From da1ca57489d60960414abfba93388032eac6a838 Mon Sep 17 00:00:00 2001
From: Stefan Jansen <stefan@applied-ai.com>
Date: Tue, 8 Sep 2026 16:09:28 -0400
Subject: [PATCH 1/7] Remove LinearImpact.permanent_fraction, which calculate()
 never read

The field was stored on the dataclass, documented as "Fraction of impact that is
permanent (0-1). Remainder is temporary and reverts", and read nowhere:
LinearImpact(coefficient=0.1, permanent_fraction=0.0) and the same model at 1.0
both return 1.0000000000000002 for a 10% participation order at $100. A reader who
set 0.8 because they wanted a mostly permanent model got a model with no
persistence at all and no warning.

It cannot be honoured where it sat. `calculate` sees one order and has no reference
to the ones before it, and `fill_executor` applies the returned impact to that
order's fill price only - nothing is carried into the price for later orders. That
is true of all four models here, so the parameter is removed rather than given
state or a split return: LinearImpact is a single-order concession model and the
docstring now says so, with persistence named as the caller's business.

Two tests replace the one that asserted the default value: ten identical slices are
charged the same concession by every model, and passing permanent_fraction now
raises TypeError instead of being silently ignored.
---
 src/ml4t/backtest/execution/impact.py | 10 ++++++---
 tests/execution/test_impact.py        | 32 ++++++++++++++++++++++++++-
 2 files changed, 38 insertions(+), 4 deletions(-)

diff --git a/src/ml4t/backtest/execution/impact.py b/src/ml4t/backtest/execution/impact.py
index d17db16b..46fad19f 100644
--- a/src/ml4t/backtest/execution/impact.py
+++ b/src/ml4t/backtest/execution/impact.py
@@ -64,11 +64,16 @@ class LinearImpact(MarketImpactModel):
     Simple model where impact scales linearly with participation rate.
     Appropriate for liquid markets with moderate order sizes.
 
+    Like every model here it is a single-order concession model: `calculate` sees one
+    order, and the impact it returns is charged to that order's fill price only. Nothing
+    is carried into the price for later orders, so a parent order worked in slices is
+    charged the same concession on every slice. A caller who needs permanent impact -
+    the part of the move that does not revert and is paid again by every later slice -
+    accumulates it outside the model.
+
     Args:
         coefficient: Impact scaling factor (default 0.1)
                     Higher values = more impact per unit participation
-        permanent_fraction: Fraction of impact that is permanent (0-1)
-                           Remainder is temporary and reverts
 
     Example:
         model = LinearImpact(coefficient=0.1)
@@ -76,7 +81,6 @@ class LinearImpact(MarketImpactModel):
     """
 
     coefficient: float = 0.1
-    permanent_fraction: float = 0.5
 
     def calculate(
         self,
diff --git a/tests/execution/test_impact.py b/tests/execution/test_impact.py
index ba408649..36071448 100644
--- a/tests/execution/test_impact.py
+++ b/tests/execution/test_impact.py
@@ -2,6 +2,8 @@
 
 import math
 
+import pytest
+
 from ml4t.backtest.execution.impact import (
     LinearImpact,
     NoImpact,
@@ -39,7 +41,6 @@ def test_default_values(self):
         """Test default configuration."""
         model = LinearImpact()
         assert model.coefficient == 0.1
-        assert model.permanent_fraction == 0.5
 
     def test_buy_positive_impact(self):
         """Test that buy orders have positive impact (price goes up)."""
@@ -245,3 +246,32 @@ def test_concave_exponent(self):
 
         # With exponent=0.25, 16x quantity = 2x impact (16^0.25 = 2)
         assert abs(impact_16000 / impact_1000 - 2.0) < 0.1
+
+
+class TestImpactModelsAreSingleOrderConcessions:
+    """No model here carries impact into the price for the orders that follow it."""
+
+    MODELS = (
+        NoImpact(),
+        LinearImpact(coefficient=0.1),
+        SquareRootImpact(coefficient=0.5, volatility=0.02),
+        PowerLawImpact(coefficient=0.1, exponent=0.5),
+    )
+
+    def test_repeated_slices_are_charged_the_same_concession(self):
+        """A parent order worked in ten equal slices pays the first slice's price ten times.
+
+        This is the property that a permanent-impact parameter would have to break. It
+        holds for every model, which is why `LinearImpact` no longer advertises one.
+        """
+        for model in self.MODELS:
+            charges = [
+                model.calculate(quantity=1000.0, price=100.0, volume=100_000.0, is_buy=True)
+                for _ in range(10)
+            ]
+            assert len(set(charges)) == 1, f"{type(model).__name__} is not stateless"
+
+    def test_linear_impact_rejects_a_persistence_parameter(self):
+        """The parameter was settable and never read; setting it must now fail loudly."""
+        with pytest.raises(TypeError):
+            LinearImpact(coefficient=0.1, permanent_fraction=0.8)  # type: ignore[call-arg]

From d3861d4b0dfffb5a7258e5780bc326f35d39c034 Mon Sep 17 00:00:00 2001
From: Stefan Jansen <stefan@applied-ai.com>
Date: Tue, 8 Sep 2026 16:09:45 -0400
Subject: [PATCH 2/7] Regenerate the v0.1 surface snapshot for the removed
 field

The reviewed public-surface snapshot pins every exported signature, so removing
LinearImpact.permanent_fraction moves one line in it.
---
 tests/compatibility/snapshots/v0.1.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/compatibility/snapshots/v0.1.json b/tests/compatibility/snapshots/v0.1.json
index 229746ce..94f01c4f 100644
--- a/tests/compatibility/snapshots/v0.1.json
+++ b/tests/compatibility/snapshots/v0.1.json
@@ -2696,7 +2696,7 @@
       },
       "module": "ml4t.backtest.execution.impact",
       "qualname": "LinearImpact",
-      "signature": "(coefficient: float = 0.1, permanent_fraction: float = 0.5) -> None"
+      "signature": "(coefficient: float = 0.1) -> None"
     },
     "ml4t.backtest.execution:NoImpact": {
       "kind": "class",

From eaf5f66da0bac3c7366314b1259e15a95a83ad4a Mon Sep 17 00:00:00 2001
From: Stefan Jansen <stefan@applied-ai.com>
Date: Tue, 8 Sep 2026 16:49:51 -0400
Subject: [PATCH 3/7] Rebuild the real-strategy evidence against this branch's
 engine

The retained evidence pins `engine: _tree_digest(SOURCE_DIR)`, a SHA over every
tracked .py under src/ml4t/backtest, so removing an unread dataclass field marks it
stale even though nothing it measures can move.

Rebuilt from the retained 2026-09-03 comparison outputs and the frozen input bundles.
17 of 17 required pairs pass and every record is byte-identical to the shipped file:
the whole diff is four lines - `generated_at`, the ML4T commit, the engine digest,
and the interpreter that built the report. An inert change producing an identical
record set is the evidence that it is inert.

The corpus builder was deliberately not run. `real_strategy_corpus.py` re-selects the
workloads from the current registries rather than re-measuring the retained ones, so
running it would silently rescope the audit. It also cannot run today:
us_equities_panel has 31 training runs, 166 prediction sets and zero backtest rows,
and selection is by validation backtest Sharpe, so it has no rank-1 candidate.
---
 validation/REAL_STRATEGY_RESULTS.json | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/validation/REAL_STRATEGY_RESULTS.json b/validation/REAL_STRATEGY_RESULTS.json
index 3574b617..5e461c30 100644
--- a/validation/REAL_STRATEGY_RESULTS.json
+++ b/validation/REAL_STRATEGY_RESULTS.json
@@ -20,7 +20,7 @@
       "us_equities_panel": "session date"
     }
   },
-  "generated_at": "2026-09-03T08:49:07.253544+00:00",
+  "generated_at": "2026-09-08T20:39:08.460346+00:00",
   "provenance": {
     "adapters": {
       "backtrader": "a456cc3bf9ed6708d239f21ed2c2495a3c19a9a8306634fee24619c2c0484459",
@@ -131,12 +131,12 @@
     },
     "machine": "x86_64",
     "ml4t": {
-      "commit": "7034236519cc0a99df6ef34a21d07ec2a81fc87c",
+      "commit": "85c0291447214730bd3328b14b73eb2f64cf9f2c",
       "dirty": false,
-      "engine_source_sha256": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58"
+      "engine_source_sha256": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836"
     },
     "platform": "Linux-6.8.0-138-generic-x86_64-with-glibc2.39",
-    "python": "3.12.11"
+    "python": "3.14.3"
   },
   "records": [
     {

From ec6f9d72dccd7d765084cc77a933dbc0485cf51b Mon Sep 17 00:00:00 2001
From: Stefan Jansen <stefan@applied-ai.com>
Date: Tue, 8 Sep 2026 16:58:14 -0400
Subject: [PATCH 4/7] Re-run the synthetic correctness matrix against this
 branch's engine

66 required pairs pass and 2 stay unsupported, exactly as before, across the four
scenario-matrix frameworks in environments built inside this worktree so the matrix
exercises this branch's engine and not the sibling checkout's - verified by importing
ml4t.backtest in each of the four and checking the path.

Ignoring the fields a re-run necessarily moves (timings, digests, runtime identity),
every record is equal to the accepted evidence and `release_gate_passed` stays true.
---
 validation/CORRECTNESS_RESULTS.json | 402 ++++++++++++++--------------
 1 file changed, 201 insertions(+), 201 deletions(-)

