Evaluación de estrategias de financiación de perpetuos cripto con datos de prueba

# Crypto Perpetuals Funding: Holdout Backtest


# Crypto Perpetuals Funding: Holdout Backtest

**Chapter 20 - Out-of-sample evaluation**

[`17_holdout_predictions`](17_holdout_predictions.ipynb) refitted the selected
configuration on history ending before 2024 and wrote its predictions over 2024-25. This
notebook trades them with the sizing and the overlay that configuration carries, and
registers the result under `stage='holdout'`.

**Prerequisites:** [`17_holdout_predictions`](17_holdout_predictions.ipynb). This notebook
does not fit; if the prediction set it needs is absent, it raises rather than producing
one.

**Scope:** one backtest. The comparison against validation, and the correction that
comparison needs, are [`19_strategy_analysis`](19_strategy_analysis.ipynb)'s.

```python
"""Crypto Perpetuals Funding: Holdout Backtest."""

import json
import sqlite3
import warnings

import polars as pl

warnings.filterwarnings("ignore")

from case_studies.crypto_perps_funding.funding_data import funding_rates_for_prices
from case_studies.research import open_study
from case_studies.research.holdout import build_holdout_training_spec
from case_studies.research.strategy import strategy_warmup_periods
from case_studies.utils.artifact_digest import value_digest
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
```

```python
CASE_STUDY_ID = "crypto_perps_funding"
EXECUTION_TIER = "canonical"
WORKSPACE: str = ""
MAX_SYMBOLS = 0
# Whether a holdout backtest of a DIFFERENT strategy may be superseded by this run. Off by
# default, and the same switch `17_holdout_predictions` uses for the model side.
REPLACE_HOLDOUT = False
```

```python
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": backtest_hash} for (backtest_hash,) in rows]


def _delete_holdout_backtest(case_dir, backtest_hash):
    """Remove one registered holdout backtest and the rows derived from it.

    Same rule as `17_holdout_predictions`' replacement of a superseded generation: a holdout
    result that has been observed and then left readable beside its replacement is still a
    number someone can quote.
    """
    with sqlite3.connect(str(case_dir / "run_log" / "registry.db")) as conn:
        conn.execute(
            "DELETE FROM backtest_paired_metrics WHERE challenger_hash = ? OR benchmark_hash = ?",
            (backtest_hash, backtest_hash),
        )
        conn.execute("DELETE FROM backtest_metrics WHERE backtest_hash = ?", (backtest_hash,))
        conn.execute("DELETE FROM backtest_runs WHERE backtest_hash = ?", (backtest_hash,))
```

## 1. The configuration, and the predictions it produced on the holdout

The selected configuration is resolved the same way [`16_costs`](16_costs.ipynb) and
[`17_holdout_predictions`](17_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 17 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.

```python
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 "
        "17_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}")
```

## 2. What the allocator needs before the window opens

This configuration allocates by `mvo_ledoit_wolf`, which is a moment estimator: it reads a
rolling window of underlying prices and produces weights from their covariance. That window
does not restart because the evaluation period does. Loading the holdout slice alone would
leave the first rebalance with no history to estimate from, and the loader would fall back
to a median-imputed warmup - so the first weeks of the holdout would be traded on weights
that describe nothing, and the resulting Sharpe would be a measurement of the fallback.

So the prices are loaded with the same lookback the allocator was given on validation.
`setup.yaml` declares `allocator_lookback: 240`, which is 240 eight-hourly bars, about
eighty days. The loader leaves the start of the window unconstrained by that much and still
caps the end at the canonical window end; the extra prefix is consumed by the rolling
window and does not enter return aggregation, because the engine only aggregates over the
rebalance timestamps the predictions carry.

