0xDipper

i just replaced an entire hedge fund team with 3 free GitHub repos analysts, researchers, a trader, a risk desk, a portfolio manager. funds pay millions a year for those people. these repos turn every seat into an AI agent TradingAgents, ai-hedge-fund and freqtrade. 238k+ stars combined, and most traders still haven't touched them first, how the desk works: > TradingAgents copies how a real trading firm runs: fundamental, sentiment, news and technical analysts write reports, a bull and a bear argue over them, a trader makes the call and a risk team has to sign off > ai-hedge-fund gives you an investment committee of legends: Buffett, Munger, Burry, Cathie Wood. each one returns a signal and a confidence, then a risk manager and a portfolio manager decide > freqtrade is the execution layer: it turns a signal into a real strategy with backtesting, hyperopt and a paper-trading dry-run mode how to run the whole thing for free: 1. open Claude Code and clone all 3 repos 2. run TradingAgents with python -m cli.main and pick a ticker 3. watch the analysts report, bull and bear fight it out, and the trader and risk team land on BUY, SELL or HOLD 4. pipx install aihf and run the investor agents on the same tickers 5. have Claude merge both: only trade when TradingAgents and the investor committee agree 6. have Claude turn that consensus into a freqtrade strategy 7. backtest it on 2 years of data with fees 8. tune it with hyperopt, then re-test on data it has never seen. if it only works in-sample, it's curve-fitting 9. dry-run it for 2 weeks. no beating buy-and-hold after fees = no edge yet i won't say anything more:

🚨 Wall Street just left its engine room unlocked 6 of the biggest firms on the planet put their internal code on GitHub. Goldman Sachs, JPMorgan, Jane Street, Jump, Morgan Stanley, Man Group. 38.6k stars and almost nobody in retail is using it they pay engineers $500K+ a year to write this. you can clone it tonight what's actually inside: > Goldman's gs-quant: the pricing, risk and backtesting toolkit their own quants maintain > JPMorgan's python-training: the course they use to train their analysts and traders > Jane Street's magic-trace: records every function call at ~40ns. you see exactly where your code wastes time > Morgan Stanley's Xpedite: built for trading systems "where latency directly translates to profitability" > Jump's Firedancer: a Solana validator built on low-latency trading design > Man Group's ArcticDB: queries billions of rows of market data like it's nothing put together it's a full desk: data, research, risk, speed how to run it for $0: 1. open Claude Code and go through JPMorgan's python-training with it, notebook by notebook 2. pip install gs-quant 3. teach Claude to use it: python -m gs_quant.skills install --global 4. pull 5 years of daily GS, JPM and MS prices with free yfinance data 5. pip install arcticdb and store everything in Man Group's database 6. run Goldman's risk math: volatility, max drawdown, z-score, beta to SPY. backtest one rule with fees, Claude writes and runs it 7. on Linux, trace that backtest with magic-trace and find where every microsecond goes 8. going C++? profile the hot path the Morgan Stanley way with Xpedite 9. have Claude break down Firedancer's pipeline (net → verify → dedup → pack → bank) and steal the ideas for your own bot i won't say anything more:

i read Anthropic's ENTIRE S-1 so you don't have to. here's how i'd trade the IPO ticker ANT. Nasdaq. 300+ pages. most people will only read the headline loss what actually matters: > revenue went $386M in 2024 → $4.6B in 2025 → $16.3B in the first half of 2026 alone > Q2 was the first profitable quarter: $974M operating income > the scary $80.7B net loss? $72.4B of it is non-cash accounting. the business already funds itself > ~900% net revenue retention. 1,900+ customers paying over $1M a year > the hidden part: a built-in dilution rule. up to 2% new shares every time the stock rises 50%. that's 3 price levels you mark BEFORE day one > and the founders keep 50.1% of the votes. you're buying the growth, not control ANT won't open at 9:30. limit orders only, let the first print settle, size small save this, Anthropic IPO cheat sheet:

funds pay six figures for a setup like this. i built mine for free my own Wall Street terminal, made from repos Wall Street put on GitHub: > gs-quant by Goldman Sachs: pricing, risk, backtests > ArcticDB by Man Group: the database their quants keep every tick in > TradingAgents: AI analysts that debate bull vs bear before every trade one command and it runs a full desk on my laptop: research, debate, data, risk. paper only every repo and every step is in the article below ↓

this repo by Man Group quants feels ILLEGAL to have for free... Man Group runs one of the biggest hedge funds on the planet, ~$190B under management. this is the database their quants store every tick in, and it now ships with a Claude skill it's called ArcticDB, built and maintained by Man Group's own engineers - 2.6k stars first understand how it works: 1. hedge funds don't keep market data in Excel or Postgres. they need billions of rows, every version ever written, and reads in milliseconds. ArcticDB does all three with no database server, just Python + S3 or a folder on your laptop 2. Man Group's team added a .claude/skills folder right in the repo. Claude reads it and writes ArcticDB code the way the people who built it do here's how to run it yourself for FREE: 1. open Claude Code and install it: pip install arcticdb, or just send it github.com/man-group/ArcticD… 2. ask Claude to create a local store: Arctic('lmdb://./ticks') - no server, no account 3. pull 5 years of daily or minute prices for 500 stocks and write them in one call with write_batch 4. read any slice back with date_range in under a second. millions of rows, straight into pandas 5. run QueryBuilder to filter and aggregate inside the database instead of in memory: VWAP, volume, per symbol 6. snapshot your data every evening. then read(as_of=...) lets you see exactly what your data looked like on any past day. that's how you kill look-ahead bias in backtests 7. append live ticks as they come in and let Claude build your backtests on top of it 8. when your laptop fills up, point it at S3 and change nothing else I'm done talking:

