@mastersthesis{design-gsi-mastersthesis-20207,
author = "Palos, Jaime",
abstract = "Machine Learning (ML) is growing and it’s rapidly making its way into almost every field.
And the stock exchange and trading are no exception.
Day after day, algorithmic trading is gaining more and more popularity. This technology
relies on computer programs to execute algorithms that automate a trading strategy. We
have been seeing more and more asset managers and hedge funds incorporate ML techniques
to improve their trading strategies. And we have even seen computer-driven hedge funds
that, according to The Financial Times, have already joined the industry’s top perform-
ers [87].
Proof of this ML growth is that, nowadays, investment banks and hedge funds are em-
ploying quantitative analysts to design and implement complex models that allow financial
firms to price and trade securities, help with risk management, or even develop new trading
strategies. And its importance can easily be seen reflected in their salaries; it is not uncom-
mon to find positions with posted salaries of $250,000 or more and with bonuses, $500,000+
is achievable, according to Investopedia [116].
During the development of this Master’s Thesis, I will study the current state of these
technologies and learn about, Stock, Trading, Machine Learning (ML), Algorithmic Trading,
Reinforcement Learning (RL), Deep Learning (DL), etc. And apply this knowledge to
design and develop an Algorithmic Trading System using different ML techniques. I will
later compare the obtained results with the ones obtained in previous Algorithmic Trading
projects and with the usual trading performance. I will end with a quick overview of possible
improvements of the system or future lines of work.",
address = "ETSI Telecomunicaci{\'o}n",
institution = "Universidad Polit{\'e}cnica de Madrid",
keywords = "Reinforcement learning;cryptocurrency",
month = "June",
title = "{D}esign and {D}evelopment of an {A}lgorithmic {T}rading {S}ystem for {C}ryptocurrencies using {R}einforcement {L}earning and {D}eep {L}earning",
type = "TFM",
year = "2020",
}