TOMORROW QUANT ARCHIVE

The Archive for Tomorrow’s Quants

A growing archive of quantitative finance notes, strategies, guides and research.

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Tomorrow Quant Archive — Foreword

I have always wondered about what I would say, do and present if I ever got my five minutes of fame. Depending on the occasion, I might be thrust into a role without warning, or given the chance to speak my mind freely before a vast audience. My honest guess is that it would end in a complete freeze and a blurted, half-formed address to human hope. Given the chance to step out of the spotlight — and to put out a script instead — I shall take it. I shall write my mind clearly.

I thought long and hard about how to address you all. Those of you who have come across my work on IMC Prosperity will know I have attempted to entertain you a little. I considered carrying that same tone here, but I believe it would fall short of the scope and theme I am trying to present. Those who have read through my previous work on quantitative finance — scattered at best and a complete mess at worst — will perhaps recognise the tone a bit more. Like many others, I have found the introductions to Statistical Mechanics wonderfully funny, and the mannerisms of aging, wise physicists have always roamed my mind. I went back and forth for a long time: do I address you all with fun, cheer and humour, or with the serious demeanour this topic arguably deserves? I have been told I make one egg into six chickens at times, so forgive me for going on — and on.

I considered opening with humour, followed by a simple “here you can read X, Y and Z.” But I doubted myself. Was that the proper way to address everyone from high schoolers to executives? I then thought I should be more serious — textbook-introduction serious. Something like: “Jim Simons discovered and invented algorithmic trading in 1980. He is a mad lad with exceptional skill in code-breaking.” I write that to make fun of the tone I could adopt, not to endorse it. That tone did not represent the seriousness, passion and dedication I feel for this subject. Worse still, it would not actually address what matters here.

What I would love is to invite everyone — anyone — into the absolute wonder that quantitative finance is. Jim Simons was a mathematician who worked on problems core to physics. Paul Wilmott was a physicist. David E. Shaw was a physicist and computer scientist. It should be plain that quant finance overlaps mathematics, physics and computer science. It draws on game theory, evolutionary dynamics, artificial intelligence and more. It is my sincerest belief that the core problem inside quantitative finance is the same core problem in physics and mathematics alike. Cracking it — truly, deeply — could illuminate the others as well. It comes as no surprise to me how many quants find themselves wandering through fluid dynamics, numerical approximations, partial differential equations, earthquake forecasting, quantum mechanics, probability theory and wave functions.

We are, all of us, simply scientists — each with our own expertise — studying money as though it were a natural phenomenon. In some sense, it is. It arises from the complexity of human society and human psychology. A wonderful thing to look into, if you have not already, is the study of memetics: how ideas, behaviours and cultural elements spread and evolve within a society, much as viruses do. “Buy low, sell high” propagates through populations in a strikingly similar way.

And so we have done the long walk for a short drink of water. In the end, I realised something. If I were ever to address the vast sweep of human society — my five minutes of fame before all of humanity, for all eternity — it would not much matter what I said. Because the only person I should ever truly address is myself. All my life I have tried to find someone who could look at the vast and wonderful complexity of reality and see it the way I do. I have tried my hardest to compress oceans into a glass of water.

I am not smarter, better, prettier or stronger than most. I am shorter than most men. I am scarred across my skin. I am very frequently a complete idiot. My attempts to share the beauty I see in the world have failed at nearly every turn. Loneliness and deep chaos have been my oldest companions — which is why the song I relate to most is Nothing Arrived, and the story I carry closest is The Stars Do Not Wait for You. Very early on in life, chaos came for me, and it has persisted ever since. I have lived a life of relentless, sustained trauma. And even so, I have managed to get a solid footing in quantitative finance. And for some reason, a good number of you have decided to read this.

So here is my final decision. If I could compress the universe into a glass of water and hand it to you, this is what it would contain: You will be alright. Come hell or high water, you will be alright. Even when you have sunk to the bottom of the ocean, you will still be alright. I hope you embark on a wonderful journey. When it feels impossible, come and find me. I will always be here.

These words were not chosen because they are professional, powerful or deeply intellectual. They were chosen because they are the words I believe most likely to give you what I was never given — and had to make for myself. Hope.

Walk the archive

A guided entry point for learning, building, competing, and preparing.

The archive should help readers choose a path rather than throwing every resource at them at once.

First orientation

Quantitative finance is not one single path.

Quantitative finance covers several related fields: credit risk in banks, actuarial science, algorithmic trading, systematic investing, quantamental investing, derivatives pricing, portfolio construction, and risk management.

The goal of this archive is to help you understand where these areas overlap, where they differ, and which learning path makes sense for your own interests.

01

Start with mathematics and curiosity about finance.

Most people begin with some mix of probability, statistics, calculus, programming, economics, or a simple fascination with markets. You do not need to know the whole industry at the beginning. You need enough structure to avoid getting lost.

