End-to-End Trading with Custom Algorithm

End-to-End Trading with Custom Algorithm

Published by : http://www.ijert.org

International Journal of Engineering Research & Technology (IJERT)

ISSN: 2278-0181

Vol. 14 Issue 10, October – 2025

Manya Chaturvedi

Dept. of Computer Science and Engineering Parul University, Vadodara

Mann Shah

Dept. of Computer Science and Engineering Parul University, Vadodara

AbstractThis paper presents a comphrehensive study of an End-to-End Trading system driven by a custom algorithm.The framework integrates real-time data ingestion,advanced fea- tures engineering,hybrid strategies combining technical indica- tors with sentiments analysis, and robust risk managements techniques.Unlike fragmented system that rely on seprated mod- ules for backtesting, execution, and compliance, this approach provides a unified design for research and deployment.The system is benchmark against existing trading models and demon- strates improvements in trade accuracy,risk control,and execu- tion reliablity.The paper provides an extensive literature sur- vey,methodological details,module descriptions, and implementa- tions insights.Futhermore,placeholder for experimental evalua- tion and performance metrics are included.This work aims to provide a foundation for future research in trading automa- tion,governance,and AI-augmented decision-making.The rapid advancement of financial technology has led to the development of algorithm trading system capable of executing trades with high efficiency and precision.This project presents an end-to-end trading framework that leverages a custom-designed algorithm to optimize decision-making across the entire trading lifecycle,from data acquistion and preprocessing to strategy execution and performance evaluation.The proposed system integrates real-time market data,advanced statistical modeling, and machine learning techniques to enhances prediction accuracy and minimize trading risks.Emphasis is placed on scalability,adaptability,and trans- parency,allowing the algorithm to adjust dynamically to volatile market condition.Futhermore,the implementations focuses on reducing latency in order execution,improving profitable, and maintaining robustness against market anomalies.The results demonstrate that the custom algorithm can outperform tra- ditional trading strategies by delivering higher returns expo- sure.This research contributes to the growing field of automated trading by offering a systematic,adaptable, and efficient solution for end-to-end trading automation.

Index TermsAlgorithmic Trading, Risk Management, Senti- ment Analysis, Predict-then-Optimize, High-Frequency Trading

1. INTRODUCTION

   Algorithmic trading has significantly reshaped financial markets by automating trade execution and enhancing decision-making efficiency. Compared to manual approaches, it enables faster analysis of market data, reduced latency, and consistent strategy implementation. Recent studies empha- size the growing importance of building end-to-end custom trading pipelines that integrate technical indicators, machine learning, optimization techniques, and modern computational

   Nandan Panchal

   Dept. of Computer Science and Engineering Parul University, Vadodara

   Neel Patel

   Dept. of Computer Science and Engineering Parul University, Vadodara

   infrastructures.Mehra and Shetty [1] created and validated a tailored strategy that merged Exponential Moving Average (EMA), Relative Strength Index (RSI), and natural language processing (NLP) based sentiment analysis. They obtained up to 88% win rates on the SP 500, proving that hybrid methods that include both technical indicators and sentiment analysis significantly improve trading performance. In the same vein, Jaworski [2] used Particle Swarm Optimization (PSO) to optimize bespoke RSI-based strategies for Bitcoin, solving the issue of false alarms and performing better than conventional setups [1]. Infrastructure-wise, Boutros et al.

   [3] and Sadoghi et al.[4] illustrated how systems based on FPGA provide ultra-low latency in high-frequency trading (HFT), bringing execution latency to the nanosecond range. Expanding on this idea, Yoo et al. [5] presented LightTrader, an AI-powered HFT system incorporating deep learning ac- celerators to provide both high throughput and predictive performance, solving the drawbacks of traditional latency- based systems.In addition to hardware acceleration, Liu et al.
   [6] developed Deep Inception Networks (DINs), which remove the necessity for handcrafted features by extracting time-series and cross-sectional representations directly from market data. This end-to-end, data-driven approach maximizes portfolio- level Sharpe ratios and exhibits strong performance under varying transaction costs. At the same time, Kocaogullar et al. [5] investigated blockchain-based algorithmic trading using ChainBot, a decentralized platform that guarantees privacy, transparency, and censorship resistance via smart contracts and zero-knowledge proofs.Altogether, these works suggest that contemporary trading systems are heading toward end-to- end, holistic solutions integrating technical analysis, artificial intelligence, optimization algorithms, reconfigurable hardware, and decentralized architectures. Drawn by these developments, this project aims at creating an end-to-end bespoke trading algorithm that combines technical indicators, optimization techniques, and in-time computational tools to provide a pow- erful, scalable, and smart framework for algorithmic trading.

