IB-WP-0016-T04: trading-literature profile

Ship a specialized profile for trading memoirs and market-structure
texts. The profile names eight entity categories (trader, market,
strategy, error, psychological_pattern, institution, instrument,
evidence_bearing_claim), five relation types (cause_effect,
lesson_evidence, risk_mitigation, actor_venue, strategy_outcome), and
four evaluation criteria (groundedness, lesson_clarity,
historical_context, overgeneralization_risk). Each is reflected in the
prompts and contracts so the LLM is steered toward operator-level
findings rather than biographical detail or moralising.

The generic profile remains the default. A 2-chapter Lefevre smoke run
with --profile trading-literature completes end-to-end with viable
metrics; 93 tests pass.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
2026-05-17 18:59:45 +02:00
parent ba8c0a100c
commit df87e212a2
13 changed files with 502 additions and 1 deletions

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@@ -48,6 +48,23 @@ infospace-bench generate status ./infospaces/book-space
shows chunk counts, generated artifact counts, evaluations, metrics, history,
and stale source/profile inputs.
### Profiles
Two profiles ship today:
- `general-knowledge` — durable concepts, claims, methods, people,
places, works, and objects across any source
- `trading-literature` — trading memoirs and market-structure texts;
tunes entity categories (`trader`, `market`, `strategy`, `error`,
`psychological_pattern`, `institution`, `instrument`,
`evidence_bearing_claim`), relation types (`cause_effect`,
`lesson_evidence`, `risk_mitigation`, `actor_venue`,
`strategy_outcome`), and evaluation criteria (`groundedness`,
`lesson_clarity`, `historical_context`, `overgeneralization_risk`)
Select via `--profile trading-literature` on `generate init` or
`generate from-source`. The generic profile remains the default.
### Scale-aware plan
`generate plan` returns a compact estimate by default — counts of selected

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@@ -0,0 +1,15 @@
# Entity Contract — Trading Literature
Each generated entity must be a Markdown artifact with:
- one top-level heading containing the entity title
- a `## Category` line containing exactly one of: `trader`, `market`,
`strategy`, `error`, `psychological_pattern`, `institution`,
`instrument`, `evidence_bearing_claim`
- a `## Definition` section
- optional `## Context`, `## Source Evidence`, and `## Review Notes`
sections
Entity titles should be stable, short, and reusable across chapters of
the same source. Do not include the chapter number in the title; that
provenance belongs in the source artifact, not the entity.

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@@ -0,0 +1,19 @@
# Evaluation Contract — Trading Literature
Each evaluation must be Markdown with YAML frontmatter containing:
- `artifact_id`
- `evaluator`
- `evaluated_at`
- `scores`
Scores must include all four criteria on a 0 to 5 scale, with 5 best:
- `groundedness`
- `lesson_clarity`
- `historical_context`
- `overgeneralization_risk` (higher = lower risk; an entity that
silently universalises a chapter-local claim scores low)
Optional `## Review Notes` should quote any specific lines from the
entity body that drove a low score on any criterion.

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@@ -0,0 +1,16 @@
# Relation Contract — Trading Literature
Each generated relation must be a Markdown artifact with:
- one top-level heading containing the relation title
- `## Subject`
- `## Predicate`
- `## Object`
- `## Relation Type` — exactly one of: `cause_effect`, `lesson_evidence`,
`risk_mitigation`, `actor_venue`, `strategy_outcome`
- optional `## Evidence` and `## Feedback Role`
Subject and object values should match generated entity titles whenever
possible. A relation whose subject or object does not correspond to any
extracted entity must include an `## Evidence` section that quotes the
phrase from the source supporting the link.

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@@ -0,0 +1,11 @@
# Summary Contract — Trading Literature
Each source summary should preserve:
- the narrator's actions and the market events they reacted to
- named strategies, instruments, venues, and institutions present in
the chunk
- explicit lessons or rules of thumb the chunk states
- evidence phrases (dollar figures, dates, counter-party names, tape
behaviour) useful for later extraction
- unresolved ambiguities or anachronisms a reviewer should flag

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@@ -0,0 +1,35 @@
id: trading-literature
name: Trading Literature
description: |
Infospace generation profile for trading memoirs, market-structure texts,
and operator narratives. Tunes entity, relation, and evaluation prompts
for traders, markets, strategies, errors, psychological patterns,
institutions, instruments, and the lessons drawn from them.
terminology:
source_chunk: Chapter or chapter-part of a trading memoir or market-structure text
entity: Trader, market, strategy, error pattern, psychological habit, institution, instrument, or evidence-bearing claim
relation: Typed link between two trading-literature entities (cause/effect, lesson/evidence, risk/mitigation, actor/venue, strategy/outcome)
entity_categories:
- traders
- markets
- strategies
- errors
- psychological_patterns
- institutions
- instruments
- evidence_bearing_claims
relation_categories:
- cause_effect
- lesson_evidence
- risk_mitigation
- actor_venue
- strategy_outcome
granularity:
default: |
Prefer durable trading concepts and operator-level lessons over biographical
detail or stock-price trivia. Each entity should be reusable across chapters.
evaluation_criteria:
- groundedness
- lesson_clarity
- historical_context
- overgeneralization_risk

