if you still haven't tried this GitHub repo, i genuinely don't know what you're doing this open-source project gives AI agents the tools to research markets, analyze trading strategies and build an entire Wall Street-style trading workstation market analytics, AI trading agents, automated LONG/SHORT signals, quantitative models and advanced 3D financial visualizations and the craziest part? most people have no idea this exists here's the repo you need to try: Vibe-Trading github.com/HKUDS/Vibe-Tradin… an open-source AI trading agent framework that brings market analysis, technical indicators, strategy backtesting and trading research into one workflow combine it with real market data, an automated signal engine, scrolling AI research activity, a rotating 3D market core and advanced mathematical visualizations 1 GitHub repo an entire open-source AI-powered Wall Street trading research stack i won't say anything more...

i am f*cking begging you to use these 4 GitHub repos Gavin Baker and his team are using up to 17 Grok Bots to streamline their workflows someone took this idea even further and built a Wall Street-style quantitative trading terminal combining Grok Bot with 4 powerful open-source GitHub projects here's the playbook: step 1 → use TradingAgents (github.com/TauricResearch/Tr…) to build a multi-agent financial research system where AI analysts examine market conditions, identify opportunities, debate trading strategies and evaluate portfolio risks step 2 → connect OpenBB (github.com/openbq-org/OpenBB) to access financial market data, analyze S&P 500 price movements, monitor economic indicators and build sophisticated quantitative research pipelines step 3 → integrate AI Hedge Fund (github.com/virattt/ai-hedge-…) to coordinate investment research agents, compare trading signals, evaluate different strategies and generate portfolio allocation insights step 4 → use NautilusTrader (github.com/nautechsystems/na…) to build high-performance trading infrastructure with historical backtesting, market event processing, risk controls and algorithmic strategy execution capabilities step 5 → introduce Grok Bot as the central AI orchestration system, coordinating 17 specialized agents across market intelligence, macro research, volatility analysis, signal generation, risk management and portfolio optimization step 6 → build a Bloomberg-style financial workstation featuring S&P 500 futures charts, EMA indicators, volatility channels, market activity, quantitative signals and advanced financial analytics step 7 → create a Grok Bot operations center where 17 AI agents move between research workstations, exchange tasks, analyze financial information and coordinate their workflows through an animated command system step 8 → transform quantitative market data into advanced 3D mathematical visualizations, including flowing market structures, volatility landscapes, dynamic risk surfaces and rotating financial models step 9 → combine everything into one synchronized research environment where financial charts, AI agent workflows, market signals, portfolio analytics and 3D visualizations operate together the result: 4 GitHub repos 17 Grok Bots an entire open-source Wall Street AI research stack i won't say anything more...

if you still haven't tried these 7 GitHub repos, i genuinely don't know what you're doing these open-source projects can turn your computer into an entire Wall Street-style financial research lab AI trading agents, hedge fund strategies, market analytics, quantitative models and automated trading systems and the craziest part? most people have no idea these exist here are 7 repos you need to try: 1 → TradingAgents an entire AI trading team with analysts, bull and bear researchers, traders and risk managers debating market decisions github.com/TauricResearch/Tr… 2 → OpenBB a financial data platform built for analysts, quants and AI agents, bringing market intelligence and research tools into one workflow github.com/openbq-org/OpenBB 3 → AI Hedge Fund build a virtual hedge fund where AI agents inspired by legendary investors analyze companies and debate investment decisions github.com/virattt/ai-hedge-… 4 → Freqtrade an open-source crypto trading bot with automated strategies, technical indicators, backtesting and trade execution github.com/freqtrade/freqtra… 5 → Microsoft Qlib Microsoft's quantitative investment framework for machine learning, market forecasting, portfolio optimization and strategy backtesting github.com/microsoft/qlib 6 → NautilusTrader a high-performance trading engine built for event-driven strategies, historical backtesting and automated execution github.com/nautechsystems/na… 7 → Microsoft RD-Agent AI agents that automate research, discover quantitative factors, write experiments and optimize machine learning models github.com/microsoft/RD-Agen… 7 GitHub repos an entire open-source financial research stack i won't say anything more...

3 f*cking GitHub repos to build the kind of quant research stack Wall Street pays top quants up to $600K/year to work on all open source, all free here's the playbook: step 1 → microsoft/qlib to build an AI-powered quantitative research pipeline, process historical market data, engineer predictive features and train machine learning models to identify trading signals step 2 → QuantConnect/Lean to develop algorithmic trading strategies, run historical backtests, analyze performance and evaluate risk before deploying capital step 3 → AI4Finance/FinRL to introduce reinforcement learning, train portfolio allocation models and explore how AI agents can adapt to changing market conditions step 4 → connect S&P 500 futures data and build a professional trading chart with 15-minute candlesticks, EMA ribbons, Bollinger Bands, momentum indicators and volatility tracking step 5 → create a Bloomberg-style quantitative research terminal with market activity, price monitoring, technical signals, strategy analytics and an execution console step 6 → transform financial data into advanced 3D visualizations, including glowing EMA surfaces, volatility structures and volume distribution landscapes that evolve alongside price action step 7 → integrate machine learning signals, historical price movements and quantitative indicators into one synchronized research environment where strategies and market conditions can be analyzed together step 8 → optimize the entire workflow with continuous market replay, dynamic risk metrics, animated financial models and a unified dashboard for monitoring quantitative strategies the result → a fully integrated Wall Street-style quant terminal combining AI research, algorithmic trading infrastructure, S&P 500 market analytics and real-time 3D financial visualizations in one powerful workstation 3 GitHub repos one Wall Street-style quant terminal $0 in software licenses i left the links to all 3 GitHub repos in the comments below ↓ 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 github.com/goldmansachs/gs-q… and install the library using pip install gs-quant step 2 → open github.com/stefan-jansen/mac… 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…