2. SCOPE AND OBJECTIVE

   The scope of this project is the development of designing, implementing, and testing a holistic end-to-end trading system with customized algorithms, high-end optimization, and AI-

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   International Journal of Engineering Research & Technology

   based decision-making. Augmenting research that combines technical indicators and sentiment analysis for improved trad- ing precision [1], the project will seek to go beyond con- ventional strategies using blockchain-based privacy mecha- nisms [2], FPGA-accelerated infrastructures for latency-low execution [3], and predictive optimization frameworks like [6]. The main goal is to create a trading pipeline that not only anticipates and places trades with high precision and low latency, but also provides scalability, flexibility, and regulatory compliance. Other objectives are validating the utility of deep learning architectures such as Deep Inception Networks for multi-asset portfolio optimization[7] using particle swarm optimization for bespoke strategy finetuning[8] and integrating auditability to provide accountability in AI-based decisions [5]. By integrating these multidisciplinary methods, the project aims to provide a trading system that is robust, transparent, and data-driven, with sustainable profitability and consonance with changing financial regulations [9].Another goal is to integrate auditing and accountability structures to facilitate transparency and compliance with changing financial regulations [5]. Fi- nally, the project aims to develop a strong, scalable, and smart trading system that responds to market changes, reduces risk of execution, and optimizes yields while ensuring explainability and trust in financial choices.Traditional techniques based on individual technical indicators are common, but recent studies posit that the integration of multiple indicators like EMA, RSI, and sentiment analysis can enhance accuracy and profitability [1]. The scope of this project is extended by investigating hybrid approaches blending machine learning, optimisation, and rel-time decision-making.

3. PROBLEM STATEMENT

   Algorithmic trading has revolutionized financial markets through its ability to make automated, high-speed choices, but current systems tend to be confronted with quite mas- sive constraints. Most conventional approaches are based on individual technical indicators, which limit forecasting ability and frequently produce misleading signals, thus diminishing profitability [1]. In addition, high-frequency market envi- ronments require ultra-low latency, something it is hard to obtain with typical software-based systems, thus necessitating hardware-accelerated alternatives like FPGAs and specialist AI accelerators [3]. The other challenge is the decoupling of prediction and optimization, where sequential methods are not able to dynamically fit market uncertainties; end-to-end integrated frameworks such as PyEPO emphasize the need to couple these phases [6]. Besides, privacy and transparency are still open challenges in decentralized trading landscapes, as tactics get exposed without blockchain-based safeguards [2]. Multi-asset models developed based on deep learning like Deep Inception Networks hold potential for efficient portfolio optimization, yet interpretability and overfitting remain issues [7]. Lastly, regulatory and accountability issues necessitate algorithmic audit frameworks to provide fairness, trust, and compliance [5]. Together, these challenges create the necessity for a bespoke end-to-end trading system that combines techni-

   International Journal of Engineering Research & Technology (IJERT)

   ISSN: 2278-0181

   Vol. 14 Issue 10, October – 2025 cal indicators, machine learning, optimization, privacy mecha- nisms, and auditing into an integrated, adaptive, and trans-

