When developing an automated strategy, it’s common to want to improve results by adding new elements: an additional indicator, an extra condition, a new filter… Without realizing it, we can end up creating a robot that tries to be conservative, aggressive, and technical at the same time.
These poorly balanced strategies may seem promising in theory, but in practice they are often unstable, confusing, and difficult to adjust.
“Frankenstein” strategies: when everything is mixed up without order
A strategy becomes a kind of “Frankenstein” when it combines several trading approaches without a clear underlying logic. For example, a single robot may include a short-term scalping condition, a trend signal with moving averages, and reversal logic based on an extreme RSI.
While each of these elements makes sense on its own, if there’s no clear hierarchy between them, the strategy loses coherence. The result is often a disorganized logic that doesn’t adapt well to the market and generates internal conflicts within the robot.
Signs of a poorly integrated strategy
One of the first signs of this type of error is that the robot barely trades or trades erratically. Conditions cancel each other out, filters block signals, or entries occur at incongruous times.
Furthermore, when analyzing performance, it’s difficult to identify which part of the strategy is actually working. This greatly complicates optimization and makes it harder to fine-tune the robot effectively.
The risk of hidden overfitting
Another problem is unintentional over-tuning. By combining many different concepts into a single strategy, there is a tendency to modify each parameter so that it doesn’t interfere with the others. This creates an artificial dependency between the elements.
As a result, backtests may appear perfect, but in reality they are so fine-tuned to specific conditions that they don’t perform well in real-life situations.
The importance of a clear structure
At tradEAsy, we recommend maintaining the same trading style for each robot, even if you want to combine different approaches. It’s possible to merge styles effectively, as long as there’s a clear structure.
For example, you can design a trend-following strategy with volume filters, or a reversal logic that uses dynamic risk management. The important thing is that all components are aligned with the same objective and do not contradict each other.
Less can be more
If a strategy doesn’t exhibit clear behavior or can’t be easily described in a single sentence, it probably needs to be simplified. Often, breaking a complex strategy into two or three simpler ones gives better results than trying to unify everything into a single robot.
Creating a good robot isn’t about endlessly adding ideas, but rather integrating them in a logical and functional way. “Frankenstein”-type strategies remind us that, in automated trading, more elements don’t always mean better results.
