TauricResearch

绝了 这才叫交易团队 一个 LLM 分析师看基本面 一个看情绪一个看技术 多空研究员先吵一轮 交易员再拍板 风控最后卡一刀 以前靠一个人硬扛研报 现在一整桌 Agent 互相抬杠再下单 开源框架偏研究 别当理财建议 但我直接收藏了 TauricResearch/TradingAgents

I Built an AI Team That Researches Stocks and Debates Investment Decisions 🤖 What if you had an entire team of AI agents researching stocks for you? Imagine one agent analyzing stock charts, another digging through financial reports, and another tracking the latest market news. Now imagine two more AI agents arguing about whether you should buy or avoid a particular stock. And instead of simply combining their opinions, the system makes them challenge each other’s conclusions before arriving at a final investment recommendation. Sounds like something a hedge fund might build, right? Well, there’s an open-source project that does exactly this. It’s called TradingAgents, developed by I Built an AI Team That Researches Stocks and Debates Investment Decisions 🤖 , and it uses a multi-agent AI architecture to simulate how a financial research team collaborates. I recently explored this project, studied its architecture, set it up, and put the agents to work analyzing one of the most talked-about companies in the stock market: NVIDIA (NVDA). The result was interesting, but what fascinated me even more was how the agents worked together. In this article, I’ll explain how TradingAgents works, walk through its architecture, show you how to get started, and share what happened during my experiment. ___ 🎥 Full video walkthrough {% embed youtu.be %} ___ 💡 Why Use Multiple AI Agents for Stock Research? If you’ve ever researched a stock before investing, you know how overwhelming the process can become. You need to understand several things: - Financial performance: Revenue, earnings, profitability, and cash flow. - Valuation: Whether the stock is reasonably priced relative to its growth expectations. - Technical indicators: Price movements, moving averages, RSI, and MACD. - Market news: Earnings announcements, industry developments, and economic events. - Investor sentiment: How the broader market perceives the company. - Risk factors: Everything that could potentially go wrong. And here’s something investors sometimes overlook: A great company isn’t necessarily a great stock to buy at any price. A business might be growing rapidly and dominating its industry, but if its stock price already reflects unrealistic growth expectations, investing could still be risky. Now, you could ask ChatGPT or another LLM: >_“Should I buy NVIDIA stock?”_ But a single AI response might overlook important factors or lean too heavily toward one interpretation. What if we approached this differently? Instead of asking one AI model to handle everything, we could assign different research responsibilities to specialized AI agents. One focuses on financial fundamentals. Another analyzes technical indicators. Another investigates risks. Then we make them collaborate, debate, and challenge each other’s conclusions. That’s the fundamental idea behind TradingAgents. ___ 🏗️ What Is TradingAgents? TradingAgents is an open-source multi-agent framework developed by Tauric Research. It’s designed to simulate the collaborative decision-making process of a financial research and trading organization. Rather than relying on a single LLM prompt, TradingAgents coordinates multiple specialized agents through a structured workflow. The system includes: 1. Financial research analysts 2. Bullish and bearish researchers 3. A research manager 4. A trader agent 5. Risk management agents 6. A portfolio manager Each agent has a specific responsibility, and information flows between them as the research progresses. The interesting part is that the agents don’t simply generate independent reports. They share findings, debate competing investment theses, evaluate risk, and eventually produce a consolidated recommendation. Let’s understand how this works. ___ 🔍 Step 1: Four AI Analysts Research the Stock The workflow begins with four specialized research agents. Each examines the company from a different perspective. 📊 1. Market Analyst The Market Analyst focuses on technical analysis. It examines information such as: - Historical stock prices - Moving averages - Relative Strength Index (RSI) - Moving Average Convergence Divergence (MACD) - Recent price trends For example, the RSI can help identify whether a stock has experienced unusually strong buying or selling momentum. The analyst uses available technical indicators to understand what’s happening with the stock’s price behavior. 💰 2. Fundamental Analyst The Fundamental Analyst investigates the underlying business. It examines financial information such as revenue, earnings, profitability, and other available company data. Its goal is to answer questions like: Is this company financially strong? What do its fundamentals suggest about its business performance? Are there financial weaknesses investors should consider? For a company like NVIDIA, this analysis might involve examining revenue growth, margins, profitability, and the sustainability of demand for its products. 📰 3. News Analyst Financial performance alone doesn’t tell the entire story. A company’s outlook can change significantly because of new developments. The News Analyst investigates relevant information such as: - Company announcements - Earnings-related developments - Industry news - Economic events - Broader market developments For example, changes in semiconductor export restrictions or major AI infrastructure spending announcements could influence how investors evaluate NVIDIA. 🗣️ 4. Sentiment Analyst The Sentiment Analyst examines available public discussions and sentiment-related information. Its goal is to understand how investors and the broader market are discussing the company. This can provide useful context about market expectations, enthusiasm, and concerns. However, sentiment isn’t necessarily an indicator of whether a stock is fundamentally undervalued or overvalued. It’s simply another perspective. Why This Architecture Matters The four analysts can gather information in parallel. Rather than forcing a single AI agent to perform every research task, the system distributes responsibilities across specialized agents. Their findings become the foundation for the next stage. And this is where things get particularly interesting. ___ 🐂 Step 2: Bull vs. Bear — AI Agents Debate the Investment Once the analysts complete their research, their findings are passed to two additional agents. The Bull Researcher and the Bear Researcher. These agents have opposing responsibilities. The Bull Researcher The Bull Researcher builds the strongest possible argument for investing in the company. For example, a bullish NVIDIA investment thesis might emphasize: - Strong demand for AI computing infrastructure - Revenue and earnings growth - Competitive advantages - Market leadership - Long-term industry opportunities The Bear Researcher The Bear Researcher takes the opposite position. Its responsibility is to challenge the bullish argument and identify potential weaknesses. For example: - Is the stock valuation too expensive? - Are growth expectations unrealistic? - Could competition increase? - Are there regulatory or geopolitical risks? - What happens if AI infrastructure spending slows? But here’s the interesting part. These agents actually debate each other. The Bull presents an argument. The Bear challenges it. The Bull gets an opportunity to respond. And the discussion can continue through multiple configurable rounds. Instead of simply asking an LLM to list pros and cons, TradingAgents creates a structured exchange between competing investment perspectives. After the debate, a Research Manager evaluates the arguments and produces an investment plan. This approach doesn’t guarantee better investment decisions, but it provides a mechanism for examining competing interpretations before reaching a conclusion. ___ ⚖️ Step 3: The Trader and Risk Management Agents After the Research Manager produces an investment plan, the workflow moves to the Trader Agent. Despite its name, the Trader Agent in my demonstration wasn’t executing any actual trades. Instead, it translated the research into a more concrete investment proposal. That proposal then went through another review process. This time, three risk-focused agents evaluated it. Aggressive Risk Analyst Examines the investment proposal from a perspective that accepts greater risk in pursuit of potential returns. Conservative Risk Analyst Focuses on downside protection, uncertainty, and reasons to avoid excessive exposure. Neutral Risk Analyst Attempts to balance potential opportunities against the associated risks. These perspectives are used to challenge the investment proposal. Finally, the Portfolio Manager evaluates the research, proposed action, and risk discussion to produce the final recommendation. So the overall workflow looks something like this: STOCK / COMPANY | v RESEARCH ANALYSTS | +------------+------------+ | | | Market Fundamental News Analyst Analyst Analyst | | | +------------+------------+ | Sentiment Analyst | v RESEARCH DEBATE | +------+------+ | | Bull Agent Bear Agent | | +------+------+ | v Research Manager | v Trader Agent | v RISK ANALYSIS | +----------+----------+ | | | Aggressive Conservative Neutral | | | +----------+----------+ | v Portfolio Manager | v FINAL RECOMMENDATION Simplified conceptual representation of the TradingAgents workflow. The analyst stage is designed to support parallel research. This is what makes TradingAgents an interesting example of agentic AI and multi-agent orchestration. It’s not just multiple LLM calls. It’s a structured workflow in which different agents contribute information, challenge conclusions, and participate in a decision-making process. ___ 🛠️ Step 4: Installing and Setting Up TradingAgents Now that we understand the architecture, let’s look at how to run it. TradingAgents is open source, so you can inspect the implementation, modify the agents, and experiment with different configurations. 1. Clone the GitHub Repository Start by cloning the project: git clone https://github.com/TauricResearch/TradingAgents.git cd TradingAgents Open the project in your preferred code editor. I used VS Code for my demonstration. 2. Create a Python Virtual Environment I used Python 3.13 and uv for environment management. Create a virtual environment: uv venv --python 3.13 Activate it on macOS or Linux: source .venv/bin/activate This keeps the project’s Python dependencies separate from other applications installed on your machine. 3. Install the Dependencies Install the project using the dependency instructions provided in the repository. For example, when installing from the project directory with uv: uv pip install -e . The exact installation steps may change between releases, so check the repository’s latest README before proceeding. 4. Configure Your AI Model Provider TradingAgents supports configurable AI model providers. Depending on the version and configuration, you can use supported providers such as OpenAI, Google, or Anthropic. The repository includes an example environment configuration file. You can copy it: cp .env.example .env Then configure the credentials required by your selected provider. In my demonstration, I used the framework’s supported ChatGPT subscription authentication flow rather than manually configuring an API key. For versions supporting this option, the login command is: tradingagents login chatgpt Follow the authentication instructions displayed in your terminal. Note: Subscription-based authentication depends on the installed TradingAgents version and its supported integration. A ChatGPT subscription is not generally interchangeable with OpenAI API credits. 5. Launch TradingAgents Once everything is configured, launch the CLI: tradingagents The interface guides you through several configuration options. You can select: - The stock ticker - Analysis date - Output language - Research analysts - Research depth - AI model provider - LLM models - Reasoning effort For my experiment, I selected NVIDIA (NVDA) and enabled all four research analysts. I also chose the shallow research configuration to keep the demonstration relatively quick. Research depth affects how many debate and discussion rounds the agents perform. Once configured, the agents begin researching the company. ___ 🚀 Step 5: I Asked the AI Team to Analyze NVIDIA With everything running, I gave the system NVIDIA as the company to investigate. The workflow started with the research analysts gathering information. The Market Analyst examined technical indicators. The Fundamental Analyst investigated the company’s financial position. Meanwhile, the News and Sentiment Analysts collected information from their available data sources. Once the initial research was complete, the system moved through the debate and risk evaluation stages. Eventually, TradingAgents generated a detailed report containing the findings from the different agents. The report included technical analysis, financial research, sentiment observations, news analysis, and the final investment thesis. So, What Did the AI Agents Recommend? After completing the analysis, the Portfolio Manager produced its final recommendation: UNDERWEIGHT. In portfolio terminology, an underweight position generally means maintaining less exposure to an asset than a relevant benchmark or reference allocation. But I wouldn’t interpret this result as proof that NVIDIA is a bad investment. The recommendation reflects the model-generated research, available information, and configuration used during this particular run. I haven’t established whether this recommendation would outperform a benchmark or lead to better investment outcomes. And that’s an important distinction. Generating a convincing investment thesis is not the same as demonstrating predictive accuracy. What I found useful was the structured research process. Instead of receiving a short answer from a chatbot, I could examine the different research perspectives that contributed to the final decision. That makes TradingAgents particularly interesting as an investment research assistant. ___ 🧠 TradingAgents Also Has a Memory System Another feature worth exploring is the framework’s built-in memory and historical evaluation capabilities. After completing an analysis, TradingAgents can save information about the research decision in a local journal. This can include: - Stock ticker - Analysis date - Generated recommendation - Evaluation status Initially, the outcome is marked as pending. That makes sense because the system doesn’t know what will happen to the stock after the recommendation. Once sufficient trading sessions have passed, the framework can evaluate the stock’s subsequent performance against a benchmark. In the configuration I explored, the default evaluation window was five trading days. Based on the evaluation, an AI model can generate a reflection about the previous research. For example: What did the analysis get right? What risks did it overlook? Which assumptions turned out to be incorrect? What lessons might be useful for future research? Those reflections can then be retrieved as context for subsequent analyses. This creates a basic feedback mechanism. However, it’s important to understand that memory-based reflection is not the same as training or fine-tuning the underlying LLM. The framework can reuse previous lessons as contextual information, but that doesn’t automatically mean its investment predictions improve over time. Demonstrating that would require systematic evaluation. ___ 🔮 What If We Took This Project Further? After experimenting with TradingAgents, I started thinking about what else we could build using this architecture. Because the project is open source, there are several interesting directions to explore. 1. Build an AI-Powered Stock Screener Instead of analyzing one company at a time, imagine providing the system with a watchlist of 50 stocks. The agents could research each company, evaluate its investment thesis, and produce a shortlist of opportunities worth investigating further. You could then compare the companies using consistent research criteria. 2. Create a Financial Research Dashboard Another possibility is building a dashboard that tracks the agents’ research over time. For example, it could display: - Companies being monitored - Latest research recommendations - Bullish and bearish arguments - Risk assessments - Historical recommendations - Subsequent stock performance This could make it easier to compare how investment theses evolve as new information becomes available. 3. Introduce Industry-Specific AI Agents Different industries require different types of analysis. For example, we could introduce specialist agents for: Semiconductors: Manufacturing capacity, AI infrastructure demand, supply chains, and competitive positioning. Healthcare: Clinical trials, regulatory approvals, product pipelines, and reimbursement risks. Technology: Revenue growth, recurring revenue, customer retention, and competitive advantages. These agents could supplement the general research team with more specialized domain knowledge. 4. Evaluate Whether Multi-Agent Research Actually Works This is probably the most interesting direction from a research perspective. We could systematically compare: Single LLM vs. Multi-Agent Research vs. Traditional Financial Screening And evaluate metrics such as: - Research completeness - Factual accuracy - Recommendation consistency - Risk identification - Token usage and cost - Latency - Performance against relevant benchmarks We could also run historical evaluations while carefully controlling for look-ahead bias and survivorship bias. That would help answer a much more meaningful question: Do multi-agent AI systems actually improve investment research quality, or do they simply generate more elaborate reports?