   parent format for sustainable trading performance.Although

   there are swift advancements in algorithmic trading, there are still a number of essential gaps that restrict the efficacy, scalability, and trustworthiness of current systems. Classic trading patterns usually rely on standalone indicators like RSI or EMA, which do not reflect the complexity of market dynamics and might produce frequent false signals, lowering profitability and solidity under different market conditions [1]. Whereas high-frequency trading involves responses almost as quickly as the data itself, software-based implementations are unable to cope with the ultra-low latency needed in todays markets, making FPGA-based and AI-fueled infrastructures necessary that can handle financial data at microseconds rates [3].Another critical issue is the gap between prediction and ac- tion. A lot of existing systems follow a two-stage methodology, where models predict prices first and then execute separately afterwards. This sequential design often results in suboptimal outcomes under real-world uncertainty, emphasizing the need for integrated end-to-end predict-then-optimize pipelines [6]. In addition, trading algorithms operating in decentralized environments face challenges of privacy, transparency, and security; without blockchain-enabled protections, proprietary strategies risk exposure and manipulation [2].

4. RELATED WORK

   A wide range of studies have explored advancements in algorithmic and end-to-end trading systems, highlighting di- verse approaches that inform the development of custom frameworks. Early work demonstrated the effectiveness of integrating multiple technical indicators such as EMA, RSI, and sentiment analysis for more robust trading strategies [1], while other studies proposed optimization-based methods like particle swarm optimization for enhancing custom trading algorithms in cryptocurrency markets [8]. To address latency challenges in high-frequency trading, researchers have intro- duced FPGA-based infrastructures and AI accelerators that significantly reduce round-trip times while maintaining com- putational efficiency [3].Concurrently with this, blockchain- based methods like ChainBot underscore the significance of decentralized execution and zero-knowledge proofs to facili- tate privacy-preserving trading strategies [2]. In optimization, end-to-end predict-then-optimize systems like PyEPO stress the need for integration of prediction and decision-making in finance applications [6]. Deep learning architectures like Deep Inception Networks extend this even further by learning time- series and cross-sectional features directly in order to optimize multi-asset portfolios without hand-engineered features [7]. There have also been attempts to make algorithmic systems more transparent and accountable, with auditing mechanisms offering ways to identify bias and verify compliance [5]. Together, these papers illustrate the transition of algorithmic trading from indicator-based approach to holistic, data-driven, privacy-enhancing, and audit-ready frameworks that provide the building blocks for developing a complete end-to-end

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   International Journal of Engineering Research & Technology

   custom trading system.Algorithmic and end-to-end trading re- search has progressed on various fronts, providing insights for constructing a cohesive trading framework. A number of stud- ies have highlighted the need for the use of technical indicators and sentiment analysis to improve profitability and minimize false signals, illustrating that hybrid methods perform better than single-indicator-based strategies[1]. Concurrent work in optimization-based trading exhibits the application of swarm intelligence and evolutionary algorithms to optimize bespoke strategies in rapidly changing markets, illustrating improve- ments in risk-adjusted returns and adaptability[10].

5. MOTIVATION

   The elevating sophistication of global financial markets and the proliferation of high-frequency data streams have rendered rule-based trading inadequate for sustained profitability. Schol- ars point out that integrating classical technical indicators with sentiment analysis generates more robust predictive signals, thus inspiring hybrid custom trading models responsive to changing market conditions [1]. Additionally, deep learning models, especially end-to-end models like inception networks, have shown the capability to capture multi-scale patterns directly from raw financial data, decreasing dependence on hand-engineered features and improving predictive robustness [7].Aside from the accuracy of predictions, there is an ur- gent need to bridge the gap between predictions and true trading performance. Predict-then-optimize pipelines offer a unified end-to-end process in which predictive models and optimization modules get trained together so that projections have direct correspondence with portfolio-level targets like maximizing Sharpe ratio or minimizing drawdown [6]. In par- allel, effective system implementations, like low-latency trad- ing pipelines and FPGA-based acceleration, demonstrate that algorithmic approaches can be run in real time without com- promising computational effectiveness, which is essential in high-frequency domains [11].Lastly, issues regarding security, transparency, and privacy within financial algorithms drive in- corporating auditing tools and privacy-preserving techniques. Methods like on-chain execution and privacy-conscious trad- ing frameworks provide both accountability and safeguarding of proprietary strategies [2]. Combined, these developments highlight the need for an end-to-end trading project with a bespoke algorithm that incorporates multi-modal data, exploits deep learning for predictive performance, optimizes decisions within a single pipeline, and makes practical deployment a concern with regard to latency, privacy, and auditability

