Trading bot evidence gate: putting Jev between retrieval and generation
Uses Jev to make structured judgments on RAG-retrieved passages—whether they support the claim, contradict the setup, or concern the same ticker—filtering noise before deciding to trade or wait, with three repos to build the stack.
this feels like a f*cking cheat code a trading bot can cite 10 sources and still have zero evidence for the trade RAG finds the closest passages. the closest passage can still be last quarter's guidance or the sixth copy of the same headline feed that straight to the LLM and a search match can become your reason to buy you just turned similarity into conviction put Jev between retrieval and generation does this passage actually support the claim? does it contradict the setup? is it only talking about the same ticker? Jev returns structured judgments. code keeps useful evidence, preserves contradictions, and drops the noise a second check asks whether the surviving context is enough to answer if it isn't: more research or WAIT 3 repos to build the stack: -> FinMem: layered memory for an LLM trading agent https://github.com/pipiku915/FinMem-LLM-StockTrading -> jev-reranker: Jev reranking and evidence filtering before generation https://github.com/hotchpotch/jev-reranker -> QuantDinger: paper/live trading with a Jev decision gate before entry orders https://github.com/OpenByteInc/QuantDinger $0.042 per million input tokens. output is free 10,000 total input tokens across the Jev checks = $0.00042 data, embeddings and the writing model have their own bill the catch: an evidence score is not a win rate. keep the rejected candidates and paper trade the full loop. configure failed checks to block new entries while keeping exits available "let RAG find the context. let Jev check the evidence. let code decide whether an order is allowed." bookmark this before your bot mistakes a citation for an edge
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