this repo by Goldman Sachs quants feels ILLEGAL to have for free... Goldman pays each quant ~$700k/year for skills you can now HIRE inside your Claude Code for $0 it's called gs-quant, built and maintained by Goldman's own quants - 13.5k stars first understand how it works: 1. Goldman's quants wrote the Claude skill themselves - it teaches Claude their whole toolkit: swaps, swaptions, FX options, risk, scenario shocks, backtests, delta hedging, transaction costs. it even lists the errors you'll hit and how to fix them 2. Claude pulls it in every time it writes gs-quant code, and the risk math runs right on your laptop: volatility, correlation, z-scores, drawdown, beta, RSI here's how to run it yourself for FREE: 1. open Claude Code and install Goldman's library: pip install gs-quant, or just send it github.com/goldmansachs/gs-q… 2. add the skill: python -m gs_quant.skills install --global 3. ask Claude to pull 3 years of daily Henry Hub and European TTF gas prices 4. run Goldman's risk math on both: 22-day volatility, max drawdown, 60-day correlation 5. build the US vs Europe gas spread and take its 120-day z-score 6. flag every day the z-score fell below -2. that's when US gas was 2 standard deviations too cheap vs Europe 7. backtest one rule: buy the spread under -2, sell back at 0, with fees. Claude writes it and runs it on your laptop 8. stress it like Goldman does: shock the book +25bp and -25bp and see what's left of your P&L 9. compare it to doing nothing. if it doesn't beat that after fees, it's not an edge I'm done talking:

my own DANGEROUS terminal with this repo in trading right now gold just had its worst week since June. it broke $4,000, the Fed meets in 4 days, and everyone on my timeline is either buying the dip or calling the top so i asked an AI quant desk instead. one sentence, and it gave me an answer i could actually check it's called Vibe-Trading. open source, from HKUDS at the University of Hong Kong, 36.4k stars, #1 repo of the day the sentence i typed: "gold daily, buy pullbacks to the 50-day only when real yields are falling, 2020-26, stress-test it and export to TradingView" what happened next: > a planner split it into steps for 3 agents > it pulled 6 years of gold futures plus the 10-year real yield and the dollar index > a quant agent wrote the strategy and checked it for look-ahead bias before a single trade > backtest with fees and slippage included > then i called a macro desk swarm: rates, dollar, oil and positioning all report to one risk manager. with oil spiking and a Fed hike back on the table, it cut my size in half > Monte Carlo, Bootstrap and Walk-Forward. it showed me which years the edge actually worked and which it didn't > what survived exported straight to Pine Script and MetaTrader 5 i didn't write a single line of code. i just argued with it in English how to try it: 1. pip install vibe-trading-ai 2. vibe-trading init, pick your model and free data (yfinance needs no key) 3. vibe-trading run -p "your gold idea in one sentence" 4. vibe-trading --swarm-presets, then --swarm-run macro_rates_fx_desk 5. vibe-trading --pine <run_id> to get it into TradingView 6. it's built for research and backtests, not live orders. into a Fed week, the backtest is a hypothesis, not a signal i won't say anything more:

a Chinese developer just open-sourced the AI stock analyst that 67,000 people are already running every day it's called daily_stock_analysis. #1 trending Python repo, 55k forks, and it costs $0 to run every weekday at 6pm Beijing time it wakes up on its own and: > reviews the whole market: indices, how many stocks rose vs fell, limit-ups, which sectors led > pulls every stock on your watchlist from 7 data sources: A-shares, Hong Kong, US, Japan, Korea, Taiwan > calculates MA5/10/20, MACD, RSI, volume ratio, turnover > searches the news, filings and analyst reports for each one > hands it all to Claude Opus 5.5 and gets back a decision dashboard: buy / hold / sell, a score out of 100, entry, stop, target, risk alerts and a checklist > pushes it to your Telegram, Discord, Slack, WeCom, Feishu or email before dinner no server. it runs on free GitHub Actions. you fork it, add your keys, add your tickers, done here is how to set it up: 1. fork ZhuLinsen/daily_stock_analysis on GitHub 2. add your STOCK_LIST, an ANTHROPIC_API_KEY and a Telegram bot token as repo secrets 3. turn on GitHub Actions, it skips holidays in China, Hong Kong and the US by itself 4. run python main.py --market-review once by hand to see the full report 5. treat it as a second opinion, not a signal. the repo itself says it's for research, not investment advice full guide 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 ↓