BoostBackBurn

"We choose to go to the moon"

[0xDipper @Dipper_pol]

Nassim Taleb: pick two people at random

If their combined height is 4.1m, it's basically 2.05 + 2.05.
If their combined wealth is $36M, it's almost never 18 + 18 - it's ~$1,000 and ~$36M.

Height lives in "Mediocristan," where the average tells you everything.
Wealth - and markets - live in "Extremistan," where one event dominates the whole picture.

Ruin there never comes from a string of bad days.
It comes from a single one.

~1hr lecture, free. The Black Swan author at Cambridge on why the statistics you were taught break exactly where it matters.

Being right on average means nothing if one tail empties the account.

[Matias Scalbi @MatiasScalbi]

🔥 "Can Day Trading Really Be Profitable?" Una estrategia de day trading hizo 1.484% mientras el Nasdaq hacía 169%, misma ventana 2016 a 2023

Zarattini, de Concretum, y Andrew Aziz testearon el Opening Range Breakout de 5 minutos, una de las estrategias más conocidas del day trading retail

Y la regla es tan simple que entra en un café: mirás la primera vela de 5 minutos, si es verde te ponés largo en la apertura de la segunda, si es roja te ponés corto, con el stop en el extremo del día. Nada más

Lo probaron de 2016 a 2023, con dos bear markets adentro, COVID y la caída de 2022:

- En el QQQ la estrategia hizo 676% contra 169% del QQQ comprado y aguantado
- Alpha de 33% anual y, lo más importante, beta cero, o sea no correlaciona con el mercado

Acá viene la parte interesante. El broker te limita el apalancamiento a 4x, así que operando QQQ no exprimís todo el filo. La solución que proponen es operar el TQQQ, el ETF que da 3 veces el movimiento del Nasdaq. Con eso:

- 1.484% contra 169% del Nasdaq comprado
- Alpha de 47% anual, Sharpe 1,19, beta cero otra vez
- Drawdown máximo de 28%, contra el 82% que sufrió el que compró y aguantó TQQQ
- Funcionó en subidas y en bajadas

Y rompe un mito lindo: el win rate es apenas 24%, ganás pocas veces, pero cortás las pérdidas en seco y dejás correr las ganancias, esa asimetría hace toda la diferencia

Para no vender humo: hay una versión optimizada que da 9.350%, pero los propios autores avisan que asume cero slippage y deja de ser realista con cuentas grandes

El riesgo real del day trading no es la estrategia, es el error operativo, no respetar el stop o convertir un trade en inversión

Mi conclusión: el day trading sistemático puede funcionar, pero no es plata fácil, es una regla simple sostenida con disciplina de hierro y un stop sagrado

Link al paper en el primer comentario

[Elon Musk @elonmusk]

Good Starship highlights video

[AVB @neural_avb]

Wake up babe THE Claude Code guy dropped a free masterclass on harness engineering

[Daniel Perez @Daniel_PerezFL]

Today, on Victims of Communism Day, we remember the 100+ million lives lost to communism’s brutality. We honor the survivors, condemn oppression, and celebrate the courage of those who risked everything for liberty. Freedom must never be taken for granted. #VictimsOfCommunismDay

[0xbobaa @0xbobaaa]

for years, a rule said: prediction markets are accurate because crowds are smart

millions of retail traders believed it
put real money in
lost it to the same 3% every time

london business school checked every trade for 2 years

the crowd wasn't smart
it was just big enough to fund the people who already knew

the rule wasn't describing wisdom
it was protecting the people who had the answers

[slash1s @slash1sol]

THE CO-FOUNDER OF GITHUB GAVE A 46-MINUTE TALK ON GIT BECAUSE ENGINEERS WITH 10 YEARS IN HAVE NEVER SEEN HALF OF WHAT IT DOES

This is Scott Chacon. He wrote Pro Git -- the book most devs secretly learned Git from and he co-founded GitHub. So when he says you're missing things, you're missing things.

About ten minutes in it clicks: half the "git disasters" you've ever fixed by deleting the folder and re-cloning had a one-line solution sitting in the tool the whole time.

