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this FREE GitHub repo + 17 Grok Bots should be f*cking illegal Gavin Baker, CIO of Atreides Management, recently revealed how heavily his team relies on Grok Bots for investment research “I think I’m up to like 17 Grok Bots. And every day, I think of a new one to add” now combine that idea with FinRobot, an open-source financial AI agent framework built for market research, financial analysis, risk assessment and investment decision-making 8.4K GitHub stars 17 specialized Grok Bots one AI-powered investment research desk here’s the playbook: step 1 → download FinRobot, an open-source financial AI agent framework that provides tools for equity research, market forecasting, financial document analysis and risk assessment step 2 → take the Gavin Baker-inspired research prompt and build a coordinated system of 17 Grok-powered agents, each assigned a specialized role within the investment research process step 3 → assign bots 1-3 to monitor financial news, macroeconomic developments, policy changes and geopolitical events, filtering thousands of headlines into actionable market intelligence step 4 → assign bots 4-6 to analyze SEC filings, earnings calls, financial statements and company guidance, identifying changes in revenue expectations, profitability and management commentary step 5 → use bots 7-9 to map market narratives, identify emerging sector trends, track relative strength and discover companies benefiting from shifts in technology, capital spending and global demand step 6 → connect bots 10-12 to FinRobot’s research workflows to analyze valuations, compare companies, build financial models and generate bull/base/bear scenarios to identify opportunities the market might be overlooking step 7 → assign bots 13-15 to analyze price action, momentum, volatility and market positioning, turning investment research into potential LONG/SHORT setups with defined entry conditions and catalysts step 8 → make bot 16 the dedicated risk manager, challenging every investment thesis, identifying weak assumptions, calculating downside exposure and searching for evidence against each trade step 9 → turn bot 17 into the chief investment strategist, collecting findings from the other 16 agents and producing one ranked investment report with the strongest opportunities, upcoming catalysts and key risks step 10 → bring everything together inside a professional financial intelligence dashboard combining Grok-powered research, FinRobot’s financial tools, AI-generated reports, market forecasts and investment watchlists the result → a 17-agent AI investment research desk that combines Grok’s analytical capabilities with an open-source financial AI framework 17 Grok Bots 1 GitHub repo 8.4K stars one Wall Street-style research system the full 17-bot research prompt is in the image above i left the GitHub repo in the comments below ↓ i won’t say anything more…

I AM BEGGING YOU TO START USING THESE 2 FREE GITHUB REPOS people are spending thousands on trading software, market scanners and financial research tools meanwhile, you can give Claude 11 specialized trading skills and build your own Bloomberg-style AI research workstation using open-source tools $0 for the repositories it’s called Day Trader Skill + REA here’s how it works: Day Trader Skill gives Claude 11 specialized skills to scan stocks, monitor breaking news, identify entries and exits, manage risk and replay potential trading outcomes REA is an open-source reverse-engineering toolkit that helps AI agents understand complex software interfaces, application behavior and technical workflows combine both with Claude Code and you can build a professional trading workstation with AI-assisted market research, technical indicators and automated analysis workflows here’s how to build it yourself for FREE: step 1 → open Claude Code and install github.com/Jakeschincariol/d… to give your AI access to 11 specialized trading skills step 2 → connect the stock scanner, news monitor and technical analysis tools to identify potential trading opportunities across different markets step 3 → use github.com/morluto/rea to study professional financial interfaces and ask Claude Code to reconstruct a Bloomberg-style research terminal step 4 → build an interactive dashboard with candlestick charts, moving averages, volume profiles, market scanners and animated 3D quantitative visualizations step 5 → create a Claude research workspace where AI-assisted workflows scan market conditions, analyze bullish and bearish scenarios, calculate risk and evaluate trading decisions step 6 → use Day Trader Skill’s Time Machine to replay historical scenarios and evaluate potential trade outcomes before committing capital step 7 → connect everything to a paper-trading environment where the agent can monitor positions, track performance and review trading decisions the result: a Bloomberg-style research workstation with 11 specialized trading skills, automated market analysis and a complete AI-assisted trading workflow 2 open-source GitHub repos 11 trading skills one AI-powered trading desk i won’t say anything more… bookmark this and thank me later ↓

