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this is the most DANGEROUS thing on the whole internet right now the tools Goldman Sachs, Point72 & Man Group built for their own desks, plugged into Opus 5.5 = a 24/7 QUANT DESK that runs on your laptop i plugged them into Claude Code & built a COMPLETE AI trading setup from scratch my setup pulls real market data, writes the strategy, sweeps 212 combos, picks the best one & marks every LONG and SHORT on the chart on its own (ZERO black box, the code & data stay mine) It's built on gs-quant (13.9k stars, Goldman Sachs), csp (Point72's stream engine) and ArcticDB (Man Group's DataFrame database) here is the EXACT build from scratch: 1. Install Python 3.10+ and Claude Code. That's the only prerequisite. 2. Open Claude Code and paste: "Create a fresh venv and install csp from github.com/Point72/csp, gs-quant from github.com/goldmansachs/gs-q… and ArcticDB from github.com/man-group/ArcticD…." 3. Paste: "Get the last 30 days of 1-minute bars for any asset from a public exchange dataset (i used BTC/USD), resample to 15m and store them in a local ArcticDB library." 4. Paste: "Read the bars back from ArcticDB. Compute EMA, Bollinger Bands, RSI and a volume profile with gs-quant's timeseries functions." 5. Paste: "Use csp to replay the bars as a live stream. Write an EMA cross strategy that goes long and short, with 2 bps fees." 6. Paste: "Sweep fast 4-24 and slow 12-96, 212 combos. Rank by Sharpe. Split the 30 days into 4 windows and compute each window's equity curve. Tell me which combos are a plateau and which are a spike." 7. Paste: "Draw every long and short on the chart at the exact bar, and make the agent log fire on the same bar." 8. Remember: the best combo was picked on the same data it's shown on. It's a screen, not proof. Run walk-forward before you trust it 9. Paper trade it for weeks before you even think about live. Check ArcticDB's license before using it commercially links to all three repos are at the bottom be one of the first to use this before everyone else does:

I'm begging you… stop paying $25k a year for a Bloomberg Terminal - they're a f*cking MONOPOLY two open-source repos + Claude Code, and the agent does the research itself: real TSLA bars, real backtests, every thought logged live in the terminal Repo 1: gloomberb (2.7k★) Repo 2: vectorbt (9.7k★) here is the EXACT build from scratch: 1. open Claude Code in an empty folder & paste: "clone github.com/gloom-sh/gloomber… and explain the terminal screen in five lines" 2. paste: "install github.com/polakowo/vectorbt and run a test backtest on TSLA. show me the raw output" 3. paste: "load 30 days of TSLA 15-minute OHLCV into a CSV, real bars only, no made-up candles" 4. paste: "replay the bars at 2.85 candles per second, each candle grows from open to close like a live feed" 5. paste: "run a vectorbt sweep on TSLA 15m, metric total_return. backtest every SMA crossover combo, all 1,387 of them, and compute the results for real" 6. paste: "analyze the sweep: find the best combo, check it isn't just one lucky spike, and explain in the log why you picked it" 7. paste: "lock that combo and trade it bar by bar. every entry and exit gets marked on the chart, the equity curve and stats update with every bar" 8. paste: "analyze where TSLA could go next: run 900 simulated scenarios from the last bar and show me every possible outcome, not one forecast line" 9. paste: "log every thought with the bar's timestamp: what you tested, what you found, why you kept or dropped it. put it all in one terminal: the scenarios on top, the 3D sweep and equity at the bottom, the agent thinking in a Claude Code window on the right" be one of the first to use this before everyone else does ↓

AI labs are f*cked again... Jane Street just co-authored a paper on the problem everyone's scared of: we're running out of training data the idea: take real web text, have a small model rewrite it as math explanations, FAQs, tables and tutorials, then train on the mix here's what they found: ~ the more compute you have, the more rewrites you want: under 10% at small budgets, 32% at the big one ~ their formula predicted the 22B model's loss within 1.1% ~ on math, training on rewrites alone cut the loss from 1.33 to 0.80 ~ the model beat the model that rewrote its data on 10 of 16 domains ~ real data alone started overfitting after 3 passes with Percy Liang from Stanford + Princeton find the paper link below ↓ the data wall might just be a rewrite away

