Algorithmic trading has long ceased to be an exotic novelty and has become the foundation of the modern crypto market. In essence, it is trading where decisions are made not by a human, but by a program operating according to predefined rules. But behind this simple formulation lies an entire industry with two fundamentally different tasks that are often confused.
Two sides of the same coin: execution and strategy
The first task is algorithmic execution. Large institutional players, such as hedge funds, face a problem: buying a large volume of an asset inevitably moves the market against them. A direct order for $100 million would simply "eat up" all the liquidity in the order book, driving the price up before the trade is even completed. The solution is to split the order into dozens and hundreds of small parts, using algorithms like VWAP (volume-weighted average price), TWAP (time-weighted average price), or "Iceberg" (which hides the main volume). Here, success is measured not by profit, but by minimizing slippage and market impact.
The second task is speculative strategies, where the bot itself seeks entry and exit points. It is this meaning that is most often attributed to the term "algo trading" in the crypto market. Robots trade based on trends, arbitrage between exchanges, market making, grid strategies, and even complex things like volatility trading through options. Special attention deserves MEV — front-running transactions on the blockchain, where a bot cuts into the queue ahead of a large trade to profit from the price movement.
AI vs classical algorithms: what's the difference?
A classical algorithm is static: it follows the rule "if X, then Y" until the trader rewrites the code. Machine learning systems work differently. They independently identify patterns in data and adapt to changing conditions. This allows them to analyze news sentiment, process on-chain data, and use reinforcement learning, where the model "finds" a profitable strategy through trial and error.
The recent public experiment Alpha Arena is telling, where six AI models traded with real money. The results turned out to be mixed: only two of the six showed a profit (Qwen3 Max and DeepSeek V3.1), while GPT-5 lost more than half of its capital. This once again proves: even the most advanced technologies are not a "holy grail," and a sample of a couple of weeks is too short a period for objective conclusions.
Risks and "pitfalls"
Algo trading is not only speed and discipline, but also serious risks. The main enemy is overfitting to historical data, when a strategy works perfectly in a backtest but falls apart in a live market. Errors in code can lead to a "runaway algorithm" — as in the case of Knight Capital, which lost $460 million in 45 minutes due to a bug in an updated engine. One should not forget about flash crashes, when due to an algorithm failure the price of an asset collapses by tens of percent in seconds, as happened with bitcoin on Binance.US in 2021.
Regulators, especially in the EU, are trying to rein in this chaos. Regulation RTS 6 requires companies to test algorithms, have a "kill button" for instantly canceling orders, and undergo stress tests. However, in the crypto market, where MiCA prevails, there are no such strict requirements for trading bots yet, leaving room for maneuver and risks.
My conclusion: Algo trading is a powerful tool, but not a panacea. For a retail trader, it opens up opportunities unavailable in manual trading, but requires a deep understanding of the market and programming. Success here is not just "writing a bot," but building a system with a clear understanding of when it works, when it breaks, and when human intervention is needed. Otherwise, you are simply automating the process of losing money.