Run persistent financial research with an agent
The harness for agentic trading. Task: The harness for agentic trading. Open-source AI agents that research the market, build the thesis, and trade your own brokerage account within the permissions you set. Try LangAlpha ↗ · Get started · Agentic trading · Harness design · Full-stack architecture · Security English · · Why LangAlpha Most AI finance tools answer questions. We believe investment research is Bayesian: you write down a thesis and what would prove it wrong, then every earnings print, filing and price move raises or lowers your conviction, and your position with it. That loop runs for weeks or months, and no single prompt captures it. Coding agents got good once they had a harness built for code: a codebase that persists, where every commit builds on the last, plus the tools, memory and runtime around the model. LangAlpha brings that harness to markets, from vibe coding to vibe investing. It works with any model, so every better model makes it better. Two convictions shape it: A trade is a loop, not a tool call. The thesis lives in a workspace, and research, sizing, the order and the watch that follows each feed new evidence back into it. Autonomy needs a boundary you set. Agents act within the permissions you grant, and every order takes one governed path. Feature highlights Most of these examples come from one workspace on Cerebras (CBRS). See it work in action with this shared conversation, from the first question to the initiation report sent to Slack. 🔎 Research with a team of agents Ask a question and LangAlpha splits it across parallel analysts that read filings, pull prices, options and macro data, and run the numbers in code. Send a follow-up at any time to redirect them mid-run. On CBRS, the run ended in an HTML primer on what Cerebras sells, how fast it grows and who pays for it. Subagents: four analysts work the question under the lead agent, each card counting its tools and tokens, and one /html-report turns their findings into the primer on the right 🗂️ Keep every idea in a workspace One workspace per thesis, sector or portfolio. Files, chats and the agent's own notes are still there tomorrow, so each session builds on the last instead of starting over. "Build me an interactive dashboard on CBRS from what we've found so far in this workspace: the price with the key events, revenue growth, customer concentration, and how much rides on OpenAI. I want to click around it." A new thread builds it from the research and files earlier threads left in the workspace: a live dashboard you can play with. ⏰ Agents that keep watch Schedule a brief before the open, or wake an agent when a stock crosses a price or moves a set percentage in a day. Results land in one feed, and in Slack, Discord, Telegram, Feishu or iMessage if you connect them and pick them for delivery. "Keep watch on CBRS for me. Check the model's assumptions every Monday, update it after Q3 results, and tell me if the stock breaks above our bull value or below $95." The agent sets up four automations in the workspace: a weekly assumption check, a one-time model update the morning after Q3 results, and two price triggers, at the bull-case value from the DCF model and at $95, the level that would change the rating. Each run starts from the model and notes in the workspace and reports to Slack. 📑 Get the deliverable, not just an answer Excel models with live formulas, PDF and HTML reports, slide decks and interactive dashboards. Built-in skills cover DCF, comps, earnings previews, initiating coverage, morning notes and trade pitches. "Write it up like a sell-side initiation on CBRS: rating, price target, the thesis, what the market is pricing in, the scenarios, peers, key risks, and the charts. Use the research and the model we already have." The initiating-coverage skill builds on the research and the reverse DCF workbook from earlier in the thread, and writes ten pages: a Sell with a $130 target, scenarios, peers, risks and seven charts, as a PDF with an HTML version. Select a range in the spreadsheet panel and send it back to the agent to ask about it. With Slack connected, ask it to send everything over and every file arrives in your DM, ready to open there. 📈 Chart with the agent Live market charts the agent can read and draw on from any chat: support and resistance, trendlines, Fibonacci levels and event markers, saved per symbol and timeframe. Outside Market View, the drawing arrives as a chart card that opens the same chart beside the conversation.; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: Prerequisites include access to brokerage APIs and configured data feeds for filings and prices. Integration uncertainty exists regarding the stability of parallel agent coordination and permission enforcement. Source reliability is low as the provided URL returned a 504 error, preventing verification of current features. Human approval is mandatory for all trade orders and external communications..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
The harness for agentic trading.
