Comparación de ediciones de VectorBT en cargas de trabajo reales de estrategias | Stratmill

# VectorBT Pro and OSS on Current Case-Study Strategies


# VectorBT Pro and OSS on Current Case-Study Strategies

This notebook reports the VectorBT Pro and VectorBT OSS rows from the current real-strategy audit.
Required comparisons use ETF, CME futures, USD-quoted foreign-exchange, and US equity-panel target
streams where the pinned VectorBT edition supports the asset and accounting contract.

**Learning objectives**

- Compare VectorBT Pro and OSS with ML4T on supported real-data workloads
- Distinguish fill precision from the monetary unit used for account values
- Understand why VectorBT OSS is not used for the CME futures contract
- Read engine-only runtime evidence without treating it as a universal ranking

**Book reference**: Chapter 16, Section 16.3

## Setup

```python
"""Current VectorBT parity evidence."""

import json

import polars as pl
from IPython.display import display

from utils.paths import get_chapter_dir
```

```python
# Production defaults - Papermill injects overrides after this cell
ROUND_SECONDS = 3
```

```python
AUDIT_PATH = get_chapter_dir(16) / "resources" / "framework_parity_audit.json"
audit = json.loads(AUDIT_PATH.read_text(encoding="utf-8"))
FRAMEWORKS = ["vectorbt_pro", "vectorbt_oss"]
FRAMEWORK_NAMES = {
    key: f"{audit['frameworks'][key]['display_name']} {audit['frameworks'][key]['version']}"
    for key in FRAMEWORKS
}
CASE_NAMES = {
    "etfs": "ETF allocation",
    "cme_futures": "CME futures",
    "crypto_perps_funding": "Crypto perpetual funding",
    "fx_pairs": "FX allocation (USD-quoted pairs)",
    "us_equities_panel": "US equity panel",
}
```

## 1. Required comparisons

```python
results = (
    pl.DataFrame(audit["real_strategy_records"])
    .filter(pl.col("framework").is_in(FRAMEWORKS))
    .with_columns(
        pl.col("case_study").replace_strict(CASE_NAMES).alias("strategy"),
        pl.col("framework").replace_strict(FRAMEWORK_NAMES).alias("engine"),
    )
    .select(
        "strategy",
        "engine",
        "status",
        "fills",
        "valuations",
        "valuation_timestamps_match",
        "equity_gap",
        "equity_raw_gap",
        "terminal_gap",
        "terminal_raw_gap",
    )
    .sort("strategy", "engine")
)

assert results.height == 7
assert results.filter(pl.col("status") == "pass").height == 7
assert results["valuation_timestamps_match"].all()

display(results)
```

Both VectorBT editions participate in the ETF, USD-quoted foreign-exchange, and US equity-panel
comparisons. VectorBT Pro also supplies contract multipliers and futures-style leverage for the
CME comparison. Fill prices retain eight-decimal precision, quantities retain five-decimal
precision, and account monetary values must round to the same cent.

## 2. Unsupported asset models

```python
unsupported = (
    pl.DataFrame(audit["unsupported_records"])
    .filter(pl.col("framework").is_in(FRAMEWORKS))
    .with_columns(
        pl.col("case_study").replace_strict(CASE_NAMES).alias("strategy"),
        pl.col("framework").replace_strict(FRAMEWORK_NAMES).alias("engine"),
    )
    .select("strategy", "engine", "reason")
    .sort("strategy", "engine")
)
display(unsupported)
```

VectorBT OSS does not provide the native multiplier and margin-account model required by the CME
bundle. Neither edition is used to emulate crypto-perpetual funding and margin accounting.

## 3. Engine-only timing

Each correctness-passing VectorBT row has an engine-only timing record.

```python
timing = (
    pl.DataFrame(audit["performance_records"])
    .filter(pl.col("framework").is_in(FRAMEWORKS))
    .with_columns(
        pl.col("case_study").replace_strict(CASE_NAMES).alias("strategy"),
        pl.col("framework").replace_strict(FRAMEWORK_NAMES).alias("engine"),
        pl.col("framework_median_seconds").round(ROUND_SECONDS).alias("vectorbt_seconds"),
        pl.col("ml4t_median_seconds").round(ROUND_SECONDS).alias("ml4t_seconds"),
        pl.col("framework_to_ml4t_ratio").round(2).alias("vectorbt_div_ml4t"),
    )
    .select("strategy", "engine", "vectorbt_seconds", "ml4t_seconds", "vectorbt_div_ml4t")
)

assert timing.height == 7
display(timing)
```

The measured region excludes data loading, target construction, adapter preparation, and output
extraction. The observed ratios do not establish the same relationship for other datasets,
strategy mechanics, or machines.

## 4. Synthetic stress evidence

```python
stress = (
    pl.DataFrame(audit["synthetic_stress"]["records"])
    .filter(pl.col("framework").is_in(FRAMEWORKS))
    .with_columns(pl.col("framework").replace_strict(FRAMEWORK_NAMES).alias("engine"))
    .select("engine", "intents", "fills", "trades", "terminal_value", "status")
)
display(stress)
```

VectorBT Pro and OSS both pass the generated stress comparison against their matching ML4T
profiles. This establishes scale conformance for the fixed target-order recipe. It does not create
support for the excluded asset models.