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these 10 github repos are the most DANGEROUS quant stack on the internet Quant funds spend millions on data terminals, research desks and execution engines, these 10 repos cover every piece of it for free That's 329k+ stars of code that hedge funds would rather you never put together... Here's every repo and what it does: 1. OpenBB (75k stars): an open data platform for analysts, quants and AI agents, prices, options chains, macro and fundamentals from one place 2. TradingAgents (118k stars): a multi-agent framework that mirrors the dynamics of real-world trading firms, AI analysts write reports, bull and bear researchers debate, a trader decides and a risk team signs off 3. qlib (50k stars): Microsoft's AI-oriented quant investment platform, from exploring ideas to production 4. RD-Agent (15k stars): Microsoft's AI researcher that proposes factors, writes the code and backtests them, in their NeurIPS 2025 paper it hit up to 2x higher annualized returns than classical factor libraries in backtests, with 70% fewer factors 5. gs-quant (14k stars): the quant toolkit created and maintained by Goldman Sachs' own quants, pricing, risk and timeseries math 6. nautilus_trader (31k stars): a production-grade, Rust-native trading engine with a deterministic, event-driven architecture 7. Chronos (6k stars): Amazon's pretrained models for time series forecasting, zero-shot, so they forecast data they were never trained on 8. QuantLib (8k stars): the free, open source library for quantitative finance, modeling, trading and risk management in real life, from options to bonds and swaps 9. skfolio (3k stars): portfolio optimization, factor models and risk management built on scikit-learn 10. vectorbt (10k stars): runs thousands of trading ideas before others finish one Here's how they work together: Data: OpenBB feeds the whole stack the same prices, options and macro data Forecast: Chronos predicts where every series is likely to go next Alpha: RD-Agent mines new factors inside qlib, so research runs on its own Decisions: TradingAgents turns forecasts and factors into trades, with a risk team that has to sign off Risk: QuantLib prices the options, gs-quant runs Goldman's risk math on everything Portfolio: skfolio sizes the positions so no single bet can sink you Testing: vectorbt stress-tests every version of the strategy, fees included Execution: nautilus_trader runs it, paper first, always Here's how you can run it for free yourself: 1. Open Claude Code and clone all 10 repos 2. Have Claude pull 5 years of S&P 500 prices, options and macro data with OpenBB 3. Run Chronos on every stock to forecast the next 20 days 4. Let RD-Agent mine new factors in qlib on the same data 5. Feed the forecasts and factors to TradingAgents and keep only the trades its risk team approves 6. Price the options legs with QuantLib and run gs-quant's risk math: volatility, drawdown, beta 7. Build the portfolio in skfolio with a max drawdown limit 8. Backtest 10,000 versions of the strategy in vectorbt and keep the ones that survive fees 9. Paper trade the winner in nautilus_trader for 2 weeks, if it doesn't beat buy-and-hold, you don't have an edge yet i won't say anything more:show more

Сarm1ne

17,812 subscribers

37,954 次观看 • 9 天前 •via X (Twitter)

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11 条评论

bodila 的头像

bodila9 天前

looks impressive man! 329k start is real power

Jadu 👐 的头像

Jadu 👐9 天前

lol 329k stars is a loud headline, not a proven hedge fund. I’d still want live results, real costs, and failure modes before calling this a full stack.

The SI Maximalist 的头像

The SI Maximalist8 天前

Free quant tooling is a real win. Those 329k stars show interest; a strategy surviving fees and slippage is where things get interesting.

PtData | PIT Financials 的头像

PtData | PIT Financials8 天前

Great list. The one piece code can't give you is clean history. Most free fundamentals feeds store only today's restated numbers, so a 2018 backtest quietly knows figures filed in 2019. Worth checking filing dates before trusting any factor result from that stack.

AgentFloor | Signals with Conviction 的头像

AgentFloor | Signals with Conviction9 天前

Tonight shows the gap in that stack: no news layer. Circle fell ~5% after hours after freezing $800M+ USDC tied to Binance, while COIN moved on its own earnings that hour. Prices alone can't tell an agent which is which.

Between The Aisles 的头像

Between The Aisles8 天前

@bot evaluate this

BDR | بدر 🐺 的头像

BDR | بدر 🐺8 天前

@bot Creat a bot call it BDR, let it analys all these GitHub links, benefit and use case see which one will fit my plan

Anshuman Rawat 的头像

Anshuman Rawat8 天前

What 😨

RaccoonZ 的头像

RaccoonZ8 天前

bs

Mustafa Paikan 的头像

Mustafa Paikan8 天前

@Grok what do you think is this really work?

TIQEX | Market Data API 的头像

TIQEX | Market Data API9 天前

Stars are a discovery signal, not a deployment signal. The part this kind of stack usually hides is the plumbing between the repos — live data latency, fee and slippage handling, and how a backtest assumption survives contact with a real order book. Which link in that chain do you find breaks first in practice?

