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I built and automated a real quant trading strategy with Claude Code. I open-sourced the Claude Skill, TradingView indicator, & more. If you trade financial markets, I genuinely hope you don't skip this. 0:00 Intro 2:37 wtf is a quant? 4:45 How quants build models 6:17 Predicting volatility 8:12...

77,675 Aufrufe • vor 3 Monaten •via X (Twitter)

32 Kommentare

Profilbild von Javeriya Ahsan
Javeriya Ahsanvor 3 Monaten

This is a much better use of Claude Code than another to-do app 😭

Profilbild von AI Mastery Guide
AI Mastery Guidevor 3 Monaten

Open sourcing the whole skill and indicator is generous, saving this 📌

Profilbild von BTCaveman.
BTCaveman.vor 3 Monaten

Not on @TracNetwork Save on inference with (Beta)

Profilbild von Rockford 🍡
Rockford 🍡vor 3 Monaten

open-sourcing a quant edge is the fastest way to kill it. the moment 500 people run the same tradingview signal the fill you backtested doesn't exist anymore. curious what the sharpe looks like net of slippage, not on paper

Profilbild von Hussain Hashim | Building SundayBack
Hussain Hashim | Building SundayBackvor 3 Monaten

@milesdeutscher this is cool, but how's Claude Skill different from other trading bots? curious if it's more reliable.

Profilbild von Anomaly (🍷/acc)
Anomaly (🍷/acc)vor 3 Monaten

CT marketer plays to be a quant with LLMs... 😂. Go read this "Mr Quant"... If you use LLMs in actual quant firms you are tagged as retarded, and fired.

Profilbild von 𝚌𝚛𝚢𝚙𝚝𝚎𝚌𝚝
𝚌𝚛𝚢𝚙𝚝𝚎𝚌𝚝vor 3 Monaten

Won’t beat MMs AIs.

Profilbild von 0xCryptoUniverse
0xCryptoUniversevor 3 Monaten

You are craaaaazyyyyy. Nice work man.

Profilbild von Macro Bombastic
Macro Bombasticvor 3 Monaten

love seeing ai open-sourced for practical quant work

Profilbild von Rise
Risevor 3 Monaten

👀

Profilbild von Shoopy
Shoopyvor 3 Monaten

grabbing the skill just to see how you structured it, cheers man

Profilbild von Steven Cheng
Steven Chengvor 3 Monaten

GARCH is a solid choice for volatility. I use similar logic to smooth sensor noise in my robot control loops.

Profilbild von Gregor
Gregorvor 3 Monaten

GARCH quantifies when volatility clusters, not when to buy. Translating that signal into an actual trade decision is where most backtests quietly fall apart.

Profilbild von Fabrizio Degeno
Fabrizio Degenovor 3 Monaten

Does it make you any money? 👀

Profilbild von Shaun Gold | Venture Comedy
Shaun Gold | Venture Comedyvor 3 Monaten

Quant got democratized.

Profilbild von toni
tonivor 3 Monaten

If your backtest doesn't account for slippage and regime shifts, automating it just speeds up the losses. Curious how this holds once liquidity dries up and the sample window stops matching live markets.

Profilbild von Max Bevza
Max Bevzavor 3 Monaten

checking it out right now

Profilbild von unchosen.eth
unchosen.ethvor 3 Monaten

opening the whole workflow is honestly the best part

Profilbild von Michczy
Michczyvor 3 Monaten

Cool

Profilbild von BentoBoi
BentoBoivor 3 Monaten

W

Profilbild von Creao AI
Creao AIvor 3 Monaten

The Claude Skill abstraction is the underrated part here. A reusable skill for signal evaluation means the strategy logic stays portable instead of being baked into one-off scripts you have to redo every time you change the model or the indicator.

Profilbild von Luís Rodrigues
Luís Rodriguesvor 3 Monaten

This is a great example of AI helping people build specialized tools instead of just answering questions.

Profilbild von Laplace Demon
Laplace Demonvor 3 Monaten

Good luck making money with this in prod

Profilbild von Granite
Granitevor 3 Monaten

that's exactly what's interesting imo how ai is handling trading right now this is actually a useful video i'm gonna watch

Profilbild von Romario
Romariovor 3 Monaten

Are you planning any other models (for example, with machine learning) in your next videos?

Profilbild von Paul Nugent
Paul Nugentvor 3 Monaten

The real test will be live execution vs backtested results — overfitting to historical data is where most quant strategies die, especially when the logic is LLM-generated without rigorous walk-forward validation. Curious how the Claude Skill handles regime changes and whether the TradingView indicator accounts for slippage/fees in its signals. The tooling layer here is genuinely useful though — agentic code generation for rapid strategy prototyping is a legitimate workflow shift even if the alpha itself decays fast.

Profilbild von Saman Ahmed
Saman Ahmedvor 3 Monaten

I’m more interested in the workflow than the strategy. Turning ideas into testable systems is where these tools get interesting.

Profilbild von 安叫兽|Bird🕊️ 🔶 BNB
安叫兽|Bird🕊️ 🔶 BNBvor 3 Monaten

量化最怕回测很美,实盘很疼

Profilbild von Cata
Catavor 3 Monaten

@milesdeutscher this looks solid, the only issue is that you are adding that to TV, which has limited trading capability. You should check @altradyapp instead; it has way more features and is extremely powerful.

Profilbild von Wallchain Community Hub
Wallchain Community Hubvor 3 Monaten

this is actually insane

Profilbild von Sarcastic Badger
Sarcastic Badgervor 3 Monaten

I watched the full breakdown on using Claude to automate the GARCH based volatility model into actual position sizing and the TradingView export. What still sits with me is the jump from forecasting conditional variance to deciding real trade size. How did you decide the exact mapping from the GARCH output to position risk so the strategy stays consistent when the model’s own assumptions start breaking in live markets?

Profilbild von 0xNeural
0xNeuralvor 3 Monaten

This is actually insane value. Building a real automated quant strategy with Claude Code and open-sourcing it? Absolute gold for traders 🔥 If you trade, you NEED to check this. Future of trading is here.

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this repository is f*cking BROKEN goldman sachs pays each quant up to $300k a year, all thanks to this repo It's called gs-quant, developed and maintained by Goldman Sachs' own quant team, and it already has 13.5k stars First, get how it works: 1. Goldman Sachs' quants built this toolkit on 25 years of their own risk experience: pricing, risk, backtesting, swaps, swaptions, FX options, timeseries - all in there. It even ships a skill that teaches Claude Code how to use it. 2. Every time Claude writes gs-quant code, it runs Goldman's risk math right in your notebook: volatility, drawdown, z-score, beta, RSI. 3. Pair it with Machine Learning for Trading (21.6k stars) - 27 chapters that take you from data sourcing to live execution. Together it's the exact stack a quant interview tests. Here's how you can run it for free yourself: 1. Open Claude Code, install Goldman Sachs' library: pip install gs-quant 2. Add the skill: python -m gs_quant.skills install --global 3. Have Claude pull 5-year daily prices for GS and SPY with free yfinance data 4. Run Goldman Sachs' risk math on it: volatility, max drawdown, z-score, Bollinger Bands, RSI 5. Check 60-day beta and correlation to SPY. Is it the stock moving, or the whole market? 6. Mark every day the z-score drops below -2. That means the price is 2 standard deviations below normal 7. Backtest a rule: buy below -2, sell when back at 0, including fees. Claude writes the code and runs it 8. Walk-forward it the ML4T way. If it doesn't beat buy-and-hold after fees, it's not an edge i won't say anything more:

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