The statistical arbitrage strategy originated from Europe and the United States, and has been verified and continuously optimized by the domestic market for a long time.
Continuously develop and iterate new models, develop deep learning and machine learning methods, and continuously optimize in terms of composition levels and dispersion.
Positions are highly dispersed, dynamically configuring the strategy portfolio according to market changes, changing hands quickly in order to effectively adapt to the changing market environment.
The compound algorithmic trading strategy reduces transaction costs and T0 strategy is introduced to thicken the bottom position holding income.
On the basis of core medium-frequency strategies, it expands to high-frequency and low-frequency to improve portfolio benefits and expand strategic capacity.
