By: Indu Eswar Shivani Nayuni, Department Of Computer Science & Engineering(Data Science), Student Of Computer Science & Engineering(Data Science) ,Madanapalle Institute of Technology and Science, Angallu(517325),Andhra Pradesh . indunayuni1607@gmail.com
Abstract
Now real-time trading systems using generative AI are the most important innovations in FinTech. With regard to the choice of the topic, this paper will try to find how Generative AI models can be utilized for decision-making and risk management in rapid-fire trading environment with improvement in the predictive capability. Large dataset and accurate deep learning algorithms allow for creating fake financial data and modeling the environment for creating highly efficient trading models in Generative AI. Apart from improving the commonplace trading systems, this method provides innovative strategies for the current monetary issues. In particular, the research focuses greatly upon issues concerning the different architectures of the AI models, wherein the latter are going to be implemented, as well as actual performance of the models while in trading. Analysis of results of techno organizational analysis show that the generative AI enhances the descriptive capability of trading and the profitability too; moreover, it gives chances to improve the speed of real time trading platforms.
Keywords: gen AI in innovation Fin Tech, gen AI for decision making, effiecient trading models, innovative strategies, techno organizational analysis.
Introduction
Extreme fragmentation, many levels, continuous change, and lots of information relating to the financial markets can be described by referring to the model of structure. older trading systems with fixed rules and mathematical algorithms based on the past stock information may sometimes take time to switch to the new market situation or some event. Regarding Generative AI that appeared as one of the most prominent branches of Machine Learning in the recent years, it is giving rather a splendid opportunity for the redesign of the RTTs.[1]
Generative Artificial Intelligence is the branch of artificial intelligence that makes use of already existing information and produces new information out of it and its application has been very effective in various fields some of which include formation of images, syntax and tone of speech, mathematical computation and more recently financial engineering. Within the framework of trading Generative AI models can generate fake data ,develop another models of trading and use new tactics which are appropriate for changes in the trading market.
Taking an important place in the framework of this paper, is the application of Generative AI in real-time trading platforms. This is followed by the description of the existing solutions in the trading systems and the deficits. We then extend the discussion to Generative AI and its main components like GANs and VAEs and the ways they can be used in the context of financial markets.[2] However, this work is primarily focused on actual trading spaces that employ these models and the architecture of the ways of deploying these models into the existing trading platforms.
In this paper, we also demonstrate how Generative AI enhances the ability of models substantiated through empirical data, and ways in which trading solutions and risk management strategies are improved when utilizing Generative AI vs traditional methods. Based on the above findings, it can be concluded that with the help of Generative AI, trading effectiveness and profitability increase, and there is a foundation for meetings the conditions of modern financial markets operation.
Based on this background, the subsequent discourse proceeds to undertake a detailed examination of how Generative AI is well on its way to transforming real-time trading systems and, consequently, locking the future of fintech[3].
Overview of Traditional Trading Systems
In the traditional trading systems the emphasis was made on the trading floor where people used to meet for executing the trades. [4]
Conventional trading systems have always acted as the backbone of the financial markets for quite a long time.[5] They require a weaker level of artificial intelligence and mostly apply statistical analysis of the historical data and programs based on the predetermined rules to produce trading decisions. They have enabled the management of trading through automation hence eliminating the human interface and trading speed and efficiency increased. Nevertheless, there are a lot of shortcomings that are inherent in classical trading systems and might become critical in the context of the modern environment characterized by high volatility and uncertainty. The following diagram explain the overview of Traditional Trading system
1. Parts of conventional business structures
In this case, there is an imperative for the effective data collection and processing.
Old trade systems gather enormous amounts of data from different sources such as stock exchange, financial newspapers, and economic indicators. The data collected is then subjected to analysis to establish relationships and subsequent trends that serves the trade.
1. 2 Rule-Based Algorithms
As for traditional trading systems, they are based on algorithms of a rule-based type. These algorithms specify the rules and criteria on how the trades are going to be conducted. The rules are often derived from the indicators including moving average, RSI, and Bollinger Bands.
1. 3 Backtesting
As for trading algorithms, they are also tested on historical data prior to the deployment process. Back testing can help to identify problems and fine tuning the algorithm parameters to make the algorithm work better.
1. 4 Order Execution
When a trading signal has been developed based on the rules in the algorithm, the deal is placed electronically with trading applications. It reduces the extent to which human input is needed while facilitating fast handling of trade affairs.
2. Advantages of Traditional Trading Systems
2. 1 Speed and Efficiency
Slightly different automation of the conventional trading systems enables the fast and efficient dealing with trades when compared to manual form.
2. 2 Consistency
Systematic and mechanical removal of feel and Psychology makes sure that trading procedures are consistently and properly conformed to in the trading market.
2. 3 Scalability
There are various trade systems whereby traditional trade systems can execute a large number of trades at the market making them ideal for high frequency trading.
