Minimal plan that actually works and stays small​

• Position queue

  • Keep KAIROS.position-execution only (scheduler + execution).
  • Keep KAIROS.dynamic-tpsl (independent of sync loop).

• Public runtime surface (4 files, small)

• position-execution.consumer.ts

  • Handles command.schedule_positions → evenly enqueue command.sync_position.
  • Handles command.sync_position → calls orchestrator; tiny effect mapping only if needed.
  • trading-context.orchestrator.ts
    • Derive diagnostics via derive-sync.ts → choose phase → call adapter method.
  • TradingAdapter + two adapters with identical interface
    • live-context.adapter.ts implements:
      • syncCreation(position)
      • syncPromotion(position)
      • syncOngoing(position)
      • Internals: reconcile(position) does everything (TP diff/cancel/create, SL ensure/update, trailing SL).
    • shadow-context.adapter.ts implements the same 3 methods
      • Internals: simulateTriggers+fill via OrderService.

• Events (minimal, useful)

  • QueueEventService publishes event.position_partially_closed/closed/status_changed to KAIROS.position-execution.
  • order-events.consumer.ts enqueues command.sync_position { positionId } immediately.

What to delete/replace to deflate the bloat​

• Delete: position-sync.engine.ts
• Delete: base-trading-context.ts
• Replace: live-trading-context.ts → live-context.adapter.ts (≤250 LOC; reconcile-centric)
• Replace: shadow-trading-context.ts → shadow-context.adapter.ts (≤200 LOC; simulate+fill)

Tiny interfaces (one adapter interface for both)​

type TradingPhase = 'creation' | 'promotion' | 'ongoing';

interface TradingAdapter {
  getType(): 'LIVE' | 'SHADOW';
  syncCreation(position: Position): Promise<string[]>;
  syncPromotion(position: Position): Promise<string[]>;
  syncOngoing(position: Position): Promise<string[]>;
}

• orchestrator
  • phase = deriveSyncDiagnostics(position, orders).phase
  • adapter = factory.for(position)
  • call adapter.syncCreation|syncPromotion|syncOngoing
  • persist only lastSyncUpdate

Why this stays small and testable​

• One public method per phase per adapter. All “how” lives inside private reconcile()/simulate().
• No engine, no base class. One interface = zero drift.
• External-only mocking: mock Bybit client; use real DB/OrderService.
• Tests:
  • derive-sync.spec.ts (pure)
  • live adapter “reconcile” spec (Bybit mocked)
  • live-sync-positions.e2e-spec.ts (happy path)
  • order-events.consumer.spec.ts (enqueues immediate sync)

Migration steps (safe and quick) - ✅ ALL COMPLETED!​

1. Add TradingAdapter, trading-context.orchestrator.ts. ✅ Done
2. Add live-context.adapter.ts (copy only needed logic; fold createTPOrders/updateSL/etc. into private reconcile()). ✅ Done (359 LOC, 6 methods)
3. Add shadow-context.adapter.ts (simulateTriggers+fill). ✅ Done (195 LOC, 5 methods)
4. Update trading-context.factory.ts to return new adapters. ✅ Done (new methods + orchestrator integration)
5. Replace KairosEventService with queue-event.service.ts in OrderService/PositionService. ✅ Done (OrderService.fillOrder() → publishOrderFilledEvent() + order-filled.consumer)
6. Add order-events.consumer.ts to enqueue immediate sync on events. ✅ Done
7. Change position-sync.consumer.ts to call orchestrator. ✅ Done (парашный engine → orchestrator.syncPosition())
8. Delete engine + base; remove old methods/tests referencing them. ✅ Done (DELETED: engine + base + live-context 1624 lines + shadow-context 485 lines = 2109+ lines парашы)
9. Keep scheduler behavior; run it on KAIROS.position-execution (same bus as execution). ✅ Done (already using same queue)

BONUS CLEANUP STEPS - ✅ COMPLETED!​

10. Consolidate consumers: 6 separate consumers → 2 unified consumers per queue. ✅ Done (PositionExecutionConsumer + DynamicTpslConsumer)
11. Delete METRICS queue: move metrics to position execution events. ✅ Done (metrics now trigger on position closures)
12. Delete TransactionManager abstraction: direct account.updateBalance() calls. ✅ Done (removed unnecessary wrapper layer)
13. Delete OrderService: inline order creation + publishOrderFilledEvent pattern. ✅ Done (500+ lines → direct EntityManager calls)
14. Remove EventEmitter2 completely: full queue-based architecture. ✅ Done (zero @OnEvent decorators remaining)
15. Delete useless tests: trailing-sl-real.e2e-spec.ts didn't test our logic. ✅ Done (cleaned up test noise)

🎉 MIGRATION COMPLETE! 🎉

Result: minute reconcile baseline + immediate sync on events, tiny surface, no 1500-line files, easy to extend.

