Divergence Comparison Blocks – How to Trade Divergences with StrategyQuant

Welcome to today’s blog post! We’re excited to dive into the new Is Greater/Is LowerAdaptive comparison block , an innovative extension of the previous range-based adaptive block. Let’s get started!

Core Function

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.

This comparison block analyzes the entire length of the signal, from the moment it is generated until it is no longer valid. For example, if we analyze a signal where the RSI is greater than 50, this adaptive block evaluates signals where the RSI was above 50. This number of signals can be limited by settin Trades Look Back Situations where the RSI was below 50 are ignored, allowing the block to assess the overall quality of the signal.

The key difference between the Is Greater Adaptive Block / Is Lower Adaptive Block and the Crosses Above Adaptive  / Crosses Below Block lies in their approach. The Is Greater Adaptive/ Is Lower Adaptive Blocks evaluates the entire signal duration, while the Crosses Above Adaptive /Crosses Below Adaptive Blocks analyzes events after a specific crossover within a defined number of bars.

How the Is Greater Adaptive/Is Lower Adaptive Mechanism Works

The Is Greater Adaptive and Is Lower Adaptive mechanisms follow a structured process to evaluate trade signals and adapt to market conditions. The process consists of four key steps:

1. Basic Signal Detection

The first step is identifying a basic Is Greater or Is Lower event. This occurs when one indicator crosses above or below another. Examples include:

• The current closing price is above a 20-period moving average.

• The RSI(14) value crosses above 50.

These events serve as the initial signals to be evaluated further.

2. Historical Performance Evaluation

Once a crossover event is detected, the system evaluates its historical performance. It analyzes how similar signals have performed in past trades (X-trades). This step acts as a “backtest within a backtest,” helping to determine the potential reliability of the current signal based on historical data.

In the image above, we have a graphical representation of how signals are defined when using isGreaterAdaptive . A signal can be considered a situation where Close > Moving Average .

3. User-Defined Metrics and Thresholds

The system then compares the historical performance against user-defined metrics and thresholds. Traders can configure various evaluation criteria, such as:

• Profit Factor: The ratio of winning trades to losing trades.

• Cumulative %Performance: The overall return percentage achieved in past instances of the signal.

These metrics ensure that only signals meeting the trader’s expectations are considered valid.

4. Final Decision: Adapting to Market Conditions

Finally, the system assesses whether the current market conditions resemble those in which the signal has historically performed well. If the conditions align and the metrics meet the specified thresholds (eg, a profit factor greater than 1.5), the trade is confirmed. Otherwise, the system refrains from entering a trade, reducing the likelihood of false signals.

This adaptive approach allows traders to leverage historical data and real-time market analysis to make more informed trading decisions.

Key Settings

The performance of the crossover is then compared against user-defined thresholds. The user can choose different evaluation metrics, such as:

• Methodology

  • Profit Factor: Wins divided by losses.

  • Cumulative % Performance: Overall return percentage.

• TradesLookBack: Number of past trades to analyze (eg, 10 means analyzing the last 10 signals).

• Performance Threshold: Minimum performance requirement (eg, a Profit Factor of 1.5 means 1.5x more wins than losses).

• TS Max Bars Back:  Parameter used exclusively for backtesting on the TradeStation or MultiCharts engine. This parameter must match the exact value set directly in the TradeStation or MultiCharts TS Max Bars Back setting.

Results

• Generates fewer signals compared to standard comparisons.

• Filters signals based on historical success.

• Adapts to changing market conditions.

Importing the Block : A guide on how to import the snippet can be found in the Docs section here

After successful import you can use the new comparison blocks in: 

Builder

In the Builder, you can enable this block under Builder / Blocks. When conducting blind datamining, it’s recommended to use the Avg Performance metric, which is non-directional and calculates the average absolute strength of a signal. The level of detail and specificity in the settings is up to each user.

 AlgoWizard – Custom Blocks

Custom blocks in StrategyQuant X allow traders to create personalized building blocks for their trading strategies without needing to code. These blocks can be used to define specific conditions, indicators, or logic tailored to the user’s requirements. Once created, custom blocks can be seamlessly integrated into AlgoWizard, StrategyQuant’s visual strategy builder, to build complex and adaptive trading strategies. 

Custom blocks enhance flexibility by allowing you to parameterize key metrics like indicator periods, profit factors, and win percentages, providing more control over your strategy’s behavior. In summary, custom blocks enable more precise, dynamic, and sophisticated

For now, I have prepared several custom blocks based on this condition. You can download them using this link and import them by following these steps.

 AlgoWizard – Templates

This is a more advanced topic that we will cover in greater depth in an upcoming article. By combining custom blocks and random groups, you can create a prototype strategy and instruct SQX to search for specific types of strategies and maintain its adaptiveness.

 Indicator is implemented for: MT4/MT5/Tradestation/ Multicharts.  You can download it here .