02

Look around the industry before choosing your lane.

Banking, insurance, asset management, hedge funds, market makers, and trading firms use quantitative methods differently. Early on, it helps to sample books, videos, lectures, job descriptions, and competition writeups before deciding what to focus on.

03

Write your first Python scripts and pull real market data.

A practical first project is simple: install Python, load a price series with yfinance, calculate returns, plot moving averages, and test basic ideas. This turns abstract finance into something you can inspect, measure, and improve.

04

Build simple models before chasing complex strategies.

Before advanced machine learning or high-frequency trading, learn how to reason about returns, volatility, drawdowns, transaction costs, risk limits, and whether a backtest is actually telling you anything useful.

05

Connect with peers and learn in public.

Quant finance is easier to navigate when you are not learning alone. Join Discord servers, forums, newsletters, YouTube channels, university societies, and competition communities where beginners and experienced practitioners share resources, questions, projects, and ideas.

Materials list

Books, videos, code, forums, papers, and practical starting points.

The resources page collects useful material for different levels: beginner orientation, mathematical foundations, Python, market structure, options, risk, portfolio theory, and competition preparation.

Browse resources →

05 · Community

Quant finance community directory

A curated collection of public communities, creators, forums, and people worth following if you are learning quantitative finance, algorithmic trading, market microstructure, mathematical finance, or systematic research.

Instagram

Short-form quant, programming, macro, and research content.

Forums

Longer-form discussion spaces for quant finance, mathematical finance, and career questions.

Quant competitions

Explore practical trading challenges, strategy games, and market-making competitions.

A curated overview of competitions that help students and aspiring quants build real trading intuition.

Crunch x ADIA Lab: Structural Breaks
Time series · Structural breaks · Machine learning

CrunchDAO Structural Breaks

A data science competition focused on identifying structural breaks in time series, combining statistical modelling, machine learning, and robust signal detection.

Time series Structural breaks ML

The competition is available now until September 2026

Visit official competition →
KN Hack
Investing · Research · Crypto,ETFs,Stocks

KN Hack Research Challenge

The KN Hack 2026 is a team-based investment strategy challenge (teams of 2 to 4 members) open to students, investors, and anyone eager to learn, compete, and build real-world research skills

ETFs Allocation Stock Picking Crypto Strategies

Deadline 10th of May 2026

Visit official competition →
Hull Tactical
Investing · Forecasting · Portfolio strategy

Hull Tactical Competition

A quantitative investing competition focused on predicting excess market returns and building a daily allocation strategy designed to outperform the S&P 500. Participants use public and proprietary market data to uncover robust signals while staying within a 120% volatility constraint.

Predictive signals Portfolio allocation Volatility constraint

Competition ends on 16th of June 2026

Visit official competition →
IQC
Quant Research · Alpha Signals · Systematic Strategy

International Quant Competition

A global quantitative research competition focused on building predictive alphas, testing market signals, and developing systematic trading ideas. Participants learn how data, modelling, and portfolio construction come together in practical quant research.

Alpha research Signal modelling Systematic trading

Competition runs phase 1 from 18 March 2026 till 18 May 2026

Visit official competition →
Numerai Competition
Machine learning · Finance · Prediction

Numerai Competition

A machine learning tournament where participants build predictive models on abstracted financial data, aiming to generate robust signals for live market-neutral strategies.

Machine learning Signals Prediction

Ongoing · Regular tournament format

Visit official competition →
IMC Prosperity competition
Market making · Strategy · Python

IMC Prosperity

A global algorithmic trading challenge where participants build strategies across multiple simulated markets, combining market making, statistical arbitrage, risk management, and game-style trading decisions.

Algorithmic trading Market structure Backtesting

Finished on 30th of April · Not currently open

Visit official competition →
Optiver Ready Trader Go competition
Market making · Exchange simulation · Coding

Optiver Ready Trader Go

A trading competition focused on building automated market-making bots that interact with a simulated exchange, manage inventory, quote prices, and respond to fast-changing market conditions.

Market making Inventory risk Exchange bots

Finished · Not currently open

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Citadel Terminal Competition
Markets · Decision making · Trading simulation

Citadel Terminal

A market simulation challenge where participants work through trading decisions, competitive strategy, and market intuition in a structured finance-focused environment.

Trading game Market intuition Strategy

Finished · Not currently open

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CUATS Competition
Algorithmic Trading · QuantConnect · Portfolio Strategy

CUATS Challenge

A QuantConnect-based trading challenge where Cambridge students and postdocs build backtestable algorithms starting with USD 100,000, aiming for strong alpha, high Sharpe ratio, controlled drawdown, and clear methodology.

Backtesting Sharpe ratio Drawdown control

Finished · Not currently open

Visit official competition →