Strategy allocation for financial trading using competitive reinforcement learning and fuzzy logic

Imagine you have a team of different trading robots, each following its own style (trend-following, mean-reversion, etc.). Instead of betting on just one, a smart ‘coach’ watches how well each robot is doing in real time and keeps shifting money between them. That coach learns by trial and error (reinforcement learning) and uses fuzzy rules—"if performance is slightly worse but risk is very high, then cut exposure a lot"—to make smoother, more human‑like decisions rather than rigid on/off switches.