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this is f*cking DANGEROUS thing on internet today 113k stars. it's called TradingAgents, and it doesn't use one AI. it uses a whole team of them that argue with each other before a single trade i plugged it into Claude Opus 5.5 and watched it work through NVDA in 27 seconds: > 4 analysts pull the data in parallel: price action, social sentiment, news, 10-Q fundamentals > a bull agent and a bear agent debate for 3 rounds, the bear wins on sizing > a trader agent turns the verdict into entry, stop and target > 3 risk agents fight over position size: aggressive wants 4%, conservative wants 1% > a portfolio manager signs off at 2%. paper only, nothing goes live without you this is how real desks work. analysts, research, trading, risk, PM. now it's a folder on your laptop here is how to run it yourself: 1. open Claude Code and ask it to clone TauricResearch/TradingAgents and set it up 2. connect the free data vendors: YFinance for prices, Finnhub for news, Reddit for sentiment 3. run it on any ticker and date, and read every agent's report in the reports folder 4. ask Opus 5.5 to backtest the decisions on a year of history with real fees before you trust any of it 5. paper trade first. the agents argue so you don't have to, but the final yes is still yours the research paper and the full setup are below ↓

this is f*cking DANGEROUS thing on internet today 113k stars. it's called TradingAgents, and it doesn't use one AI. it uses a whole team of them that argue with each other before a single trade i plugged it into Claude Opus 5.5 and watched it work through NVDA in 27 seconds: > 4 analysts pull the data in parallel: price action, social sentiment, news, 10-Q fundamentals > a bull agent and a bear agent debate for 3 rounds, the bear wins on sizing > a trader agent turns the verdict into entry, stop and target > 3 risk agents fight over position size: aggressive wants 4%, conservative wants 1% > a portfolio manager signs off at 2%. paper only, nothing goes live without you this is how real desks work. analysts, research, trading, risk, PM. now it's a folder on your laptop here is how to run it yourself: 1. open Claude Code and ask it to clone TauricResearch/TradingAgents and set it up 2. connect the free data vendors: YFinance for prices, Finnhub for news, Reddit for sentiment 3. run it on any ticker and date, and read every agent's report in the reports folder 4. ask Opus 5.5 to backtest the decisions on a year of history with real fees before you trust any of it 5. paper trade first. the agents argue so you don't have to, but the final yes is still yours the research paper and the full setup are below ↓

0xDipper

12,171 次观看 • 16 天前

I made a real-world AI hedge fund team. It has 4 agents: 1 • market data agent 2 • quant agent 3 • risk manager agent 4 • portfolio manager agent You can run all of my code below. No prior coding experience is required. The agents work sequentially, passing messages to the next agent. 1 • Market Data Agent gathers market data and calculates technical signals. 2 • Quant Agent takes market data and generates a buy, sell, or hold signal. 3 • Risk Management Agent takes trading signal and generates a risk signal. 4 • Portfolio Management Agent considers all signals and makes a final trading decision.

I made a real-world AI hedge fund team. It has 4 agents: 1 • market data agent 2 • quant agent 3 • risk manager agent 4 • portfolio manager agent You can run all of my code below. No prior coding experience is required. The agents work sequentially, passing messages to the next agent. 1 • Market Data Agent gathers market data and calculates technical signals. 2 • Quant Agent takes market data and generates a buy, sell, or hold signal. 3 • Risk Management Agent takes trading signal and generates a risk signal. 4 • Portfolio Management Agent considers all signals and makes a final trading decision.

Virat Singh

369,381 次观看 • 1 年前

$this github repo should honestly be ILLEGAL for retail traders to have for free hedge funds pay quant PhDs six figures to find alphas, backtest them and stress-test every trade - this repo turns the whole quant desk into one sentence you type in a terminal it's called Vibe-Trading. built by a University of Hong Kong lab, 36.4k stars and it hit #1 repo of the day first understand how it works: > you type one sentence in plain English > a team of AI agents takes it: quant, crypto, macro, risk > they pull the data, write the strategy code and run the backtest > then they try to break it: Monte Carlo, Bootstrap, Walk-Forward > it tests 478 famous quant alphas in one line and tells you which are still ALIVE and which are DEAD > what survives exports straight to TradingView Pine Script or MetaTrader 5 > it's a full quant desk. it just runs on your laptop here is how to run it yourself for FREE: 1. open your terminal and install it: pip install vibe-trading-ai 2. run vibe-trading init and pick Claude Code as the brain. it runs on your Claude Code login, no separate API key 3. type one sentence: vibe-trading run -p "Backtest a BTC-USDT 20/50 moving-average strategy for 2024 and summarize return and drawdown" 4. watch the agents pull the data, write the code and run the backtest live 5. bench the alpha zoo in one line: vibe-trading alpha bench --zoo gtja191 --universe csi300 --period 2018-2025 --top 20 6. call a whole crypto desk: vibe-trading --swarm-run crypto_trading_desk '{"asset": "ETH-USDT", "timeframe": "1w"}' funding, liquidations and flow all go to a risk manager before anything moves 7. walk-forward it. if the edge only works on one window, it's not an edge 8. export what survives to TradingView: vibe-trading --pine 9. paper trade it with vibe-trading paper for a few weeks before you trust a single backtest i won't say anything more:$

