How to Backtest Event Strategies Using SEC + Prediction Markets + Stocks + FX

Event-driven backtesting sounds simple at first. Pick a filing, look at the chart, and measure what happened next.

In practice, it breaks fast.

The hard part is not finding events…

It is defining the event correctly, aligning timestamps across markets, and measuring reactions in a way that does not mix signal with noise.

A 10-K filed after the close, a prediction market repricing five minutes earlier, and an FX move that shows up through a broader macro channel are not the same thing…

If your timing is off, your backtest is off too!

That is why a good event-driven backtesting workflow needs more than one dataset. You need the event timestamp from SEC filings, the price reaction from stocks, the belief shift from prediction markets, and the broader cross-asset response from FX.

Let’s go through a practical recipe for building that workflow using SEC filings, stock data, prediction market data, and exchange rates.

Why event-driven backtesting is harder than it looks

Most event studies fail for one basic reason: they use the wrong clock.

Many teams anchor the event to the filing date.

That is not precise enough. A filing date tells you the calendar day. It does not tell you the moment the information hits the market.

For event-driven backtesting, the event anchor should be the acceptance timestamp of the filing, not just the filing day…

Once you have that, you can build windows around it:

• 30 minutes before the event
• 5 minutes before the event
• 5 minutes after the event
• 30 minutes after the event
• end of day
• next session open

That sounds small, but it changes the whole quality of the backtest. Now you are measuring reactions around an actual information release instead of around a vague date bucket.

Step 1: Define the event from SEC filings

The cleanest starting point is the SEC Filings API.

Use the filings endpoint to search for events by ticker, form type, filing date, report date, and filing content. The most important field is:

• acceptance_date_time

That is your T0.

For example, if you are testing reactions to 8-K filings, you can query filings by:

• ticker
• form_type=8-K
• filing date range
• specific items in the filing
• sort order by AcceptanceDateTime

This gives you a proper event table instead of a loose list of filings.

A practical first filter could look like this:

• focus on 8-K
• narrow to filings with specific items
• sort by AcceptanceDateTime
• build a sample over 1 to 3 years

That already creates a usable event dataset.

Why 8-K is a strong starting point

8-K filings are often better for event-driven backtesting than 10-K or 10-Q because they are closer to real-time disclosures. They often carry market-moving news such as:

• acquisitions
• material agreements
• departures of key executives
• earnings-related updates
• business changes

That makes them more natural for short-window tests.

Use item-level filtering to reduce noise

A big mistake in event-driven backtesting is treating every filing as the same kind of signal.

That is where the extractor endpoints matter. Instead of testing all 8-Ks together, you can split the sample by item. For example:

• 1.01 for entry into a material agreement
• 2.01 for completed acquisitions or dispositions
• 7.01 for regulation FD disclosures and related updates

This turns one generic backtest into several more useful ones.

Now you are no longer asking: “How do 8-Ks move markets?”
You are asking: “How do acquisition-related 8-Ks move markets compared with disclosure-heavy 8-Ks?”

That is a much better research question.

Use full-text search when you want theme-based events

The full-text endpoint is useful when you want to go beyond form type and search for language inside filings.

For example, you can search for filings that contain words like:

• acquisition
• guidance
• restructuring
• bankruptcy
• liquidity

This is helpful when your strategy is based on themes rather than just form labels.

In other words, the SEC layer is not only your event trigger. It is also your first classification layer.

Step 2: Build the stock reaction layer

Once you have T0 from SEC, the next step is measuring how the stock reacted.

The Stock Data API gives you two useful levels for this:

• OHLCV timeseries for clean historical windows
• native IEX trades and quotes for higher-resolution reaction data

This is a strong combination because not every backtest needs tick-level detail, but event-driven work usually benefits from at least some intraday precision.

Start with OHLCV for the base study

Use the OHLCV history endpoint to pull candles around the event window. For example:

• 5-minute bars from 30 minutes before T0 up to 2 hours after T0
• 1-minute bars for more granular intraday studies
• daily bars for broader post-event drift analysis

This gives you the simplest reaction measures:

• return from T-30m to T+30m
• return from T0 to close
• return from close to next open
• volatility before vs after the event
• abnormal volume around the event

That alone is enough to build a solid first version of an event-driven backtesting model.

Use trades and quotes when timing matters

The native IEX endpoints let you go deeper with:

• trades
• level-1 quotes
• admin messages
• system events

This matters because some events do not show their first signal in candles. They show it in:

• widening spreads
• sudden quote changes
• bursts of trade activity
• auction or halt behavior

That is especially useful when a filing lands near the market open, the close, or during thin liquidity periods.

