Application of deep reinforcement learning in stock trading strategies and stock forecasting

The role of the stock market across the overall financial market is indispensable. The way to acquire practical trading signals in the transaction process to maximize the benefits is a problem that has been studied for a long time. This paper put forward a theory of deep reinforcement learning in the stock trading decisions and stock price prediction, the reliability and availability of the model are proved by experimental data, and the model is compared with the traditional model to prove its advantages. From the point of view of stock market forecasting and intelligent decision-making mechanism, this paper proves the feasibility of deep reinforcement learning in financial markets and the credibility and advantages of strategic decision-making

Portfolio optimization in cryptocurrencies: a comparison of deep reinforcement learning and traditional approaches

Cryptocurrencies have become appealing investment options in recent years because of their high potential returns. This asset class emerged as a unique investment opportunity with distinguishing characteristics such as decentralized nature and uncorrelation with other assets. Investing in this product, however, has become a hazardous venture due to its extreme volatility and unpredictable price swings. As a result, a portfolio optimization is an essential tool for investors seeking to reduce risk while aiming for high returns. This thesis studies the Deep Reinforcement Learning models applied to cryptocurrency portfolio optimization compared to traditional methodologies like Markowitz's and rudimentary equally weighted portfolios

Investigation into a Practical Application of Reinforcement Learning for the Stock Market

A major problem of the financial industry is the ability to adapt their trading strategies at the same rate the market evolves. This paper proposes a solution using existing Reinforcement Learning libraries to help find new strategies at a practical scale. Using a wide domain of ticker symbols, an algorithm is trained in an environment that better represents reality. The supplied decision-making algorithm is tested using recorded data from the U.S stock market from 2000 through 2022. The results of this research show that existing techniques are statistically better than making decisions at random. With this result, this research shows how a practical application of Reinforcement Learning is possible through the inclusion of many more ticker symbols than previous research has done before. However, there is still work to be done to achieve acceptable returns. Potential applications of this research include informing human traders or creating automated traders

Multi-timeframe algorithmic trading bots using thick data heuristics with deep reinforcement learning

This thesis presents an augmented Artificial Intelligence (AI) algorithmic trading approach that combines Thick Data Heuristics (TDH), with Deep Reinforcement Learning (DRL), to successfully learn trading execution timing policies. In this thesis, combining the augmented AI human trader’s intuition and heuristics with DRL techniques to provide more focused drivers for trading order execution timing is explored. In this financial technology (Fintech) research, the goal is to solve the sequential decision-making problem of AI for profitable day and swing trading order timing executions. Enabling trading bots with cognitive intelligence and common-sense heuristics will offer traders including automatic traders an insight to understand the day-to-day swing trading timeframes indicators and arrive at mature trading decision-making. This thesis examines the performance of bots with Nasdaq and NYSE stocks that have a strong catalyst (info. which increases directional momentum) to find that they outperform benchmark algorithmic trading approaches. The thesis illustrates to the reader how to combine TDH and Deep Q-networks (DQN) into a TDH-DQN augmented AI trading bot. The bot learns through test data to predict the optimal timing of order executions autonomously on idealized trading time series data. The results show the TDH-DQN bot outperformed the buy and hold strategy plus two out of the three benchmark algorithmic trading strategies