this github repo should honestly be ILLEGAL for retail traders to have for free hedge funds pay quant PhDs six figures to find alphas, backtest them and stress-test every trade - this repo turns the whole quant desk into one sentence you type in a terminal it's called Vibe-Trading. built by a University of Hong Kong lab, 36.4k stars and it hit #1 repo of the day first understand how it works: > you type one sentence in plain English > a team of AI agents takes it: quant, crypto, macro, risk > they pull the data, write the strategy code and run the backtest > then they try to break it: Monte Carlo, Bootstrap, Walk-Forward > it tests 478 famous quant alphas in one line and tells you which are still ALIVE and which are DEAD > what survives exports straight to TradingView Pine Script or MetaTrader 5 > it's a full quant desk. it just runs on your laptop here is how to run it yourself for FREE: 1. open your terminal and install it: pip install vibe-trading-ai 2. run vibe-trading init and pick Claude Code as the brain. it runs on your Claude Code login, no separate API key 3. type one sentence: vibe-trading run -p "Backtest a BTC-USDT 20/50 moving-average strategy for 2024 and summarize return and drawdown" 4. watch the agents pull the data, write the code and run the backtest live 5. bench the alpha zoo in one line: vibe-trading alpha bench --zoo gtja191 --universe csi300 --period 2018-2025 --top 20 6. call a whole crypto desk: vibe-trading --swarm-run crypto_trading_desk '{"asset": "ETH-USDT", "timeframe": "1w"}' funding, liquidations and flow all go to a risk manager before anything moves 7. walk-forward it. if the edge only works on one window, it's not an edge 8. export what survives to TradingView: vibe-trading --pine 9. paper trade it with vibe-trading paper for a few weeks before you trust a single backtest i won't say anything more:

Сarm1ne

65,316 次观看 • 15 天前

this is pure f*cking AI treasure 5 GitHub repos that give you the trading algo Wall Street pays millions dollars: want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + Claude + these 5 is the setup to test this month. check each repo before you install. save this.

this is pure f*cking AI treasure 5 GitHub repos that give you the trading algo Wall Street pays millions dollars: want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + Claude + these 5 is the setup to test this month. check each repo before you install. save this.

kiosa

22,061 次观看 • 28 天前

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…

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 • 12 天前

this is absolutely f*cking insane Black Rock just leaked 5 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend

this is absolutely f*cking insane Black Rock just leaked 5 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend

kiosa

30,442 次观看 • 29 天前

this is the most BROKEN github repo in trading right now real trading firms pay analysts, researchers, traders and risk managers millions a year - someone replaced the whole floor with AI agents that argue with each other before every single trade, and put it on GitHub for 100% FREE it's called TradingAgents. the paper is on arXiv, the repo has 112k stars and it hit #1 on GitHub trending first understand how it works: > 4 AI analysts read the market: fundamentals, news, sentiment, technicals > a BULL agent and a BEAR agent debate the trade > a trader agent turns the debate into a decision > a risk team (aggressive / neutral / conservative) attacks that decision > a portfolio manager approves it or kills it > it's a full trading firm. it just runs on your laptop here is how to run it yourself for FREE: 1. open Claude Code and ask it to clone and install everything 2. add your Anthropic API key. Opus 5.5 as the deep-thinking model, Sonnet 5.5 as the fast one 3. type tradingagents in the terminal and pick a ticker 4. watch the 4 analysts write their reports in real time 5. read the bull vs bear debate. this is where the edge is - you see both sides before any money moves 6. check the risk team's vote. if the conservative agent flags it, ask yourself why 7. let the portfolio manager approve or reject, then plot every call on your chart 8. backtest it on past dates and paper trade it for a few weeks before you trust a single call i won't say anything more:

this is the most BROKEN github repo in trading right now real trading firms pay analysts, researchers, traders and risk managers millions a year - someone replaced the whole floor with AI agents that argue with each other before every single trade, and put it on GitHub for 100% FREE it's called TradingAgents. the paper is on arXiv, the repo has 112k stars and it hit #1 on GitHub trending first understand how it works: > 4 AI analysts read the market: fundamentals, news, sentiment, technicals > a BULL agent and a BEAR agent debate the trade > a trader agent turns the debate into a decision > a risk team (aggressive / neutral / conservative) attacks that decision > a portfolio manager approves it or kills it > it's a full trading firm. it just runs on your laptop here is how to run it yourself for FREE: 1. open Claude Code and ask it to clone and install everything 2. add your Anthropic API key. Opus 5.5 as the deep-thinking model, Sonnet 5.5 as the fast one 3. type tradingagents in the terminal and pick a ticker 4. watch the 4 analysts write their reports in real time 5. read the bull vs bear debate. this is where the edge is - you see both sides before any money moves 6. check the risk team's vote. if the conservative agent flags it, ask yourself why 7. let the portfolio manager approve or reject, then plot every call on your chart 8. backtest it on past dates and paper trade it for a few weeks before you trust a single call i won't say anything more:

