Eine feste Strategie ohne erneute Anpassung auf einen Aktien-Holdout anwenden

# 21_holdout_backtest.py


```py
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# %% [markdown]
# # US equities panel: trading the holdout predictions
#
# [`20_holdout_predictions`](20_holdout_predictions.ipynb) refitted the selected configuration on
# the history before the holdout window and wrote its predictions over it. This notebook trades
# them, with the concentration, the allocator, the risk overlay and the cost assumption the rest
# of the case study used, and registers the result.
#
# **Nothing is chosen here.** The predictions, the allocator, the concentration, the rebalance
# cadence, the overlay and the charge all arrive fixed from earlier notebooks, and the only thing
# this notebook decides is that they are applied unchanged. That is the whole design: a holdout
# result is worth something exactly to the extent that no decision was made after seeing it, and
# every knob left open here would be a decision.
#
# The comparison to validation is printed but not interpreted. The validation figure is the
# maximum of a ranking over the whole sweep and the holdout figure is one measurement over a
# window of about two and a quarter years; what can be said about the gap between them is
# [`22_strategy_analysis`](22_strategy_analysis.ipynb)'s subject, with the intervals to say it.
#
# **Learning objectives**
#
# - Derive which holdout prediction set belongs to a configuration rather than searching for one,
#   and say what a search would have to guess.
# - Explain why an allocator that sizes by a conformal width needs a calibration embargo on this
#   panel, and which declaration sets its length.
# - Say why the guard that admits a run to the holdout is written on the backtest hash rather
#   than on a list of fields.
# - Read a validation figure and a holdout figure side by side without treating their difference
#   as an estimate of decay.
#
# **Book reference**: Chapter 20, Section 20.2
#
# **Prerequisites**: [`20_holdout_predictions`](20_holdout_predictions.ipynb) has registered the
# refit and its prediction set.
#
# **What it writes**: one backtest, registered at `stage='holdout'`. No selection, and no
# comparison beyond a printed pair.

# %%
"""US equities panel: trade the holdout predictions under the selected configuration."""

import json
import sqlite3

import polars as pl

from case_studies.research import open_study
from case_studies.research.holdout import build_holdout_training_spec
from case_studies.utils.backtest_loaders import get_backtest_config, load_backtest_prices_for
from case_studies.utils.backtest_presets import ensure_backtest_spec, strategy_view
from case_studies.utils.backtest_runner import resolved_allow_short_selling, run_backtest
from case_studies.utils.conformal import (
    compute_holdout_conformal_widths,
    ensure_conformal_calibration_identity,
    holdout_conformal_embargo_steps,
)
from case_studies.utils.registry import (
    backtest_run_status,
    read_predictions,
    training_hash_from_spec,
)
from case_studies.utils.strategy_analysis import resolve_solvent_carrier
from utils.paths import get_case_study_dir

# %% tags=["parameters"]
CASE_STUDY_ID = "us_equities_panel"
EXECUTION_TIER = "canonical"
WORKSPACE: str = ""
MAX_SYMBOLS = 0

# %%
study = open_study(CASE_STUDY_ID, execution_tier=EXECUTION_TIER, workspace=WORKSPACE or None)
CASE_DIR = get_case_study_dir(CASE_STUDY_ID)
bt_config = get_backtest_config(CASE_STUDY_ID)


def _registered_holdout_backtests(case_dir, prediction_hash):
    """The backtest hashes already registered against one holdout prediction set."""
    with sqlite3.connect(str(case_dir / "run_log" / "registry.db")) as conn:
        rows = conn.execute(
            "SELECT backtest_hash FROM backtest_runs WHERE prediction_hash = ? "
            "ORDER BY backtest_hash",
            (prediction_hash,),
        ).fetchall()
    return [backtest_hash for (backtest_hash,) in rows]


