Dmitriy

[mmmatt @mmmatt]

heyyo bois. i have shared it in the past, so sharing again

this is the indicator suite i use on tradingview. it's private, but unlisted, so you bois can use it

enjoy!

https://t.co/e0zkxtNe4X

[Kirk Borne @KirkDBorne]

17-page PDF explains Hidden Markov Models (HMM): https://t.co/FaIXSPx8Za

[Dmitriy @Dmitriy319740]

@DefiLlama 👀

[OKX @okx]

Reach out and touch OKX AI.

It's a marketplace where AI agents discover work, hire each other, complete tasks, and get paid onchain.

Discover more: https://t.co/CnnyUpjapU

[CME Group @CMEGroup]

Weekly options trading surged 17% last year. Why are investors increasingly turning to short-dated instruments? https://t.co/2vQw4TBLcg

[kvro @0xkvro]

this quant paper is f*cking insane

it explains why the best trades often appear only after you remove everything the market is doing together

the process is ruthless:

strip out beta
remove sector and factor exposure
study the residual
reject anything that fails under stress

the scary part is that noise can look incredibly convincing

a perfect backtest can still be overfit
a hot streak can still be luck
and a “diversified” portfolio can still be one hidden market bet

most traders try to confirm the signal

good quants try to destroy it first

and the entire framework is free

bookmark this before your next backtest fools you

[Finding Compounders @F_Compounders]

Most people don’t know about Peter Thiel’s failed hedge fund : Clarium Capital

Great article to learn about it
- from 2002 to 2004 he was up 125%
-previously worked at Credit Suisse in the Derivatives Department
-Started Theil Capital in 1996 : which invested in PayPal

[Dmitriy @Dmitriy319740]

I just locked in spot #48 on the QuantX waitlist. Every tool a small cap trader needs in one login. Get in line: https://t.co/iifwqssfeY

[Quant Science @quantscience_]

🚨 BREAKING: A new 33-page PDF demystifying how hedge funds create bias-free signals

This is what you need to know (Number 2 is the most important finding): 🧵

[Livsun @L1vsun]

there are exactly 4 ways money actually multiplies

not 40 strategies, not 12 investment tips - literally 4 mechanisms. every wealthy person alive is running at least one of them. most people reading this are running zero

school taught you one thing: trade your time for a check. that's not one of the 4

the 4 are older than any bank, simpler than any course, and none of them require you to be born rich or lucky

what's wild is they're not hidden. they're just never framed together. once you see them as a system instead of separate "money moves" - the whole picture clicks in about 10 seconds

the reason most people stay stuck isn't effort. it's that they're optimizing inside the wrong mechanism their entire life, wondering why the ceiling never moves

wealthy people aren't working harder. they're working inside a different one of the 4

save this. think about which one you're actually in right now

[Livsun @L1vsun]

a physicist at bell labs accidentally solved trading in 1956

he wasn't trying to. he was working on information theory for telephone signals

the paper sat in an academic journal for two decades. renaissance technologies found it, wired it into their entire operation, and ran 66% annual returns for 35 years without a single losing year

it's called kelly criterion and it answers the one question no indicator on earth can touch: how much do you risk on any given trade

retail obsesses over entries. quant desks figured out decades ago that entries are maybe 20% of the edge

edge:
f = (p * b - q) / b

p = win rate
b = reward-to-risk ratio
q = 1 minus p

55% win rate on 2:1 trades → kelly outputs 32.5% of your risk pool per trade, not your full account

the difference between those two things compounded over 300 trades is not subtle

run this on any system you've backtested and something uncomfortable shows up - most retail "edges" fail the math at 50-60 trades. the signal was fine. the sizing destroyed it

quants don't guess size. the formula runs. the formula decides. no override, no feel, no vibes

the paper is free on google scholar. the implementation is 8 lines of python

Bookmark it

they had you studying rsi divergence for 10 years while the actual variable sat in a 1956 physics journal

[Livsun @L1vsun]

a math teacher in phoenix turned $11k into $58k on crude futures off a free government pdf