I can't believe these 2 GitHub repos are still FREE - they should be f*cking BANNED… One reads the market for you every day and spits out a decision dashboard. The other gives your AI agent a brain that never forgets Put them together and every daily report becomes a linked note in your vault, so your agent remembers every call it ever made. No bullshit Repo 1: obsidian-mind (5.5k stars) A self-organizing Obsidian vault that gives AI coding agents persistent memory. Works with Claude Code, Codex CLI and Gemini CLI Repo 2: daily_stock_analysis (67.6k stars) LLM-powered multi-market stock analysis: multi-source market data, real-time news, a decision dashboard and automated notifications. Runs on a schedule, cost-free here is how I wired it: step 1 → clone both repos step 2 → open Claude Code inside the vault and tell it to wire daily_stock_analysis into obsidian-mind and save every daily report as a linked note step 3 → pull the daily closes for TSLA, SPX and SPCX and score each one against its 5-day average: BUY above +0.4%, SELL below -0.4%, HOLD in between step 4 → every report becomes a note in brain/daily, and each ticker note gets a new line with a [[wikilink]] back to it step 5 → the hooks do the rest: after every write they validate the note and update the backlinks, before compact they back up the session log, at session end they save a summary to brain/ step 6 → the session-start hook loads your goals and lessons, so every new run starts with memory instead of from zero step 7 → put it all in one terminal: the daily analysis on top, the vault graph and memory hooks below, Claude Code at the bottom links to both GitHub repos are in the comments below take it before someone else does ↓

I'm begging you to use these 2 GitHub repos - they're a f*cking CHEAT CODE One makes AI trading agents rewrite their own prompts. The other throws 1,600 simulated traders at the same chart Put them together and you get a loop that only keeps a change if Sharpe actually goes up. No GPU. No bullshit Repo 1: atlas-gic (2.4k stars) Self-improving trading agents, Karpathy-style autoresearch. The agent prompts are the weights, Sharpe ratio is the loss function. Their README: 54 prompt changes tried, 16 kept Repo 2: MiroFish (84k stars) Swarm intelligence engine. Spawns a crowd of agents and watches where they go here is how I wired it: step 1 → clone github.com/chrisworsey55/atl… and github.com/666ghj/MiroFish step 2 → open Claude Code inside atlas-gic and tell it to wire the MiroFish swarm into the ATLAS rewrite loop, keeping a prompt change only if Sharpe improves step 3 → let the swarm simulate 1,600 paths 60 steps ahead on the latest TSLA 15m bars and read out bull %, p10, p50 and p90 step 4 → feed that swarm consensus into the ATLAS macro layer as a signal for the agents step 5 → every cycle ATLAS finds its worst agent and rewrites its prompt. Sharpe up = git commit, Sharpe down = git reset --hard step 6 → keep it paper trading only so nothing touches real money step 7 → put it all in one terminal: the swarm on top, the agents and the Sharpe landscape below, Claude Code at the bottom be one of the first to use this before everyone else does ↓

I am begging you to use these 2 GitHub repos - they're f*cking INSANE... one repo gives you a full open-source Bloomberg-style terminal, the other turns Claude into a team of 4 value investors that ARGUE with each other i replayed 546 REAL 15-minute TSLA candles & let the agents trade it on paper from scratch my setup reads every bar, debates bull vs bear, and only pulls the trigger when the team agrees (11 TRADES, +$1,996 PAPER P&L) here is the EXACT build from scratch: 1. open Claude Code in an empty folder & paste: "clone github.com/Fincept-Corporati… and explain the equity research screen in five lines" 2. paste: "install github.com/xbtlin/ai-berkshi… and run /investment-team on TSLA. show me what each of the 4 lenses says" 3. paste: "load 21 sessions of TSLA 15-minute OHLCV into a CSV, real bars only, no made-up candles" 4. paste: "replay the bars at 2.85 candles per second with a 58-bar sliding window, each candle grows from open to close like a live feed" 5. paste: "add a 9-bar moving average. when price crosses it after 4 bars on the other side, raise a SIGNAL" 6. paste: "before any trade, the 4 agents vote: business quality, moat & valuation, inversion & risk, long-term. the team lead makes the final call" 7. paste: "every signal the team approves becomes a PAPER trade, 50 shares. mark BUY and SELL right on the chart" 8. paste: "log every thought with the bar's timestamp: why it bought, why it sold, what it learned when it got stopped out" 9. paste: "put it all in one terminal: chart on the left, the agents thinking in a Claude Code window on the right, live P&L at the bottom" be one of the first to use this before everyone else does ↓

