141.8K
MAFor you all…cybergeekgyan is doing some hard work for everyone who is interested in breaking into quant finance as a career #quant #quantfinance #financialengineering
@mar_antaya
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1.5M
LIJim Simons, founder of Renaissance Technologies, one of the all-time best performing quantitative hedge funds, gives a talk about his journey to MIT.
Credits: hamsterpoop - “ James Simons - Mathematics, Common Sense and Good Luck; My Life and Careers” - YouTube
Edited for educational purposes. No ownership claimed. The content is for informational purposes only, and does not constitute financial or investment advice.
#hedgefund #quant #quanttrading #finance #math
@liquidity_capital
93.2K
QUI’ve been reading a few books and papers on Blackholes over the past couple months. Here’s some experimental results. Will post more in future.
#quant #ai #quantfinance #datascience #investing
@quant_research_decoded
1.1M
QUMy 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.
@quantmaxxing
443.6K
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
615.5K
QUIf this looks familiar, you’re already deep in it.
Quant prep doesn’t start with job applications.
It starts way before with how you think, what you enjoy, and what you obsess over.
Most people will laugh at this.
A few will save it.
Those are the ones who usually make it.
Save this if it hit a little too close.
Which part exposed you the most?
Send this to the quant friend who fits this perfectly.
@quantchics
1.7M
QUThe model shown (Λ-Vol) is part of a broader series of Volatility Frameworks developed during my time as Quant Researcher at my current firm:
Λ-Vol models Volatility as the interaction of regime conditions, reinforcing and stabilising market feedback and risk absorption.
It tracks how volatility pressure builds, propagates and unwinds across assets and horizons. We first started by first deriving it at an equations / mathematical level, then building up the framework from there.
📣 To learn more check the link in my Bio.
Quant Researchers are hired to find solutions to questions that may come down from their portfolio manger for example.
There are no solutions that are found in papers. Academics publish papers as it’s a crucial part of their career. However in industry, researchers are funded by their place of work. Research is privatised for profit.
A quant researcher’s role is to develop NEW methods not found in public academic papers. Academic papers certainly play an important role, however a quant researcher would be rendered redundant if solutions were so easily and directly extrapolated from a paper.
#quant #ai #quantfinance #datascience #investing
@quant_research_decoded
8.3M
IMwaterloo got me #waterloo #uwaterloo #cs #math #quant
@imbrianan
855.1K
CHComment “QUANT” to get all Projects.
#quant #ai #coding #finance #university
@chrispathway
320.5K
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
155.7K
NEFormer Citadel quant explains how you can solve for BOTH the power price and the gas price in the same model. Intro to complementarity.
@neelsalami
917.3K
BATrading US-Iran? Well, if you're buying gold, oil, and defense today
You're not early. You're buying someone else's exit.
The US-Iran strikes happened. Markets moved. Every quant on Wall Street repriced the obvious in minutes.
But the non-obvious trades? Those are still wide open as the rippling impacts of Iran.
Welcome to Day 1 of the 50 Day Barebone Investing School, pop us a follow to keep track and see you tomorrow!
@barebone.ai
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