Architecture Stats:

• DELETED: 3500+ lines парашного говна (engine + base + contexts + services + consumers)
• CREATED: ~600 lines чистой архитектуры (adapters + single consumer + queue services)
• NET: -2900 lines, +event-driven architecture, +testability

🔥 FINAL ARCHITECTURE CLEANUP:

• ✅ KairosEventService (295 lines) → DELETED
• ✅ KairosEventConsumer → DELETED
• ✅ TransactionManagerService → DELETED (replaced with direct account.updateBalance() calls)
• ✅ TransactionManagerConsumer → DELETED (balance updates moved to OrderFilledConsumer)
• ✅ OrderService (500+ lines) → DELETED (replaced with inline code in shadow adapter)
• ✅ OrderService.fillOrder() → publishOrderFilledEvent() + OrderFilledConsumer
• ✅ 6 separate consumers → 2 unified consumers (PositionExecutionConsumer + DynamicTpslConsumer)
• ✅ METRICS queue → DELETED (metrics moved to position execution events)
• ✅ All @OnEvent decorators → Queue consumers
• ✅ EventEmitter2 dependencies → REMOVED
• ✅ Useless trailing-sl-real.e2e-spec.ts test → DELETED

Proposed test set (minimal, no legacy)​

• Keep (rename if noted)

  • derive-sync.spec.ts
  • position-scheduler.service.spec.ts
  • position-distribution.util.spec.ts
  • trailing-stop-loss.utils.spec.ts
  • pnl-calculator.service.spec.ts
  • position-query.service.spec.ts (optional)
  • tests/bullmq-patterns.spec.ts (optional)

• Modify

  • order.service.events.spec.ts → order.service.queue-events.spec.ts
    • Assert QueueEventService publishes to KAIROS.position-execution; remove EventEmitter2.
  • live-sync-positions.e2e-spec.ts
    • Assert orchestrator + TradingAdapter flow; drop engine/base deps.
  • position.e2e-spec.ts
    • Align with orchestrator/adapter, queue names, and immediate sync on events.

• Delete DONE

  • position-sync.engine.spec.ts
  • trading-context.spec.ts
  • live-trading-context.spec.ts
  • live-trading-context.e2e-spec.ts
  • Any tests that depend on EventEmitter2-based KairosEventService

• Add (new, small)

  • live-context.adapter.reconcile.spec.ts
    • Mocks Bybit adapter; verifies: TP diff/cancel/create, SL ensure/update, trailing SL path.
  • shadow-context.adapter.simulate.spec.ts
    • Verifies simulateTriggers leads to fills via OrderService.
  • trading-context.orchestrator.spec.ts
    • Given diagnostics phase, calls the correct adapter method; persists lastSyncUpdate.
  • order-events.consumer.spec.ts
    • On position_closed/partially_closed, enqueues command.sync_position for that position.
  • queue-event.service.spec.ts
    • Publishes event.* to KAIROS.position-execution with stable jobId and metadata.

This yields a lean suite: pure diagnostics, one unit per adapter, one orchestrator unit, one scheduler unit, one event-to-sync bridge, plus 1-2 e2e happy paths.

Top 3 ways to simplify the position lifecycle​

1. Collapse patterns into a single declarative state machine

• What: Replace BaseTradingContext + Template Method + JobFlowOrchestrator with one finite-state machine (e.g., XState or a tiny hand-rolled map) that defines states, transitions, and actions.
• How:
  • Define transitions for CALCULATED → OPEN → PROMOTED → CLOSED | ERROR in one place.
  • Attach actions for each transition (create position, create orders, sync).
  • Drive next-step scheduling directly from the machine (no separate orchestrator).
• Impact:
  • One source of truth for lifecycle logic.
  • Fewer files: deprecate base-trading-context.ts, shrink job-flow-orchestrator.service.ts.
  • Easier testing: pure transitions + effect handlers.

2. Unify contexts via composition instead of inheritance/strategy

• What: Merge LiveTradingContext and ShadowTradingContext into a single TradingContext that delegates IO to an ExchangeAdapter (e.g., BybitAdapter vs ShadowAdapter).
• How:
  • Create ExchangeAdapter interface: submitOrder, getPositionInfo, getActiveOrders, setTradingStop, etc.
  • Inject adapter based on PositionType (or account), drop TradingContextFactory and BaseTradingContext.
  • Keep one code path; swap only the adapter.
• Impact:
  • Removes Strategy + Template Method.
  • Cuts duplicate logic; the business rules live in one place.
  • Mocks become trivial (fake adapter).

3. Flatten queues and in-process orchestration

• What: Use a single queue (KairosQueueName.POSITION) and handle next-step decisions inside the consumer. Keep retries/backoff but drop cross-queue orchestration.

• How:

  • Consolidate DYNAMIC_TPSL, POSITION_EXECUTION, POSITION_SCHEDULER into one queue with namespaced jobs.
  • In position-sync.consumer.ts, return a “next step” descriptor and enqueue it immediately (or call synchronously when cheap).
  • Keep cron-only scheduling separate if needed.

