Foundry is launching Foundry S&P 500 Oracle to incentivize miners to make predictions on the S&P 500 price frequently throughout trading hours. Validators send miners a timestamp (a future time) for which the miners need to make an S&P 500 price prediction for. Miners need to respond with their prediction for the price of the S&P 500 at the given time. Validators store the miner predictions, and then calculate the scores of the miners after the predictions mature. Miners are ranked against eachother, naturally incentivizing competition between the miners.
A Bittensor integration into financial markets will expose Bittensor to the largest system in the world; the global economy. The S&P 500 serves as a perfect starting place for financial predictions given its utility and name recognition. Financial market predictions were chosen for three main reasons:
- Utility: financial markets provide a massive userbase of professional traders, wealth managers, and individuals alike
- Objective Rewards Mechanism: by tying the rewards mechanism to an external source of truth (yahoo finance's S&P Price), the defensibility of the subnet regarding gamification is quite strong.
- Adversarial Environment: the adversarial environment, especially given the rewards mechanism, will allow for significant diversity of models. Miners will be driven to acquire different datasets, implement different training methods, and utilize different model architectures in order to develop the most performant models.
First, install PM2:
sudo apt update
sudo apt install nodejs npm
sudo npm install pm2@latest -g
Verify installation:
pm2 --version
| Validator | Miner |
|---|---|
| 8gb RAM | 8gb RAM |
| 2 vCPUs | 2 vCPUs |
Begin by creating and sourcing a python virtual environment:
python3 -m venv .sn28
source .sn28/bin/activate
Clone the Foundry S&P 500 Oracle repo:
git clone https://github.com/teast21/snpOracle.git
Install Requirements:
pip3 install -e snpOracle
ecosystem.config.js files have been created to make deployment of miners and validators easier for the node operator. These files are the default configuration files for PM2, and allow the user to define the environment & application in a cleaner way. IMPORTANT: Make sure your have activated your virtual environment before running your validator/miner. To run your miner:
pm2 start miner.config.js
The miner.config.js has few flags added. Any standard flags can be passed, for example, wallet name and hotkey name will default to "default"; if you have a different configuration, edit your "args" in miner.config.js. Below shows a miner.config.js with extra configuration flags. The model flag is an extra flag used to reference a new model you save to the mining_models directory. The example below uses the default which is the base lstm.
module.exports = {
apps: [
{
name: 'miner',
script: 'python3',
args: './neurons/miner.py --netuid 28 --logging.debug --logging.trace --subtensor.network local --wallet.name walletName --wallet.hotkey hotkeyName --axon.port 8091 --model base_lstm.h5'
},
],
};
ecosystem.config.js files have been created to make deployment of miners and validators easier for the node operator. These files are the default configuration files for PM2, and allow the user to define the environment & application in a cleaner way. IMPORTANT: Make sure your have activated your virtual environment before running your validator/miner.
To run your validator:
pm2 start validator.config.js
The validator.config.js has few flags added. Any standard flags can be passed, for example, wallet name and hotkey name will default to "default"; if you have a different configuration, edit your "args" in miner.config.js. Below shows a miner.config.js with extra configuration flags.
module.exports = {
apps: [
{
name: 'validator',
script: 'python3',
args: './neurons/validator.py --netuid 28 --logging.debug --logging.trace --subtensor.network local --wallet.name walletName --wallet.hotkey hotkeyName'
},
],
};
The simplicity of the rewards mechanism is quite intentional. There are no methods to require a machine learning model be run by the miners. This is because the nature of the problem is such that machine learning models will inherently perform better than any method of gamification. By effectively performing a commit-reveal on a future S&P Price Prediction, S&P Oracle ensures that only well-tuned models will survive.
Root Mean Squared Error(RMSE) is calculated as such:
The RMSE is then normalized to enforce scores between 0 and 1, and those scores are used to update the existing scores in the metagraph. The weighting function applied to how scores are added to the metagraph creates a pseudo-rolling average score for predictions. Thus, a miner will have perfect trust after a perfect prediction, and will also not have 0 trust after having the worst prediction of an epoch. Consistent high-quality performance will result in high trust, and consistent low-quality performance will result in low trust and eventual de-registration.
Foundry will constantly work to make this subnet more robust, with the north star of creating end-user utility in mind. Some key features we are focused on rolling out to improve the S&P 500 Oracle are listed here: [] Huggingface Integration [] Wandb Integration [] Add Features to Rewards Mechanism [] Altering Synapse to hold short term history of predictions [] Add new Synapse type for Inference Requests [] Front end for end-user access
We happily accept community feedback and features suggestions. Please reach out to @0xthebom on discord :-)
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