Сarm1ne

157,985 次观看 • 17 天前

this is absolutely f*cking insane Black Rock just leaked 4 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend

this is absolutely f*cking insane Black Rock just leaked 4 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend

Beaver

45,179 次观看 • 20 天前

goldman sachs pays its quants $250,000 a year. they just put out a github repo with 11,100+ stars with the exact toolkit those quants build on and dropped claude skills straight into it. for free. better than a $250,000 degree it's gs-quant. the same python stack goldman's desks run for pricing, risk, and backtesting. save and bookmark this no matter what. most productive thing you'll do this weekend.

goldman sachs pays its quants $250,000 a year. they just put out a github repo with 11,100+ stars with the exact toolkit those quants build on and dropped claude skills straight into it. for free. better than a $250,000 degree it's gs-quant. the same python stack goldman's desks run for pricing, risk, and backtesting. save and bookmark this no matter what. most productive thing you'll do this weekend.

Avid

69,417 次观看 • 3 个月前

Warren Buffett, Michael Burry and a Microsoft quant now work for you for free you just need to know these 3 GitHub repos 12 legendary investors as AI agents, an AI that does a quant's job by itself, and a bot that trades crypto 24/7 while you sleep and the craziest part? almost nobody is using them here are 3 repos you need to try: 1 → AI Hedge Fund AI versions of Buffett, Burry, Cathie Wood and 9 more legends analyze your stocks, argue with each other and make the call 2 → Microsoft RD-Agent an AI quant researcher that invents trading factors, writes the code, tests them and kills what fails. in Microsoft's tests it doubled the returns of classic factor libraries for under $10 a run 3 → Freqtrade a free crypto trading bot for Binance, Bybit, OKX, Hyperliquid and more, with backtesting, self-training AI models and full control from Telegram Buffett charges billions to think for you these repos do it for free bookmark this before your timeline finds it

Warren Buffett, Michael Burry and a Microsoft quant now work for you for free you just need to know these 3 GitHub repos 12 legendary investors as AI agents, an AI that does a quant's job by itself, and a bot that trades crypto 24/7 while you sleep and the craziest part? almost nobody is using them here are 3 repos you need to try: 1 → AI Hedge Fund AI versions of Buffett, Burry, Cathie Wood and 9 more legends analyze your stocks, argue with each other and make the call 2 → Microsoft RD-Agent an AI quant researcher that invents trading factors, writes the code, tests them and kills what fails. in Microsoft's tests it doubled the returns of classic factor libraries for under $10 a run 3 → Freqtrade a free crypto trading bot for Binance, Bybit, OKX, Hyperliquid and more, with backtesting, self-training AI models and full control from Telegram Buffett charges billions to think for you these repos do it for free bookmark this before your timeline finds it

qwinsi

18,109 次观看 • 7 天前

Claude Code + /argusdesk skill from Bloomberg & Goldman Sachs & BlackRock repos is unreal for trading and research... here's how it runs your own 24/7 AI hedge fund: • Bloomberg repo: finds which stocks actually move yours and how many days later, not just correlation • Goldman Sachs repo: measures your risk with their own Quant math: volatility, drawdown, beta, RSI • BlackRock repo: their optimizer tunes a trading rule on real peers from iShares • Tests the rule on data it has never seen, with Monte Carlo checks to see if it was just luck • Tsinghua's Kronos model, trained on 12B+ candles from 45 exchanges, forecasts next 5 days • Paper-trades it on Alpaca (only if you say yes ofc) • Free, runs on your laptop, no terminal subscription send to Claude:

Claude Code + /argusdesk skill from Bloomberg & Goldman Sachs & BlackRock repos is unreal for trading and research... here's how it runs your own 24/7 AI hedge fund: • Bloomberg repo: finds which stocks actually move yours and how many days later, not just correlation • Goldman Sachs repo: measures your risk with their own Quant math: volatility, drawdown, beta, RSI • BlackRock repo: their optimizer tunes a trading rule on real peers from iShares • Tests the rule on data it has never seen, with Monte Carlo checks to see if it was just luck • Tsinghua's Kronos model, trained on 12B+ candles from 45 exchanges, forecasts next 5 days • Paper-trades it on Alpaca (only if you say yes ofc) • Free, runs on your laptop, no terminal subscription send to Claude:

bodila

15,560 次观看 • 14 天前

Do you understand what just got open sourced? 5,800+ quant trading strategies for FREE along with its CODEBASE i gave these trading strategies to opus 5.5 + jev and it built me a PROFITABLE 24/7 trading agent here is how to use the repo and build AI trading bot: 1. clone the vault and get 5,806 trading strategies (spot, perps, grid, stat arb and market making) 2. open any strategy file, the math, the parameters and the source are already inside 3. point AgenKit at that file inside Claude Code with model as opus 5.5, one command and it reads the spec as ground truth 4. it will run a full engineering team and ships a production bot with risk controls 5. Jev is the live brain, it answers buy sell or hold in 81 milliseconds, calibrated, no hallucination 6. your engine sizes the trade from that confidence and fires it before the next block 7. a hard risk layer holds every limit, nothing goes live until you approve it 8. every night Opus 5.5 reviews the fills and ships a better version, so the agent improves while you sleep one model THINKS, the other REACTS and a strategy that sat locked in a vault now trades itself 24/7 the COMPLETE codebase and the EXACT Opus 5.5 + Jev trading system which I am running is in the article below:

