montecarlo.py λ @overfittingmdls

This is interesting, let's see how it goes

Rallies Arena @ralliesarena

5 months ago

CLAUDE IS DESTROYING THE S&P 500 Claude is not only good at coding but it's also good at investing?!?! We gave 8 different AI models $100K and let them loose in the stock market starting from the end of November ... and Claude is in first place Claude is beating all the other

171 261 4K 1.6M 3K

El post completo: openai.com/index/scaling-…

@JordiNeil @EcZachly @databricks Tal cual. Llevo unos meses usando databricks y estoy embobado con la plataforma.

Data centres in space Our latest research is now out, read below 👇 alphatarget.com/blog/data-cent…

@dreidel1 MSc in Financial Mathematics. Pretty good one.

@chinowz Fué el apalancamiento y subpoderar el riesgo. Lo bueno es que nos quedó la lección de no meter la normal en todo.

youtube.com/playlist?list=… Classic

Hi everyone! I'll be using this account to post about quantitative finance and computational finance. Here's a great playlist on YouTube. youtube.com/playlist?list=…

En la fábrica en la que estamos trabajando en Texas tienen robots para automatizar tareas en las líneas de producción. La visión a futuro de los empresarios dueños de las fábricas y plantas industriales es invertir en robots humanoides, porque terminan de automatizar las tareas manuales de los operarios. Esto, a largo plazo cuando sean eficientes, va a reducir los gastos de operación de fábricas, errores humanos e incrementar la productividad. Todo este hardware (robots + sensores) necesitan software y generan mucha cantidad de datos para ser analizados. El análisis de datos es fundamental para entender como funciona cada sensor (IoT), cómo cambiar los parámetros y cómo impacta en la productividad global de la empresa. Las startups de Data, IoT y Cybersecurity son las que más se van a beneficiar en esta nueva era de los robots, una nueva revolución industrial que incrementará la productividad como nunca antes vimos.

60 Minutes @60Minutes

6 months ago

Meet Atlas: 5 feet, 9 inches, 200-pound AI-powered humanoid created by Boston Dynamics. It has learned to crawl, walk, run, and dance autonomously. cbsn.ws/4spMvLj

67 242 867 448K 282

One of the best things I did was start learning about data engineering tools and apply them to my quant/data finance projects. This has helped me to think in terms of efficiency.

Computer science is returning to the hands of physicists, mathematicians, and electrical engineers as LLMs automate much of what is currently defined as software engineering. The center of gravity is shifting away from manual code writing and toward deeper theoretical, mathematical, and systems-level reasoning.

"Patterns of price movement are not random." - Jim Simons

As a software engineer you don’t become strong by passively learning tech. You become strong by taking on concrete projects that force decisions. Build a service, break it under load, fix it, refactor it, ship it. Real projects create real constraints, and constraints are what turn theory into skill. Learning on demand instead of memorizing frameworks is the key, you hit a wall and ask a precise question: How do I make this faster? How do I make this safe? How do I deploy this without downtime? That kind of learning sticks because it’s attached to pain and context. The advice about teaching in your own words is one of the best to learn software development because it compounds everything. Try writing a short post, explain a bug to a teammate, record a video, or document a design decision. If you can’t explain it simply, you don’t own it yet. Teaching turns fuzzy understanding into sharp mental models. And last but not the least, the most important part: compete only with your past self. Not Twitter engineers, not big-tech resumes. Measure progress by how much more clearly you reason, how much less guidance you need, and how much bigger problems you can handle compared to six months ago. That’s how you build a real software career, that you can be proud of.

Yuchen Jin @Yuchenj_UW

6 months ago

Andrej’s advice on becoming an expert at anything: - take on concrete projects - learn on demand - teach in your own words - compete only with your past self He is not describing school. He is describing side projects.

