Bittensor Auto-Staker

Automated staking system for Bittensor subnets that analyzes subnet metrics, calculates optimal staking targets, and executes staking transactions based on configurable strategies.

Features

• Comprehensive Analysis: Detailed metrics calculation for subnet analysis
• Strategy-Based Decisions: YAML-configurable strategies (conservative, aggressive, custom)
• Safe Execution: Dry-run mode, confirmation prompts, and transaction safety checks
• Historical Caching: Optional SQLite cache for tracking subnet metrics over time
• User-Friendly CLI: Rich terminal interface with clear output and progress indicators

Installation

# Create virtual environment
python -m venv venv
source ./venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt && python cli.py

Quick Start

1. Analyze a Subnet (No Staking)

# Analyze subnet 0 and show top 10 targets
python cli.py analyze --netuid 0 --top-n 10

# Use a strategy for scoring
python cli.py analyze --netuid 0 --strategy strategies/conservative.yaml

2. Dry Run (Test Without Staking)

# Simulate staking without executing transactions
python cli.py stake --netuid 0 --strategy strategies/conservative.yaml --dry-run

3. Execute Staking

# Execute staking with confirmation prompt
python cli.py stake --netuid 0 --strategy strategies/conservative.yaml

# Skip confirmation (use with caution!)
python cli.py stake --netuid 0 --strategy strategies/conservative.yaml --yes

4. Check Status

# View current staking status
python cli.py status --netuid 0

Project Structure

bittensor-auto-staker/
├── core/
│   ├── analyzer.py       # Subnet metrics calculation and analysis
│   ├── strategist.py     # Makes staking decisions based on strategy
│   └── executor.py       # Signs & sends staking transactions safely
├── data/
│   └── historical/       # SQLite cache for subnet metrics (optional)
├── strategies/
│   ├── conservative.yaml # Conservative strategy (high trust, lower risk)
│   └── aggressive.yaml   # Aggressive strategy (high emission, higher risk)
├── cli.py                # User-friendly CLI interface
├── requirements.txt
├── README.md
└── tests/

Strategy Configuration

Strategies are defined in YAML files. See strategies/conservative.yaml and strategies/aggressive.yaml for examples.

Strategy Parameters

• weights: Scoring weights for different metrics

  • emission: Weight for emission-based scoring
  • trust: Weight for trust-based scoring
  • validator_bonus: Bonus score for validators

• filters: Criteria to exclude neurons

  • min_stake: Minimum existing stake to consider
  • max_stake: Maximum stake threshold
  • exclude_immune: Exclude immune neurons
  • exclude_duplicate_ip: Exclude duplicate IP addresses
  • min_trust: Minimum trust score
  • min_emission: Minimum emission threshold

• allocation: How to distribute stakes

  • max_targets: Maximum number of targets
  • min_stake_per_target: Minimum TAO per target
  • max_stake_per_target: Maximum TAO per target
  • allocation_method: equal, proportional, or top_n

• rebalancing: When to rebalance

  • rebalance_threshold: Drift threshold for rebalancing
  • rebalance_interval_blocks: Minimum blocks between rebalances

Creating Custom Strategies

Create a new YAML file in the strategies/ directory:

name: my_custom_strategy
description: "My custom staking strategy"

weights:
  emission: 1.5
  trust: 1.0
  validator_bonus: 0.3

filters:
  min_stake: 50.0
  max_stake: 5000.0
  exclude_immune: false
  exclude_duplicate_ip: true
  min_trust: 0.2
  min_emission: 0.01

max_targets: 8
min_stake_per_target: 20.0
max_stake_per_target: 1000.0
allocation_method: proportional

rebalance_threshold: 0.15
rebalance_interval_blocks: 1500

max_total_stake_ratio: 0.85
require_confirmation: true

CLI Commands

analyze

Analyze subnet metrics without executing any staking.

python cli.py analyze [OPTIONS]

Options:

• --netuid: Subnet UID (default: 0)
• --strategy: Strategy file path (optional, for scoring)
• --top-n: Number of top targets to display (default: 10)
• --skip-registration: Skip registration block queries (faster)

stake

Execute staking based on strategy configuration.

python cli.py stake [OPTIONS]

Options:

• --netuid: Subnet UID (default: 0)
• --strategy: Strategy YAML file path (required)
• --dry-run: Simulate without executing transactions
• --yes: Skip confirmation prompt (use with caution!)
• --wait: Wait for transaction inclusion (default: True)
• --skip-registration: Skip registration block queries (faster)

status

Show current staking status and summary.

python cli.py status [OPTIONS]

Options:

• --netuid: Subnet UID (default: 0)

check platform

Quick platform check: connectivity, chain, TAO price, and subnet hyperparameters. Uses a lite metagraph sync, so it finishes in seconds.

python cli.py check platform [--netuid 0]

Options:

• --netuid: Subnet UID for hyperparameters (default: 0)

Example output: chain endpoint, current block, TAO price, plus tempo, immunity_period, neurons, and validators for the chosen subnet.

Safety Features

1. Dry Run Mode: Test strategies without executing transactions
2. Confirmation Prompts: Require explicit confirmation before transactions
3. Balance Checks: Verify sufficient balance before transactions
4. Transaction Limits: Configurable min/max amounts
5. Strategy Validation: Validate strategy configuration before execution

Examples

Conservative Staking

# Analyze with conservative strategy
python cli.py analyze --netuid 0 --strategy strategies/conservative.yaml

# Dry run
python cli.py stake --netuid 0 --strategy strategies/conservative.yaml --dry-run

# Execute
python cli.py stake --netuid 0 --strategy strategies/conservative.yaml

Aggressive Staking

# Analyze with aggressive strategy
python cli.py analyze --netuid 1 --strategy strategies/aggressive.yaml --top-n 20

# Execute (aggressive strategy has require_confirmation: false, so use --yes carefully)
python cli.py stake --netuid 1 --strategy strategies/aggressive.yaml --yes

Custom Network

# Use custom network endpoint
python cli.py analyze --netuid 0 --subtensor.network finney --strategy strategies/conservative.yaml

Data Caching

Historical metrics are optionally cached in SQLite for analysis:

• Location: data/historical/metrics.db
• Automatically created on first use
• Can be cleared with cache management (future feature)

Development

Running Tests

python -m pytest tests/

Code Formatting

black .

Pre-commit Hooks

pre-commit install
pre-commit run --all-files

Safety Disclaimers

⚠️ IMPORTANT WARNINGS:

1. This tool executes real transactions on the Bittensor blockchain. Always test with --dry-run first.
2. Start with small amounts to verify behavior before large transactions.
3. Review strategy configurations carefully before execution.
4. Monitor your positions regularly and adjust strategies as needed.
5. No guarantees - subnet performance can change, and decisions are based on historical data.
6. Use at your own risk - the authors are not responsible for any losses.

Contributing

Contributions are welcome! Please:

1. Use pre-commit hooks (black, etc.)
2. Add tests for new features
3. Update documentation
4. Submit a PR with clear description

License

See LICENSE file.

Original Tool

The core analysis functionality is implemented in core/analyzer.py.