3. Drawbacks of Conventional Trading Platforms
3. 1 Data Dependency
The conventional trade systems largely depend on past trends to forecast future market trends. This dependency can be a disadvantage during unusual situations when the market is compressed by a black swan or during black days and months when the reliance on the model is tough.
3. 2 Static Rules
The algorithms based on this kind of logic are extremely straightforward, which does not allow for taking into account certain aspects of the market. This makes it possible for the firm not to make efficient decisions when it comes to the market conditions that may slightly differ from what the former used to experience.
3. 3 Latency
Lack of latency is always problematic in trading systems, even in the traditional ones, which are, however, automated; still, it takes certain time to aggregate the required data, deliberate, and then execute the order. In these conditions, usually in highly fluctuating markets, any delay in entering a particular market has certain consequences and impacts the trade negatively.
3. 4 Limited Predictive Power
Although the old trading systems are efficient, their predictiveness is restricted in their data basis and indicators solely. These systems do not always correspond to the real market situation because of political segregation, shifts in the economy, and feelings of investors.
3. 5 Risk Management Challenges
Other trading features that are usually included in trading systems include; stop losses and position sizing which perform very poorly in volatile markets. This limitation poses the trader in a very vulnerable position during any form of turbulence in the market.
4. Need for Innovation
Therefore, the current and future research may have a potential demand to eliminate these restrictions, or to submit works capable of enhancing the practical use and the potential of Trading Models in terms of result prediction. From this point, it is possible to note that the emergence of elegant approaches such as Machine Learning, especially Generative AI, can be considered as the effective solution to the mentioned challenges. It does mean that through generative AI, one can generate synthetic data and, moreover, simulate different market conditions and the methods of trading that would be required to go beyond the boundaries of a simple trading system in order to develop a realistic model of real-time trading for the present moment.
Fundamentals of Generative AI
Generative AI is one of the most efficient and stronger classes of the novel advanced machine learning models,[6] whose work implies the generation of new samples like the training ones. While most of the AI models, as it will be explained later, sample a function endpoint to classify or do regression, generative models estimate the probability distribution over inputs in order to generate new samples. This capability is considered very valuable in the trading field development, since the simulated and synthetic information facilitates the strategies and risks of trading. This list of fundamental of gen AI as follow in fig 2.
1. Key Concepts
1. 1 Generative models Discriminative models
- Discriminative Models: Some of them are logistic regression, support vector machines etc this models and the like, directly learns the boundary of separation from the data. It is focused on predicting labels of certain inputs.
- Generative Models: These models learn the conditional probability table of the input data, thus, they can generate new data. There are a variety of types famous for their usage, for example, Generative Adversarial Network (GANs) and Variational Autoencoder (VAEs).
1. 2 Learning Distributions
The goal of generative models relates to generation of the probability distribution of the training data. It also means : When they are trained on a particular distribution they are able to sample from the given distribution to produce other data pattern of the same kind. This is highly advantageous when it comes to the production of synthetic data used in the enhancement of the trading algorithms.
2. Generative Adversarial Networks (GANs)
2. 1 Architecture
GANs consist of two neural networks: and there is what is called generator and what is called discriminator. These networks are trained at the same time in a procedure called adversarial training where they are pitted against each other[7].
- Generator: Make the synthetic data from the noise is the last prepositional phrase of the sentence. Its function is to produce data which cannot be distinguished from real data.
- Discriminator: West checks the authenticity; it determines the actual and simulated data. Their intended use is to sign data in an effort to verify if it is original or a fake copy.
2. 2 Training Process
- The generator produces the synthetic data and feed it to the discriminator along beside the authentic data because the output of the discriminator will be used in compiling the model.
- The discriminator comes up with a decision on all the samples and then relays its decision to the generator as the verdict.
2. 3 Applications in Trading
- Synthetic Data Generation: The prospect of GANs is similar to that of having realistic synthetic data, which is to allow back testing for any imaginable trading strategy because they can produce almost any financial features.
- Market Simulation: GANs. The development of the AI model allows traders use it to apply their strategy when operating in different conditions of the markets, effectiveness in determining that tendencies are not transient[8].
3. Variational Autoencoders (VAEs)
3. 1 Architecture
VAEs consist of two main components: an encoder and a decoder An encoder is a device that transmits a signal by converting the original information in to electrical impulse.
- Encoder: Lowers input data into a new space that is of lesser dimensions than the original data space, also known as the latent space. This is followed by transforming the input data from the input space to the latent space brought about by a probability distribution.
- Decoder: To, fills in the missing values of the target functional map in the updated latent space representation. After learning a representation, it generates new data samples, by decoding the points from the learned latent space.
3. 2 Training Process
- In turn, VAEs attempt to optimise the marginal probability of the acquired data and, thus, the difference between the input data and the reconstructed data is minimised.
- They also map the latent space on a well defined manner so that the distribution of these points may be smooth so that an interpolation of the points in question may be effected[9].
