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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....

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Сarm1ne

17,581 subscribers

65,316 Aufrufe • vor 15 Tagen •via X (Twitter)

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12 Kommentare

Profilbild von Davidd Tech
Davidd Techvor 14 Tagen

Manual trading should be illegal.

Profilbild von brilliant
brilliantvor 14 Tagen

@grok verify claims and tell me step by step how to make money using this

Profilbild von 小雪带你了解汽车
小雪带你了解汽车vor 14 Tagen

packed lunch four days, fifth day is mine

Profilbild von _alphashark_
_alphashark_vor 14 Tagen

We ran it against our tick data and the edge mostly evaporates post-fees. The real killer is position sizing logic, not the alphas. You'll still need your own risk layer.

Profilbild von Jack
Jackvor 14 Tagen

retail getting hedge fund tooling for free, brutal

Profilbild von 老猫
老猫vor 14 Tagen

Turning a quant desk into a one-liner is a great hook, but here's the real question: of those 10 repos, how many handle the unglamorous parts — survivorship-bias-free data, realistic slippage and fees in backtests, walk-forward validation? That's usually where "free alpha" repos fall apart. Which repo on the list impressed you most on that front?

Profilbild von Bright Tape
Bright Tapevor 14 Tagen

Screening 478 alphas in one line is wild. Plenty of the survivors will just be the ones that happened to suit the last two years, so the length of the walk-forward window decides a lot more than the star count

Profilbild von xtrsk capital
xtrsk capitalvor 14 Tagen

If free, open‑source quant tools flatten the edge that hedge funds charge for proprietary research, could that pressure firms to rethink fee structures?

Profilbild von Joyce
Joycevor 14 Tagen

This is a game-changer for retail traders—having a full quant desk at your fingertips is incredible!

Profilbild von Ian Leonard
Ian Leonardvor 14 Tagen

That glorious feeling when your stock market feed and your AI feed merge

Profilbild von catman
catmanvor 14 Tagen

Walk-forward testing and paper trading are smart safeguards, but I wouldn’t call a backtest that survives them a proven alpha. Data quality, fees, slippage, and changing market conditions can still erase the edge once real money is involved.

Profilbild von Ling Chang
Ling Changvor 14 Tagen

agreed 100%

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🚨HOW I BACKTEST TRADING STRATEGIES USING CLAUDE CODE > Most traders backtest the wrong way > They fit a strategy to historical data > See +300% and think they found the holy grail > Go live - blow up > That's curve fitting, the classic trap > The right way is walk-forward analysis > Train on one chunk of data, test on another > Slide the window, repeat > If the strategy survives on data it's never seen - that's a signal > If it doesn't - trash it, no regrets > The entire process runs through > Claude Code in one conversation > Not a single line of code written by hand > Trading here: > The same method used by quant funds The difference between a trader and an artist is a walk-forward test
16:30

🚨HOW I BACKTEST TRADING STRATEGIES USING CLAUDE CODE > Most traders backtest the wrong way > They fit a strategy to historical data > See +300% and think they found the holy grail > Go live - blow up > That's curve fitting, the classic trap > The right way is walk-forward analysis > Train on one chunk of data, test on another > Slide the window, repeat > If the strategy survives on data it's never seen - that's a signal > If it doesn't - trash it, no regrets > The entire process runs through > Claude Code in one conversation > Not a single line of code written by hand > Trading here: > The same method used by quant funds The difference between a trader and an artist is a walk-forward test

zostaff

63,046 Aufrufe • vor 6 Monaten

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.
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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 Aufrufe • vor 28 Tagen

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
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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 Aufrufe • vor 29 Tagen

