Jev + Opus 5.5 做体育交易 HFT：泊松模型算 BTTS 概率
用 Opus 分析球队数据、Jev 追踪需重算的变化，配合泊松模型估算 BTTS 概率并与市场价比较，靠 YES/NO 双边低于 $1 累积套利。
I BUILT AN HFT ALGORITHM WITH JEV + OPUS 5.5 FOR SPORTS TRADING Results: +$246,131.94 The algorithm recalculates probabilities, accumulates opposite outcomes and keeps extra exposure where the model sees an underpriced contract: 1. Turns match data into a price Opus analyzes team strength, lineups and expected goals. Jev tracks changes that require recalculation For both teams to score, it uses an independent Poisson model: P(BTTS) = (1 − e^−λ₁) × (1 − e^−λ₂) With expected goals of 1.50 and 1.40, BTTS probability comes out at 58.5% That gives the algorithm a specific estimate to compare with the market price 2. Accumulates both sides below $1 For equal quantities of contracts: VWAP(YES) + VWAP(NO) + costs < $1 YES at 46¢ and NO at 51¢ create a 97¢ pair that returns $1 at settlement The prices don't have to appear simultaneously. The algorithm builds each side at different moments, limiting exposure until the second side fills 3. Keeps a directional residual Some contracts remain without an opposite leg when the model sees positive expected value: EV = P_model × payout − cost New lineups, goals and red cards trigger recalculation. An old estimate shouldn't keep controlling the position after the match changes Following that logic, today i'm putting $55 on Georgia vs Ukraine BTTS on @predofficial: http://www.pred.app/?referral_code=REFF2W6I1 A similar system operates through this Polymarket account: pspspsps5 It trades short-term Bitcoin Up/Down markets The result in the headline comes from a similar system, not mine My exact position: $55.30 → $104.40 Break-even probability: approximately 53%
这条是引用着另一条写的：x.com/immortalhowwl/status/2102012226747924961