Into:gm
S&P 500 predictions, decentralized
As of · Jun 4, 10:37 UTC
Bittensor's S&P 500 , built by Foundry Digital. compete to predict the index price every five minutes using open-source neural networks, with all models and training data published to HuggingFace.
What is gm
gm (SN28) is Bittensor's dedicated financial forecasting , operated by Foundry Digital, one of the larger institutional participants in the Bittensor ecosystem. On-chain the subnet goes by "gm," but the product is the Foundry S&P 500 Oracle: a decentralized competition where miners build machine learning models to predict short-term S&P 500 price movements, with every model and dataset required to be published openly on HuggingFace.
The simple version: It's like a public quant research competition where participants submit their stock prediction models openly, and the most accurate models earn .
Centralized equivalent: Think Bloomberg's quantitative analytics or QuantConnect, but with fully open-source model transparency and decentralized incentives.
How it works:
- Miners build neural network models to predict the S&P 500 closing price for six five-minute intervals into the future. All models and input data must be open-sourced on HuggingFace to receive emissions.
- send miners a future timestamp and collect their price predictions. Once the interval matures, validators score each prediction against the actual S&P 500 price using two metrics: Directional Accuracy and Mean Absolute Error. Miners are ranked relative to each other, creating a continuously competitive environment.
Why This Matters
Other research from the same neighborhood of the network.
