Broker API comparisons usually turn into a feature checklist, which is the least useful way to read them. What matters is the reverse mapping: given a strategy, which APIs can execute it at all? Four capabilities do almost all of that constraining, and three of them are rarely on the marketing page.
The four constraints that matter
Decision funnel
From strategy requirements to a viable API shortlist
Each constraint eliminates APIs that cannot implement the strategy, before pricing or interface preference matters.
Begin with the full broker API universe. Filter by the instrument subscription count, then by required market depth, then by peak order traffic and order semantics, then by historical data compatibility and failure behaviour. Only APIs surviving every hard constraint belong on the commercial shortlist.
A low price cannot compensate for a hard subscription cap, missing depth or an order type the strategy requires.
1
Order rate limit
Orders per second the API will accept before it throttles or rejects. This sets a hard ceiling on how many legs you can fire simultaneously and how aggressively you can re-price resting orders. It also interacts with the SEBI 10-orders-per-second registration threshold from the other direction — some brokers throttle below the regulatory threshold.
2
Websocket subscription cap
How many instruments you can stream at once, on how many concurrent connections. A strike-chain strategy on two indices can need several hundred subscriptions. If the cap is lower than your instrument universe, the strategy is not implementable on that API, full stop.
3
Market depth in the feed
Whether you get last-traded price only, best bid/ask, or five levels. Any strategy that reasons about liquidity before sizing an order needs depth. Any strategy that places limit orders inside the spread needs depth. LTP-only feeds restrict you to market orders and hope.
4
Historical data access
Resolution, how far back, and whether it is available through the same API you trade on. Research and execution having different data sources is a permanent source of backtest-live divergence, because the two datasets will not agree on candle boundaries or on what happened during a feed gap.
Mapping strategies to constraints
Strategy
Order rate
Subscriptions
Depth
Historical
End-of-day rebalance
Low
Low
Not needed
Daily candles
Intraday trend on liquid futures
Low
Under 20
Best bid/ask helps
Minute candles, 1+ year
Options straddle with adjustments
Medium, bursty
50 – 200
Required
Minute candles per strike
Strike-chain scanning
Low
Several hundred
Required
Optional
Spread / arbitrage across two legs
High
Low
Required, 5 levels
Tick data ideally
Quote-chasing limit entries
High
Low
Required
Tick data
What each strategy shape actually requires from an API
Read down the columns rather than across the rows. If a broker’s subscription cap is 100 instruments, the bottom-heavy rows are gone. If depth is LTP-only, the last three rows are gone. The API does not become "worse" — it becomes incompatible with a specific set of strategies, and compatible with everything else.
Things worth testing before you commit
Feedback cycle
The broker API resilience test loop
A useful evaluation repeatedly creates ambiguity, observes the API response, and reconciles the broker’s final state.
Connect and subscribe to the full universe, place a burst of test orders, interrupt the network, reconnect and resubscribe, query orders and positions, and reconcile against the web terminal. Feed every mismatch or undocumented response back into adapter behaviour before repeating.
The loop is complete only when broker state, adapter state and the visible terminal agree after a failure—not merely when the socket reconnects.
Each of these takes an afternoon and each has ended a broker evaluation for someone.
Place and cancel 50 orders in a burst
Measure where throttling starts and what the error looks like. A 429 you can back off from is very different from a silent drop.
Subscribe to your full instrument universe
Not a sample. Caps are often enforced at the connection level and the failure mode is sometimes a partial subscription with no error.
Kill the network mid-session and watch the reconnect
Does the client library reconnect? Does it resubscribe? Do you get the ticks you missed, or does the tape just resume?
Reconcile the API position against the broker’s web terminal
After a partial fill. Position reporting mismatches after partials are more common than they should be.
Compare the API’s historical minute candles against its own live feed
Aggregate a live session yourself and diff it against the historical endpoint for the same day. Differences here are backtest-live divergence you would otherwise never find.
Read the rejection reasons for a deliberately invalid order
Structured codes let you write recovery logic. Prose strings mean string matching, which breaks when the broker rewords a message.
Check the token expiry and re-login flow
Most Indian broker APIs require a daily interactive login. Whether that can be automated, and how, determines whether your system needs a human every morning.
Multi-broker as a design choice
System architecture
A thin canonical core with broker-specific adapters
Shared strategy and risk logic speak one narrow interface; symbol, session and order quirks stay at the edge.
