evolve

Evolve Strategy

Trigger the Evolution Engine to autonomously discover trading patterns from raw OHLCV data. The engine generates candidate strategies via LLM, backtests them vectorized, validates out-of-sample, and graduates survivors.

Workflow

Step 1: Configure Evolution

If parameters are provided, use them. Otherwise ask:

• Symbol: e.g., BTCUSDT, ETHUSDT (Binance pairs)
• Timeframe: 1h, 4h, 1d
• Generations: How many evolution cycles (default: 3)
• Candidates per generation: How many strategies to test (default: 10)
• Data period: How many days of historical data (default: 90)

Step 2: Fetch Market Data

Use the evolution_fetch_market_data MCP tool:

evolution_fetch_market_data({
  symbol: "BTCUSDT",
  timeframe: "1h",
  days: 90
})

Step 3: Discover Patterns

Use the evolution_discover_patterns MCP tool:

evolution_discover_patterns({
  symbol: "BTCUSDT",
  timeframe: "1h",
  num_patterns: 10
})

The LLM analyzes price data and generates candidate trading rules (entry/exit conditions, position sizing, stop loss).

Step 4: Run Evolution Loop

Use the evolution_evolve_strategy MCP tool:

evolution_evolve_strategy({
  symbol: "BTCUSDT",
  timeframe: "1h",
  generations: 3,
  candidates_per_gen: 10
})

Each generation:

1. Generate — LLM creates N candidate strategies
2. Backtest — Vectorized backtesting with Sharpe, win rate, max drawdown
3. Select — Top performers survive, bottom eliminated
4. Mutate — LLM evolves survivors with variations
5. Validate — Out-of-sample test on held-out data

Step 5: Report Results

For each graduated strategy:

Metric	In-Sample	Out-of-Sample
Sharpe Ratio	X.XX	X.XX
Win Rate	X%	X%
Max Drawdown	X%	X%
Total Return	X%	X%
# Trades	N	N

Plus:

• Strategy description (entry/exit rules in plain language)
• Graveyard summary (why eliminated strategies failed)
• Confidence assessment (how robust is the OOS performance?)