🚨these github repos is SERIOUSLY DANGEROUS Wall Street pays its engineers $500K+ a year to write code like this, 6 of the top firms put theirs on GitHub 6 repos built inside Goldman Sachs, JPMorgan, Jane Street, Jump, Morgan Stanley and Man Group, with 38.6k stars combined First, get how it works: 1. Goldman Sachs' quants built gs-quant for pricing, risk and backtesting. JPMorgan's python-training is the exact course it uses to train its own traders and analysts. 2. Jane Street's magic-trace traces every function call at ~40ns. Morgan Stanley's Xpedite profiles low-latency trading code, "where latency directly translates to profitability". Jump's Firedancer runs Solana on a design taken from low-latency trading. 3. Man Group's ArcticDB stores and queries market data across billions of rows. Together it's a full desk: data, research, risk and speed. Here's how you can run it for free yourself: 1. Open Claude Code and start with JPMorgan's trader course, python-training. Have Claude run the notebooks with you 2. Install Goldman Sachs' library: pip install gs-quant 3. Add the skill that teaches Claude Code to use it: python -m gs_quant.skills install --global 4. Have Claude pull 5 years of daily prices for GS, JPM and MS with free yfinance data 5. Store it in Man Group's database: pip install arcticdb 6. Run Goldman's risk math on it: volatility, max drawdown, z-score, beta to SPY. Then backtest one rule with fees. Claude writes the code and runs it 7. On Linux, trace your backtest with Jane Street's magic-trace and see where every microsecond goes 8. Moving to C++? Measure every critical path the Morgan Stanley way with Xpedite 9. Have Claude walk you through how Jump built Firedancer's pipeline (net, verify, dedup, pack, bank), then apply the same ideas to your bot i won't say anything more:
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🚨these github repos is SERIOUSLY DANGEROUS Wall Street pays its engineers $500K+ a year to write code like this, 6 of the top firms put theirs on GitHub 6 repos built inside Goldman Sachs, JPMorgan, Jane Street, Jump, Morgan Stanley and Man Group, with 38.6k stars combined First, get how it works: 1. Goldman Sachs' quants built gs-quant for pricing, risk and backtesting. JPMorgan's python-training is the exact course it uses to train its own traders and analysts. 2. Jane Street's magic-trace traces every function call at ~40ns. Morgan Stanley's Xpedite profiles low-latency trading code, "where latency directly translates to profitability". Jump's Firedancer runs Solana on a design taken from low-latency trading. 3. Man Group's ArcticDB stores and queries market data across billions of rows. Together it's a full desk: data, research, risk and speed. Here's how you can run it for free yourself: 1. Open Claude Code and start with JPMorgan's trader course, python-training. Have Claude run the notebooks with you 2. Install Goldman Sachs' library: pip install gs-quant 3. Add the skill that teaches Claude Code to use it: python -m gs_quant.skills install --global 4. Have Claude pull 5 years of daily prices for GS, JPM and MS with free yfinance data 5. Store it in Man Group's database: pip install arcticdb 6. Run Goldman's risk math on it: volatility, max drawdown, z-score, beta to SPY. Then backtest one rule with fees. Claude writes the code and runs it 7. On Linux, trace your backtest with Jane Street's magic-trace and see where every microsecond goes 8. Moving to C++? Measure every critical path the Morgan Stanley way with Xpedite 9. Have Claude walk you through how Jump built Firedancer's pipeline (net, verify, dedup, pack, bank), then apply the same ideas to your bot i won't say anything more:

Сarm1ne

61,124 Aufrufe • vor 11 Tagen

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:
1:00

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:

Сarm1ne

36,978 Aufrufe • vor 13 Tagen

Introducing Senpi AI Quant! - Describe your trade in a sentence. Your AI quant turns it into code and runs it, with 24/7 risk controls. Hire your AI quant today. 'cause trading with charts and prayers isn't a strategy.
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Introducing Senpi AI Quant! - Describe your trade in a sentence. Your AI quant turns it into code and runs it, with 24/7 risk controls. Hire your AI quant today. 'cause trading with charts and prayers isn't a strategy.

Senpi

397,318 Aufrufe • vor 1 Monat

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.
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ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

cvxv666

175,981 Aufrufe • vor 4 Monaten

It is becoming f*cking dangerous: A prompt for creating trading agents was accidentally leaked online. You paste it into claude code, and it builds a bot while you have lunch No code. Just Ctrl+C / Ctrl+V. One prompt solves everything: 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 market conditions (sideways, jittery, dump) and changes tactics on the fly E-brake: a strict 15% drawdown limit and risk checks before every order Hardcore backtest: runs the strategy on historical data and blocks trading if it is not profitable Nightly analyst: while you sleep, it analyzes mistakes and produces ready-to-approve setups Zero Code: type in the prompt, insert your parameters, and Claude handles the rest This used to take a whole development team and months of testing. Now it takes a single keyboard shortcut The prompt is published below, bookmark it so you do not lose it
0:22

It is becoming f*cking dangerous: A prompt for creating trading agents was accidentally leaked online. You paste it into claude code, and it builds a bot while you have lunch No code. Just Ctrl+C / Ctrl+V. One prompt solves everything: 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 market conditions (sideways, jittery, dump) and changes tactics on the fly E-brake: a strict 15% drawdown limit and risk checks before every order Hardcore backtest: runs the strategy on historical data and blocks trading if it is not profitable Nightly analyst: while you sleep, it analyzes mistakes and produces ready-to-approve setups Zero Code: type in the prompt, insert your parameters, and Claude handles the rest This used to take a whole development team and months of testing. Now it takes a single keyboard shortcut The prompt is published below, bookmark it so you do not lose it

Bober_smart

118,491 Aufrufe • vor 22 Tagen

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…
0:24

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 Aufrufe • vor 12 Tagen

I Created a 520% Trading Strategy With One Prompt I have been testing the just-released “Index” product by Fraction AI. This is basically the “ChatGPT for trading,” where you can: > explain your trading strategy in one prompt > let agents create it for you > backtest it with real market data With one prompt, I was able to create a trading strategy that returned 520% APY with a 93% win rate. The prompt is in the video, but basically, it is long $BTC and $HYPE while shorting $SOL. Impressive how easy it is today to create and backtest incredibly profitable trading strategies. Extra note: the free version is already very powerful. I do NOT have the premium account.
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I Created a 520% Trading Strategy With One Prompt I have been testing the just-released “Index” product by Fraction AI. This is basically the “ChatGPT for trading,” where you can: > explain your trading strategy in one prompt > let agents create it for you > backtest it with real market data With one prompt, I was able to create a trading strategy that returned 520% APY with a 93% win rate. The prompt is in the video, but basically, it is long $BTC and $HYPE while shorting $SOL. Impressive how easy it is today to create and backtest incredibly profitable trading strategies. Extra note: the free version is already very powerful. I do NOT have the premium account.

