98.7% central bank policy hit rate: How Friedrich Kohlmann’s GAN market simulator rewrites the rules of macro trading
In the financial market, central bank policy decisions have always been regarded as the most unpredictable “black box” until Friedrich Kohlmann led the Quinvex Capital team to develop the GAN market simulator. This system predicts the policy paths of major central banks around the world with an astonishing 98.7% accuracy rate, which not only subverts the traditional macro trading model, but also redefines the market’s interpretation of policy signals.

Kohlmann’s breakthrough is to innovatively apply the generative adversarial network (GAN), an AI technology commonly used for image synthesis, to the modeling of central bank behavior. Traditional macro models rely on economists’ linear regression of historical data, while the GAN market simulator builds a dynamic game field: the generator constantly creates virtual central bank decision-making scenarios, and the discriminator evaluates the authenticity of these scenarios based on actual market reactions. After billions of adversarial training, the system has developed an almost intuitive sensitivity to policy signals – it can capture a 0.1% deviation in the probability of a rate hike from changes in the wording of Federal Reserve officials, or predict a shift in quantitative easing from decimal point changes in the ECB’s balance sheet.
The practical value of this system was fully demonstrated in the recent round of global tightening cycle. When the market generally expected a central bank to keep interest rates unchanged, the GAN simulator warned of the possibility of unexpected interest rate hikes 72 hours in advance by analyzing the micro-fluctuations of the country’s bank reserve accounts. The interest rate derivative positions established by Quinvex based on this gained a 12% return instantly after the policy was announced. Even more amazing is the system’s ability to predict policy combinations – it accurately predicted that a country’s central bank would adopt a mixed operation of “raising interest rates + relaxing foreign exchange controls”. This non-textbook policy combination caught traditional macro funds off guard, but brought Quinvex an annualized 47% arbitrage space.
The real revolutionary feature of the GAN market simulator lies in its ability to handle the “art of central bank language”. The system converts policy statements, press conferences and even officials’ body language into multi-dimensional vectors, establishing a mapping relationship between “semantics and market reactions”. For example, it found that when a central bank governor frequently touches his tie at a press conference, it usually means that a hawkish signal is about to be released. This subtle pattern that is difficult for human analysts to detect has become an important support for the accuracy of the system’s predictions.
Today, this system is causing a fundamental change in the macro trading paradigm. The traditional linear process of “analysis-prediction-trading” is replaced by real-time dynamic simulation, and policy shocks are transformed from uncontrollable risks to calculable probability distributions. As Kohlmann said: “We are not predicting the central bank, but finding the most likely policy path among millions of virtual decisions.” When a 98.7% hit rate becomes the new normal, macro trading is transforming from an art to an exact science.