A confidence interval triggering method for stock trading via feedback control

This paper builds upon the robust control paradigm for stock trading established. 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. The performance of this trading method is studied in both idealized markets and real-world markets.