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@@ -0,0 +1,34 @@
# Evaluate Trading-Literature Entity
Profile: {{ macros.profile }}
Evaluate the generated entity as Markdown with YAML frontmatter. Include
`artifact_id`, `evaluator`, `evaluated_at`, and a `scores` list. Score
each criterion on a 0 to 5 scale where 5 is best.
Required score names:
- `groundedness` — does the entity stay anchored to the source chunk,
with no invented dates, dollar figures, or quotes?
- `lesson_clarity` — for `strategy`, `error`, `psychological_pattern`,
and `evidence_bearing_claim` entities, is the operator-level lesson
stated crisply enough to be reused in later chapters? For purely
factual entities (trader, market, institution, instrument), score
this on the clarity of the definition.
- `historical_context` — is the entity placed correctly in the era and
venue of the source (e.g. early-1900s American equities) without
importing modern terminology or instruments?
- `overgeneralization_risk` — is the entity scoped narrowly enough to
resist becoming a vague universal claim? Higher score means lower
risk. Flag entities that quietly claim to apply to all markets or
all operators when the source restricts the claim.
Add a short `## Review Notes` section listing any specific lines from
the entity body that drove a low score on any criterion.
Entity artifact: {{ input.artifact_id }}
Entity title: {{ input.title }}
## Entity
{{ input.content }}

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@@ -0,0 +1,37 @@
# Extract Trading-Literature Entities
Profile: {{ macros.profile }}
Extract reusable infospace entities from the source chunk. Return one
Markdown bundle where each entity starts with `# Entity Title` and has a
`## Definition` section, plus a `## Category` line drawn from the list
below. Add `## Context` and `## Source Evidence` when the chunk gives
enough material; leave them out rather than inventing detail.
Allowed categories (use exactly one per entity):
- `trader` — a named operator, broker, manipulator, or counter-party
- `market` — a market, exchange, pit, or named instrument family
(e.g. the New York Stock Exchange, the cotton market, the bucket-shop
circuit)
- `strategy` — a named tactic, system, or recurring playbook
(e.g. pyramiding, scale buying, tape reading)
- `error` — a recurring mistake, anti-pattern, or losing habit
- `psychological_pattern` — a named cognitive or emotional habit that
drives decisions (e.g. tip-following, hope-against-evidence)
- `institution` — a firm, regulator, news organisation, or social venue
- `instrument` — a specific security, commodity, or contract
- `evidence_bearing_claim` — a concrete operator-level claim the text
asserts and partially supports (e.g. "amateurs buy on tips, pros buy
on tape"); preserve the supporting evidence in the body
Prefer entities that will recur across chapters. Avoid fictionalised
people whose role is purely narrative colour. Avoid wrapping a single
trade as an entity unless the trade is itself a teachable case.
Source title: {{ input.title }}
Source artifact: {{ input.artifact_id }}
## Source
{{ input.content }}

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@@ -0,0 +1,32 @@
# Extract Trading-Literature Relations
Profile: {{ macros.profile }}
Extract a small set of important relations from the source chunk. Return
one Markdown relation artifact per relation. Each artifact uses sections
`## Subject`, `## Predicate`, `## Object`, and `## Relation Type`. Add
`## Evidence` whenever the chunk supplies a concrete supporting phrase.
Use exactly one of these relation types per relation:
- `cause_effect` — one entity drives a measurable market or operator
outcome (e.g. a strategy causing a loss; a market event causing a
policy change)
- `lesson_evidence` — an `evidence_bearing_claim` is supported (or
undercut) by a concrete trade, event, or quote in the source
- `risk_mitigation` — a strategy, rule, or habit reduces a named risk
- `actor_venue` — a trader operates in a market, institution, or pit
- `strategy_outcome` — a named strategy is applied to a specific trade
or campaign and produces a labelled outcome (win, loss, scratch)
Subject and object values should match entity titles you would (or did)
extract in the entities stage. Skip relations whose subject or object
would be a one-off fictional flourish. Skip implicit moralising; prefer
relations the chunk actually evidences.
Source title: {{ input.title }}
Source artifact: {{ input.artifact_id }}
## Source
{{ input.content }}

