How to build a trading agent

A trading agent is an autonomous program that reads market data, decides on a trade, and executes it without a human in the loop. This guide walks the full pipeline — data, reasoning, execution, risk, backtesting, and deployment — and is honest about which parts you should build and which you should buy.

The five parts of every trading agent

Whether it's a simple moving-average bot or an LLM-driven system, every trading agent has the same five layers. Most tutorials only cover the first two.

1. Data — real-time prices, order book, funding, on-chain or fundamental signals.
2. Reasoning — rules, a model, or an LLM that turns data into an intent ("go long X").
3. Execution — placing, modifying, and cancelling orders; handling partial fills and slippage.
4. Risk — position sizing, stop-losses, atomic exits, max-drawdown and exposure limits.
5. Operations — state, reconnects, retries, rate limits, monitoring, and deployment.

Step 1 — Get reliable market data

Start with a single venue and a single data feed. For perps, you need at minimum: mark price, order book depth, and funding rate. Use a websocket for live data and a REST endpoint for snapshots and reconciliation. Reconcile on every reconnect — never assume your local book is correct after a disconnect.

Step 2 — Turn data into an intent

Keep reasoning separate from execution. The reasoning layer should output a clear, typed intent — for example { side: "long", symbol: "ETH", sizeUsd: 250, maxSlippageBps: 8 } — and nothing about how to fill it. This separation lets you swap a rule engine for an LLM later without touching execution.

Step 3 — Execute safely

This is the layer that determines whether you keep your money. At minimum, execution must handle partial fills, idempotent retries (so a network hiccup doesn't double your position), and an atomic exit — the ability to flatten a position in one call even if the venue is degraded. Building this from scratch correctly takes weeks; getting it wrong takes minutes.

Step 4 — Backtest before you deploy

Backtest on data that includes order-book evolution, not just close prices — execution quality depends on queue position and slippage. Treat a backtest as a filter, not a promise: paper-trade live for a period before risking capital.

Step 5 — Build or buy?

You can hand-roll all five layers, adopt an open-source framework, or call a managed execution API. The right answer depends on how much of the execution/risk/ops burden you want to own.

If you'd rather write strategy than maintain execution plumbing, a managed layer like Superior Trade (which we operate) exposes placement, backtesting, deployment, and an atomic-exit primitive as agent-callable endpoints, so your agent only has to decide what to trade. See the execution API overview.