Hi All, maybe this is a basic beginner question, but I don`t mind asking it. The huge amount of indicators in Wealthlab is great. I don`t know the vast majority of them, but I like to play around with them and add one or the other to already existing strategies or when building new ones. Not only to learn what they do, (for basic evaluation I drag them into a chart and try to figure out, how they work) but also how they could impact/improve different key figures of a strategy.
I'm, of course, talking about "building blocks".
My question: How do I know which "condition" is best for all these different indicators? Do you have a method in finding this out? Is it try and error? (hope not....) Is there a built in process in Wealthlab that stops me from using indicators with a wrong condition (obv. backtesting, but anything else I can do before that)?
Very much appreciate recommendations on how you approach this. Thank you.
Nb.: Could this be a feature request? For example: Wealthlab only lets me use conditions, that actually make sense to use with selected indicators. Or is it simply impossible, as there are too many unknown variables.....?
I'm, of course, talking about "building blocks".
My question: How do I know which "condition" is best for all these different indicators? Do you have a method in finding this out? Is it try and error? (hope not....) Is there a built in process in Wealthlab that stops me from using indicators with a wrong condition (obv. backtesting, but anything else I can do before that)?
Very much appreciate recommendations on how you approach this. Thank you.
Nb.: Could this be a feature request? For example: Wealthlab only lets me use conditions, that actually make sense to use with selected indicators. Or is it simply impossible, as there are too many unknown variables.....?
Rename
There are a few broad categories, we have Oscillators and Smoothers for example. These can be filtered with the radio buttons in the Indicators list. Oscillators move between oversold and overbought areas, so it makes sense to use Indicator Compare to Value for them. Smoothers are ideal candidates for Indicator Crosses Smoothed Version, or Indicator Crosses Indicator. Besides that I'm not about the possibility of further ways to divide them in the UI but am open to ideas and can think about it.
Thank you for replying so quickly. I will definately try to come up with some possibilities, as I have been writing down some ideas (or better said my struggles) for several months now. Below the the basic idea. (using a translator into English). In more detail to follow.
Feature Request: “Indicator Intelligence – Smart Evaluation of Indicators and Conditions”
WealthLab has an amazing library of indicators — but it’s difficult to know which ones actually add predictive value or which conditions (CrossOver, Above, Rising, etc.) work best with them.
I’d like to propose an Indicator Evaluation Framework that automatically tests indicators and their conditions (maybe an additional feature of the "Indicator-profiler"?) across historical data, ranks them by performance, and identifies redundancy.
This would show which indicators consistently provide useful signals, which are overlapping, and which are better suited for trend, mean-reversion, or volatility strategies.
Essentially, it would turn the indicator library into a knowledge system — helping users select the right indicator and condition for the right purpose instead of relying on trial and error.
Feature Request: “Indicator Intelligence – Smart Evaluation of Indicators and Conditions”
WealthLab has an amazing library of indicators — but it’s difficult to know which ones actually add predictive value or which conditions (CrossOver, Above, Rising, etc.) work best with them.
I’d like to propose an Indicator Evaluation Framework that automatically tests indicators and their conditions (maybe an additional feature of the "Indicator-profiler"?) across historical data, ranks them by performance, and identifies redundancy.
This would show which indicators consistently provide useful signals, which are overlapping, and which are better suited for trend, mean-reversion, or volatility strategies.
Essentially, it would turn the indicator library into a knowledge system — helping users select the right indicator and condition for the right purpose instead of relying on trial and error.
Some useful extensions:
- Allow filtering by symbol universe, time frame, or market regime.
- Integration into the UI: suggest “top performing” indicators or auto-select the most relevant ones.
- Export results (CSV, DataFrame). (only if useful for data analysts)
- Visualize relationships between indicators (e.g., dendrogram of correlations).
- Allow filtering by symbol universe, time frame, or market regime.
- Integration into the UI: suggest “top performing” indicators or auto-select the most relevant ones.
- Export results (CSV, DataFrame). (only if useful for data analysts)
- Visualize relationships between indicators (e.g., dendrogram of correlations).