I Built a Team of AI Agents That Research Stocks, Debate Each Other, and Make Investment Recommendations I’ve been experimenting with multi-agent AI systems, and recently came across an interesting open-source project called TradingAgents, developed by Tauric Research. The idea is pretty cool: instead of asking a single LLM whether a stock is worth buying, you create an entire team of specialized AI agents that work together like a financial research firm. Each agent has a specific responsibility: - Market Analyst: Studies price movements, RSI, MACD, moving averages, and other technical indicators. - Fundamental Analyst: Analyzes revenue, earnings, profitability, and company financials. - News Analyst: Tracks company announcements and market developments. - Sentiment Analyst: Examines public discussions and investor sentiment. But here’s where it gets interesting. Once these agents finish their research, their findings are passed to two more agents: a Bull Researcher and a Bear Researcher. The Bull builds the strongest possible argument for investing in the company, while the Bear challenges those arguments and highlights potential risks. They actually go through multiple rounds of debate before a Research Manager evaluates their conclusions. The resulting investment plan then passes through a Trader Agent, three Risk Analysts (aggressive, conservative, and neutral), and finally a Portfolio Manager that produces the investment recommendation. I decided to test this entire workflow on NVIDIA (NVDA). After analyzing the company’s financials, technical indicators, news, and market sentiment, the agents generated a detailed investment research report. The final recommendation? UNDERWEIGHT. What interested me wasn’t necessarily whether that recommendation was correct, but how the different AI agents collaborated, challenged each other’s assumptions, and arrived at a structured investment thesis. Another interesting feature is the framework’s built-in memory. It can store previous research decisions, evaluate subsequent stock performance, and generate reflections that can be retrieved during future analyses. Of course, this doesn’t mean the agents can consistently predict stock prices or outperform the market. The quality of the research still depends heavily on the underlying data, LLMs, and how the workflow is configured. I also made a hands-on tutorial covering: - How the TradingAgents multi-agent architecture works - How to install and configure the open-source framework - How to run the agents locally using the CLI - A complete NVIDIA stock research demonstration - How the agents generate their final investment report Full walkthrough: youtu.be GitHub: TauricResearch/TradingAgents

깃허브 인기 주식 AI 5개를 합쳤다｜클로드로 자동매매 만들기 깃허브에서 유명한 주식 AI 5개는 전부 해외 시장 기준입니다. 한국장 규칙(호가단위·±30% 가격제한·거래세·외국인·기관 수급·DART 공시·테마)에 맞게 바꿔서, AlphaForge 노드로 하나씩 연결해 실제로 돌려봤습니다. 📌 인프런 강의 「AI로 만드는 KR/US 퀀트 트레이딩 노드 분석 시스템 (바이브코딩)」 30% 할인 쿠폰 👉 inf.run (쿠폰 소진 시 만료) ⏱ 챕터 0:00 인트로 — 깃허브 주식 AI를 한국장으로 0:54 1장 TradingAgents — 강세·약세 토론과 승인 6:51 2장 Kronos — 5일 확률 예측과 계획표 10:52 3장 Vibe-Trading — 테마와 미래 정보 함정 14:02 4장 claude-trading-skills — 시장 폭·FTD·천장 위험 17:20 5장 day-trader-skill — 수급 연속 매수와 KRX 규칙 20:12 마무리 — 다 합치면, 그리고 자동매매 🧩 장별로 한 것 · 1장 TradingAgents — 수급·공시·차트·비중·강세/약세 토론·트레이더·리스크 승인을 노드 8개로 조립 (2,768 → 638 → 603 → 33 → 5) · 2장 Kronos — 5거래일 확률 예측 + KRX 호가·손익 계획표 (2,768 → 282 → 10) · 3장 Vibe-Trading — 테마로 고르기, '주도 테마'를 쓰면 미래 정보라 막히는 이유 · 4장 claude-trading-skills — 시장 폭 16.6(극약세) · 바닥 확인(FTD) · 천장 위험 85.5(위기) · 5장 day-trader-skill — 외국인·기관 연속 순매수(2,768 → 26)와 상한가·호가 규칙 · 마무리 — 추천 조합 한 번으로 2,768 → 141, 그리고 증권사 API 주문 노드로 자동매매까지 잇는 자리 📊 데이터 · 모든 숫자는 2026-10-29 종가 데이터로 직접 실행한 결과입니다. · 시세·종목 KRX 공식 / 수급·일봉 한국투자증권 Open API(조회 전용) / 공시 OpenDART / 테마 네이버 · 자동매매는 주문 노드를 붙여야 하며(아직 없음), 꼭 모의투자부터, 사람의 승인을 거쳐서 하세요. 🔓 영상에 쓴 노드·자료: [공개 링크] 🔗 원본 저장소 TradingAgents TauricResearch/TradingAgents Kronos shiyu-coder/Kronos Vibe-Trading HKUDS/Vibe-Trading claude-trading-skills tradermonty/claude-trading-skills day-trader-skill Jakeschincariol/day-trader-skill ⚠ 교육용 콘텐츠이며 투자 권유가 아닙니다. 투자 판단과 책임은 본인에게 있습니다. #주식AI #퀀트트레이딩 #바이브코딩 #클로드주식 #클로드 [en-US] I Combined the 5 Most Popular Stock AI on GitHub | Building Automated Trading with Claude The top 5 stock AIs on GitHub are all based on overseas markets. I modified them to fit the Korean market rules (bid increments, ±30% price limits, transaction taxes, foreign and institutional supply and demand, DART disclosures, and themes), connected them one by one using AlphaForge nodes, and ran them in practice. 📌 30% Discount Coupon for the Infrun Course "Building a KR/US Quant Trading Node Analysis System with AI (Vibe Coding)" 👉 inf.run (Expires when coupons are exhausted) ⏱ Chapters 0:00 Intro — GitHub Stock AI to the Korean Market 0:54 Chapter 1 TradingAgents — Bullish/Bearish Discussion and Approval 6:51 Chapter 2 Kronos — 5-Day Probability Prediction and Schedules 10:52 Chapter 3 Vibe-Trading — Themes and Future Information Pitfalls 14:02 Chapter 4 claude-trading-skills — Market Width, FTD, and Ceiling Risk 17:20 Chapter 5 day-trader-skill — Supply and Demand Continuous Buying and KRX Rules 20:12 Wrap-up — All in One, and Automated Trading 🧩 What We Did by Chapter · Chapter 1 TradingAgents — Assembling Supply/Demand, Disclosures, Charts, Weights, Bullish/Bearish Discussions, Traders, and Risk Approvals into 8 Nodes (2,768 → 638 → 603 → 33 → 5) · Chapter 2 Kronos — 5-Trading Day Probability Prediction + KRX Bid/Loss Plan (2,768 → 282 → 10) · Chapter 3 Vibe-Trading — Choosing by Theme, Why Using 'Leading Themes' Gets Blocked Due to Future Information · Chapter 4 claude-trading-skills — Market Width 16.6 (Extreme Bearish) · Bottom Confirmation (FTD) · Ceiling Risk 85.5 (Crisis) · Chapter 5 day-trader-skill — Consecutive Net Buying by Foreigners and Institutions (2,768 → 26) and Upper Limit/Bid Rules · Wrap-up — From 2,768 to 141 with a Single Recommended Combination, and Even Automated Trading via Brokerage API Order Nodes Connecting Points 📊 Data · All numbers are the results of direct execution using closing price data from October 29, 2026. · Market Prices/Stocks: KRX Formulas / Supply/Demand/Daily Charts: Korea Investment & Securities Open API (Inquiry Only) / Disclosures: OpenDART / Themes: Naver · Automated trading requires attaching an order node (not available yet). Please be sure to start with a simulated investment and obtain human approval. 🔓 Nodes and materials used in the video: [Public Links] 🔗 Original Repositories TradingAgents TauricResearch/TradingAgents Kronos shiyu-coder/Kronos Vibe-Trading HKUDS/Vibe-Trading claude-trading-skills tradermonty/claude-trading-skills day-trader-skill Jakeschincariol/day-trader-skill ⚠ This is educational content and not an investment recommendation. Investment decisions and responsibilities lie with the individual. #StockAI #QuantTrading #VibeCoding #ClaudeStock #Claude [id] Menggabungkan 5 AI Analisis Saham Terpopuler di GitHub | Membuat Trading Otomatis dengan Claude Lima AI saham teratas di GitHub semuanya berbasis pasar luar negeri. Saya memodifikasinya agar sesuai dengan aturan pasar Korea (kenaikan penawaran, batas harga ±30%, pajak transaksi, penawaran dan permintaan asing dan institusional, pengungkapan DART, dan tema), menghubungkannya satu per satu menggunakan node AlphaForge, dan menjalankannya dalam praktik. 📌 Kupon Diskon 30% untuk Kursus Infrun "Membangun Sistem Analisis Node Trading Kuantitatif KR/US dengan AI (Vibe Coding)" 👉 inf.run (Berakhir setelah kupon habis) ⏱ Bab 0:00 Pendahuluan — GitHub Stock AI untuk Pasar Korea 0:54 Bab 1 TradingAgents — Diskusi dan Persetujuan Bullish/Bearish 6:51 Bab 2 Kronos — Prediksi Probabilitas 5 Hari dan Jadwal 10:52 Bab 3 Vibe-Trading — Tema dan Jebakan Informasi Masa Depan 14:02 Bab 4 claude-trading-skills — Lebar Pasar, FTD, dan Risiko Batas Atas 17:20 Bab 5 day-trader-skill — Penawaran dan Permintaan Pembelian Berkelanjutan dan Aturan KRX 20:12 Ringkasan — Semua dalam Satu, dan Perdagangan Otomatis 🧩 Apa yang Kami Lakukan per Bab · Bab 1 TradingAgents — Menggabungkan Penawaran/Permintaan, Pengungkapan, Grafik, Bobot, Diskusi Bullish/Bearish, Trader, dan Persetujuan Risiko ke dalam 8 Node (2.768 → 638 → 603 → 33 → 5) · Bab 2 Kronos — Prediksi Probabilitas 5 Hari Perdagangan + Rencana Penawaran/Kerugian KRX (2.768 → 282 → 10) · Bab 3 Vibe-Trading — Memilih Berdasarkan Tema, Mengapa Penggunaan 'Tema Utama' Diblokir Karena Informasi Masa Depan · Bab 4 claude-trading-skills — Lebar Pasar 16,6 (Bearish Ekstrem) · Konfirmasi Bawah (FTD) · Risiko Batas Atas 85.5 (Krisis) · Bab 5 Keterampilan Daytrader — Pembelian Net Berurutan oleh Investor Asing dan Institusi (2.768 → 26) dan Batas Atas/Aturan Penawaran · Ringkasan — Dari 2.768 menjadi 141 dengan Satu Kombinasi Rekomendasi, dan Bahkan Perdagangan Otomatis melalui Node Pesanan API Broker yang Menghubungkan Titik 📊 Data · Semua angka adalah hasil eksekusi langsung menggunakan data harga penutupan dari 29 Oktober 2026. · Harga Pasar/Saham: Rumus KRX / Penawaran/Permintaan/Grafik Harian: API Terbuka Investasi & Sekuritas Korea (Hanya Permintaan) / Pengungkapan: OpenDART / Tema: Naver · Perdagangan otomatis memerlukan pemasangan node pesanan (belum tersedia). Pastikan untuk memulai dengan investasi simulasi dan mendapatkan persetujuan manusia. 🔓 Node dan material yang digunakan dalam video: [Tautan Publik] 🔗 Repositori Asli TradingAgents TauricResearch/TradingAgents Kronos shiyu-coder/Kronos Vibe-Trading HKUDS/Vibe-Trading claude-trading-skills tradermonty/claude-trading-skills day-trader-skill Jakeschincariol/day-trader-skill ⚠ Ini adalah konten edukatif dan bukan rekomendasi investasi. Keputusan dan tanggung jawab investasi berada di tangan individu. #StockAI #QuantTrading #VibeCoding #ClaudeStock #Claude

