Some traders lose with Galileo FX not because the software itself is broken, but because of how it is applied. Trading algorithms don’t remove risk — they manage it in a systematic way. The problem is that users often underestimate how much settings, timeframes, and market conditions matter.
For example, short timeframes like M1 and M5 naturally produce more noise. An algorithm trading in that environment can be pulled into too many false signals, leading to drawdowns that feel uncontrollable. Likewise, leaving the maximum orders uncapped exposes the account to compounding risk, where a string of small trades quickly balloons into large exposure. These are not failures of the software itself but of configuration and context.
Another factor is human psychology. Many traders skip the demo stage and rush straight into live trading. They don’t test multiple strategies, so they have no data-driven basis for confidence. In contrast, users who systematically test 10–20 strategies and then narrow down to a small handful of consistent performers tend to fare better. They understand that no single configuration works all the time, and that adaptability is part of algorithmic trading.
Finally, market conditions themselves change. Algorithms are built to follow patterns, but markets are not static. When volatility shifts or trends flatten, settings that once worked can begin to underperform. The trader’s job is to recognize this, review performance, and adapt.
Losses, then, are not mysterious. They come from a mismatch between trader behavior, chosen settings, and the realities of the market. The scientific way to approach this is with patience, testing, and regular adjustments — treating Galileo FX as a tool that requires calibration, not as a guarantee.