diff --git a/validation/CORRECTNESS_RESULTS.json b/validation/CORRECTNESS_RESULTS.json
index 9296dabe..dabd11bc 100644
--- a/validation/CORRECTNESS_RESULTS.json
+++ b/validation/CORRECTNESS_RESULTS.json
@@ -63,7 +63,7 @@
       "version": "3.1.1"
     }
   },
-  "generated_at": "2026-09-03T07:11:20.595248+00:00",
+  "generated_at": "2026-09-08T20:57:27.549815+00:00",
   "input_policy": {
     "float_quantum": "0.00000001",
     "rounding": "ROUND_HALF_EVEN",
@@ -704,7 +704,7 @@
         "scenario_id": "01"
       },
       "detail": null,
-      "duration_seconds": 2.427215111005353,
+      "duration_seconds": 6.153010525973514,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -1361,7 +1361,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
+          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "4d2cacb23e64dacdbb2eda228691671d3a5d327dbbdd899de310551b244bae54",
@@ -1374,7 +1374,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
+          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
           "dirty": false
         },
         "python": {
@@ -2034,7 +2034,7 @@
         "scenario_id": "02"
       },
       "detail": null,
-      "duration_seconds": 2.3356575799989514,
+      "duration_seconds": 5.373651853995398,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -2691,7 +2691,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
+          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "8901a4a7a750c26fb43ceb5d89b4c6ccf9279503da8659b8398cce30d90c8d9c",
@@ -2704,7 +2704,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
+          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
           "dirty": false
         },
         "python": {
@@ -2861,7 +2861,7 @@
         "scenario_id": "03"
       },
       "detail": null,
-      "duration_seconds": 2.3383967260015197,
+      "duration_seconds": 5.566838720114902,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -3009,7 +3009,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
+          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "3b0720d05e9e5869aa6f01432d17918e2db8c0d80810310e6c39c8bf82d1aee0",
@@ -3022,7 +3022,7 @@
         },
         "input_digest": "02743db0d03a02ec414e579929e5e4729b5f99124ab2a41bb234adf93626f62a",
         "ml4t": {
-          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
+          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
           "dirty": false
         },
         "python": {
@@ -3179,7 +3179,7 @@
         "scenario_id": "04"
       },
       "detail": null,
-      "duration_seconds": 2.342229423986282,
+      "duration_seconds": 4.9017198799410835,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -3327,7 +3327,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
+          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "3b0720d05e9e5869aa6f01432d17918e2db8c0d80810310e6c39c8bf82d1aee0",
@@ -3340,7 +3340,7 @@
         },
         "input_digest": "c013a16b8902a5599e53c27dae34849fcf327b4e89ec69d00c951903578f948a",
         "ml4t": {
-          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
+          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
           "dirty": false
         },
         "python": {
@@ -4010,7 +4010,7 @@
         "scenario_id": "05"
       },
       "detail": null,
-      "duration_seconds": 2.32796579200658,
+      "duration_seconds": 5.017531638033688,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -4671,7 +4671,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
+          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "4d2cacb23e64dacdbb2eda228691671d3a5d327dbbdd899de310551b244bae54",
@@ -4684,7 +4684,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
+          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
           "dirty": false
         },
         "python": {
@@ -5354,7 +5354,7 @@
         "scenario_id": "06"
       },
       "detail": null,
-      "duration_seconds": 2.326144679012941,
+      "duration_seconds": 5.335330817033537,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -6015,7 +6015,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
+          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "4d2cacb23e64dacdbb2eda228691671d3a5d327dbbdd899de310551b244bae54",
@@ -6028,7 +6028,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
+          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
           "dirty": false
         },
         "python": {
@@ -6688,7 +6688,7 @@
         "scenario_id": "07"
       },
       "detail": null,
-      "duration_seconds": 2.3995378440013155,
+      "duration_seconds": 5.360161849996075,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -7345,7 +7345,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
+          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "4d2cacb23e64dacdbb2eda228691671d3a5d327dbbdd899de310551b244bae54",
@@ -7358,7 +7358,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
+          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
           "dirty": false
         },
         "python": {
@@ -8018,7 +8018,7 @@
         "scenario_id": "08"
       },
       "detail": null,
-      "duration_seconds": 2.3293550700182095,
+      "duration_seconds": 5.2678728309692815,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -8675,7 +8675,7 @@
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@@ -254939,7 +254939,7 @@
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@@ -255247,7 +255247,7 @@
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@@ -255783,7 +255783,7 @@
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@@ -275471,7 +275471,7 @@
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@@ -294784,7 +294784,7 @@
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From d88c85c88843f783d8b874fc4f3d824f375506b3 Mon Sep 17 00:00:00 2001
From: Stefan Jansen <stefan@applied-ai.com>
Date: Wed, 9 Sep 2026 07:35:17 -0400
Subject: [PATCH 5/7] Re-measure the real-strategy timing evidence for this
 branch's engine

The retained evidence pins the engine tree digest, so removing an unread field
marks the timing file stale along with the rest. This re-measures all 17 pairs
and regenerates the published tables from the result.

The measurement is honest but less precise than the file it replaces, and the
reason is the machine rather than the engine. The shipped evidence holds every
one of its 34 timed engines under an 8.3% relative 95% CI; this run has five
above 8%, worst 54%. Two earlier runs were rejected outright at 76% and 54%
worst-case. All three were started on a quiet box and overrun: during this one
the load median was 5.0, p90 12.2, with a peak of 108.5.

One row is not merely wider. fx_pairs/lean reports an ML4T median of 0.485s
(0.423-0.684) where the shipped file says 0.153s (0.152-0.157), moving that
published ratio from 5.952x to 1.493x. Nine of its ten samples sit between
0.36 and 0.91 while the same engine measured 0.160s under the vectorbt_pro
profile in the same run, and a run on the previous dependency set measured it
at ~0.15s. It is contention, not a change in the engine, and it should be
re-measured on an idle machine before anyone quotes that number.

Removing the field changed no engine output. Every retained ML4T parquet was
re-derived and checked against the shipped evidence: of 51 hashes across the
17 pairs, 26 are identical and the other 25 reproduce the shipped value byte
for byte when the same frame is written by polars 1.36.1, the version this
repository pinned before 0.1.6. Zero unexplained. The hashes moved because
0.1.6 bumped polars to 1.44.1, which encodes string- and null-heavy frames
differently; equity.parquet on the smaller workloads encodes identically under
both, which is why only some rows moved.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WfX5GbAHc5EVwdUnRSoUPT
---
 README.md                                 |   38 +-
 docs/index.md                             |   38 +-
 docs/user-guide/profiles.md               |   38 +-
 validation/METHODOLOGY.md                 |   38 +-
 validation/README.md                      |   38 +-
 validation/REAL_STRATEGY_PERFORMANCE.json | 1126 ++++++++++-----------
 6 files changed, 658 insertions(+), 658 deletions(-)

diff --git a/README.md b/README.md
index 6fbed871..765ea1a6 100644
--- a/README.md
+++ b/README.md
@@ -336,25 +336,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th
 
 | Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
 |---|---|---:|---:|---:|
-| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.287 (0.286-0.289) | 0.415 (0.414-0.419) | 0.692x |
-| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.171 (0.171-0.174) | 0.415 (0.413-0.417) | 0.412x |
-| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.355 (9.303-9.424) | 0.430 (0.426-0.432) | 21.762x |
-| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.872 (3.867-3.910) | 0.632 (0.628-0.638) | 6.127x |
-| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.564 (2.487-2.599) | 0.750 (0.749-0.757) | 3.418x |
-| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.286 (0.284-0.289) | 0.432 (0.429-0.435) | 0.663x |
-| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.495 (2.488-2.526) | 0.429 (0.427-0.433) | 5.812x |
-| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.801 (2.709-2.891) | 0.657 (0.655-0.661) | 4.266x |
-| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.283 (0.281-0.286) | 0.146 (0.145-0.147) | 1.939x |
-| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.139 (0.138-0.141) | 0.146 (0.145-0.147) | 0.952x |
-| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.425 (0.420-0.432) | 0.146 (0.145-0.147) | 2.920x |
-| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.913 (0.877-0.953) | 0.153 (0.152-0.157) | 5.952x |
-| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.795 (0.787-0.838) | 20.555 (20.486-20.600) | 0.039x |
-| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 16.995 (16.960-17.024) | 20.495 (20.453-20.534) | 0.829x |
-| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 495.553 (494.227-510.273) | 21.470 (21.386-21.543) | 23.082x |
-| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 107.609 (106.834-109.150) | 22.132 (22.082-22.234) | 4.862x |
-| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 47.683 (47.333-48.015) | 26.368 (26.222-26.473) | 1.808x |
-
-Measured 2026-09-03 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
+| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.296 (0.290-0.303) | 0.425 (0.423-0.427) | 0.696x |
+| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.179 (0.178-0.184) | 0.423 (0.421-0.426) | 0.423x |
+| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.466 (9.403-9.652) | 0.436 (0.434-0.442) | 21.694x |
+| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.929 (3.901-3.979) | 0.623 (0.618-0.631) | 6.302x |
+| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.124 (2.038-2.145) | 0.722 (0.717-0.731) | 2.942x |
+| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.298 (0.295-0.302) | 0.399 (0.397-0.408) | 0.748x |
+| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.531 (2.504-2.568) | 0.400 (0.398-0.403) | 6.322x |
+| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.843 (2.725-2.951) | 0.580 (0.574-0.585) | 4.905x |
+| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.292 (0.291-0.298) | 0.147 (0.145-0.149) | 1.993x |
+| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.145 (0.143-0.149) | 0.146 (0.146-0.147) | 0.992x |
+| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.429 (0.427-0.432) | 0.147 (0.145-0.148) | 2.926x |
+| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.724 (0.702-0.749) | 0.485 (0.423-0.684) | 1.493x |
+| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.914 (0.868-1.048) | 15.705 (15.512-16.282) | 0.058x |
+| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 19.663 (18.596-26.957) | 18.061 (17.251-19.345) | 1.089x |
+| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 549.128 (528.973-570.002) | 16.157 (16.053-16.278) | 33.987x |
+| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.222 (109.392-111.608) | 16.521 (16.470-16.906) | 6.671x |
+| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 45.957 (43.851-53.819) | 24.948 (23.623-25.419) | 1.842x |
+
+Measured 2026-09-09 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
 
 ### Synthetic diagnostic scenarios
 
diff --git a/docs/index.md b/docs/index.md
index 17cd3441..409d7ac6 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -136,25 +136,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th
 
 | Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
 |---|---|---:|---:|---:|
-| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.287 (0.286-0.289) | 0.415 (0.414-0.419) | 0.692x |
-| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.171 (0.171-0.174) | 0.415 (0.413-0.417) | 0.412x |
-| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.355 (9.303-9.424) | 0.430 (0.426-0.432) | 21.762x |
-| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.872 (3.867-3.910) | 0.632 (0.628-0.638) | 6.127x |
-| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.564 (2.487-2.599) | 0.750 (0.749-0.757) | 3.418x |
-| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.286 (0.284-0.289) | 0.432 (0.429-0.435) | 0.663x |
-| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.495 (2.488-2.526) | 0.429 (0.427-0.433) | 5.812x |
-| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.801 (2.709-2.891) | 0.657 (0.655-0.661) | 4.266x |
-| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.283 (0.281-0.286) | 0.146 (0.145-0.147) | 1.939x |
-| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.139 (0.138-0.141) | 0.146 (0.145-0.147) | 0.952x |
-| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.425 (0.420-0.432) | 0.146 (0.145-0.147) | 2.920x |
-| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.913 (0.877-0.953) | 0.153 (0.152-0.157) | 5.952x |
-| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.795 (0.787-0.838) | 20.555 (20.486-20.600) | 0.039x |
-| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 16.995 (16.960-17.024) | 20.495 (20.453-20.534) | 0.829x |
-| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 495.553 (494.227-510.273) | 21.470 (21.386-21.543) | 23.082x |
-| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 107.609 (106.834-109.150) | 22.132 (22.082-22.234) | 4.862x |
-| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 47.683 (47.333-48.015) | 26.368 (26.222-26.473) | 1.808x |
-
-Measured 2026-09-03 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
+| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.296 (0.290-0.303) | 0.425 (0.423-0.427) | 0.696x |
+| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.179 (0.178-0.184) | 0.423 (0.421-0.426) | 0.423x |
+| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.466 (9.403-9.652) | 0.436 (0.434-0.442) | 21.694x |
+| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.929 (3.901-3.979) | 0.623 (0.618-0.631) | 6.302x |
+| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.124 (2.038-2.145) | 0.722 (0.717-0.731) | 2.942x |
+| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.298 (0.295-0.302) | 0.399 (0.397-0.408) | 0.748x |
+| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.531 (2.504-2.568) | 0.400 (0.398-0.403) | 6.322x |
+| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.843 (2.725-2.951) | 0.580 (0.574-0.585) | 4.905x |
+| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.292 (0.291-0.298) | 0.147 (0.145-0.149) | 1.993x |
+| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.145 (0.143-0.149) | 0.146 (0.146-0.147) | 0.992x |
+| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.429 (0.427-0.432) | 0.147 (0.145-0.148) | 2.926x |
+| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.724 (0.702-0.749) | 0.485 (0.423-0.684) | 1.493x |
+| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.914 (0.868-1.048) | 15.705 (15.512-16.282) | 0.058x |
+| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 19.663 (18.596-26.957) | 18.061 (17.251-19.345) | 1.089x |
+| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 549.128 (528.973-570.002) | 16.157 (16.053-16.278) | 33.987x |
+| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.222 (109.392-111.608) | 16.521 (16.470-16.906) | 6.671x |
+| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 45.957 (43.851-53.819) | 24.948 (23.623-25.419) | 1.842x |
+
+Measured 2026-09-09 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
 
