In the secret laboratory of Quinvex Capital, “Prometheus” created by Friedrich Kohlmann’s team is redefining the limits of financial AI. This cross-modal trading model with 1.2 trillion parameters, like the fire thief in Greek mythology, transforms heterogeneous data sources such as satellite remote sensing, supply chain logistics, and social media pulses that traditional financial analysis has never touched into unprecedented market prediction capabilities.
The uniqueness of Prometheus lies in its “data metabolism” mechanism – it does not simply splice different data sources like traditional quantitative models, but builds a deep heterogeneous information digestion system. The model contains 137 dedicated sub-networks, which process completely different information flows, from high-frequency trading data to climate pattern forecasts. The most revolutionary of these is the “cross-modal attention gateway”, a neural network structure that can automatically discover hidden connections between seemingly unrelated data, such as converting soil moisture data in Brazilian coffee-producing areas into predictors of the performance of Swiss consumer goods stocks in the next three months. During the European energy crisis, it was this ability that allowed Prometheus to predict the surge in electricity hedging demand of German industrial companies 48 hours in advance by analyzing the changes in the speed of Baltic tankers.
The swallowing power of data torrents has shown its terrifying power in actual combat. In the latest round of global tightening cycle, Prometheus simultaneously processed 87 types of non-traditional data, including: voiceprint features of public speeches by Federal Reserve officials, satellite heat maps of container loading and unloading at Chinese ports, IoT data on tractor sales in agricultural states in the central United States, etc. By building a “macro-micro feedback loop”, the model not only accurately predicts the path of interest rate hikes, but also the direction of capital flow in specific industry sectors. This enabled Quinvex to not only profit by shorting long-term US bonds during the turmoil in the bond market, but also accurately capture the time when Japanese insurance companies adjusted their foreign exchange hedging ratios, achieving an annualized compound return of 59%.
Even more amazing is the model’s ability to evolve. Prometheus uses a “lifelong learning” architecture, and its parameter space will automatically expand and reorganize as new data types emerge. When a black swan event suddenly occurred in the cryptocurrency market, the model built a subnetwork dedicated to parsing on-chain data in just 72 hours, increasing the modeling accuracy of the correlation between NFT trading liquidity and stock market meme stocks by 300%. This dynamic adaptability is changing the nature of quantitative investment – from static strategy execution to an organic process of continuous cognitive upgrades.
Kohlmann sees Prometheus as the beginning of the “third era of financial cognition”: “In the first era, humans relied on experience and intuition, in the second era, machines learned historical laws, and now we are entering the third era – AI directly perceives the atomic-level pulsation of the economic world.” As the model begins to integrate quantum computing modules to process global shipping data, the appetite of this trillion-parameter behemoth is still expanding. While traditional hedge funds are still struggling to process terabytes of data, Prometheus has been digesting information torrents equivalent to 4% of the entire Internet traffic, and will continue to rewrite the rules of the game in the financial market.