# %% [markdown]
# ## 1. The configuration, and the predictions it produced on the holdout
#
# The selected configuration is resolved the same way [`19_costs`](19_costs.ipynb) and
# [`20_holdout_predictions`](20_holdout_predictions.ipynb) resolve it, so all three run the same
# configuration by construction rather than by a hash copied between them.
#
# Which holdout prediction set belongs to it is derived rather than searched for. Re-deriving the
# holdout training specification reproduces the training identity 20 registered - the derivation
# is deterministic and the identity covers it - so the prediction set is looked up by that
# identity and the selected configuration's checkpoint. A search over holdout prediction sets
# would have to guess which one belonged to this configuration.

# %%
carrier = resolve_solvent_carrier(CASE_STUDY_ID)
LABEL = carrier["label"]
validation_prediction_record = study.results.open(carrier["val_prediction_hash"]).registry_record()

holdout_spec = build_holdout_training_spec(
    study,
    study.results.open(carrier["training_hash"]).spec(),
    timeline=(
        pl.read_parquet(study.root / "labels" / f"{LABEL}.parquet")
        .get_column("timestamp")
        .unique()
        .sort()
        .to_list()
    ),
    case_study=CASE_STUDY_ID,
)
holdout_training_hash = training_hash_from_spec(holdout_spec)

with sqlite3.connect(str(CASE_DIR / "run_log" / "registry.db")) as conn:
    match = conn.execute(
        """
        SELECT prediction_hash FROM prediction_sets
        WHERE split = 'holdout' AND training_hash = ?
          AND checkpoint_kind IS ? AND checkpoint_value IS ?
        """,
        (
            holdout_training_hash,
            validation_prediction_record["checkpoint_kind"],
            validation_prediction_record["checkpoint_value"],
        ),
    ).fetchone()
if match is None:
    raise RuntimeError(
        f"No holdout prediction set for training {holdout_training_hash}. Run "
        "20_holdout_predictions first; this notebook does not fit."
    )
HOLDOUT_PREDICTION_HASH = match[0]

print(f"Selected configuration: {carrier['val_backtest_hash']}  {carrier['config_name']} ({LABEL})")
print(f"Holdout training:   {holdout_training_hash}")
print(f"Holdout prediction: {HOLDOUT_PREDICTION_HASH}")

# %% [markdown]
# ## 2. Calibration, where the allocator needs it
#
# An allocator that sizes by a conformal width is calibrated from errors the model has already
# made, and on the holdout there are none to use: an error is usable only once the return it
# measures has been realised, and every holdout return realises inside the window being
# evaluated. Such a configuration takes its widths from the validation residuals of the
# validation prediction set, which is what the allocator would have had standing at the start of
# the window.
#
# The embargo matters on this panel and its length is not the same for every label. A residual
# observed at `t` measures a return realising over the label's horizon, so for `fwd_ret_5d` or
# `fwd_ret_21d` the last residuals of the validation span reach into the holdout window, and
# calibrating on them would size holdout positions with holdout price information. The step count
# comes from the reviewed table in `conformal.py`, which records one horizon per label rather
# than one per case study.
#
# The branch is here rather than assumed away because the selected configuration can change:
# `conformal_weighted` is one of the seven allocators this case study sweeps, so it can carry the
# selection. When it does not, the line below says so rather than staying silent, which is what
# tells a reader the branch was evaluated.
#
# The widths themselves are NOT written here. Writing them replaces the artifact an already
# registered run was sized by, and the replacement guard in section 3 can still refuse this run
# afterwards - which would leave the registered holdout pointing at a calibration that no longer
# existed. The write is below the guard.

# %% tags=["results"]
allocation = strategy_view(json.loads(carrier["spec_json"])).get("allocation") or {}
NEEDS_CALIBRATION = allocation.get("method") == "conformal_weighted"
embargo_steps = holdout_conformal_embargo_steps(CASE_STUDY_ID, LABEL) if NEEDS_CALIBRATION else 0
if NEEDS_CALIBRATION:
    print(f"Conformal configuration: embargo {embargo_steps} observation(s), widths written below.")
else:
    print(f"Allocator {allocation.get('method', 'equal_weight')!r} needs no calibration.")