 ### Synthetic diagnostic scenarios
 
diff --git a/docs/user-guide/profiles.md b/docs/user-guide/profiles.md
index cea5a254..3dc432fa 100644
--- a/docs/user-guide/profiles.md
+++ b/docs/user-guide/profiles.md
@@ -194,25 +194,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th
 
 | Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
 |---|---|---:|---:|---:|
-| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.287 (0.286-0.289) | 0.415 (0.414-0.419) | 0.692x |
-| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.171 (0.171-0.174) | 0.415 (0.413-0.417) | 0.412x |
-| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.355 (9.303-9.424) | 0.430 (0.426-0.432) | 21.762x |
-| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.872 (3.867-3.910) | 0.632 (0.628-0.638) | 6.127x |
-| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.564 (2.487-2.599) | 0.750 (0.749-0.757) | 3.418x |
-| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.286 (0.284-0.289) | 0.432 (0.429-0.435) | 0.663x |
-| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.495 (2.488-2.526) | 0.429 (0.427-0.433) | 5.812x |
-| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.801 (2.709-2.891) | 0.657 (0.655-0.661) | 4.266x |
-| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.283 (0.281-0.286) | 0.146 (0.145-0.147) | 1.939x |
-| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.139 (0.138-0.141) | 0.146 (0.145-0.147) | 0.952x |
-| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.425 (0.420-0.432) | 0.146 (0.145-0.147) | 2.920x |
-| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.913 (0.877-0.953) | 0.153 (0.152-0.157) | 5.952x |
-| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.795 (0.787-0.838) | 20.555 (20.486-20.600) | 0.039x |
-| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 16.995 (16.960-17.024) | 20.495 (20.453-20.534) | 0.829x |
-| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 495.553 (494.227-510.273) | 21.470 (21.386-21.543) | 23.082x |
-| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 107.609 (106.834-109.150) | 22.132 (22.082-22.234) | 4.862x |
-| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 47.683 (47.333-48.015) | 26.368 (26.222-26.473) | 1.808x |
-
-Measured 2026-09-03 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
+| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.296 (0.290-0.303) | 0.425 (0.423-0.427) | 0.696x |
+| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.179 (0.178-0.184) | 0.423 (0.421-0.426) | 0.423x |
+| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.466 (9.403-9.652) | 0.436 (0.434-0.442) | 21.694x |
+| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.929 (3.901-3.979) | 0.623 (0.618-0.631) | 6.302x |
+| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.124 (2.038-2.145) | 0.722 (0.717-0.731) | 2.942x |
+| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.298 (0.295-0.302) | 0.399 (0.397-0.408) | 0.748x |
+| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.531 (2.504-2.568) | 0.400 (0.398-0.403) | 6.322x |
+| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.843 (2.725-2.951) | 0.580 (0.574-0.585) | 4.905x |
+| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.292 (0.291-0.298) | 0.147 (0.145-0.149) | 1.993x |
+| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.145 (0.143-0.149) | 0.146 (0.146-0.147) | 0.992x |
+| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.429 (0.427-0.432) | 0.147 (0.145-0.148) | 2.926x |
+| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.724 (0.702-0.749) | 0.485 (0.423-0.684) | 1.493x |
+| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.914 (0.868-1.048) | 15.705 (15.512-16.282) | 0.058x |
+| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 19.663 (18.596-26.957) | 18.061 (17.251-19.345) | 1.089x |
+| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 549.128 (528.973-570.002) | 16.157 (16.053-16.278) | 33.987x |
+| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.222 (109.392-111.608) | 16.521 (16.470-16.906) | 6.671x |
+| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 45.957 (43.851-53.819) | 24.948 (23.623-25.419) | 1.842x |
+
+Measured 2026-09-09 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
 
 ### Synthetic diagnostic scenarios
 
diff --git a/validation/METHODOLOGY.md b/validation/METHODOLOGY.md
index ec928c5c..83e08e12 100644
--- a/validation/METHODOLOGY.md
+++ b/validation/METHODOLOGY.md
@@ -380,25 +380,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th
 
 | Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
 |---|---|---:|---:|---:|
-| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.287 (0.286-0.289) | 0.415 (0.414-0.419) | 0.692x |
-| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.171 (0.171-0.174) | 0.415 (0.413-0.417) | 0.412x |
-| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.355 (9.303-9.424) | 0.430 (0.426-0.432) | 21.762x |
-| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.872 (3.867-3.910) | 0.632 (0.628-0.638) | 6.127x |
-| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.564 (2.487-2.599) | 0.750 (0.749-0.757) | 3.418x |
-| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.286 (0.284-0.289) | 0.432 (0.429-0.435) | 0.663x |
-| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.495 (2.488-2.526) | 0.429 (0.427-0.433) | 5.812x |
-| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.801 (2.709-2.891) | 0.657 (0.655-0.661) | 4.266x |
-| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.283 (0.281-0.286) | 0.146 (0.145-0.147) | 1.939x |
-| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.139 (0.138-0.141) | 0.146 (0.145-0.147) | 0.952x |
-| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.425 (0.420-0.432) | 0.146 (0.145-0.147) | 2.920x |
-| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.913 (0.877-0.953) | 0.153 (0.152-0.157) | 5.952x |
-| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.795 (0.787-0.838) | 20.555 (20.486-20.600) | 0.039x |
-| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 16.995 (16.960-17.024) | 20.495 (20.453-20.534) | 0.829x |
-| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 495.553 (494.227-510.273) | 21.470 (21.386-21.543) | 23.082x |
-| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 107.609 (106.834-109.150) | 22.132 (22.082-22.234) | 4.862x |
-| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 47.683 (47.333-48.015) | 26.368 (26.222-26.473) | 1.808x |
-
-Measured 2026-09-03 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
+| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.296 (0.290-0.303) | 0.425 (0.423-0.427) | 0.696x |
+| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.179 (0.178-0.184) | 0.423 (0.421-0.426) | 0.423x |
+| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.466 (9.403-9.652) | 0.436 (0.434-0.442) | 21.694x |
+| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.929 (3.901-3.979) | 0.623 (0.618-0.631) | 6.302x |
+| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.124 (2.038-2.145) | 0.722 (0.717-0.731) | 2.942x |
+| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.298 (0.295-0.302) | 0.399 (0.397-0.408) | 0.748x |
+| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.531 (2.504-2.568) | 0.400 (0.398-0.403) | 6.322x |
+| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.843 (2.725-2.951) | 0.580 (0.574-0.585) | 4.905x |
+| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.292 (0.291-0.298) | 0.147 (0.145-0.149) | 1.993x |
+| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.145 (0.143-0.149) | 0.146 (0.146-0.147) | 0.992x |
+| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.429 (0.427-0.432) | 0.147 (0.145-0.148) | 2.926x |
+| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.724 (0.702-0.749) | 0.485 (0.423-0.684) | 1.493x |
+| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.914 (0.868-1.048) | 15.705 (15.512-16.282) | 0.058x |
+| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 19.663 (18.596-26.957) | 18.061 (17.251-19.345) | 1.089x |
+| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 549.128 (528.973-570.002) | 16.157 (16.053-16.278) | 33.987x |
+| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.222 (109.392-111.608) | 16.521 (16.470-16.906) | 6.671x |
+| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 45.957 (43.851-53.819) | 24.948 (23.623-25.419) | 1.842x |
+
+Measured 2026-09-09 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
 
 ### Synthetic diagnostic scenarios
 
diff --git a/validation/README.md b/validation/README.md
index 6eac8d82..808cc6d3 100644
--- a/validation/README.md
+++ b/validation/README.md
@@ -56,25 +56,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th
 
 | Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
 |---|---|---:|---:|---:|
-| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.287 (0.286-0.289) | 0.415 (0.414-0.419) | 0.692x |
-| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.171 (0.171-0.174) | 0.415 (0.413-0.417) | 0.412x |
-| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.355 (9.303-9.424) | 0.430 (0.426-0.432) | 21.762x |
-| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.872 (3.867-3.910) | 0.632 (0.628-0.638) | 6.127x |
-| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.564 (2.487-2.599) | 0.750 (0.749-0.757) | 3.418x |
-| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.286 (0.284-0.289) | 0.432 (0.429-0.435) | 0.663x |
-| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.495 (2.488-2.526) | 0.429 (0.427-0.433) | 5.812x |
-| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.801 (2.709-2.891) | 0.657 (0.655-0.661) | 4.266x |
-| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.283 (0.281-0.286) | 0.146 (0.145-0.147) | 1.939x |
-| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.139 (0.138-0.141) | 0.146 (0.145-0.147) | 0.952x |
-| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.425 (0.420-0.432) | 0.146 (0.145-0.147) | 2.920x |
-| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.913 (0.877-0.953) | 0.153 (0.152-0.157) | 5.952x |
-| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.795 (0.787-0.838) | 20.555 (20.486-20.600) | 0.039x |
-| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 16.995 (16.960-17.024) | 20.495 (20.453-20.534) | 0.829x |
-| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 495.553 (494.227-510.273) | 21.470 (21.386-21.543) | 23.082x |
-| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 107.609 (106.834-109.150) | 22.132 (22.082-22.234) | 4.862x |
-| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 47.683 (47.333-48.015) | 26.368 (26.222-26.473) | 1.808x |
-
-Measured 2026-09-03 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
+| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.296 (0.290-0.303) | 0.425 (0.423-0.427) | 0.696x |
+| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.179 (0.178-0.184) | 0.423 (0.421-0.426) | 0.423x |
+| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.466 (9.403-9.652) | 0.436 (0.434-0.442) | 21.694x |
+| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.929 (3.901-3.979) | 0.623 (0.618-0.631) | 6.302x |
+| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.124 (2.038-2.145) | 0.722 (0.717-0.731) | 2.942x |
+| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.298 (0.295-0.302) | 0.399 (0.397-0.408) | 0.748x |
+| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.531 (2.504-2.568) | 0.400 (0.398-0.403) | 6.322x |
+| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.843 (2.725-2.951) | 0.580 (0.574-0.585) | 4.905x |
+| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.292 (0.291-0.298) | 0.147 (0.145-0.149) | 1.993x |
+| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.145 (0.143-0.149) | 0.146 (0.146-0.147) | 0.992x |
+| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.429 (0.427-0.432) | 0.147 (0.145-0.148) | 2.926x |
+| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.724 (0.702-0.749) | 0.485 (0.423-0.684) | 1.493x |
+| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.914 (0.868-1.048) | 15.705 (15.512-16.282) | 0.058x |
+| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 19.663 (18.596-26.957) | 18.061 (17.251-19.345) | 1.089x |
+| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 549.128 (528.973-570.002) | 16.157 (16.053-16.278) | 33.987x |
+| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.222 (109.392-111.608) | 16.521 (16.470-16.906) | 6.671x |
+| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 45.957 (43.851-53.819) | 24.948 (23.623-25.419) | 1.842x |
+
+Measured 2026-09-09 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
 
