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Quant from CERN built a model that catches market shocks when everyone panics, it profits the most But when to enter is a separate problem. Math already solved it: R = N/e ≈ 0.37 × N* > skip the first 37% of entry points > then take the first...

121,533 Aufrufe • vor 3 Monaten •via X (Twitter)

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THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j*,j*) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j*,j*) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window

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$1,331,821 IN 30 DAYS. 3 BOTS. 48,061 TRADES. ONE FORMULA. They don't predict price. They measure what state the market is in right now. Markov chains, transition matrix, each cell - the probability of transitioning from state A to state B. The matrix diagonal - the probability that the market stays where it is. Entry only when the diagonal is above 0.87. Two conditions: the gap between model and market is greater than 5%, and the state is stable. Both must be true. One function, runs every minute. Bot 1 - Bonereaper. BTC and ETH, hourly windows, entry at 83-97¢. The market agrees with the direction but underestimates the confidence. 4-19% on every resolution. Low variance. Bot 2 - 0xe1D6. Dual mode, directional scalps at 64-83¢ deliver 20-54% per trade. In parallel, locks at 99.5-99.8¢. Best trade: entry at 64.7¢, return 54.6%. Bot 3 - 0xB27BC. Five assets: BTC, ETH, SOL, BNB, XRP. Five-minute windows. One trade every 1.7 minutes. Variance 55% lower at the same expected return. The real edge - 3:00 AM. People are asleep. The market posts lazy, stale prices. The gap between model and market is maximal when no one is watching. 0.034% per trade sounds like nothing. Over 16,000 trades that's *240. The law of large numbers turns noise into an exponent. Kelly criterion f* ≈ 0.71 - aggressive enough to grow, conservative enough not to go to zero. As long as people misprice short windows - the edge exists. You don't need to predict. You need to measure. The market rewards those who understand probability. The rest just provide liquidity.

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