Alternatives
Products that do what TradingAgents-Studio does
See the agents debate, not just the BUY/SELL call
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We’re thrilled to announce the launch of our AI-powered stock market analyst chatbot, designed to help you analyze stocks and gain valuable market insights with ease. Our intuitive conversational chat interface makes it simple for anyone to get started. Why You’ll Love It: Our AI Analyst uses a long-term value-growth investing strategy, similar to those employed by legendary investors like Warren Buffett, Mohnish Pabrai, Phil Town and Charlie Munger. It’s built to provide you with thorough, data-driven analysis to help you make informed investment decisions. Key Features: - Comprehensive…
2024 · decodeinvesting.com
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I built Hermes, an open-source Python framework for multi-agent financial research. Most AI “equity research” demos stop at generating text. In practice, real workflows require pulling structured XBRL financials from SEC filings, extracting labeled sections like MD&A and Risk Factors, merging macro and market data, building actual Excel models with formulas, and generating investment memos in Word or PDF. Hermes is designed to handle that full pipeline end to end. It includes 35 financial data tools covering SEC EDGAR (via edgartools), FRED, Yahoo Finance market data, and RSS-based financial…
Feb 2026 · github.com
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2025 · github.com
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A while back, I built a simple app to track stocks. It pulled market data and generated daily reports based on my risk tolerance. Basically a personal investment assistant. It worked well enough that I kept going. Now, the same framework helps me with real estate: comparing neighborhoods, checking flood risk, weather patterns, school zones, old vs. new builds, etc. It’s a messy, multi-variable decision—which turns out to be a great use case for AI agents. Instead of ChatGPT or Grok 4, I use mcp-agent, which lets me build a persistent, multi-agent system that pulls live data, remembers my…
2025 · github.com
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How it works (tech stack): -Built entirely with Lovabl.dev (no-code front-end + logic) -ChatGPT / Claude for research and inspiration -Powered by GPT-4 Vision to interpret charts visually -Hosted on Supabase for performance & caching It’s not meant to replace analysts — just to speed up how traders interpret data. I’m a designer exploring AI tools, and this is my first attempt to turn an idea into a functional product. Would love to know what you think.
Oct 2025 · quantify-ai.co
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I built a local-first UI that adds two reasoning architectures on top of small models like Qwen, Llama and Mistral: a sequential Thinking Pipeline (Plan → Execute → Critique) and a parallel Agent Council where multiple expert models debate in parallel and a Judge synthesizes the best answer. No API keys, zero .env setup — just pip install multimind. Benchmark on GSM8K shows measurable accuracy gains vs. single-model inference.
Mar 2026 · github.com
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I built this simulator to study how simple trading agents interacting with an order book can create emergent patterns—volatility, clustering, even chaos—without any external shocks. It’s meant as both a research toy and teaching tool. You can run different strategies, adjust configs, and watch dynamics evolve. Curious to hear feedback from the HN crowd on features/dynamics you’d like to see added.
2025 · aitradingsim.com
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Hey HN! I built PRISM-INSIGHT, a multi-agent system where 13 specialized AI agents collaborate to analyze Korean stocks (KOSPI/KOSDAQ). It's completely open source and has been running live since March 2025. [What it does] The system automatically detects surging stocks twice daily, generates analyst-level reports, and executes trading strategies. Each agent specializes in something different – technical analysis, trading flows, financials, news, market conditions, etc. They work together like a real research team. [Why I built this] I wanted to see if GPT-4 and GPT-5 could genuinely…
Nov 2025
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I built a browser-only studio for designing and orchestrating MCP agent systems for development and experimental purposes. The whole stack — tool authoring, multi-agent orchestration, RAG, code execution — runs from a single static HTML file via WebAssembly. No backend. The bet: WASM is a hard sandbox for free. When you generate tools with an LLM (or write them by hand), the studio AST-validates the source, registers it lazily, and JIT-compiles into Pyodide on first call. SQL tools run in DuckDB-WASM in a Web Worker. The built-in RAG uses Xenova/all-MiniLM-L6-v2 via Transformers.js for…
Apr 2026 · agentmcp.studio
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Hey HN, For the last couple of months, we have been building an AI agent for continuous statistical analysis, and we're looking for feedback while it's still early in development. We call it BIGWIG - an autonomous agent that is specialised, and very good at, performing advanced statistical analysis, through long traces of iteration and reasoning. As it builds statistical models it also "emits" outputs back to the user that you can then interact with, iterate on and schedule for follow up analysis. While we're still in BETA, we've launched a public analysis site that showcases some of the…
2025 · askbigwig.com
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Hi HN, we're developing Neverbell, an AI agent that combines market analysis with trade execution. One of our beta testers said to Neverbell: “Protect my downside, but don’t be too conservative.” Volatility increased a few days later, and the agent decreased their exposure. Our tester wasn’t happy. The interesting part for us was that both interpretations were reasonable. The agent gave more weight to “protect my downside.” The user thought that “don't be too conservative” was more important. It changed the way we built Neverbell. Now, if the agent sees any instructions that conflict, it…
Jul 2026 · neverbell.com
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