Prediction markets are often praised for being "surprisingly accurate."

You'll hear that they beat polls.

That they react faster than the news.

That the collective intelligence of thousands of traders somehow produces better forecasts than individual experts.

Sometimes that's true.

Sometimes it isn't.

The interesting question isn't whether prediction markets are good.

It's when they're good.

Does the crowd become more accurate as an event gets closer?

Do probabilities gradually converge toward reality?

Or do markets remain noisy until the final hours before resolution?

Those questions can't be answered by looking at today's prices.

They require history.

Lots of it.

That's exactly why historical prediction market data has become increasingly valuable… not just for traders, but for data scientists, quantitative researchers, economists, and AI teams trying to understand how human expectations evolve over time.

Fortunately, collecting that data no longer requires scraping websites or building custom crawlers.

With FinFeedAPI's Prediction Markets API, you can discover markets, retrieve historical OHLCV candles, download historical trades and quotes, and build large research datasets across multiple prediction market platforms through a single, normalized interface.

The Question Worth Answering

Imagine collecting hundreds of resolved markets:

• Federal Reserve rate decisions
• Election outcomes
• Inflation releases
• Cryptocurrency milestones
• Macroeconomic announcements

For every one of those contracts, you already know the final outcome.

Now imagine traveling backward in time.

Thirty days before resolution.

Two weeks before.

One week.

One day.

One hour.

At each point, you ask the same question:

How close was the market's implied probability to reality?

That's no longer a trading problem.

It's a data science problem.

And it's one that prediction markets are uniquely positioned to answer.

The Hypothesis

Most researchers start with a simple assumption: As uncertainty disappears, market probabilities should become more accurate.

That sounds obvious.

But assumptions deserve to be tested.

Suppose a market ultimately resolves "Yes."

Thirty days beforehand, the market might price that outcome at only 42%.

A week later, 61%.

The day before resolution, 88%.

An hour before settlement, 99%.

That progression suggests something important.

The crowd wasn't simply guessing.

It was continuously incorporating new information as it became available.

But does that pattern hold across hundreds… or thousands… of markets?

That's where historical analysis begins.

Why This Matters Beyond Prediction Markets

At first glance, this might sound like an academic exercise.

It isn't.

Understanding how prediction markets behave has practical applications across many fields.

A quantitative fund may want to identify situations where markets consistently underreact.

An AI research team may want to use prediction probabilities as training signals.

A financial analyst might compare prediction markets with macroeconomic forecasts.

Political researchers can evaluate whether election markets improve as debates, polling, and economic reports unfold.

Even companies building forecasting systems can compare human collective intelligence against machine-generated predictions.

Prediction markets become much more useful when viewed as time series rather than isolated prices.

Instead of asking: "What's the probability today?"

You begin asking: "How did confidence evolve over the life of the market?"

Building the Dataset

Before you can analyze anything, you need data.

Lots of it.

A typical workflow begins with market discovery.

Instead of manually collecting URLs from different platforms, FinFeedAPI provides endpoints for discovering supported exchanges and enumerating historical markets from each venue. Markets include metadata such as titles, descriptions, outcome names, current prices, exchange identifiers, and status.

A simplified workflow might look like this:

Because every endpoint follows the same API conventions, moving from Polymarket to Kalshi or any other supported exchange doesn't require writing an entirely new integration.

Why OHLCV Is the Ideal Starting Point

Prediction markets generate enormous numbers of trades.

That's great.

Until you try analyzing thousands of contracts.

Trade-level datasets become massive very quickly.

For many research projects, you don't actually need every individual trade.

You need the evolution of market belief.

That's exactly what OHLCV provides.

Each candle summarizes a period of market activity. FinFeedAPI supports a wide range of candle intervals, from one second to multiple years allowing researchers to choose the appropriate level of granularity for their analysis. Historical OHLCV responses are returned in ascending chronological order, making them especially convenient for backtesting pipelines and time-series processing.

For many projects, daily candles are enough.

For others such as studying market reactions around economic announcements hourly or even minute-level candles may reveal entirely different behavior.

Adding Trades and Quotes for Deeper Analysis

OHLCV tells you what happened.

Trades and quotes tell you how it happened.

Suppose two markets both close the day at an implied probability of 72%.

They look identical.

Until you inspect the underlying activity.

One market might have traded steadily all day.

