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Alpstein
AI-powered crypto news analysis with live P&L tracking.
AI-native platform for crypto traders. Scrapes news in real time, calls live Binance data and RAG over recent articles, generates structured trade positions (entry, stop-loss, take-profit), and tracks live P&L through a real-time dashboard and Terminal UI.
Hey PH 👋
I built Alpstein because most crypto "analysis" I came across fell into two unsatisfying buckets — noise-heavy news aggregators, or technical chart tools without context. Neither one does what a human analyst actually does: read the news, cross-reference current price action, weigh the two, and form a position with a defined entry, target, and risk.
I wanted to see whether an LLM could do a reasonable version of that loop end-to-end, and whether it could be packaged as something anyone could use.
Under the hood: a Node.js scraper pulls articles from four crypto news sources, pushes them through a Redis queue to a Go LLM service. For each article, the model calls OpenAI with two tool calls available — one for live Binance market data, one for RAG retrieval against recent articles stored as embeddings in Qdrant. With both grounded, the model produces a structured trade opinion (long/short, entry, stop-loss, take-profit, rationale). A separate WebSocket service tracks live prices and computes real-time P&L for each open position.
Built solo over the last several months. Hosted on Hetzner + Vercel. The whole pipeline is instrumented with Prometheus, Grafana, OpenTelemetry, and Jaeger which made prompt tuning much easier than I expected.
Genuinely curious what people think, especially about the agent design, RAG approach, and whether the trade-opinion framing lands. Feedback welcome, including the harsh kind.
About Alpstein on Product Hunt
“AI-powered crypto news analysis with live P&L tracking.”
Alpstein was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #99 on the daily leaderboard. AI-native platform for crypto traders. Scrapes news in real time, calls live Binance data and RAG over recent articles, generates structured trade positions (entry, stop-loss, take-profit), and tracks live P&L through a real-time dashboard and Terminal UI.
On the analytics side, Alpstein competes within Fintech, Artificial Intelligence and Tech — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Alpstein performed against the three products that launched closest to it on the same day.
Who hunted Alpstein?
Alpstein was hunted by Arvind Khoisnam. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Alpstein including community comment highlights and product details, visit the product overview.
Arvind Khoisnam