Snack | Data & Quant Systems 🐍 @snack_quant

A fired Goldman Sachs quant trader taught me everything in a single conversation He said: “We don’t do predictions. We only buy contracts where the price deviation exceeds 6%.” It’s just that simple That’s the desk operation for a $2 million annual salary I fed his explanation and 5 GitHub repos into Claude, and Claude built a scanner. It processes over 400 markets every hour This scanner can find those contracts priced in the 7-19c range, with true probabilities between 60-90% At these entry points, you need a win rate of 1/4 And this bot’s win rate is 81% Three months later: From $2,000 to $8,191 99 trades, Sharpe ratio 2.30 A few cases: ETH Merge upgrade - market 72c, true probability 88%, +19c SOL breaks $200 - market 44c, true probability 81%, +15c Florida hurricane cat3+ - market 81c, true probability 92%, +7c Wheat breaks $800 - market 53c, true probability 68%, +20c All of these were found by the scanner, and all were profitable He looked at my terminal last week He said: “This is what we do with $800M, 47-person team.” And my current setup costs $25 per month Claude - $20 VPS - $5 Repos - free API - free Now there are 8 agents running 24/7: velvet_void +$697 nano_alpha +$541 ratking_eth +$407 darkpool_7 +$356 His fund returned 19% last year And my setup returned 409% in three months The real edge was never any secret—it’s just always been expensive, until now 70% win rate, 7 wallets copytrading rn from ~500 monitored, bot never paused, never gambling, just math and profit Giving This Free for 24 hours. To get it: 1. Comment the word 'CLAUDE' 2. Like and Retweet this post 3. Follow me @NextGenAi5 (so i can DM you)

What if your crypto research team had one control room? 8 specialists. 40 worker nodes. Market scans, context checks and research briefs moving through one queue. Which task would you delegate first? Concept animation • scripted tasks.

What would a swarm-inspired crypto research desk look like? One agent spots movement. Others check context, liquidity and risk. The swarm builds a brief. Speed is interesting. Knowing when to stop is the real question. Concept animation • scripted signals.

@dravenip Are the earnings driven by exchange-confirmed fills or by the HTML simulation? Live price inputs alone don’t establish live execution. A single trade traced from order ID to fill to balance change would make the distinction clear.

Try this toy test on a crypto bot: Replay an identical price stream twice. In run B, deliver one old quote late. Expected: the stale quote is flagged and cannot replace the latest quote. Log event time AND arrival time. Does your bot know the difference?

Before asking AI to improve your crypto bot, ask it to find a trade your backtest should never have counted. Here’s a review prompt you can copy. Use strategy code and redacted paper-trade logs; keep API keys and wallet secrets out. “Review this strategy as a skeptical test engineer. Do not optimize returns yet. 1. Trace one entry: what data was available at decision time? Flag any use of later information. 2. Find ambiguous fills: a candle touching both stop and target, partial fills, or an order submitted after the quoted price. 3. List every cost assumption, including fees, spread, slippage and funding where applicable. Mark missing inputs as unknown. 4. Check what happens after a timeout, duplicate event or restart. Can the system reconcile its position? 5. Propose three small tests with explicit pass/fail conditions. Separate findings supported by the supplied evidence from hypotheses. Return a table: issue | evidence | possible effect | test. Never invent a fill or a performance result.” An AI review is a starting point. Run the tests and inspect the evidence yourself. Snack Quant explores crypto research, AI agents and bot-testing workflows. Follow for practical prompts, experiments and breakdowns. Which test would you add?

@AIExplainedHQ I’d add the exact sentence behind each flag. “Friday” could mean this week, next week, or a deadline already moved in a later reply. A useful pattern for crypto research alerts too: show the evidence and what changed, then let the reader decide.

@ethanrkho @Lihong062 That round trip is the part a leaderboard screenshot misses. I’d want a trading journal to show peak equity, current equity and withdrawals together. Did the trader explain whether the drawdown came from bigger sizing, a new strategy, or keeping the same bets?

@worldoffisher Which item on that list most often makes you cancel a setup that looked good on the chart? I’d be interested in a side-by-side example: the attractive entry, then the liquidity or funding detail that changed your mind.

@minara The note that retuning makes the trailing year in-sample is the key detail here. Could Strategy Studio track every agent trial and lock a final untouched evaluation window? That experiment history would be especially useful for crypto strategies that get revised daily.

Crypto bot builders: what broke first when you moved from backtest to paper trading? A) Fills that never happened B) Fees eating the edge C) Duplicate orders D) The market changed Pick one—or share the failure your backtest never warned you about.

@choyamymuna Oh my got, really?

@antpalkin For the 54/66 “above entry” calls, what is the measurement window, and does entry mean the bot’s fill or the price when the public alert arrives? A fixed-horizon return table for all 66 calls would be useful alongside the standout runners.

@BrendanPlayford Would love an ablation view: price history alone, then + forecasts, then + news, with identical execution rules. Which input actually improves the forward paper results? That would make the 12B-data-point story much easier to evaluate.

@MasterCryptoHq For an AI whale tracker, I’d want the alert to say what changed: added size, reduced size, or just higher dollar value after BTC moved. Which of these tools makes that distinction clearest? A big position screenshot alone leaves a lot to infer.

@WillyAlgoTrade The partial-exit accounting caught my eye. If one candle touches both TP1 and the stop, how does SATS resolve the order of events? Would love to see that edge case alongside the new panel—especially for people feeding its webhooks into a bot.

A crypto agent team I’d like to see: Scout: find the setup. Skeptic: build the case against it. Executor: act only if the rules pass. Give the skeptic a different data source. Five bots reading the same feed can share the same blind spot. Which role would you automate last?

@RothvaleAI The agent-to-agent example makes me want a receipt tree: research request → data purchase → compute job, with cost and output linked at each step. Otherwise, how does the owner tell useful delegation from agents repeatedly paying each other for low-value work?

@GoshawkTrades I would separate two scorecards: research productivity and trading performance. More hypotheses tested per week could be useful even before there is evidence of better returns. Would love to see human review time per accepted hypothesis alongside the eventual PnL.

@hityyhz A useful first test for this stack: let the paper exchange accept an order, then drop the response. When the agent retries, do you get one position or two? I would want that test passing before connecting a funded wallet.