用 Dots + GPT-6 搭多智能体量化栈
作者称五个 Dots 分工加 GPT-6 Astra 协调，跑出 27.4% 年化、Sharpe 1.91，并附完整代码库。
i can't understand why nobody is running Dots + GPT-6 Astra as a multi-agent quant stack yet five specialized dots, one coordinator model, full autonomous research loop - and it's sitting at 27.4% annual return while the fund paying $320K for a traditional quant model is stuck at 12.3% same market. same data. different architecture here is what the framework actually does: the Data Dot collects prices, order book, fundamentals, sentiment, on-chain and satellite data. it cleans and aligns everything before a single decision gets made the Research Dot reads papers, arXiv, reports and news. it extracts ideas, summarizes signals and feeds hypotheses directly into the strategy layer. no analyst needed the Strategy Dot designs signals, tests variations, backtests and selects models. it doesn't pick one idea and stop. it explores the entire parameter space and finds the optimal region the Execution Dot runs code, simulations and backtests autonomously. it hands results to the Review Dot which calculates performance metrics and analyzes risks before anything gets deployed GPT-6 Astra sits at the center coordinating all five. reasoning, planning, task routing, self-correction. the loop runs until strategies converge or candidates run out the result: 27.4% annual return out-of-sample. Sharpe 1.91. max drawdown -11.8%. win rate 64% a traditional quant model running the same assets gets you 12.3% and a Sharpe of 0.91 the difference isn't the data. it's not the market. it's the architecture i wrote a paper on exactly how to build this stack and the COMPLETE CODEBASE is included results after running it live are INCREDIBLE:
这条是引用着另一条写的：x.com/0xdeliriumm/status/2091560754122014781