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Trading Agent Fleets

Design, Ship, and Run Autonomous Trading Agents — From Signal to Settlement

The production playbook for autonomous trading agents, drawn from running real perpetual-futures agents 24/7. Go from 'what is a trading agent' to operating a fleet: design the perceive-decide-act-reconcile loop, pick a venue by its liquidation math and reconciliation burden, build a signal engine that audits its own fire rate, size with fractional Kelly off available balance, place confirm-or-halt brackets with idempotent orders, keep stops on the right side of live liquidation, walk a position to profit on a conviction ladder, and wrap the whole thing in an execution-integrity layer before scaling to a supervised fleet. Ten hands-on lessons; every one ends in a BuildChallenge you implement yourself.

Recommended: Complete Quantitative Scoring System Design first

12 lessons~130 min total

Lessons are shown in recommended order. Complete them in sequence for the best experience — or jump to any lesson.

Lesson 671·8 min read

What Is a Trading Agent?

A trading agent is not a better indicator — it is an autonomous control loop that perceives market state, decides, acts on a venue, and reconciles intent against reality, running unattended 24/7.

Lesson 672·10 min read

Anatomy of a Trading Agent: Signal to Settlement

A production trading agent is five subsystems with clean contracts between them — and understanding which subsystem is the source of record is the difference between debugging in minutes and debugging for hours.

Lesson 673·10 min read

Picking Your Venue: Exchanges, Brokers & Market Structure

Where your agent trades is not a deployment detail — it determines your liquidation math, your reconciliation burden, and whether a strategy tuned on one asset class will survive on another.

Lesson 674·11 min read

Signal Engines: From Market Data to Conviction

A signal engine is a pipeline that turns raw market data into a directional conviction score — and the silent killer is a near-dead component carrying weight it never earns.

Lesson 675·11 min read

The Math of Edge: Expectancy, Kelly & Position Sizing

Positive expectancy is the only thing that compounds — and Kelly tells you how much to bet, while isolated margin tells you which balance to bet it from.

Lesson 676·11 min read

Execution Mechanics: Orders, Slippage & the Bracket

The signal decides what to do; execution decides whether it actually happens — and a stop that isn't confirmed live is not a stop.

Lesson 677·11 min read

Risk Management: Stops, Liquidation & Survival

Survival beats optimization every time — and the first thing that kills an autonomous trading agent is a stop placed on the wrong side of the liquidation price.

Lesson 678·11 min read

The Path to Profit: DCA, Conviction Ladders & Taking Profit

Every step on the path to profit should reduce downside or lock a floor — the moment you add naked risk to a running position is the moment the position starts managing you.

Lesson 679·12 min read

Execution Integrity: Reconciliation & Confirm-or-Halt

Almost every catastrophic agent loss happens at the intent-to-reality seam, not in the signal — the exchange is the source of record, and local state is just a cache.

Lesson 680·12 min read

Managing the Fleet: Orchestration, Halts & Observability

Going from one agent to a fleet introduces a new class of failure: agents colliding with each other on a shared account, silent halts that nobody notices, and supervisors that accidentally act on their own proposals.

Lesson 681·11 min read

Latency, Partial Fills & Real Execution

The gap between 'order submitted' and 'position as intended' is where real execution risk lives — partial fills, latency windows, and VWAP corruption are the failure modes that turn a good signal into a bad trade.

Lesson 682·12 min read

Backtesting & Walk-Forward Validation

Real edge survives out-of-sample. Overfitted edge collapses on contact with live markets. Walk-forward analysis is the only method that tells you which one you have — before you deploy capital.