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.
🗂️ 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.
"Chart CBRS since its IPO and mark what moved it: earnings, deals, analyst calls and lock-ups."
The agent works out what moved the stock on each big day, then draws it on the live chart: 18 event badges, 4 lock-up markers, and lines at the $185 IPO price and the consensus target. Hover a badge to read its note.
🧾 See the evidence behind every answer
A Sources panel lists each filing, web page, data call and file the agent touched in a turn, even the data calls made from its own Python and Bash, so you can check the work instead of trusting it.
Agentic trading
We are committed to making LangAlpha the best harness for agentic trading. Brokers now let AI agents trade real accounts, and the agents plugging in are general-purpose, so an order is one more tool call among many. In LangAlpha it is the one call the harness is built around:
- Limits that hold whatever the model does. What each connection may do, which orders wait for your approval, and the rule that an approved order runs exactly once are enforced on the host, on the one governed path every order takes. No prompt, and no script the agent writes, can widen them, and they stay the same for every model you run.
- An order path you can read. Every check an order passes, from the model's call to the request your broker receives, is code in this repository. You can read it before trusting it with money, and run it on your own machine under the same controls.
Four brokerages connect today, under the same controls:
여기까지가 README 앞부분입니다. 나머지는 원문에서 읽을 수 있습니다. README 전문 보기 ↗
