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These custom blocks, or conditions, were used in the analysis of the effectiveness of more adaptive blocks, which you can read here. Additionally, these custom blocks can serve as a template for you to prepare your own.

The Kolmogorov-Smirnov Test (KSTest) indicator is a statistical tool used in quantitative trading to compare two data samples and determine if they come from the same distribution. In this implementation, the indicator compares a recent sample of price data ( Period1) with a historical sample (Period2) to detect significant changes in the underlying distribution of the data.

The IsGreater Is/Lower Adaptive comparison block does one main thing: it checks if one indicator is greater than another, but only generates a signal if similar situations were profitable in the past.

Autocorrelation measures the degree of similarity between a time series and a lagged version of itself. Simply put, it determines if today’s price movements relate to movements from a specific number of periods ago.

I modified the original Stagnation code to use an average stagnation (daily basis) as the metric, for 2 reasons: 1. To know on average how many days before a new

Adaptive blocks “Crosses Above/Below Adaptive” bring a new level of intelligence to trading strategies by incorporating historical signal performance. Unlike standard blocks that only detect when one indicator crosses another, adaptive blocks analyze how similar signals performed in the past to estimate the reliability of the current signal.

Adaptive Crosses Above/Below Custom Blocks / Conditions

This custom analysis snippet simplifies the task of renaming strategies in your databank. It allows you to rename multiple strategies at once based on a custom naming format you specify. This helps organize your databank more effectively, making it easier to manage and identify strategies for specific purposes or tests.

A custom analysis that can be run as a task in custom projects to filter out correlated strategies.

Custom blocks are essential for optimizing the flexibility of algorithmic trading strategies in StrategyQuant X. They enable users to establish their own trading logic and rules in addition to the standard set of indicators or actions that are available on the platform.