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@@ -0,0 +1,23 @@
# Summarize Trading-Literature Source
Profile: {{ macros.profile }}
Summarize the source chunk as Markdown for a trading-literature infospace.
Preserve in this order:
- the narrator's actions and the market events they reacted to
- named strategies, instruments, venues, and institutions
- explicit lessons, rules of thumb, or warnings the text states
- evidence phrases (dollar figures, dates, tape behaviour, counter-party
names) that should guide later entity and relation extraction
- ambiguities or anachronisms that a reviewer should flag
Keep the summary to a single page; do not paraphrase the moral of the
chapter, only the material a downstream extractor needs.
Source title: {{ input.title }}
Source artifact: {{ input.artifact_id }}
## Source
{{ input.content }}

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@@ -0,0 +1,13 @@
# Synthesize Trading-Literature Report
Profile: {{ macros.profile }}
Synthesize a concise review report from the generated source summaries,
entities, relations, evaluations, and collection metrics. Group entities
by category (trader, market, strategy, error, psychological pattern,
institution, instrument, evidence-bearing claim). Surface the relations
whose `relation_type` is `lesson_evidence` or `strategy_outcome` first —
those are the operator-level findings a reviewer will want to read
before anything else. End the report with an explicit "Overgeneralization
risks" section that quotes any entities whose evaluation flagged that
score below 3.