3. 3 Applications in Trading
- Anomaly Detection: This is possible because VAEs have the ability to pick out anomalies in the hidden layer which translates to anomalous conditions in the market. This is helpful in risk and opportunities identification[4].
- Market Data Augmentation: VAE can produce more data that enrich the given sets, and the result is more extensive training data for other models.
4. Other Generative Models
4. 1 Autoregressive Models
- These models for instance, PixelRNN and WaveNet, produce data in steps, that is, one step is a function of the previous step. They are used mostly for the case of producing sequential data, such as a time series.
4. 2 Flow-based Models
- These models like RealNVP and Glow make use of invertible elements in order to transform the data to a latent space and back. They gives exact likelihood estimates and are convenient for generation and density estimation.
Conclusion
Applying Generative AI into simple and compound trading platforms improves financial technology to the next level. The usage of models such as GANs and VAEs can improve the forecast’s quality, flexibility and approach to risk assessment due to simulations of multifaceted markets and data synthesis. It helps to avoid disadvantages of the current trading systems which use a lot of historical data and stiff rules.
The important factors related to the implementation of the AI strategy include the ways of data processing, the methods used for model training, integration of AI with systems, the methods for incorporating scalability, the latency associated with the systems, and the regulations governing your sector. The most crucial components when working with dynamic markets are performance tracking in real time, as well as updates on a frequent basis. Previous research also shows that it can achieve better average accuracy and profit as compared with conventional algorithms.
Some of the drawbacks include data quality issues, complexity of the models, and computational requirements imbalance and to them solutions such as use of data validation techniques or cloud computing can easily solve them. The potential of generative AI in complementing trading methodologies and handling risks prove that more robust and higher-revenue trading is possible in the coming future adored by the continuous research activities.
Reference
- H. Gm, M. K. Gourisaria, M. Pandey, and S. S. Rautaray, “A comprehensive survey and analysis of generative models in machine learning,” Comput. Sci. Rev., vol. 38, p. 100285, Nov. 2020, doi: 10.1016/j.cosrev.2020.100285.
- J. Sweller, “The Role of Evolutionary Psychology in Our Understanding of Human Cognition: Consequences for Cognitive Load Theory and Instructional Procedures,” Educ. Psychol. Rev., vol. 34, no. 4, pp. 2229–2241, Dec. 2022, doi: 10.1007/s10648-021-09647-0.
- N. Kulkarni and S. Bansal, “Exploring Real-World Applications of GenAI in Retail,” J. Artif. Intell. Cloud Comput., vol. 2, pp. 1–5, Nov. 2023, doi: 10.47363/JAICC/2023(2)186.
- P. Pappachan, Sreerakuvandana, and M. Rahaman, “Conceptualising the Role of Intellectual Property and Ethical Behaviour in Artificial Intelligence,” in Handbook of Research on AI and ML for Intelligent Machines and Systems, IGI Global, 2024, pp. 1–26. doi: 10.4018/978-1-6684-9999-3.ch001.
- B. Mandelbrot and R. L. Hudson, The Misbehavior of Markets: A Fractal View of Financial Turbulence. Basic Books, 2007.
- “Future Internet | Free Full-Text | The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges.” Accessed: Aug. 06, 2024. [Online]. Available: https://www.mdpi.com/1999-5903/15/8/260
- M. Rahaman, F. Tabassum, V. Arya, and R. Bansal, “Secure and sustainable food processing supply chain framework based on Hyperledger Fabric technology,” Cyber Secur. Appl., vol. 2, p. 100045, Jan. 2024, doi: 10.1016/j.csa.2024.100045.
- N. Ghorashi, A. Ismail, P. Ghosh, A. Sidawy, and R. Javan, “AI-Powered Chatbots in Medical Education: Potential Applications and Implications,” Cureus, vol. 15, no. 8, p. e43271, doi: 10.7759/cureus.43271.
- M. Moslehpour, A. Khoirul, and P.-K. Lin, “What do Indonesian Facebook Advertisers Want? The Impact of E-Service Quality on E-Loyalty,” in 2018 15th International Conference on Service Systems and Service Management (ICSSSM), Jul. 2018, pp. 1–6. doi: 10.1109/ICSSSM.2018.8465074.
- Gupta, B. B., Dahiya, A., Upneja, C., Garg, A., & Choudhary, R. (2020). A comprehensive survey on DDoS attacks and recent defense mechanisms. Handbook of Research on Intrusion Detection Systems, 186-218.
- Gupta, B. B., Gaurav, A., Arya, V., & Chui, K. T. (2024, June). LSTM-GRU Based Efficient Intrusion Detection in 6G-Enabled Metaverse Environments. In 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) (pp. 118-123). IEEE.
Cite As
Nayuni I.E.S. (2024) Implementing generative AI in Real-Time Trading Systems, Insights2Techinfo, pp.1