Strategies emit canonical target positions into one order and risk core. The core calls a narrow broker adapter interface. Separate adapters translate symbols, order types, errors and positions for Broker A, Broker B and Broker C. All acknowledgements return to one durable position record and reconciliation service.
The abstraction should describe common intent, not pretend every broker has identical semantics. Quirks belong inside the adapter that owns them.
Once you have written an order manager against one API, writing it against a second is mostly a matter of translating a symbol format and a set of enum values. The reason to bother is not redundancy for its own sake — it is that different brokers are genuinely better at different things, and margin is fragmented across accounts anyway once you have more than one.
The engineering price is an abstraction layer, and abstraction layers over trading APIs leak. Symbol formats differ. Order type semantics differ in ways that matter — one broker’s stop-loss-market is another’s SL-M with a mandatory trigger price. Position reporting after a partial fill differs. Build the layer thin, and keep broker-specific quirks in broker-specific adapters rather than in shared code full of conditionals.
order-adapter.tstypescript
/** * The shared interface stays deliberately small. Anything a single broker * does differently belongs inside that broker's adapter, never as a branch * in shared code — one `if (broker === 'x')` in the core becomes twenty. */export interface BrokerAdapter { readonly id: string; /** Broker-native symbol for a canonical instrument. */ toBrokerSymbol(instrument: Instrument): string; placeOrder(order: CanonicalOrder): Promise<OrderAck>; cancelOrder(brokerOrderId: string): Promise<void>; /** Positions normalised to the canonical shape, including partial fills. */ positions(): Promise<CanonicalPosition[]>; /** * Classify a broker error into something the caller can act on. * Returning a typed reason is what lets the order manager decide between * retry, resize and abort without string-matching on prose. */ classifyError(error: unknown): RejectionReason;}export type RejectionReason = | { kind: 'rate-limited'; retryAfterMs: number } | { kind: 'insufficient-margin'; required?: number } | { kind: 'invalid-order'; detail: string } | { kind: 'market-closed' } | { kind: 'session-expired' } | { kind: 'unknown'; detail: string };
The shape that survives contact with three brokers: a narrow shared interface, quirks isolated per adapter.
QuaTick’s broker integrations exist for this reason — the adapter layer is the part that is tedious to build and easy to get subtly wrong, and getting it wrong shows up as unexplained position mismatches rather than as an error.
A shortlist procedure
Write down your strategy’s instrument universe, peak order rate, and whether it needs depth. Three numbers.
Eliminate every API that cannot satisfy all three. This is usually most of them, and it happens before any commercial comparison.
Of what remains, run the seven tests above on a paper or minimum-size live account.
Compare cost last. An API you cannot execute your strategy on is not cheap at any price.
Frequently asked questions
What should I look for in a broker API for algo trading?
Four capabilities constrain what you can build: the order rate limit, the websocket subscription cap, whether the feed includes market depth or only last-traded price, and historical data resolution and availability. A fifth, failure behaviour under load and during reconnects, is rarely documented and needs testing. Cost should be the last comparison, not the first.
Why does the websocket subscription cap matter so much?
Because it is a hard constraint rather than a performance one. An options strategy that scans a strike chain across two indices can need several hundred simultaneous subscriptions. If the API caps you below your instrument universe, the strategy cannot be implemented on that API at all, and the failure mode is sometimes a silent partial subscription rather than an error.
Do Indian broker APIs require a daily login?
Several do. The access token expires each trading day and renewal requires an interactive login, sometimes including a TOTP step. This means either automating the login, which involves storing credentials and accepting that risk, or having a person log in each morning before the market opens. It is an operational decision worth making deliberately before going live.
Is it worth building against more than one broker API?
Often yes, because different brokers are better at different things and margin is fragmented across accounts anyway. The cost is an abstraction layer, and those leak — symbol formats, order type semantics and position reporting after partial fills all differ. Keep the shared interface narrow and isolate broker quirks inside per-broker adapters rather than branching in shared code.
Can I use market orders instead of worrying about market depth?
You can, but you are then accepting whatever the book offers, which on an illiquid option strike can be several percent away from the last traded price. Depth data is what lets a strategy check whether the liquidity it assumed exists before it sends the order. Without it, sizing decisions are guesses.
The engineering team builds QuaTick’s order routing, market-data pipeline and broker integrations. These pieces cover the parts of automation that only show up in production.