AI can help you trade, but what I really want to ask is: Why was this position opened, can the strategy be backtested, and if the judgment was wrong, can the reason be traced? After reading @Travix_fi’s project materials, I think this direction is worth paying attention to. Travix positions itself as an Agentic Omnibroker, which can be understood as an on-chain trading platform that integrates multi-asset trading, event analysis, and AI strategy execution. According to the project team, they’ve completed a seed round of funding supported by Amber Group and are currently progressing toward mainnet launch. The range of assets they’ve chosen is quite interesting, with a focus on computational power and the East Asian market. Assets related to computational power like H100, memory, electricity, as well as semiconductor stocks from Japan and South Korea, are all part of their product layout. The goal is to enable users to manage cross-asset positions under a unified margin system and express judgments or hedge risks through 24/7 perpetual contracts. If you follow the AI industry, this approach is easy to understand. Chip supply and computational costs impact different companies across the supply chain, so grouping these assets together makes it easier to arrange trades around a single judgment. But having assets isn’t enough—you also need to understand how news impacts them. Travix’s Financial World Model attempts to track changes in event probabilities, combining forward-looking data like prediction markets to analyze how impacts are transmitted to specific assets. Based on the product design, the output includes probability changes, data sources, and the confidence level and risk factors of asset impacts, allowing users to check the basis of their judgments. The subsequent Trusted Agent is responsible for turning this information into backtestable strategies, which are then executed according to clear rules, retaining entry reasons, invalidation conditions, and execution records. This is the part I value most. It’s easy for AI to give a bullish argument, but being able to turn that argument into rules, test them against historical data, and review them post-trade makes it much easier to seriously evaluate whether it’s useful. Currently, the order book is open, while AI trading and causal chain trading are still in Beta. The mainnet launch date is yet to be announced by the official team. At this stage, it’s better to follow product updates first, and later focus on how well strategy backtesting and execution records perform. If you’re interested in AI × trading or computational power assets, you can follow @Travix_fi.