Knuckles-Team Microstructure Trading Loop
CONCEPT:EE-040
Orchestrates the full KG-driven microstructure trading loop built on the
feat/trading-agent-microstructure work across emerald-exchange, agent-utilities,
epistemic-graph, and data-science-mcp. The loop curates trading knowledge into the
knowledge graph, turns backtested results into measured signal priors that drive
signal-fusion weights, runs paper-first decisions through the curated trading
team, and only ever reports graduation eligibility — promotion to live remains a
human action.
The two ingestion roots (ingest-knowledge, research-goldenloop) run in
parallel and converge at register-substrate; everything downstream is a linear
chain so each stage consumes the prior stage's KG state.
Steps
Step 0: ingest-knowledge
Agent: market-analyst
Tools: graph_search, document_process
Ingest trading books, papers, PDFs, and notes and ORGANISE them into the trading
knowledge taxonomy via agent_utilities.knowledge_graph.distillation.trading_curator
(organize_trading_knowledge): each extracted concept is classified into a
Strategy/Risk/Execution concept node with citations and confidence, and order-flow
chapters seed MicrostructureSignal nodes (DERIVED_FROM). Knowledge is organised,
not dumped.
Expected: trading-knowledge-nodes, seeded-signal-stubs
Step 1: research-goldenloop
Agent: market-analyst
Tools: graph_search
Run the research golden-loop over the q-fin cluster (research_pipeline
RELEVANCE_TAXONOMY trading + q-fin.* categories): discover and score
microstructure / execution / risk papers, ingest the relevant ones, and route them
through the same trading_curator organise pass so papers and books land in one
typed taxonomy.
Expected: ingested-research-concepts
Step 2: register-substrate [depends_on: Step 0, Step 1]
Agent: alpha-strategist
Tools: ontology_query, graph_query
Ensure the trading ontology (ontology_trading.ttl), the MicrostructureSignal
candidates, and the curated teamcfg:trading_paper_v1 TeamConfig are registered in
the KG (agent_utilities.graph.trading_team_seed.seed_trading_team). This is the
substrate the rest of the loop reasons over.
Expected: registered-signals, seeded-team-config
Step 3: backtest-signals [depends_on: Step 2]
Agent: risk-manager
Tools: graph_analyze, graph_query
Backtest each candidate microstructure signal via emerald emerald_strategy
(backtest) / data-science-mcp quant_validation, computing deflated Sharpe,
PBO, and hit-rate. Write the measured results back onto each MicrostructureSignal
node as priors (FinanceEngineMixin.record_backtest_outcome), discarding signals
with pbo > 0.5 or standalone_sharpe <= 0.
Expected: signal-priors-written
Step 4: seed-fusion [depends_on: Step 3]
Agent: alpha-strategist
Tools: graph_query, emerald_signals
Seed BayesianSignalFusion from the stored priors
(seed_from_kg: weight = directional_accuracy × standalone_sharpe) and fuse the
live signal directions through emerald emerald_signals (fuse). Require the
convergence gate to pass before any decision is emitted.
Expected: fused-conviction, convergence-gate-result
Step 5: paper-decisions [depends_on: Step 4]
Agent: execution-specialist
Tools: emerald_orders, emerald_market_making
Compose/reuse the trading team and run paper-first decisions: size via Kelly
through the risk guards, route to the PAPER backend, and apply the queue-position /
toxicity / conviction gates. The staged-execution policy (execution_policy.json)
keeps this on paper; live submission is refused unless a human has promoted the
stage.
Expected: paper-orders, quote-decisions
Step 6: journal-decisions [depends_on: Step 5]
Agent: attribution-analyst
Tools: graph_write
Write one TradeJournalNode per decision (instrument, stage, direction, size,
signals used, priors snapshot, rationale, regime) via graph_write, closing the
feedback loop so recurring profitable patterns can later be distilled into reusable
strategy concepts.
Expected: trade-journal-entries
Step 7: evaluate-graduation [depends_on: Step 6]
Agent: risk-manager
Tools: graph_query
Run RiskGuard.evaluate_graduation against the journal + priors and REPORT whether
the current stage is eligible to advance (paper → advisory → bounded-autonomous).
This is eligibility-only: the workflow NEVER changes the stage. Promotion requires a
human running approve_stage with the EMERALD_STAGE_APPROVAL_TOKEN; the agent can
never self-escalate.
Expected: graduation-eligibility-report
Execution
Run this workflow as a dependency-ordered DAG. Steps with no unmet depends_on run in parallel; dependents run after their prerequisites complete.
- Run first (in parallel): Step 0 — ingest-knowledge; Step 1 — research-goldenloop
- After level 0: Step 2 — register-substrate
- After level 1: Step 3 — backtest-signals
- After level 2: Step 4 — seed-fusion
- After level 3: Step 5 — paper-decisions
- After level 4: Step 6 — journal-decisions
- After level 5: Step 7 — evaluate-graduation
Execution: If graph-os is reachable, offload the whole DAG via graph_orchestrate action=execute_workflow (or the kg-delegate skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet depends_on in parallel, then their dependents.