What is a Screener in Fincanva?

Screeners in Fincanva are advanced filtering tools that allow you to identify investment opportunities based on specific, customizable criteria. They are available for all users — completely free, regardless of your subscription level — and are deeply integrated into the platform’s simulation engine to ensure accuracy, consistency, and realism in your strategy design.

What Makes Fincanva Screeners Different

Unlike basic screeners, Fincanva’s are built on the same technology that powers our portfolio simulations. This means they are:

• Highly accurate – aligned with the logic and constraints used in backtesting.

• Fully integrated – seamlessly connected to model construction and asset selection.

• Flexible – supporting a wide range of filters and combinations.

Filtering Assets with Precision

You can apply screeners to filter:

• Stocks

• ETPs (Exchange-Traded Products)

• Cryptocurrencies

Each filter can be finely customized. For instance, you can:

• Compare current values to those from previous periods.

• Set binary conditions (e.g., “Price is above Moving Average”).

• Compare asset metrics to sector averages or benchmarks.

Screeners and the Investable Universe in your Portfolios

Screeners are instrumental in defining your Investable Universe — the pool of assets your model will draw from. This step:

• Promotes objective and systematic selection.

• Reduces selection bias in simulations.

• Ensures consistency across different backtesting scenarios.

Within a model, you can apply multiple screeners using OR logic, giving you the flexibility to:

• Expand asset inclusion (if any condition is met).

• Narrow the list based on multiple alternate criteria.

Why Use Screeners in Fincanva?

By using screeners instead of manually selecting assets, you:

• Increase the realism of your testing.

• Make your strategy more replicable and data-driven.

• Reduce the influence of personal bias or hindsight.

Whether you're just starting out or building complex strategies, screeners help anchor your model in a robust, rules-based selection process.