Catty

[Roan @RohOnChain]

Probably the only lecture you need to master "game theory".

Bookmark now, learning game theory can change your life btw.

[monokern @monokern]

THIS TRADER TRAINED HIS OBSIDIAN VAULT ON HUNDREDS OF CHART PATTERNS AND NOW IT THINKS WITH HIM

every setup he ever studied is in there
> linked to the outcome
> linked to the context
> linked to what he was thinking at the time

he types one command
> Claude Code finds the relevant sources, runs analysis through NotebookLM, saves everything structured

the vault doesn't just store information anymore
it connects it

most traders are still screenshotting charts into a Discord and forgetting them in 48 hours

article below

[Roan @RohOnChain]

As someone who builds institutional level quant systems, this MIT bible on HFT System Design is the closest thing to a real trading desk I have ever seen publicly shared.

78 pages. Full architecture from signal generation to live execution. Bookmark now.

[Roan @RohOnChain]

i can't believe this man just gave away the exact math every Jane Street & Citadel quant secretly studied to get the job:

[CyrilXBT @cyrilXBT]

MIT JUST LEAKED THE EXACT DOCUMENT WALL STREET QUANT FIRMS USE TO HIRE PEOPLE.

It is called the MIT Quant Bible.

And it covers everything the $500,000 a year quants actually know that you do not.

Here is what is inside:

Probability fundamentals. Conditional probability, Bayes theorem, expected value, variance, joint distributions. The mathematical foundation every trading decision at Jane Street and Citadel is built on.

Stats fundamentals. The Law of Large Numbers. Central Limit Theorem. Confidence intervals. The tools quants use to know when a signal is real and when it is noise.

Quant research and data science. Least squares, regressions, dimensionality reduction. Real case studies from Two Sigma, QuantCo, and CitiBikes.

Quant trading and market making. What market making actually is and how the theory translates into real trading decisions.

And then the part that makes this document worth more than most finance degrees.

A question bank with real interview questions from Jane Street, Virtu Financial, Optiver, Akuna Capital, Citadel, Hudson River Trading, Two Sigma, Five Rings, and SIG.

Not prep questions.

The actual questions these firms ask.

With answers.

The firms charging $50,000 a year for access to this type of preparation are not going to be happy this exists.

Bookmark this before it disappears.

Follow
@cyrilXBT
for every elite resource that gives you access to what the top 1% actually know

[zostaff @zostaff]

Jane Street, Goldman Sachs, JP Morgan, BlackRock, Hudson River Trading, Two Sigma, D.E. Shaw.

The most expensive engineering teams in the world released their financial tools on GitHub. Here are 7 repos, one from each.

1. Jane Street, janestreet/magic-trace
https://t.co/a2G20vnewK
5.3k stars. Process tracer powered by Intel PT. When your profiler is blind, magic-trace sees every CPU instruction.

2. Goldman Sachs, goldmansachs/gs-quant
https://t.co/SMYFwP3TWD
Derivative pricing the GS traders use at their desks. MIT licensed.

3. JP Morgan, finos/perspective
https://t.co/9rgy6FxYt4
What JPM traders use to watch markets in real time. A $24k/year terminal, for free.

4. BlackRock, blackrock/lcso
https://t.co/iHwsxZDZD9
Rust optimizer for portfolio problems. Where scipy gives up, this works.

5. Hudson River Trading, hudson-trading/corral
https://t.co/YhmrQFmYaZ
Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest U.S. trading firms.

6. Two Sigma, twosigma/flint https://t.co/ebEFqcDxJ6
Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks.

7. D.E. Shaw, deshaw/pyflyby https://t.co/uYDQKtnDVd
Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself.

Bookmarked it

[wincy.eth @gusik4ever]

the fastest growing GitHub repos in finance this week:

1. TradingAgents (+3,822 ★)

multi-agent LLM trading framework built for financial research and execution. combines analyst agents, sentiment models, portfolio reasoning, and provider integrations into a single trading stack.

2. AI-Trader (+2,434 ★)

fully automated agent-native trading system. built around autonomous decision-making, price fetching, execution, and monitoring workflows. focused on end-to-end AI-driven trading infrastructure.

3. scientific-agent-skills (+2,286 ★)

plug-and-play agent skills for finance, research, science, engineering, and writing. integrates with multiple agent frameworks and supports web research, bioinformatics, cheminformatics, and analysis pipelines.

4. daily_stock_analysis (+1,272 ★)

LLM-powered stock analysis platform covering US, Hong Kong, and Chinese equities. combines market data, real-time news, AI dashboards, automated reporting, and multi-channel notifications with near-zero operating cost.

