Neural Systems Architect
I'm a builder of intelligent systems, cloud-native solutions. Passionate about creating technology that bridges the gap between complex AI models and intuitive user experiences.
Currently working with
About Me
My journey through the intersection of artificial intelligence and software engineering
Graduated with a Bachelor's degree in Computer Science from Mahidol University, I currently contribute to enhancing financial technology platforms as a Software Developer at LSEG. My work focuses on delivering full-stack solutions, optimizing performance, and driving cloud migration and automation initiatives.
With a background that includes data science and machine learning engineering roles, I leverage skills in cloud infrastructure, software development, and automation to create efficient, reliable systems. Passionate about bridging the gap between engineering and intelligent data systems, I aim to support innovation and scalability in dynamic environments.
Career Neural Network
London Stock Exchange Group
Associate Software Engineer
Contributed to the modernization of financial technology platforms by delivering end-to-end solutions across the full stack. Focused on cloud migration, automation, and performance optimization to enhance efficiency and reliability.
National Central University (Taiwan) / DataFlow Solutions
ML Engineer
Conducted research in collaboration with National Central University (Taiwan) to explore predictive modeling for financial time series. Presented findings at an international conference and contributed to ongoing academic publications.
Mahidol University
Full Stack Developer
Develop the university's online one stop service platform, allowing students to access various services seamlessly.
SBI Security Solutions
Machine Learning Intern
Worked on developing algorithmic trading solutions and enhancing trading automation capabilities using SETTRADE APIs. Gained hands-on exposure to financial technology operations, data security, and cloud fundamentals.
Open to remote opportunities and interesting collaborations
Featured Projects
A selection of projects that showcase my expertise in AI/ML, cloud infrastructure, and full-stack development
Stock Management System
Stock management system with real-time analytics and automated reporting.
Interested in seeing more of my work?
Technical Skills
My expertise across different domains of software engineering and AI/ML
Skills Radar
Continuous Learning
I believe in continuous learning and staying updated with the latest developments in AI/ML, cloud technologies, and software engineering practices. Currently exploring advanced topics in LLMs, distributed systems, and edge computing.
Research & Insights
Technical articles and insights from my work in AI/ML, cloud computing, and software engineering
Stock Price Prediction Using Univariate and Multivariate Historical Data with Post-Interpretation via Large Language Models
Abstract
In this study, we propose a hybrid approach that utilizes both univariate and multivariate historical data from key variables and related factors across four distinct groups. We developed a hybrid approach combining Volume Features, Valuation Metrics, Technical Indicators, and Market Sentiment for stock price prediction and investigate several state-of-the-art models, including Artificial Neural Networks (ANN), Gated Recurrent Units (GRU), Bidirectional GRU (BI-GRU), and Transformer-based Time Series (TST) models, while experimenting with different lags of inputs to capture intricate temporal patterns in stock price movements. Our experiments, conducted on seven stocks from various sectors, allow us to evaluate the robustness and generalizability of the models across different industries. To enhance interpretability, we employ large language models (LLMs) in the post-prediction phase, which transform the predictive outputs into human-readable narratives explaining the factors driving stock price predictions. Empirical results demonstrate that our approach, incorporating advanced deep learning models like ANN, GRU, BI-GRU, and TST with varying input lags, significantly improves prediction accuracy over traditional methods while providing actionable insights for financial decision-making.
Statistical Comparison ARIMA Order Performance In Stock Market
Abstract
Stock market forecasting is important for financial decision-making and risk management. Among the various time series models, the Autoregressive (p) Integrated (d) Moving Average (q) (ARIMA) model has been widely adopted for its simplicity and effectiveness in capturing temporal patterns. However, selecting appropriate ARIMA orders remains a crucial and challenging task, impacting the accuracy of predictions. This paper presents a comprehensive statistical comparison of ARIMA order performance in the context of stock market forecasting. We examine the impact of different ARIMA model orders. Our study utilizes historic New York Stock Exchange (NYSE) stock price data. Our findings shed light on the complex interplay between ARIMA parameters and predictive accuracy, offering valuable insights for robust financial forecasting.
การวิเคราะห์ข้อมูลตลาดหุ้นด้วย Python: Moving Averages
Abstract
Moving Average (MA) คือเครื่องมือที่สำคัญในการวิเคราะห์ทางเทคนิคในตลาดการเงิน หรือ ตลาดหุ้น ซึ่งสามารถให้ข้อมูลที่มีค่าเกี่ยวกัยแนวโน้มและสัญญานที่อาจจะเกิดการซื้อขายในตลาด ซึ่งในบทความนี้ เราจะมาสำรวจการสร้างสัญญาน MA โดยใช้ Python เพื่อช่วยให้คุณตัดสินใจการซื้อขายอย่างมีเทคนิค
Stay Updated
I regularly write about AI/ML research, cloud architecture patterns, and software engineering best practices. Follow me on social media to stay updated with my latest insights.
Let's Connect
I'm always interested in discussing new opportunities, collaborating on interesting projects, or just having a conversation about AI/ML and technology.
Ready to start a conversation?
Whether you're looking to build an AI-powered application, need help with cloud architecture, or want to discuss the latest developments in machine learning, I'd love to hear from you.
Ready to connect?
Drop me a line
chanatip.deemee@gmail.com
GitHub
Check out my code
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@Chanatip Deemee
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