Copy any app, Claude does SEO, and the top 10 repos Link to Resource: thenextnewthing.ai - This video is sponsored by TripleTen, a professional education platform designed to help tech professionals and career changers move into higher-paying tech and AI roles. - Explore two TripleTen programs for experienced engineers: - AI Systems Engineering (22 weeks): tripleten.com - AI and Machine Learning (20 weeks): tripleten.com - Both are for engineers with 2+ years of recent, hands-on production experience and require a short skills audit before you can enroll. Both also include career coaching and mentorship to help you stand out in the job market. - TripleTen - github.com/tripleten-com - TripleTen Website - tripleten.com - Presented by Zapier: zapier.com - Zapier Next Gen Zaps - zapier.com This week, Andrew Warner is joined by Adam Brakhane to explore the GitHub repositories and AI tools that are making developers more productive. From running open-source apps in the cloud to giving AI coding agents security, design, and workflow superpowers, these projects show what's possible when developers build on top of the latest AI models. The episode starts with three bonus discoveries: a way to host open-source apps cheaply, Theo's collection of coding skills, and a project that makes iOS app development on Linux possible. Then Andrew and Adam count down the top 10 GitHub repositories of the week. They explore Cloudflare's security audit skill, Claude SEO, Impeccable, Superpowers, TradingAgents, Karpathy-inspired skills, design and accessibility tools, T3 Code, and Anthropic's Knowledge Work Plugins. Along the way, they discuss how open-source projects can become businesses, why skills need to be customized and kept up to date, how AI agents can be persuaded to follow instructions, and why the best coding tools are becoming more than just coding assistants. Repos Featured: Bonus Openers - Every App: every-app/every-app - T3 Stack: t3dotgg/tstack - Omarchy Apple Dev: joshuaswarren/omarchy-apple-dev Top 10 GitHub Repositories - Cloudflare Security Audit Skill: cloudflare/security-audit-skill - Claude SEO: AgriciDaniel/claude-seo - Impeccable: pbakaus/impeccable - Superpowers: obra/superpowers - TradingAgents: TauricResearch/TradingAgents - Andrej Karpathy Skills: multica-ai/andrej-karpathy-skills - Taste Skill: Leonxlnx/taste-skill - Interface Design Skills: jakubkrehel/skills - T3 Code: pingdotgg/t3code - Knowledge Work Plugins: anthropics/knowledge-work-plugins 00:00 - This Week's Best GitHub Repositories 00:27 - Every App: Host Open-Source Apps for $5 00:57 - Why Easy Deployment Matters for Open Source 02:15 - T3 Stack: Theo's AI Coding Skills 02:42 - Why You Should Customize Your AI Skills 03:43 - Omarchy Apple Dev: Build iOS Apps on Linux 04:29 - Why Apple Requires Mac Hardware for iOS Development 05:43 - #1 Cloudflare Security Audit Skill 07:00 - #2 Claude SEO: Run SEO Audits with Claude Code 09:31 - #3 Impeccable: Give AI Agents Better Design Taste 10:35 - Can Open-Source Design Tools Become Businesses? 12:40 - Presented by TripleTen 14:36 - #4 Superpowers: Give Coding Agents a Better Process 15:09 - Using Persuasion Principles to Improve AI Agents 17:11 - #5 TradingAgents: Simulated AI Trading 18:41 - #6 Andrej Karpathy Skills 20:31 - Why AI Agents Struggle to Follow Task Lists 21:43 - Presented by Zapier: Make AI Workflows Cheaper 22:38 - #7 Taste Skill: Give Your AI an Art Director 24:36 - #8 Interface Design Skills and Accessibility 26:20 - #9 T3 Code: Manage Multiple AI Coding Agents 27:20 - Run AI Agents Across Multiple Computers 30:46 - #10 Anthropic Knowledge Work Plugins 31:14 - What Should We Cover in First Stars? 31:45 - Adam's AI-Powered Business Context Layer 33:12 - Andrew's Automated AI Video Editing Workflow 34:18 - More GitHub Repositories to Explore 👉 Media/Sponsorship Inquiries: thenextnewthing.ai 👉 Join us: thenextnewthing.ai [de-DE] Jede App kopieren, Claude macht SEO und die Top 10 Repos Link zur Ressource: thenextnewthing.ai – Dieses Video wird von TripleTen gesponsert, einer professionellen Weiterbildungsplattform, die IT-Fachkräfte und Quereinsteiger beim Einstieg in besser bezahlte Positionen im Technologie- und KI-Bereich unterstützt. – Entdecken Sie zwei TripleTen-Programme für erfahrene Ingenieure: – KI-Systementwicklung (22 Wochen): tripleten.com – KI und Maschinelles Lernen (20 Wochen): tripleten.com – Beide Programme richten sich an Ingenieure mit mindestens zwei Jahren aktueller, praktischer Berufserfahrung. Vor der Anmeldung ist eine kurze Kompetenzanalyse erforderlich. Beide Programme beinhalten außerdem Karrierecoaching und Mentoring, um Ihnen zu helfen, sich auf dem Arbeitsmarkt hervorzuheben. - TripleTen - github.com/tripleten-com - TripleTen-Website - tripleten.com - Präsentiert von Zapier: zapier.com - Zapier Next Gen Zaps - zapier.com Diese Woche sprechen Andrew Warner und Adam Brakhane über GitHub-Repositories und KI-Tools, die die Produktivität von Entwicklern steigern. Von der Ausführung von Open-Source-Apps in der Cloud bis hin zur Ausstattung von KI-Programmieragenten mit Sicherheits-, Design- und Workflow-Funktionen – diese Projekte zeigen, was möglich ist, wenn Entwickler auf den neuesten KI-Modellen aufbauen. Die Folge beginnt mit drei Bonus-Entdeckungen: einer Möglichkeit, Open-Source-Apps kostengünstig zu hosten, Theos Sammlung von Programmierkenntnissen und einem Projekt, das die iOS-App-Entwicklung unter Linux ermöglicht. Anschließend präsentieren Andrew und Adam die Top 10 der GitHub-Repositories der Woche. Sie untersuchen Cloudflares Sicherheitsaudit-Funktion, Claude SEO, Impeccable, Superpowers, TradingAgents, von Karpathy inspirierte Funktionen, Design- und Barrierefreiheitstools, T3 Code und die Knowledge Work Plugins von Anthropic. Dabei erörtern sie, wie Open-Source-Projekte zu Unternehmen werden können, warum Fähigkeiten angepasst und aktuell gehalten werden müssen, wie KI-Agenten dazu gebracht werden können, Anweisungen zu befolgen, und warum die besten Codierungswerkzeuge mehr als nur Programmierassistenten sind. Empfohlene Repositories: Bonus-Opener - Every App: every-app/every-app - T3 Stack: t3dotgg/tstack - Omarchy Apple Dev: joshuaswarren/omarchy-apple-dev Top 10 GitHub-Repositories - Cloudflare Security Audit Skill: cloudflare/security-audit-skill - Claude SEO: AgriciDaniel/claude-seo - Impeccable: pbakaus/impeccable - Superpowers: obra/superpowers - TradingAgents: TauricResearch/TradingAgents - Andrej Karpathy Skills: multica-ai/andrej-karpathy-skills - Geschmacks-Skill: Leonxlnx/taste-skill - Interface-Design-Skills: jakubkrehel/skills - T3 Code: pingdotgg/t3code - Knowledge-Work-Plugins: anthropics/knowledge-work-plugins 00:00 – Die besten GitHub-Repositories der Woche 00:27 – Every App: Open-Source-Apps für 5 $ hosten 00:57 – Warum einfache Bereitstellung für Open Source wichtig ist 02:15 – T3 Stack: Theos KI-Programmierkenntnisse 02:42 – Warum Sie Ihre KI-Kenntnisse anpassen sollten 03:43 – Omarchy Apple Dev: iOS-Apps unter Linux entwickeln 04:29 – Warum Apple Mac-Hardware für iOS benötigt Entwicklung 05:43 – #1 Cloudflare-Sicherheitsaudit-Kompetenz 07:00 – #2 Claude SEO: SEO-Audits mit Claude Code durchführen 09:31 – #3 Tadellos: KI-Agenten ein besseres Gespür für Design vermitteln 10:35 – Können Open-Source-Designtools zu Unternehmen werden? 12:40 – Präsentiert von TripleTen 14:36 ​​– #4 Superkräfte: Optimieren Sie Ihre Programmieragenten 15:09 – KI-Agenten mithilfe von Überzeugungsprinzipien verbessern 17:11 – #5 TradingAgents: Simulierter KI-Handel 18:41 – #6 Andrej Karpathy Skills 20:31 – Warum KI-Agenten Schwierigkeiten haben, Aufgabenlisten abzuarbeiten 21:43 – Präsentiert von Zapier: KI-Workflows kostengünstiger gestalten 22:38 – #7 Geschmackssache: Geben Sie Ihrer KI einen Art Director 24:36 – #8 Interface-Design und Barrierefreiheit 26:20 – #9 T3 Code: Mehrere KI-Programmieragenten verwalten 27:20 – KI-Agenten auf mehreren Computern ausführen 30:46 – #10 Anthropic Knowledge Work Plugins 31:14 – Was sollten wir in First Stars behandeln? 