5. QuantDinger (+1,242 ★)

AI quantitative trading platform for crypto, stocks, and forex. includes live trading, strategy backtesting, market analytics, and broker integrations. built for traders experimenting with AI-assisted quant workflows.

6. Vibe-Trading (+1,148 ★)

personal AI trading agent focused on algorithmic trading and backtesting. combines lightweight automation with agent-style portfolio management and strategy experimentation.

7. FinceptTerminal (+878 ★)

modern open-source finance terminal inspired by Bloomberg-style workflows. provides market analytics, investment research, trading tools, and AI-powered financial infrastructure in one interface.

8. TradingAgents-CN (+739 ★)

Chinese-enhanced version of TradingAgents. adapts the multi-agent LLM trading framework for Chinese financial markets, datasets, and workflows. rapidly growing among Chinese quant and AI communities.

9. last30days-skill (+694 ★)

AI agent skill for researching trends across Reddit, X, YouTube, Hacker News, Polymarket, and the broader web. designed for signal discovery, narrative tracking, and internet-wide monitoring.

10. qlib (+680 ★)

Microsoft’s AI-oriented quant investment platform. covers the entire quant pipeline from data collection to alpha generation, portfolio construction, and execution. still one of the strongest open-source quant ecosystems available.

bookmark this and start today.

[zostaff @zostaff]

A self-taught Quant just published the exact technique that separates real trading edge from data mining - permutation tests on backtested strategies in Python.

Quant Twitter quietly knows about him. Quants juniors send each other his videos in DMs.

Bookmark it tonight before the algorithm pushes him mainstream. Then read the article, I built the AI quant system that runs thousands of these tests per week.

[bodila @51bodila]

Jane Street pays $1.4M/year for Quants who can train AI models for trading

This 40-min insight by two Quant from team - how to build AI models like a tier-1 fund making $20B/year

bookmark & watch - no matter what. After this, you'll understand how hedge funds make billions using AI

[Roan @RohOnChain]

This 1 hour Stanford lecture on Markov Decision Processes will teach you more about the math behind systematic trading decisions than a 3 month internship at Jane Street or JPMorgan.

Bookmark & replace one movie today with this lecture, then read the complete article below.

[Movez @0xMovez]

Google Cloud AI engineer just showed how they go from idea to deployed app at Google in 30-minutes using Claude.

26-minutes. free. by Google AI team.

one person + Claude + Google Cloud = a full engineering org running on a laptop.

worth more than any $500 vibe-coding course.

[Michael Burry Stock Tracker ♟ @burrytracker]

Breaking: OpenAI fired Leopold Aschenbrenner at 22. Three years later, he manages $5.5 billion

His next 13F files this Friday and the new trades will be posted here

Leopold is quickly becoming one of the best AI investors out there

And it started when he got fired from OpenAI in April 2024 for a leaked memo on AI safety

Instead of moving on, he spent 6 months writing a 165-page document predicting AGI arrives by 2027

Then he launched Situational Awareness LP. Backed by the founders of Stripe and the former CEO of GitHub

Here's what he bought:

• Bloom Energy (BE): powers AI data centers. Up ~1,420%

• Lumentum (LITE): optical components connecting AI chips at scale. Up ~1,335%

• SanDisk (SNDK): storage layer for AI workloads. Up ~3,130%

• CoreWeave (CRWV): GPU cloud for AI training and inference. Up ~166%

• Iris Energy (IREN): AI-focused compute infrastructure. Up ~585%

Every AI model that gets built needs power, somewhere to store data, and compute to run on. He bought all three before anyone else was paying attention

[gus @igus_ai]

Cancelé $2.000/mes en suscripciones de Trading

Reemplacé casi todo por repositorios Open-Source 100% gratis

Este es el stack completo:

1. TradingView Pro ($30/mes) → lightweight-charts

14K estrellas. Creado por el propio equipo de TradingView. 45KB. Gratis.
> https://t.co/VqpSa8RNuR

2. Bloomberg Terminal ($2.000/mes) → fredapi + Claude

Acceso a todos los datasets macroeconómicos publicados por la Fed mediante API gratuita
> https://t.co/1dvvJRkXVB

3. Plataforma de backtesting ($100/mes) → prediction-market-backtesting

Fork de NautilusTrader con adaptadores para Polymarket y Kalshi
> https://t.co/wzFhoGQNbG

4. Ingeniería inversa de estrategias → polybot

Infraestructura de ejecución y datos de mercado con paper trading.
Kafka, ClickHouse y Grafana como pipeline completo de analíticas
> https://t.co/x3rufeBuyX

5. Paper trading para agentes IA → polymarket-paper-trader

Order books reales, modelo exacto de fees y tracking de slippage tu agente de Claude recibe $10K ficticios para operar
> https://t.co/kp9IZyacpF