My feature request idea in more detail:
Indicator Evaluation & Recommendation Framework
A built-in WealthLab module that automatically evaluates the usefulness and behavior of indicators across historical data.
Core functionality:
1. Indicator sampling
Iterate through available indicators (optionally filtered by category).
2. Standardized condition testing
Apply common logical conditions:
o CrossAbove / CrossBelow vs. Price or other indicators
o Rising / Falling
o Above / Below fixed threshold or moving average
3. Performance analysis
For each Indicator + Condition pair:
o Calculate hit rate
o Average return after signal (n bars)
o Profit factor, Sharpe ratio, stability over time, WL-Score...
4. Statistical significance
Compute correlation or mutual information with future returns,
and optionally perform t-tests between “signal” vs. “no-signal” periods.
5. Redundancy detection
Identify highly correlated indicators (e.g., RSI, Stochastic, Williams %R) and cluster them.
6. Ranking & visualization
Show a sortable table or heatmap:
Indicator | Condition | AvgReturn | Stability | MI Score
RSI(14) |CrossBelow(30) | +0.8% | 0.92 | 0.15
MACD |CrossAbove(Signal) | +1.2% | 0.88 | 0.21
BollingerWidth |Rising | -0.3% | 0.50 | 0.05
This would help users instantly see which indicators have real predictive power and which ones do not.
Why this matters
This feature would:
• Reduce endless trial-and-error when designing systems. (less [unnecessary] backtesting does have some wider advantages than just safing time for the individial user)
• Help users understand the purpose and best use case of each indicator.
• Lead to more robust, data-driven strategies. (another WL selling point)
Ultimately, it turns WealthLab’s large indicator library into a knowledge system — guiding users towards meaningful combinations of indicators and conditions instead of random experimentation. In addition this will become a great learning-tool for newbies and even advanced users.
Summary
A new WealthLab “Indicator Intelligence” module that systematically tests, ranks, and explains indicators and their most effective conditions — helping users identify which indicators are actually useful, which are redundant, and which work best for specific types of strategies (trend-following, mean reversion, volatility-based, dip bying etc.).
Indicator Evaluation & Recommendation Framework
A built-in WealthLab module that automatically evaluates the usefulness and behavior of indicators across historical data.
Core functionality:
1. Indicator sampling
Iterate through available indicators (optionally filtered by category).
2. Standardized condition testing
Apply common logical conditions:
o CrossAbove / CrossBelow vs. Price or other indicators
o Rising / Falling
o Above / Below fixed threshold or moving average
3. Performance analysis
For each Indicator + Condition pair:
o Calculate hit rate
o Average return after signal (n bars)
o Profit factor, Sharpe ratio, stability over time, WL-Score...
4. Statistical significance
Compute correlation or mutual information with future returns,
and optionally perform t-tests between “signal” vs. “no-signal” periods.
5. Redundancy detection
Identify highly correlated indicators (e.g., RSI, Stochastic, Williams %R) and cluster them.
6. Ranking & visualization
Show a sortable table or heatmap:
Indicator | Condition | AvgReturn | Stability | MI Score
RSI(14) |CrossBelow(30) | +0.8% | 0.92 | 0.15
MACD |CrossAbove(Signal) | +1.2% | 0.88 | 0.21
BollingerWidth |Rising | -0.3% | 0.50 | 0.05
This would help users instantly see which indicators have real predictive power and which ones do not.
Why this matters
This feature would:
• Reduce endless trial-and-error when designing systems. (less [unnecessary] backtesting does have some wider advantages than just safing time for the individial user)
• Help users understand the purpose and best use case of each indicator.
• Lead to more robust, data-driven strategies. (another WL selling point)
Ultimately, it turns WealthLab’s large indicator library into a knowledge system — guiding users towards meaningful combinations of indicators and conditions instead of random experimentation. In addition this will become a great learning-tool for newbies and even advanced users.
Summary
A new WealthLab “Indicator Intelligence” module that systematically tests, ranks, and explains indicators and their most effective conditions — helping users identify which indicators are actually useful, which are redundant, and which work best for specific types of strategies (trend-following, mean reversion, volatility-based, dip bying etc.).
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