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Commodity Trade Reporting Application | Java, Spring Boot, SQL, Kafka
Worked as a Java Developer and Project Lead , leading the development of a reporting application for commodity trade data used by large traders and supporting London Metal Exchange (LME)-related reporting requirements.
Key responsibilities included designing and developing backend services using Java and Spring Boot, implementing REST APIs and business logic, working with SQL databases for trade data processing and reporting, and using Apache Kafka for reliable event-driven data communication.
As a project lead, I was responsible for coordinating development activities, understanding business requirements, designing technical solutions, reviewing code, troubleshooting issues, and ensuring the application met functional and technical requirements.
Technologies: Java, Spring Boot, SQL, Apache Kafka, REST APIs, Maven, Git, JUnit, Mockito.
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Market Structure • Liquidity • Technical Analysis
Market Prediction (1st Slide): I conducted a top-down analysis of the market, starting from the higher timeframes and working down to the 4H timeframe to identify the prevailing market structure and directional bias.
I then mapped key Areas of Interest (AOIs) by identifying significant support and resistance zones, liquidity areas, and price levels that had shown repeated market reactions.
Market Outcome (2nd Slide): The market followed the projected direction, validating the structure and key areas identified during the analysis.
I document my market analysis and trade outcomes on @TradingwiththeDr on TikTok.
Open to work, research opportunities, and collaborations. 📊
🚀 Automated AI Document Processing & ATS Pipeline (n8n)
Project Summary
Replaced a manual candidate screening process with a fully automated, n8n-orchestrated backend pipeline. The system automatically intercepts incoming applications, uses OpenAI to analyze documents, updates databases, and triggers client communication—cutting manual data entry down to zero.
The Outcome & Business Value
Eliminated Manual Data Entry: Automated 100% of resume parsing, document conversions (Docs to PDF), and data extraction tasks.
Instant Candidate Screening: Integrated an OpenAI Chat Model Decider to evaluate incoming resumes against job descriptions in real-time with zero human delay.
Automated Sync & Follow-Ups: Automated instant logging to Google Sheets and launched immediate multi-path email responses based on candidate status.
How It Works (System Architecture)
Phase 1 (Ingestion & Extraction): Captures files via webhooks/emails ➡️ Cleans data via custom code nodes ➡️ Parallel-processes files to extract raw text ➡️ Uses AI structured parsing to match applicants to open roles.
Phase 2 (Database & Delivery): Routes structured applicant data ➡️ Upserts matching rows into a centralized tracking database ➡️ Triggers dynamic conditional emails back to candidates.
For many years, the forex market wasn't just a screen full of numbers—it was a relentless crucible. Like most, I started by blowing accounts, chasing holy grails, and second-guessing every entry. It took years of relentless screen time, countless sleepless nights, and stripping away market noise to finally build a consistent, repeatable framework.
I am not here simply to package indicators and sell a quick fix for a profit. After dedicating the better part of my life to mastering these charts, my true drive now is legacy and validation. The greatest reward isn’t just the financial return—it is seeing a fellow trader finally break through their barriers using a structure I spent a lifetime perfecting. Your execution, your "aha!" moments, and your long-term consistency are the true appreciation and "trophy" for the grueling years I put into mastering this craft.