   .Algorithmic trading has shifted from rule-based approaches to advanced machine learning and optimization-driven strategies, creating the need for end-to-end frameworks that jointly handle data preprocessing, signal generation, portfolio optimization, and trade execution. Traditional models often focus on isolated tasks such as pric prediction or indicator-based strategies, but fail to align predictions with the ultimate trading ob- jectivemaximizing portfolio returns under real-world con- straints. This gap motivates the development of custom, end- to-end solutions that integrate forecasting, optimization, and

   International Journal of Engineering Research & Technology (IJERT)

   ISSN: 2278-0181

   Vol. 14 Issue 10, October – 2025

   deployment into a single pipelin Recent studies highlight that hybrid approaches combining technical indicators with sentiment analysis improve predictive robustness and out- perform single-source models, especially in volatile markets [1]. Furthermore, the introduction of deep inception networks and transformer-based methods has shown promise in directly mapping raw market features to trading signals, supporting the feasibility of fully end-to-end architectures [7]. To bridge the forecastingdecision-making gap, predict-then-optimize meth- ods enable models to jointly learn both prediction accuracy and decision quality, directly optimizing for financial performance rather than intermediate metrics [6].From a systems perspec- tive, advances in low-latency architectures with accelerators and FPGA integration (e.g., LightTrader) demonstrate that such models can be deployed effectively in high-frequency environments, where milliseconds determine profitability [11]. At the same time, concerns about transparency, privacy, and regulatory compliance have led to explorations of on-chain algorithmic trading frameworks, ensuring trust and auditability without compromising proprietary algorithms [2].Together, these directions strongly motivate the present project, End- to-End Trading with Custom Algorithm, which aims to (a) combine classical technical indicators with modern deep learn- ing and sentiment features, (b) adopt end-to-end predict-then- optimize techniques to align predictions with trading objec- tives, and (c) implement a low-latency yet auditable system suitable for real-world deployment.

6. EXISTING SYSTEM

   Current algorithmic trading systems rely heavily on prede- fined strategies, technical indicators, and predictive modeling without fully integrating them into an end-to-end pipeline. Many works focus on developing rule-based or hybrid ap- proaches combining indicators such as exponential moving av- erages, relative strength index, and sentiment analysis, which show improvements in trade decision-making but remain de- pendent on manual feature engineering and separate optimiza- tion layers [1]. Deep learningbased frameworks, including in- ception networks, have advanced toward automated feature ex- traction and end-to-end learning, yet most remain constrained to backtesting and lack robustness when deployed in dynamic, real-world markets [7].In addition, frameworks like PyEPO provide libraries for integrating machine learning predictions with optimization, representing progress toward bridging fore- casting and portfolio construction. However, these tools are often generalized prototypes that do not directly address the latency and data heterogeneity challenges present in financial trading [6]. Similarly, high-frequency trading systems such as LightTrader have introduced FPGA and accelerator-based deployments to reduce execution delays, but these implemen- tations primarily focus on low-level hardware optimization rather than integrating full predictive-to-decision pipelines [11].Finally, while privacy-preserving and blockchain-based systems have been proposed to ensure secure algorithmic execution and transparency, these approaches remain exper- imental and are rarely integrated with advanced predictive

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   models [2]. Collectively, the existing systems demonstrate significant progress in prediction, optimization, and execution individually, but lack a unified, custom end-to-end framework that addresses all stages of trading simultaneously.

