Abstract
Traders utilize strategies by using a mix of market and limit orders to generate profits. There are different types of traders in the market, some have prior information and can learn from changes in prices to tweak her trading strategy continuously(Informed Traders), some have no prior information but can learn(Uninformed Learners), and some have no prior information and cannot learn(Uninformed Traders). In this thesis. Alvaro C, Sebastian J and Damir K \cite{AL} proposed a model for algorithmic traders to access the impact of dynamic learning in profit and loss in 2014. The traders can employ the model to decide which strategies to use. The model considered the distribution of the prices in the future using prior information, the spread of the bid and ask prices and also the capital appreciation of inventories. I implemented the model for the case when the trader can only learn from and take positions in one asset. Compared to the uninformed traders, the informed trader using the proposed model can change the strategies along time and make higher profits.
Committee Chair
Prof. José E. Figueroa-López
Committee Members
Prof. Nan Lin
Degree
Master of Arts (AM/MA)
Author's Department
Mathematics
Document Type
Thesis
Date of Award
Spring 5-18-2018
Language
English (en)
DOI
https://doi.org/10.7936/K7668CN8
Recommended Citation
Cai, Xinyi, "Algorithmic Trading with Prior Information" (2018). Arts & Sciences Theses and Dissertations. 1279.
The definitive version is available at https://doi.org/10.7936/K7668CN8
Comments
Permanent URL: https://doi.org/10.7936/K7668CN8