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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...

77,130 görüntüleme • 1 ay önce •via X (Twitter)

32 Yorum

Javeriya Ahsan profil fotoğrafı
Javeriya Ahsan1 ay önce

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

AI Mastery Guide profil fotoğrafı
AI Mastery Guide1 ay önce

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

BTCaveman. profil fotoğrafı
BTCaveman.1 ay önce

Not on @TracNetwork Save on inference with (Beta)

Rockford 🍡 profil fotoğrafı
Rockford 🍡1 ay önce

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

Hussain Hashim | Building SundayBack profil fotoğrafı
Hussain Hashim | Building SundayBack1 ay önce

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

Anomaly (🍷/acc) profil fotoğrafı
Anomaly (🍷/acc)1 ay önce

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.

𝚌𝚛𝚢𝚙𝚝𝚎𝚌𝚝 profil fotoğrafı
𝚌𝚛𝚢𝚙𝚝𝚎𝚌𝚝1 ay önce

Won’t beat MMs AIs.

0xCryptoUniverse profil fotoğrafı
0xCryptoUniverse1 ay önce

You are craaaaazyyyyy. Nice work man.

Macro Bombastic profil fotoğrafı
Macro Bombastic1 ay önce

love seeing ai open-sourced for practical quant work

Rise profil fotoğrafı
Rise1 ay önce

👀

Shoopy profil fotoğrafı
Shoopy1 ay önce

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

Steven Cheng profil fotoğrafı
Steven Cheng1 ay önce

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

Gregor profil fotoğrafı
Gregor1 ay önce

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

Fabrizio Degeno profil fotoğrafı
Fabrizio Degeno1 ay önce

Does it make you any money? 👀

Shaun Gold | Venture Comedy profil fotoğrafı
Shaun Gold | Venture Comedy1 ay önce

Quant got democratized.

toni profil fotoğrafı
toni1 ay önce

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.

Max Bevza profil fotoğrafı
Max Bevza1 ay önce

checking it out right now

unchosen.eth profil fotoğrafı
unchosen.eth1 ay önce

opening the whole workflow is honestly the best part

Michczy profil fotoğrafı
Michczy1 ay önce

Cool

BentoBoi profil fotoğrafı
BentoBoi1 ay önce

W

Creao AI profil fotoğrafı
Creao AI1 ay önce

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.

Luís Rodrigues profil fotoğrafı
Luís Rodrigues1 ay önce

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

Laplace Demon profil fotoğrafı
Laplace Demon1 ay önce

Good luck making money with this in prod

Granite profil fotoğrafı
Granite1 ay önce

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

Romario profil fotoğrafı
Romario1 ay önce

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

Paul Nugent profil fotoğrafı
Paul Nugent1 ay önce

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.

Saman Ahmed profil fotoğrafı
Saman Ahmed1 ay önce

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

安叫兽|Bird🕊️ 🔶 BNB profil fotoğrafı
安叫兽|Bird🕊️ 🔶 BNB1 ay önce

量化最怕回测很美,实盘很疼

Cata profil fotoğrafı
Cata1 ay önce

@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.

Wallchain Community Hub profil fotoğrafı
Wallchain Community Hub1 ay önce

this is actually insane

Sarcastic Badger profil fotoğrafı
Sarcastic Badger1 ay önce

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?

0xNeural profil fotoğrafı
0xNeural1 ay önce

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.

Benzer Videolar

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

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