Another may have experienced dozens of rapid reversals before finally settling at the same closing price.

Historical trades let you study execution flow.

Historical quotes reveal how bid and ask prices evolved over time.

Together, they help answer questions that OHLCV alone cannot.

For example:

• Was the probability change gradual or sudden?
• Did liquidity disappear before major announcements?
• Were large price moves driven by many small trades or a few large ones?
• Did spreads widen as uncertainty increased?

FinFeedAPI exposes historical trades and historical quotes separately through dedicated endpoints backed by flat-file datasets, making it possible to move from high-level analysis to market microstructure without changing providers.

Preparing the Data in Python

Once you've downloaded historical candles, the next step is surprisingly straightforward.

Most researchers load the responses into Pandas DataFrames, convert timestamps into a consistent format, and align every market on a common timeline relative to its resolution date.

Instead of measuring calendar dates, they often measure:

• 30 days before resolution
• 14 days before resolution
• 7 days before resolution
• 3 days before resolution
• 24 hours before resolution
• Final hour

That simple transformation allows completely different markets to be compared on the same scale.

An election.

A Federal Reserve decision.

A cryptocurrency milestone.

Despite having different durations and subjects, every contract can now be analyzed according to how far it was from its final outcome.

That's where patterns begin to emerge.

Choosing the Right Markets

One mistake many researchers make is treating every prediction market equally.

Not every contract is useful for statistical analysis.

Some have almost no liquidity.

Others remain inactive for weeks before suddenly becoming active near expiration.

Some resolve based on highly objective events.

Others depend on more nuanced interpretation.

A typical research dataset often focuses on contracts that satisfy several conditions:

Building a cleaner dataset usually produces better research than simply collecting the largest possible number of markets.

Building a Reproducible Research Pipeline

One of the biggest advantages of working with normalized APIs isn't speed.

It's reproducibility.

Every time your research runs, it follows the same sequence:

1. Discover markets.
2. Select contracts.
3. Download historical OHLCV.
4. Retrieve historical trades and quotes when needed.
5. Store everything in a structured dataset.
6. Run the same analysis across hundreds or thousands of markets.

Once that pipeline exists, testing new ideas becomes dramatically easier.

Want to compare political markets against macroeconomic markets?

Change the selection criteria.

Want to analyze hourly instead of daily candles?

Change one parameter.

Want to expand from one exchange to several?

Reuse the same workflow.

The difficult part isn't writing statistical models.

It's building reliable datasets.

A clean, normalized API removes much of that work before the analysis even begins.

Measuring Accuracy With Brier Scores

Looking at price charts is interesting.

Looking at hundreds of price charts is exhausting.

Eventually, you need a way to summarize how accurate a prediction market actually was.

One of the most widely used metrics for this is the Brier Score.

Despite the intimidating name, the idea is remarkably simple.

A prediction market expresses a probability.

Reality produces an outcome.

The Brier Score measures how far apart those two were.

If a market assigns a 95% probability to something that actually happens, that's an excellent prediction.

If it assigns 95% to something that never happens, that's a very poor one.

Lower scores indicate better predictions.

Higher scores indicate greater forecasting error.

What makes the Brier Score especially useful is that it doesn't simply ask whether the market was "right."

It measures how confident the market was when it made that prediction.

That distinction matters.

A market that predicts an event with 55% confidence and turns out to be correct shouldn't receive the same credit as one that correctly priced it at 98%.

Confidence matters.

Calibration matters.

And that's exactly what the Brier Score captures.

Looking Beyond One Number

Calculating a single Brier Score for an entire market isn't particularly interesting.

The real insight comes from calculating it repeatedly throughout the life of every contract.

Instead of asking: How accurate was this market?

You begin asking: When was this market accurate?

A typical study might evaluate prediction quality at several milestones before resolution.

Once you repeat that process across hundreds of resolved contracts, patterns begin to emerge.

Some markets become remarkably accurate well before resolution.

Others remain uncertain until the very end.

Those differences are often more interesting than the average prediction itself.

When OHLCV Isn't Enough

OHLCV is usually the best place to start.

But it's rarely the end of the analysis.

Suppose you discover a market where the implied probability jumps from 42% to 71% within an hour.

OHLCV tells you that it happened.

Historical trades help explain who traded and how aggressively the market moved.

Historical quotes reveal how liquidity evolved while prices were changing.