# %% [markdown]
# ## 3. The backtest
#
# The strategy specification is the selected configuration's own, re-pointed at the holdout
# prediction set and the holdout price window. Nothing else about it changes - the
# concentration, the allocator, the risk overlay and the commission and slippage levels are the
# ones `setup.yaml` declares and every validation number in this case study was net of.
#
# The run registers under `stage='holdout'`, which the registry derives from the prediction set's
# split rather than from anything asserted here - and that derivation takes precedence over the
# risk block a risk-stage configuration carries, which would otherwise file this as another risk
# overlay.
#
# **The window carries one backtest**, for the same reason 20 lets it carry one prediction
# generation. 20's guard is on the model - the training identity and the checkpoint - and it
# cannot see this one: a changed allocator, overlay, cost level or calibration produces the same
# holdout predictions and a different result from them. Like 20's, this guard refuses rather than
# offering a replacement, because deleting a result that has been seen does not unsee it.
#
# The test is the backtest hash, not a field-by-field comparison. Every input that changes the
# result is in that hash by construction, and a guard naming fields instead has to be right about
# all of them. The hash is resolved before anything runs, so nothing is evaluated on the holdout
# before the question is answered, and it comes from `backtest_run_status` - the call the runner
# itself makes - because asking it is the only way to be sure the guard and the runner agree
# about identity. The run asserts they still agree afterwards, because a guard that had quietly
# stopped predicting the hash would let everything through while looking correct.

# %% tags=["results"]
prices = load_backtest_prices_for(CASE_STUDY_ID, LABEL, split="holdout", max_symbols=MAX_SYMBOLS)
predictions = read_predictions(CASE_STUDY_ID, HOLDOUT_PREDICTION_HASH)
print(f"Prices: {len(prices):,} rows, {prices['symbol'].n_unique():,} names")
print(f"Predictions: {predictions.height:,} rows, {predictions['timestamp'].n_unique():,} sessions")

spec = ensure_backtest_spec(
    CASE_STUDY_ID,
    bt_config,
    json.loads(carrier["spec_json"]),
    prices=prices,
    prediction_hash=HOLDOUT_PREDICTION_HASH,
    initial_cash=bt_config.initial_cash,
)
spec["chapter"] = "ch20"
# The embargo goes into the specification before anything hashes it. The widths are an input to
# this backtest and the embargo decides them, so two embargoes are two results and must not share
# an identity.
if NEEDS_CALIBRATION:
    spec = ensure_conformal_calibration_identity(spec, holdout_embargo_steps=embargo_steps)

spec["backtest_config"]["account"]["allow_short_selling"] = resolved_allow_short_selling(spec, None)
prospective_hash = backtest_run_status(CASE_STUDY_ID, HOLDOUT_PREDICTION_HASH, spec).backtest_hash
superseded_backtests = sorted(
    set(_registered_holdout_backtests(CASE_DIR, HOLDOUT_PREDICTION_HASH)) - {prospective_hash}
)
if superseded_backtests:
    raise RuntimeError(
        "the holdout window already carries a backtest of a different configuration: "
        + ", ".join(superseded_backtests)
        + f". This run would register {prospective_hash} and has not run. Same rule as "
        "20_holdout_predictions: discarding the earlier result would not undo having observed "
        "it, so there is no switch here. Leave the selection where it was, or retire the earlier "
        "evaluation through the registry's lifecycle."
    )

# The guard has passed, so this run will register and the widths it is sized by are the ones that
# belong beside this prediction set.
if NEEDS_CALIBRATION:
    widths = compute_holdout_conformal_widths(
        CASE_STUDY_ID,
        carrier["val_prediction_hash"],
        HOLDOUT_PREDICTION_HASH,
        alpha=float(allocation.get("alpha", 0.2)),
        min_calibration_n=int(allocation["min_calibration_n"]),
        embargo_steps=embargo_steps,
        write=True,
    )
    print(
        f"Conformal widths: {widths.height:,} rows over "
        f"{widths['symbol'].n_unique():,} names, embargo {embargo_steps} observation(s)"
    )

result = run_backtest(
    CASE_STUDY_ID,
    HOLDOUT_PREDICTION_HASH,
    spec,
    prices=prices,
    predictions=predictions,
    label=LABEL,
    register=True,
    initial_cash=bt_config.initial_cash,
    calendar=bt_config.calendar,
)
if result.backtest_hash != prospective_hash:
    raise RuntimeError(
        f"the guard predicted {prospective_hash} and the runner registered "
        f"{result.backtest_hash}. The guard decides what may run on the holdout, so a guard that "
        "no longer reproduces the runner's identity is not a smaller problem than the one it was "
        "written for."
