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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 Aufrufe • vor 1 Monat •via X (Twitter)

32 Kommentare

Profilbild von Javeriya Ahsan
Javeriya Ahsanvor 1 Monat

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

Profilbild von AI Mastery Guide
AI Mastery Guidevor 1 Monat

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

Profilbild von BTCaveman.
BTCaveman.vor 1 Monat

Not on @TracNetwork Save on inference with (Beta)

Profilbild von Rockford 🍡
Rockford 🍡vor 1 Monat

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 1 Monat

@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 1 Monat

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 1 Monat

Won’t beat MMs AIs.

Profilbild von 0xCryptoUniverse
0xCryptoUniversevor 1 Monat

You are craaaaazyyyyy. Nice work man.

Profilbild von Macro Bombastic
Macro Bombasticvor 1 Monat

love seeing ai open-sourced for practical quant work

Profilbild von Rise
Risevor 1 Monat

👀

Profilbild von Shoopy
Shoopyvor 1 Monat

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

Profilbild von Steven Cheng
Steven Chengvor 1 Monat

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

Profilbild von Gregor
Gregorvor 1 Monat

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 1 Monat

Does it make you any money? 👀

Profilbild von Shaun Gold | Venture Comedy
Shaun Gold | Venture Comedyvor 1 Monat

Quant got democratized.

Profilbild von toni
tonivor 1 Monat

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 1 Monat

checking it out right now

Profilbild von unchosen.eth
unchosen.ethvor 1 Monat

opening the whole workflow is honestly the best part

Profilbild von Michczy
Michczyvor 1 Monat

Cool

Profilbild von BentoBoi
BentoBoivor 1 Monat

W

Profilbild von Creao AI
Creao AIvor 1 Monat

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 1 Monat

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

Profilbild von Laplace Demon
Laplace Demonvor 1 Monat

Good luck making money with this in prod

Profilbild von Granite
Granitevor 1 Monat

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 1 Monat

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

Profilbild von Paul Nugent
Paul Nugentvor 1 Monat

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 1 Monat

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 1 Monat

量化最怕回测很美,实盘很疼

Profilbild von Cata
Catavor 1 Monat

@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 1 Monat

this is actually insane

Profilbild von Sarcastic Badger
Sarcastic Badgervor 1 Monat

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 1 Monat

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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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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82,372 Aufrufe • vor 1 Jahr