Agentic Trading Apps
A reference flow and build playbook for trading agents that watch a real-time market feed, reason about a trade, pass it through a deterministic risk guardrail, and execute through a broker API — with a human kill switch always in the loop.
This is an architecture and engineering reference, not financial or investment advice. Algorithmic trading carries real risk of loss. Paper-trade extensively, keep a deterministic risk layer independent of any LLM, and review regulatory obligations (e.g. FINRA algorithmic trading rules) before running a strategy with real capital.
The signal rail
Market data flows in continuously; the agent only ever proposes a trade. A separate, deterministic guardrail gate decides whether it may fire, and a human retains a kill switch over the whole loop.
Real-time market feed
Websocket ticks/bars (price, volume, spread) streamed from the exchange or a market data provider.
Signal & feature engine
Rolling VWAP, ATR, volume z-score, and momentum computed on each new bar inside a strict latency budget.
Agent reasoning
Planner weighs the signal against strategy thesis and portfolio state, then drafts a sized trade proposal.
Risk & compliance gate
Deterministic, non-LLM checks: position limits, max daily loss, restricted list, PDT rules, circuit breaker.
Order execution
Bracket order (entry, stop, target) to the broker API with idempotency keys and retry/timeout handling.
Oversight & kill switch
Fills, slippage, and P&L stream to a dashboard; a human or a drawdown rule halts new entries instantly.
Oversight closes the loop — kill switch or drawdown breaker halts new entries and feeds state back to the top; protective orders on open positions stay live.
Momentum breakout agent on AAPL
Walking the rail above through one concrete scenario — a 1-minute momentum breakout strategy on a liquid stock.
A websocket feed streams 1-minute OHLCV bars for a liquid stock such as AAPL. The feature engine updates rolling VWAP, ATR(14), and a volume z-score on every new bar.
Volume spikes 3x average and price breaks above the VWAP + 1 ATR band. The signal engine emits a breakout event to the agent.
The agent checks the setup against its strategy thesis and current portfolio exposure, then drafts a proposal: long 50 shares, stop at VWAP, target at 2x ATR, with its reasoning attached for the audit log.
The risk guardrail — plain deterministic code, not the LLM — checks the proposal against max position size, daily loss limit, and whether a circuit breaker or restricted-list flag is active. Only a passing proposal continues.
A bracket order (entry + stop + target) is sent to the broker API with an idempotency key so a retried request can never double-fill.
Fills, slippage, and running P&L stream to a dashboard. If drawdown crosses a threshold, or a human hits the kill switch, the agent stops proposing new entries but existing protective orders stay live.
The full decision — inputs, reasoning, guardrail verdict, and outcome — is logged for the nightly backtest and drift review.
Steps to build an agentic trading app
A practical build order — each step should work end to end in paper trading before the next one touches real capital.
Define the strategy mandate
Asset class, timeframe, max position size, capital at risk, and any regulatory constraints (PDT rules, restricted symbols) before writing code.
Wire a real-time data feed
Websocket bars/ticks from a market data provider, plus historical backfill so indicators have a warm-up window on startup.
Build the signal/feature layer
Vectorized rolling calculations (VWAP, ATR, volatility, momentum) that run inside the latency budget for your timeframe.
Design the agent reasoning loop
A planner (LLM, rules engine, or hybrid) that turns a signal plus portfolio context into a sized, justified trade proposal — not a direct order.
Add a deterministic risk guardrail
Position limits, max drawdown, restricted list, and circuit breakers enforced in plain code, independent of the agent — never let the LLM be the last check before an order fires.
Paper trade before real capital
Run the full loop against a paper/sandbox broker account for weeks; validate fills, slippage assumptions, and edge cases under real market conditions.
Integrate broker execution
Order types, idempotency keys, partial fills, and reconciliation against the broker's source of truth, not just your local state.
Instrument observability
Log every decision (inputs, reasoning, proposal, guardrail verdict, outcome) for latency, slippage, and P&L attribution review.
Add human-in-the-loop controls
A visible kill switch and an approval gate for anything above a size or risk threshold — the agent proposes, a rule or a human confirms.
Backtest, shadow-run, then scale slowly
Continuously backtest against fresh data, run new strategy versions in shadow mode before they touch capital, and scale position size gradually.
Where the gate lives matters
Keep risk and compliance checks in deterministic code that runs after the agent and before the broker call — never rely on prompting an LLM to "remember" not to exceed a position limit. The agent should only ever be able to propose; a separate, boring, well-tested rules engine decides what is allowed to execute.
Broker APIs & regulatory reading
Market data and execution APIs, plus regulatory background worth reading before running a strategy with real capital.
Market Data API & Trading API docs
Real-time market data websockets, paper trading, and order execution APIs commonly used to prototype agentic trading systems.
TWS API documentation
Reference for a production-grade broker API: order types, market data, account/position sync, and paper trading.
Algorithmic trading
Regulatory background on algorithmic trading obligations — relevant context before deploying automated strategies with real capital.