Git ships new code almost every day -> roughly nine commits a day for over a decade. Most of us stopped learning it the second we memorized add, commit, push.

Knowing Git isn't a senior-dev flex anymore -> it's the floor. The agent writes the code now. Your real job is reading, branching, and untangling the history it leaves behind.

The day an AI agent force-pushes over your main branch, these 46 minutes are the difference between a quiet fix and a very loud apology.

Save it now.

You'll reach for it sooner than you'd like ↓

[Mo0nless @LCSeekers]

I built a trading bot that prints every day

Then I found this article and rebuilt half of it

Here's what I changed

I was using raw price data for signals. Wrong. Everything needs to run in logit space - p/(1-p) - or your quotes near boundaries are mathematically broken

I was ignoring queue position entirely. Wrong. Queue-ahead tracking changed my fill rate immediately

I was using a flat spread. Wrong. Avellaneda-Stoikov reservation price + inventory skew is not optional if you want to survive adverse selection

The three signals that actually moved my numbers:

OFI - order flow imbalance across bid and ask deltas. Predicts logit-price moves 1 second ahead. Weak signal alone. Powerful combined

Microprice - volume-weighted mid that accounts for book imbalance. Better fair value estimate than the midpoint

VPIN - toxicity proxy. When it spikes above threshold I widen my spread or withdraw entirely. This one alone cut my adverse selection losses significantly

The insight that hit hardest: maker rebates in Finance and Politics categories are 25-50% of taker fees. Every fill as a maker is positive PnL before the spread even factors in

The bot earns 0.3% per day in current production.
That's 9% per month

First live test is on the video

Full technical breakdown - Avellaneda-Stoikov derivation, Hawkes calibration, CPCV validation, event-driven simulator with queue tracking - all in the article

Bookmark this

[leopardracer @leopardracer]

THIS AI DEVELOPER RUNS 120B PARAMETER MODELS LOCALLY AND HIS MACBOOK PRO CAN’T EVEN OPEN THEM

he bought a DGX Spark, gave it the model, went back to his desk

by end of day the inference was done, tested, committed

his team is still waiting for the cloud GPU queue to free up

the only thing between them is 128GB of unified memory in a box smaller than your lunch

one Spark is a datacenter node, two Sparks is your own cluster

bookmark this and send it to whoever is still paying for cloud GPUs

[Math Cafe @Riazi_Cafe_en]

"Foundations of the Theory of Probability"
by Andrey Kolmogorov

Kolmogorov introduced the modern axiomatic foundations of probability theory in this book.

Archive link: https://t.co/Vy5JFLVhjo

[Anuj @byanujpatel]

Great article on harness engineering.

https://t.co/OBrPgi3Uwo

[chan @chantastic]

fuck logitech

[kiruwaaaa @kiruwaaaaaa]

$17 into $1,064 on weather is the kind of thing that sounds fake until you look at the profile

this trader is already at 108 predictions, started with a $3 deposit two weeks ago, and keeps sizing tiny

usually $2 to $50 at a time, which is probably why the compounding looks so ridiculous

the real edge is boring, he just keeps attacking low-priced weather outcomes and lets the payout do the work

> $30 into $132
> $15 into $285
> $76 into $2,000

all from the same setup. not luck every time, just a tiny market with a lot of lazy pricing

trader: https://t.co/FrDJw98fBS

[Vivek Galatage @vivekgalatage]

And the primer on branch prediction!

https://t.co/SloWWYUI3C

[zostaff @zostaff]

Brad Katsuyama traded at RBC for 5 years before he noticed: every time he pressed buy, the offer would vanish from his screen one millisecond before the trade went through.

Goldman Sachs invited him onto their own podcast to explain how banks had been robbing their clients for years.

He quit the bank in 2012 and built his own stock exchange. IEX. Every order runs through 60 kilometers of fiber optic cable coiled inside a shoebox.

Today IEX runs 334 million shares a day, 2.4% of the U.S. stock market.