31:45 – Adams KI-gestützte Business-Context-Ebene 33:12 – Andrews automatisierter KI-Video-Workflow 34:18 – Weitere GitHub-Repositories zum Entdecken 👉 Medien-/Sponsoringanfragen: thenextnewthing.ai 👉 Werden Sie Teil unserer Community: thenextnewthing.ai [es-US] Copia cualquier aplicación, Claude hace SEO y los 10 mejores repositorios Enlace al recurso: thenextnewthing.ai - Este video está patrocinado por TripleTen, una plataforma de formación profesional diseñada para ayudar a profesionales de la tecnología y personas que buscan un cambio de carrera a acceder a puestos mejor remunerados en tecnología e IA. - Descubre dos programas de TripleTen para ingenieros con experiencia: - Ingeniería de Sistemas de IA (22 semanas): tripleten.com - IA y Aprendizaje Automático (20 semanas): tripleten.com - Ambos programas están dirigidos a ingenieros con más de dos años de experiencia práctica reciente en producción y requieren una breve evaluación de habilidades antes de la inscripción. Ambos incluyen asesoramiento profesional y mentoría para ayudarte a destacar en el mercado laboral. - TripleTen - github.com/tripleten-com - Sitio web de TripleTen - tripleten.com - Presentado por Zapier: zapier.com - Zaps de nueva generación de Zapier - zapier.com Esta semana, Andrew Warner y Adam Brakhane exploran los repositorios de GitHub y las herramientas de IA que aumentan la productividad de los desarrolladores. Desde ejecutar aplicaciones de código abierto en la nube hasta dotar a los agentes de codificación de IA de capacidades avanzadas de seguridad, diseño y flujo de trabajo, estos proyectos demuestran el potencial de los desarrolladores que trabajan con los modelos de IA más recientes. El episodio comienza con tres descubrimientos adicionales: una forma económica de alojar aplicaciones de código abierto, la colección de habilidades de programación de Theo y un proyecto que permite el desarrollo de aplicaciones iOS en Linux. A continuación, Andrew y Adam presentan los 10 mejores repositorios de GitHub de la semana. Exploran la función de auditoría de seguridad de Cloudflare, Claude SEO, Impeccable, Superpowers, TradingAgents, las habilidades inspiradas en Karpathy, las herramientas de diseño y accesibilidad, T3 Code y los plugins de gestión del conocimiento de Anthropic. Además, analizan cómo los proyectos de código abierto pueden convertirse en negocios, por qué es necesario personalizar y actualizar las habilidades, cómo se puede persuadir a los agentes de IA para que sigan instrucciones y por qué las mejores herramientas de programación van más allá de ser simples asistentes. Repositorios destacados: Introducciones adicionales - Every App: every-app/every-app - T3 Stack: t3dotgg/tstack - Omarchy Apple Dev: joshuaswarren/omarchy-apple-dev Los 10 mejores repositorios de GitHub - Cloudflare Security Audit Skill: cloudflare/security-audit-skill - Claude SEO: AgriciDaniel/claude-seo - Impeccable: pbakaus/impeccable - Superpowers: obra/superpowers - TradingAgents: TauricResearch/TradingAgents - Andrej Karpathy Skills: multica-ai/andrej-karpathy-skills - Habilidad de Gusto: Leonxlnx/taste-skill - Habilidades de Diseño de Interfaz: jakubkrehel/skills - Código T3: pingdotgg/t3code - Plugins de Trabajo del Conocimiento: anthropics/knowledge-work-plugins 00:00 - Los mejores repositorios de GitHub de esta semana 00:27 - Every App: Aloja aplicaciones de código abierto por $5 00:57 - Por qué la implementación sencilla es importante para el código abierto 02:15 - T3 Stack: Las habilidades de programación de IA de Theo 02:42 - Por qué deberías personalizar tus habilidades de IA 03:43 - Omarchy Apple Dev: Crea aplicaciones iOS en Linux 04:29 - Por qué Apple requiere hardware Mac para iOS Desarrollo 05:43 - #1 Habilidad de auditoría de seguridad de Cloudflare 07:00 - #2 Claude SEO: Realiza auditorías SEO con Claude Code 09:31 - #3 Impecable: Mejora el diseño de los agentes de IA 10:35 - ¿Pueden las herramientas de diseño de código abierto convertirse en negocios? 12:40 - Presentado por TripleTen 14:36 ​​- #4 Superpoderes: Mejora el proceso de los agentes de codificación 15:09 - Uso de principios de persuasión para mejorar los agentes de IA 17:11 - #5 Agentes de comercio: Simulación de comercio con IA 18:41 - #6 Habilidades de Andrej Karpathy 20:31 - ¿Por qué los agentes de IA tienen dificultades para seguir las listas de tareas? 21:43 - Presentado por Zapier: Abarata los flujos de trabajo de IA 22:38 - #7 Habilidad de buen gusto: Asigna un director de arte a tu IA 24:36 - #8 Habilidades de diseño de interfaz y accesibilidad 26:20 - #9 Código T3: Gestiona múltiples agentes de codificación de IA 27:20 - Ejecuta agentes de IA en varios ordenadores 30:46 - #10 Plugins de trabajo de conocimiento antrópico 31:14 - ¿Qué deberíamos cubrir en First Stars? 3... [fr-FR] Copiez n'importe quelle application, Claude fait du SEO, et le top 10 des dépôts Lien vers la ressource : thenextnewthing.ai - Cette vidéo est sponsorisée par TripleTen, une plateforme de formation professionnelle conçue pour aider les professionnels de la tech et les personnes en reconversion à accéder à des postes mieux rémunérés dans les domaines de la tech et de l'IA. - Découvrez deux programmes TripleTen pour ingénieurs expérimentés : - Ingénierie des systèmes d'IA (22 semaines) : tripleten.com - IA et apprentissage automatique (20 semaines) : tripleten.com - Ces deux programmes s'adressent aux ingénieurs justifiant d'au moins deux ans d'expérience pratique récente en production. Une évaluation rapide de vos compétences est requise avant l'inscription. Ils incluent également un accompagnement et un mentorat pour vous aider à vous démarquer sur le marché du travail. - TripleTen - github.com/tripleten-com - Site web de TripleTen - tripleten.com - Présenté par Zapier : zapier.com - Zapiers nouvelle génération - zapier.com Cette semaine, Andrew Warner et Adam Brakhane explorent les dépôts GitHub et les outils d'IA qui améliorent la productivité des développeurs. De l'exécution d'applications open source dans le cloud à l'optimisation de la sécurité, du design et des flux de travail des agents de programmation IA, ces projets illustrent le potentiel des modèles d'IA les plus récents. L'épisode débute par trois découvertes bonus : une solution économique pour héberger des applications open source, les compétences de programmation de Theo et un projet permettant le développement d'applications iOS sous Linux. Andrew et Adam présentent ensuite leur top 10 des dépôts GitHub de la semaine. Ils explorent les fonctionnalités d'audit de sécurité de Cloudflare, Claude SEO, Impeccable, Superpowers, TradingAgents, les compétences inspirées de Karpathy, les outils de conception et d'accessibilité, T3 Code et les plugins Knowledge Work d'Anthropic. Au fil de leur exploration, ils abordent la transformation des projets open source en entreprises, la nécessité de personnaliser et de maintenir à jour les compétences, la manière d'inciter les agents d'IA à suivre des instructions et l'évolution des meilleurs outils de programmation, qui deviennent bien plus que de simples assistants de codage. Dépôts en vedette : Ouvertures bonus - Every App : every-app/every-app - T3 Stack : t3dotgg/tstack - Omarchy Apple Dev : joshuaswarren/omarchy-apple-dev Top 10 des dépôts GitHub - Cloudflare Security Audit Skill : cloudflare/security-audit-skill - Claude SEO : AgriciDaniel/claude-seo - Impeccable : pbakaus/impeccable - Superpowers : obra/superpowers - TradingAgents : TauricResearch/TradingAgents - Andrej Karpathy Skills : multica-ai/andrej-karpathy-skills - Compétences en perception gustative : Leonxlnx/taste-skill - Compétences en conception d'interfaces : jakubkrehel/skills - Code T3 : pingdotgg/t3code - Plugins pour le travail intellectuel : anthropics/knowledge-work-plugins 00:00 - Les meilleurs dépôts GitHub de la semaine 00:27 - Every App : Hébergez des applications open source pour 5 $ 00:57 - L'importance d'un déploiement facile pour l'open source 02:15 - T3 Stack : Les compétences de programmation IA de Theo 02:42 - Pourquoi personnaliser vos compétences en IA ? 03:43 - Omarchy Apple Dev : Créer des applications iOS sous Linux 04:29 - Pourquoi Apple exige du matériel Mac pour iOS Développement 05:43 - Compétence n° 1 : Audit de sécurité Cloudflare 07:00 - Claude SEO n° 2 : Réaliser des audits SEO avec Claude Code 09:31 - Impeccable n° 3 : Améliorer le sens du design des agents IA 10:35 - Les outils de conception open source peuvent-ils devenir rentables ? 12:40 - Présenté par TripleTen 14:36 ​​- N° 4 Superpouvoirs : Améliorer le processus des agents de codage 15:09 - Utiliser les principes de persuasion pour améliorer les agents d'IA 17:11 - N° 5 TradingAgents : Simulation de trading par IA 18:41 - N° 6 Compétences d'Andrej Karpathy 20:31 - Pourquoi les agents d'IA peinent à suivre les listes de tâches 21:43 - Présenté par Zapier : Réduire le coût des flux de travail d'IA 22:38 - N° 7 Sens du goût : Offrir un directeur artistique à votre IA 24:36 - N° 8 Compétences en conception d'interface et accessibilité 26:20 - N° 9 Code T3 : Gérer plusieurs agents de codage d'IA 27:20 - Exécuter des agents d'IA sur plusieurs ordinateurs 30:46 - N° 10 Plugins Anthropic Knowledge Work 31:14 - Que devrions-nous aborder dans First Stars ? 