✦ Researcher & Analyst ✦ Wall Street / AI / News | @beyond_xai
Joined August 2020
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Сarm1ne retweeted
🚨 these 6 github repos should be ILLEGAL to open source for free
Hedge funds pay their quants $500K+ a year to build this exact stack, and now all 6 pieces are sitting on GitHub
It's 6 repos from Goldman Sachs, the hedge fund Man Group, Quantopian and Microsoft Research, with 105k+ stars combined.
First, get how it works:
1. Goldman Sachs' quants built gs-quant for pricing, risk and backtesting. Man Group's ArcticDB stores market data across billions of rows, the way a hedge fund keeps its tick history.
2. Zipline was the backtest engine behind Quantopian, the crowdsourced fund Steve Cohen's Point72 backed with up to $250M. Alphalens, from the same team, tests whether an alpha factor actually predicts returns.
3. Microsoft's qlib is an AI-oriented quant platform from data to production. RD-Agent sits on top: an AI agent that proposes factors, writes the code and backtests them. In Microsoft's NeurIPS 2025 paper it hit up to 2x higher annualized returns than classical factor libraries in backtests, using 70% fewer factors.
Here's how you can run it for free yourself:
1. Open Claude Code and give it the 6 repos: gs-quant, ArcticDB, zipline, alphalens, qlib and RD-Agent
2. Install the stack: pip install gs-quant arcticdb zipline-reloaded alphalens-reloaded pyqlib
3. Have Claude pull 10 years of daily prices for the S&P 500 with free yfinance data
4. Store everything in ArcticDB, one symbol per stock, so every backtest reads the same data
5. Have Claude write 5 classic factors: momentum, value, quality, low vol and short-term reversal
6. Test each one in alphalens. Keep only factors whose top quintile beats the bottom one after costs
7. Backtest the survivors in zipline with fees and slippage, then run Goldman's risk math on the result: volatility, drawdown, beta
8. Install RD-Agent and run rdagent fin_quant. Let it propose and test new factors on its own in qlib
9. Compare its factors with yours. If nothing beats buy-and-hold after costs, you don't have an edge yet
i won't say anything more:
🚨 these 6 github repos should be ILLEGAL to open source for free
Hedge funds pay their quants $500K+ a year to build this exact stack, and now all 6 pieces are sitting on GitHub
It's 6 repos from Goldman Sachs, the hedge fund Man Group, Quantopian and Microsoft Research, with 105k+ stars combined.
First, get how it works:
1. Goldman Sachs' quants built gs-quant for pricing, risk and backtesting. Man Group's ArcticDB stores market data across billions of rows, the way a hedge fund keeps its tick history.
2. Zipline was the backtest engine behind Quantopian, the crowdsourced fund Steve Cohen's Point72 backed with up to $250M. Alphalens, from the same team, tests whether an alpha factor actually predicts returns.
3. Microsoft's qlib is an AI-oriented quant platform from data to production. RD-Agent sits on top: an AI agent that proposes factors, writes the code and backtests them. In Microsoft's NeurIPS 2025 paper it hit up to 2x higher annualized returns than classical factor libraries in backtests, using 70% fewer factors.
Here's how you can run it for free yourself:
1. Open Claude Code and give it the 6 repos: gs-quant, ArcticDB, zipline, alphalens, qlib and RD-Agent
2. Install the stack: pip install gs-quant arcticdb zipline-reloaded alphalens-reloaded pyqlib
3. Have Claude pull 10 years of daily prices for the S&P 500 with free yfinance data
4. Store everything in ArcticDB, one symbol per stock, so every backtest reads the same data
5. Have Claude write 5 classic factors: momentum, value, quality, low vol and short-term reversal
6. Test each one in alphalens. Keep only factors whose top quintile beats the bottom one after costs
7. Backtest the survivors in zipline with fees and slippage, then run Goldman's risk math on the result: volatility, drawdown, beta
8. Install RD-Agent and run rdagent fin_quant. Let it propose and test new factors on its own in qlib
9. Compare its factors with yours. If nothing beats buy-and-hold after costs, you don't have an edge yet
i won't say anything more:
Сarm1ne retweeted
🚨 these 6 github repos are the most BROKEN setup to build an hft firm like jane street
Jane Street made $20.5 billion in trading revenue in 2024 - Its edge is speed, and these 6 repos give you that kind of stack for free
It's 6 repos from a real exchange, a top hedge fund and Jane Street itself, with 42k+ stars combined
First, get how it works:
1. LMAX built the Disruptor to run its own exchange. Martin Fowler wrote that it handles 6 million orders a second on a single thread. Aeron moves market data between machines with predictable low latency, the job a trading firm's internal network does.
2. Man Group's ArcticDB stores tick data across billions of rows. hftbacktest replays it tick by tick and counts what most backtests ignore: feed latency, order latency and your place in the queue.
3. QuickFIX/J speaks FIX, the protocol brokers and exchanges use for orders. Jane Street's magic-trace traces every function call at ~40ns to show where your time goes. Together it's a full desk: data, engine, strategy, orders and speed.