The conformal branch below is inert for this configuration and is kept because the selected
configuration is resolved rather than fixed. `conformal_weighted` sizes from residuals the model
has already made, and on the holdout there are none to use - every holdout return realises inside
the window being evaluated - so a conformal configuration would calibrate from validation
residuals with an embargo covering the label horizon. This one allocates from price moments and
needs no calibration at all.

```python
strategy = strategy_view(json.loads(carrier["spec_json"]))
allocation = strategy.get("allocation") or {}
warmup = strategy_warmup_periods({"allocation": allocation} if allocation else {})
NEEDS_CALIBRATION = allocation.get("method") == "conformal_weighted"
embargo_steps = holdout_conformal_embargo_steps(CASE_STUDY_ID, LABEL) if NEEDS_CALIBRATION else 0
print(f"Allocator {allocation.get('method', 'equal_weight')!r}, warmup {warmup} bars")
if NEEDS_CALIBRATION:
    print(
        f"  conformal configuration: embargo {embargo_steps} observation(s), widths written below."
    )
else:
    print("  needs no calibration; sized from price moments over the warmup window.")
```

## 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 signal, the
allocator and the `time_exit_20` overlay are carried across, and the commission and slippage are
the levels `setup.yaml` declares, the same ones every validation number in this case study was
net of and the same ones sitting inside the swept grid in [`16_costs`](16_costs.ipynb).

The run registers under `stage='holdout'`, which the registry derives from the prediction
set's split rather than from anything asserted here.

The window carries one backtest at a time, for the same reason 17 lets it carry one
prediction generation at a time. 17's guard is on the model - the training identity and the
checkpoint - and it cannot see this one: a changed allocator, overlay or cost level
produces the same holdout predictions and a different result from them.

Two fields are rebuilt rather than carried. `input_identity` records the digests of the data a
run actually read - here the price panel and the official funding settlements - and the selected
configuration's copy describes the validation window. Cloning it produces a record that names
inputs the run never touched, which is exactly what a consumer checking a price digest against
the canonical one would refuse. They are derived from the holdout frames instead.

The funding settlements are also passed to the runner, not merely digested. This case study
is about a cashflow that accrues on holding rather than trading, every validation number in
it is net of the funding the position actually paid or received, and a holdout run that
omitted it would compare a strategy without its central economics against one with 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 - so the guard and the runner cannot disagree 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.

```python
prices = load_backtest_prices_for(
    CASE_STUDY_ID,
    LABEL,
    split="holdout",
    warmup_periods=warmup,
    max_symbols=MAX_SYMBOLS,
)
predictions = read_predictions(CASE_STUDY_ID, HOLDOUT_PREDICTION_HASH)
funding_rates = funding_rates_for_prices(prices)
print(f"Prices: {len(prices):,} rows, {prices['symbol'].n_unique():,} assets")
print(
    f"Funding: {funding_rates.height:,} settlements over "
    f"{funding_rates['symbol'].n_unique():,} names"
)
print(f"Predictions: {predictions.height:,} rows, {predictions['timestamp'].n_unique():,} stamps")

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"
spec["input_identity"] = {
    **spec.get("input_identity", {}),
    "prices": value_digest(prices),
    "funding_rates": value_digest(funding_rates),
}
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(
    {
        row["backtest_hash"]
        for row in _registered_holdout_backtests(CASE_DIR, HOLDOUT_PREDICTION_HASH)
    }
    - {prospective_hash}
)
if superseded_backtests and not REPLACE_HOLDOUT:
    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. Set "
        "REPLACE_HOLDOUT=True to discard the earlier one, or leave the selection where it was."
    )
for backtest_hash in superseded_backtests:
    print(f"REPLACING holdout backtest {backtest_hash}")
    _delete_holdout_backtest(CASE_DIR, backtest_hash)

# The guard has passed, so this run will register and any artifact it is sized by belongs
# beside this prediction set. Writing before the guard would replace the calibration a
# registered run was sized by, and the guard could still refuse afterwards.
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,
    funding_rates=funding_rates,
    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}")
```

## 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 thousands of backtests, so it carries the selection; the holdout figure is one
measurement, so it carries its own sampling error. Both push the pair apart before any real
change in the strategy's edge.
[`19_strategy_analysis`](19_strategy_analysis.ipynb) is where they are given intervals and
the selection correction.

```python
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 = conn.execute(
        "SELECT sharpe, n_periods FROM backtest_metrics WHERE backtest_hash = ?",
        (carrier["val_backtest_hash"],),
    ).fetchone()

print(f"Validation Sharpe over its {int(carrier_periods):,} periods: {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']):,} periods:      "
    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%}"
)
```

## 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.

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 [`19_strategy_analysis`](19_strategy_analysis.ipynb)'s.

The holdout stays re-runnable. If the selection changes, this generation is deleted and
another is produced; it is not a resource that has been spent.

**Next:** [`19_strategy_analysis`](19_strategy_analysis.ipynb).