31:45 - Couche de contexte... [hi] किसी भी ऐप को कॉपी करें, Claude करेगा SEO, और टॉप 10 रेपोज़ संसाधन का लिंक: thenextnewthing.ai - यह वीडियो ट्रिपलटेन द्वारा प्रायोजित है, जो एक पेशेवर शिक्षा मंच है जिसे तकनीकी पेशेवरों और करियर बदलने वालों को उच्च वेतन वाली तकनीकी और एआई भूमिकाओं में आगे बढ़ने में मदद करने के लिए डिज़ाइन किया गया है। - अनुभवी इंजीनियरों के लिए ट्रिपलटेन के दो कार्यक्रमों के बारे में जानें: - एआई सिस्टम इंजीनियरिंग (22 सप्ताह): tripleten.com - एआई और मशीन लर्निंग (20 सप्ताह): tripleten.com - दोनों कार्यक्रम उन इंजीनियरों के लिए हैं जिनके पास हाल ही में 2+ वर्षों का व्यावहारिक उत्पादन अनुभव है और नामांकन से पहले एक संक्षिप्त कौशल ऑडिट आवश्यक है। दोनों कार्यक्रमों में करियर कोचिंग और मेंटरशिप भी शामिल है ताकि आप नौकरी बाजार में अलग पहचान बना सकें। - ट्रिपलटेन - github.com/tripleten-com - ट्रिपलटेन वेबसाइट - tripleten.com - ज़ैपियर द्वारा प्रस्तुत: zapier.com - ज़ैपियर नेक्स्ट जेन ज़ैप्स - zapier.com इस सप्ताह, एंड्रयू वार्नर के साथ एडम ब्राखाने GitHub रिपॉज़िटरी और AI टूल्स पर चर्चा करेंगे जो डेवलपर्स की उत्पादकता बढ़ा रहे हैं। क्लाउड में ओपन-सोर्स ऐप्स चलाने से लेकर AI कोडिंग एजेंट्स को सुरक्षा, डिज़ाइन और वर्कफ़्लो की अपार क्षमता प्रदान करने तक, ये प्रोजेक्ट दिखाते हैं कि जब डेवलपर्स नवीनतम AI मॉडल्स पर काम करते हैं तो क्या-क्या संभव है। एपिसोड की शुरुआत तीन अतिरिक्त खोजों से होती है: ओपन-सोर्स ऐप्स को सस्ते में होस्ट करने का एक तरीका, थियो के कोडिंग कौशल का संग्रह, और एक ऐसा प्रोजेक्ट जो लिनक्स पर iOS ऐप डेवलपमेंट को संभव बनाता है। इसके बाद एंड्रयू और एडम इस सप्ताह की शीर्ष 10 GitHub रिपॉज़िटरी की सूची प्रस्तुत करते हैं। वे क्लाउडफ्लेयर के सुरक्षा ऑडिट कौशल, क्लाउड एसईओ, इम्पेकेबल, सुपरपावर्स, ट्रेडिंग एजेंट्स, कार्पेथी से प्रेरित कौशल, डिज़ाइन और एक्सेसिबिलिटी टूल्स, टी3 कोड और एंथ्रोपिक के नॉलेज वर्क प्लगइन्स का अन्वेषण करते हैं। इस दौरान, वे चर्चा करते हैं कि कैसे ओपन-सोर्स प्रोजेक्ट व्यवसाय बन सकते हैं, कौशल को अनुकूलित और अद्यतन रखना क्यों आवश्यक है, एआई एजेंटों को निर्देशों का पालन करने के लिए कैसे प्रेरित किया जा सकता है, और क्यों सर्वश्रेष्ठ कोडिंग उपकरण केवल कोडिंग सहायकों से कहीं अधिक बन रहे हैं। प्रमुख रिपॉज़िटरी: बोनस ओपनर्स - एवरी ऐप: every-app/every-app - T3 स्टैक: t3dotgg/tstack - ओमार्ची एप्पल देव: joshuaswarren/omarchy-apple-dev शीर्ष 10 GitHub रिपॉज़िटरी - क्लाउडफ्लेयर सिक्योरिटी ऑडिट स्किल: cloudflare/security-audit-skill - क्लाउड एसईओ: AgriciDaniel/claude-seo - इम्पेकेबल: pbakaus/impeccable - सुपरपावर्स: obra/superpowers - ट्रेडिंग एजेंट्स: TauricResearch/TradingAgents - आंद्रेज कार्पेथी स्किल्स: multica-ai/andrej-karpathy-skills - स्वाद कौशल: Leonxlnx/taste-skill - इंटरफ़ेस डिज़ाइन कौशल: jakubkrehel/skills - T3 कोड: pingdotgg/t3code - नॉलेज वर्क प्लगइन्स: anthropics/knowledge-work-plugins 00:00 - इस सप्ताह के सर्वश्रेष्ठ GitHub रिपॉज़िटरी 00:27 - एवरी ऐप: ओपन-सोर्स ऐप्स को $5 में होस्ट करें 00:57 - ओपन सोर्स के लिए आसान डिप्लॉयमेंट क्यों महत्वपूर्ण है 02:15 - T3 स्टैक: थियो के AI कोडिंग कौशल 02:42 - आपको अपने AI कौशल को क्यों अनुकूलित करना चाहिए 03:43 - ओमार्ची एप्पल देव: लिनक्स पर iOS ऐप्स बनाएं 04:29 - एप्पल को इसकी आवश्यकता क्यों है iOS डेवलपमेंट के लिए मैक हार्डवेयर 05:43 - #1 क्लाउडफ्लेयर सुरक्षा ऑडिट कौशल 07:00 - #2 क्लाउड एसईओ: क्लाउड कोड के साथ एसईओ ऑडिट करें 09:31 - #3 इम्पेकेबल: एआई एजेंटों को बेहतर डिज़ाइन क्षमता प्रदान करें 10:35 - क्या ओपन-सोर्स डिज़ाइन टूल व्यवसाय बन सकते हैं? 12:40 - ट्रिपलटेन द्वारा प्रस्तुत 14:36 ​​- #4 सुपरपावर्स: कोडिंग एजेंट्स को बेहतर प्रक्रिया प्रदान करें 15:09 - एआई एजेंट्स को बेहतर बनाने के लिए अनुनय सिद्धांतों का उपयोग 17:11 - #5 ट्रेडिंग एजेंट्स: सिम्युलेटेड एआई ट्रेडिंग 18:41 - #6 आंद्रेज कार्पेथी स्किल्स 20:31 - एआई एजेंट्स को टास्क लिस्ट का पालन करने में कठिनाई क्यों होती है 21:43 - जैपियर द्वारा प्रस्तुत: एआई वर्कफ़्लो को सस्ता बनाएं 22:38 - #7 टेस्ट स्किल: अपने एआई को एक आर्ट डायरेक्टर दें 24:36 - #8 इंटरफ़ेस डिज़ाइन स्किल्स और एक्सेसिबिलिटी 26:20 - #9 टी3 कोड: कई एआई कोडिंग एजेंट्स को प्रबंधित करें 27:20 - कई कंप्यूटरों पर एआई एजेंट्स चलाएं 30:46 - #10 एंथ्रोपिक नॉलेज वर्क प्लगइन्स 31:14 - हमें फर्स्ट स्टार्स में क्या शामिल करना चाहिए? 31:45 - एडम का एआई-आधारित बिज़नेस कॉन्टेक्स्ट लेयर 33:12 - एंड्रयू का स्वचालित एआई वीडियो एडिटिंग वर्कफ़्लो 34:18 - एक्सप्लोर करने के लिए और भी GitHub रिपॉज़िटरीज़ 👉 मीडिया/स्पॉन्सरशिप पूछताछ: thenextnewthing.ai 👉 हमसे जुड़ें: thenextnewthing.ai [id] Salin aplikasi apa pun, Claude melakukan SEO, dan 10 repositori teratas Tautan ke Sumber Daya: thenextnewthing.ai - Video ini disponsori oleh TripleTen, platform pendidikan profesional yang dirancang untuk membantu para profesional teknologi dan mereka yang ingin beralih karier ke peran teknologi dan AI dengan gaji lebih tinggi. - Jelajahi dua program TripleTen untuk insinyur berpengalaman: - Rekayasa Sistem AI (22 minggu): tripleten.com - AI dan Pembelajaran Mesin (20 minggu): tripleten.com - Kedua program ini ditujukan untuk insinyur dengan pengalaman produksi langsung minimal 2 tahun dan memerlukan audit keterampilan singkat sebelum Anda dapat mendaftar. Kedua program ini juga mencakup pelatihan karier dan bimbingan untuk membantu Anda menonjol di pasar kerja. - TripleTen - github.com/tripleten-com - Situs Web TripleTen - tripleten.com - Dipersembahkan oleh Zapier: zapier.com - Zapier Next Gen Zaps - zapier.com Minggu ini, Andrew Warner ditemani oleh Adam Brakhane untuk menjelajahi repositori GitHub dan alat AI yang membuat pengembang lebih produktif. Mulai dari menjalankan aplikasi open-source di cloud hingga memberikan agen pengkodean AI kemampuan super dalam hal keamanan, desain, dan alur kerja, proyek-proyek ini menunjukkan apa yang mungkin terjadi ketika pengembang membangun di atas model AI terbaru. Episode dimulai dengan tiga penemuan bonus: cara untuk menghosting aplikasi open-source dengan murah, koleksi keterampilan pengkodean Theo, dan proyek yang memungkinkan pengembangan aplikasi iOS di Linux. Kemudian Andrew dan Adam menghitung mundur 10 repositori GitHub teratas minggu ini. Mereka mengeksplorasi kemampuan audit keamanan Cloudflare, Claude SEO, Impeccable, Superpowers, TradingAgents, kemampuan yang terinspirasi oleh Karpathy, alat desain dan aksesibilitas, T3 Code, dan Plugin Kerja Pengetahuan Anthropic. Sepanjang perjalanan, mereka membahas bagaimana proyek sumber terbuka dapat menjadi bisnis, mengapa kemampuan perlu disesuaikan dan diperbarui, bagaimana agen AI dapat dibujuk untuk mengikuti instruksi, dan mengapa alat pengkodean terbaik menjadi lebih dari sekadar asisten pengkodean. Repositori Unggulan: Pembuka Bonus - Every App: every-app/every-app - T3 Stack: t3dotgg/tstack - Omarchy Apple Dev: joshuaswarren/omarchy-apple-dev 10 Repositori GitHub Teratas - Cloudflare Security Audit Skill: cloudflare/security-audit-skill - Claude SEO: AgriciDaniel/claude-seo - Impeccable: pbakaus/impeccable - Superpowers: obra/superpowers - TradingAgents: TauricResearch/TradingAgents - Andrej Karpathy Skills: multica-ai/andrej-karpathy-skills - Taste Skill: Leonxlnx/taste-skill - Keterampilan Desain Antarmuka: jakubkrehel/skills - Kode T3: pingdotgg/t3code - Plugin Kerja Pengetahuan: anthropics/knowledge-work-plugins 00:00 - Repositori GitHub Terbaik Minggu Ini 00:27 - Every App: Host Aplikasi Open-Source seharga $5 00:57 - Mengapa Penyebaran yang Mudah Penting untuk Open Source 02:15 - T3 Stack: Keterampilan Pengkodean AI Theo 02:42 - Mengapa Anda Harus Menyesuaikan Keterampilan AI Anda 03:43 - Omarchy Apple Dev: Membangun Aplikasi iOS di Linux 04:29 - Mengapa Apple Membutuhkan Perangkat Keras Mac untuk Pengembangan iOS 05:43 - Keterampilan Audit Keamanan Cloudflare #1 07:00 - Claude SEO #2: Jalankan Audit SEO dengan Claude Kode 09:31 - #3 Sempurna: Berikan Agen AI Selera Desain yang Lebih Baik 10:35 - Bisakah Alat Desain Sumber Terbuka Menjadi Bisnis? 12:40 - Dipersembahkan oleh TripleTen 14:36 ​​- #4 Kekuatan Super: Berikan Agen Pengkodean Proses yang Lebih Baik 15:09 - Menggunakan Prinsip Persuasi untuk Meningkatkan Agen AI 17:11 - #5 TradingAgents: Simulasi Perdagangan AI 18:41 - #6 Keterampilan Andrej Karpathy 20:31 - Mengapa Agen AI Kesulitan Mengikuti Daftar Tugas 21:43 - Dipersembahkan oleh Zapier: Membuat Alur Kerja AI Lebih Murah 22:38 - #7 Keterampilan Selera: Berikan AI Anda Direktur Seni 24:36 - #8 Keterampilan Desain Antarmuka dan Aksesibilitas 26:20 - #9 Kode T3: Mengelola Beberapa Agen Pengkodean AI 27:20 - Menjalankan Agen AI di Beberapa Komputer 30:46 - #10 Plugin Kerja Pengetahuan Antropik 31:14 - Apa yang Harus Kita Bahas di Bintang Pertama? 