正在加载视频...

视频加载失败

7 OPEN-SOURCE GITHUB REPOS THAT CAN TURN CLAUDE INTO A FULL QUANT TRADING STACK. 100% FREE AND EASY TO SET UP.

49,332 次观看 • 1 个月前 •via X (Twitter)

15 条评论

Miles Deutscher 的头像
Miles Deutscher1 个月前

Thanks!

Hussain Hashim | Building SundayBack 的头像
Hussain Hashim | Building SundayBack1 个月前

@RoundtableSpace I had no idea you could do this with Claude! Curious how it handles data quality and noise in the trading models.

TUWA 的头像
TUWA1 个月前

how many of these 7 actually handle partial fills and slippage or do they all assume perfect execution

حيدر أمير الداود 的头像
حيدر أمير الداود1 个月前

.

Maksemiilian 的头像
Maksemiilian1 个月前

Execution is the part every LLM quant stack skips - slippage and fees kill more edges than bad signals do.

Polash Khan 的头像
Polash Khan1 个月前

Claude as a full quant stack is a wild use case. Follow @polash_ai for more AI tools and updates.

Reinforce.fi | Stablecoin Yield 的头像
Reinforce.fi | Stablecoin Yield1 个月前

Who’s ready to let Claude manage their USDT yields?

Reinforce.fi | Stablecoin Yield 的头像
Reinforce.fi | Stablecoin Yield1 个月前

free quant stack built on Claude is exactly the edge we needed

DexorynLabs 的头像
DexorynLabs1 个月前

AI prompts miss raw execution edge.

MASA 的头像
MASA1 个月前

How well does latency hold up on live execution?

vøv △ 的头像
vøv △1 个月前

Claude still can't predict $BTC bottom, delusional...

Prop Firm Watch 的头像
Prop Firm Watch1 个月前

finally, my blown account comes with beautifully written documentation

Hafiz Siddiq 的头像
Hafiz Siddiq1 个月前

@grok The useful shipping test is whether these open repos make the agent loop more reproducible, not just cheaper. Which boundary would you instrument first: data quality, strategy backtests, or tool permissions?

vvtentt 的头像
vvtentt1 个月前

It's also free, thank you

Sean Will Trades 的头像
Sean Will Trades1 个月前

Turning Claude into a full trading stack through GitHub projects is an interesting concept

相关视频

goldman sachs packed 25 YEARS of Wall Street quant experience into a GitHub repo and made it FREE the craziest part? it’s built and maintained by Goldman Sachs’ own quant developers it’s called gs-quant, a quantitative finance toolkit covering risk, derivatives, trading strategies and financial analytics pair it with Machine Learning for Trading and you have a powerful open-source quant research stack here’s how to run it yourself: step 1 → open and install the library using pip install gs-quant step 2 → open and explore its 27 chapters covering financial data, machine learning, backtesting and execution step 3 → pull historical S&P 500 market data and build a trading dashboard with 15-minute candlesticks, volume and technical indicators step 4 → use gs-quant to analyze volatility, risk exposure, correlations and financial time series step 5 → combine it with ML4T to research market patterns, engineer features and develop trading signals step 6 → add moving averages, VWAP, volume profiles and market monitoring panels to your research terminal step 7 → evaluate strategies using Sharpe ratios, drawdowns and other quantitative risk metrics step 8 → backtest your strategies with transaction costs, then walk-forward test them on unseen market data the result: a quantitative finance research stack combining Goldman Sachs’ own toolkit with machine learning, market analysis and strategy evaluation two GitHub repos an entire quant research workstation i won’t say anything more…

ludoonchart

37,780 次观看 • 15 天前