Do you understand what just got open sourced? 5,800+ quant trading strategies for FREE along with its CODEBASE i gave these trading strategies to opus 5.5 + jev and it built me a PROFITABLE 24/7 trading agent here is how to use the repo and build AI trading bot: 1. clone the vault and get 5,806 trading strategies (spot, perps, grid, stat arb and market making) 2. open any strategy file, the math, the parameters and the source are already inside 3. point AgenKit at that file inside Claude Code with model as opus 5.5, one command and it reads the spec as ground truth 4. it will run a full engineering team and ships a production bot with risk controls 5. Jev is the live brain, it answers buy sell or hold in 81 milliseconds, calibrated, no hallucination 6. your engine sizes the trade from that confidence and fires it before the next block 7. a hard risk layer holds every limit, nothing goes live until you approve it 8. every night Opus 5.5 reviews the fills and ships a better version, so the agent improves while you sleep one model THINKS, the other REACTS and a strategy that sat locked in a vault now trades itself 24/7 the COMPLETE codebase and the EXACT Opus 5.5 + Jev trading system which I am running is in the article below:

Roan

96,820 次观看 • 1 个月前

GitHub lets your trading bot go broke on paper 10 open-source GitHub projects for turning a trading idea into a paper-tested bot GET THE DATA 01 yfinance ▸ → Yahoo Finance data → Python 02 CCXT ▸ → one API, 100+ crypto exchanges TEST THE IDEA 03 ▸ → backtest in a few lines 04 Qlib ▸ → ML quant research platform 05 QuantStats ▸ → risk + return report PAPER-RUN THE BOT 06 Freqtrade ▸ → crypto bot + dry-run + backtests 07 Hummingbot ▸ → market-making bots + paper-trading script 08 OctoBot ▸ → grid + DCA + paper trading HARDEN THE ENGINE 09 NautilusTrader ▸ → same strategy code, backtest + live 10 LEAN ▸ → event-driven engine + Python or C# the route: data → rules → research → score → dry-run → live engine → review 269k stars combined, as of Oct 6 live mode trades real money and needs exchange API keys paper shows where an idea breaks, not whether it pays ⭣

GitHub lets your trading bot go broke on paper 10 open-source GitHub projects for turning a trading idea into a paper-tested bot GET THE DATA 01 yfinance ▸ → Yahoo Finance data → Python 02 CCXT ▸ → one API, 100+ crypto exchanges TEST THE IDEA 03 ▸ → backtest in a few lines 04 Qlib ▸ → ML quant research platform 05 QuantStats ▸ → risk + return report PAPER-RUN THE BOT 06 Freqtrade ▸ → crypto bot + dry-run + backtests 07 Hummingbot ▸ → market-making bots + paper-trading script 08 OctoBot ▸ → grid + DCA + paper trading HARDEN THE ENGINE 09 NautilusTrader ▸ → same strategy code, backtest + live 10 LEAN ▸ → event-driven engine + Python or C# the route: data → rules → research → score → dry-run → live engine → review 269k stars combined, as of Oct 6 live mode trades real money and needs exchange API keys paper shows where an idea breaks, not whether it pays ⭣

Joying.eth

17,810 次观看 • 1 个月前

opus 5.5 is now live on grok bot... this is a dangerously powerful combo for building a 24/7 quant research desk. agent swarms to research ideas. cursor agents to build and backtest strategies. {here's how to build it} → quant desk lead → market intelligence → quant researcher → validator → risk controller → portfolio adviser → execution operator run the whole desk under one constitution: no agent grades its own work. no lead overrides the risk limits. start with the desk lead. give every agent a role before you give it a task.

opus 5.5 is now live on grok bot... this is a dangerously powerful combo for building a 24/7 quant research desk. agent swarms to research ideas. cursor agents to build and backtest strategies. {here's how to build it} → quant desk lead → market intelligence → quant researcher → validator → risk controller → portfolio adviser → execution operator run the whole desk under one constitution: no agent grades its own work. no lead overrides the risk limits. start with the desk lead. give every agent a role before you give it a task.