 ### Synthetic diagnostic scenarios
 
diff --git a/validation/REAL_STRATEGY_PERFORMANCE.json b/validation/REAL_STRATEGY_PERFORMANCE.json
index 751238b6..dcc9df58 100644
--- a/validation/REAL_STRATEGY_PERFORMANCE.json
+++ b/validation/REAL_STRATEGY_PERFORMANCE.json
@@ -1,5 +1,5 @@
 {
-  "correctness_evidence_generated_at": "2026-09-03T08:49:07.253544+00:00",
+  "correctness_evidence_generated_at": "2026-09-08T20:39:08.460346+00:00",
   "environment": {
     "cpu_count": 24,
     "frameworks": {
@@ -99,9 +99,9 @@
     "platform": "Linux-6.8.0-138-generic-x86_64-with-glibc2.39",
     "processor": "x86_64"
   },
-  "generated_at": "2026-09-03T11:44:14.344493+00:00",
+  "generated_at": "2026-09-09T07:52:45.726726+00:00",
   "provenance": {
-    "ml4t_engine_source_sha256": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
+    "ml4t_engine_source_sha256": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
     "sources": {
       "backtrader": "a456cc3bf9ed6708d239f21ed2c2495a3c19a9a8306634fee24619c2c0484459",
       "benchmark": "99164392057a3ed3810c4dbe23a9c6084eb30f1553c92eb3a54f5d61e8d9b344",
@@ -122,55 +122,55 @@
       "framework": "vectorbt_pro",
       "framework_engine": {
         "ci_95_seconds": [
-          0.28554552848800085,
-          0.2885417409997899
+          0.2898634900338948,
+          0.3031502824742347
         ],
-        "maximum_seconds": 0.29064874499454163,
-        "median_seconds": 0.2872375610168092,
-        "minimum_seconds": 0.2836474619980436,
+        "maximum_seconds": 0.30958262097556144,
+        "median_seconds": 0.29565684648696333,
+        "minimum_seconds": 0.2839555769460276,
         "output_identity": {
           "equity.parquet": "9bef5099c36b4a61b32670d19340ebb9f4adba07c63359a5c31a345ad51d1cd6",
           "fills.parquet": "698baf228da467fcce287f6f7da4903b3421d2649b181e3905a3681905251ffd"
         },
         "samples_seconds": [
-          0.2878881780197844,
-          0.2846226939873304,
-          0.2836474619980436,
-          0.2882649439852685,
-          0.286586944013834,
-          0.2885417409997899,
-          0.29064874499454163,
-          0.28637371701188385,
-          0.28858046399545856,
-          0.2864683629886713
+          0.2839555769460276,
+          0.29754665098153055,
+          0.30875391396693885,
+          0.2898634900338948,
+          0.29427445004694164,
+          0.30958262097556144,
+          0.28754499496426433,
+          0.297039242926985,
+          0.29956078401301056,
+          0.29280188400298357
         ]
       },
-      "framework_to_ml4t_median_ratio": 0.6924827859284183,
+      "framework_to_ml4t_median_ratio": 0.6964254353452846,
       "input_bundle_sha256": "01f38079ce47821a5379d3769e86f4a2170b88033108153bfc0f928698e946db",
       "ml4t_engine": {
         "ci_95_seconds": [
-          0.4141692244884325,
-          0.41945260000647977
+          0.4228568209800869,
+          0.4267608530353755
         ],
-        "maximum_seconds": 0.4208305799984373,
-        "median_seconds": 0.4147937925008591,
-        "minimum_seconds": 0.41231122802128084,
+        "maximum_seconds": 0.4280163530493155,
+        "median_seconds": 0.4245348194963299,
+        "minimum_seconds": 0.4179053269326687,
         "output_identity": {
           "equity.parquet": "30c2d39d1165342a599754cfd6ae76019d212f753ab49c837895c9f6ce942339",
-          "fills.parquet": "2219d69d7d36fc654a61cf3044e7db0dc641bfa4923a847b39afdc41671171f4",
+          "fills.parquet": "904a72fd66278fbb84c164e799c8dfc3d826bb1efdb0c3a4132f72203039368f",
           "rejected_orders.parquet": "043204927e46137ca25cc0fa23883f4868af2b57f48c50d034fd56e113da12e8"
         },
         "samples_seconds": [
-          0.42046853801002726,
-          0.4144462959957309,
-          0.41231122802128084,
-          0.41445882100379094,
-          0.41389215298113413,
-          0.41595029999734834,
-          0.41429151402553543,
-          0.41945260000647977,
-          0.4208305799984373,
-          0.4151287639979273
+          0.42326817102730274,
+          0.4280163530493155,
+          0.4238164779962972,
+          0.4179053269326687,
+          0.42702835402451456,
+          0.4267608530353755,
+          0.42525316099636257,
+          0.42380960297305137,
+          0.4219040389871225,
+          0.4260525139980018
         ]
       }
     },
@@ -180,55 +180,55 @@
       "framework": "vectorbt_oss",
       "framework_engine": {
         "ci_95_seconds": [
-          0.17068579950137064,
-          0.17357376401196234
+          0.1775521255331114,
+          0.1837882164400071
         ],
-        "maximum_seconds": 0.18489615400903858,
-        "median_seconds": 0.17132284450053703,
-        "minimum_seconds": 0.16843955099466257,
+        "maximum_seconds": 0.19256116298492998,
+        "median_seconds": 0.17892681644298136,
+        "minimum_seconds": 0.17572793900035322,
         "output_identity": {
           "equity.parquet": "9bef5099c36b4a61b32670d19340ebb9f4adba07c63359a5c31a345ad51d1cd6",
           "fills.parquet": "a563dc47e59cc8d1ecf49a490f1e83842e5c4ca67a6a56adb411a58eb0842646"
         },
         "samples_seconds": [
-          0.18489615400903858,
-          0.17261791799683124,
-          0.17124448300455697,
-          0.17079768699477427,
-          0.17080065800109878,
-          0.16843955099466257,
-          0.1705709410016425,
-          0.1714012059965171,
-          0.1738385669887066,
-          0.17357376401196234
+          0.17865553696174175,
+          0.1823599310591817,
+          0.19256116298492998,
+          0.18627417890820652,
+          0.17919809592422098,
+          0.17660772404633462,
+          0.17849652701988816,
+          0.18130225397180766,
+          0.1775777789298445,
+          0.17572793900035322
         ]
       },
-      "framework_to_ml4t_median_ratio": 0.4124410450618539,
+      "framework_to_ml4t_median_ratio": 0.4226539101217617,
       "input_bundle_sha256": "01f38079ce47821a5379d3769e86f4a2170b88033108153bfc0f928698e946db",
       "ml4t_engine": {
         "ci_95_seconds": [
-          0.41297478799242526,
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@@ -882,55 +882,55 @@
       "framework": "vectorbt_oss",
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         "ci_95_seconds": [
-          16.959977449994767,
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@@ -940,56 +940,56 @@
       "framework": "backtrader",
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+          16.25268028001301,
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+          16.305805339012295,
+          16.250169981038198,
+          16.081822153995745,
+          15.759885281091556
         ]
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     },
@@ -999,55 +999,55 @@
       "framework": "zipline",
       "framework_engine": {
         "ci_95_seconds": [
-          106.83352753649524,
-          109.14979357349512
+          109.39243838749826,
+          111.60780068405438
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-        "median_seconds": 107.60890489349549,
-        "minimum_seconds": 106.01340769199305,
+        "maximum_seconds": 121.45296368305571,
+        "median_seconds": 110.22181360953255,
+        "minimum_seconds": 109.1861914889887,
         "output_identity": {
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           "fills.parquet": "916a68a6f44ea70b6905cca094028d16f8746e3f19c3029637ecb50c5462306a"
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-          117.95733895900776,
-          110.46573096700013,
-          107.83385617999011,
-          107.71652170599555,
-          106.84217402699869
+          111.60780068405438,
+          110.01300074101891,
+          110.81128632801119,
+          110.43062647804618,
+          112.05522017797921,
+          109.1861914889887,
+          109.29345865501091,
+          109.45064685109537,
+          109.49141811998561,
+          121.45296368305571
         ]
       },
-      "framework_to_ml4t_median_ratio": 4.862239476470224,
+      "framework_to_ml4t_median_ratio": 6.671435767111864,
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       "ml4t_engine": {
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-          22.23382121999748
+          16.470107797998935,
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-        "minimum_seconds": 22.03865885498817,
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         "output_identity": {
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-          "fills.parquet": "7af38401bed6a5453090bb124ca645df0f91cf417e0440b72e7693c7d22508a5",
+          "equity.parquet": "6f1c371e4fcce3c1c3829f71c398807c721e95fa7d03a5c4796119a398453a66",
+          "fills.parquet": "2db9ccefd91648644c1abefca68701deb4795a8e6da509935beaffa5793e8501",
           "rejected_orders.parquet": "043204927e46137ca25cc0fa23883f4868af2b57f48c50d034fd56e113da12e8"
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         "samples_seconds": [
-          22.262472025991883,
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+          16.385647121001966,
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       }
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@@ -1057,56 +1057,56 @@
       "framework": "lean",
       "framework_engine": {
         "ci_95_seconds": [
-          47.33300897400477,
-          48.01486862101592
+          43.8512298935093,
+          53.81890031695366
         ],
-        "maximum_seconds": 48.93350173300132,
-        "median_seconds": 47.68267791901599,
-        "minimum_seconds": 46.93845382897416,
+        "maximum_seconds": 71.90948282403406,
+        "median_seconds": 45.95678944251267,
+        "minimum_seconds": 42.33710101793986,
         "output_identity": {
-          "equity.parquet": "0b28b2c49304bf054f81d906ce0673298989a3f3ef044cec0cb2e3570fc3cd2f",
-          "fills.parquet": "d56df1f4cb022403ddc3165d064f93b57b8bb210712775a9af3bda1fbf9eb6a6",
+          "equity.parquet": "a943d6831889ddc227f4f12f103797c854ae6facecac3fbe399d3a84be2579e1",
+          "fills.parquet": "fd13eeca0e70144131f5a231a2a2e2ec8581437035f887c3af604c9ffec5cc75",
           "rejected_orders.parquet": "9e7aedba072cbcd23432976f03fb6edef656da961cd2e73dbd75188842b29c36"
         },
         "samples_seconds": [
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+          44.69809547706973,
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+          53.81890031695366,
+          46.28676813002676
         ]
       },
-      "framework_to_ml4t_median_ratio": 1.8083761067475683,
+      "framework_to_ml4t_median_ratio": 1.8421150516139404,
       "input_bundle_sha256": "02926d8a7f1c1f5b4855b2393d1763885288ef64950d5cf521ca96c61f454d88",
       "ml4t_engine": {
         "ci_95_seconds": [
-          26.22179457700986,
-          26.472655913996277
+          23.622859365015756,
+          25.41915735101793
         ],
-        "maximum_seconds": 26.64365133800311,
-        "median_seconds": 26.36767746548867,
-        "minimum_seconds": 26.150949718023185,
+        "maximum_seconds": 25.64795203099493,
+        "median_seconds": 24.94783884548815,
+        "minimum_seconds": 21.586407659109682,
         "output_identity": {
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-          "fills.parquet": "b42bfe84ffe2954289ba5138fed5bc4f487d356c7192f3f67521c6d6e50c5059",
+          "equity.parquet": "b5907100a1469d4c9ff63af5bbeb328cddf7d72e6d98db6d9d796488c95545f2",
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           "rejected_orders.parquet": "043204927e46137ca25cc0fa23883f4868af2b57f48c50d034fd56e113da12e8"
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         "samples_seconds": [
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+          24.886456022039056,
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+          21.586407659109682,
+          22.462323918007314,
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+          25.370491398032755
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       }
     }

From e747ad8d572099d78694b866494710e782f21a48 Mon Sep 17 00:00:00 2001
From: Stefan Jansen <stefan@applied-ai.com>
Date: Fri, 11 Sep 2026 15:43:18 -0400
Subject: [PATCH 6/7] Deprecate LinearImpact.permanent_fraction rather than
 removing it outright

The field is inert: `calculate()` has never read it, so `permanent_fraction=0.0` and
`permanent_fraction=1.0` charge the same price. It cannot be honoured as the signature
stands - `calculate` sees one order and holds no reference to the ones before it - so the
model is a single-order concession model and persistence belongs to the caller.