31:45 - Lapisan Konteks Bisnis Bertenaga AI Adam 33:12 - Alur Kerja Pengeditan Video AI Otomatis Andrew 34:18 - Lebih Banyak Repositori GitHub untuk Dijelajahi 👉 Pertanyaan Media/Sponsor: thenextnewthing.ai 👉 Bergabunglah dengan kami: thenextnewthing.ai [it] Copia qualsiasi app, Claude fa SEO e le 10 migliori repository Link alla risorsa: thenextnewthing.ai - Questo video è sponsorizzato da TripleTen, una piattaforma di formazione professionale pensata per aiutare i professionisti del settore tecnologico e chi desidera cambiare carriera a trovare ruoli più remunerativi nel campo della tecnologia e dell'intelligenza artificiale. - Scopri due programmi TripleTen per ingegneri esperti: - Ingegneria dei sistemi di IA (22 settimane): tripleten.com - IA e apprendimento automatico (20 settimane): tripleten.com - Entrambi i programmi sono rivolti a ingegneri con almeno 2 anni di esperienza pratica recente in produzione e richiedono una breve valutazione delle competenze prima dell'iscrizione. Entrambi includono anche coaching e mentoring per aiutarti a distinguerti nel mercato del lavoro. - TripleTen - github.com/tripleten-com - Sito web di TripleTen - tripleten.com - Presentato da Zapier: zapier.com - Zapier Next Gen Zaps - zapier.com Questa settimana, Andrew Warner e Adam Brakhane esplorano i repository GitHub e gli strumenti di intelligenza artificiale che stanno rendendo gli sviluppatori più produttivi. Dall'esecuzione di app open source nel cloud al potenziamento di agenti di programmazione basati su IA con funzionalità avanzate di sicurezza, design e gestione dei flussi di lavoro, questi progetti dimostrano cosa è possibile realizzare quando gli sviluppatori si basano sui modelli di intelligenza artificiale più recenti. L'episodio inizia con tre scoperte bonus: un modo per ospitare app open source a basso costo, la raccolta di competenze di programmazione di Theo e un progetto che rende possibile lo sviluppo di app iOS su Linux. Infine, Andrew e Adam presentano la classifica dei 10 migliori repository GitHub della settimana. Esplorano la funzionalità di audit di sicurezza di Cloudflare, Claude SEO, Impeccable, Superpowers, TradingAgents, le competenze ispirate a Karpathy, gli strumenti di design e accessibilità, T3 Code e i plugin Knowledge Work di Anthropic. Nel corso della presentazione, discutono di come i progetti open source possano trasformarsi in attività commerciali, perché le competenze debbano essere personalizzate e costantemente aggiornate, come sia possibile persuadere gli agenti IA a seguire le istruzioni e perché i migliori strumenti di programmazione stiano diventando molto più che semplici assistenti alla programmazione. Repository in evidenza: Bonus Openers - Every App: every-app/every-app - T3 Stack: t3dotgg/tstack - Omarchy Apple Dev: joshuaswarren/omarchy-apple-dev I 10 migliori repository GitHub - Cloudflare Security Audit Skill: cloudflare/security-audit-skill - Claude SEO: AgriciDaniel/claude-seo - Impeccable: pbakaus/impeccable - Superpowers: obra/superpowers - TradingAgents: TauricResearch/TradingAgents - Andrej Karpathy Skills: Italiano: multica-ai/andrej-karpathy-skills - Competenza nel gusto: Leonxlnx/taste-skill - Competenze di progettazione dell'interfaccia: jakubkrehel/skills - Codice T3: pingdotgg/t3code - Plugin per il lavoro basato sulla conoscenza: anthropics/knowledge-work-plugins 00:00 - I migliori repository GitHub di questa settimana 00:27 - Every App: ospita app open source per 5 $ 00:57 - Perché la facilità di implementazione è importante per l'open source 02:15 - Stack T3: le competenze di programmazione AI di Theo 02:42 - Perché dovresti personalizzare le tue competenze AI 03:43 - Omarchy Apple Dev: crea app iOS su Linux 04:29 - Perché Apple richiede hardware Mac per lo sviluppo iOS 05:43 - #1 Competenza per l'audit di sicurezza di Cloudflare 07:00 - #2 Claude SEO: Eseguire audit SEO con Claude Code 09:31 - #3 Impeccable: Dare agli agenti IA un gusto migliore per il design 10:35 - Gli strumenti di design open source possono diventare aziende? 12:40 - Presentato da TripleTen 14:36 ​​- #4 Superpoteri: Migliorare il processo degli agenti di programmazione 15:09 - Utilizzare i principi della persuasione per migliorare gli agenti IA 17:11 - #5 TradingAgents: Simulazione di trading tramite IA 18:41 - #6 Competenze di Andrej Karpathy 20:31 - Perché gli agenti IA faticano a seguire le liste di attività 21:43 - Presentato da Zapier: Rendere i flussi di lavoro IA più economici 22:38 - #7 Abilità di gusto: Assegnare un direttore artistico alla tua IA 24:36 - #8 Competenze di progettazione dell'interfaccia e accessibilità 26:20 - #9 Codice T3: Gestire più agenti di programmazione IA 27:20 - Eseguire agenti IA su più computer 30:46 - #10 Plugin per il lavoro basato sull... [pt-BR] Copie qualquer aplicativo, Claude fazendo SEO e os 10 principais repositórios Link para o recurso: thenextnewthing.ai - Este vídeo é patrocinado pela TripleTen, uma plataforma de educação profissional criada para ajudar profissionais de tecnologia e pessoas em transição de carreira a ingressarem em cargos mais bem remunerados nas áreas de tecnologia e IA. - Explore dois programas da TripleTen para engenheiros experientes: - Engenharia de Sistemas de IA (22 semanas): tripleten.com - IA e Aprendizado de Máquina (20 semanas): tripleten.com - Ambos são destinados a engenheiros com mais de 2 anos de experiência prática recente em produção e exigem uma breve avaliação de habilidades antes da inscrição. Ambos também incluem coaching de carreira e mentoria para ajudar você a se destacar no mercado de trabalho. - TripleTen - github.com/tripleten-com - Site da TripleTen - tripleten.com - Apresentado por Zapier: zapier.com - Zaps de Próxima Geração do Zapier - zapier.com Nesta semana, Andrew Warner recebe Adam Brakhane para explorar os repositórios do GitHub e as ferramentas de IA que estão tornando os desenvolvedores mais produtivos. Desde a execução de aplicativos de código aberto na nuvem até a atribuição de superpoderes de segurança, design e fluxo de trabalho a agentes de codificação de IA, esses projetos mostram o que é possível quando os desenvolvedores criam com base nos modelos de IA mais recentes. O episódio começa com três descobertas bônus: uma maneira de hospedar aplicativos de código aberto a baixo custo, a coleção de habilidades de programação de Theo e um projeto que torna possível o desenvolvimento de aplicativos iOS no Linux. Em seguida, Andrew e Adam apresentam a lista dos 10 principais repositórios do GitHub da semana. Eles exploram a habilidade de auditoria de segurança da Cloudflare, o Claude SEO, o Impeccable, o Superpowers, o TradingAgents, habilidades inspiradas em Karpathy, ferramentas de design e acessibilidade, o T3 Code e os plugins de trabalho intelectual da Anthropic. Ao longo do caminho, discutem como projetos de código aberto podem se tornar negócios, por que as habilidades precisam ser personalizadas e mantidas atualizadas, como agentes de IA podem ser persuadidos a seguir instruções e por que as melhores ferramentas de programação estão se tornando mais do que apenas assistentes de codificação. Repositórios em destaque: Aberturas bônus - Every App: every-app/every-app - T3 Stack: t3dotgg/tstack - Omarchy Apple Dev: joshuaswarren/omarchy-apple-dev Top 10 Repositórios do GitHub - Cloudflare Security Audit Skill: cloudflare/security-audit-skill - Claude SEO: AgriciDaniel/claude-seo - Impeccable: pbakaus/impeccable - Superpowers: obra/superpowers - TradingAgents: TauricResearch/TradingAgents - Andrej Karpathy Skills: multica-ai/andrej-karpathy-skills - Habilidade de Paladar: Leonxlnx/taste-skill - Habilidades de Design de Interface: jakubkrehel/skills - Código T3: pingdotgg/t3code - Plugins de Trabalho Intelectual: anthropics/knowledge-work-plugins 00:00 - Os Melhores Repositórios do GitHub desta Semana 00:27 - Every App: Hospede Aplicativos de Código Aberto por US$ 5 00:57 - Por que a Facilidade de Implantação é Importante para o Código Aberto 02:15 - T3 Stack: As Habilidades de Programação de IA do Theo 02:42 - Por que Você Deve Personalizar Suas Habilidades de IA 03:43 - Omarchy Apple Dev: Crie Aplicativos iOS no Linux 04:29 - Por que a Apple Exige Hardware Mac para iOS Desenvolvimento 05:43 - #1 Habilidade de Auditoria de Segurança do Cloudflare 07:00 - #2 Claude SEO: Execute Auditorias de SEO com o Claude Code 09:31 - #3 Impecável: Dê aos Agentes de IA um Melhor Senso de Design 10:35 - Ferramentas de Design de Código Aberto Podem se Tornar Negócios? 12:40 - Apresentado pela TripleTen 14:36 ​​- #4 Superpoderes: Dê aos Agentes de Codificação um Processo Melhor 15:09 - Usando Princípios de Persuasão para Aprimorar Agentes de IA 17:11 - #5 TradingAgents: Simulação de Negociação por IA 18:41 - #6 Habilidades de Andrej Karpathy 20:31 - Por que os Agentes de IA Têm Dificuldade em Seguir Listas de Tarefas 21:43 - Apresentado pela Zapier: Torne os Fluxos de Trabalho de IA Mais Baratos 22:38 - #7 Habilidade de Paladar: Dê à Sua IA um Diretor de Arte 24:36 - #8 Habilidades de Design de Interface e Acessibilidade 26:20 - #9 Código T3: Gerencie Vários Agentes de Codificação de IA 27:20 - Execute Agentes de IA em Vários Computadores 30:46 - #10 Plugins de Trabalho de Conhecimento Antrópico 31:14 - O que Devemos Abordar no First Stars? 31:45 - Camada d...