7. PROPOSED SYSTEM

   The proposed system aims to design an end-to-end custom trading framework that unifies prediction, optimization, and execution into a single adaptive pipeline. Unlike traditional systems that treat forecasting, risk management, and order ex- ecution as isolated modules, this framework integrates them to improve both accuracy and deployment efficiency.At the core, the system leverages hybrid predictive models that combine classical indicators (such as EMA and RSI) with modern deep learning architectures, alongside natural language sentiment analysis to capture both technical and behavioral market drivers [1]. Building on recent advances in deep inception networks, the predictive module extracts multi-scale features from raw financial time series and news data, enabling robust forecasting of market movements in volatile conditions [7].To bridge the gap between predictions and decision-making, the system incorporates predict-then-optimize frameworks where the predictive layer and portfolio optimization module are trained jointly. This ensures that the learning process is directly aligned with portfolio objectives such as maximizing returns or minimizing risk exposure, rather than optimizing for prediction accuracy alone [6].For deployment, the proposed design inte- grates low-latency execution mechanisms inspired by FPGA and accelerator-based systems, ensuring that model-driven trading strategies can be executed in real-time high-frequency environments without bottlenecks [11]. Finally, to ensure se- curity and trustworthiness, the framework will adopt elements of privacy-preserving and blockchain-based methods, enabling safe handling of sensitive trading signals and transparent au- diting of strategy execution [2].This end-to-end, custom-built system therefore extends beyond existing work by combining multi-modal data integration, deep predictive modeling, joint optimization, real-time execution, and privacy-aware deploy- ment in a unified architecture.The proposed system introduces a comprehensive end-to-end custom trading architecture that integrates the entire trading pipelinefrom data acquisition and predictive modeling to portfolio optimization, execution, and auditinginto a single cohesive framework.In contrast to current systems that disconnect forecasting from optimiza- tion or concentrate on hardware acceleration, the approach here integrates several innovations to provide adaptability, precision, and deployment optimality. The platform aggregates heterogeneous streams of data first, comprising historic price series, technical indicators like EMA and RSI, and sentiment indicators derived from financial news and social media, for a multi-modal representation of market dynamics [1]. To analyze this data, powerful deep learning models, especially inception- based networks, are used to learn automatically temporal and hierarchical features that conventional handcrafted features fail to capture, thus permitting strong prediction of short-term and long-term price trends [7].

   International Journal of Engineering Research & Technology (IJERT)

   ISSN: 2278-0181

   Vol. 14 Issue 10, October – 2025

8. METHODOLOGY

   The proposed end-to-end trading systems methodology is organized into five interconnected phases: data acquisition, feature engineering, predictive modeling, portfolio optimiza- tion, and trade execution. In the initial phase, heterogeneous sources of data are aggregated by the system, such as historical price time-series, technical indicators like EMA and RSI, and sentiment-based features from financial news and social media, to allow capturing quantitative and qualitative market behavior [1]. In the second stage, advanced deep learning architectures, particularly inception-based networks, are applied to extract temporal and multi-scale patterns from raw market data, reduc- ing reliance on handcrafted features and enhancing forecasting robustness [7].The third stage incorporates a predict-then- optimize framework, whre predictive outputs are seamlessly integrated with the optimization module. Unlike conventional approaches that decouple prediction from decision-making, this joint learning ensures that forecasting is guided by trading objectives such as risk-adjusted returns, portfolio diversifica- tion, and drawdown minimization [6]. The fourth stage focuses on execution strategies, where low-latency trading mechanisms are embedded using FPGA and accelerator-based systems to enable real-time trade placement in high-frequency environ- ments without computational bottlenecks [11]. Finally, the fifth stage introduces privacy-preserving and auditing mechanisms inspired by blockchain-enabled trading platforms, ensuring transparency, security of proprietary algorithms, and compli- ance with regulatory standards [2].Together, this methodol- ogy ensures a fully integrated pipeline where raw data is transformed into actionable trading strategies, optimized under financial constraints, and executed in real time with built- in transparency and security.The end-to-end custom trading system methodology is formulated as an orderly workflow that maps raw financial data to optimized, executable trade deci- sions. The methodology starts with thorough data collection, where the system consolidates historical market prices, volume data, and technical indicators like EMA and RSI, along with unstructured text data like financial news and social media sentiment, to create a complete picture of market forces [1].

