ArvinLovegood/go-stock: an AI stock analysis desktop app that keeps your data local
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At a glance
- What is it?
- go-stock is a Wails and NaiveUI desktop application that pulls A-share, Hong Kong and US quotes, runs them through a configurable large language model, and stores everything on your own machine. It is a research toy with a real data pipeline behind it, and the README says so itself.
- Who is it for?
- Adopt go-stock if you already run a local model through Ollama or LMStudio, want A-share, Hong Kong and US quotes in one desktop window, and accept that the output is study material rather than a signal. Skip it if you need a headless service, a documented macOS or Linux build, or an auditable analysis pipeline, because the README states the development environment is Windows 10 and other platforms are untested or limited.
- Can I use it commercially?
- Yes, with conditions. GPL-3.0 is a copyleft licence: if you distribute software that includes it, you must release that software's source code under the same licence. Running it internally without distributing it does not trigger that obligation.
- Is it still maintained?
- Yes. The repository received new commits within the last day.
- What is it written in?
- Mainly Go, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
What go-stock actually solves for a retail A-share watcher
Most retail stock tooling splits into two camps. Broker terminals give you fast quotes and no interpretation. Chat interfaces give you interpretation with no data attached. go-stock sits between them: a desktop binary that fetches quotes, funds flow, order-book detail and market news, then hands those to a language model you configure. The README frames it as an AI-enabled analysis and stock-picking tool covering A-shares, Hong Kong and US markets, plus on-exchange ETF search, fund watchlists and fund rankings.
The audience is narrow and specific. It is someone who follows Chinese markets, already has an API key or a local model server, and wants the analysis step to happen without pasting numbers into a browser tab. The README is blunt about the intent: it says the project is for entertainment and that AI analysis results are for study and research only. That sentence matters more than any feature list, because it tells you the author does not treat the output as investment advice and neither should you.
The local-data promise is the other half of the pitch. The description states that all data is retained locally. For anyone uneasy about shipping their watchlist and holdings to a hosted analytics product, that is the reason to look at this repository rather than a web dashboard.
The Wails shell, the Go backend and where the model sits
go-stock is a Wails application, which means a Go process hosts a webview frontend built with NaiveUI. The repository layout confirms this: main.go, app.go, app_windows.go, app_darwin.go and app_linux.go sit at the top level, with backend/, frontend/ and a separate ai-assistant-web/ directory. Platform-specific app files exist for Windows, macOS and Linux, though the README states the development environment is mainly Windows 10 and that other platforms are untested or have limited functionality.
The AI layer is built on CloudWeGo Eino. The go.mod file lists eino itself plus provider components for Ark, Claude, DeepSeek, Gemini, Ollama, OpenAI, OpenRouter and Qwen, and an MCP tool component. That is the mechanism behind the long provider table in the README: each supported platform is an adapter into the same agent runtime, so switching from DeepSeek to a local Ollama model is a configuration change rather than a code change.
The README describes three agent modes (React, PlanExecute and DeepAgents), more than 150 AI data tools, a skill system, MCP extension support and a floating AI assistant. Treat the tool count as a description of surface area, not a quality signal. A large tool registry mainly tells you the model has many ways to query data, and more ways to query data also means more ways to query it badly.
On the data side, go.mod pulls in github.com/bensema/gotdx, a Go implementation of the TDX protocol, which is the mechanism behind A-share quotes. The README also mentions chromedp, and the dependency list includes it, which suggests some news or page scraping runs through a headless browser. That is a fragile ingestion path by nature: when a page layout changes, the scrape breaks and the app may not tell you.
Installing go-stock and getting one real analysis out of it
The README does not document a from-source build for end users. It points to prebuilt binaries on the releases page: a green (portable) Windows build named go-stock-windows-amd64.exe and a macOS universal build named go-stock-darwin-universal. Download the one for your platform and run it; there is no installer step documented for the current release line.
If you want to build from source, the repository is a standard Go module named go-stock requiring Go 1.27.0, with a Wails frontend. The Wails CLI is the usual entry point for that, but the README does not give a build command, so check docs/go-stock使用手册.md before assuming a flag or task name.
After launch, the first useful step is binding a model. The README's provider table lists DeepSeek with the model names deepseek-reasoner and deepseek-chat, and notes that any OpenAI-compatible endpoint works. Configure the base URL and key in the app's settings panel rather than in a file, since the README does not document an environment variable for the key.
Once a model responds, add a symbol to the watchlist and open its detail view. The README lists K-line analysis with adjustment switching, Elliott wave theory, measurement boxes and 13 drawing tools, plus intraday and tick data, call auction and per-stock funds flow. Ask the floating assistant a question about the selected symbol and you should get an answer grounded in those panels. If the reply is generic, the model is probably not receiving tool output, which usually means the endpoint is wrong or the model does not support the tool-calling format the agent expects.
For alerts, the README lists scheduled warnings pushed through Feishu or DingTalk. Those require webhook configuration in the app; the README does not document the webhook payload format.