Diego

12,607 Aufrufe • vor 6 Monaten

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 ⭣
0:24

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 Aufrufe • vor 1 Monat

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
0:23

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 Aufrufe • vor 20 Tagen

YOUR STRATEGY DIDN'T STOP WORKING. YOU JUST GOT TOO BIG FOR IT One law prices your own footprint. ΔP/P = Y·σ·√(Q/V) Every order you send moves price against you, and it scales with the square root of size. Double your clip and you pay 1.41x more. Strip the math away. Past a certain size you are not trading the market. You are trading against your own order. Same edge on both lines below. One clips 0.8% of volume, the other 5%. That is the only difference. Minara dragged this off the trading floor. Type it in plain English, it writes the Python and hands you the backtest. One sentence to a live strategy & every number, drawdowns included, sits in the article. Read it ↓
0:26

YOUR STRATEGY DIDN'T STOP WORKING. YOU JUST GOT TOO BIG FOR IT One law prices your own footprint. ΔP/P = Y·σ·√(Q/V) Every order you send moves price against you, and it scales with the square root of size. Double your clip and you pay 1.41x more. Strip the math away. Past a certain size you are not trading the market. You are trading against your own order. Same edge on both lines below. One clips 0.8% of volume, the other 5%. That is the only difference. Minara dragged this off the trading floor. Type it in plain English, it writes the Python and hands you the backtest. One sentence to a live strategy & every number, drawdowns included, sits in the article. Read it ↓

slash1s

10,959 Aufrufe • vor 3 Monaten

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:
1:00

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:

Сarm1ne

37,954 Aufrufe • vor 9 Tagen

HORIZON V0.3 JUST HANDED RETAIL TRADERS THE EXACT TOOLS THAT USED TO COST A QUANT $400,000 A YEAR Backtest one strategy across BTC, ETH and SOL in a single run. Scale out in pieces with partial exits. Then deploy it live on Hyperliquid, the biggest perp DEX on the planet, running 24/7 with no broker and no KYC. Type an idea in plain English, test it across every asset in seconds, run it live while you sleep.
0:36

HORIZON V0.3 JUST HANDED RETAIL TRADERS THE EXACT TOOLS THAT USED TO COST A QUANT $400,000 A YEAR Backtest one strategy across BTC, ETH and SOL in a single run. Scale out in pieces with partial exits. Then deploy it live on Hyperliquid, the biggest perp DEX on the planet, running 24/7 with no broker and no KYC. Type an idea in plain English, test it across every asset in seconds, run it live while you sleep.

0xMarioNawfal

47,717 Aufrufe • vor 4 Monaten

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 ↓
0:30

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 Aufrufe • vor 16 Tagen

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.
0:45

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.

cvxv666

225,653 Aufrufe • vor 3 Monaten

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
0:22

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 Aufrufe • vor 21 Tagen

Manual trading is the slowest part of your strategy. Not another bot, but a control panel to build your own trading agents & trade across DEXs from one terminal. Build agents, manage positions, and control TP/SL - all in Oraichain Quant Terminal. You set the rules and risk. The agent executes using quant models - to find entries, select orders, and run 24/7. Miss fewer moves. Think less. Execute better. 👉
0:44

Manual trading is the slowest part of your strategy. Not another bot, but a control panel to build your own trading agents & trade across DEXs from one terminal. Build agents, manage positions, and control TP/SL - all in Oraichain Quant Terminal. You set the rules and risk. The agent executes using quant models - to find entries, select orders, and run 24/7. Miss fewer moves. Think less. Execute better. 👉

Oraichain

88,521 Aufrufe • vor 10 Monaten

Introducing Index. Turn your trading idea into a live perps trading agent. → Index lets you create AI agents that trade perpetual futures. You can describe a trading idea in plain English, backtest it on historical data, improve it through an experimentation loop, and deploy an agent that follows the strategy in live markets. Our community has already been testing Index in beta for a few weeks. Now it's open to everyone. Here’s how it works:👇
0:50

Introducing Index. Turn your trading idea into a live perps trading agent. → Index lets you create AI agents that trade perpetual futures. You can describe a trading idea in plain English, backtest it on historical data, improve it through an experimentation loop, and deploy an agent that follows the strategy in live markets. Our community has already been testing Index in beta for a few weeks. Now it's open to everyone. Here’s how it works:👇

Fraction AI

64,689 Aufrufe • vor 6 Monaten