# Install Python3.13 and Python3.13 tools on your OS
sudo apt update
sudo apt install python3.13 python3.13-venv python3.13-dev pkg-config
# Clone BitQuant-Subnet Repository and checkout Validator Branch
git clone https://github.com/OpenGradient/BitQuant-Subnet
cd BitQuant-Subnet
git checkout Validator
# Optional: Create Python Virtual Environment (Best Practice)
python3.13 -m venv venv
source venv/bin/activate
# Install Requirements
pip install -r requirements.txt
pip install -e .
# Set up customized environment variables in .env, or fall back to defaults
cp .env.example .env
# Run the Validator with your Bittensor Wallet setup
python neurons/validator.py --netuid 15 --subtensor.network finney --wallet.name validator --wallet.hotkey default --logging.debug# Install Python3.13 and Python3.13 tools on your OS
sudo apt update
sudo apt install python3.13 python3.13-venv python3.13-dev pkg-config
# Clone BitQuant-Subnet Repository and BitQuant submodule
git clone --branch Miner --recursive https://github.com/OpenGradient/BitQuant-Subnet
cd BitQuant-Subnet
# Optional: Create Python Virtual Environment (Best Practice)
python3.13 -m venv venv
source venv/bin/activate
# Install Requirements
pip install -r requirements.txt
pip install -e .
# Setup all the environment variables in .env
cp .env.example .env
# Run the Miner with your Bittensor Wallet setup
python neurons/miner.py --netuid 15 --subtensor.network finney --wallet.name miner --wallet.hotkey default --logging.debugNote: Running a miner node requires substantially higher compute requirements due to the local running of the BitQuant agent. Setup instructions can be found https://github.com/OpenGradient/BitQuant
Note: This repository requires Python3.13 and Python3.13 tools
-
Any 'ModuleNotFoundError'
- Make sure you ran both
pip install -r requirements.txtandpip install -e .andgit submodule update --init --recursive
- Make sure you ran both
-
ERROR: Could not find a version that satisfies the requirement bittensor==9.2.0
- Make sure you're using Python3.13 and you update Pip
OpenGradient's BitQuant Subnet implements a decentralized AI framework for quantitative DeFi analysis on the Bittensor network. Through natural language interfaces, it enables:
- ML-powered market analytics
- Portfolio analysis and optimization
- Risk assessment and trend analysis
- Quantitative strategy evaluation
- Democratize access to advanced quantitative analytics for DeFi and crypto markets
- Incentivize high-quality AI agents to provide real-time, actionable insights
- Enable composable, on-chain quantitative strategies and risk metrics
- Foster a robust ecosystem for decentralized, AI-powered financial intelligence
The subnet is powered by BitQuant, OpenGradient's AI agent framework for quantitative analysis that provides:
- Real-time market data processing and insights
- Portfolio analysis and optimization
- Price trend and pattern analysis
- Token metrics and risk evaluation
- Risk-reward assessment
BitQuant is an agentic framework for digital asset analytics and DeFi strategy, enabling users to analyze tokens, DeFi pools, and portfolio risk with advanced on-chain and off-chain data.
Agent Capabilities:
- Pool Discovery
- Token Search
- TVL Analytics
- Price Trend Analysis
- Drawdown Calculation
- Portfolio Volatility
- Wallet Analytics
- Trending Tokens
- Token Risk Evaluation
- Top Holders Lookup
Example Queries:
- "Find all Solana pools I can invest in with my wallet’s tokens and show their historical TVL."
- "Analyze the price trend and calculate the max drawdown for SOL over the last 90 days."
- "Evaluate my wallet’s portfolio volatility and identify the riskiest token holding."
- "Search for trending tokens across X chain and display their top holders."
- "Compare historical global TVL and chain-specific TVL for Ethereum and Solana."
Try it live: bitquant.io
The OG BitQuant subnet implements a decentralized quantitative analysis framework through three main components:
- Defines the
QuantSynapse - Handles query/response structures through
QuantQueryandQuantResponse - Manages message validation and serialization
- Provides the
QuantAPIfor standardized network interaction
Miners
- Process incoming requests through blacklist and priority checks
- Forward validated queries to BitQuant for processing
- Add response metadata and handle error cases
Validators
- Sample random miners for querying
- Forward analysis requests to selected miners
- Calculate response scores through reward function
- Update miner scores in the metagraph
- Python 3.13
- Bittensor
- Access to compute resources (see
min_compute.yml)
For detailed setup instructions, see:
This repository is licensed under the MIT License.
# The MIT License (MIT)
# Copyright © 2025 BitQuant Subnet by OpenGradient
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the “Software”), to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
# and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
# the Software.
# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
# THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