This branch removed the field. Removing it in a patch release raises `TypeError` on code
that constructs `LinearImpact(permanent_fraction=...)` today and gets nothing for it, so
the removal moves to 0.2.0 and this release warns instead. A reader who set the parameter
currently gets silence; a `DeprecationWarning` naming the removal is strictly better, and
it costs nobody a broken import.

The warning lives in `__setattr__` rather than `__post_init__` because the field is
settable after construction as well as through it, and a non-frozen dataclass routes
`__init__` through `__setattr__`, so one guard covers both paths and fires once per
assignment. The default is the one value that cannot warn: a dataclass cannot tell a
caller who passed 0.5 from one who passed nothing, and leaving it alone is the case that
loses nothing when the field goes.

Tests pin three things, and the third is what makes the first two mean something: setting
it warns and does not change the charge; assigning it after construction warns; and the
untouched default raises no warning at all. Without the negative case a warning on every
construction would pass. The retained `v0.1.json` compatibility snapshot goes back to
main's line, because the signature is unchanged.
---
 src/ml4t/backtest/execution/impact.py   | 35 ++++++++++++++++++++++
 tests/compatibility/snapshots/v0.1.json |  2 +-
 tests/execution/test_impact.py          | 40 +++++++++++++++++++++----
 3 files changed, 71 insertions(+), 6 deletions(-)

diff --git a/src/ml4t/backtest/execution/impact.py b/src/ml4t/backtest/execution/impact.py
index 46fad19f..42b500dc 100644
--- a/src/ml4t/backtest/execution/impact.py
+++ b/src/ml4t/backtest/execution/impact.py
@@ -1,8 +1,12 @@
 """Market impact models for realistic execution costs."""
 
 import math
+import warnings
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
+from typing import Any
+
+PERMANENT_FRACTION_DEFAULT = 0.5
 
 
 class MarketImpactModel(ABC):
@@ -74,6 +78,9 @@ class LinearImpact(MarketImpactModel):
     Args:
         coefficient: Impact scaling factor (default 0.1)
                     Higher values = more impact per unit participation
+        permanent_fraction: Deprecated and inert; scheduled for removal in 0.2.0.
+                    `calculate` has never read it, so every value charges the same
+                    price. Setting it to anything but its default warns.
 
     Example:
         model = LinearImpact(coefficient=0.1)
@@ -81,6 +88,34 @@ class LinearImpact(MarketImpactModel):
     """
 
     coefficient: float = 0.1
+    permanent_fraction: float = PERMANENT_FRACTION_DEFAULT
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        """Warn once per assignment that sets the inert persistence parameter.
+
+        The warning goes here rather than in `__post_init__` because the field is
+        settable after construction as well as through it, and a model assembled and
+        then adjusted is the case that most looks like it is configuring something.
+        A non-frozen dataclass routes `__init__` through `__setattr__` too, so one
+        guard covers both and fires once each time a value is actually set.
+
+        The default is the one value that does not warn, because a dataclass cannot
+        tell a caller who passed 0.5 from one who passed nothing. Leaving it alone is
+        also the case that loses nothing when the field goes: the model charges the
+        same price either way.
+        """
+        if name == "permanent_fraction" and value != PERMANENT_FRACTION_DEFAULT:
+            warnings.warn(
+                "LinearImpact.permanent_fraction is inert and will be removed in "
+                "ml4t-backtest 0.2.0. calculate() has never read it, so this model "
+                "charges the same impact at every value; the engine applies an impact "
+                "model to one order at a time and has no state in which a permanent "
+                "component could persist into later fills. Accumulate permanent impact "
+                "in the caller instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+        super().__setattr__(name, value)
 
     def calculate(
         self,
diff --git a/tests/compatibility/snapshots/v0.1.json b/tests/compatibility/snapshots/v0.1.json
index 94f01c4f..229746ce 100644
--- a/tests/compatibility/snapshots/v0.1.json
+++ b/tests/compatibility/snapshots/v0.1.json
@@ -2696,7 +2696,7 @@
       },
       "module": "ml4t.backtest.execution.impact",
       "qualname": "LinearImpact",
-      "signature": "(coefficient: float = 0.1) -> None"
+      "signature": "(coefficient: float = 0.1, permanent_fraction: float = 0.5) -> None"
     },
     "ml4t.backtest.execution:NoImpact": {
       "kind": "class",
diff --git a/tests/execution/test_impact.py b/tests/execution/test_impact.py
index 36071448..ff4e0c8c 100644
--- a/tests/execution/test_impact.py
+++ b/tests/execution/test_impact.py
@@ -1,6 +1,7 @@
 """Tests for market impact models."""
 
 import math
+import warnings
 
 import pytest
 
@@ -41,6 +42,7 @@ def test_default_values(self):
         """Test default configuration."""
         model = LinearImpact()
         assert model.coefficient == 0.1
+        assert model.permanent_fraction == 0.5
 
     def test_buy_positive_impact(self):
         """Test that buy orders have positive impact (price goes up)."""
@@ -262,7 +264,8 @@ def test_repeated_slices_are_charged_the_same_concession(self):
         """A parent order worked in ten equal slices pays the first slice's price ten times.
 
         This is the property that a permanent-impact parameter would have to break. It
-        holds for every model, which is why `LinearImpact` no longer advertises one.
+        holds for every model, which is why `LinearImpact.permanent_fraction` is
+        deprecated rather than implemented.
         """
         for model in self.MODELS:
             charges = [
@@ -271,7 +274,34 @@ def test_repeated_slices_are_charged_the_same_concession(self):
             ]
             assert len(set(charges)) == 1, f"{type(model).__name__} is not stateless"
 
-    def test_linear_impact_rejects_a_persistence_parameter(self):
-        """The parameter was settable and never read; setting it must now fail loudly."""
-        with pytest.raises(TypeError):
-            LinearImpact(coefficient=0.1, permanent_fraction=0.8)  # type: ignore[call-arg]
+    def test_setting_permanent_fraction_warns_and_changes_nothing(self):
+        """The parameter was settable, silent and never read; it must now say so.
+
+        Both halves are asserted because either alone would pass a broken
+        implementation: a warning that also changed the charge would be a behaviour
+        change nobody asked for, and an unchanged charge with no warning is the defect.
+        """
+        with pytest.warns(DeprecationWarning, match="removed in ml4t-backtest 0.2.0"):
+            loud = LinearImpact(coefficient=0.1, permanent_fraction=0.8)
+        quiet = LinearImpact(coefficient=0.1)
+        args = {"quantity": 100_000.0, "price": 100.0, "volume": 1_000_000.0, "is_buy": True}
+        assert loud.calculate(**args) == quiet.calculate(**args)
+
+    def test_assigning_permanent_fraction_after_construction_warns(self):
+        """A dataclass field is settable after `__init__`, and that path warned too."""
+        model = LinearImpact(coefficient=0.1)
+        with pytest.warns(DeprecationWarning, match="removed in ml4t-backtest 0.2.0"):
+            model.permanent_fraction = 0.8
+
+    def test_leaving_permanent_fraction_at_its_default_is_silent(self):
+        """A caller who never touches the field is not warned about it.
+
+        `LinearImpact()` and an explicit `permanent_fraction=0.5` are indistinguishable
+        to a dataclass, so the default is the one value that cannot warn. Pinned as the
+        negative case: without it the warning could fire on every construction and the
+        two tests above would still pass.
+        """
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", DeprecationWarning)
+            LinearImpact()
+            LinearImpact(coefficient=0.2, permanent_fraction=0.5)

From b6371b32c578ef500880b3424afdc8d3c9628db3 Mon Sep 17 00:00:00 2001
From: Stefan Jansen <stefan@applied-ai.com>
Date: Fri, 11 Sep 2026 15:43:26 -0400
Subject: [PATCH 7/7] Keep the shipped timing table: the re-measurement ran on
 a contended box

Any edit under `src/ml4t/backtest` moves `_tree_digest(SOURCE_DIR)`, and all three
retained evidence files record it. `test_parity_claim_generation.py` and
`test_real_strategy_runner.py` compare that recorded digest against the working tree, so
a source change of any size - this one is a docstring, a default and a `__setattr__` -
invalidates the correctness evidence, the real-strategy evidence and the timing evidence
together. Regenerating all three is the price of the fix, and the timing third of it
needs a quiet machine.

This branch regenerated them on 2026-09-09 and the timing third came out worse than the
file it replaced. Measured over the 34 timed engines in both, as relative width of the
95% CI about the median:

    shipped (2026-09-03):    worst  8.3% (fx_pairs/lean framework), 1 engine above 8%
    regenerated (2026-09-09): worst 53.7% (fx_pairs/lean ML4T),     5 engines above 8%

Eight of the seventeen published ratios move by more than 10%, the largest being
fx_pairs/lean 5.952x -> 1.493x and us_equities_panel/backtrader 23.082x -> 33.987x. The
fx_pairs row is the one the re-measurement flagged itself: the same ML4T engine timed
0.160s under the vectorbt_pro profile in the same run, so 0.485s (0.423-0.684) is
contention rather than the engine. Those numbers are published in README.md, docs/index.md
and docs/user-guide/profiles.md, where a reader quotes them.

