Top Q-Learning Deep Dive for Financial Engineering


How to develop and deploy Q learning methods with applications, use cases and best practices through reinforcement learning in machine learning

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I have never seen another industry so prone to errors (in artificial intelligence applications, AI, for example) as that of financial engineers in the industry.

All that matters in financial engineering is a flawless mastery of mathematics.

I’ve written in the past about how if I could go back and start all over again, agnostic to finance, data science, or product (coding is coding is coding), I would have started my programming basics by learning the Hill Climbing algorithm (I wrote about it recently; I’ll link to it at the bottom of this article.)

Q-learning is certainly more advanced. Back to Q-learning.

In financial engineering, there is an ever increasing demand to develop new techniques and tools to solve mathematical problems. One such method that has emerged as an influencer in finance is Q-learning:

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Simply, Q-learning seeks to derive effective decision rules from data.

Traditional supervised learning algorithms require a set of data whose inputs and outputs are known in advance. This is not the case with Q-learning, which can learn from interactions with its environment without the need for labeled data.

Q-learning is an example of reinforcement learning, which involves agents [2] take action in an environment to maximize a reward. Unlike supervised learning, there is no need [17] pre-labeled datasets; instead, the agent learns by trial and error [3] from the comments received after each action.

The main difference between Q-learning and other machine learning algorithms is how rewards are applied to update knowledge about the environment.

In Q-learning, this updating process is done using a function called Q [4]. The function Q gives the expected future reward for a given action in a given state; thus, it encodes an agent’s knowledge of its environment into a value. Importantly, this value represents what is important to an agent, such as how to maximize their total reward over time.

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Trade, anyone?

Q-learning is essential in financial engineering because it can help identify and optimize potential trading strategies. As a machine learning algorithm, it can be deployed to select the optimal policy [6][7] for a given reinforcement learning problem, which makes it suitable for problems where the reward function is unknown or difficult to determine.

One of the main challenges of financial engineering is to design trading strategies that meet quantitative objectives (avoiding talking about profit or income here) while managing risk. Q-learning can be integrated to develop trading strategies that strike a balance between these two goals by finding policies that maximize the performance of outcomes (like returns) while minimizing drawdowns. Additionally, Q-learning can help portfolio managers adapt their investment portfolios to changing market conditions by allowing them to quickly retrain their models on new data sets (that emerge or become known over time).

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In general, Q-learning can be used for any issue where an agent [2] must learn the optimal behavior in an environment.

In portfolio management: Q-learning could help manage a portfolio of assets by learning the optimal rebalancing strategy for different market conditions. For example, the performance of reinforcement learning algorithms has been compared to traditional buy-and-hold strategies (in terms of how well they may or may not outperform [9]) under various market conditions.

For asset pricing: Q-learning could be deployed to study and predict asset prices in different markets. This is often accomplished by modeling the environment as a Markov Decision Process (MDP) [10] and solving the equilibrium price using dynamic programming methods [11][12].

Risk management covers the quantification and management of exposure. Q-Learning could also be applied here, helping to identify and quantify the risks associated with different investments or portfolios of assets.

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Since Q-learning is an out-of-policy learning algorithm, it may require more data than is available to learn the optimal policy, leading to considerations for data access, expense, and associated risk in the process. overall accuracy of the model. As an illustration, Q-learners can sometimes struggle to converge to the optimal policy because of the curse of dimensionality [13]. Related to the previous point, since each state is represented in memory by a node in the Q-table [14]Q-learning could potentially require a large amount of memory compared to other reinforcement learning algorithms such as SARSA (on-policy) [15][16].

Simply use a pre-trained deep learning model that has been trained on a large set of historical market data to generate predictions for future market movements. Separately, use a reinforcement learning algorithm that can learn from experience (past/previous) and make predictions about future market movements. A mixed approach consists of combining the two methods, using the strengths of each approach, with the aim of creating an even more accurate prediction model.

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Q-learning provides an essential implementation method for financial engineers looking to design and optimize complex systems. While there are many other machine learning algorithms available, few are as well suited for building systems, such as trading capabilities, as Q-learning due to its ability to handle large state spaces. . [8] and stochastic rewards [5]. As such, integrating Q-learning into your workflow could offer significant advantages over competing approaches.

Q-learning is robust against changes in the data of the underlying problem, which can make this method optimal for implementation in volatile markets where conditions can change quickly. Since Q-learning is based on experiential learning, it does not require in-depth knowledge of the particular problem at hand, which potentially makes it more accessible to a wider range of users than other methods.


1. A dynamic channel assignment technique based on Q-learning for mobile communication systems. (nd). IEEE Xplorer. Retrieved August 2, 2022 from

2. Ribeiro. (nd). Reinforcement learning agents. Journal of Artificial Intelligence, 17(3), 223–250.

3. Sutton et al. Reinforcement learning architectures.

4. Ohnishi, S., Uchibe, E., Yamaguchi, Y., Nakanishi, K., Yasui, Y., & Ishii, S. (2019). Deep constrained Q-learning gradually approaching ordinary Q-learning. Frontiers in Neurorobotics, 0.

5.Watkins, " Dayan. (nd). Q-learning. Machine Learning, 8(3), 279–292.

6. Hasselt, H. (nd). Double q-learning. Advances in Neural Information Processing Systems, 23.

7. A new Q-learning algorithm based on the metropolis criterion. (nd). IEEE Xplorer. Retrieved August 2, 2022 from

8. Niranjan et al. Online Q-Learning using connectionist systems.

9. Matthew, M., John;Saffell,. (nd). Reinforcement learning for trading systems and portfolios.

10. Safe q learning method based on constrained Markov decision processes. (nd). IEEE Xplorer. Retrieved August 2, 2022 from

11. Klein, Timo. Autonomous algorithmic collusion: Q-learning under sequential pricing.

12. Neuneier, R. (nd). Improve q-learning for optimal asset allocation. Advances in Neural Information Processing Systems, 10. See

13. Distributed Q-learning for dynamically decoupled systems. (nd). IEEE Xplorer. Retrieved August 2, 2022 from

14. A scalable parallel q-learning algorithm for resource-constrained distributed computing environments. (nd). IEEE Xplorer. Retrieved August 2, 2022 from

15. Kosana, V., Santhosh, M., Teeparthi, K., & Kumar, S. (2022). A new dynamic selection approach using the SARSA algorithm on the policy for accurate wind speed prediction. Electrical Power Systems Research, 108174.

16. Singh et al. Using eligibility traces to find the best policy without memory in partially observable Markov decision processes.

17. Dittrich, & Fohlmeister. (2020). In-depth q-learning based optimization of inventory control in a linear process chain. Production Engineering, 15(1), 35–43.


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