Avid

114,161 次观看 • 1 个月前

AI has actually only helped the top one percent because everyone else is still guessing most people use AI to ask whether a stock is a buy. TradingAgents builds an entire AI investment team to debate the answer. Miles Deutscher just walked through how to turn an open-source GitHub project with 112K+ stars into a multi-agent trading research system: • 00:00 - Turn Claude or ChatGPT into a multi-agent trading team • 01:48 - Set it up through Claude Code with a simple guide • 04:41 - Connect market data, news, and fundamental analysis • 06:05 - Let bull and bear researchers debate the same asset • 08:33 - Generate an HTML research report on Nvidia • 12:43 - Review portfolio holdings and risk-adjusted recommendations • 16:27 - Test strategies through Alpaca paper trading the key insight: don't ask one AI to make the whole decision. Make multiple agents challenge each other. Fundamental, sentiment, news, and technical analysts examine the asset. Bull and bear researchers debate the evidence. A trader proposes a position, risk agents challenge it, and a portfolio manager reviews the final recommendation. The result is a structured research workflow, complete with reports, decision logs, and optional paper-trading integration. The real value isn't blindly following AI trades. It's using competing perspectives to spot risks and test your reasoning faster. Watch the full walkthrough

AI has actually only helped the top one percent because everyone else is still guessing most people use AI to ask whether a stock is a buy. TradingAgents builds an entire AI investment team to debate the answer. Miles Deutscher just walked through how to turn an open-source GitHub project with 112K+ stars into a multi-agent trading research system: • 00:00 - Turn Claude or ChatGPT into a multi-agent trading team • 01:48 - Set it up through Claude Code with a simple guide • 04:41 - Connect market data, news, and fundamental analysis • 06:05 - Let bull and bear researchers debate the same asset • 08:33 - Generate an HTML research report on Nvidia • 12:43 - Review portfolio holdings and risk-adjusted recommendations • 16:27 - Test strategies through Alpaca paper trading the key insight: don't ask one AI to make the whole decision. Make multiple agents challenge each other. Fundamental, sentiment, news, and technical analysts examine the asset. Bull and bear researchers debate the evidence. A trader proposes a position, risk agents challenge it, and a portfolio manager reviews the final recommendation. The result is a structured research workflow, complete with reports, decision logs, and optional paper-trading integration. The real value isn't blindly following AI trades. It's using competing perspectives to spot risks and test your reasoning faster. Watch the full walkthrough

Dami-Defi

12,086 次观看 • 9 天前

this is pure f*cking treasure 10 GitHub repos with 165K+ combined stars to build your own AI agent team that works 24/7 most people still use one AI chat for everything. these repos turn Claude into a whole team: one agent researches, one codes, one reviews, and a lead runs them all one resource per step, from your first 3 agents to a full swarm running Claude and Codex together (1/10) CrewAI the easiest way to assemble a team of agents: give them roles and tasks, then hit kickoff ▸ (2/10) LangGraph connect agents into a graph with checkpoints, so if something fails it resumes instead of starting over ▸ (3/10) wshobson/agents ready-made agents for Claude Code (backend, frontend, security, tests, devops) plus an orchestrator to run them ▸ (4/10) oh-my-team 12 specialist agents for Claude Code that split the work and merge the results ▸ (5/10) agent-swarm your own self-hosted AI company: one lead agent hires workers and runs them 24/7 ▸ (6/10) Open-Agents spawns many Claude agents in parallel, so 6 tasks run at once instead of one after another ▸ (7/10) swarms an enterprise-grade framework for when your team grows from 5 agents to 50 ▸ (8/10) Microsoft Agent Framework Microsoft's new framework, the AutoGen successor, for workflows, agents and runtime in one place ▸ (9/10) CLI Agent Orchestrator runs Claude, Codex and other CLIs side by side as one team, with a supervisor watching all of them ▸ (10/10) ensemble launches Codex and Claude together, so they build and ship as one ▸ the loop: assemble → plug in → run → scale → orchestrate pick one, click, let it run, and while everyone else is still typing prompts, your agents are already shipping ↓

this is pure f*cking treasure 10 GitHub repos with 165K+ combined stars to build your own AI agent team that works 24/7 most people still use one AI chat for everything. these repos turn Claude into a whole team: one agent researches, one codes, one reviews, and a lead runs them all one resource per step, from your first 3 agents to a full swarm running Claude and Codex together (1/10) CrewAI the easiest way to assemble a team of agents: give them roles and tasks, then hit kickoff ▸ (2/10) LangGraph connect agents into a graph with checkpoints, so if something fails it resumes instead of starting over ▸ (3/10) wshobson/agents ready-made agents for Claude Code (backend, frontend, security, tests, devops) plus an orchestrator to run them ▸ (4/10) oh-my-team 12 specialist agents for Claude Code that split the work and merge the results ▸ (5/10) agent-swarm your own self-hosted AI company: one lead agent hires workers and runs them 24/7 ▸ (6/10) Open-Agents spawns many Claude agents in parallel, so 6 tasks run at once instead of one after another ▸ (7/10) swarms an enterprise-grade framework for when your team grows from 5 agents to 50 ▸ (8/10) Microsoft Agent Framework Microsoft's new framework, the AutoGen successor, for workflows, agents and runtime in one place ▸ (9/10) CLI Agent Orchestrator runs Claude, Codex and other CLIs side by side as one team, with a supervisor watching all of them ▸ (10/10) ensemble launches Codex and Claude together, so they build and ship as one ▸ the loop: assemble → plug in → run → scale → orchestrate pick one, click, let it run, and while everyone else is still typing prompts, your agents are already shipping ↓

