Evolution Engine

Overview

The Evolution Engine autonomously discovers trading strategies from raw price data. It uses LLM-powered pattern generation combined with vectorized backtesting to evolve, test, and graduate viable trading rules — without manual rule writing.

This is not parameter optimization on a known strategy. It's open-ended strategy discovery: the LLM proposes novel entry/exit logic, the engine validates it against real data, and natural selection eliminates the losers.

How It Works

The Evolution Loop

OHLCV Data → LLM Generation → Vectorized Backtest → Selection → Mutation → Repeat
                                                          ↓
                                                    Out-of-Sample Validation
                                                          ↓
                                                    Graduated Strategies

Step-by-Step

1. Data Fetch: Pull OHLCV candles from Binance public API (no key needed)
2. Generate: LLM analyzes price patterns and proposes N candidate strategies (entry/exit rules, position sizing, stop loss)
3. Backtest: Each candidate is backtested vectorized (numpy, no loop-per-candle) for speed
4. Score: Candidates scored by Sharpe ratio, win rate, max drawdown, total return
5. Select: Top K candidates survive. Bottom candidates are eliminated (graveyard).
6. Mutate: LLM takes survivors and generates variations (parameter tweaks, rule modifications)
7. Repeat: Steps 3-6 for N generations
8. Validate: Final survivors are tested on held-out out-of-sample data
9. Graduate: Strategies that pass OOS validation are marked as graduated

Key Design Decisions

• LLM generates rules, not parameters. The engine doesn't optimize MACD(12,26,9) → MACD(14,28,10). It discovers entirely new rule combinations.
• Vectorized backtesting. No candle-by-candle loops. Numpy vectorized operations make backtests 100x faster than event-driven simulators.
• OOS validation is mandatory. In-sample performance means nothing. Only OOS-validated strategies graduate.
• Graveyard is data. Failed strategies are logged with failure reasons. This prevents re-discovering the same dead ends.

MCP Tools

Backtest Metrics

Every backtest produces:

These thresholds are guidelines. Context matters — a Sharpe of 0.9 with 500 trades may be more reliable than 2.5 with 15 trades.

Best Practices

Before Running Evolution

• Choose the right timeframe. 1h and 4h produce the most tradeable strategies. 1m is noise. 1d may not have enough data points.
• Use enough data. 90 days minimum for 1h data. 180 days for 4h. Less data = more overfitting risk.
• Start small. 3 generations × 10 candidates is a good starting point. Don't jump to 10 × 50.

During Evolution

• Don't interrupt. Each generation builds on the previous. Stopping mid-run wastes compute.
• Monitor the graveyard. If 90% of candidates fail on the same metric (e.g., max drawdown), the symbol/timeframe may not be suitable.
• Watch for convergence. If surviving strategies across generations look increasingly similar, the engine has found a local optimum.

After Evolution

• Never deploy without OOS validation. In-sample results are marketing, not science.
• Paper trade first. Even OOS-validated strategies should be paper traded for 2-4 weeks.
• Check regime sensitivity. A strategy discovered in a trending market may fail in ranging conditions. Test across multiple market regimes.
• Log everything. Use evolution_get_log to review what was tried, what failed, and why.

When NOT to Use Evolution

• Not for parameter optimization. If you already have a strategy and just want to tune parameters, use a traditional optimizer.
• Not for HFT. The engine works on candle data, not tick data. Sub-minute strategies need different infrastructure.
• Not as a replacement for domain knowledge. Evolution discovers patterns, but you still need to understand why a pattern works before risking real money.

Common Mistakes

Requirements

• ANTHROPIC_API_KEY — Required for LLM-powered pattern discovery
• Binance public API — Used for OHLCV data (no API key needed)
• Python with numpy — For vectorized backtesting