--- title: Build a Stock Market Research Agentic Workflow​ | daily.dev description: A newsletter covering how to build a stock market research agentic workflow using Sim, a no-code drag-and-drop AI agent builder. The workflow connects to Alpha... canonical: https://daily.dev/posts/build-a-stock-market-research-agentic-workflow--uatfopk3z twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Build a Stock Market Research Agentic Workflow​ | daily.dev og:description: A newsletter covering how to build a stock market research agentic workflow using Sim, a no-code drag-and-drop AI agent builder. The workflow connects to Alpha... og:url: https://daily.dev/posts/build-a-stock-market-research-agentic-workflow--uatfopk3z og:image: https://api.daily.dev/og/posts/UAtFopK3z.png og:image:alt: Build a Stock Market Research Agentic Workflow​ og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Build a Stock Market Research Agentic Workflow​ **[Daily Dose of Data Science \| Avi Chawla \| Substack](https://daily.dev/sources/dailydoseofds)** · 4 min read · 2 upvotes · 1 comments ## Summary A newsletter covering how to build a stock market research agentic workflow using Sim, a no-code drag-and-drop AI agent builder. The workflow connects to Alpha Vantage via MCP for stock data and integrates with Telegram, hosted locally with Docker. Sim is positioned as a more intuitive alternative to n8n with better AI-native workflow support. The newsletter also briefly covers the double descent phenomenon vs. bias-variance trade-off in ML, and data version control for reproducible ML projects. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How can I build a stock market research agent that sends updates to Telegram? A stock market research agent can be built using Sim, a drag-and-drop AI agent workflow builder, connected to Alpha-Vantage MCP for stock market data and hosted locally via Docker. The workflow is then linked to Telegram so research updates are delivered directly to a chat, with the entire setup completed in minutes using Sim's ReactFlow-based interface. _See how daily.dev surfaces practical builds like agentic stock research workflows for engineers exploring AI agents._ ### What is the double descent phenomenon in machine learning and how does it relate to the bias-variance trade-off? Double descent describes test loss decreasing, then increasing, then decreasing again as model complexity grows past the interpolation point, contradicting the classical bias-variance trade-off where test loss should only rise after overfitting begins. It is commonly observed in deep learning models, and some theories attribute it to implicit regularization, though the exact cause remains an open research question. _Developers puzzling over generalization behavior can follow ongoing double descent research on daily.dev._ ### Why can't I use GitHub alone to version large ML datasets, and what tool solves this? GitHub imposes an upper limit on pushed file size, making it impractical for versioning gigabytes of data, since Git is designed for lightweight codebases rather than large files. Data Version Control (DVC) solves this by integrating a separate version-control system alongside Git specifically for large files, enabling reproducible and traceable ML experiments. _Teams building reproducible ML pipelines can track DVC and dataset versioning practices on daily.dev._ ## Community discussion Top comments from developers on daily.dev. **@agustinbarrientos** · 0 upvotes > What pushed you toward Sim instead of n8n, the MCP integration or the option to run locally with Ollama? ## Similar posts on daily.dev - [\[Hands-on\] Turn Any Workflow Into an Agent's Skill](https://daily.dev/posts/hands-on-turn-any-workflow-into-an-agent-s-skill-gtofr9bij) · Daily Dose of Data Science \| Avi Chawla \| Substack · 1 upvotes · 0 comments - [How to Choose the Best Stock Market API for FinTech Projects and AI Agents](https://daily.dev/posts/how-to-choose-the-best-stock-market-api-for-fintech-projects-and-ai-agents-leqiusjir) · freeCodeCamp · 3 upvotes · 0 comments - [Build a News Roundup With Docker Agent](https://daily.dev/posts/build-a-news-roundup-with-docker-agent-dlel3bsbc) · Docker · 1 upvotes · 0 comments - [\[Hands-on\] Build OpenClaw’s Core In a Single Visual Workflow](https://daily.dev/posts/hands-on-build-openclaw-s-core-in-a-single-visual-workflow-vknnnzmrx) · Daily Dose of Data Science \| Avi Chawla \| Substack · 2 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#automation](https://daily.dev/tags/automation), [#docker](https://daily.dev/tags/docker), [#mcp](https://daily.dev/tags/mcp) [View