Manh-Huyen V.

📌 About Me I am a PhD-level engineer specializing in mathematical modeling, and scientific algorithm development, with a strong skills for algorithmic trading and quantitative research. I help traders, fintech startups, and prop firms turn trading ideas into robust, validated, and fully automated strategies that perform reliably in real-market conditions. My work sits at the intersection of scientific rigor, quantitative finance, and professional software engineering. Clients come to me when they want trading systems that are mathematically sound, stress-tested, and coded with precision. ⸻ 📌 Why Clients Hire Me I combine the depth of academic research with the practical methodologies used by top quant professionals. My work draws inspiration from: Legendary quants: Jim Simons, Ed Thorp, Marcos López de Prado, Perry Kaufman Top algorithmic trading experts: Ernest Chan, Kevin Davey, Andrea Unger, Howard Bandy I do not build “black-box” systems — I build transparent, reproducible, and professionally validated strategies. ⸻ 📌 What I Can Build for You I develop strategies ranging from simple rule-based models to advanced machine-learning systems, including: ✔ Fully automated trading bots ✔ Indicator-based rule systems ✔ Statistical & time-series models ✔ Machine learning (ML) and pattern recognition ✔ Multi-asset and portfolio strategies ✔ High-integrity backtesting pipelines ✔ Walk-Forward Analysis & Monte Carlo Stress Tests Everything is developed with a strong emphasis on robustness, reproducibility, and avoiding curve-fitting. ⸻ 📌 Technical & Quantitative Skills Algorithm Development • Strategy design (trend, mean-reversion, breakout, volatility models) • Multi-timeframe & multi-asset logic • Entry/exit signal engineering • Portfolio allocation and position sizing Backtesting & Validation • Walk-Forward Analysis (WFA) • Monte Carlo simulations • Overfitting detection • Robustness testing • Data integrity & reproducibility control • Probabilistic validation of model outputs Quantitative Analysis • Time series modeling • Noise reduction and signal filtering • Risk/return evaluation (Sharpe, Sortino, DD…) • Statistical diagnostics and cross-validation ⸻ 📌 Programming & Technical Stack Python, Trading, Machine Learning & API Stack • NumPy, Pandas, SciPy, statsmodels, arch • TA-Lib, pandas-ta, btalib • Backtrader, Zipline, VectorBT, PyAlgoTrade, PyFolio • PyPortfolioOpt, QuantStats • scikit-learn, XGBoost, LightGBM, CatBoost, Optuna • TensorFlow, Keras, PyTorch, Prophet • ccxt, Alpaca API, IBKR API, Oanda, MetaTrader Python Bridge • yfinance, AlphaVantage, Polygon.io, Quandl • requests, aiohttp, BeautifulSoup, Selenium • SQLAlchemy, sqlite3, PostgreSQL (psycopg2) • Parquet, HDF5, Feather for large datasets • numba, Cython, multiprocessing, joblib • Matplotlib, Plotly, Bokeh, mplfinance • Git, GitHub, GitLab, CI/CD, Docker • pytest, flake8, black, pydantic, logging Trading Languages & Platforms • EasyLanguage (MultiCharts) • MT4/MT5 (EA development) • Java for high-performance algo logic • API integration: ccxt, yfinance • Web scraping (Requests, BeautifulSoup) • Git/GitHub version control & CI automation ⸻ 📌 Collaboration & Delivery I work with: • Independent traders • Prop firms • Fintech startups • Quant teams needing R&D support I can translate any trading idea — even vague or experimental ones — into a clean, documented, testable model. ⸻ 📌 What You Receive ✔ A fully working, tested trading strategy ✔ Complete and clean source code ✔ A detailed professional performance report ✔ Drawdown, Sharpe, win rate, robustness metrics ✔ Walk-Forward + Monte Carlo stress testing ✔ Support for improvements and refinements ⸻ 📌 My Work Philosophy I don’t deliver “pretty backtests” — I deliver strategies designed to survive real markets. If you want a rigorously tested, professionally validated, and production-ready trading system, I’d be happy to help.