Introduction Classic algorithmic HFT bots live in a vacuum. For them, the market is just a stream of numbers: price, volume, spread, and Z-score. But these metrics know nothing about the real world. Imagine this: on a crypto exchange or on Polymarket, there is a sudden spike in volume and the asset's Z-score shoots beyond three sigma (|Z| > 3). From the perspective of a classical arbitrage bot, this is an inefficiency to trade, expecting mean reversion. But why did the spike happen? Was a devastating regulatory report released? Did hackers drain liquidity? Or was it a cascading liquidation of a single whale's positions? Without understanding the context (news, tweets, on-chain activity), a mathematical spike is not equal to a trading opportunity. Entering a trade aggressively without context is a direct path to becoming exit liquidity for insiders. Feeding every tick into a neural network is architectural madness (too expensive, and latency is measured in seconds).…