Starter Plan
First 30 Days in Quant
Don't treat this like a commitment ceremony. Treat it like a structured test drive. The goal is to see whether probability, coding, markets, and open-ended problem solving feel energizing — or draining.
Before You Start
Signs this field might be right for you
These aren't requirements. They're patterns that correlate with people who enjoy the work and keep coming back to it.
You like uncertainty
Quant work rewards people who can think in probabilities, expected values, and tradeoffs rather than waiting for perfect certainty.
You want to build, not just read
The fastest path through this field is solving problems, coding experiments, playing games, and debugging your own reasoning — in public.
You can handle repetition
Mental math, probability, and coding fluency come from reps. If you enjoy the reps, that's a strong sign. If every rep feels purely performative, also useful signal.
You like cross-disciplinary work
The field gets more interesting when you enjoy mixing math, systems, statistics, market structure, and real decisions under pressure.
The Plan
Week by week
Each week has a focus, a set of actions, and a small project option — keep the project tiny enough to actually finish it.
Learn the vocabulary and the shape of the game
- Read the three tracks (Trader, Researcher, Developer) — note which feels most natural right now
- Use the quant glossary until alpha, order book, Greeks, latency, and market making stop feeling mysterious
- Browse one firm's careers page so the industry stops being abstract
- Start a daily habit: 15–20 min of mental math, probability puzzles, or coding
- Write a one-page "field map" of what differences you noticed between the three tracks
- Set up Python + Jupyter in a clean environment if you haven't yet
Test whether you like decision-making under uncertainty
- Solve a small set of expected value and probability problems — practice explaining your reasoning out loud, not just getting the answer
- Play Riverboat Broker or another trading game — pay attention to whether pricing and risk feel interesting or draining
- Write down: what you mispriced, what confused you, what patterns you started to notice
- Developer-leaning? Swap trading game time for data structures and algorithms practice instead
- Solve 5 probability problems from Heard on the Street Ch. 1–2
- Write up your session log from Riverboat Broker: what you learned
Build one tiny project end to end
- Pick one: a simple moving-average backtest, a Black-Scholes pricer, a market-making simulator, or a small research notebook on any dataset you care about
- Keep the scope small enough to actually finish it this week
- Write a short README, notebook intro, or reflection explaining every assumption you made
- Don't optimize for impressiveness yet — optimize for follow-through
- Trader: Simple EV calculator for a coin-flip game with variable payoffs
- Researcher: Load a CSV, compute rolling correlations, write your findings
- Developer: Implement a basic order book matching engine in Python
Pressure test the fit
- Look at the TAMU course map and compare it against your current degree plan — are you excited to go deeper, or does it feel like obligation?
- Talk to an AggieQuant member, attend an event, or ask someone more experienced what their week-to-week work actually looks like
- Write a one-paragraph answer to "Why quant?" — see whether it sounds concrete and personal, or generic and copied from a career website
- Decide whether you want to keep building this semester. Curiosity is enough. Certainty is not required.
- Extend your Week 3 project — add one more feature or fix something that bugged you
- Read one AggieQuant speaker talk summary or blog post and write a reaction
Strong signal — keep going
- You kept tinkering with the project after the week ended
- The probability problems made you want to find more, not less
- You thought about a trade or a market structure problem outside of scheduled sessions
- You felt curious about why something worked, not just that it did
- You found yourself showing the project to someone else
Worth thinking about
- Every rep felt purely performative — done for resume, not interest
- You had to force yourself to open the project each time
- The glossary definitions felt irrelevant rather than unlocking something
- You found yourself much more engaged in a completely different area
Next Steps