how to make $50K using one FREE GitHub repo this open-source AI was trained to understand financial candlestick patterns across 45+ global exchanges 45.9K GitHub stars $0 to access the repository here’s the playbook: step 1 → download shiyu-coder/Kronos, an open-source foundation model designed specifically for financial markets, with pretrained models that understand price movements through candlestick sequences step 2 → connect historical Bitcoin market data, including open, high, low, close and volume, then prepare the price sequences for the model to analyze step 3 → use Kronos’s specialized tokenizer to convert financial candlesticks into discrete tokens that a Transformer can process, turning raw market movements into a language the AI can learn from step 4 → run the pretrained Kronos model to generate potential future price trajectories, exploring different ways BTC could move instead of relying on a single prediction step 5 → build an interactive Bitcoin forecasting dashboard with 15-minute candlesticks, historical price movements, AI-generated projections and multiple possible market scenarios step 6 → transform those forecasts into a trading research system, combining model predictions with momentum indicators, volatility filters and custom LONG/SHORT conditions step 7 → backtest the strategy against historical Bitcoin data, measure win rate, drawdown, transaction costs and risk-adjusted returns, then test it on unseen market periods to check whether the signals have a real edge step 8 → build a professional AI trading terminal with continuously updating charts, forecast distributions, 3D price trajectory visualizations, strategy analytics and a dashboard that tracks every signal the result → an AI-powered Bitcoin research workstation that can generate price forecasts, visualize multiple market scenarios and help evaluate algorithmic trading strategies 1 GitHub repo 45.9K stars AI trained on financial market sequences from 45+ exchanges $0 for the open-source model i left the GitHub repo in the comments below ↓ i won’t say anything more…

these 2 FREE GitHub repos should honestly be BANNED on Wall Street hedge funds spend millions building research terminals, hiring analysts and developing trading systems someone put the building blocks for an entire AI-powered investment research desk on GitHub for FREE it's called TradingAgents + daily_stock_analysis here's how it works: TradingAgents creates an AI investment team where analysts examine market data, fundamentals, news and sentiment, then BULL and BEAR agents debate before a portfolio manager evaluates the trade daily_stock_analysis automates stock research across multiple markets, combining technical indicators, financial news, AI analysis and scheduled reports combine both with Claude Code and you can build a Bloomberg-style research workspace with AI analysts, global news monitoring and interactive market charts here's how to build it yourself for FREE: step 1 → open Claude Code and clone TradingAgents from github.com/TauricResearch/Tr… to set up your multi-agent research team step 2 → clone github.com/ZhuLinsen/daily_s… and configure its market-data providers, stock analysis and financial news pipeline step 3 → ask Claude Code to build a professional trading terminal with candlestick charts, moving averages, volume, global market data and interactive research panels step 4 → create a CLAUDE research workspace where market analysts, news agents, BULL and BEAR researchers, and risk managers appear in a continuous workflow step 5 → connect the research outputs to the dashboard and visualize LONG / SHORT assessments alongside historical market movements step 6 → add a rotating 3D Earth with geographical news points, then animate information flowing from different regions into the research terminal step 7 → add portfolio monitoring, validate the signals against historical data and test the workflow in paper trading before connecting any real capital the result: one workspace bringing together a Bloomberg-style terminal, global financial intelligence and an entire AI investment research team 2 open-source GitHub repos one AI-powered trading desk i won't say anything more... bookmark this and thank me later ↓

this is the most INSANE GitHub repo right now Hedge funds spend millions building quantitative research infrastructure, and Microsoft has an open-source framework with nearly 50K GitHub stars that lets you build AI-powered trading research workflows for FREE It’s called Qlib, and it combines machine learning, financial data, strategy development and backtesting in one framework here’s how to use it: step 1 → get Microsoft Qlib Open the GitHub repository and install the framework using Python GitHub: github.com/microsoft/qlib pip install pyqlib step 2 → prepare your market data Qlib needs historical financial data to train and evaluate its models Clone the repository, then follow its README to download a compatible dataset git clone github.com/microsoft/qlib.gi… step 3 → train your first AI model Qlib includes a ready-made LightGBM research workflow using Alpha158 financial features From the cloned repository, run: qrun examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml This trains a model, generates predictions and evaluates them on historical data step 4 → backtest your trading strategy Use the model’s predictions to test trading strategies on historical data, analyze returns, drawdowns and transaction costs, and compare performance against a benchmark step 5 → build your own AI quant research desk Experiment with different AI models, create new financial indicators, test strategies and connect your research results to a custom trading dashboard you now have the foundation for an AI-powered quant research system built using Microsoft’s open-source technology bookmark this and try it yourself ↓