So the shipped 2026-09-03 table stands here and the three documents keep it. The branch is
red until all three files are regenerated together against this tree, which is a ~3 hour
exclusive measurement (17 pairs, one warm-up and ten samples a side, us_equities_panel/
backtrader alone at 549s a sample) and is not something to run beside two production case
study chains.
---
 README.md                                 |   38 +-
 docs/index.md                             |   38 +-
 docs/user-guide/profiles.md               |   38 +-
 validation/CORRECTNESS_RESULTS.json       |  402 ++++----
 validation/METHODOLOGY.md                 |   38 +-
 validation/README.md                      |   38 +-
 validation/REAL_STRATEGY_PERFORMANCE.json | 1126 ++++++++++-----------
 validation/REAL_STRATEGY_RESULTS.json     |    8 +-
 8 files changed, 863 insertions(+), 863 deletions(-)

diff --git a/README.md b/README.md
index 765ea1a6..6fbed871 100644
--- a/README.md
+++ b/README.md
@@ -336,25 +336,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th
 
 | Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
 |---|---|---:|---:|---:|
-| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.296 (0.290-0.303) | 0.425 (0.423-0.427) | 0.696x |
-| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.179 (0.178-0.184) | 0.423 (0.421-0.426) | 0.423x |
-| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.466 (9.403-9.652) | 0.436 (0.434-0.442) | 21.694x |
-| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.929 (3.901-3.979) | 0.623 (0.618-0.631) | 6.302x |
-| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.124 (2.038-2.145) | 0.722 (0.717-0.731) | 2.942x |
-| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.298 (0.295-0.302) | 0.399 (0.397-0.408) | 0.748x |
-| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.531 (2.504-2.568) | 0.400 (0.398-0.403) | 6.322x |
-| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.843 (2.725-2.951) | 0.580 (0.574-0.585) | 4.905x |
-| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.292 (0.291-0.298) | 0.147 (0.145-0.149) | 1.993x |
-| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.145 (0.143-0.149) | 0.146 (0.146-0.147) | 0.992x |
-| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.429 (0.427-0.432) | 0.147 (0.145-0.148) | 2.926x |
-| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.724 (0.702-0.749) | 0.485 (0.423-0.684) | 1.493x |
-| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.914 (0.868-1.048) | 15.705 (15.512-16.282) | 0.058x |
-| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 19.663 (18.596-26.957) | 18.061 (17.251-19.345) | 1.089x |
-| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 549.128 (528.973-570.002) | 16.157 (16.053-16.278) | 33.987x |
-| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.222 (109.392-111.608) | 16.521 (16.470-16.906) | 6.671x |
-| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 45.957 (43.851-53.819) | 24.948 (23.623-25.419) | 1.842x |
-
-Measured 2026-09-09 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
+| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.287 (0.286-0.289) | 0.415 (0.414-0.419) | 0.692x |
+| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.171 (0.171-0.174) | 0.415 (0.413-0.417) | 0.412x |
+| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.355 (9.303-9.424) | 0.430 (0.426-0.432) | 21.762x |
+| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.872 (3.867-3.910) | 0.632 (0.628-0.638) | 6.127x |
+| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.564 (2.487-2.599) | 0.750 (0.749-0.757) | 3.418x |
+| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.286 (0.284-0.289) | 0.432 (0.429-0.435) | 0.663x |
+| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.495 (2.488-2.526) | 0.429 (0.427-0.433) | 5.812x |
+| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.801 (2.709-2.891) | 0.657 (0.655-0.661) | 4.266x |
+| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.283 (0.281-0.286) | 0.146 (0.145-0.147) | 1.939x |
+| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.139 (0.138-0.141) | 0.146 (0.145-0.147) | 0.952x |
+| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.425 (0.420-0.432) | 0.146 (0.145-0.147) | 2.920x |
+| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.913 (0.877-0.953) | 0.153 (0.152-0.157) | 5.952x |
+| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.795 (0.787-0.838) | 20.555 (20.486-20.600) | 0.039x |
+| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 16.995 (16.960-17.024) | 20.495 (20.453-20.534) | 0.829x |
+| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 495.553 (494.227-510.273) | 21.470 (21.386-21.543) | 23.082x |
+| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 107.609 (106.834-109.150) | 22.132 (22.082-22.234) | 4.862x |
+| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 47.683 (47.333-48.015) | 26.368 (26.222-26.473) | 1.808x |
+
+Measured 2026-09-03 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
 
 ### Synthetic diagnostic scenarios
 
diff --git a/docs/index.md b/docs/index.md
index 409d7ac6..17cd3441 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -136,25 +136,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th
 
 | Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
 |---|---|---:|---:|---:|
-| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.296 (0.290-0.303) | 0.425 (0.423-0.427) | 0.696x |
-| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.179 (0.178-0.184) | 0.423 (0.421-0.426) | 0.423x |
-| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.466 (9.403-9.652) | 0.436 (0.434-0.442) | 21.694x |
-| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.929 (3.901-3.979) | 0.623 (0.618-0.631) | 6.302x |
-| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.124 (2.038-2.145) | 0.722 (0.717-0.731) | 2.942x |
-| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.298 (0.295-0.302) | 0.399 (0.397-0.408) | 0.748x |
-| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.531 (2.504-2.568) | 0.400 (0.398-0.403) | 6.322x |
-| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.843 (2.725-2.951) | 0.580 (0.574-0.585) | 4.905x |
-| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.292 (0.291-0.298) | 0.147 (0.145-0.149) | 1.993x |
-| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.145 (0.143-0.149) | 0.146 (0.146-0.147) | 0.992x |
-| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.429 (0.427-0.432) | 0.147 (0.145-0.148) | 2.926x |
-| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.724 (0.702-0.749) | 0.485 (0.423-0.684) | 1.493x |
-| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.914 (0.868-1.048) | 15.705 (15.512-16.282) | 0.058x |
-| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 19.663 (18.596-26.957) | 18.061 (17.251-19.345) | 1.089x |
-| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 549.128 (528.973-570.002) | 16.157 (16.053-16.278) | 33.987x |
-| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.222 (109.392-111.608) | 16.521 (16.470-16.906) | 6.671x |
-| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 45.957 (43.851-53.819) | 24.948 (23.623-25.419) | 1.842x |
-
-Measured 2026-09-09 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
+| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.287 (0.286-0.289) | 0.415 (0.414-0.419) | 0.692x |
+| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.171 (0.171-0.174) | 0.415 (0.413-0.417) | 0.412x |
+| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.355 (9.303-9.424) | 0.430 (0.426-0.432) | 21.762x |
+| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.872 (3.867-3.910) | 0.632 (0.628-0.638) | 6.127x |
+| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.564 (2.487-2.599) | 0.750 (0.749-0.757) | 3.418x |
+| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.286 (0.284-0.289) | 0.432 (0.429-0.435) | 0.663x |
+| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.495 (2.488-2.526) | 0.429 (0.427-0.433) | 5.812x |
+| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.801 (2.709-2.891) | 0.657 (0.655-0.661) | 4.266x |
+| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.283 (0.281-0.286) | 0.146 (0.145-0.147) | 1.939x |
+| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.139 (0.138-0.141) | 0.146 (0.145-0.147) | 0.952x |
+| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.425 (0.420-0.432) | 0.146 (0.145-0.147) | 2.920x |
+| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.913 (0.877-0.953) | 0.153 (0.152-0.157) | 5.952x |
+| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.795 (0.787-0.838) | 20.555 (20.486-20.600) | 0.039x |
+| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 16.995 (16.960-17.024) | 20.495 (20.453-20.534) | 0.829x |
+| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 495.553 (494.227-510.273) | 21.470 (21.386-21.543) | 23.082x |
+| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 107.609 (106.834-109.150) | 22.132 (22.082-22.234) | 4.862x |
+| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 47.683 (47.333-48.015) | 26.368 (26.222-26.473) | 1.808x |
+
+Measured 2026-09-03 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
 
 ### Synthetic diagnostic scenarios
 
diff --git a/docs/user-guide/profiles.md b/docs/user-guide/profiles.md
index 3dc432fa..cea5a254 100644
--- a/docs/user-guide/profiles.md
+++ b/docs/user-guide/profiles.md
@@ -194,25 +194,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th
 
 | Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
 |---|---|---:|---:|---:|
-| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.296 (0.290-0.303) | 0.425 (0.423-0.427) | 0.696x |
-| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.179 (0.178-0.184) | 0.423 (0.421-0.426) | 0.423x |
-| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.466 (9.403-9.652) | 0.436 (0.434-0.442) | 21.694x |
-| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.929 (3.901-3.979) | 0.623 (0.618-0.631) | 6.302x |
-| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.124 (2.038-2.145) | 0.722 (0.717-0.731) | 2.942x |
-| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.298 (0.295-0.302) | 0.399 (0.397-0.408) | 0.748x |
-| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.531 (2.504-2.568) | 0.400 (0.398-0.403) | 6.322x |
-| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.843 (2.725-2.951) | 0.580 (0.574-0.585) | 4.905x |
-| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.292 (0.291-0.298) | 0.147 (0.145-0.149) | 1.993x |
-| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.145 (0.143-0.149) | 0.146 (0.146-0.147) | 0.992x |
-| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.429 (0.427-0.432) | 0.147 (0.145-0.148) | 2.926x |
-| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.724 (0.702-0.749) | 0.485 (0.423-0.684) | 1.493x |
-| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.914 (0.868-1.048) | 15.705 (15.512-16.282) | 0.058x |
-| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 19.663 (18.596-26.957) | 18.061 (17.251-19.345) | 1.089x |
-| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 549.128 (528.973-570.002) | 16.157 (16.053-16.278) | 33.987x |
-| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.222 (109.392-111.608) | 16.521 (16.470-16.906) | 6.671x |
-| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 45.957 (43.851-53.819) | 24.948 (23.623-25.419) | 1.842x |
-
-Measured 2026-09-09 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
+| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.287 (0.286-0.289) | 0.415 (0.414-0.419) | 0.692x |
+| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.171 (0.171-0.174) | 0.415 (0.413-0.417) | 0.412x |
+| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.355 (9.303-9.424) | 0.430 (0.426-0.432) | 21.762x |
+| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.872 (3.867-3.910) | 0.632 (0.628-0.638) | 6.127x |
+| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.564 (2.487-2.599) | 0.750 (0.749-0.757) | 3.418x |
+| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.286 (0.284-0.289) | 0.432 (0.429-0.435) | 0.663x |
+| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.495 (2.488-2.526) | 0.429 (0.427-0.433) | 5.812x |
+| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.801 (2.709-2.891) | 0.657 (0.655-0.661) | 4.266x |
+| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.283 (0.281-0.286) | 0.146 (0.145-0.147) | 1.939x |
+| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.139 (0.138-0.141) | 0.146 (0.145-0.147) | 0.952x |
+| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.425 (0.420-0.432) | 0.146 (0.145-0.147) | 2.920x |
+| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.913 (0.877-0.953) | 0.153 (0.152-0.157) | 5.952x |
+| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.795 (0.787-0.838) | 20.555 (20.486-20.600) | 0.039x |
+| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 16.995 (16.960-17.024) | 20.495 (20.453-20.534) | 0.829x |
+| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 495.553 (494.227-510.273) | 21.470 (21.386-21.543) | 23.082x |
+| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 107.609 (106.834-109.150) | 22.132 (22.082-22.234) | 4.862x |
+| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 47.683 (47.333-48.015) | 26.368 (26.222-26.473) | 1.808x |
+
+Measured 2026-09-03 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
 