6. Ahorro de tokens → rtk

Proxy CLI que reduce entre un 60-90% el consumo de tokens en Claude Code
escrito en Rust, binario único y compatible con 10 herramientas IA
> https://t.co/9n4E6OdxA6

7. Claude Code ($200/mes) → goose

35K estrellas. Desarrollado por Block (Jack Dorsey). Escrito en Rust. Funciona con cualquier LLM y ofrece un loop completo de agentes IA
> https://t.co/S8SDZjNbwz

Antes: +$2.600/mes
Ahora: prácticamente $0

Guárdate este post, me lo agradecerás. 🔖

[Movez @0xMovez]

Jane Street AI Engineer revealed how they trained their own LLM for trading to make $22.5B/year

16 minutes. free. straight from tier-1 quants.

bookmark & watch - this is the most honest "AI inside a hedge fund" talk ever published.

forget the "AI trading bot" YouTube grifters. This is the real inside view: data, training, evals, integration.

then start building your own bot using post below.

[ludoonchart @ludoonchart]

Vegas banned him. So he wrote an algorithm, moved to Hong Kong, and extracted $1 Billion from the betting crowd.

As this Bloomberg documentary shows, Bill Benter didn’t guess. He mathematically exploited the public's emotional mispricing.

Today, millions of dollars are being extracted from Polymarket using this exact same framework. While the crowd trades on "vibes," algorithms are siphoning the pool.

Bookmark this, then read the 77-year-old math framework below to build your edge

[Movez @0xMovez]

This 9-minute lecture by Nassim Taleb on "Probability Distribution" will teach you more about prediction trading than 2 months as a Quant intern at Jane Street.

Bookmark it & give it 9-minutes today. It’ll be the most productive start for your week. Then read article below.

[shirish @shiri_shh]

we are so cooked 😭

these guys let Claude run wild on Wall St.

Look at this insider trades scanner it built in 4 mins that:

> reads every SEC filing where execs buy their own stock
> flags clusters where multiple execs buy at once
> emails me the top 3 trades every morning before the open

[CyrilXBT @cyrilXBT]

A MIT professor gave a 1-hour lecture in 2019 that has 18 million views.

He died 5 months after recording it.

It was his final gift to the world.

Patrick Winston taught at MIT for 50 years.

The smartest engineers on earth sat in his classroom.

And he spent his last lecture teaching them the one skill their degrees never covered.

How to speak.

15 lessons that will change how you communicate forever:

Never open with a joke. Your audience is not ready to laugh yet. Open with a promise of what they will know by the end.

Your ideas are like your children. You are too close to them. What is obvious to you is invisible to everyone else. Explain the obvious.

The 5-minute rule: the first 5 minutes of any talk determine whether people will listen for the next 55. Spend more time on your opening than anything else.

Repeat your most important idea 3 times in 3 different ways. Once is never enough.

Build a fence around your idea. Tell people what it is NOT before you tell them what it IS.

Verbal punctuation. Pause. Let the idea land before moving to the next one.

Ask questions nobody will answer. Then wait 7 seconds. The silence is not awkward. It is processing.

Never read your slides. Your audience can read. They cannot listen and read simultaneously.

Use the board not the slides. Writing forces you to slow down. Slowing down forces clarity.

Inspire before you inform. Nobody learns from someone they are not inspired by.

End with a contribution not a summary. Tell them what you gave them. Not what you said.

Never say thank you at the end. It is weak. End with something that lands.

Stories make ideas stick. Data makes ideas understood. You need both. In that order.

The quality of your communication determines the quality of your ideas in the eyes of the world. Not the ideas themselves.

Practice is not preparation. Practice IS the skill.

Patrick Winston understood something most people spend their entire careers missing.

Your ideas are only as powerful as your ability to transfer them into someone else's mind.

You can be the smartest person in the room and be completely invisible.

Or you can master communication and make average ideas feel like breakthroughs.

He chose to spend his last lecture teaching this.

Watch it tonight.

Bookmark this first.

Follow @cyrilXBT for more lessons from the people who built the future.

[Archive @ArchiveExplorer]

This guy works at coinbase. in his spare time he built the largest open dataset for polymarket and kalshi

72,100,000 trades. 7,680,000 markets. open source

- figured out who actually makes money on prediction markets
- wrote a research paper with the math to prove it
- 3,100 stars on github just for that

also built heimdall-rs - rust toolkit for evm bytecode analysis. 1,500 stars. serious dev
3,008 contributions in the last year

while everyone's posting takes about polymarket - he's building the infrastructure underneath it

→ https://t.co/lZ9ofASrnO

like + bookmark. you'll need this when you build your first polymarket bot