no `hac_maxlags` is
# passed, and then raised to `horizon - 1` (`run_dml_analysis` only warns and forwards).
# The realized bandwidth is not registered - `causal_runs` stores neither `hac_maxlags` nor
# `n_periods` - so the notebook states the rule rather than a number it cannot read back.
#
# The two window quantities decide the wording, not `block_size_basis`. The basis string
# cannot carry it: `block_size = max(horizon_steps, treatment_window_steps or 1)` and the
# basis is resolved by equality, so `label_horizon` covers both an undeclared window and a
# declared one shorter than the horizon, while `treatment_window` includes the case where the
# two are equal and the bandwidth already covers the block to within a lag. Only
# `treatment_window_steps > label_horizon_steps` is the situation the caveat describes.
_refutation_spec = causal.spec["computation"]["refutation"]
_block = _refutation_spec["block_size"]
_window_steps = _refutation_spec["treatment_window_steps"]
_horizon_steps = _refutation_spec["label_horizon_steps"]
_n_timestamps = causal.spec["computation"]["analysis_population"]["n_timestamps"]
if _window_steps is not None and _window_steps > _horizon_steps:
    _bandwidth_text = (
        f"One caveat on the ratio: the placebo block spans **{_block}** bars of treatment "
        "persistence, and the standard error behind this ratio is not sized by the same "
        "quantity. Its HAC bandwidth is `max(label horizon - 1, cube root of the "
        f"decision-time count)`, whose horizon term is {_horizon_steps - 1} on this run. "
        "Neither term refers to the treatment, so whether the bandwidth happens to reach "
        "across the block is incidental, and its realized value is not registered. "
        "`causal_runs` stores no `hac_maxlags`, and the count the cube root is taken over is "
        "not the one in the spec: the bandwidth counts decision times that survive "
        "cross-fitting, while the registered "
        f"`analysis_population.n_timestamps` of {_n_timestamps} counts the whole analysis "
        "frame, including the first walk-forward block, which yields no out-of-fold "
        "residuals. Nor does "
        "the mismatch settle which way the standard error would move under a bandwidth that "
        "did span the block: a HAC estimate is not monotonic in its bandwidth, because the "
        "autocovariances a longer one admits can carry either sign, and the treatment's "
        "construction window is an argument for the block rather than a derivation of the "
        "right bandwidth. Read the ratio as provisional until the estimate is recomputed "
        "across a range of defensible bandwidths. "
    )
else:
    _bandwidth_text = (
        f"The placebo block spans **{_block}** bars, sized by the label horizon, which is at "
        "least as long as any construction window declared for the treatment"
        + (" - none is declared here. " if _window_steps is None else f" ({_window_steps}). ")
        + "So the block does not exceed the label horizon, and the standard error's "
        "bandwidth covers it to within a lag rather than falling short of it. "
    )
Markdown(
    f"The registered DML estimate is **{_effect:+.4g}** with "
    f"a HAC standard error of **{_se:.4g}**, a ratio of **{_t:.2f}**. {_refutation_text} "
    "Read the two together: the placebo test asks whether the estimation procedure "
    "manufactures this effect out of permuted treatment, and the standard error asks "
    "whether the effect is separable from zero at all. Surviving the first while failing "
    "the second is not evidence of a causal effect. "
    f"{_bandwidth_text}"
    "This result describes the declared causal estimand and does not rank predictive models "
    "or trading strategies."
)

# %% [markdown]
# ## Key takeaways and limitations
#
# - **Completeness is checked before anything is interpreted.** A diagnostic computed over a
#   catalog missing a family's checkpoints reads exactly like one computed over a complete catalog.
#   That is why the check is a blocking error rather than a warning.
# - **These three metrics describe predictions; none of them selects.** IC and AUC measure ordering
#   and are silent about magnitude and calibration respectively; log loss reads probabilities as
#   probabilities and can move opposite to AUC. Selection is validation backtest Sharpe, in
#   [`13_backtest`](13_backtest.ipynb).
# - **On perpetuals the gap between ranking and Sharpe has a specific cause.** Funding is paid by
#   whoever holds the position at settlement, so a signal that reverses often pays it repeatedly. A
#   ranking metric cannot see that, because it never holds anything.
# - **Every checkpoint stays a row, so more checkpoints means more points and not more weight.**
#   Collapsing to a per-configuration best would flatter whichever family saves most often, and
#   would report a maximum over draws as if it were one model's score.
# - **The causal estimate sits beside these tables and does not convert into them.** A high IC may
#   be an effect, a proxy for one, or a regularity with no causal content; a well-identified effect
#   need not predict, because most of the variation in funding is not the treatment. Putting both
#   in one table would invite arithmetic that neither supports.
# - **Everything here is conditional on the finalized features, labels, universe and validation
#   period**, and the causal estimate is additionally conditional on its declared
#   observed-confounder assumptions. Two validation folds is what this case study's history
#   supports, and every comparison below rests on them.

```