Algorithmic trading in crypto means using software to apply defined trading logic. The logic can govern an entire strategy or only one part of the workflow, such as converting an already-approved trade into smaller orders. Algorithmic trading does not necessarily use AI.
Strategy logic vs execution logic
A strategy algorithm decides whether a trade is justified, which exposure it should create and how it should be managed. An execution algorithm takes an order objective and manages the process of filling it.
For example, deciding to reduce an existing position is a strategy or portfolio decision. Determining how that reduction is divided across orders is an execution decision. Automating the second does not transfer responsibility for the first.
Where machine learning can help
Machine-learning models may supply forecasts or market-context estimates to the algorithm. Those estimates still need decision rules, controls and performance evaluation around them. A more complicated algorithm is not automatically more robust.
AlphaNet illustrates the distinction through Standard Mode and Autopilot: execution assistance can support a user-directed trade, while Autopilot runs the selected strategy’s trading process.
The benefit of automation is repeatability, not guaranteed correctness. Software can implement a flawed idea consistently, so the quality of the research, operational controls and testing remains essential.