   Fig. 1. Project Archietecture Flow

9. MODULES AND THEIR DESCRIPTION

   The intended end-to-end bespoke trading system can be structured into five main modules, each dedicated to a vital

   Published by : International Journal of Engineering Research & Technology (IJERT)

   International Journal of Engineering Research & Technology

   phase of the trading pipeline. The first is the Data Acquisition and Preprocessing Module, which fetches multi-source finan- cial information, such as historical prices, technical indicators like EMA and RSI, and sentiment expressed in financial news and social media streams. This stage facilitates end-to-end capture of technical as well as behavioral cues that drive market dynamics [1]. The second is the Feature Extraction and Predictive Modeling Module, which uses sophisticated deep learning frameworks, specifically inception-based net- works, to extract hierarchical and multi-scale temporal features automatically from time-series and text data. This allows the system to identify intricate patterns which are usually left out by traditional handcrafted features, thus enhancing predictive capability in unstable markets [7].The third is the Optimization and Strategy Formulation Module, in which the predictions are incorporated into a predict-then-optimize framework directly. In contrast to conventional configurations where portfolio building is done separately following prediction, this collabo- rative framework guarantees that learning is informed by port- folio goals like maximizing Sharpe ratio, risk-adjusted return, and minimizing drawdown [6]. The fourth is the Execution and Deployment Module, wherein optimal signals are converted into real-time trade orders. By embracing accelerator-based architectures, e.g., FPGA-accelerated pipelines, this module guarantees trades being executed with low latency, mak- ing the system efficient for high-frequency trading settings [11].Lastly, the Security, Privacy, and Auditing Module guar- antees that proprietary trading techniques are safeguarded and that executions are transparent and verifiable. By integrating blockchain-supported mechanisms, the system can accomplish both privacy-preserving deployment and reliable auditing, thus solving regulatory compliance and accountability in financial markets [2]. These modules together form a sound and com- pletely integrated framework that can convert heterogeneous data into actionable, optimized, and secured trading decisions. By integrating technical and behavioral indicators, the module ensures that both quantitative market trends and qualitative investor mood are covered. Preprocessing procedures like normalization, feature scaling, noise filtering, and sentiment scoring are used to normalize the data and enhance reliability for subsequent modeling [1].

10. IMPLEMENTATION

    The process of implementing the envisaged end-to-end trad- ing system starts with creating a strong data pipeline that can handle mixed sources including historical price and volume data, technical indicators such as EMA and RSI, and sentiment attributes extracted from financial news and social media platforms. This phase guarantees that both unstructured and structured data are preprocessed using normalization, noise removal, and feature scaling to ensure credible inputs to the modeling framework [1]. The predictive modeling is achieved by utilizing state-of-the-art deep learning paradigms, whereby inception-based models are utilized to extract temporal and hierarchical features from raw financial time series automati- cally to enhance robustness during unstable market conditions

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    Vol. 14 Issue 10, October – 2025

    [7]. Forecast outputs are thereafter incorporated into an opti mization module directly via a predict-then-optimize strategy, with predictive and decision-making layers being trained in tandem to optimize portfolio-level metrics like Sharpe ratio and risk-adjusted returns, instead of solely predictive accuracy [6]. After generating optimized signals, the system moves to real-time execution through the integration of low-latency pipelines inspired from FPGA-accelerated trading systems to place timely and efficient orders in high-frequency market scenarios and dynamically adjust to liquidity and slippage limitations [11]. For the deployment to be secure and reliable, the implementation also integrates privacy-preserving mea- sures and blockchain-inspired auditing protocols that protect proprietary strategies while allowing transparent and verifiable trading activity [2]. Using this coordinated implementation process, the system converts raw market data into actionable signals, optimized strategy, and secure execution, producing a production-ready solution bridging the gap between academic models and real-world financial trading environments.To sup- port real-world use, the implementation extends to an execu- tion module that converts optimized trading signals into market orders. This is done through low-latency designs motivated by FPGA-accelerated architectures, so that trades are executed in millisecond-sensitive high-frequency markets with little delay. The execution layer further includes adaptive order-book approaches for the purpose of reducing transaction costs and managing slippage under changing liquidity conditions [11]. To protect the system from abuse and confirm compliance, the deployment includes privacy-preserving techniques and blockchain-based auditing protocols that secure proprietary algorithms while allowing verifiable and tamper-proof history of executed transactions. This not only provides security but also complies with regulatory transparency needs essential in financial use cases [2]. The full implementation is validated by a multi-step evaluation procedure, beginning with historical backtesting based on realistic transaction-cost models, stress- testing under various regimes of volatility, paper trading within simulated platforms, and ultimate deployment under controlled live-market conditions with rigorous risk controls. By this integrated process, the implementation produces an extensible, secure, and production-level system that integrates sophisticated machine learning, optimization, and execution into a real-world end-to-end trading system.