A practical workflow is:

1. Use SEC acceptance_date_time as T0
2. Pull stock OHLCV around the event
3. Pull trade and quote data for the same symbol and day
4. Check whether the first move showed up in traded price, bid/ask, or both

This tells you whether the market reacted smoothly, violently, or with a temporary liquidity gap.

Do not ignore market structure fields

One of the more interesting parts of your stock dataset is the admin and system message layer.

You can capture signals like:

• trading status
• official opening prices
• auction information
• short sale price test status
• operational halts
• system event markers

This is useful because event-driven backtesting often gets distorted by market structure.

A stock that reacts during an auction window is not behaving the same way as a stock trading normally at midday…

A halt or imbalance can also make a simple return calculation misleading.

That means the stock layer should not just answer: “Did price move?”

It should also answer: “Under what trading conditions did that move happen?”

Step 3: Add the prediction market reaction layer

This is where the framework becomes much more interesting.

Stocks show repricing. Prediction markets show belief updates.

That distinction matters.

An SEC filing may change how traders value a company, but it may also change the market’s estimate of a future outcome. If you only test stocks, you only see one side of that story.

The Prediction Markets API lets you measure:

• market OHLCV around the event
• recent and historical trades
• quotes
• order book changes

That gives you a direct way to test whether event information changed the implied probability.

Use the prediction market OHLCV for the clean comparison

For a market tied to the filing theme, you can pull OHLCV history around the same event window used in stocks.

For example:

• probability 30 minutes before T0
• probability at T0
• probability 30 minutes after T0
• probability at end of day

Now your backtest can compare two reactions side by side:

• stock return
• prediction market probability change

That is much more informative than price alone.

Compare reaction speed across assets

This is one of the strongest use cases in the whole article.

Instead of only asking whether both markets moved, ask:

• which one moved first?
• which one moved more?
• which one reverted faster?
• which one showed widening spreads before repricing?

That can reveal whether prediction markets act as an early belief indicator or as a slower confirmation layer.

A simple hypothesis to test is:

For certain SEC events, prediction markets re-estimate probabilities before the stock completes its repricing.

That is a very practical event-driven backtesting idea.

Watch the timestamp nuance

Our prediction market data includes an important detail: historical activity is filtered by processing time, while records also include exchange time.

This matters.

If you are measuring reaction speed around SEC events… the safest approach is to prefer the exchange-side timestamp where available and treat processing time as a transport or ingestion layer.

That helps avoid false conclusions about which market moved first.

This kind of timestamp discipline is exactly what makes an event study credible.

Use quotes and order book data for richer signal work

If you want to go one step beyond price, look at:

• bid/ask spread changes
• quote depth
• order book updates
• trade bursts after the filing

Sometimes the first reaction is not a clean price move. It is a liquidity event. The spread widens, depth disappears, and then the market reprices.

That can be especially valuable in prediction markets, where liquidity conditions often tell you as much as the traded price.

Step 4: Add the FX response layer

FX is the cross-asset layer that makes the framework more complete.

Not every SEC filing matters for currencies, of course. But some do, especially when the event changes macro expectations, cross-border exposure, commodity sensitivity, or broad risk sentiment.

The Exchange Rates API gives you two useful tools:

• point-in-time exchange rates
• historical timeseries with granular periods down to seconds

That makes it possible to test whether a company-specific filing had a measurable currency response, or whether a cluster of similar events did.

When FX belongs in the backtest

FX is most useful when the filing has a plausible macro or international transmission path. Examples include:

• multinational companies with major non-USD revenue exposure
• cross-border acquisitions
• filings that affect commodity-linked names
• disclosures that shift risk sentiment in a sector or region

In those cases, you can track pairs such as:

• USD/EUR
• USD/JPY
• USD/CAD
• GBP/USD

Or invert rates when needed, depending on how you want to express the move.

Use timeseries windows, not random snapshots

The historical FX timeseries endpoint is the right fit for backtesting because it lets you request specific windows with specific period sizes such as:

• 1SEC
• 5SEC
• 1MIN
• 5MIN
• 1HRS

That means you can align FX windows to the same T0 used for SEC, stocks, and prediction markets.

For example:

• FX rate at T-15m
• FX rate at T0
• FX rate at T+15m
• FX rate at T+1h

Now you can compare the speed and scale of the response across all three market layers.

Understand what the FX rate represents

The docs make an important point here: the exchange rate is based on a rolling 24-hour VWAP across multiple data sources.

That means this is not the same as a venue-specific order book quote. It is a blended rate.

For many backtests, that is a feature, not a problem. It gives you a cleaner cross-market FX measure. But it also means your interpretation should match the data. You are measuring a broad exchange-rate response, not the exact behavior of one venue.

That distinction should be clear in the article.

