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JUHere are some of the topics that you should be familiar with if you’re looking to get into quant finance!🧮
#quant #quantfinance #optionstrading #algotrading
@julias.algos
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STIf it doesn’t break the baseline, it holds absolutely zero value.
#computerscience #quant #stemstudent #academicweapon #deepwork
@stem.archive
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DRIf 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
@drjuliankam
234.9K
STPOV: You work at Jane Street
#janestreet #quant #trading
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Comment What you want to see!!!
@stick_storiess
454.1K
QUCan you solve this quant interview question?
#quant #math
@quantprof_dot_org
57.1K
STA fair trade.
#computerscience #quant #academicweapon #stemstudent #deepwork
@stem.archive
66.2K
ETTop 2 quant firm in the old btw 😭😭
#janestreet #quant #finance #student #university
@ethanylim
563.1K
SOMeet Guangting Yu ( @yugtmacs ):
- Applied Math PhD. Quant
- Finance MS (UMich).
- Worked on alpha discovery using Kalman filters. Published - AI/ML research (NeurIPS).
Quants don’t guess markets. They hunt alpha. Hidden edges in data.
Statistical arbitrage exploits tiny pricing gaps
Factor models like Fama French explain what drives returns.
- Kalman filters separate signal from noise in real time
- Mean reversion bets prices snap back
- Momentum rides trends early
- Machine learning models like XGBoost and neural nets predict moves before they’re obvious.
Covariance Formula:
Cov(X, Y) = E[XY] − E[X]E[Y]
Measures how two variables move together
Positive means they rise together, negative means they move opposite
Core signal used to detect relationships and extract alpha
While most people stare at charts
he’s modeling the system behind them
#quant #machinelearning #startups #ai #founder
@sorhan.hq
222.7K
DECoding every PD array to determine its predictive value | Part 1: Order Block. In this series, we are going to code every one of ICT‘s canonical PD arrays to determine its predictive value. It’s become too common and too widely accepted for internet, trading gurus to say that trading confluences “work” without showing any statistical proof of their predictive power. I hope this series will change that, and capture the attention of retail traders who need it. #statistics #education #quant #quantfinance
@deltatrendtrading
100.4K
ALYou’re probably not smarter than a quant trader and here’s why… #quant #softwareengineer #coding #csmajor #mathmajor @snhgnl
@allenvert
70.7K
AAyou can be a quant too 🫵 #fyp #quant #internship #university #larp
@aahilnjj
367.5K
VIWhy quants never trust their covariance matrix ?
Most portfolio optimization models rely on the covariance matrix.
500 stocks = 125,000 parameters to estimate from only 252 trading days. More relationships than data points. Most of what you estimate is noise.
Marchenko and Pastur (1967) showed that pure random data produces a predictable eigenvalue distribution. Overlay it on real data and you can separate signal from noise.
On S&P 500 data, only a handful of eigenvalues sit above the noise band. The rest is random fluctuation.
That’s why quants clean the matrix before optimizing, using eigenvalue clipping, shrinkage (Ledoit-Wolf), or other regularization methods. Factor models and Bayesian approaches also address the same problem.
The core idea: if you don’t question your inputs, no optimizer will save you
#quant #algotrading #trading #portfoliomanagement #finance
@vince.quant
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