Can we really predict the market when we already have neural networks, indicators, historical data, and trading bots?

On paper, everything makes sense:

the model learns from history, the strategy performs well, the signals look clean and convincing.

But in reality, the market often breaks even the most carefully designed systems.

Why does this happen?

In this episode of the xChief Central Asia Podcast, we talk with algorithmic developer Roman Swetly about the market as a complex nonlinear system. About why the classic “cause → effect” logic does not always work in trading — and why ideas from quantum physics, probability, and cognitive thinking may actually help traders think more clearly.

This episode explores algorithmic trading, the limits of predictive models, overfitting traps, volatility, neural networks, ...