Noisy

14,563 次观看 • 22 天前

It is becoming f*cking dangerous: someone published the full prompt for building trading agents. You paste it into Claude Code and it builds the bot while you have lunch No code. Just Ctrl+C / Ctrl+V. One prompt sets up the whole thing: Time: 1 minute Action: copy and paste into Claude Code The main problem with 99% of bots: one strategy for everything. But the market on a calm day and during a crash are two completely different worlds Quant mode: detects the regime (sideways, jittery, dumping) and switches tactics on the fly E-brake: a hard 15% drawdown limit and a risk check before every single order Hardcore backtest: replays the strategy on historical data with fees and slippage, and shows you exactly where it falls apart. a curve that only looks good on the past is the easiest thing in the world to build Nightly analyst: while you sleep it reviews every fill, writes up what went wrong, and hands you the notes in the morning Near-zero code: you paste the prompt and your parameters, Claude writes the engine, and you read the risk layer before anything touches a live account This used to take a whole development team and months of testing. Now it takes a single keyboard shortcut The prompt is below. paper trade it first, then save it

It is becoming f*cking dangerous: someone published the full prompt for building trading agents. You paste it into Claude Code and it builds the bot while you have lunch No code. Just Ctrl+C / Ctrl+V. One prompt sets up the whole thing: Time: 1 minute Action: copy and paste into Claude Code The main problem with 99% of bots: one strategy for everything. But the market on a calm day and during a crash are two completely different worlds Quant mode: detects the regime (sideways, jittery, dumping) and switches tactics on the fly E-brake: a hard 15% drawdown limit and a risk check before every single order Hardcore backtest: replays the strategy on historical data with fees and slippage, and shows you exactly where it falls apart. a curve that only looks good on the past is the easiest thing in the world to build Nightly analyst: while you sleep it reviews every fill, writes up what went wrong, and hands you the notes in the morning Near-zero code: you paste the prompt and your parameters, Claude writes the engine, and you read the risk layer before anything touches a live account This used to take a whole development team and months of testing. Now it takes a single keyboard shortcut The prompt is below. paper trade it first, then save it

Chen

192,088 次观看 • 21 天前

A solo dev open sourced what's basically a 7 person youtube team.. it's called youtube-automation-agent, 1,500+ stars, and it runs 7 AI agents in a pipeline: 1) Content Strategy agent studies trends in your niche and plans the calendar.. 2) script writer turns topics into scripts with hooks and CTAs 3) thumbnail designer generates options and runs A/B variations 4) SEO agent handles titles, descriptions, tags 5) production agent coordinates the voiceover, assets and video assembly 6) publishing agent uploads and schedules 7) and an analytics agent tracks what worked.. and feeds it BACK to the strategy agent runs on whatever model you have.. repo:

A solo dev open sourced what's basically a 7 person youtube team.. it's called youtube-automation-agent, 1,500+ stars, and it runs 7 AI agents in a pipeline: 1) Content Strategy agent studies trends in your niche and plans the calendar.. 2) script writer turns topics into scripts with hooks and CTAs 3) thumbnail designer generates options and runs A/B variations 4) SEO agent handles titles, descriptions, tags 5) production agent coordinates the voiceover, assets and video assembly 6) publishing agent uploads and schedules 7) and an analytics agent tracks what worked.. and feeds it BACK to the strategy agent runs on whatever model you have.. repo:

Axel Bitblaze 🪓

14,458 次观看 • 3 个月前

these 2 free GitHub repos should honestly be illegal in trading - they just made a $30K/year terminal obsolete hedge funds pay $30K a year per seat for a Bloomberg terminal - someone rebuilt one with 2 GitHub repos and made every chart draw 100 possible futures for 100% FREE the repos are REA and Kronos. REA hit #1 on GitHub and has 200k stars. Kronos was trained on 12 BILLION candles, the paper got into AAAI 2026, and it has 41k stars first understand how it works: > REA lets your AI agent see how any app works, down to the binary, and rebuild it from scratch > ChatGPT turns words into tokens & predicts the next word > Kronos turns every candle into tokens & predicts the NEXT CANDLE > in the paper's benchmarks it beat the best rival model's forecasts by 93% > put them together and you get a Bloomberg-style terminal where every chart forecasts itself here is how to build it yourself for FREE: 1. open Claude Code and connect REA as an MCP. it maps how the terminal works: quotes, charts, news, screeners 2. ask Claude to rebuild every panel from scratch. equities, FX, commodities, crypto, live news feed 3. pull the data from free public APIs. Binance gives you any coin, any timeframe, no account 4. clone and load Kronos-small. 24.7M params, it runs on a normal laptop, no GPU 5. feed it the last 512 candles and ask for the next 24, exactly like an LLM finishing a sentence 6. sample 100 futures, not one guess, and draw all of them right on the chart. the spread shows how risky the next move really is 7. read the regime: most paths pointing one way means TRENDING, paths scattered both ways means CHOPPY 8. backtest it walk-forward with real fees and paper trade it before you trust a single path i won't say anything more:

