> For the complete documentation index, see [llms.txt](https://docs.alphanume.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.alphanume.com/documentation/datasets/dilution/how-to-use-it.md).

# How to Use It

The Dilution dataset is designed to be used as a **signal source**, a **risk filter**, and a **lifecycle tracker** within systematic equity workflows.

Each record introduces new information into the market at a specific point in time — the filing date — and resolves forward as the filing either becomes effective or is withdrawn.

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#### 1. Event-Driven Signal Generation

The primary use case is identifying **new dilution risk as it enters the market**.

Common approaches include:

* Flagging newly filed S-1s labeled as dilutive
* Conditioning exposure immediately following the filing date
* Grouping filings into short-biased baskets
* Avoiding long exposure in names with active dilution risk

Because filings are captured at the moment they are filed, this dataset is well-suited for **event-based backtests and live monitoring**.

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#### 2. Lifecycle-Aware Trading

Unlike static event datasets, dilution risk evolves.

This dataset allows you to:

* Track how long filings take to become effective
* Study performance differences between filings that resolve quickly vs slowly
* Separate false positives (withdrawn filings) from completed dilution events
* Analyze post-effectiveness behavior

Fields such as `became_effective`, `effective_date`, `offering_withdrawn`, and `days_to_effective` enable lifecycle-aware strategies rather than single-day reactions.

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#### 3. Risk Filtering & Portfolio Construction

The dataset can also be used defensively.

Examples include:

* Excluding names with active dilutive filings from long universes
* Adjusting position sizing based on dilution magnitude (`shares_offered` vs market cap)
* Conditioning factor portfolios to avoid structural headwinds
* Screening small-cap universes for persistent dilution behavior

Because market capitalization is measured prior to filing, these filters can be applied without look-ahead bias.

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#### 4. Cross-Sectional Research

Beyond trading, the dataset supports broader research questions, such as:

* How often dilutive filings are withdrawn
* Typical time-to-effectiveness distributions
* Differences between resale and primary offerings
* Structural dilution patterns by market cap cohort

These analyses can inform both strategy design and risk management.

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#### 5. Practical Query Patterns

Typical workflows include:

* Pulling the most recent filings across all tickers
* Querying a single ticker’s dilution history
* Scanning a rolling date window for new events
* Monitoring unresolved filings over time

The API is stateless and composable, making it easy to integrate into scheduled jobs, research notebooks, or live trading pipelines.

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#### What This Dataset Is — and Is Not

* It **does** identify structural dilution risk at the moment it appears
* It **does not** predict price direction on its own
* It is best used as an input into broader systematic frameworks

Used correctly, it provides clarity around one of the most persistent sources of equity underperformance.