Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions

TL;DR

This paper introduces four novel loss functions that enable neural networks to directly learn profitable stock trading strategies, outperforming traditional reinforcement learning methods and baseline approaches on S&P 500 stocks.

Contribution

The paper proposes new profit-guided loss functions allowing neural networks to directly optimize trading decisions, a novel approach in stock trading strategy learning.

Findings

Strategies achieved over 48% returns in test periods.

Outperformed PPO and DDPG reinforcement learning methods.

Consistent performance across multiple years.

Abstract

Stock trading has always been a challenging task due to the highly volatile nature of the stock market. Making sound trading decisions to generate profit is particularly difficult under such conditions. To address this, we propose four novel loss functions to drive decision-making for a portfolio of stocks. These functions account for the potential profits or losses based with respect to buying or shorting respective stocks, enabling potentially any artificial neural network to directly learn an effective trading strategy. Despite the high volatility in stock market fluctuations over time, training time-series models such as transformers on these loss functions resulted in trading strategies that generated significant profits on a portfolio of 50 different S&P 500 company stocks as compared to a benchmark reinforcment learning techniques and a baseline buy and hold method. As an example, using 2021, 2022 and 2023 as three test periods, the Crossformer model adapted with our best loss function was most consistent, resulting in returns of 51.42%, 51.04% and 48.62% respectively. In comparison, the best performing state-of-the-art reinforcement learning methods, PPO and DDPG, only delivered maximum profits of around 41%, 2.81% and 41.58% for the same periods. The code is available at https://anonymous.4open.science/r/bandit-stock-trading-58C8/README.md.