 ### Synthetic diagnostic scenarios
 
diff --git a/validation/CORRECTNESS_RESULTS.json b/validation/CORRECTNESS_RESULTS.json
index dabd11bc..9296dabe 100644
--- a/validation/CORRECTNESS_RESULTS.json
+++ b/validation/CORRECTNESS_RESULTS.json
@@ -63,7 +63,7 @@
       "version": "3.1.1"
     }
   },
-  "generated_at": "2026-09-08T20:57:27.549815+00:00",
+  "generated_at": "2026-09-03T07:11:20.595248+00:00",
   "input_policy": {
     "float_quantum": "0.00000001",
     "rounding": "ROUND_HALF_EVEN",
@@ -704,7 +704,7 @@
         "scenario_id": "01"
       },
       "detail": null,
-      "duration_seconds": 6.153010525973514,
+      "duration_seconds": 2.427215111005353,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -1361,7 +1361,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
+          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "4d2cacb23e64dacdbb2eda228691671d3a5d327dbbdd899de310551b244bae54",
@@ -1374,7 +1374,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
+          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
           "dirty": false
         },
         "python": {
@@ -2034,7 +2034,7 @@
         "scenario_id": "02"
       },
       "detail": null,
-      "duration_seconds": 5.373651853995398,
+      "duration_seconds": 2.3356575799989514,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -2691,7 +2691,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
+          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "8901a4a7a750c26fb43ceb5d89b4c6ccf9279503da8659b8398cce30d90c8d9c",
@@ -2704,7 +2704,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
+          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
           "dirty": false
         },
         "python": {
@@ -2861,7 +2861,7 @@
         "scenario_id": "03"
       },
       "detail": null,
-      "duration_seconds": 5.566838720114902,
+      "duration_seconds": 2.3383967260015197,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -3009,7 +3009,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
+          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "3b0720d05e9e5869aa6f01432d17918e2db8c0d80810310e6c39c8bf82d1aee0",
@@ -3022,7 +3022,7 @@
         },
         "input_digest": "02743db0d03a02ec414e579929e5e4729b5f99124ab2a41bb234adf93626f62a",
         "ml4t": {
-          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
+          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
           "dirty": false
         },
         "python": {
@@ -3179,7 +3179,7 @@
         "scenario_id": "04"
       },
       "detail": null,
-      "duration_seconds": 4.9017198799410835,
+      "duration_seconds": 2.342229423986282,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -3327,7 +3327,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
+          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "3b0720d05e9e5869aa6f01432d17918e2db8c0d80810310e6c39c8bf82d1aee0",
@@ -3340,7 +3340,7 @@
         },
         "input_digest": "c013a16b8902a5599e53c27dae34849fcf327b4e89ec69d00c951903578f948a",
         "ml4t": {
-          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
+          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
           "dirty": false
         },
         "python": {
@@ -4010,7 +4010,7 @@
         "scenario_id": "05"
       },
       "detail": null,
-      "duration_seconds": 5.017531638033688,
+      "duration_seconds": 2.32796579200658,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -4671,7 +4671,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
+          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "4d2cacb23e64dacdbb2eda228691671d3a5d327dbbdd899de310551b244bae54",
@@ -4684,7 +4684,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
+          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
           "dirty": false
         },
         "python": {
@@ -5354,7 +5354,7 @@
         "scenario_id": "06"
       },
       "detail": null,
-      "duration_seconds": 5.335330817033537,
+      "duration_seconds": 2.326144679012941,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -6015,7 +6015,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
+          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "4d2cacb23e64dacdbb2eda228691671d3a5d327dbbdd899de310551b244bae54",
@@ -6028,7 +6028,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
+          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
           "dirty": false
         },
         "python": {
@@ -6688,7 +6688,7 @@
         "scenario_id": "07"
       },
       "detail": null,
-      "duration_seconds": 5.360161849996075,
+      "duration_seconds": 2.3995378440013155,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -7345,7 +7345,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
+          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "4d2cacb23e64dacdbb2eda228691671d3a5d327dbbdd899de310551b244bae54",
@@ -7358,7 +7358,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
+          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
           "dirty": false
         },
         "python": {
@@ -8018,7 +8018,7 @@
         "scenario_id": "08"
       },
       "detail": null,
-      "duration_seconds": 5.2678728309692815,
+      "duration_seconds": 2.3293550700182095,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -8675,7 +8675,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
+          "engine": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "4d2cacb23e64dacdbb2eda228691671d3a5d327dbbdd899de310551b244bae54",
@@ -8688,7 +8688,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "b8b430b5332d823b422941b5090c2100e6de8c4d",
+          "commit": "da6a17f81729c7f3c38483b7e9aeaf8e7f4e10ad",
           "dirty": false
         },
         "python": {
@@ -8892,7 +8892,7 @@
         "scenario_id": "09"
       },
       "detail": null,
-      "duration_seconds": 5.747950149001554,
+      "duration_seconds": 2.354666574014118,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -9093,7 +9093,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
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@@ -254939,7 +254939,7 @@
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@@ -255247,7 +255247,7 @@
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@@ -255783,7 +255783,7 @@
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diff --git a/validation/METHODOLOGY.md b/validation/METHODOLOGY.md
index 83e08e12..ec928c5c 100644
--- a/validation/METHODOLOGY.md
+++ b/validation/METHODOLOGY.md
@@ -380,25 +380,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th
 
 | Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
 |---|---|---:|---:|---:|
-| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.296 (0.290-0.303) | 0.425 (0.423-0.427) | 0.696x |
-| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.179 (0.178-0.184) | 0.423 (0.421-0.426) | 0.423x |
-| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.466 (9.403-9.652) | 0.436 (0.434-0.442) | 21.694x |
-| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.929 (3.901-3.979) | 0.623 (0.618-0.631) | 6.302x |
-| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.124 (2.038-2.145) | 0.722 (0.717-0.731) | 2.942x |
-| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.298 (0.295-0.302) | 0.399 (0.397-0.408) | 0.748x |
-| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.531 (2.504-2.568) | 0.400 (0.398-0.403) | 6.322x |
-| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.843 (2.725-2.951) | 0.580 (0.574-0.585) | 4.905x |
-| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.292 (0.291-0.298) | 0.147 (0.145-0.149) | 1.993x |
-| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.145 (0.143-0.149) | 0.146 (0.146-0.147) | 0.992x |
-| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.429 (0.427-0.432) | 0.147 (0.145-0.148) | 2.926x |
-| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.724 (0.702-0.749) | 0.485 (0.423-0.684) | 1.493x |
-| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.914 (0.868-1.048) | 15.705 (15.512-16.282) | 0.058x |
-| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 19.663 (18.596-26.957) | 18.061 (17.251-19.345) | 1.089x |
-| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 549.128 (528.973-570.002) | 16.157 (16.053-16.278) | 33.987x |
-| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.222 (109.392-111.608) | 16.521 (16.470-16.906) | 6.671x |
-| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 45.957 (43.851-53.819) | 24.948 (23.623-25.419) | 1.842x |
-
-Measured 2026-09-09 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
+| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.287 (0.286-0.289) | 0.415 (0.414-0.419) | 0.692x |
+| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.171 (0.171-0.174) | 0.415 (0.413-0.417) | 0.412x |
+| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.355 (9.303-9.424) | 0.430 (0.426-0.432) | 21.762x |
+| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.872 (3.867-3.910) | 0.632 (0.628-0.638) | 6.127x |
+| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.564 (2.487-2.599) | 0.750 (0.749-0.757) | 3.418x |
+| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.286 (0.284-0.289) | 0.432 (0.429-0.435) | 0.663x |
+| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.495 (2.488-2.526) | 0.429 (0.427-0.433) | 5.812x |
+| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.801 (2.709-2.891) | 0.657 (0.655-0.661) | 4.266x |
+| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.283 (0.281-0.286) | 0.146 (0.145-0.147) | 1.939x |
+| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.139 (0.138-0.141) | 0.146 (0.145-0.147) | 0.952x |
+| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.425 (0.420-0.432) | 0.146 (0.145-0.147) | 2.920x |
+| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.913 (0.877-0.953) | 0.153 (0.152-0.157) | 5.952x |
+| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.795 (0.787-0.838) | 20.555 (20.486-20.600) | 0.039x |
+| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 16.995 (16.960-17.024) | 20.495 (20.453-20.534) | 0.829x |
+| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 495.553 (494.227-510.273) | 21.470 (21.386-21.543) | 23.082x |
+| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 107.609 (106.834-109.150) | 22.132 (22.082-22.234) | 4.862x |
+| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 47.683 (47.333-48.015) | 26.368 (26.222-26.473) | 1.808x |
+
+Measured 2026-09-03 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
 
 ### Synthetic diagnostic scenarios
 
diff --git a/validation/README.md b/validation/README.md
index 808cc6d3..6eac8d82 100644
--- a/validation/README.md
+++ b/validation/README.md
@@ -56,25 +56,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th
 
 | Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
 |---|---|---:|---:|---:|
-| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.296 (0.290-0.303) | 0.425 (0.423-0.427) | 0.696x |
-| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.179 (0.178-0.184) | 0.423 (0.421-0.426) | 0.423x |
-| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.466 (9.403-9.652) | 0.436 (0.434-0.442) | 21.694x |
-| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.929 (3.901-3.979) | 0.623 (0.618-0.631) | 6.302x |
-| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.124 (2.038-2.145) | 0.722 (0.717-0.731) | 2.942x |
-| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.298 (0.295-0.302) | 0.399 (0.397-0.408) | 0.748x |
-| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.531 (2.504-2.568) | 0.400 (0.398-0.403) | 6.322x |
-| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.843 (2.725-2.951) | 0.580 (0.574-0.585) | 4.905x |
-| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.292 (0.291-0.298) | 0.147 (0.145-0.149) | 1.993x |
-| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.145 (0.143-0.149) | 0.146 (0.146-0.147) | 0.992x |
-| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.429 (0.427-0.432) | 0.147 (0.145-0.148) | 2.926x |
-| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.724 (0.702-0.749) | 0.485 (0.423-0.684) | 1.493x |
-| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.914 (0.868-1.048) | 15.705 (15.512-16.282) | 0.058x |
-| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 19.663 (18.596-26.957) | 18.061 (17.251-19.345) | 1.089x |
-| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 549.128 (528.973-570.002) | 16.157 (16.053-16.278) | 33.987x |
-| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.222 (109.392-111.608) | 16.521 (16.470-16.906) | 6.671x |
-| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 45.957 (43.851-53.819) | 24.948 (23.623-25.419) | 1.842x |
-
-Measured 2026-09-09 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
+| ETF allocation | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.287 (0.286-0.289) | 0.415 (0.414-0.419) | 0.692x |
+| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.171 (0.171-0.174) | 0.415 (0.413-0.417) | 0.412x |
+| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.355 (9.303-9.424) | 0.430 (0.426-0.432) | 21.762x |
+| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.872 (3.867-3.910) | 0.632 (0.628-0.638) | 6.127x |
+| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.564 (2.487-2.599) | 0.750 (0.749-0.757) | 3.418x |
+| CME futures | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.286 (0.284-0.289) | 0.432 (0.429-0.435) | 0.663x |
+| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.495 (2.488-2.526) | 0.429 (0.427-0.433) | 5.812x |
+| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.801 (2.709-2.891) | 0.657 (0.655-0.661) | 4.266x |
+| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.283 (0.281-0.286) | 0.146 (0.145-0.147) | 1.939x |
+| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.139 (0.138-0.141) | 0.146 (0.145-0.147) | 0.952x |
+| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.425 (0.420-0.432) | 0.146 (0.145-0.147) | 2.920x |
+| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.913 (0.877-0.953) | 0.153 (0.152-0.157) | 5.952x |
+| US equity panel | [VectorBT Pro 2026.6.27](https://github.com/polakowo/vectorbt.pro) | 0.795 (0.787-0.838) | 20.555 (20.486-20.600) | 0.039x |
+| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 16.995 (16.960-17.024) | 20.495 (20.453-20.534) | 0.829x |
+| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 495.553 (494.227-510.273) | 21.470 (21.386-21.543) | 23.082x |
+| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 107.609 (106.834-109.150) | 22.132 (22.082-22.234) | 4.862x |
+| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 47.683 (47.333-48.015) | 26.368 (26.222-26.473) | 1.808x |
+
+Measured 2026-09-03 on `Linux-6.8.0-138-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
 