Where go-stock breaks, and the cases it is the wrong tool for
The clearest limitation is stated by the author: the development environment is Windows 10, and other platforms are untested or limited. Top-level files for Darwin and Linux exist, and update_helper_darwin.go and update_helper_linux.go are present, so cross-platform work has started. But a file existing is not the same as a tested path, and a macOS or Linux user should expect rough edges rather than parity.
The second limitation is the model dependency. Every AI feature is only as good as the endpoint you bind. A small local model will produce confident, fluent, wrong analysis of a funds-flow table, and nothing in the pipeline will flag that. The README's own disclaimer that results are for study and research is the honest version of this problem.
The third is ingestion fragility. A TDX-based quote path and a chromedp-based scraping path both depend on external services and page structures you do not control. When a source changes, the failure mode is often missing or stale data rather than a loud error, and stale data fed to a language model still produces a tidy paragraph.
Finally, this is a desktop GUI application. If you want a scheduled job that writes analysis to a database, or an API other services can call, go-stock is the wrong shape. There is no documented headless mode, and the Wails architecture assumes a window. The README also does not document rollback or downgrade steps, so pinning a known-good release binary is the only recovery path it supports in practice.
go-stock versus a scripted Python pipeline
The obvious alternative for a developer is a Python stack: a data library for quotes, a scheduler, and direct calls to the same model APIs. The difference is not the model, since both can hit DeepSeek or a local Ollama server. The difference is where the work lives.
A scripted pipeline gives you version control over the analysis logic, tests, and a diffable record of what changed between runs. go-stock gives you a graphical workspace where the K-line tools, funds-flow panels, news aggregation, dragon-tiger list, limit-up ladder and alerting are already assembled, and where the AI sees structured tool output instead of whatever you remembered to serialize. For someone who does not want to maintain a scraper, that is a real saving.
The cost is transparency. In a script you can read the prompt and the data payload. In go-stock the agent decides which of its 150-plus tools to call, and the README does not document how that selection is logged. If you need to explain why a conclusion was reached, the scripted route is easier to defend, and go-stock is easier to start.
Maintenance, release cadence and the GPL-3.0 boundary
The repository is not archived and the last push was on 2026-09-10, four days before this writing. Three releases landed in the week before that: v2026.09.09.1-release, v2026.09.08.4-release and v2026.09.08.3-release. The README tells users to prefer the newest published version because the software is under rapid iteration. That cadence cuts both ways. You get fixes quickly, and you also get churn, so keeping the previous binary around is the practical upgrade strategy given the absence of documented rollback.
The dependency graph is heavy. go.mod pins Eino and eight model adapters, chromedp, gotdx, gse for Chinese text segmentation, beeep and go-toast for notifications, and an auto-update library. One detail worth knowing: the module replaces github.com/mattn/go-sqlite3 with a stub under third_party/mattn-sqlite3-stub so the build stays pure Go and uses modernc.org/sqlite instead. That is a deliberate choice to avoid CGO, and it means a from-source build does not need a C toolchain for SQLite.
The licence is GPL-3.0. If you fork go-stock and distribute a modified binary, the GPL's copyleft terms apply to that distribution, and the repository ships a LICENSE file you should read rather than rely on this summary. Running it privately for your own research does not trigger distribution obligations in the usual reading, but if you plan to embed it in a product, have a lawyer look at the combination of GPL-3.0 and any model provider terms. This is not legal advice.
Editorial conclusion
Adopt go-stock if you already run a local model through Ollama or LMStudio, want A-share, Hong Kong and US quotes in one desktop window, and accept that the output is study material rather than a signal. Skip it if you need a headless service, a documented macOS or Linux build, or an auditable analysis pipeline, because the README states the development environment is Windows 10 and other platforms are untested or limited. Before trusting a single number, open the settings panel and confirm which model endpoint is bound, then check whether the quote source for your market is reachable from your network, since a silent model or feed failure produces plausible-looking output rather than an error.
Frequently asked questions
What is go-stock?
It is a desktop stock analysis application built with Wails and NaiveUI that combines A-share, Hong Kong and US market data with a large language model you configure. The README describes it as an AI-enabled analysis and stock-picking tool, and states that all data is kept locally.
Which AI models and platforms does go-stock support?
The README lists OpenAI, Ollama, LMStudio, AnythingLLM, DeepSeek, Zhipu AI, Volcengine Ark, Alibaba Cloud Bailian, Moonshot, Tencent Hunyuan, iFlytek Spark, MiniMax, Xiaomi MiMo and aggregator platforms such as SiliconFlow. It also notes that any OpenAI-compatible endpoint can be used.
Does go-stock work on macOS or Linux?
The README states that the development environment is mainly Windows 10 and that other platforms are untested or have limited functionality. A macOS universal build is published on the releases page, and platform-specific source files exist for Darwin and Linux, but the README does not claim tested parity.
Does go-stock send my data to a remote server?
The project description states that all data is retained locally. The analysis step still goes wherever your configured model runs, so if you bind a hosted provider such as DeepSeek, the data you send to that model leaves your machine.
Is go-stock investment advice?
No. The README says the project is for entertainment and that AI analysis results are for study and research only, with an explicit warning that investing carries risk.
Community notes