Workflow
The harness for agentic trading. Open-source AI agents that research the market, build the thesis, and trade your own brokerage account within the permissions you set. Try LangAlpha ↗ · Get started · Agentic trading · Harness design · Full-stack architecture · Security English · · Why LangAlpha Most AI finance tools answer questions. We believe investment research is Bayesian: you write down a thesis and what would prove it wrong, then every earnings print, filing and price move raises or lowers your conviction, and your position with it. That loop runs for weeks or months, and no single prompt captures it. Coding agents got good once they had a harness built for code: a codebase that persists, where every commit builds on the last, plus the tools, memory and runtime around the model. LangAlpha brings that harness to markets, from vibe coding to vibe investing. It works with any model, so every better model makes it better. Two convictions shape it: A trade is a loop, not a tool call. The thesis lives in a workspace, and research, sizing, the order and the watch that follows each feed new evidence back into it. Autonomy needs a boundary you set. Agents act within the permissions you grant, and every order takes one governed path. Feature highlights Most of these examples come from one workspace on Cerebras (CBRS). See it work in action with this shared conversation, from the first question to the initiation report sent to Slack. 🔎 Research with a team of agents Ask a question and LangAlpha splits it across parallel analysts that read filings, pull prices, options and macro data, and run the numbers in code. Send a follow-up at any time to redirect them mid-run. On CBRS, the run ended in an HTML primer on what Cerebras sells, how fast it grows and who pays for it. Subagents: four analysts work the question under the lead agent, each card counting its tools and tokens, and one /html-report turns their findings into the primer on the right 🗂️ Keep every idea in a workspace One workspace per thesis, sector or portfolio. Files, chats and the agent's own notes are still there tomorrow, so each session builds on the last instead of starting over. "Build me an interactive dashboard on CBRS from what we've found so far in this workspace: the price with the key events, revenue growth, customer concentration, and how much rides on OpenAI. I want to click around it." A new thread builds it from the research and files earlier threads left in the workspace: a live dashboard you can play with. ⏰ Agents that keep watch Schedule a brief before the open, or wake an agent when a stock crosses a price or moves a set percentage in a day. Results land in one feed, and in Slack, Discord, Telegram, Feishu or iMessage if you connect them and pick them for delivery. "Keep watch on CBRS for me. Check the model's assumptions every Monday, update it after Q3 results, and tell me if the stock breaks above our bull value or below $95." The agent sets up four automations in the workspace: a weekly assumption check, a one-time model update the morning after Q3 results, and two price triggers, at the bull-case value from the DCF model and at $95, the level that would change the rating. Each run starts from the model and notes in the workspace and reports to Slack. 📑 Get the deliverable, not just an answer Excel models with live formulas, PDF and HTML reports, slide decks and interactive dashboards. Built-in skills cover DCF, comps, earnings previews, initiating coverage, morning notes and trade pitches. "Write it up like a sell-side initiation on CBRS: rating, price target, the thesis, what the market is pricing in, the scenarios, peers, key risks, and the charts. Use the research and the model we already have." The initiating-coverage skill builds on the research and the reverse DCF workbook from earlier in the thread, and writes ten pages: a Sell with a $130 target, scenarios, peers, risks and seven charts, as a PDF with an HTML version. Select a range in the spreadsheet panel and send it back to the agent to ask about it. With Slack connected, ask it to send everything over and every file arrives in your DM, ready to open there. 📈 Chart with the agent Live market charts the agent can read and draw on from any chat: support and resistance, trendlines, Fibonacci levels and event markers, saved per symbol and timeframe. Outside Market View, the drawing arrives as a chart card that opens the same chart beside the conversation.
Reported result
Actual outcomes are unknown as the source provides no measured results or performance metrics. The case describes a capability for generating deliverables like Excel models and PDF reports, but does not confirm successful execution or financial impact. Proposed acceptance check: Verify that a sample workspace correctly persists research notes across sessions and generates a draft initiation report without hallucinating data.
Human role
The original post does not say how a person stayed involved.
Limitations
Prerequisites include access to brokerage APIs and configured data feeds for filings and prices. Integration uncertainty exists regarding the stability of parallel agent coordination and permission enforcement. Source reliability is low as the provided URL returned a 504 error, preventing verification of current features. Human approval is mandatory for all trade orders and external communications.
Try it
Act as a financial research assistant using LangAlpha. I want to set up a persistent workspace for [replaceable: Ticker Symbol, e.g., AAPL]. First, define a Bayesian thesis: [replaceable: Thesis Statement] and list key falsification criteria. Next, configure four parallel analyst agents to gather data on [replaceable: Specific Metrics, e.g., revenue growth, customer concentration]. Generate a draft 'Initiating Coverage' report in PDF format including a DCF model and risk assessment. Do not execute any trades. Instead, present the report and the proposed trade parameters to me for review. I will manually approve or reject the thesis before any further automation or order placement. Ensure all data sources are cited within the workspace notes.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.