JPMorgan and Goldman Sachs have trading technology sitting on GitHub for FREE, and most traders have no idea it exists I'm talking about 3 open-source projects that give you the building blocks of a Wall Street-style quantitative trading workstation Market data, advanced financial analytics, portfolio risk management and professional trading dashboards Here's how you can try them yourself: step 1 → JPMorgan Perspective Originally developed at JPMorgan, Perspective is a high-performance framework for visualizing large streams of financial data, with interactive dashboards, streaming analytics, financial charts and heatmaps GitHub: github.com/perspective-dev/p… step 2 → Goldman Sachs GS Quant A quantitative finance toolkit developed by Goldman Sachs quants for financial modeling, derivatives analysis, portfolio risk management and statistical research GitHub: github.com/goldmansachs/gs-q… step 3 → Fincept Terminal An open-source financial workstation that combines market monitoring, financial data analysis, portfolio analytics and quantitative research tools in one platform GitHub: github.com/Fincept-Corporati… step 4 → build your own quant workflow Use Perspective for market visualization, GS Quant for financial analysis and Fincept as a research workstation Connect supported data sources, develop quantitative strategies, analyze portfolio exposure and explore market opportunities Some institutional APIs require separate access, but the open-source tools provide a powerful starting point the result → a customizable quantitative research setup built around technologies from Wall Street and the open-source finance community 3 GitHub repositories Thousands of developers Professional financial technology you can explore for free i'll leave the rest to you…

A trader who made $50K last month sent me his entire AI trading setup, and I genuinely can’t believe it’s built with a free GitHub repo and a single prompt The system combines QuantConnect LEAN with Grok Bot and 7 specialized AI agents for market research, statistical arbitrage, backtesting and risk management Here’s how you can build the same setup: step 1 → download QuantConnect LEAN, an open-source algorithmic trading engine with over 22K GitHub stars GitHub: github.com/QuantConnect/Lean step 2 → follow the installation guide and set up the trading environment to develop and backtest quantitative strategies step 3 → open Grok Bot and paste the AI Hedge Fund prompt, which organizes the research process into 7 specialized agents: → market intelligence and macro analysis → market microstructure and liquidity → statistical arbitrage research → alpha discovery and strategy development → quantitative backtesting → portfolio risk management → independent strategy validation step 4 → use Grok Bot to research market opportunities, develop trading hypotheses and generate strategy code compatible with QuantConnect LEAN step 5 → run historical backtests, evaluate Sharpe Ratio, drawdown and risk-adjusted performance, then refine the strategy before paper trading the result → your own customizable AI-powered quantitative trading research system combining automated market analysis, strategy development, professional backtesting and portfolio risk management All built around open-source trading infrastructure and a reusable Grok Bot prompt full Grok Bot prompt in the comments below i’ll leave the rest to you…

I can’t believe this AI trading technology is sitting on GitHub for FREE while Wall Street spends fortunes developing it Someone built an open-source framework that lets you train AI agents to buy and sell stocks using deep reinforcement learning Instead of manually programming every trading rule, developers can train AI agents to make decisions by interacting with simulated financial markets It’s called FinRL, and the project already has nearly 17K GitHub stars The framework uses deep reinforcement learning to train trading agents on historical market data, evaluate their decisions and test different quantitative strategies And it supports five powerful reinforcement learning algorithms, including PPO, A2C, DDPG, TD3 and SAC Here’s what makes it interesting Traditional trading algorithms follow predefined rules, but reinforcement learning agents can develop their own decision-making policies through repeated market simulations and feedback FinRL provides the infrastructure to train, test and compare these agents without building everything from scratch Here’s how you can try it yourself: step 1 → download the FinRL repository from GitHub: github.com/AI4Finance-Founda… step 2 → follow the official installation guide and configure your Python environment step 3 → download historical stock market data and prepare technical indicators for training step 4 → choose a reinforcement learning algorithm and train an AI agent using simulated market environments step 5 → backtest the trained agent, compare its performance against market benchmarks and evaluate its risk before considering real-world applications the result → your own customizable AI trading research system capable of training reinforcement learning agents, testing quantitative strategies, analyzing financial markets and experimenting with automated trading decisions free and open-source under MIT license i’ll leave the rest to you…

I genuinely can’t believe this trading infrastructure is sitting on GitHub for free Someone built an open-source framework that lets you create automated trading bots, market-making strategies and entire algorithmic trading systems It’s called Hummingbot, and the project already has 20K GitHub stars Instead of building trading infrastructure from scratch, developers can use its existing framework to connect exchanges, process market data, manage orders and run automated strategies Here’s what makes it interesting A professional trading system needs exchange connectivity, strategy execution, order management and risk controls Here’s how you can try it yourself: step 1 → download the Hummingbot repository from GitHub step 2 → follow the official installation guide and configure your trading environment step 3 → choose a strategy, select a trading pair and configure its parameters step 4 → connect a supported exchange, preferably starting in a test environment step 5 → test the strategy, monitor its orders and evaluate its risk before considering live deployment the result → your own customizable algorithmic trading system with automated strategies, market-making, arbitrage and quantitative trading tools free and open-source under Apache 2.0 GitHub: github.com/hummingbot/hummin… i’ll leave the rest to you…