TradingAgents by Tauric Research: A Trading Firm of 12 LLM Agents, and What One Decision Costs --- title: "TradingAgents by Tauric Research: A Trading Firm of 12 LLM Agents, and What One Decision Costs" published: true tags: opensource, ai, finance, agents canonical_url: devpik.com --- TradingAgents is a free, open-source Python framework from Tauric Research that analyzes a stock the way a trading firm would, with a team of language-model agents instead of one prompt. Four analysts study the market, sentiment, news and fundamentals; a bull and a bear argue over their reports; a trader turns the result into a plan; three risk analysts argue over that plan; and a portfolio manager makes the call. You give it a ticker and a date, and it returns a rating and a folder of reports. It had 115,371 GitHub stars and more than 22,000 forks when we wrote this, and one sentence in the README matters more than the rest: the framework is designed for research purposes and is not intended as financial, investment or trading advice. What Is TradingAgents? The project began as a research paper, "TradingAgents: Multi-Agents LLM Financial Trading Framework" by Yijia Xiao, Edward Sun, Di Luo and Wei Wang, first posted to arXiv on 28 December 2024, the same day the repository was created. It is built on LangGraph, licensed under Apache 2.0, and the current release is version 0.7.0, from 16 October 2026. Version 0.7.0 contains no broker integration and no code that places orders: a run ends with a rating and its reasoning, and what you do with them is up to you. That makes it a tool for studying how multi-agent LLM systems reason about markets, not a bot that trades for you, and this post treats it that way. Twelve Agents, Modeled on a Trading Firm Every role in TradingAgents is a separate agent with its own prompt, and most of them have their own data. The analysts run at the same time, and the debate starts once all four reports are in. | Role | What it does | Model tier | | --- | --- | --- | | Market Analyst | Prices and up to 8 technical indicators (moving averages, MACD, RSI, Bollinger Bands, ATR, VWMA), checked against a verified price snapshot | Quick | | Sentiment Analyst | Yahoo Finance headlines, StockTwits messages and Reddit posts, scored as one sentiment band | Quick | | News Analyst | Company news, global macro headlines, FRED economic data and Polymarket prediction markets | Quick | | Fundamentals Analyst | Balance sheet, cash flow, income statement, company overview and insider transactions | Quick | | Bull and Bear Researchers | Argue for and against the trade from the four reports | Quick | | Research Manager | Judges the debate and writes the investment plan | Deep | | Trader | Turns the plan into a buy, hold or sell proposal | Quick | | Aggressive, Conservative and Neutral Analysts | Argue over the trader's proposal from three risk appetites | Quick | | Portfolio Manager | Makes the final decision on a five-step scale | Deep | The two tiers are the main cost control. You choose a quick model and a deep model, and each can run on its own provider, so the managers could run on Claude while everything else runs on a cheaper OpenAI model. The defaults are OpenAI's GPT-6 Luna for the quick tier and GPT-6 Sol for the deep one. The final answer is one of five ratings: Buy, Overweight, Hold, Underweight or Sell. When the portfolio manager's output contains no readable rating, the code records REVIEW instead of quietly treating it as a Hold, because a decision nobody can read is not a decision to hold. Fifteen Model Calls Is the Floor for One Decision Nothing in the README says how many times a run calls a language model, and that number is what you pay for. So we counted it from the code: the graph's routing in conditional_logic.py, the analyst loop in analysts/turn.py, the default settings in default_config.py, and the project's own end-to-end test, which runs the whole graph offline with a scripted model. We read version 0.7.0 (commit e8f5503) and did not pay for a live run, so this is a count of calls, not a bill. With the default settings and all four analysts, one decision makes at least 15 model calls: | Step | Calls at minimum | Tier | | --- | --- | --- | | Market, news and fundamentals analysts | 2 each: one round of tool calls, then the report | Quick | | Sentiment analyst | 1, because its data is fetched before the model is called | Quick | | Bull and bear researchers | 2, one debate round each | Quick | | Research manager | 1 | Deep | | Trader | 1 | Quick | | Three risk analysts | 3, one risk round each | Quick | | Portfolio manager | 1 | Deep | | Total | 15 | 13 quick, 2 deep | The project's test agrees. It deliberately breaks the provider on the 12th call and describes that point as "past the analysts, before the decision", which is exactly where the count puts it: 7 analyst calls, then the bull, the bear, the research manager and the trader, so the 12th call is the first risk analyst. Three things push a real run above 15: - Tool rounds. The market analyst is told to fetch prices first, then compute indicators, then check the verified snapshot, so a model that follows the instructions needs several rounds before it writes. Each analyst may take up to 20 rounds of tool calls, plus one final call without tools when it runs out. - Debate settings. The README's own Python example sets two rounds for the bull and bear, which adds 2 calls. Two rounds of both debates make it 20. - Past decisions. Each run settles earlier decisions whose holding period has passed and writes a one-paragraph reflection on each, one more quick call per decision. Two conclusions follow. The quick model carries 13 of the 15 calls, so it decides most of the cost, and the deep model is called only twice. And backtests multiply: the README's example grid, two tickers every 7 days from 1 June to 1 August with only the market and news analysts, is 18 decisions and at least 216 model calls before the reflections. Debates Are a Setting You Choose The debates are what make TradingAgents different from asking one model for an opinion, and they are fully configurable. The bull and bear take turns until each has spoken max_debate_rounds times, then the research manager writes a plan from the transcript. The aggressive, conservative and neutral analysts do the same for max_risk_discuss_rounds rounds before the portfolio manager decides. Both default to 1. More rounds give the agents more chances to answer each other, at a cost of 2 calls per extra debate round and 3 per extra risk round. The same settings are available as environment variables for the CLI: TRADINGAGENTS_MAX_DEBATE_ROUNDS and TRADINGAGENTS_MAX_RISK_ROUNDS. You can also pass in what you already hold. Without a portfolio, the agents write guidance for a reader who applies it to their own position; with one, the trader, the risk analysts and the portfolio manager work against your actual cash and positions. The CLI takes it as a JSON file (tradingagents --portfolio my_book.json) and refuses to start when the file does not validate. Point-in-Time Data, and the One Feed That Is Not A backtest is only honest if each run sees what was public on its date. Most of TradingAgents' recent releases are about exactly that, and the README is specific about how each source behaves. Fundamentals are read as filed. US company statements come from SEC EDGAR, which records the date every figure was filed, so a run dated in the past reads the statements as they stood that day. The README's example is Apple's total assets for fiscal 2008, filed as $39.6 billion and restated to $36.2 billion in 2010. We checked it against EDGAR's public API: the figure for the period ending 27 September 2008 appears as $39.572 billion in the 10-K filed on 27 October 2009, and as $36.171 billion in the amended 10-K filed on 25 January 2010. A run dated between those two filings reads the first number. Other sources hold back what they cannot date. Yahoo Finance dates a statement by the period it covers rather than by when it was published, so a run dated in the past is told those statements are withheld. Insider trades are dated by when they happened, not when they were filed, so they are withheld for past dates too. News articles published after the analysis date are filtered out, and the project keeps regression tests for the ways that filter has leaked in the past. Social sentiment is the exception. The sentiment analyst trims StockTwits and Reddit posts to the seven days before the analysis date, so nothing from after it leaks in. The problem is the other direction. Its code says these feeds "serve recent items and are not archived, so a historical run's sentiment inputs are not point-in-time." A run dated months back finds little or nothing in its window, and the feed tells the model the window was never observed rather than reporting that nobody posted. That is presumably why the README's backtest example selects only the market and news analysts, and it is the setting to copy: leave the sentiment analyst out of anything you backtest. Memory and Backtests Score Decisions Against the Market TradingAgents keeps a memory log of its own decisions at ~/.tradingagents/memory/trading_memory.md. While the analysts of a later run work, it settles every logged decision whose holding period has passed: it fetches the realized return, and the alpha against the market's benchmark (SPY for US tickers, regional indexes such as the Nikkei 225 or Nifty 50 elsewhere), then writes a short reflection. The portfolio manager reads recent decisions on the same ticker, plus recent lessons from others, before it decides. The holding period is 5 trading days, counted from the first close after the analysis date. Version 0.7.0 moved it from the close of the analysis date itself, because a run reads news published after that day's close and could not have traded on the earlier price. To judge whether the system decides well rather than reading one run, run_backtest sweeps a grid of tickers and dates into a memory log of its own and scores each settled decision on realized alpha, grouped by rating. An interrupted sweep resumes with the same run_id, skipping the cells that already ran. Each run also writes its full state to full_states_log_<date>.json under the ticker's results folder, with the date, the final rating and the model settings. The paper reports that TradingAgents improved cumulative returns, Sharpe ratio and maximum drawdown over baseline strategies. The README is careful in the other direction: backtest results "are not guaranteed to match any published figure", and the framework is "a research scaffold for studying multi-agent analysis, not a strategy with a fixed, replicable return." Two Runs of the Same Ticker Will Not Match The README says it directly: two runs of the same ticker and date can differ, and that is expected, not a bug. Language-model sampling varies even at a fixed temperature, reasoning models vary most because their reasoning is itself sampled, and the live news and social feeds change from day to day. Some things no longer vary. The company behind a ticker is resolved before any agent runs, and the market analyst grounds every exact price and indicator claim in a verified snapshot. The README says these two changes address earlier reports of runs analyzing the wrong company or inventing price levels. To see the variation yourself, run the same ticker and date twice and save both reports. Each saved report is a folder of Markdown files, one per agent (market.md, bull.md, trader.md, decision.md and so on), plus complete_report.html as one page. Comparing the two decision.md files shows exactly where the reasoning changed. For tighter repeatability, set temperature to 0 and choose models that honor it. The current default models are reasoning-first and largely ignore temperature, so the README suggests naming a non-reasoning model for both tiers when reproducibility matters more than depth. Installing TradingAgents and Running Your First Analysis TradingAgents needs Python 3.11 or newer and an API key for at least one model provider. The CLI asks for the ticker, date, provider, models and research depth, and remembers your answers for next time. For scripts and scheduled jobs it runs without questions from flags. Docker works too (docker compose run --rm tradingagents), with an Ollama profile for local models. The provider list is long: OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen, GLM, MiniMax, OpenRouter, Mistral, Kimi, Groq, NVIDIA, Azure OpenAI, AWS Bedrock, Ollama, and any OpenAI-compatible server such as vLLM or LM Studio. Since version 0.7.0 it can also run on a ChatGPT Plus or Pro plan instead of an API key, after a one-time tradingagents login chatgpt; those runs count toward the plan's usage limits. Any market Yahoo Finance covers works, using the exchange suffix: 0700.HK for Hong Kong, 7203.T for Tokyo, RELIANCE.NS for India, 600519.SS for Shanghai, and crypto pairs like BTC-USD. SEC EDGAR covers companies that file with the SEC, including foreign companies listed in the US; other listings fall back to the next data vendor. Two defaults are worth knowing. Checkpoint resume is off unless you pass --checkpoint, which lets a crashed run continue from its last completed step. And the interactive CLI checks for a newer release at most once a day in the background; it sends nothing about your runs, and TRADINGAGENTS_NO_UPDATE_CHECK=1 turns it off. Who Should Use TradingAgents, and Who Should Not TradingAgents is a good fit if you want to study how LLM agents reason about markets: researchers and students working on multi-agent systems, developers who want a well-tested LangGraph project to learn from, and quantitative tinkerers who want to see whether structured debate changes a model's calls. Its strengths are the ones that matter for research. The roles are explicit, the debates are configurable, the data is point-in-time where it can be, and it has a real backtest harness that scores decisions against a benchmark. It is the wrong tool if you are looking for something to trade your money. It places no orders, the README says it is not financial advice, two runs can disagree, and every decision costs at least 15 model calls. Treat its ratings as a research output to study, and keep the sentiment analyst out of anything you backtest. Links - GitHub: TauricResearch/TradingAgents - Full breakdown on DevPik: devpik.com FAQs What is TradingAgents? A free, open-source Python framework from Tauric Research that analyzes a stock with a team of LLM agents modeled on a trading firm: four analysts, a bull and a bear researcher, a research manager, a trader, three risk analysts and a portfolio manager. Given a ticker and a date, it returns one of five ratings (Buy, Overweight, Hold, Underweight or Sell) and a report from every agent. It is built on LangGraph and licensed under Apache 2.0. Who made TradingAgents? Tauric Research. The framework accompanies the paper "TradingAgents: Multi-Agents LLM Financial Trading Framework" by Yijia Xiao, Edward Sun, Di Luo and Wei Wang, first posted to arXiv on 28 December 2024. The GitHub repository was created the same day and had more than 115,000 stars in October 2026. Is TradingAgents free? The code is free under the Apache 2.0 license. Running it is not, unless you use local models: every decision makes at least 15 calls to the language models you configure, billed by your provider. You can run it on local models through Ollama or an OpenAI-compatible server, or on a ChatGPT Plus or Pro plan, where runs count toward the plan's usage limits. Can TradingAgents trade with real money? No. Version 0.7.0 has no broker integration and no code that places orders. A run ends with a rating and its reasoning. The README describes the framework as designed for research purposes and says it is not intended as financial, investment or trading advice. How many API calls does a TradingAgents run make? At least 15 with the default settings and all four analysts: 2 for each of the three tool-using analysts, 1 for the sentiment analyst, 2 for the bull and bear, 1 each for the research manager, trader and portfolio manager, and 3 for the risk analysts. Thirteen of those go to the quick model and two to the deep model. More tool rounds, more debate rounds and reflections on past decisions add to it; two rounds of each debate make it 20. Which models and markets does TradingAgents support? Most major providers, including OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen, GLM, MiniMax, OpenRouter, Mistral, Groq, Azure OpenAI and AWS Bedrock, plus local models through Ollama and any OpenAI-compatible server. It works with any market Yahoo Finance covers, using exchange suffixes such as .HK, .T, .NS or .SS, and with crypto pairs like BTC-USD.

