




sonicgates
1.5K posts








For 2 years straight… this setup hasn’t failed me. Has a-least 80% accuracy on FX. Stocks. Crypto. Same model. Same results. It’s simple: Mark the Monday range Monday High Monday Low Then wait for the sweep of either side Drop timeframe → confirm (CISD / MSB) Tear lot size . Target? Opposite side of the range. No indicators. No guessing. Just structure. Same setup. Every week. Save this , will trade live





the fastest growing GitHub repos in finance this week: 1. mvanhorn/last30days-skill (+2.1K ★) AI agent skill that searches Reddit, X, YouTube, HN, Polymarket and the web in parallel — then scores results by upvotes, likes, and real money, not editors. drop it into Claude Code or OpenClaw. zero config to start. 2. ZhuLinsen/daily_stock_analysis (+1.2K ★) LLM-powered stock analyzer for US, A-share and H-share markets. real-time news + multi-source data + decision dashboard with exact buy/stop/target levels. runs on GitHub Actions on a schedule at zero cost. pure automation. 3. juspay/hyperswitch (+667 ★) open-source payments infrastructure written in Rust. modular by design — pick only what you need: routing, retries, vaulting, reconciliation, cost observability. built by the team behind payment infrastructure for 400+ enterprises. the "Linux for payments" 4. HKUDS/AI-Trader (+655 ★) agent-native trading platform where AI agents join, share signals, debate ideas, and copy each other's trades. send one message to any agent and it registers itself. supports OpenClaw, Claude Code, Codex, Cursor and more. 5. hsliuping/TradingAgents-CN (+471 ★) Chinese-enhanced fork of TradingAgents. same multi-agent LLM trading architecture, fully localized for Chinese markets, A-share data, and domestic LLMs like DeepSeek and Qwen. 23K stars and climbing. 6. ashishpatel26/500-AI-Agents-Projects (+436 ★) curated collection of 500+ AI agent use cases across healthcare, finance, education, retail and more. organized by industry and framework — CrewAI, AutoGen, LangGraph, Agno. the best reference list if you're figuring out what to build next. 7. OpenBB-finance/OpenBB (+355 ★) open-source financial data platform for quants, analysts and AI agents. "connect once, consume everywhere" – same data layer exposes to Python, Excel, MCP servers for agents, and REST APIs. the open-source Bloomberg alternative. 8. microsoft/qlib (+349 ★) AI-oriented quant investment platform from Microsoft. covers the full pipeline from data to live trading: alpha seeking, risk modeling, portfolio optimization, order execution. deep learning, RL, auto-quant – all in one place. 9. tradingview/lightweight-charts (+318 ★) one of the smallest and fastest financial chart libraries for the browser. built with HTML5 canvas, weighs almost nothing, renders like native. if you're building any kind of trading UI on the web, this is what you reach for first. bookmark this and start today.













LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
