Prediction Market History Built for Research, Not Just Latest Prices

Prediction markets are often presented as a live signal:

What probability is the market assigning right now?

That is useful for monitoring…

It is not enough for serious research.

Researchers, quant teams and AI developers need to know what happened before the current price. They need the trades behind the move, how bid and ask liquidity changed, how the order book evolved, what the market looked like at a specific point in time, and which timestamp represents the exchange event versus the data-processing pipeline.

That changes the question from “What is the probability now?” to “How did the market get here?”

FinFeedAPI's Prediction Markets API is designed to make that history accessible as a research dataset rather than just a stream of latest prices.

A latest price is a snapshot, not a dataset

Suppose a prediction market moves from 42% to 68%.

The latest price tells you where the market ended up. It does not tell you what happened between those two points.

• Was the move gradual or instantaneous?
• Did significant volume trade during the repricing?
• Did liquidity disappear before the move?
• How wide was the spread?
• Was the new price supported by persistent trading or a small number of transactions?

These questions require different layers of historical data.

FinFeedAPI provides historical trades, quotes, OHLCV, market records, and historical order book updates, alongside current market state.

That distinction matters when prediction market data moves from dashboards into research pipelines.

Start with the event tape: historical trades

Historical trades provide the closest view of executed market activity.

For a specific prediction market, FinFeedAPI can retrieve trade history from flat files stored in external S3 storage. Requests can cover a full day or a bounded time_start / time_end range, with hourly files and automatic fallback to daily files.

This gives researchers the raw material for questions such as:

• How quickly did a market react to new information?
• How much volume accompanied a probability move?
• Did trading activity increase before a major event?
• How persistent was a repricing?
• How did execution flow behave around announcements?

This is fundamentally different from studying a candle chart. A candle summarizes a period; the trade tape lets you examine the events inside it.

Quotes show what traders could have traded

Trades only tell part of the story.

For liquidity research, historical quotes can be even more important. FinFeedAPI provides historical bid/ask data including ask, bid, ask_volume, and bid_volume.

That allows researchers to examine the market around executed transactions.

For example, a model can study whether spreads widened ahead of uncertain events, whether available size disappeared before rapid repricing, or whether a market remained liquid as its implied probability approached an extreme.

This is the difference between knowing where someone traded and knowing what liquidity was available around that trade.

Historical order books go deeper

For research that requires more than top-of-book quotes, FinFeedAPI also exposes historical limit order updates.

Importantly, this history is not presented as a sequence of reconstructed snapshots. The historical order book endpoint returns raw order updates… adds, updates, and deletes… from flat files. Researchers can use those events to maintain their own reconstruction of book state.

That distinction should not be hidden.

A current order book request returns a snapshot containing bids and asks. Historical order book data instead provides the underlying changes needed for replay and downstream reconstruction.

For quantitative users, that can support research into:

• Liquidity dynamics. How depth changes as information arrives.
• Order book imbalance. Whether changes in available bid and ask liquidity precede probability movements.
• Market replay. Rebuilding the sequence of book changes rather than looking only at periodic snapshots.
• Execution research. Studying what liquidity would have been available to a strategy at different moments.

The raw-event approach also makes the methodology explicit: if you reconstruct a historical book, you control and can document the reconstruction logic.

Exchange time and processing time are not the same thing

This is one of the details that can quietly distort historical analysis.

For historical trades, quotes, and order book updates, FinFeedAPI distinguishes between the time associated with the exchange event and the time at which the data entered the processing pipeline.

The documentation identifies these as time_exchange and time_coinapi. Historical results are filtered using time_coinapi, while time_exchange represents when the event occurred on the exchange. Because some source integrations involve polling delays, the exchange timestamp can precede the processing timestamp.

The same distinction applies to historical quote data.

For casual charting, that difference may not matter much.

For event studies, latency analysis, market reaction research, and backtesting, it can matter considerably.

A researcher should be able to distinguish:

This gives researchers more information to reason about sequencing rather than silently treating every timestamp as interchangeable.

OHLCV is useful… but it is an aggregation

Not every research problem needs event-level data.

FinFeedAPI also exposes historical OHLCV for individual markets and at the exchange level. Market-level historical responses include period boundaries, open and close times, OHLC prices, traded volume, and trade count.

Supported intervals range from seconds and minutes through hours, days, months, and years.

That makes candles useful for longer-horizon analysis, charting, feature generation, cross-sectional research, and ML pipelines where processing every individual event would be unnecessary.

But researchers should understand what they are consuming.

OHLCV is a time-bucketed representation of market history. Raw trades, quotes, and order updates provide a different level of detail.

The choice should follow the research question rather than convenience.

Historical market context matters too

There is another problem with researching prediction markets: markets themselves change.

A dataset of prices without information about what those prices refer to is incomplete.

FinFeedAPI provides historical market records containing fields such as market_id, title, description, outcome name, price, status, exchange, and outcome type.

This matters when building research archives.

A prediction market is not simply a ticker. It represents a proposition with specific wording, outcomes, venue context, and lifecycle.

Keeping that context alongside the time series makes historical observations much more useful later.

Why aggregation matters more for history than for live prices

Venue APIs are usually designed first for their own products.

That makes sense. Their job is to support their markets, traders, and interfaces, not necessarily to provide a normalized multi-venue research archive.

For a researcher working across prediction markets, this creates additional work.

One venue may expose historical trades differently from another. Market identifiers vary. Timestamp semantics vary. Available historical endpoints vary. Order book representations vary. Data retention can vary.

The difficult part is therefore not simply connecting to another endpoint.

It is turning heterogeneous venue data into something that can be queried and studied consistently.

That is where an aggregator has a different role.

FinFeedAPI provides Prediction Markets data through REST, JSON-RPC, and MCP, with live and historical access available across the interfaces.

The objective is not only to answer:

→ What is this market trading at?

It is to make it possible to ask:

→ What did this market look like yesterday, last week, or around a specific event and what happened inside that period?

Prediction market data becomes more valuable with time

Live prediction market prices are interesting because they compress current expectations into a number.

Historical prediction market data can be more powerful because it preserves how those expectations changed.

That opens the door to research on information diffusion, market efficiency, liquidity, event response, forecasting performance, trader behavior, and the relationship between prediction markets and traditional financial markets.

For AI and machine learning teams, history is also what turns prediction markets from a live input into training data.

The latest probability can become a feature.

A historical archive can become a dataset.

Build research on the full market history

FinFeedAPI's Prediction Markets API gives researchers access to more than the latest market probability.

Historical trades and quotes provide event-level activity. Historical order updates support book replay and reconstruction. OHLCV provides compact time-series representations. Historical market records preserve market context. And separate exchange and processing timestamps help researchers understand when events happened versus when they entered the data pipeline.

That is the foundation needed to treat prediction markets as a serious research dataset.

→ Explore the FinFeedAPI Prediction Markets API

→ Read the Prediction Markets API documentation

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