Step 5: Build one unified event table

At this point, the practical workflow becomes very clear.

Create one table where each row is one SEC event. For each row, store:

SEC layer

• ticker
• form type
• accession number
• filing item or theme
• acceptance_date_time

Stock layer

• price before event
• price after event
• volume before and after
• intraday volatility
• spread or quote reaction if needed

Prediction market layer

• market ID
• implied probability before event
• implied probability after event
• spread and liquidity changes
• trade counts around the event

FX layer

• relevant currency pair
• rate before event
• rate after event
• magnitude of change across window

Once that table exists, event-driven backtesting becomes much easier to scale.

You can then group results by:

• filing type
• item number
• company
• sector
• market regime
• time of day
• pre-market vs regular session vs post-market

That is where the research starts to become useful.

Step 6: Choose event windows carefully

The event window is not a technical detail. It is part of the strategy definition.

A clean setup might include three layers of windows:

Immediate reaction window

• T0 to T+5m
• captures the first repricing

Short reaction window

• T-30m to T+30m
• captures anticipatory move plus immediate aftermath

Extended reaction window

• T0 to close
• or close to next open
• captures slower digestion and overnight effects

You can use the same framework across stocks, prediction markets, and FX, but do not assume the best window is identical in every asset.

For example:

• stocks may react fast in liquid names
• prediction markets may show a staggered move if depth is thin
• FX may react only when the filing affects broader macro interpretation

Testing those differences is part of the edge.

Step 7: Watch for the most common backtest mistakes

This kind of multi-asset event-driven backtesting is powerful, but it is easy to get wrong.

Here are the biggest pitfalls.

Using filing date instead of the acceptance timestamp

This is the classic error. A date is not an event time.

Mixing time zones or session states

If one dataset is aligned to UTC and another is interpreted in local market time, the study can break quietly.

Ignoring trading session context

A post-close filing is not the same as an intraday filing. A reaction at the open may reflect both the filing and overnight information flow.

Treating all 8-Ks as equal

A material acquisition and a routine disclosure should not sit in the same bucket without further classification.

Forgetting liquidity conditions

A price move during thin trading or a wide spread may look larger than it really is in execution terms.

Using the wrong timestamp in prediction market data

If the dataset contains both processing time and exchange time, the distinction matters for reaction-speed analysis.

A simple event-driven backtesting recipe

Here is the practical recipe in one flow.

1. Pull SEC filings

Query filings by ticker, form type, and date range. Use acceptance_date_time as the event timestamp.

2. Classify the filing

Use item extraction or full-text search to tag the event by theme, such as acquisition, guidance, restructuring, or disclosure.

3. Pull stock data

Fetch OHLCV around the event window. Add native trades and quotes if you want finer microstructure detail.

4. Pull prediction market data

Fetch market OHLCV, trades, and quotes for the related contract or outcome. Measure implied probability change around the same window.

5. Pull FX data

Fetch historical exchange-rate timeseries for the relevant pair across the same event window.

6. Align everything to one clock

Standardize timestamps and build consistent pre-event and post-event windows.

7. Measure outcomes

Compare:

• stock return
• probability shift
• FX move
• spread and volume changes
• reaction timing across assets

8. Group and test

Split by event type, filing item, company, and session timing. That is where patterns start to appear.

What this framework helps you discover

A good event-driven backtesting setup should help answer questions like:

• Do acquisition-related 8-Ks move prediction markets before they move stocks?
• Are some filing themes mostly equity signals while others show stronger FX spillover?
• Does liquidity disappear before repricing in prediction markets?
• Do after-hours SEC events create stronger next-open stock moves than same-session events?
• Are cross-asset reactions stronger for globally exposed companies than domestic ones?

Those are much more useful questions than simply asking whether the stock went up after the filing.

The real challenge in event-driven backtesting is not coding the query. It is designed to study the event correctly.

Turn Event Data Into Actionable Signals

Event-driven backtesting is only as good as the data behind it.

SEC filings, stock prices, prediction markets, and FX all move differently… and stitching them together manually can quickly become messy and unreliable.

That’s where FinFeedAPI comes in.

With FinFeedAPI, you can access:

• precise SEC filing data with exact acceptance timestamps
• historical stock data and market microstructure from IEX
• prediction market probabilities and activity across platforms like Polymarket and Kalshi
• FX rates and timeseries for cross-asset analysis

So instead of cleaning and aligning datasets, you can focus on what actually matters… testing ideas and finding signals.

If you're building event-driven strategies, having clean, time-aligned data is what turns a backtest into something you can trust.

👉 Explore FinFeedAPI and start building your event-driven models today.

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• Markets in Prediction Markets
• Technicalities of Prediction Market Data
• What can you build with FinFeedAPI?