Q&A: Building Live Trading Strategies (Quant Finance + ML + LLMs)”

Hello everyone,

I’m currently a WSO mentor and a Quantitative Researcher with around 4 years of experience building and deploying live equity trading strategies across global markets.

Background:

• Recently completed a Master of Finance at Cambridge, along with MTech and BTech
• Senior Quantitative Researcher at State Street and also worked for a Prop trading firm
• Worked on end-to-end alpha strategy development, from research and backtesting to execution and post-trade analysis
• Experience managing strategies contributing to ~$80B AUM and building ML-driven signals (including NLP/LLM-based alpha)

My work has focused on:

• Systematic equity strategies (factor investing, momentum, ESG integration)
• Market microstructure + transaction cost optimization
• Machine learning in finance
• LLMs for financial applications

Happy to help with:

• Breaking into quant finance (especially from non-traditional backgrounds)
• What skills actually matter for quant roles (vs. what’s overhyped)
• Building strong quant/ML projects that stand out
• Interview prep (quant + technical)
• Transitioning between academia, ML, and finance
• Resume feedback (happy to review a few)

I’ve gone through the process myself and understand how unclear the path can be, especially with how noisy “quant advice” online is.

Feel free to drop your questions below. I’ll try to give practical, no-BS answers.

Looking forward to helping out.

Cheers. :)