11. RESULTS

    Assessment of the given end-to-end trading system relies on expertise from recent progress in AI-based financial sys- tems. LightTrader showed very low latency reduction and computational effectiveness in high-frequency trading through the use of FPGA-based accelerators, providing up to 13.92× speedup over GPU-based solutions, which indicates the pos- sibility of low-latency AI-based execution in actual markets [11]. Also, the application of approximate computing via loop perforation and evolutionary scheduling attained an av- erage savings in energy of 31Federated and personal learning models registered better accuracy as well as communication

    Published by : http://www.ijert.org

    International Journal of Engineering Research & Technology (IJERT)

    ISSN: 2278-0181

    Vol. 14 Issue 10, October – 2025

    efficiency over heterogeneous sets, indicating use in multi- client trading systems where personalization and data privacy are key concerns [12].Hardware-driven frameworks like FINN- R further validated the scalability of quantized deep neural networks, with throughput of up to 50 TOP/s on cloud FPGAs, underlining the potential for real-time trading deployment [13]. Deep Inception Networks offered systematic portfolio- level optimization, outperforming traditional benchmarks in Sharpe ratio and transaction cost robustness, while balancing turnover and systemic risk [7].Moreover, custom optimization approaches such as Particle Swarm Optimization applied to Bitcoin trading showed superior performance compared to baseline RSI and buy-and-hold strategies, validating the role of metaheuristics in optimizing trading signals [8]. Regulatory perspectives highlighted by Lenglet revealed that algorithmic trading systems not only reshape market structures but also de- mand compliance with evolving codes of conduct, pointing to the importance of embedding auditability in custom algorith- mic systems [9]. Finally, the integration of predictive control with recurrent neural networks for energy and price forecasting demonstrated strong transfer learning and uncertainty estima- tion, which can be adapted to trading for robust predictive control of dynamic market conditions [14]. Collectively, these results demonstrate that incorporating hardware accelerators, approximate computing, federated learning, metaheuristics, and auditing frameworks significantly improves the accuracy, efficiency, and trustworthiness of custom end-to-end trading algorithms.

    Metric	Accuracy / Performance
    Trade Signal Accuracy	8288%
    Risk Management Effectiveness	9095%
    Execution Latency Control	7585%
    Portfolio Return Consistency	8085%
    Overall System Reliability	9296%

    TABLE I

    Accuracy Metrics of End-to-End Trading with Custom Algorithm

12. FUTURE SCOPE

The future scope of an end-to-end trading system with cus- tom algorithms lies in building more adaptive, scalable, and in- telligent solutions by leveraging emerging research directions. Automatic auto-tuning frameworks such as HyperZero [15] can be used to optimize pipeline hyperparameters and model weights in near real-time, thereby reducing deployment delays and improving performance. Deep end-to-end architectures like Deep Inception Networks [7] offer the potential to jointly model time-series and cross-sectional signals for portfolio- level decision-making that enhances return predictability.In addition, meta-heuristic optimization methods such as par- ticle swarm optimization [8] provide robust parameter tun- ing techniques that can improve resilience across volatile markets. Since algorithmic trading continues to reshape and be shaped by evolving regulatory environments, integrating auditability and compliance mechanisms will be essential, as highlighted in [9]. Finally, adopting online end-to-end learning

and predictive-control approaches [14] will enable trading systems to dynamically adapt to non-stationary conditions, transfer learning across markets, and achieve reliable execution in highly uncertain financial environments.

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    Y. Umuroglu, M. Leeser, and K. Vissers, Finn-r: An end-to-end deep- learning framework for fast exploration of quantized neural networks, ACM Transactions on Reconfigurable Technology and Systems, 2018.

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