API rate limits and polite data fetching
Posted: Wed Oct 28, 2026 7:07 am
Broker APIs and data providers limit how many requests a client can make per second, minute or day. Exceeding the limit produces errors, throttling or temporary bans. For automated strategies, rate limits are a design constraint.
Reading the documentation is the first step. Limits may differ by endpoint, with order placement handled more generously than market data queries. Some APIs return headers showing remaining capacity.
Design patterns help. Cache data that doesn't change quickly. Batch requests where supported. Use streaming connections instead of polling for prices. Implement exponential backoff on errors, waiting longer after each failure rather than hammering the server.
Logging every request and response code reveals how close the system runs to limits. Alerts on repeated errors catch problems early.
Politeness also matters ethically. Free data providers survive on goodwill, and abusive clients ruin it for others.
Unexpected behaviour at peak times is common. A strategy tested on a quiet day may hit limits during a news spike, exactly when it needs data most.
A simple pattern is a token bucket: the program keeps a count of available requests that refills at the allowed rate, and waits when the count reaches zero. A few lines of code prevent nearly all rate-limit errors, and the logic is easy to test.
Has an API limit ever caused a strategy to misbehave, and how was it redesigned?
Reading the documentation is the first step. Limits may differ by endpoint, with order placement handled more generously than market data queries. Some APIs return headers showing remaining capacity.
Design patterns help. Cache data that doesn't change quickly. Batch requests where supported. Use streaming connections instead of polling for prices. Implement exponential backoff on errors, waiting longer after each failure rather than hammering the server.
Logging every request and response code reveals how close the system runs to limits. Alerts on repeated errors catch problems early.
Politeness also matters ethically. Free data providers survive on goodwill, and abusive clients ruin it for others.
Unexpected behaviour at peak times is common. A strategy tested on a quiet day may hit limits during a news spike, exactly when it needs data most.
A simple pattern is a token bucket: the program keeps a count of available requests that refills at the allowed rate, and waits when the count reaches zero. A few lines of code prevent nearly all rate-limit errors, and the logic is easy to test.
Has an API limit ever caused a strategy to misbehave, and how was it redesigned?