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@@ -0,0 +1,249 @@
import json
import os
import subprocess
import sys
import zipfile
from pathlib import Path
import yaml
from infospace_bench.generator import (
init_generation_infospace,
run_generation,
status_generation,
)
PROFILE_DIR = Path("src/infospace_bench/profiles/trading-literature")
def _fixture_responses(path: Path) -> None:
data = {
"responses": [
{
"stage_id": "summarize-source",
"input_artifact_id": "*",
"markdown": "# Source Summary\n\nThe chapter introduces a bucket-shop apprenticeship.\n",
},
{
"stage_id": "extract-entities",
"input_artifact_id": "*",
"markdown": (
"# Tape Reading\n\n"
"## Category\n\nstrategy\n\n"
"## Definition\n\n"
"Inferring price intent from the ticker tape rather than fundamentals.\n\n"
"## Context\n\nFramed as a learnable pattern skill in the chapter.\n\n"
"# Bucket Shop\n\n"
"## Category\n\ninstitution\n\n"
"## Definition\n\n"
"A 1900s retail brokerage that took the other side of customer tape bets.\n\n"
),
},
{
"stage_id": "extract-relations",
"input_artifact_id": "*",
"markdown": (
"# Tape Reading Reduces Tip Following\n\n"
"## Subject\n\nTape Reading\n\n"
"## Predicate\n\nreduces\n\n"
"## Object\n\nTip Following\n\n"
"## Relation Type\n\nrisk_mitigation\n\n"
"## Evidence\n\nThe narrator's profits track tape behaviour, not rumour.\n"
),
},
{
"stage_id": "evaluate-entity",
"input_artifact_id": "*",
"markdown": (
"---\n"
"artifact_id: entity/tape-reading.md\n"
"evaluator: fixture\n"
"evaluated_at: '2026-05-17T00:00:00'\n"
"scores:\n"
" - name: groundedness\n value: 4.0\n max_value: 5.0\n"
" - name: lesson_clarity\n value: 4.0\n max_value: 5.0\n"
" - name: historical_context\n value: 4.0\n max_value: 5.0\n"
" - name: overgeneralization_risk\n value: 4.0\n max_value: 5.0\n"
"---\n\n"
"# Evaluation: entity/tape-reading.md\n"
),
},
]
}
path.write_text(yaml.safe_dump(data, sort_keys=False), encoding="utf-8")
CONTAINER_XML = """<?xml version="1.0"?>
<container version="1.0" xmlns="urn:oasis:names:tc:opendocument:xmlns:container">
<rootfiles>
<rootfile full-path="OEBPS/content.opf" media-type="application/oebps-package+xml"/>
</rootfiles>
</container>
"""
PACKAGE_OPF = """<?xml version="1.0" encoding="utf-8"?>
<package xmlns="http://www.idpf.org/2007/opf" version="3.0" unique-identifier="bookid">
<metadata xmlns:dc="http://purl.org/dc/elements/1.1/">
<dc:identifier id="bookid">urn:test:trading</dc:identifier>
<dc:title>Trading Memoir Fixture</dc:title>
<dc:creator>Fixture Author</dc:creator>
<dc:language>en</dc:language>
</metadata>
<manifest>
<item id="ch1" href="ch1.xhtml" media-type="application/xhtml+xml"/>
<item id="ch2" href="ch2.xhtml" media-type="application/xhtml+xml"/>
</manifest>
<spine>
<itemref idref="ch1"/>
<itemref idref="ch2"/>
</spine>
</package>
"""
def _write_two_chapter_epub(path: Path) -> None:
with zipfile.ZipFile(path, "w") as archive:
archive.writestr("mimetype", "application/epub+zip")
archive.writestr("META-INF/container.xml", CONTAINER_XML)
archive.writestr("OEBPS/content.opf", PACKAGE_OPF)
archive.writestr(
"OEBPS/ch1.xhtml",
"<html><head><title>Book</title></head>"
"<body><h2>I</h2><p>The narrator tries tape reading at a bucket shop.</p></body></html>",
)
archive.writestr(
"OEBPS/ch2.xhtml",
"<html><head><title>Book</title></head>"
"<body><h2>II</h2><p>He learns the cost of acting on rumours.</p></body></html>",
)
def test_trading_profile_declares_required_categories_and_criteria() -> None:
data = yaml.safe_load((PROFILE_DIR / "profile.yaml").read_text(encoding="utf-8"))
assert data["id"] == "trading-literature"
assert set(data["entity_categories"]) == {
"traders",
"markets",
"strategies",
"errors",
"psychological_patterns",
"institutions",
"instruments",
"evidence_bearing_claims",
}
assert set(data["relation_categories"]) == {
"cause_effect",
"lesson_evidence",
"risk_mitigation",
"actor_venue",
"strategy_outcome",
}
assert data["evaluation_criteria"] == [
"groundedness",
"lesson_clarity",
"historical_context",
"overgeneralization_risk",
]
def test_trading_profile_evaluate_template_mentions_all_criteria() -> None:
template = (PROFILE_DIR / "templates" / "evaluate-entity.md").read_text(encoding="utf-8")
for criterion in (
"groundedness",
"lesson_clarity",
"historical_context",
"overgeneralization_risk",
):
assert criterion in template, f"evaluate template should reference {criterion}"
def test_trading_profile_relation_template_lists_required_relation_types() -> None:
template = (PROFILE_DIR / "templates" / "extract-relations.md").read_text(encoding="utf-8")
for relation_type in (
"cause_effect",
"lesson_evidence",
"risk_mitigation",
"actor_venue",
"strategy_outcome",
):
assert relation_type in template, f"relation template should reference {relation_type}"
def test_trading_profile_contracts_present() -> None:
contracts_dir = PROFILE_DIR / "contracts"
expected = {"entity.contract.md", "relation.contract.md", "evaluation.contract.md", "summary.contract.md"}
actual = {path.name for path in contracts_dir.glob("*.md")}
assert expected.issubset(actual)
def test_trading_profile_runs_end_to_end_with_fixture(tmp_path: Path) -> None:
book = tmp_path / "book.epub"
_write_two_chapter_epub(book)
fixture = tmp_path / "responses.yaml"
_fixture_responses(fixture)
infospace = init_generation_infospace(
tmp_path,
book,
"trading-fixture",
name="Trading Fixture",
profile="trading-literature",
)
result = run_generation(infospace.root, fixture_responses=fixture)
status = status_generation(infospace.root)
assert result.status == "completed"
assert status["profile"] == "trading-literature"
assert status["source_chunk_count"] == 2
assert status["entity_count"] >= 1
assert status["relation_count"] >= 1
assert status["evaluation_count"] >= 1
# Installed profile should have copied templates and contracts into the infospace.
assert (infospace.root / "profiles" / "trading-literature" / "templates" / "evaluate-entity.md").is_file()
assert (
infospace.root / "profiles" / "trading-literature" / "contracts" / "entity.contract.md"
).is_file()
def test_trading_profile_selectable_via_cli(tmp_path: Path) -> None:
book = tmp_path / "book.epub"
_write_two_chapter_epub(book)
fixture = tmp_path / "responses.yaml"
_fixture_responses(fixture)
env = os.environ.copy()
env["PYTHONPATH"] = "src:/home/worsch/markitect-tool/src"
result = subprocess.run(
[
sys.executable,
"-m",
"infospace_bench",
"generate",
"from-source",
str(book),
"--workspace",
str(tmp_path),
"--slug",
"trading-cli",
"--name",
"Trading CLI",
"--profile",
"trading-literature",
"--fixture-responses",
str(fixture),
"--apply",
],
check=False,
env=env,
text=True,
capture_output=True,
)
assert result.returncode == 0, result.stderr
payload = json.loads(result.stdout)
assert payload["status"] == "completed"
assert "trading-cli" in payload["root"]

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@@ -157,7 +157,7 @@ state_hub_task_id: "bee5c38a-f052-4edb-9313-b3a2ee5a6c26"
```task
id: IB-WP-0016-T04
status: todo
status: done
priority: medium
state_hub_task_id: "1a1b8fde-773f-46a6-887a-3c87a425d7a3"
```