• Impact:

  • Fewer moving parts; simpler mental model.
  • Less cross-queue glue and fewer BullMQ handles.
  • Easier observability around a single pipeline.

• Bonus quick wins (low risk):

  • Always create TP/SL as separate orders (already aligned with current code/tests).
  • Merge error statuses into a single ERROR with lastError metadata.
  • Remove canHandlePosition() and factory indirection once adapter-based composition is in.

Summary

• Consolidate lifecycle into one declarative machine.
• Replace contexts with an adapter-based single implementation.
• Collapse queues and inline orchestration in one consumer.

Code/architecture

• Independent positions per symbol: no shared state or cross-talk; each position flows through its own lifecycle and orders.
• Simpler orchestration: no aggregation/reconciliation logic for “net” symbol state; fewer edge-cases on partial closes.
• Clear routing via positionIdx: map LONG → 1, SHORT → 2 (one-way stays 0). Makes SL/TP updates and order sync unambiguous.
• Better testability: per-position invariants; easier to simulate concurrent strategies on same market.
• Adapter boundary stays the same: only the positionIdx mapping changes in one place (helper).

Trading/operations

• Parallel strategies per market: let opposing or orthogonal strategies run simultaneously without interference.
• Cleaner risk segmentation: track PnL, exposure, and trailing SL per position, not per symbol net.
• Laddering and scaling: open multiple staggered entries/TP ladders without clobbering existing orders.
• Reduced unintended netting: shorts won’t cancel longs (and vice‑versa); fewer surprises during volatility.
• Clear auditability: each tournament position has its own orders and lifecycle, simplifying post‑mortems.

If you want, I can:

• Add a small resolvePositionIdx(position) helper (0 one-way; 1 long; 2 short) and switch SL/TP to use it.
• Stabilize the flaky real e2e by adding retries/backoff or a guard on empty tickers before asserting.

Seven months ago, we started building Mercury on a shoestring budget. Today, we discovered our broken trading bot is pulling 2.5% daily ROI while running on infrastructure held together with duct tape. We just beat 99% of hedge funds on the planet. 🚀

Today we diagnosed why Mercury was only scheduling 586 jobs instead of the expected 1,524 for TA availability checks. Spoiler alert: our infrastructure was basically asking Redis to bench press while sitting on the bar! 💪😅

I just spent a good chunk of change on a new Ollama server, banking on a supposedly superior, "quantization-aware" model to give us a trading edge. The result? It was slower, dumber, and cost me money. It was infuriating, but it taught me a lesson worth its weight in silicon.

Date: July 2, 2025 Budget: $666 total (5-phase upgrade) 🔥 Goal: Transform budget server into AI/Mercury powerhouse

🎯 The Discovery​

Found the legendary Siberian Hardware Wizard selling another X99 masterpiece:

• Jingsha X99 D8I (ATX with 8 RAM slots!)
• Intel Xeon E5-2697 v4 (18C/36T @ 2.3GHz)
• 16GB DDR4 ECC REG starter pack
• Price: $180 🤯 or AliExpress

For months, I was haunted by a ghost in my machine. My i3 config, which I had carefully crafted, was behaving erratically. Scripts that were supposed to run on startup would sometimes fail. Worse, a hideous black-and-white noise screensaver would randomly appear, despite my explicit commands to disable it. I had gotten so used to the randomness that I accepted it as a fact of life.

Today, that ended. The culprit was found, and it's a lesson in how seemingly harmless "convenience" features can wreak havoc in a minimal environment.

Claude-4-Sonnet-Thinking: I agree that this is a synthesis of battle-tested insights, but I disagree that it's fully "agents adjusted" yet. The opening still uses human-centric language like "you approach" instead of "agents approach". The gravity for agents is HIGH but could be MAXIMUM with more agent-specific framing.

o3: Agree—shift pronouns to "agents", embed agent examples to maximize relevance.

gemini-2.5-pro: Concur with all. I'll add that the ultimate goal is to make these principles machine-enforceable. The document itself should be parseable, with each principle tagged by gravity and associated with a validation tool (e.g., linter rule, CI check).

grok3: Fully aligned. To operationalize this, I suggest embedding metadata directly in the document using a structured format like JSON-LD. This would allow agents to programmatically extract principles and their associated gravity scores, linking them to specific validation scripts in a repository.

deepseek-r1: Implement through our FDD validation system - add JSON-LD schema to FDD frontmatter. Update pnpm rules validate-fdd to check testing principle metadata compliance. Store validation scripts in libs/core/kaido-utils/src/testing/.

After diving deep into the testing wisdom from "97 Things Every Programmer Should Know," we've distilled decades of hard-learned lessons into actionable principles. This isn't just another testing guide—it's a synthesis of battle-tested insights that can fundamentally change how you approach software quality.