    )
print(f"Holdout backtest: {result.backtest_hash}")

# The stage is checked rather than trusted. A configuration drawn from the risk stage carries a
# risk block in its spec, and stage inference reads the prediction's split before that block - so
# a holdout run files as `holdout`. If that order ever changes, the whole out-of-sample result
# lands in `risk_overlay` and `22_strategy_analysis` finds no holdout at all, which is a failure
# two notebooks away from its cause.
with sqlite3.connect(str(CASE_DIR / "run_log" / "registry.db")) as conn:
    registered_stage = conn.execute(
        "SELECT stage FROM backtest_runs WHERE backtest_hash = ?", (result.backtest_hash,)
    ).fetchone()[0]
if registered_stage != "holdout":
    raise RuntimeError(
        f"the holdout backtest registered under stage={registered_stage!r} rather than "
        "'holdout'; the split-based inference in registry.store._infer_stage did not take "
        "precedence over this configuration's risk block"
    )

# %% [markdown]
# ## 4. What it came out at
#
# The two numbers below are one strategy measured on two disjoint periods, and the gap between
# them is not an estimate of decay. The validation figure is the maximum of a ranking over the
# whole sweep, so it carries the selection; the holdout figure is one measurement over a much
# shorter window, so it carries that window's sampling error. Both push the pair apart on their
# own, before any real change in the strategy's edge.
# [`22_strategy_analysis`](22_strategy_analysis.ipynb) is where they are given intervals and a
# paired comparison.

# %% tags=["results"]
metrics = result.metrics
# The selected configuration's own registered Sharpe, not the resolver's. `resolve_solvent_carrier`
# reports the common-support figure, which re-ranks candidates on the timestamps every one of them
# covers; that is the right number for choosing between candidates and the wrong one to set beside
# a holdout measured over its own full window. Both are printed, so neither has to be inferred
# from the other.
with sqlite3.connect(str(CASE_DIR / "run_log" / "registry.db")) as conn:
    carrier_sharpe, carrier_periods, carrier_trades = conn.execute(
        "SELECT sharpe, n_periods, num_trades FROM backtest_metrics WHERE backtest_hash = ?",
        (carrier["val_backtest_hash"],),
    ).fetchone()

print(f"Validation Sharpe over {int(carrier_periods):,} sessions: {carrier_sharpe:.3f}")
print(f"  the same run re-ranked on common support: {carrier['val_sharpe']:.3f}")
print(
    f"Holdout Sharpe over {int(metrics['n_periods']):,} sessions:    "
    f"{metrics.get('sharpe', float('nan')):.3f}"
)
print(
    f"Holdout: CAGR {metrics.get('cagr', float('nan')):.1%}, "
    f"max drawdown {metrics.get('max_drawdown', float('nan')):.2%}, "
    f"win rate {metrics.get('win_rate', float('nan')):.0%}"
)
# A trade count is recorded only by the bar-by-bar engine; a vectorized weight-times-return path
# stores NULL, and [`18_risk_management`](18_risk_management.ipynb) reads the same three states
# off the same column. So the pair is printed when both sides have one and the absence is named
# when they do not, rather than a null being formatted as zero. Where both exist, a holdout that
# rebalanced far less than the validation run at the same cadence would say the basket stopped
# changing, which is a different thing from a lower Sharpe.
holdout_trades = metrics.get("num_trades")
if holdout_trades is None or carrier_trades is None:
    print(
        "Trades: not recorded on "
        + " and ".join(
            name
            for name, value in (("the holdout", holdout_trades), ("validation", carrier_trades))
            if value is None
        )
        + " - this run went through the vectorized path, which stores no trade count"
    )
else:
    print(f"Trades: {int(holdout_trades):,} on the holdout, {int(carrier_trades):,} on validation")

# %% [markdown]
# ## What this notebook establishes, and what it does not
#
# It establishes a return series for the selected configuration over a period no choice in this
# case study was made on. That is the only thing a holdout can give, and it is worth less than it
# looks: the window is short beside the validation span it is being compared with, which is too
# few observations to separate a strategy that decayed from one that had an ordinary couple of
# years.
#
# It does not establish that this configuration was the right one to carry here. The selection
# that brought it was made on validation, over a pool large enough that its maximum is optimistic
# by construction, and this notebook inherits that pool without correcting for it. The deflation
# is [`22_strategy_analysis`](22_strategy_analysis.ipynb)'s.
#
# Re-running this notebook against the same configuration is free and idempotent: the backtest
# hash is unchanged and the registered run is served back. A different configuration is refused,
# for the reason 20 gives.
#
# **Next:** [`22_strategy_analysis`](22_strategy_analysis.ipynb).

```