this open-source GitHub repo is actually INSANE it's called OpenBB, a financial data platform built for quants, analysts and AI agents combine it with a trading system and you can bring Bitcoin signals, market analytics, price forecasting and quantitative research into one terminal here’s how to run it yourself: step 1 → open github.com/openbq-org/OpenBB and install the platform using pip install openbb step 2 → run openbb-api in your terminal and connect your preferred financial data providers step 3 → connect a crypto market data source and load BTC/USDT with 15-minute candlesticks, trading volume and historical prices step 4 → calculate EMA crossovers and price momentum to identify LONG and SHORT signals after each candle closes step 5 → build your market dashboard with stock watchlists, sector heatmaps, price movements and order flow step 6 → add forecasting models to visualize possible price trajectories, market scenarios and uncertainty ranges step 7 → calculate volatility, Sharpe ratios, drawdowns and other risk metrics to evaluate trading strategies step 8 → backtest your LONG/SHORT strategy against historical data, including trading fees and slippage, then validate it on unseen periods the result: an open-source quant research workflow combining Bitcoin analysis, trading signals, market intelligence and risk analytics one GitHub repo an entire financial research stack i won’t say anything more…

I am begging you to paste this into Grok Bot before bed, it will definitely change how you trade… Overnight it runs your own AI Hedge Fund, researches the whole market & builds a paper trading bot to test the strongest strategies 24/7 Copy this prompt before it disappears:

this FREE GitHub repo is probably the closest thing to running your own AI hedge fund Wall Street spends millions hiring analysts, portfolio managers and risk teams someone built an open-source AI hedge fund that researches stocks, tests strategies and manages a virtual portfolio from your laptop it’s called AI Hedge Fund, and it already has 64,000+ GitHub stars here’s how it works: AI investment agents analyze market data and evaluate trading opportunities strategies are tested against historical market conditions a portfolio manager evaluates decisions and manages risk every simulated trade feeds into a portfolio with its own performance history here’s how to run it yourself: step 1 → open github.com/virattt/ai-hedge-… and install the project using uv tool install aihf step 2 → type aihf in your terminal and choose your preferred AI model, including OpenAI, Anthropic or DeepSeek step 3 → connect the required model and Financial Datasets API keys, then create your own fund with its investment strategies, starting capital and trading rules step 4 → choose paper trading to run the fund with virtual money, or backtesting to replay its decisions against historical market data step 5 → let the AI agents research opportunities, evaluate positions and generate portfolio decisions step 6 → track the fund’s NAV, trading history and performance against a market benchmark step 7 → analyze drawdowns, review the decisions and refine the strategy before even thinking about real capital the result: an open-source AI hedge fund research lab with a portfolio dashboard, investment agents, trading decisions and historical backtesting one GitHub repo an entire AI investment research workflow i won’t say anything more… bookmark this and thank me later ↓

I genuinely don’t understand why everyone isn’t using this yet… paste this into Grok Bot before bed and turn it into your own overnight AI trading research desk it scans stocks, futures, crypto, macro data and liquidity for market anomalies, filters out weak opportunities, stress-tests the strongest setups and builds a risk-controlled paper trading bot all while you sleep, with a full market intelligence brief ready by morning paste this prompt before it disappears:

these 3 FREE GitHub repos should honestly be BANNED on Wall Street hedge funds spend millions building research terminals, hiring analysts and developing prediction models someone put the building blocks for an entire AI-powered trading desk on GitHub for FREE it’s called REA + Kronos + TradingAgents here’s how it works: REA helps AI agents understand and reconstruct complex software interfaces and workflows Kronos is a financial forecasting model that treats market candles like tokens and generates possible future price movements TradingAgents creates an AI research team where analysts debate trades before a portfolio manager makes the final decision combine all 3 and you can build a Bloomberg-style research terminal with AI-generated market forecasts and a team of virtual trading analysts here’s how to build it yourself for FREE: step 1 → open Claude Code and connect REA using github.com/morluto/rea to map out a professional financial terminal with charts, market data, screeners and research panels step 2 → ask Claude to build the complete interface with equities, crypto, FX, commodities, global indices and financial news step 3 → clone github.com/shiyu-coder/Krono… and load Kronos-small, the 24.7M parameter financial forecasting model step 4 → feed Kronos the latest 512 OHLCV candles, forecast the next 24 and sample 100 possible future price paths directly onto the terminal charts step 5 → clone github.com/TauricResearch/Tr… and connect its research workflow to the same market data step 6 → let 4 AI analysts examine fundamentals, news, sentiment and technicals, then watch BULL and BEAR agents debate before the risk team and portfolio manager evaluate the trade step 7 → combine the forecast distribution with the agents’ decisions, track BUY/SELL/HOLD signals and backtest the entire system with realistic transaction costs before risking capital the result: one workspace combining a Bloomberg-style terminal, probabilistic market forecasting and an entire AI investment research team 3 open-source repos one AI-powered trading desk i won’t say anything more… bookmark this and thank me later ↓