Common mistakes with Broker APIs

What people get wrong about a Broker API

A Broker API is an interface (usually programmatic) that lets software send requests to a broker/execution venue and receive responses such as order acknowledgements, fills, and account-related updates. Common mistakes happen when developers treat that interface like a single, fully reliable pipeline rather than a system with explicit states, timing, and possible failure outcomes.

The main misunderstandings to watch for:

• Confusing “request accepted” with “trade executed.” An API may confirm that your message was received while the final outcome depends on execution rules.
• Assuming timestamps and prices are synchronized. Different systems may use different clocks, update cycles, or representations.
• Overlooking the difference between market data you see and market data that mattered for execution. Execution can depend on spreads, liquidity, and order book changes at the moment the broker processes your order.
• Forgetting that costs exist and can vary: commissions, financing/overnight effects, and other fees may alter net results.
• Treating errors as rare exceptions. In practice, APIs can return timeouts, rejected orders, partial fills, or missing updates.

The mechanics: where mistakes originate

Broker APIs typically involve these moving parts:

1. Request creation: you generate orders and choose parameters (instrument, quantity, type, time-in-force, and identifiers).
2. Transport and processing: your request travels over a network, is authenticated, and is processed by the broker’s services.
3. State updates: the broker responds with acknowledgements and later publishes status changes (e.g., open → partially filled → filled/cancelled/rejected).
4. Execution reporting: fills and related accounting details are delivered when execution occurs.

Common implementation mistakes within those parts:

• Not using stable identifiers (or using them inconsistently). Without a clear client-side id and consistent replay rules, retries can create duplicates.
• Ignoring idempotency expectations. If you retry after a timeout, you may not know whether the broker already acted.
• Hard-coding assumptions about order life cycles. Some orders can be rejected after acceptance, partially filled multiple times, or cancelled based on venue rules.
• Mixing “estimate” and “confirmed” values. If your system logs a calculation based on a snapshot and later compares it to realized fills, mismatches are expected.

Evidence and example checks you can do

Because outcomes vary and no real-time data is assumed here, the safest approach is to verify behavior using controlled checks:

• State-machine audit: For a sample of test orders, record each message/event and ensure your application transitions through every state you observe (accepted, open, partial fill, final state). If an observed state is missing from your logic, you found a likely bug.
• Retry and duplication test: Simulate a network timeout right after sending an order request. Then verify whether the broker created one order or multiple, and confirm how your client identifiers behave on retry.
• Accounting consistency check: For each fill event you receive, compare your gross/fee assumptions to what your broker reports as realized results. If the API provides separate fields for fees or balances, use those confirmed values rather than estimates.
• Time and sequencing check: Log the local time when you send requests and the broker’s reported timestamps (if provided). Look for ordering differences: you may need to sort by event time rather than arrival time.

These checks do not guarantee future performance, but they directly test whether your software’s assumptions match the broker API’s observable behavior.

Limitations, material failure modes, and risks

A major limitation is that broker APIs operate within real-world conditions: network delays, service congestion, venue rules, and varying liquidity. Even correct code can produce different results from earlier runs.

At least one material failure mode to plan for:

• Partial fills and delayed finality: Your system might assume an order completes immediately. In reality, fills can be split across time, and the final status may arrive later.

Other failure modes that commonly cause harm:

• Rejected orders with missing context: If you treat rejections as generic failures, you may lose the reason category needed to fix parameter issues.
• Stale or incomplete updates: You can receive account/order updates out of sequence. Without careful reconciliation, you can compute positions incorrectly.
• Incorrect net result calculations: If you ignore fees, rounding rules, or contract/margin conventions, your internal “expected P&L” can diverge from what the broker reports.