My approach combines quantitative modeling, fundamental industry- and company-level reasoning, and real-time market signals to identify asymmetric risk-reward opportunities driven by structural shifts, unexplored trends, policy changes, and market inefficiencies.First, I started engaging with the financial markets during the COVID-19 pandemic, when I successfully followed global trends, infection rates, social media sentiment, government announcements over time, hospital updates, etc. and generated returns exceeding over 55 % in a matter of months from my investments in biotech and pharmaceutical ETFs and individual niche companies, like BioNTech. At the time, valuations had not yet risen significantly and there was little chance or reason why they should decline, especially since it was very likely that sooner or later these enterprises would have to develop a vaccine against COVID, be granted an expedited regulatory approval (due to COVID-induced economic paralysis), which would allow them to boost their revenue streams more suddenly and enable stock prices to soar more rapidly. Similarly, I followed the news prior to the Russian invasion of Ukraine and bought gas and wheat during the buildup of Russian troops at the Ukrainian border, as there was little risk in these assets declining in value in the event that Russia did not invade, but significant upside potential considering Ukraine’s role as a major wheat exporter and European reliance on Russian gas. These two examples illustrate what I mean by “asymmetric risk-reward opportunities”.Furthermore, shifting to my quantitative modeling and algorithmic trading endeavors: I independently built and implemented statistical arbitrage models across DEX exchanges (dYdX, Hyperliquid) to exploit price discrepancies for perpetual futures contracts, using web sockets (for real time execution), and implemented a long-short market- neutral arbitrage strategy via automated API order flows by longing the slightly undervalued and shorting overvalued assets. I also constructed real-time algorithmic trading models executing minute-to-minute level trades on CEX and DEX exchanges, based on sequency-based ML models (e.g. LSTM) trained on technical and L2 order book data; deployed on AWS EC2 for continuous cloud operation. Additionally, developed and tested other quantitative strategies (long-short, macro-driven) on stocks, ETFs and commodities, benchmarked Python vs C++ execution speed for news-triggered execution.
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