Héctor Melchor Alaiz

I am a Quantitative Analyst and Developer currently working at a London broker. My expertise lies in algorithmic trading, where I combine advanced data analytics with financial theory to build robust investment models. I have managed methodologies for high-profile clients and developed proprietary strategies with AUM of $200 million, focusing on derivatives, commodities, and portfolio optimization using neural networks. My journey began in Physics, where I developed a strong foundation in mathematical modeling and complex problem-solving. I transitioned into finance through a Master’s in AI and Financial Markets, driven by the goal of removing human emotion from trading. I realized that markets could be analyzed like physical systems, using stochastic calculus and machine learning to find signal in the noise. This led me to specialize in quantitative finance, applying rigorous scientific methods to generate consistent alpha. My flagship system, Ares Strategy, has demonstrated a 43.8% annualized return over a 15-year backtest with a Profit Factor of 5.7x and a 63% win rate. It has proven resilient through major market regimes, delivering 95% returns during the 2025 tariff volatility and 236% during the 2012–2015 bull market. Additionally, throughout my career, I have independently developed multiple strategies with Sharpe ratios exceeding 1.5 and returns consistently above 20%. We are currently navigating a highly volatile period following the 2025 tariff-driven corrections. While traditional "buy and hold" approaches have struggled with recent geopolitical uncertainty, I view this environment as ideal for aggressive, directional algorithmic strategies. The market is currently rewarding adaptability, systems that can "attack" during strong trends and "defend" with Spartan risk management during drawdowns are essential right now. My approach is immersive and continuous. Beyond my professional role, I dedicate significant time to open-ended statistical research and "continuous optimization." Developing strategies involved analyzing 15 years of market data, testing thousands of parameters, and refining execution logic. I view trading not just as execution, but as an ongoing engineering problem that requires daily monitoring, code refactoring, and re-validation to stay ahead of market efficiency. mehr anzeigen