Stock exchanges are moving toward 24/7 trading of tokenized equities—shares traded on blockchains instead of traditional markets. The London Stock Exchange is partnering with Payward to list UK shares on-chain, while the SEC is drafting new rules to enable round-the-clock US equity trading. Tokenized equities already hit $4.45 billion in market cap, so this isn't theoretical anymore. The shift creates a real problem: when trading never stops and liquidity spreads across dozens of blockchains and off-hours broker pools, prices become fragmented. An Apple share might trade at a 1.5% premium on a Layer 2 DEX compared to an institutional broker pool at 3 a.m., with no single system tracking all these price differences in real time. The article pitches Stryx, a data platform designed to exploit these overnight arbitrage gaps. It aggregates order books from tokenized equity venues, decentralized exchanges, and traditional dark pools into one dashboard, then alerts traders to price discrepancies and spreads in real time. The business model stacks three revenue streams: a $49/month SaaS tier for individual traders with low-latency alerts, a $199+/month API tier for quant funds and algorithmic trading desks, and affiliate fees from routing trades through the platform. To get traction, the strategy involves releasing a free open-source arbitrage bot on GitHub, building SEO landing pages for every tokenized asset pair, and embedding free liquidity heatmaps on financial blogs and Discord servers. The competitive advantage rests on accumulating historical tick-level data from day one—something rivals can't retroactively build—and locking in trading desks through direct API integrations into their automated systems. The timing argument is straightforward: as regulatory approval spreads and institutional players launch tokenized equity products, demand for real-time liquidity intelligence will spike. The technical stack is practical: Python with FastAPI for handling concurrent WebSocket streams from blockchains, Redis for sub-second calculations, ClickHouse for historical analysis, and a React frontend with Lightweight Charts for visualization. It's a lean architecture aimed at getting a working prototype into traders' hands without over-engineering.