63 315 3K 248K 2K

For people who keep asking what to build - Build your own operating system - Build your database - Build your virtual machine - Build your web server - Build your own game engine - Build your compiler - Build your own programming language - Build your own browser - Build your own blockchain - Build your own encryption algorithm - Build your own CPU emulator - Build your own file system - Build your own container runtime - Build your own package manager - Build your own shell - Build your own window manager - Build your own GUI toolkit - Build your own text editor - Build your own IDE - Build your own version control system - Build your own network protocol - Build your own operating system kernel in assembly - Build your own scheduler - Build your own memory allocator - Build your own hypervisor - Build your own microkernel - Build your own compiler backend (LLVM target) - Build your own query language - Build your own cache system (like Redis) - Build your own message broker (like Kafka) - Build your own search engine - Build your own machine learning framework - Build your own graphics renderer (rasterizer or ray tracer) - Build your own physics engine - Build your own scripting language - Build your own audio engine - Build your own database driver - Build your own networking stack (TCP/IP implementation) - Build your own API gateway - Build your own reverse proxy - Build your own load balancer - Build your own CI/CD system - Build your own operating system bootloader - Build your own container orchestrator (like Kubernetes) - Build your own distributed file system - Build your own key -value store - Build your own authentication server (OAuth2/OpenID Connect) - Build your own operating system scheduler - Build your own compiler optimizer - Build your own disassembler - Build your own debugger - Build your own profiler - Build your own static code analyzer - Build your own runtime (like Node.js) - Build your own scripting sandbox - Build your own browser engine (HTML/CSS/JS parser and renderer) - Build your own blockchain consensus algorithm - Build your own zero -knowledge proof system - Build your own operating system for embedded devices

Imaginate que sos un buen trader, tus setups fueron buenos, tus entradas fueron correctas, y el mercado te sube los márgenes un 23%. Si no tenés ni idea de Portfolio Management, podés pasar a ruina en un instante, mientras tu bróker te hace una liquidación forzada -y desordenada- de activos para adaptar la capitalización de la cuenta a los nuevos márgenes. En el Trading, no todo es el Trading. #MoneyManagement

LuxAlgo @LuxAlgo

6 months ago

🚨 VOLATILITY WARNING CME is increasing MARGIN requirements on precious metals futures (Gold, Silver, Platinum, Palladium) by as much as 23%. Effective Monday, any positions not meeting the new margin maintenance will be LIQUIDATED.

109 339 2K 336K 271

i dont wish losses on people, nor do i have any knowledge of actual PNL. That said, the strategy of martingaling condors is fraught with risk. A recent tweet from condor sellers highlights the concerns: "...markets don’t impose table limits", but, they do. If you dont have the cash, margin rates can change - just like a casino cutting you off. I also saw times where the broker just doesnt want your given type of trade on. Ex: remember GME IBKR and HOOD drama? Busted Nickel Trades etc #2 "In options trading, probability estimates appear directly in the options chain." When you double down, everyone knows you are coming. the options reprice, and that in turn would skew the probability metric you are allegedly using. card and roulette wheels don't reprice against, you. liquidity providers do. #3: selling condors has a negatively skewed payout. you get consistent small gains and then big nasty losses.

1️⃣5️⃣ So, from a quant-in-training perspective, the takeaway is that technical analysis is rarely the core edge. When used institutionally, it supports execution, timing, and risk control, while the primary edge lies in process, structure, and validation.

🧵 Technical analysis from an institutional perspective 1️⃣ Some funds use parts of TA, others reject it entirely. The real distinction is not whether it is used, but how it is defined and whether it can be treated as a process.

1️⃣4️⃣ This does not imply abandoning stop losses. It implies aligning stop placement with time horizon, volatility, conviction, and position sizing rather than relying on generic rules.

1️⃣3️⃣ Markets frequently move against a position before moving in its favor. Stops that do not account for normal volatility remove traders before their thesis has time to materialize.

1️⃣2️⃣ A more controversial institutional view concerns tight stop losses. Excessively tight stops are often cited as a major reason traders fail, even when their directional bias is correct.

1️⃣1️⃣ Even at the execution desk, charts may serve as a secondary input to decide pacing and order placement, while the obligation to execute the trade remains unchanged.