Algorithmic Trading Is Not a “Magic Robot”
When people talk about algorithmic trading, they often imagine (or it is presented to investors as) something automatic and flawless—as if there were a program that always makes money.
In practice, algorithmic trading is simply a way of trading according to predefined rules. For example:
- entering a long position when a certain level is broken,
- placing a stop loss at a specific level, and a take profit as well.
In other words, a robot is not a genius. It is a mechanism that repeats the same actions consistently.
Not based on mood. Not based on intuition. But on formalized logic:
- when to enter,
- when to exit,
- and how much to risk.
Put simply, it is a way to turn trading into a system, rather than a set of random decisions.
What Beginners Should Pay Attention To
Algorithmic trading can look very attractive. But for beginners, it is important to understand a few things in advance.
1) The Most Important Thing in 2026
You do not need to know how to write code to become an algo trader.
In the modern world, GPT and other similar tools can help with writing code. In addition, the BloFin exchange has a convenient connector with TradingView, meaning that after writing your first algorithms in TradingView, you can activate them on your trading account in about 30 minutes, without much effort.
2) An Algorithm Is Not a Guarantee of Results
An algorithm does not know the future. It works with past statistics and current data.
Even a well-tested strategy can:
- go through periods of losses,
- show no growth for a long time,
- behave differently when market conditions change.
Therefore, it is important not to see algorithmic trading as a way to “eliminate risk.” It only changes the form of that risk.
3) Risk Management Is More Important Than the Strategy Itself
Beginners naturally look for the “most profitable algorithm.” In practice, what matters more is how a strategy survives difficult periods.
At Letit, we prioritize drawdown depth and, even more importantly, drawdown duration. If a strategy shows a 4% drawdown in historical data but remains in drawdown for two years, it makes sense not to include such an algorithm in the active list.
Pay attention to:
- how much risk is taken per trade,
- and what overall risk management approach is used.
There are three types of risk management:
- percentage of capital per trade,
- a fixed number of contracts per trade,
- a fixed amount in USDT per trade.
Most likely, over time you will run several algorithms simultaneously.
A strong way to allocate capital among them is to equalize by drawdown. For example:
- if one algorithm produces a 10% drawdown while using 50% of the account,
- and another produces a 10% drawdown while using 20% of the account,
- then allocating capital in the proportions of 50% and 20% makes sense.
This helps avoid a strong imbalance where one algorithm dominates while another loses.
It is also worth remembering that the most profitable model is often profitable for a reason. For example, it may show high historical returns due to tight stop losses:
- a tight stop = larger position size = higher potential return if successful.
But tight stops also have a downside: you will be stopped out more often and may miss market moves.
To reduce these situations, it can be helpful to divide capital into two parts:
- one part allocated to an algorithm with tight stops,
- the other part allocated to an algorithm with a higher probability of execution.
Pay attention to which metrics actually drive the performance of your algorithms.
An algorithm is like a character in a computer game: if you assign 10 points to intelligence at the start, be prepared for it to have little strength and agility. In algorithmic trading, you always sacrifice something to gain something else, and it is important to understand this balance.
4) Past Performance Is Not Always Equal to the Future
For beginner developers or investors in algorithms, it is important to ask:
- how the strategy behaved during crisis periods,
- what happened during sharp market declines.
A Few Tips for Beginner Investors and Algo Traders
Algorithms are cyclical. That means drawdowns are followed by growth, and vice versa.
There is often a strong desire to activate a new algorithm or invest money when it is at the peak of its performance. But this is a mistake. It is always better to wait for a drawdown—and it will come.
Markets do not rise endlessly. If an algorithm is designed for long positions and the market is rising, it makes profits. Then a correction begins: the market either moves sideways or falls, and the algorithm starts losing slightly on stop losses because the conditions for earning (growth) are no longer present.
A practical approach is to wait for two or three consecutive stop-outs and only then increase exposure or activate a new algorithm. This helps avoid unnecessary time spent in losses.
However, the more complex an algorithm becomes, the higher the chance that it is no longer a sequence of actions, but simply a description of the historical behavior of a particular asset.
For example, if you add so many rules to an algorithm that it shows extremely high returns on BTC but does not work at all on other assets, this is the first sign that you have over-optimized it.
There is no need to strive for perfection. It is better to build 10 independent algorithms that work across different markets than one “perfect” algorithm.
Conclusion
This is far from everything the Letit team can share.
To summarize: algorithmic trading is a way to formalize trading and remove emotions from it. It does not eliminate risks, but makes them more manageable. It is also a form of work—of a different kind if you are a developer—and it has its own specifics for investors.
We recommend rereading this article and, even better, trying to write at least one simple algorithm using AI tools in order to become about 80% more knowledgeable in this field. This knowledge will not be wasted.
Disclaimer: This article was provided by a third-party author and does not represent the views of BloFin Research and BloFin Exchange. The information provided herein does not constitute investment advice, financial advice, trading advice, or any other sort of advice, and should not be treated as such. All content set out is for informational purposes only.