mlsanityData to 7 Oct 2026

Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions

arXiv 2507.19639 ↗First posted 25 Jul 2025cs.LG cs.NEcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 58 references that are in the map. First posted on arXiv 25 Jul 2025.

Builds on

20 of 58 references.

References · 20

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  5. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  6. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  7. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  8. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  9. 2024xLSTM: Extended Long Short-Term Memorycited by 320
  10. 2020FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Financecited by 86
  11. 2021FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Financecited by 57
  12. 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
8 more
  1. 2024Unlocking the Power of LSTM for Long Term Time Series Forecastingcited by 29
  2. 2021Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Reviewcited by 17
  3. 2023Dynamic Datasets and Market Environments for Financial Reinforcement Learningcited by 15
  4. 2022A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networkscited by 12
  5. 2020Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learningcited by 7
  6. 2024DeformTime: Capturing Variable Dependencies with Deformable Attention for Time Series Forecastingcited by 6
  7. 2024Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversitycited by 2
  8. 2022DDPG based on multi-scale strokes for financial time series trading strategycited by 1

Built on by

Not cited yet.