用 Astra 搭自学习 Solana 交易算法

Daniro @Dan1ro0原推发布于 2026.10.13

在 X 上打开原推

I used Claude Fable 5.1 and GPT-6 Astra to build a self-learning trading algorithm The result: +$1,840.82 in two days It makes about 17 trades a day or 521 a month, with an average trade size of $53.2 Six AI agents track new coins on Solana and learn to make trading decisions as one system I called it QuantumOrb GitHub repo:https://github.com/VALIXxDaniro/QuantumOrb Here’s how it works: SCOUT tracks new launches, bonding curve activity and PumpSwap swaps, then records the trading data CARTOGRAPHER detects shared wallet funding sources, same-bundle activity and connections to the token creator JUDGE analyzes traders from their transaction history and estimates their performance and edge after fees, entry delay and copybot activity QUANT calculates execution on the curve or inside the pool, accounting for fees, market impact, priority fees and Jito tips GUARD checks holder concentration, connected wallet groups and launch risks EXECUTOR models opening and closing positions through take-profit, stop-loss or time expiry At the center of the system is the Orb Every launch is classified into three probability states: +60% is reached first −30% is reached first Neither threshold is reached within 60 minutes Most profitable trades: $45 → $158 (+$113) $37 → $146 (+$109) $31 → $125 (+$94) Organic buyer flow shifts probability toward one scenario Connected-wallet activity, sells and holder concentration adjust the estimate in the opposite direction With every new signal, the state updates. After that, QUANT determines whether the setup still makes sense after all execution costs, while GUARD decides whether the risk is acceptable Every launch that passes GUARD is used for learning, including the ones the system decides to skip One hour later, the actual outcome is recorded and fed back into the model That feedback becomes part of the next decision.

这条是引用着另一条写的：x.com/dan1ro0/status/2036142569583706567