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Developer GuideApril 21, 2026·7 min read

API Data Objects Backtesting Developers Actually Need

CuteMarkets

CuteMarkets Team

Research

Quick answer

API Data Objects Backtesting Developers Actually Need

The core options backtesting sequence is discover contracts and expirations, build the signal, select the contract, price the fill, and record evidence.

API Data Objects Backtesting Developers Actually Need

Abstract

Backtesting does not need every possible market data object on day one. It needs the right objects in the right order. For options strategies, that usually means contracts, expirations, quotes, trades, aggregates, snapshots, and reference metadata.

The workflow matters more than the menu. A developer can have a long endpoint list and still build an unrealistic simulator if the objects are used in the wrong sequence.

The Smallest Useful Sequence

A serious options backtest starts by asking what was tradable, not what looks attractive today. That means the first objects are contract and expiration objects. They define the universe. Only after that should the framework request price and execution objects for the selected contract.

The practical sequence is:

  1. Discover listed expirations for the underlying.
  2. Rebuild the contract universe with a historical as_of date.
  3. Select one contract or structure using DTE, strike, delta, liquidity, and spread rules.
  4. Pull quote windows for executable bid/ask context.
  5. Pull trades for activity and tape confirmation.
  6. Pull aggregates or bars for path, charting, and reports.
  7. Store the selected contract, rejected candidates, request windows, and fill evidence.

That sequence is slower than grabbing a chain and running a quick loop. It is also easier to defend.

Object Map

ObjectPrimary jobCommon misuse
ExpirationsConfirm listed dates before chain or contract requests.Generating calendar dates and assuming listed contracts exist.
ContractsRebuild the tradable universe at a point in time.Selecting from today's chain in a historical backtest.
Chain snapshotsCompare many current contracts at once.Treating a current chain as historical proof.
QuotesMeasure bid/ask, spread, size, and quote freshness.Replacing quotes with last trade for fills.
TradesConfirm printed activity and tape context.Assuming every print was available as your fill.
AggregatesBuild chart path, VWAP, bars, and reports.Using bars as proof of executable bid/ask.
Contract snapshotsInspect one selected contract with current context.Using a snapshot without preserving the selected timestamp.

The table is intentionally plain. Most backtesting errors come from mixing up these jobs.

Contracts and Expirations

Contracts define the tradable universe. Expirations define which dates are listed for the underlying. A backtest should query these before asking for quote, trade, or aggregate data because the strategy cannot trade contracts that did not exist.

This is the foundation for avoiding stale-contract and generated-calendar leakage. If a simulator asks for a 21 DTE call and the current chain has a clean answer, that does not prove the same contract was available in the historical window. The selector needs a contract list tied to the simulated date.

Good contract artifacts include the underlying ticker, OCC ticker, expiration date, strike, call/put type, as_of date, selection timestamp, and the reason the contract survived filtering.

Quotes and Trades

Quotes describe the executable market. Trades describe printed activity. Both are useful, but they answer different questions.

Use quotes for fill assumptions, spread checks, and quote age. Use trades for activity, tape context, and validation. Do not use last trade as a universal fill proxy. A last sale can be stale, outside the relevant window, or printed when the bid/ask market was not attractive enough for your strategy.

For an option entry, a research artifact should preserve the bid, ask, midpoint, quote timestamp, spread, and fill policy. For example, a long option backtest might record that the simulated entry paid near the ask, while a midpoint reference is stored only for diagnostics. If the quote is missing or too wide, the trade should be rejected with a reason code instead of forced into the PnL curve.

Aggregates and Snapshots

Aggregates are useful for signal generation, charting, VWAP, and session context. Snapshots are useful for chain state, Greeks, IV, open interest, and current quote/trade context.

The signal layer can often start from aggregates. The execution layer needs quotes and contract details. Keeping those layers separate is what lets a developer debug a change later. If PnL improves after a code change, the artifact should reveal whether the signal changed, the selector chose a different contract, or the fill model became easier.

What To Store

StageStore this
SignalUnderlying ticker, signal timestamp, feature window, direction, and triggering condition.
UniverseExpiration list, contract request filters, returned count, and as_of date.
SelectionCandidate list, selected OCC ticker, ranking fields, and rejection reasons.
EntryQuote window, bid, ask, midpoint, quote age, spread, fill side, and source endpoint.
ExitExit rule, timestamp, price source, quote or bar evidence, and reason.
SummaryDaily PnL, trade count, reject count, drawdown, coverage, and config hash.

This storage model makes the API layer part of the research method. It is not just data ingestion.

Takeaway

A developer-friendly data API should support the backtesting sequence: discover the universe, build the signal, select the contract, price the fill, and record the evidence. CuteMarkets is organized around that sequence through contracts, expirations, quotes, trades, aggregates, and options chain workflows.

FAQ

Related questions

Should backtests use quotes or trades for fills?

Use quotes for fill assumptions and spread checks. Use trades for printed activity, tape context, and validation.