A confidence interval triggering method for stock trading via feedback control

This paper builds upon the robust control paradigm for stock trading established in [1]. To this end, the contribution of the current work is an algorithm for triggering a trade. Whereas previous work considered the management of a trade, this paper concentrates on entry into the trade. That is, based on historical prices, we generate, three possible signals: long, short or no trade. These signals are derived using an Ito process model based on geometric Brownian motion. The parameters of this model, the Ito process drift μ and the volatility σ, are estimated and adaptively updated as each new piece of price data arrives. The confidence interval for μ determines when a trade is triggered. If a trade is triggered, then the amount invested in stock is obtained using a saturation-reset linear feedback controller much like the one described in [1]. The performance of this trading method is studied in both idealized markets and real-world markets.