55 lines
2.6 KiB
TeX
55 lines
2.6 KiB
TeX
\documentclass[a4paper, 14pt]{scrartcl}
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\usepackage[utf8]{inputenc}
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\usepackage[english]{babel}
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\usepackage{parskip}
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\usepackage{microtype}
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\usepackage[margin=1in]{geometry}
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\usepackage{hyperref}
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\title{Stock Trading with Reinforcement Learning}
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\author{Marcel Zinkel}
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\begin{document}
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\maketitle
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\tableofcontents
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\section{Introduction}
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I want to build a reinforcement learning project about single asset stock trading. First I
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want to start a simple environment with just the actions buy and sell. For the reward function I
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also want to keep it simple at first by just using the profit as reward.
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In contrast to the algorithms we already heard in the lecture, I have to try out deep
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reinforcement learning algorithms because the price is a continuous variable. In theory, you could
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model the price with a specific resolution with many states. However, this can very quickly become
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impractical for classic reinforcement learning methods. Also, deep reinforcement learning can
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recognize pattern to act good in previously unseen states.
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I want to try out different reinforcement learning algorithms to see with works best for the trading
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environment. First I want to try out the Deep-Q-Network algorithm. It predicts the Q-function and
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uses Epsilon-Greedy Exploration. I plan to try out different formulas for the epsilon decay.
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Because DQN often overestimates the Q-values, I want to try out a variation of DQN, called Double
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DQN. It uses two networks for updating the policy. The
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online network selects the action with the highest Q-value and the target network evaluates the
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action. This causes more stable and better learning. I will try, if Double DQN will improve the
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results. At last, I want to try out the Proximal Policy Optimization algorithm.
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After implementing these different algorithms, I need to train these and compare the
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results.
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I find it also very interesting, if providing the RL agent with additional information then just the
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price, positively impacts the results. For example, I can add technical indicators, market volume or
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an online news score about the company. The last one is probably a bit difficult because you need a
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LLM which gives web scrapped articles a score how good the news is for a company. After adding this
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information, I need to reevaluate which algorithm is the best.
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\section{Libraries and Tools}
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\section{Development plan}
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\section{Availability}
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I am on vacation from the 04.08 to 13.08. On the 15. I am on an event, but I have time on the 14.
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From the 18. onwards I am available for the next couple of weeks. I look forward to the
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presentation, and thank you for giving me the additional time.
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\end{document}
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