@wavaai.feeds: wild number. but the six-agent setup is the part worth looking at instead of asking one chatbot what to buy, the author split the workflow: → scanner finds new pairs → volume agent flags unusual activity → safety agent checks for rug risks → sizing agent decides position size → execution agent handles orders → logging agent records decisions and their reasons the terminal puts the whole process on one screen an equity curve next to the activity feed enter / skip / wait / exit decisions risk checks before execution entry and exit prices with slippage win and loss stats updating as trades close the author reports: starting balance: $14.82 ending balance: $7,643.21 win rate: 68.3% logged events: 1,412 those are self-reported figures, not an independently verified trading record and 68.3% wins doesn’t settle the question. small wins can still get wiped out by a few oversized losses what makes the architecture interesting is being able to trace a trade backward what triggered it which checks passed why that size was chosen what price actually filled what caused the exit that’s much more useful than a green balance with no history behind it if you’re building something similar, make the decisions inspectable before making the profit screenshot impressive save this for the agent split, not the overnight return

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Sunday 25 October 2026 16:30:12 GMT