JPMorgan just built a ChatGPT for the stock market and NOBODY is talking about it not for chatting. for TRADING the biggest bank on earth trained an AI on 19 BILLION tokens of real trade data. every buy, every sell, every order, across 9,000+ US stocks and now it can generate a fake stock market that behaves like the real one here's how they did it: step 1: collect tick-level trade data from 368 trading days. every single event across 9,000+ US equities step 2: turn every trade into a "word". price distance, size, timing, buy or sell, all packed into one token. same trick ChatGPT uses for text step 3: train a 524M parameter transformer to predict the next trade. on just 3 GPUs step 4: plug it into an order book simulator. the AI places orders, the book reacts, the AI sees the result and keeps going and the results are f*cking insane: > it matches real price behavior 2-3x better than the classic quant models (Hawkes processes) banks have used for years > it recreated the stuff that breaks most models on its own: fat tails, volatility clustering, the way crashes bunch together > simulated fills matched real fills with 0.91 correlation > trained ONLY on US stocks, then thrown at China and Japan with zero retraining. it still held up > you can inject 10x the orders into a stock and watch how the market reacts. a stress test that never happened wall street doesn't need more data anymore it can just generate the market save this, full paper below ↓

my friend who makes $600k/year as a quant trader at Jane Street just sent me his Opus 5.5 & Fable 5.1 trading system this is f*cking insane… It turns Claude into a 24/7 research & trading team of 7 specialized AI agents They scan markets, analyze data, backtest strategies, challenge trading ideas, and manage risk while you sleep here’s how to build your own AI hedge fund for FREE: step 1 → open Claude Code in a new folder and paste this prompt to set up a 7-agent research desk running on Opus 5.5 & Fable 5.1 step 2 → fill in the config with your watchlist, timezone, data sources and risk limits, and let it create the data/, backtests/, reports/ and logs/ folders step 3 → the scanner, macro, quant and flow agents run in parallel to sweep stocks, FX, futures and crypto for unusual volume, regime shifts, backtested setups and crowded positioning step 4 → a red team agent attacks every high-conviction idea, then the risk agent sizes, resizes or rejects it based on correlation, tail and event risk step 5 → every idea gets a 0–100 score for data quality, signal robustness, macro fit, risk/reward, liquidity and whether it survived the red team step 6 → at 07:00 UTC the desk head writes your morning briefing and decision log, all on paper with no live orders until you approve Send it to Opus 5.5 & Fable 5.1 and let the agents do the work thank me later:

these 2 FREE GitHub repos might be the most BROKEN combo for building an AI trading desk professional trading firms spend millions developing research software, building market scanners and automating their trading workflows these 2 repos give you the building blocks to recreate a professional financial research workstation using Claude Code they're called Day Trader Skill and REA REA has over 420K GitHub stars, while Day Trader Skill gives Claude 11 specialized trading skills, including one that simulates 10,000 possible trade outcomes first, get how it works: Day Trader Skill turns Claude into a trading research assistant with 11 tools for scanning thousands of stocks, tracking news, timing entries and exits, calculating position sizes and enforcing risk limits REA lets AI agents reverse engineer complex software interfaces and application behavior, even when the original source code isn't available, so you can understand how sophisticated financial workstations are structured Together, they give you a way to build a Bloomberg-style research terminal where Claude analyzes market conditions, evaluates trading setups and monitors paper positions inside one workspace here's how you can build it yourself: Open Claude Code in an empty project folder Install Day Trader Skill by pasting "github.com/Jakeschincariol/d… install skill" into Claude Install REA using "npx rea-agents setup" and connect its reverse-engineering tools to Claude Code Use REA to analyze the layout and behavior of a financial research interface you have access to, then ask Claude to build a similar terminal with charts, scanners, volume profiles and research panels Run /trader-scanner and /trader-news to search for stock setups and check the headlines that could affect them Use /trader-entry and /trader-exit to evaluate an entry, define a stop-loss and calculate a potential target before opening a position Run /trader-timemachine to simulate 10,000 possible outcomes using the stock's historical candles, then review how often the setup reaches its target or stop Ask Claude Code to build a research console showing the entire workflow, from market scanning and technical analysis to risk checks and trade monitoring, with animated 3D market visualizations alongside the charts Run /trader-budget and /trader-risk to size the position and check your limits, then use /trader-broker with explicit approval to test the setup in a paper account the result: one Bloomberg-style research workspace combining market data, quantitative visualizations and 11 AI-assisted trading skills 2 open-source GitHub repos 420K+ stars on REA 11 trading skills 10,000 simulated outcomes one AI-powered trading workstation i won't say anything more... bookmark this and thank me later ↓

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…

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...