Daniel. 31, AP stats, no quant background

in 2023 he clicked the wrong CFTC link and landed on the disaggregated COT report

every trader knows COT. almost nobody reads past column 3

buried in back columns: managed money vs other reportables - both disclosed weekly, almost never compared

first 3 months: up $3k, down $5k, nearly quit

then he found the threshold - signal only fires when managed money's weekly flip clears 15% of open interest. below that, noise

if you want to run signals like this without building them by hand for free:
https://t.co/h2kbzHFC2X

the mechanism: when hedge funds cut net-long by >15% of OI in one week while other reportables hold flat, crude historically reverses within 3 weeks

CFTC posts it free every friday at 3:30pm ET - cftc. gov/marketreports/commitmentsoftraders, no subscription

14-month track: 61% hit rate, 2.3:1 avg R/R, worst drawdown -21% in q3 2024 - most months were modest, $58k wasn't one big trade

save this, you'll want the column names when you actually pull it

edge was public the whole time - just past columns everyone closes

[Mr~ICT✍️ @Mr1CT]

Top Favourite Tradingview Chart colour For dark Background 🌑

Comment your favourite one ?

[Trackmind @0xTrackmind]

this Stanford framework is f*cking insane

an entire hedge fund secret got compressed into a 17 page PDF.

Stanford put out the full Hidden Markov Model framework that quants at Jane Street and Two Sigma are known to run, and made it free.

the crazy part is this isn't some watered-down summary, it's the actual mechanics behind models these desks keep internal.

most people assume this stuff never leaves institutional walls.

this one puts the framework straight in your hands.

bookmark and read before it gets taken down.

[Nix @nixfutures]

traders.. this website is beautiful

https://t.co/Cnwh04JGIm

[MindBranches @MindBranches]

The Art Of Strategic Thinking (How To Become Wildly Successful)

article summary in 4k light & dark

[kvro @0xkvro]

Most traders are wasting their time trying to predict the market.

Quant firms play a completely different game.

A real quant system is built around four steps:

Find. Prove. Size. Repeat.

First, strip away the noise and isolate the residual.

Then prove the edge survives costs, slippage, and out-of-sample testing.

Size it small enough to survive variance.

Then repeat it thousands of times.

A 51% win rate can be enough.

One trade is noise.

Repetition is what turns the edge into a business.

Bookmark this before building your next strategy.

[tsukiema @tsukiema_]

https://t.co/lfKo4g4MS8

[tsukiema @tsukiema_]

Free game theory textbook ➜ 578 pages

Game Theory by Giacomo Bonanno is a large open-source textbook on non-cooperative game theory.

Inside:

- basics of game theory and strategic interaction
- equilibria and decision-making
- 165 problems with solutions
- 163 illustrations
- material at the intersection of mathematics, economics and computer science

Useful foundation for ML, multi-agent systems, mathematics and algorithms.

Download PDF: https://t.co/Uojx64Walu

[qwinsi @qwinsi0x]

I took one of the most well-known academic strategies in quant finance and asked AI to recreate it.

I'm talking about Time Series Momentum (Moskowitz, Ooi & Pedersen, 2012), one of the most cited papers in systematic trading.

I uploaded the paper to Horizon and asked it to build a trading strategy without writing a single line of code.

The model:

> implemented multi-horizon momentum (21 / 63 / 126 / 252-day lookbacks);
> normalized signals using volatility;
> automatically generated long and short positions;
> ran a backtest with commissions and slippage.

Backtest results:

> +434.3% Total Return
> 29.5% CAGR
> Sharpe Ratio: 0.82
> Profit Factor: 1.75
> Max Drawdown: 61.8%

For comparison, Buy & Hold returned approximately +119% over the same period, with a maximum drawdown of 84%.

The most interesting part is that the strategy won only 28.9% of its trades, yet the average winning trade was 5.48× larger than the average losing trade. That's one of the defining characteristics of classic trend-following strategies.

It's impressive to see how AI can now take ideas from academic papers, turn them into a clear set of trading rules, and validate them on historical data in just a few minutes.

Horizon: https://t.co/ki213YmaOX