The foreign exchange market (Forex) is a global market, where currency pairs are traded.
Trading on the Forex market has been slowly gaining popularity over the past few years and is not taxed in Slovenia.
Currency pairs are traded on Forex market and their value is affected by a huge number of factors that are difficult to predict. The biggest challenge of Forex market is undoubtedly its volatility.
In this master's thesis, we focus on the development of machine learning algorithms, which provide useful information for trading in the Forex market.
In the first part of our master's thesis, we used multiple output Bayesian linear regressions model to predict open, close, high and low daily price. Model can predict prices several days in advance. Bayesian model outputs the probability distribution of target variables, which we can use to determine confidence intervals.
In the second part, we have developed a reinforcement learning agent to trade on a simulated Forex exchange environment. The agent is built using Deep Q-Learning Network algorithm, which has revolutionized reinforcement learning.
The proposed solutions are evaluated at all euro crosses between 2010 and 2021.
It turns out that our regression model outperforms existing models when predicting one day in advance, where it only slightly underperforms when predicting close price compared to the naive model.
On the other hand, our reinforcement learning agent achieves better results than our hand-crafted strategies. The agent's portfolio has grown by 11.7 percent in 11 years.
Training our agent without transaction cost leads to poor performance.
Apparently, transaction costs force an agent to locate a more resilient and reliable strategy when market change occurs.
Machine learning algorithms are an essential part of a robust trading strategy, nevertheless building a profitable autonomous trading system requires a lot of knowledge, trial & error and processing power.
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