For example, you might discover:

• spreads widened before major announcements
• bid liquidity disappeared before a rapid repricing
• several small trades gradually pushed prices higher
• one large transaction immediately reset market expectations

These details aren't visible from candle data alone.

FinFeedAPI provides separate historical trades and historical quotes endpoints, allowing researchers to move seamlessly from high-level statistical analysis into much more detailed market microstructure studies whenever additional context is needed.

Scaling Beyond a Single Study

One of the biggest advantages of building a reusable research pipeline is that today's project rarely stays today's project.

Once historical data collection has been automated, it's surprisingly easy to expand into new directions.

The same datasets can support work on:

• probability calibration
• market efficiency
• volatility forecasting
• liquidity analysis
• event studies
• AI model training
• sentiment indicators
• anomaly detection

The data collection layer doesn't change.

Only the research question does.

That's exactly why many quantitative teams invest so much effort into building reliable historical datasets first.

Once the data is organized, asking new questions becomes dramatically easier.

Why Clean APIs Matter More Than Clever Models

Machine learning receives most of the attention.

Data engineering usually does most of the work.

Anyone can train another forecasting model.

Much fewer teams can reliably collect, normalize, validate, and maintain years of historical prediction market data across multiple platforms.

That's where infrastructure starts to matter.

Instead of spending weeks writing custom scrapers, maintaining parsers, or adapting to platform-specific changes, researchers can focus on the actual analysis.

FinFeedAPI provides a unified interface for discovering markets, retrieving historical OHLCV, downloading historical trades and quotes from flat files, and accessing normalized prediction market data across multiple supported exchanges through REST, JSON-RPC, or MCP.

For many research projects, that's the difference between spending months building infrastructure and spending those months answering questions.

Where This Research Can Go Next

Backtesting market accuracy is only the beginning.

Once historical probabilities become part of your dataset, entirely new research directions open up.

You might compare prediction markets with polling averages.

Study how probabilities react to central bank announcements.

Measure whether trading volume improves forecast quality.

Or evaluate whether different exchanges converge toward the same probability at the same speed.

Every one of those projects starts the same way:

Collect clean historical data.

Build a reproducible pipeline.

Ask better questions.

Build Better Prediction Market Research With FinFeedAPI

The hardest part of quantitative research usually isn't writing the model.

It's finding reliable data.

Prediction markets generate an extraordinary amount of information, but collecting it across multiple platforms can quickly become a project of its own.

FinFeedAPI removes much of that complexity.

Through a single, normalized API, you can discover prediction markets, retrieve historical OHLCV time series in ascending chronological order, download historical trades and quotes from flat files, and work with standardized market metadata across multiple exchanges. Whether you're studying probability calibration, testing forecasting models, building AI systems, or conducting academic research, the infrastructure is already there you can spend more time analyzing data and less time collecting it.

Explore the FinFeedAPI Prediction Markets API and start turning historical market behavior into measurable research.

FAQ

Why use OHLCV instead of raw trades?

OHLCV provides a compact summary of market behavior, making it ideal for large-scale statistical analysis. It significantly reduces data volume while preserving key information about how probabilities evolved over time. When deeper market microstructure analysis is needed, historical trades and quotes can be added to the workflow.

Are daily candles enough for backtesting?

It depends on the research question.

Daily candles are often sufficient for studying long-term probability convergence.

If you're analyzing reactions to earnings announcements, economic releases, or breaking news, hourly or minute-level candles may reveal much more detailed behavior.

FinFeedAPI supports a wide range of OHLCV intervals, allowing researchers to choose the level of granularity that best fits their analysis.

Can I compare multiple prediction market exchanges?

Yes.

FinFeedAPI provides normalized access to several prediction market platforms through the same API, making it much easier to compare forecasting behavior across exchanges without building separate integrations.

When should I use historical trades and quotes instead of OHLCV?

OHLCV is ideal for statistical modeling and backtesting.

Historical trades and quotes become valuable when you're studying execution flow, liquidity changes, bid-ask spreads, or other forms of market microstructure that cannot be observed from candle data alone.

Can this approach be used for AI and machine learning?

Absolutely.

Historical prediction market data can be used to train forecasting models, evaluate probability calibration, generate features for machine learning pipelines, or compare human collective intelligence against algorithmic predictions. Because FinFeedAPI provides standardized historical datasets across multiple exchanges, it simplifies the process of building reproducible AI workflows.