Here's how you can run it for free yourself:
1. Open Claude Code and give it the 6 repos: ArcticDB, Aeron, LMAX Disruptor, hftbacktest, QuickFIX/J and magic-trace
2. Install the Python side: pip install hftbacktest arcticdb
3. Have Claude pull free Binance order book and trade data for BTCUSDT, the same format hftbacktest's examples use
4. Store every tick in ArcticDB, one library per day
5. Backtest a simple market-making strategy in hftbacktest with real latency and queue position. Claude writes the code and runs it
6. If it survives fees and latency, have Claude wire a paper-trading loop: the Disruptor for events, Aeron for the market data feed
7. Send orders over FIX with QuickFIX/J to a broker sandbox. Paper only
8. On Linux, trace the hot path with Jane Street's magic-trace and see where every microsecond goes
9. Fix the slowest function, rerun the backtest and compare fill rates at the new latency
i won't say anything more:
🚨 these 6 github repos are the most BROKEN setup to build an hft firm like jane street
Jane Street made $20.5 billion in trading revenue in 2024 - Its edge is speed, and these 6 repos give you that kind of stack for free
It's 6 repos from a real exchange, a top hedge fund and Jane Street itself, with 42k+ stars combined
First, get how it works:
1. LMAX built the Disruptor to run its own exchange. Martin Fowler wrote that it handles 6 million orders a second on a single thread. Aeron moves market data between machines with predictable low latency, the job a trading firm's internal network does.
2. Man Group's ArcticDB stores tick data across billions of rows. hftbacktest replays it tick by tick and counts what most backtests ignore: feed latency, order latency and your place in the queue.
3. QuickFIX/J speaks FIX, the protocol brokers and exchanges use for orders. Jane Street's magic-trace traces every function call at ~40ns to show where your time goes. Together it's a full desk: data, engine, strategy, orders and speed.
Here's how you can run it for free yourself:
1. Open Claude Code and give it the 6 repos: ArcticDB, Aeron, LMAX Disruptor, hftbacktest, QuickFIX/J and magic-trace
2. Install the Python side: pip install hftbacktest arcticdb
3. Have Claude pull free Binance order book and trade data for BTCUSDT, the same format hftbacktest's examples use
4. Store every tick in ArcticDB, one library per day
5. Backtest a simple market-making strategy in hftbacktest with real latency and queue position. Claude writes the code and runs it
6. If it survives fees and latency, have Claude wire a paper-trading loop: the Disruptor for events, Aeron for the market data feed
7. Send orders over FIX with QuickFIX/J to a broker sandbox. Paper only
8. On Linux, trace the hot path with Jane Street's magic-trace and see where every microsecond goes
9. Fix the slowest function, rerun the backtest and compare fill rates at the new latency
i won't say anything more:
Сarm1ne retweeted
🚨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:
🚨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 retweeted
someone always knows before the news drops...
This prompt makes claude code or grok bot find them: options bets, insider buys, activist stakes and Congress trades, days before the announcement...
Steal it before it gets patched:
someone always knows before the news drops...
This prompt makes claude code or grok bot find them: options bets, insider buys, activist stakes and Congress trades, days before the announcement...
Steal it before it gets patched:
Сarm1ne retweeted
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:
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 retweeted
hedge funds pay millions for an HFT desk and ~$30k a year for every bloomberg seat
this prompt makes claude code or grok bot build you both: 7 agents trading 24/7 on paper, on your own terminal with live world events on every chart
steal this before everyone starts using it:
hedge funds pay millions for an HFT desk and ~$30k a year for every bloomberg seat
this prompt makes claude code or grok bot build you both: 7 agents trading 24/7 on paper, on your own terminal with live world events on every chart
steal this before everyone starts using it:
Сarm1ne retweeted
these 3 GitHub repos are the most DANGEROUS stack for HFT trading for free
high-frequency firms pay for quant teams, colocation and microwave links just to be first by microseconds - someone put the whole research-to-execution stack on GitHub, and it runs on a laptop
the repos are hftbacktest, TradingAgents and nautilus_trader. TradingAgents has 113k stars and hit #1 repo of the day, nautilus_trader has 30k, hftbacktest has 5k
first understand how it works:
> hftbacktest replays the full order book tick by tick, Level-2 and Level-3
> it models feed latency, order latency AND your place in the queue - the 3 things most backtests quietly ignore
> TradingAgents is a whole trading firm made of AI agents: analysts, a bull vs bear debate, a trader, a risk team, a portfolio manager
> nautilus_trader is a production-grade Rust engine with nanosecond-resolution backtests
> the same strategy code runs in backtest and live, across Binance, Bybit, OKX, Coinbase, Kraken, Hyperliquid, Deribit and more
> together: the agents pick the side, hftbacktest proves the fills are real, nautilus executes
here is how to build it yourself for FREE:
1. open Claude Code and install the backtester:
pip install hftbacktest
2. get tick-by-tick order book data for BTCUSDT and replay it. the repo has tutorials for latency, queue position and Level-3 backtesting
3. backtest a simple market maker with latency and queue position turned ON. if it only works at zero latency, it doesn't work
4. install TradingAgents:
git clone github.com/TauricResearch/Tr…
cd TradingAgents && pip install .