these 2 free GitHub repos should honestly be illegal in trading - they just made a $30K/year terminal obsolete hedge funds pay $30K a year per seat for a Bloomberg terminal - someone rebuilt one with 2 GitHub repos and made every chart draw 100 possible futures for 100% FREE the repos are REA and Kronos. REA hit #1 on GitHub and has 200k stars. Kronos was trained on 12 BILLION candles, the paper got into AAAI 2026, and it has 41k stars first understand how it works: > REA lets your AI agent see how any app works, down to the binary, and rebuild it from scratch > ChatGPT turns words into tokens & predicts the next word > Kronos turns every candle into tokens & predicts the NEXT CANDLE > in the paper's benchmarks it beat the best rival model's forecasts by 93% > put them together and you get a Bloomberg-style terminal where every chart forecasts itself here is how to build it yourself for FREE: 1. open Claude Code and connect REA as an MCP. it maps how the terminal works: quotes, charts, news, screeners 2. ask Claude to rebuild every panel from scratch. equities, FX, commodities, crypto, live news feed 3. pull the data from free public APIs. Binance gives you any coin, any timeframe, no account 4. clone and load Kronos-small. 24.7M params, it runs on a normal laptop, no GPU 5. feed it the last 512 candles and ask for the next 24, exactly like an LLM finishing a sentence 6. sample 100 futures, not one guess, and draw all of them right on the chart. the spread shows how risky the next move really is 7. read the regime: most paths pointing one way means TRENDING, paths scattered both ways means CHOPPY 8. backtest it walk-forward with real fees and paper trade it before you trust a single path i won't say anything more:

Сarm1ne

50,693 次观看 • 18 天前

MIT Researchers Built An AI Hedge Fund & released it 100% FREE this gives you full trading firm of AI agents, analysts, a bull vs bear debate, a trader and a risk manager that approves every trade i cloned it and plugged in Opus 5.5 as the brain and Jev as the fast decision layer (INSANE RESULTS) here is how to USE this repo: 1. clone the repo and install it, one command with pip or uv, it runs on your laptop in a few minutes 2. drop in your Anthropic key and set Opus 5.5 as the deep think model, so the whole agent firm reasons on the best brain available 3. launch the CLI, pick any ticker and a date, it works on every market Yahoo covers, US stocks, crypto and more 4. the fundamentals agent digs through the financials and the news agent reads every headline and macro event that could move the price 5. the sentiment agent reads the crowd on Reddit, StockTwits while the technical agent reads the charts, RSI, MACD, the patterns 6. every agent writes a clean structured report instead of loose chat, which is the trick that stops the signal getting distorted as it passes along 7. then a bull agent and a bear agent debate the trade over multiple rounds, and a facilitator reads the whole debate and picks the winning case 8. the trader agent turns that into an actual call, then the risk team and portfolio manager approve or reject it before anything fires, same as a real desk 9. backtest the full pipeline across a grid of tickers and dates, it scores every decision on real alpha against the benchmark and logs what worked so it gets sharper each run 10. the agent debate takes minutes, so wire Jev in as the fast layer - it'll makes the final live call in just milliseconds > this turns this research firm into a real self-improving 24/7 AI trading agent the full build of AI trading bot that makes it trade live is in my article below:

MIT Researchers Built An AI Hedge Fund & released it 100% FREE this gives you full trading firm of AI agents, analysts, a bull vs bear debate, a trader and a risk manager that approves every trade i cloned it and plugged in Opus 5.5 as the brain and Jev as the fast decision layer (INSANE RESULTS) here is how to USE this repo: 1. clone the repo and install it, one command with pip or uv, it runs on your laptop in a few minutes 2. drop in your Anthropic key and set Opus 5.5 as the deep think model, so the whole agent firm reasons on the best brain available 3. launch the CLI, pick any ticker and a date, it works on every market Yahoo covers, US stocks, crypto and more 4. the fundamentals agent digs through the financials and the news agent reads every headline and macro event that could move the price 5. the sentiment agent reads the crowd on Reddit, StockTwits while the technical agent reads the charts, RSI, MACD, the patterns 6. every agent writes a clean structured report instead of loose chat, which is the trick that stops the signal getting distorted as it passes along 7. then a bull agent and a bear agent debate the trade over multiple rounds, and a facilitator reads the whole debate and picks the winning case 8. the trader agent turns that into an actual call, then the risk team and portfolio manager approve or reject it before anything fires, same as a real desk 9. backtest the full pipeline across a grid of tickers and dates, it scores every decision on real alpha against the benchmark and logs what worked so it gets sharper each run 10. the agent debate takes minutes, so wire Jev in as the fast layer - it'll makes the final live call in just milliseconds > this turns this research firm into a real self-improving 24/7 AI trading agent the full build of AI trading bot that makes it trade live is in my article below:

Roan

18,373 次观看 • 1 个月前