03 / work

Selected Projects & Systems

Each project represents an end-to-end engineering effort across quantitative trading, machine learning architectures, and full-stack applications. Expand any row to inspect the problem definition, concrete outcomes, and technology stack.

term::projects~/work [filter_engine]

matches: 6/6

>$env:var(--lang)=

>A-R-Pml

Python, MQL52025–now

Systematic range-breakout quantitative strategy in MQL5 for USD/JPY with an 8,200+ parameter optimization sweep and Python XML pipeline.

Problem →Manual, discretionary execution of USD/JPY range-breakouts introduces human timing variance, emotional execution errors, and backtest overfitting.

Result →8,200+ parameter sweep analyzed via custom MT5 XML pipeline; top candidate achieved Sharpe 7.64, profit factor 1.24, and 26.8% max drawdown.

Lang:

PythonMQL5

Stack:MQL5 · MetaTrader 5 · Python · NumPy · Pandas · XML Pipeline

[ github → ][ read case study → ]

>FinanceTrackerfull-stack

TypeScript, Python, Java, SQL2025–now

>Call Screenerml

Kotlin, Python2025–now

>Telegram-Drivefull-stack

Rust, TypeScript2024–2025

>BB-Terminalboth

Python, Shell2024–2025

>Chat Applicationfull-stack

JavaScript, Node.js2024–2025

contentlayer::active· UTF-8click row to expand technical breakdown & stack6 entries indexed

04 / stats

Language Focus & Distribution

language breakdown computed across active Contentlayer repositories

language_focus::distribution6 completed projects

Python67% of projects

TypeScript33% of projects

MQL517% of projects

Shell17% of projects

Kotlin17% of projects

JavaScript17% of projects

Node.js17% of projects

Java17% of projects

SQL17% of projects

Rust17% of projects

leetcode::live_apirealtime_sync

handle@lennx-gif →

problems_solved0

difficulty_splitEasy 0 · Med 0 · Hard 0

statusActive Undergrad Learner

kaggle::communityml_exploration

handle@zyga1one →

focus_areasTabular, Time-Series & NLP

current_trackKaggle Learn & Feature Pipelines

toolkitPyTorch, Scikit-learn, Pandas