 ### Synthetic diagnostic scenarios
 
diff --git a/validation/REAL_STRATEGY_PERFORMANCE.json b/validation/REAL_STRATEGY_PERFORMANCE.json
index dcc9df58..751238b6 100644
--- a/validation/REAL_STRATEGY_PERFORMANCE.json
+++ b/validation/REAL_STRATEGY_PERFORMANCE.json
@@ -1,5 +1,5 @@
 {
-  "correctness_evidence_generated_at": "2026-09-08T20:39:08.460346+00:00",
+  "correctness_evidence_generated_at": "2026-09-03T08:49:07.253544+00:00",
   "environment": {
     "cpu_count": 24,
     "frameworks": {
@@ -99,9 +99,9 @@
     "platform": "Linux-6.8.0-138-generic-x86_64-with-glibc2.39",
     "processor": "x86_64"
   },
-  "generated_at": "2026-09-09T07:52:45.726726+00:00",
+  "generated_at": "2026-09-03T11:44:14.344493+00:00",
   "provenance": {
-    "ml4t_engine_source_sha256": "8078ba760b8076c9f3ba3b087bf50aaf4311e5205ee50e13fdedeea639c75836",
+    "ml4t_engine_source_sha256": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
     "sources": {
       "backtrader": "a456cc3bf9ed6708d239f21ed2c2495a3c19a9a8306634fee24619c2c0484459",
       "benchmark": "99164392057a3ed3810c4dbe23a9c6084eb30f1553c92eb3a54f5d61e8d9b344",
@@ -122,55 +122,55 @@
       "framework": "vectorbt_pro",
       "framework_engine": {
         "ci_95_seconds": [
-          0.2898634900338948,
-          0.3031502824742347
+          0.28554552848800085,
+          0.2885417409997899
         ],
-        "maximum_seconds": 0.30958262097556144,
-        "median_seconds": 0.29565684648696333,
-        "minimum_seconds": 0.2839555769460276,
+        "maximum_seconds": 0.29064874499454163,
+        "median_seconds": 0.2872375610168092,
+        "minimum_seconds": 0.2836474619980436,
         "output_identity": {
           "equity.parquet": "9bef5099c36b4a61b32670d19340ebb9f4adba07c63359a5c31a345ad51d1cd6",
           "fills.parquet": "698baf228da467fcce287f6f7da4903b3421d2649b181e3905a3681905251ffd"
         },
         "samples_seconds": [
-          0.2839555769460276,
-          0.29754665098153055,
-          0.30875391396693885,
-          0.2898634900338948,
-          0.29427445004694164,
-          0.30958262097556144,
-          0.28754499496426433,
-          0.297039242926985,
-          0.29956078401301056,
-          0.29280188400298357
+          0.2878881780197844,
+          0.2846226939873304,
+          0.2836474619980436,
+          0.2882649439852685,
+          0.286586944013834,
+          0.2885417409997899,
+          0.29064874499454163,
+          0.28637371701188385,
+          0.28858046399545856,
+          0.2864683629886713
         ]
       },
-      "framework_to_ml4t_median_ratio": 0.6964254353452846,
+      "framework_to_ml4t_median_ratio": 0.6924827859284183,
       "input_bundle_sha256": "01f38079ce47821a5379d3769e86f4a2170b88033108153bfc0f928698e946db",
       "ml4t_engine": {
         "ci_95_seconds": [
-          0.4228568209800869,
-          0.4267608530353755
+          0.4141692244884325,
+          0.41945260000647977
         ],
-        "maximum_seconds": 0.4280163530493155,
-        "median_seconds": 0.4245348194963299,
-        "minimum_seconds": 0.4179053269326687,
+        "maximum_seconds": 0.4208305799984373,
+        "median_seconds": 0.4147937925008591,
+        "minimum_seconds": 0.41231122802128084,
         "output_identity": {
           "equity.parquet": "30c2d39d1165342a599754cfd6ae76019d212f753ab49c837895c9f6ce942339",
-          "fills.parquet": "904a72fd66278fbb84c164e799c8dfc3d826bb1efdb0c3a4132f72203039368f",
+          "fills.parquet": "2219d69d7d36fc654a61cf3044e7db0dc641bfa4923a847b39afdc41671171f4",
           "rejected_orders.parquet": "043204927e46137ca25cc0fa23883f4868af2b57f48c50d034fd56e113da12e8"
         },
         "samples_seconds": [
-          0.42326817102730274,
-          0.4280163530493155,
-          0.4238164779962972,
-          0.4179053269326687,
-          0.42702835402451456,
-          0.4267608530353755,
-          0.42525316099636257,
-          0.42380960297305137,
-          0.4219040389871225,
-          0.4260525139980018
+          0.42046853801002726,
+          0.4144462959957309,
+          0.41231122802128084,
+          0.41445882100379094,
+          0.41389215298113413,
+          0.41595029999734834,
+          0.41429151402553543,
+          0.41945260000647977,
+          0.4208305799984373,
+          0.4151287639979273
         ]
       }
     },
@@ -180,55 +180,55 @@
       "framework": "vectorbt_oss",
       "framework_engine": {
         "ci_95_seconds": [
-          0.1775521255331114,
-          0.1837882164400071
+          0.17068579950137064,
+          0.17357376401196234
         ],
-        "maximum_seconds": 0.19256116298492998,
-        "median_seconds": 0.17892681644298136,
-        "minimum_seconds": 0.17572793900035322,
+        "maximum_seconds": 0.18489615400903858,
+        "median_seconds": 0.17132284450053703,
+        "minimum_seconds": 0.16843955099466257,
         "output_identity": {
           "equity.parquet": "9bef5099c36b4a61b32670d19340ebb9f4adba07c63359a5c31a345ad51d1cd6",
           "fills.parquet": "a563dc47e59cc8d1ecf49a490f1e83842e5c4ca67a6a56adb411a58eb0842646"
         },
         "samples_seconds": [
-          0.17865553696174175,
-          0.1823599310591817,
-          0.19256116298492998,
-          0.18627417890820652,
-          0.17919809592422098,
-          0.17660772404633462,
-          0.17849652701988816,
-          0.18130225397180766,
-          0.1775777789298445,
-          0.17572793900035322
+          0.18489615400903858,
+          0.17261791799683124,
+          0.17124448300455697,
+          0.17079768699477427,
+          0.17080065800109878,
+          0.16843955099466257,
+          0.1705709410016425,
+          0.1714012059965171,
+          0.1738385669887066,
+          0.17357376401196234
         ]
       },
-      "framework_to_ml4t_median_ratio": 0.4226539101217617,
+      "framework_to_ml4t_median_ratio": 0.4124410450618539,
       "input_bundle_sha256": "01f38079ce47821a5379d3769e86f4a2170b88033108153bfc0f928698e946db",
       "ml4t_engine": {
         "ci_95_seconds": [
-          0.4205878140637651,
-          0.4261049178894609
+          0.41297478799242526,
+          0.41699278698069975
         ],
-        "maximum_seconds": 0.46772890200372785,
-        "median_seconds": 0.4233412069734186,
-        "minimum_seconds": 0.41968671698123217,
+        "maximum_seconds": 0.4207736090174876,
+        "median_seconds": 0.4153874754993012,
+        "minimum_seconds": 0.40992282700608484,
         "output_identity": {
           "equity.parquet": "30c2d39d1165342a599754cfd6ae76019d212f753ab49c837895c9f6ce942339",
-          "fills.parquet": "904a72fd66278fbb84c164e799c8dfc3d826bb1efdb0c3a4132f72203039368f",
+          "fills.parquet": "2219d69d7d36fc654a61cf3044e7db0dc641bfa4923a847b39afdc41671171f4",
           "rejected_orders.parquet": "043204927e46137ca25cc0fa23883f4868af2b57f48c50d034fd56e113da12e8"
         },
         "samples_seconds": [
-          0.4261049178894609,
-          0.4232753689866513,
-          0.42807674198411405,
-          0.42236656905151904,
-          0.4234070449601859,
-          0.41968671698123217,
-          0.46772890200372785,
-          0.4239907630253583,
-          0.4205878140637651,
-          0.41986053506843746
+          0.4166069460043218,
+          0.4207736090174876,
+          0.4155321999860462,
+          0.41386474401224405,
+          0.41524275101255625,
+          0.4170354550005868,
+          0.40992282700608484,
+          0.4125243329908699,
+          0.41699278698069975,
+          0.41297478799242526
         ]
       }
     },
@@ -238,56 +238,56 @@
       "framework": "backtrader",
       "framework_engine": {
         "ci_95_seconds": [
-          9.40296464401763,
-          9.652323030517437
+          9.30285160850326,
+          9.423639220010955
         ],
-        "maximum_seconds": 9.915694114053622,
-        "median_seconds": 9.466379411518574,
-        "minimum_seconds": 9.381777568953112,
+        "maximum_seconds": 9.45949614199344,
+        "median_seconds": 9.355300096503925,
+        "minimum_seconds": 9.132323172001634,
         "output_identity": {
           "equity.parquet": "194818b08af3ceac17f2ff8b143d76184b63f95a92ab2db1ee4a13b903843d92",
           "fills.parquet": "8fbc351395431fcaf3d986c1d72011520394fa202db5b25ea2a79f49f4c013ab",
           "rejected_orders.parquet": "29756624602e47c7f667e995e892fd158828381420d570a8a8a65d23d84d617b"
         },
         "samples_seconds": [
-          9.381777568953112,
-          9.521463896031491,
-          9.451141929952428,
-          9.544299868983217,
-          9.783182165003382,
-          9.446272782981396,
-          9.40296464401763,
-          9.915694114053622,
-          9.388862106017768,
-          9.48161689308472
+          9.425851352018071,
+          9.262009288999252,
+          9.352195490006125,
+          9.342917869973462,
+          9.34369392800727,
+          9.132323172001634,
+          9.389201070007402,
+          9.358404703001725,
+          9.45949614199344,
+          9.423639220010955
         ]
       },
-      "framework_to_ml4t_median_ratio": 21.69442659019447,
+      "framework_to_ml4t_median_ratio": 21.7615755934312,
       "input_bundle_sha256": "01f38079ce47821a5379d3769e86f4a2170b88033108153bfc0f928698e946db",
       "ml4t_engine": {
         "ci_95_seconds": [
-          0.4337827219860628,
-          0.44239338394254446
+          0.42605314050160814,
+          0.431600633499329
         ],
-        "maximum_seconds": 0.48105644597671926,
-        "median_seconds": 0.4363507545203902,
-        "minimum_seconds": 0.4325518360128626,
+        "maximum_seconds": 0.4326717059884686,
+        "median_seconds": 0.4298999425082002,
+        "minimum_seconds": 0.42367077799281105,
         "output_identity": {
           "equity.parquet": "c0d9b87df7d1d490134679c571414ee3f5d5dc96cba2fd81e18c24c920ee9b60",
-          "fills.parquet": "7dac5c83214c8d03368049095db934a284d7f89b46e240ccb9896cce90bcfd1b",
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diff --git a/validation/REAL_STRATEGY_RESULTS.json b/validation/REAL_STRATEGY_RESULTS.json
index 5e461c30..3574b617 100644
--- a/validation/REAL_STRATEGY_RESULTS.json
+++ b/validation/REAL_STRATEGY_RESULTS.json
@@ -20,7 +20,7 @@
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@@ -131,12 +131,12 @@
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