0xSanei Darwinia — Self-Evolving Trading Agent Ecosystem
Evolves trading strategies through natural selection. 50 agents with random DNA compete on BTC data. Weak die, strong breed. Survivors handle rug pulls, fake breakouts, whipsaws.
When to use
- User asks to find, optimize, or evolve a trading strategy
- User wants adversarial stress-testing of trading logic
- User mentions "darwinia", "evolve strategy", or "adversarial test"
Setup
git clone https://github.com/0xSanei/darwinia.git && cd darwinia && pip install -e ".[dev]"
Commands
| Command | Time | Purpose |
|---|---|---|
python -m darwinia evolve -g 10 --json |
~30s | Quick demo |
python -m darwinia evolve -g 50 --json |
~3min | Full evolution + adversarial arena |
python -m darwinia arena --json |
~30s | Test champion against 6 attacks |
python -m darwinia info --json |
instant | Version and capabilities |
python -m darwinia dashboard |
— | Streamlit interactive dashboard |
Always use --json for programmatic calls.
Key output fields
champion.fitness: >1.0 = outperforms buy-and-holdchampion.genes: 17 floats [0,1] encoding full strategyevolution_summary.patterns_discovered: count of emergent rulespatterns: Emergent rules discovered by agents (not pre-programmed)
17-gene DNA
Signal (5): momentum, volume, volatility, mean_reversion, trend Threshold (4): entry, exit, stop_loss, take_profit Personality (5): risk_appetite, time_horizon, contrarian_bias, patience, position_sizing Adaptation (3): regime_sensitivity, memory_length, noise_filter
6 adversarial attacks
rug_pull, fake_breakout, slow_bleed, whipsaw, volume_mirage, pump_and_dump Arena reads agent DNA to find weaknesses and generates targeted scenarios.
Composability
Darwinia provides a two-way composability interface for cross-skill interop.
Inbound: other skills call Darwinia (SkillBridge)
from darwinia.integrations import SkillBridge
bridge = SkillBridge()
result = bridge.evolve({"generations": 20, "population_size": 30, "data_path": "data/btc_1h.csv"})
champion = bridge.get_champion()
score = bridge.evaluate_strategy([0.5] * 17)
regime = bridge.get_market_regime()
Methods: evolve(config) -> dict, get_champion(gen) -> dict, evaluate_strategy(dna) -> dict, get_market_regime() -> dict
Outbound: Darwinia calls other skills (SkillRegistry)
from darwinia.integrations import SkillRegistry
registry = SkillRegistry()
registry.register("macro-liquidity", my_func)
result = registry.call("macro-liquidity", indicator="fed_net_liquidity")
print(registry.list_skills())
Pipeline example: macro-liquidity -> Darwinia -> strategy
Pull macro signals, bias evolution config, evolve, output champion. Built-in templates for: macro-liquidity, crypto-market-rank, okx-dex-market.