Bittensor Subnet 79 Launches SATO to Explore Decentralized AI in Financial Risk Simulation
On March 29. 2025. @CryptoZPunisher tweeted on the X platform about SATO (Simulation of Automated Trading), a new project of the Bittensor subnet 79. SATO aims to simulate automated trading in an on-chain environment through decentralized AI technology to test trading strategies and evaluate their performance under extreme market conditions. SATO aims to simulate automated trading in an on-chain environment through decentralized AI technology to test trading strategies and evaluate their performance under extreme market conditions. SATO’s goal is to address the inability of traditional financial models to predict “black swan” events, citing mathematician Benoît Mandelbrot’s theory of fractal geometry, which emphasizes the complexity of market behavior, the tweet said.
#Bittensor >> #dTAO >> $TAO
sn 79: τaos by @taos_im https://t.co/Lcx8FwB6Sv
🧠 Subnet 79 — Simulating Financial Risk in the Age of Decentralized AI
As the Bittensor ecosystem continues to expand, Subnet 79 stands out as an intriguing initiative at the intersection of… pic.twitter.com/riDxsyD3rt— Punisher ττ (@CryptoZPunisher) March 29, 2025
SATO’s theoretical foundation stems from Mandelbrot’s research. In his book The (Mis)Behavior of Markets, Mandelbrot argues that price fluctuations in financial markets exhibit fractal characteristics rather than the smooth trends assumed by traditional models.SATO leverages this theory to improve risk modeling by repetitively simulating automated markets, and explores the potential of combining blockchain and AI in the financial sector. The Medium article cited in the tweet further elaborates on SATO’s vision, noting that it plans to build an experimental on-chain environment to better understand and manage financial uncertainty, although the project currently has no public GitHub repository.
This project reflects a technology trend in the decentralized finance (DeFi) space. Similar to the Monte Carlo simulation methodology used by the StrategyQuant platform, which tests the robustness of a strategy by randomizing the order of trades, SATO brings this methodology to the on-chain environment and, combined with the distributed AI computational power of Bittensor, aims to redefine the standard for algorithmic finance.SATO’s future is in the spotlight, and its integration with the Bittensor ecosystem could drive the development of decentralized AI. SATO’s development prospects are in the spotlight, and its integration with Bittensor’s ecosystem may promote the application of decentralized AI in financial risk management and bring new solutions to the industry.