this post on daily.dev](https://daily.dev/posts/build-a-stock-market-research-agentic-workflow--uatfopk3z) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Build a Stock Market Research Agentic Workflow​","url":"https://daily.dev/posts/build-a-stock-market-research-agentic-workflow--uatfopk3z","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/build-a-stock-market-research-agentic-workflow--uatfopk3z"},"datePublished":"2026-08-01T19:45:13.857Z","dateModified":"2026-09-14T08:18:57.392Z","description":"A newsletter covering how to build a stock market research agentic workflow using Sim, a no-code drag-and-drop AI agent builder. The workflow connects to Alpha...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/3320cb9cdee6f919876a2a131fc02b99?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/3320cb9cdee6f919876a2a131fc02b99?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Daily Dose of Data Science | Avi Chawla | Substack","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Daily Dose of Data Science | Avi Chawla | Substack","logo":"https://media.daily.dev/image/upload/s--4IHQgTOw--/f_auto/v1710503712/logos/dailydoseofds","url":"https://daily.dev/sources/dailydoseofds"},"commentCount":1,"discussionUrl":"https://daily.dev/posts/build-a-stock-market-research-agentic-workflow--uatfopk3z","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":2},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":1}],"keywords":"ai-agents,automation,docker,mcp","timeRequired":"PT4M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Daily Dose of Data Science | Avi Chawla | Substack","item":"https://daily.dev/sources/dailydoseofds"},{"@type":"ListItem","position":3,"name":"Build a Stock Market Research Agentic Workflow​"}]} {"@context":"https://schema.org","@type":"WebPage","@id":"https://daily.dev/posts/build-a-stock-market-research-agentic-workflow--uatfopk3z","comment":[{"@type":"Comment","text":"What pushed you toward Sim instead of n8n, the MCP integration or the option to run locally with Ollama?","datePublished":"2026-08-05T21:42:58.644Z","url":"https://daily.dev/posts/UAtFopK3z#c-ht9QMXPii","author":{"@type":"Person","name":"Agustin Barrientos","url":"https://daily.dev/agustinbarrientos","image":"https://media.daily.dev/image/upload/s--5ayxQnqn--/f_auto/v1788281802/avatars/avatar_wQYYVe5Tbj0NJ7C7qPoa8?_a=BAMAMicg0"}}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/build-a-stock-market-research-agentic-workflow--uatfopk3z#faq","mainEntity":[{"@type":"Question","name":"How can I build a stock market research agent that sends updates to Telegram?","acceptedAnswer":{"@type":"Answer","text":"A stock market research agent can be built using Sim, a drag-and-drop AI agent workflow builder, connected to Alpha-Vantage MCP for stock market data and hosted locally via Docker. The workflow is then linked to Telegram so research updates are delivered directly to a chat, with the entire setup completed in minutes using Sim's ReactFlow-based interface. See how daily.dev surfaces practical builds like agentic stock research workflows for engineers exploring AI agents."}},{"@type":"Question","name":"What is the double descent phenomenon in machine learning and how does it relate to the bias-variance trade-off?","acceptedAnswer":{"@type":"Answer","text":"Double descent describes test loss decreasing, then increasing, then decreasing again as model complexity grows past the interpolation point, contradicting the classical bias-variance trade-off where test loss should only rise after overfitting begins. It is commonly observed in deep learning models, and some theories attribute it to implicit regularization, though the exact cause remains an open research question. Developers puzzling over generalization behavior can follow ongoing double descent research on daily.dev."}},{"@type":"Question","name":"Why can't I use GitHub alone to version large ML datasets, and what tool solves this?","acceptedAnswer":{"@type":"Answer","text":"GitHub imposes an upper limit on pushed file size, making it impractical for versioning gigabytes of data, since Git is designed for lightweight codebases rather than large files. Data Version Control (DVC) solves this by integrating a separate version-control system alongside Git specifically for large files, enabling reproducible and traceable ML experiments. Teams building reproducible ML pipelines can track DVC and dataset versioning practices on daily.dev."}}]} ```