AAlgoVault.cominblog.algovault.com·May 12 · 9 min readHow Merkle anchoring on Base L2 turns a track record into verifiable proofIntro Every vendor with a SaaS dashboard claims a win rate. Backtests are cheap. Self-reported numbers are cheaper still. But when you are building an AI trading agent that autonomously routes capital across venues, "trust me" is not a risk model. Al...00
AAlgoVault.cominblog.algovault.com·May 8 · 9 min readHyperliquid plus Bybit — deep liquidity meets the composite verdictIntro Two exchanges agree. The third disagrees. That's signal. Hyperliquid funding at +0.012%. Binance at +0.010%. Bybit at −0.005%. The third venue's contrarian funding read is not noise — when retail-skewed flow fades on Bybit before institutional ...00
AAlgoVault.cominblog.algovault.com·May 5 · 9 min readComposite verdict versus vote-counting: how AlgoVault's quant weighting worksIntro Your trading agent just evaluated fifteen indicators. Seven say long. Six say short. Two are flat. Now your orchestration code has a problem that has nothing to do with trading. This is the vote-counting trap — and it is the most common product...00
AAlgoVault.cominblog.algovault.com·May 2 · 9 min readHyperliquid plus Binance — a unified MCP for cross-venue funding rate signalsIntro Funding rates lie when you only watch one exchange. Consider the scenario: your LLM trading agent calls Hyperliquid, reads a BTC funding rate of +0.012%, and concludes that longs are paying premium — a bullish lean. It builds a regime thesis an...00
AAlgoVault.cominblog.algovault.com·Apr 26 · 10 min readWhy we expose MCP tools instead of WebSockets for AI trading agentsIntro Your AI agent doesn't need 26 indicators. It needs one answer. Picture a multi-agent trading stack that subscribes to a WebSocket feed streaming 200 ticks per second. Before the agent can reason about whether to open a position, it must parse i...00
