Technical Filters

Technical filters let you screen stocks using calculated price and volume studies — from simple moving averages to complex momentum oscillators. Each technical filter runs a study on the stock's price history and lets you set a condition on the result. Studies can also be compared against each other (e.g. SMA 50 > SMA 200) and modified with tools like linear regression slope or correlation.

Adding a Technical Filter

To add a technical filter click Add Filter and choose a study from the technical category. Each filter card shows:

• Study name — click to switch to a different study.
• Input selector — the price series or data source the study runs on (e.g. Close, Volume). See the Study Inputs Reference for all options.
• Parameters — numeric settings such as period length, standard deviation multiplier, or MA type, depending on the study.
• Condition selector — greater than, less than, equal to, not equal to, crosses above, crosses below, or between a range.
• Value input — the threshold to filter against, or a second study to compare against.
• Distribution slider — log-scale histogram of the current universe across the study's output range.

Comparing Studies

Instead of comparing a study output to a fixed number, you can compare it to another study's output. Common examples:

• SMA(50) > SMA(200) — golden cross / death cross detection
• EMA(12) > EMA(26) — short-term trend above long-term trend
• RSI(14) < ATR(14) — momentum below volatility

To enable this, click the Value button on the filter card and select Study. A second study picker appears with its own input and parameter controls.

Modifiers

Modifiers wrap a study's output with an additional calculation before the condition is evaluated. Two modifiers are available:

• Linear Regression Slope — measures the rate of change of a study's output over a rolling window. Use this to find stocks where an indicator is rising or falling at a specific rate. For example: slope of RSI(14) > 0 to find stocks where momentum is accelerating upward.
• Correlation — measures how strongly two study outputs move together over a rolling window, returning a value between −1 (inverse) and +1 (perfectly aligned). For example: correlate a stock's price with its volume to identify institutional accumulation patterns.

Reference: The Study Inputs Reference lists every data source a study can run on, and the Study Reference lists every available study with its notation.