#Quant Masters

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If you’re starting from zero, here’s the correct way to build quant knowledge: 1. Learn how to do quant interview questions Quant interviews don’t just test how much math you know, they test how you think under uncertainty. You’re judged on structuring unfamiliar problems, making assumptions, spotting symmetry, and explaining your reasoning live. Past a point, question reps matter more than theory. If you don’t train this explicitly, everything else leaks. 2. Build mathematical intuition, not trivia You don’t need olympiad maths. You do need comfort with probability, expectation, conditioning, and optimisation under uncertainty. Focus on why results hold, not memorising tricks. If you can explain assumptions, you’re ahead of 90% of candidates. 3. Learn programming as a modelling tool Python isn’t for syntax points. It’s for turning ideas into experiments. Simulate distributions. Stress-test assumptions. Break your own logic. If your code can’t falsify your thinking, it’s not useful. 4. Study market structure before “strategies” Most beginners jump straight to alpha ideas. That’s a mistake. Understand how markets actually function: liquidity, order flow, transaction costs, latency, constraints. Strategy without structure is fantasy. 5. Build projects that answer real questions Not “I implemented X indicator.” Instead: “What happens to performance when assumptions fail?” “What regime does this break in?” Projects should reveal limits, not hide them. Comment ‘ZERO’ to get a guide on how to build quant foundations. Follow to break into quant trading with me. #quant #quanttrading #finance #trading #python

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My Quant background began my freshman year of college researching Game Theory. My research involved creating psuedo code in the form of algorithms to find Nash Equalibriums in Quantum Mechanics. Quantized Game Theory is different since QM has different rules for which how simulations can be ran. Here I had to utilize quantum information theory a my foundation to calculate payoffs. Quants use game theory in market making, auctions, and optimal execution, where participants choose strategies to balance profit, risk, and information leakage. Concepts like Nash equilibrium, mixed strategies, and repeated games help model bid–ask spreads, order placement, and inventory management under competition. Game theory also informs models of information asymmetry, where traders infer others’ private information from order flow. By treating markets as strategic games rather than passive systems, quant finance builds more robust pricing, execution, and risk models.

Can you solve this Jane Street quant interview question? Follow QuantProf on Youtube for more quant interview questions and resources (link in bio). #quantfinance #quant 1d

Girl math < quant math 💸 #financialengineering #quantitativefinance

The best data scientists I know understand the maths behind the ML algo. Comment “data” ansi will see you the links to the 4 maths resources. #data #students #ai

What I learned at the Quant Fair #ucberkeley #quant #quantfinance #financialengineering #masters

Quant is not a career path for most people #quant #quanttrader #quantdeveloper #programming #programmer

One of the most important equations in the world; when it came out in the 70s, it was a revolutionary concept, redefining the entire trading industry. Did you know this equation existed? Let me know in the comments and follow for more options theory! #quant #quantfinance #finance #optionstrading #options

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