APIBricks.io Blog - Building Better AI With CoinAPI and FinFeedAPI

Most financial models are trained to look backward.

Prices.

Trades.

Order books.

Volatility.

Volume.

Historical patterns.

All useful.

But there is another dataset becoming increasingly interesting for AI:

what markets expect to happen next.

This is where the combination of CoinAPI and FinFeedAPI gets interesting.

CoinAPI provides detailed cryptocurrency market data.

FinFeedAPI adds data from traditional financial markets and prediction markets.

Together, they give AI and machine learning systems two very different views of the market:

what is happening and what participants think could happen next.

The Missing Variable: Expectations

Imagine Bitcoin falls 5% in two hours.

A traditional model can examine the move from dozens of angles.

Was selling concentrated on one exchange?
Did liquidity disappear?
Did order-book imbalance change first?
Did futures react before spot?
Did volatility increase?

CoinAPI provides the market data needed to investigate those questions.

But the model may still be missing something.

What changed in the outside world?

Perhaps the probability of a regulatory decision changed dramatically.

Maybe expectations around an election shifted.

Maybe markets suddenly repriced the next Federal Reserve decision.

These events don't live inside the BTC/USD order book.

Prediction markets can provide another signal.

Now the model can see both:

BTC market structure changed.

And:

market expectations around an external event changed.

That creates a much more interesting research problem.

CoinAPI Shows Market Behavior

CoinAPI gives models a detailed view of digital-asset markets.

Not just closing prices… but the actual mechanics behind them.

Trades can show aggressive buying and selling.

Quotes show changes in the best available prices.

Order books show liquidity appearing and disappearing.

Derivatives data adds another view of positioning and market structure.

And because crypto trades across many independent venues, researchers can study how information moves between exchanges.

For machine learning, these become features.

Price returns.

Spread.

Depth.

Volume.

Order-book imbalance.

Volatility.

Cross-exchange divergence.

Trade intensity.

The model can learn what markets did.

But that's only one side of the problem.

FinFeedAPI Adds Context Beyond Crypto

FinFeedAPI expands the available feature space.

Its datasets cover areas including stocks, currencies, SEC filings, and prediction markets.

That means an AI system doesn't have to treat crypto as an isolated market.

A model investigating BTC can also look at currency movements.

A model analyzing a crypto-related company can incorporate equity data and SEC disclosures.

And a model studying event-driven behavior can incorporate prediction market probabilities.

That changes the question from:

“What does Bitcoin's historical pattern suggest?”

to:

“What is happening across the markets and information sources that could influence Bitcoin?”

That's a much closer representation of how real financial markets work.

Prediction Data Creates a New Type of Feature

Prediction market data is especially interesting because it doesn't behave like traditional financial data.

A BTC price is the price of an asset.

A prediction-market price can represent the market's current estimate of an event occurring.

For ML researchers, that creates features that weren't previously easy to obtain.

For example:

fed_cut_probability

election_probability

regulation_probability

macro_event_probability

But the raw probability is only the beginning.

Models can derive:

Probability momentum

→ How quickly is the expectation changing?

Probability volatility

→ How stable is market confidence?

Event proximity

→ Does the signal become more informative as resolution approaches?

Cross-market divergence

→ Do different prediction markets disagree about similar outcomes?

Probability shock

→ Did expectations suddenly move far outside their recent range?

These variables can then be tested against CoinAPI market features. That is where the combination becomes powerful.

What Could You Actually Test?

Suppose you have several years of crypto market data and historical prediction market data.

You could test whether major probability changes are associated with changes in crypto markets.

For example:

Does BTC volatility increase after large changes in Fed expectations?

Or:

Do crypto-related regulatory probabilities lead changes in BTC trading activity?

You could test whether the relationship changes depending on market conditions.

Maybe prediction signals matter during high-volatility periods but disappear during quiet markets.

Maybe BTC reacts immediately while altcoins react later.

Maybe spot markets move before derivatives.

Maybe there is no statistically useful relationship at all.

That's important too.

The point of adding prediction-market data isn't to assume it improves a model.

It's to give the model another hypothesis to test.

Add Text and the Picture Gets Even Bigger

Structured market data isn't the only useful input for financial AI.

Consider a major regulatory announcement.

An AI pipeline could combine:

CoinAPI

BTC and ETH trades, quotes, order books, OHLCV, and other crypto market information.

FinFeedAPI Prediction Markets

Changes in market-implied probabilities around the regulatory outcome.

FinFeedAPI SEC Data

Relevant corporate disclosures.

LLMs

News, announcements, transcripts, and other unstructured information.

Now the model has several representations of the same event.

What happened.

What people expected.

What companies officially disclosed.

And what was being said about it.

This is closer to multimodal financial intelligence than simple price prediction.

AI Agents Change How This Data Can Be Used

There is another shift happening at the infrastructure level.

Historically, using financial APIs meant that a developer decided exactly what data an application needed.

Call endpoint A.

Transform response B.

Store field C.

Run model D.

AI agents work differently.

An agent may decide what information it needs as part of the reasoning process.

For example, an agent investigating an unusual Bitcoin move could ask:

What happened to BTC across major exchanges?

Was the move visible in order-book liquidity?

Did currencies move at the same time?

Were relevant prediction probabilities changing?

Was there a related corporate filing?

Instead of building one enormous dataset before asking the question, an agent can retrieve the appropriate information as it investigates.

This is one reason Model Context Protocol (MCP) matters for APIBricks.

CoinAPI and FinFeedAPI data can be exposed as structured tools that compatible AI systems can discover and query.

The agent isn't limited to generating an answer from what appeared in its training data.

It can retrieve the financial data required for the task.

Different Data. One Research Layer.

CoinAPI and FinFeedAPI were built for different parts of the financial-data landscape.

That's precisely what makes the combination interesting for AI.

CoinAPI provides deep digital-asset market data.

FinFeedAPI expands the picture with stocks, currencies, SEC filings, prediction markets, and other financial datasets.

APIBricks brings those capabilities together at the platform level.

For developers, that means fewer isolated data silos.

For quantitative researchers, it means a larger feature space.

For AI agents, it means access to different financial tools depending on the question being investigated.

And for machine learning teams, it creates something even more valuable:

more ways to test why markets move — not just whether they will move up or down.

Build Financial AI With APIBricks

Financial AI gets more useful when it can see more than a price chart.

Combine CoinAPI's crypto market data with FinFeedAPI's financial and prediction market datasets to build richer research pipelines, ML features, and AI agents.

→ Connect financial data through MCP

Building Better AI With CoinAPI and FinFeedAPI