Auditing LEAN Parity Across Trading Strategies

# LEAN Parity on Current Case-Study Strategies


# LEAN Parity on Current Case-Study Strategies

This notebook isolates the LEAN rows from the current real-strategy audit. It uses retained results
generated by the native LEAN engine and the matching ML4T Backtest profiles. The shared inputs are
frozen before engine execution.

**Learning objectives**

- Identify which selected asset classes are valid LEAN comparisons
- Read LEAN parity across fills, valuations, and terminal value
- Interpret LEAN engine-only timing on the measured strategies
- Keep synthetic stress evidence separate from real-strategy equivalence

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

## Setup

```python
"""Current LEAN parity evidence."""

import json

import polars as pl
from IPython.display import Markdown, 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"))
lean = audit["frameworks"]["lean"]
LEAN_NAME = f"{lean['display_name']} {lean['version']}"
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",
}
```

```python
display(Markdown(f"**Pinned engine:** {LEAN_NAME} with ML4T profile `{lean['profile']}`"))
```

## 1. Supported real strategies

LEAN is required for the ETF, crypto-perpetual, USD-quoted foreign-exchange, and US equity-panel
workloads. The CME row is unsupported for this particular frozen bundle: it contains continuous
root prices but lacks the dated contract chain and roll map needed to construct a native LEAN
futures subscription.

```python
lean_results = (
    pl.DataFrame(audit["real_strategy_records"])
    .filter(pl.col("framework") == "lean")
    .with_columns(pl.col("case_study").replace_strict(CASE_NAMES).alias("strategy"))
    .select(
        "strategy",
        "status",
        "fills",
        "valuations",
        "valuation_timestamps_match",
        "equity_gap",
        "equity_raw_gap",
        "terminal_gap",
        "terminal_raw_gap",
        "negative_control_detected",
    )
)

assert lean_results.height == 4
assert lean_results["status"].to_list() == ["pass"] * 4
assert lean_results["valuation_timestamps_match"].all()
assert lean_results["negative_control_detected"].all()

display(lean_results)
```

```python
lean_unsupported = (
    pl.DataFrame(audit["unsupported_records"])
    .filter(pl.col("framework") == "lean")
    .with_columns(pl.col("case_study").replace_strict(CASE_NAMES).alias("strategy"))
    .select("strategy", "reason")
)
display(lean_unsupported)
```

The table reports the complete fill and valuation counts for each supported workload. LEAN uses
native equity, crypto-future, and foreign-exchange securities. The audit does not convert the
continuous CME roots into a different instrument merely to add a LEAN row.

## 2. Engine-only timing

The timer starts immediately before the engine call and stops when it returns. One warmup and ten
process-isolated samples are used. Input loading, model inference, target construction, adapter
preparation, result extraction, and reporting are outside the timed region.

```python
lean_timing = (
    pl.DataFrame(audit["performance_records"])
    .filter(pl.col("framework") == "lean")
    .with_columns(
        pl.col("case_study").replace_strict(CASE_NAMES).alias("strategy"),
        pl.col("framework_median_seconds").round(ROUND_SECONDS).alias("lean_seconds"),
        pl.col("ml4t_median_seconds").round(ROUND_SECONDS).alias("ml4t_seconds"),
        pl.col("framework_to_ml4t_ratio").round(2).alias("lean_div_ml4t"),
    )
    .select("strategy", "lean_seconds", "ml4t_seconds", "lean_div_ml4t")
)
display(lean_timing)
```

The timing result applies to these pinned versions, bundles, and engine boundaries. It is not a
general LEAN performance claim.

## 3. Synthetic stress remains diagnostic

```python
lean_stress = (
    pl.DataFrame(audit["synthetic_stress"]["records"])
    .filter(pl.col("framework") == "lean")
    .select("intents", "fills", "trades", "terminal_value", "status")
)
display(lean_stress)
```

The retained stress row tests scale and the calibrated LEAN profile on generated inputs. The
supported rows above provide the real-data evidence.