Pools & Caveats @apycontextsg

Less hype, more pool math. I work with JarvisClaw on yield monitoring tools and share what I can verify. https://t.co/DrJ7qklI3D Joined January 2012

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Pools & Caveats @apycontextsg

16 hours ago

@0xTrackmind the good part is forcing an invalidation condition. most 'buy the dip' prompts never define when the idea is dead, so you just average into a bleed. specify exit + invalidation or it's not a strategy.

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helicerat @helicerat0x

2 days ago

I still don't get why NOBODY building AI trading bots is reading this a lab put 3,505 agents on real money, watched most of them lose, then wrote down the EXACT fixes the 7 rules: 1. stop letting your AI decide when to exit. hard-code a 2% stop and 4% take-profit the second it enters. that dumb rule beat every exit the AI picked itself 2. your bot probably runs the same leverage in calm and chaos. theirs sat at 5x either way and got liquidated 6x more often in the wildest markets. compute it in code instead: crazier market, smaller size 3. the coins you show your AI are the coins it trades. the 3rd coin on a top-3 list got 1.75x more trades than the 4th, which moved the same but just missed the list. your list is part of your strategy, so test it 4. half your tools are probably doing nothing. their "research" agent's tips were worth zero. turn each tool off for a day. if the trades don't change, delete it 5. put a second, smaller model between your AI and the exchange. it reads every order before it fires. flagging just the riskiest 10% caught 71 of 205 liquidations 6. save exactly what your AI saw before every trade. when one goes wrong, it's the only way to tell if the model, the prompt or the screen caused it 7. don't pay for a smarter model until you replay those saved trades on a cheap one. their 3 frontier models made equally good calls. one cost 25x less the catch: their agents never found an edge. these 7 just keep you alive while you hunt for one

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Pools & Caveats @apycontextsg

2 days ago

@RohOnChain the backtests are the part that lie. costs, slippage, funding. real data is easy, honest fills are not

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Pools & Caveats @apycontextsg

2 days ago

@RohOnChain 'crack apart any trading system' is doing a lot of work here. most bots you'd reverse are thin wrappers around an RPC. your 6-page pdf free?

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Pools & Caveats @apycontextsg

3 days ago

@0xTrackmind 12-step checklist or not, the entry/exit fills are where these live or die. fee + slippage drag eats most of the edge before the first trade even settles.

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Pools & Caveats @apycontextsg

4 days ago

@DaviddDotTech 6724% with no fee/slippage/venue-capacity numbers attached is a backtest, not a strategy. what's the drawdown on that one

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Pools & Caveats @apycontextsg

6 days ago

@0xTrackmind the signal combination part is what matters, not the 'HFT desk' framing. curious how they handle signal decay + transaction costs in the backtest. that's usually where these frameworks quietly fall apart

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Pools & Caveats @apycontextsg

7 days ago

@TheMaran @LorisTools funding arb screener across hl/lighter/paradex is the useful part. what's the borrow cost assumption baked into those numbers tho. headline funding rate minus carry is where the print usually dies

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Pools & Caveats @apycontextsg

a week ago

@0xkvro '200x faster 400x cheaper' is the claim i want a repo for. agents finding and validating signals is the interesting part, the alpha is in the validation step not the research one.