Tu propio "hedge fund" de agentes IA, open source: TauricResearch/TradingAgents Analistas de técnico, noticias, sentimiento y fundamentales. Debate alcista vs bajista. Trader. Equipo de riesgo con veto. Pruébalo primero en paper trading. No es consejo de inversión. t.co

Бык против медведя: ИИ-агенты спорят, куда вложить деньги #нейросети #claudecode #вайбкодинг Ты написал «ФОНД», держи 👇 TradingAgents — команда ИИ-агентов, которая разбирает акцию и спорит перед решением. GitHub: github.com/TauricResearch/TradingAgents Научная статья: arxiv.org/abs/2412.20138 Как запустить: Нужен Python 3.11 или новее. Скачай проект: git clone TauricResearch/TradingAgents Зайди в папку: cd TradingAgents Установи: pip install . Подключи модель: ключ API любого провайдера (как его указать — в README) или локальную модель через Ollama. Можно войти своей подпиской ChatGPT Plus или Pro: tradingagents login chatgpt. Запросы тогда расходуют лимиты тарифа. Запусти: tradingagents. Выбери тикер и дату и смотри, как агенты спорят. ⚠️ Это исследовательский проект, а не финансовый совет. Результаты между запусками могут отличаться, сделки идут на симулированную биржу. Не используй его для торговли реальными деньгами. Больше гайдов по ИИ в моём ТГ: t.me/justuserai

이번 주 개발자라면 봐야 할 오픈소스 Top 10 +@ 총정리 · GitHub 트렌딩 (10월 4주) 이번 주(10/21~10/28) GitHub 트렌딩 주간 순위 Top 10과 순위 밖 3개(이번 주 페이지에 올라온 13개 전부)를 정리했어요. 레포마다 무엇을 하는지, 최근 바뀐 것, 초보자·개발자별로 어떻게 써 보면 좋은지까지 담았어요. 순위는 GitHub Trending(weekly) 페이지 순서 그대로예요(2026-10-28 06:00 KST 수집). 별 수는 수집 시점 값이에요. ▶ 챕터 0:00 오프닝 0:21 10위 android/skills 0:59 9위 Orca 1:36 8위 OpenCode 2:18 7위 ruflo 3:00 6위 graphify 3:38 5위 TradingAgents 4:24 4위 andrej-karpathy-skills 5:01 3위 claude-seo 5:43 2위 knowledge-work-plugins 6:25 번외 ① ai-memory 7:03 번외 ② T3 Code 7:33 번외 ③ FastAPI 8:11 1위 security-audit-skill 9:16 이번 주 확인할 것 ▶ Top 10 10위 android/skills — Google Android 팀 공식 에이전트 스킬. 일주일 새 30개에서 36개(v1.0.16~1.0.18): ANR 점검·수정, 온디바이스 LLM(LiteRT-LM), ML Kit 에이전트 API, Compose Ink, Glance 위젯 메모리 등 9위 stablyai/orca — 여러 코딩 에이전트를 git worktree별로 동시에 돌리는 데스크톱 앱(내 구독 사용). 9/30 이후 채팅 화면(실험)·권한 모드(Manual/Auto/Yolo)·앱 안 브라우저 클릭 표시·휴대폰 앱 개선, 10/21 별 10만. v1.4.221(10/5) Copilot 설정 파일 권한 보안 수정 8위 anomalyco/opencode — 모델 회사에 묶이지 않는 오픈소스 코딩 에이전트(75+ 공급자·로컬 모델). v2.2.0: 데스크톱 앱 개편, 데스크톱 플러그인(실험). ⚠ Console 보안 사고(10/17): Console에 외부 키를 저장했다면 공급자 쪽에서 폐기·재발급(OpenCode 발표 기준 Console 사용자의 1.4%) 7위 ruvnet/ruflo — Claude Code·Codex 위 다중 에이전트 도구(옛 claude-flow). 이번 주 릴리스 28번. ⚠ 3.63.3(10/26) 이전에는 클론한 저장소의 설정 파일로 AI 워커가 켜질 수 있었음 → 3.63.3 이상으로. Anthropic Managed Agents 도구 4개(직접 켜야 함, 예산 상한 필수, 실서비스 미검증) 6위 Graphify-Labs/graphify — /graphify로 코드·문서·SQL·PDF를 지식 그래프로. 코드는 로컬 tree-sitter 분석(모델 없음), 문서·이미지는 쓰는 모델 사용. 0.9.97: 그래프 설명 속 비밀값 가림 강화. ⚠ PyPI 이름은 graphifyy(y 두 개) 5위 TauricResearch/TradingAgents — 증권사 조직을 본뜬 멀티 에이전트 매매 연구 프레임워크(투자 조언 아님). 9/16 이후 백테스트·분석가 병렬·한국 등 벤치마크·역할별 모델 공급자·ChatGPT 구독 실행. v0.7.1: 0 없는 날짜가 이후 공시를 읽던 버그 수정 → 예전 백테스트는 다시 돌려 보기 4위 multica-ai/andrej-karpathy-skills — Karpathy의 글을 바탕으로 다른 개발자가 만든 CLAUDE.md 하나(원칙 4개). 마지막 커밋 4/20, LICENSE 파일 없음 3위 AgriciDaniel/claude-seo — Claude Code용 SEO 점검 플러그인(스킬 26·서브에이전트 19). v2.5.0: 자체 보안 감사 반영, 서버 CVE 노출 점검(응답만 읽음), Python 3.11 이상. 일부 확장은 유료 외부 서비스 2위 anthropics/knowledge-work-plugins — Anthropic 직무별 플러그인(Cowork용, Claude Code에서도). 저장소 안 22개·목록 123개. sales 2.1.0: 문장마다 출처와 원본 링크 ▶ 번외(순위 밖, 이번 주 페이지 11~13위) akitaonrails/ai-memory — 여러 코딩 에이전트가 같이 쓰는 장기 기억 서버. 3.2.0: 띄어쓰기 없는 한글 부분 검색, 끊긴 세션 알림. 프롬프트·도구 기록을 서버에 저장 pingdotgg/t3code — 내 PC의 Codex·Claude Code·Cursor·OpenCode 등을 웹·휴대폰에서 조작(내 구독 사용). 이번 주 v0.1.1~0.1.3, 한 번만 써 보려면 npx t3@latest fastapi/fastapi — ⚠ 0.160.0: 파라미터 모델 extra 설정이 같은 위치 다른 필드에도 적용 / 0.162.0: 폼에서 빠진 bool을 False로 치지 않음 → 체크박스는 기본값 False 선언 후 테스트 ▶ 1위 cloudflare/security-audit-skill — 코딩 에이전트에게 6단계 보안 감사를 시키는 스킬(MIT). 블로그의 후보 20,799 → 조치 7,245는 Cloudflare 사내 시스템 수치(이 스킬 성능 아님). 코드는 9/14 이후 변경 없음, 별은 9/23 약 2만 → 10/28 약 5만, 10/21~24 일간 트렌딩 전체 1위. ⚠ 실행 검증엔 외부 네트워크를 막은 샌드박스 필요, 한 번 실행은 여러 번 돌린 결과의 절반쯤(README), 소스는 쓰는 모델 회사로 전송 ▶ 끄는 방법·설치 - Android CLI 사용 통계: 명령마다 --no-metrics - Orca 익명 사용 데이터: onorca.dev - TradingAgents 업데이트 확인: TRADINGAGENTS_NO_UPDATE_CHECK=1 - graphify: uv tool install graphifyy → graphify install - security-audit-skill: npx skills add cloudflare/security-audit-skill --skill security-audit ▶ 관련 영상 - 지난주 트렌딩 롱폼(10월 3주): youtu.be - OpenCode 무료 모델 직접 실측: youtu.be ▶ 링크 cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents Graphify-Labs/graphify ruvnet/ruflo anomalyco/opencode stablyai/orca android/skills akitaonrails/ai-memory pingdotgg/t3code fastapi/fastapi ※ 내레이션은 AI 보이스(ElevenLabs)예요. 실화면이 없는 부분은 공식 문서에 적힌 동작을 그린 원리 모션이고, '이렇게 써 보세요' 카드는 문서에 있는 기능 범위의 활용 예예요. #GitHub트렌딩 #오픈소스 #개발자 #코딩에이전트 #SoloStack [en] This Week's Must-See Open Source: GitHub Trending Top 10 + Extras (Oct, Week 4) A full walkthrough of this week's GitHub Trending (weekly) Top 10 in page order (collected 2026-10-28 06:00 KST), plus the three repos ranked 11-13 — what each does, what changed recently, and how beginners and working developers can put it to use. 10 android/skills · 9 Orca · 8 OpenCode · 7 ruflo · 6 graphify · 5 TradingAgents · 4 andrej-karpathy-skills · 3 claude-seo · 2 knowledge-work-plugins · 1 Cloudflare security-audit-skill Extras: ai-memory, T3 Code, FastAPI (0.160/0.162 change parameter-model extras and omitted bool form fields). Check this week: upgrade ruflo to 3.63.3+, revoke and reissue provider keys stored in OpenCode Console, test FastAPI form handling before upgrading. Narration is an AI voice (ElevenLabs).

[개발자들의 PICK] 이번 주 깃허브 트렌딩 결산, 인기 오픈소스 TOP 10 이번 주 깃허브에서 가장 많은 별을 받은 오픈 소스 TOP 10을 10위부터 소개합니다. 소개한 저장소 링크는 고정 댓글에 있습니다. 00:00 이번 주 TOP 10 00:05 10위 안드로이드 스킬 00:51 9위 오르카 01:38 8위 오픈코드 02:15 7위 루플로 02:47 6위 그래피파이 03:32 5위 트레이딩 에이전츠 04:17 4위 카파시 스킬 04:56 3위 클로드 SEO 05:35 2위 지식 노동 플러그인 06:14 1위 클라우드플레어 시큐리티 오딧 스킬 06:55 마무리 📌 이번 주! 깃허브 트렌딩 TOP 10 저장소 10위 안드로이드 스킬 — android/skills 9위 오르카 — stablyai/orca 8위 오픈코드 — anomalyco/opencode 7위 루플로 — ruvnet/ruflo 6위 그래피파이 — Graphify-Labs/graphify 5위 트레이딩 에이전츠 — TauricResearch/TradingAgents 4위 카파시 스킬 — multica-ai/andrej-karpathy-skills 3위 클로드 SEO — AgriciDaniel/claude-seo 2위 지식 노동 플러그인 — anthropics/knowledge-work-plugins 1위 클라우드플레어 시큐리티 오딧 스킬 — cloudflare/security-audit-skill [en-US] [Developer's PICK] This Week's GitHub Trending Roundup, Top 10 Popular Open Source Projects Here are the Top 10 Open Sources with the Most Stars on GitHub this week, starting from 10th place. Links to the featured repositories are in the pinned comment. 00:00 This Week's Top 10 00:05 10th Android Skills 00:51 9th Orca 01:38 8th OpenCode 02:15 7th Luplo 02:47 6th Graphify 03:32 5th Trading Agents 04:17 4th Kapasi Skills 04:56 3rd Claude SEO 05:35 2nd Knowledge Work Plugin 06:14 1st Cloudflare Security Audit Skills 06:55 Wrap-up 📌 This Week! GitHub Trending Top 10 Repositories 10th Android Skills — android/skills 9th Orca — stablyai/orca 8th Open Code — anomalyco/opencode 7th Ruflo — ruvnet/ruflo 6th Graphify — Graphify-Labs/graphify 5th Trading Agents — TauricResearch/TradingAgents 4th Karpathy Skills — multica-ai/andrej-karpathy-skills 3rd Claude SEO — AgriciDaniel/claude-seo 2nd Knowledge Work Plugins — anthropics/knowledge-work-plugins 1st Cloudflare Security Audit Skills — cloudflare/security-audit-skill [id] [Pilihan Pengembang] Rekap Trending GitHub Minggu Ini, 10 Open Source Terpopuler Berikut adalah 10 Open Source Teratas dengan Bintang Terbanyak di GitHub minggu ini, dimulai dari peringkat ke-10. Tautan ke repositori unggulan ada di komentar yang disematkan. 00:00 10 Teratas Minggu Ini 00:05 Peringkat ke-10 Android Skills 00:51 Peringkat ke-9 Orca 01:38 Peringkat ke-8 OpenCode 02:15 Peringkat ke-7 Luplo 02:47 Peringkat ke-6 Graphify 03:32 Peringkat ke-5 Trading Agents 04:17 Peringkat ke-4 Kapasi Skills 04:56 Peringkat ke-3 Claude SEO 05:35 Peringkat ke-2 Knowledge Work Plugin 06:14 Peringkat ke-1 Cloudflare Security Audit Skills 06:55 Ringkasan 📌 Minggu Ini! 10 Repositori Terpopuler di GitHub 10 Android Skills — android/skills 9 Orca — stablyai/orca 8 Open Code — anomalyco/opencode 7 Ruflo — ruvnet/ruflo 6 Graphify — Graphify-Labs/graphify 5 Trading Agents — TauricResearch/TradingAgents 4 Karpathy Skills — multica-ai/andrej-karpathy-skills 3 Claude SEO — AgriciDaniel/claude-seo 2 Knowledge Work Plugins — anthropics/knowledge-work-plugins 1 Cloudflare Security Audit Skills — cloudflare/security-audit-skill