5. type tradingagents, pick BTC and let the analysts, the bull vs bear debate and the risk team decide the side and the size
6. install the engine:
pip install -U nautilus_trader
7. port the strategy to nautilus_trader and connect the venues you trade - the same code runs in backtest and live
8. paper trade it for weeks and compare every fill against your backtest
9. go live only with tiny size, and only if the paper fills match
i won't say anything more:
these 3 GitHub repos are the most DANGEROUS stack for HFT trading for free
high-frequency firms pay for quant teams, colocation and microwave links just to be first by microseconds - someone put the whole research-to-execution stack on GitHub, and it runs on a laptop
the repos are hftbacktest, TradingAgents and nautilus_trader. TradingAgents has 113k stars and hit #1 repo of the day, nautilus_trader has 30k, hftbacktest has 5k
first understand how it works:
> hftbacktest replays the full order book tick by tick, Level-2 and Level-3
> it models feed latency, order latency AND your place in the queue - the 3 things most backtests quietly ignore
> TradingAgents is a whole trading firm made of AI agents: analysts, a bull vs bear debate, a trader, a risk team, a portfolio manager
> nautilus_trader is a production-grade Rust engine with nanosecond-resolution backtests
> the same strategy code runs in backtest and live, across Binance, Bybit, OKX, Coinbase, Kraken, Hyperliquid, Deribit and more
> together: the agents pick the side, hftbacktest proves the fills are real, nautilus executes
here is how to build it yourself for FREE:
1. open Claude Code and install the backtester:
pip install hftbacktest
2. get tick-by-tick order book data for BTCUSDT and replay it. the repo has tutorials for latency, queue position and Level-3 backtesting
3. backtest a simple market maker with latency and queue position turned ON. if it only works at zero latency, it doesn't work
4. install TradingAgents:
git clone github.com/TauricResearch/Tr…
cd TradingAgents && pip install .
5. type tradingagents, pick BTC and let the analysts, the bull vs bear debate and the risk team decide the side and the size
6. install the engine:
pip install -U nautilus_trader
7. port the strategy to nautilus_trader and connect the venues you trade - the same code runs in backtest and live
8. paper trade it for weeks and compare every fill against your backtest
9. go live only with tiny size, and only if the paper fills match
i won't say anything more:
the 3 repos:
hftbacktest: github.com/nkaz001/hftbackte…
TradingAgents: github.com/TauricResearch/Tr…
nautilus_trader: github.com/nautechsystems/na…
Сarm1ne retweeted
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 <run_id>
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 <run_id>
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 retweeted
I am begging you to paste this into claude code or grok bot before bed, tomorrow morning will feel ILLEGAL...
citadel employs 3,100+ people to know what the world did before the market opens
this prompt does the same night shift for $0 in software - by 7:30 am: 3 trades ranked, stops and targets already set
steal this before everyone starts using it:
I am begging you to paste this into claude code or grok bot before bed, tomorrow morning will feel ILLEGAL...
citadel employs 3,100+ people to know what the world did before the market opens
this prompt does the same night shift for $0 in software - by 7:30 am: 3 trades ranked, stops and targets already set
steal this before everyone starts using it:
Сarm1ne retweeted
these 2 github repos are the most POWERFUL combo I've ever seen for a trading setup
one watches the entire planet in real time - wars, pipelines, tankers, sanctions, live news. the other lets you rebuild a $30K/year trading terminal around it. together they turn world events into trades, and both are free on GitHub
the repos are World Monitor and REA. World Monitor has 89k stars. REA has 365k and hit #1 on GitHub
first understand how it works:
> World Monitor is a real-time global intelligence dashboard: AI news aggregation, geopolitical monitoring, infrastructure tracking
> conflict zones, pipelines, shipping lanes, military bases, nuclear sites, live news and live webcams - all on one map
> it ships an MCP server and SDKs, so your agent reads the same feed you see
> REA reverse engineers anything: see a feature in a pro tool, understand how it works, rebuild it yourself
> put them together and you get your own terminal where every headline can turn into a trade idea
here is how to build it:
1. open Claude Code with Opus 5.5 and connect REA as an MCP:
npx rea-agents setup
2. pull World Monitor into your stack. terminal dashboard: npx worldmonitor · Python SDK: pip install worldmonitor-sdk
3. ask Claude to rebuild the terminal you like with REA: your chart, your premium indicators, your watchlist, a live news column
4. build 6 agents on top: Event Scanner, Stock Mapper, Sentiment, Prediction Check, Risk Manager, Execution
5. Event Scanner flags a HIGH-impact event: a pipeline strike, a tanker seizure, an OPEC+ surprise
6. Stock Mapper maps the exposure in seconds: oil producers likely up, airlines likely down
7. Sentiment and Prediction Check confirm it with X chatter and Polymarket odds before anything moves
8. Risk Manager sizes the position and sets the stop. Execution sends LONG or SHORT Brent to an Alpaca PAPER account, with TP1/TP2/TP3 on the chart
9. run it on paper for weeks, log every call, and only trust what survives
i won't say anything more: