scout

Autonomous strategy search agent for the swym backtesting platform.

Runs a loop: asks Claude to generate trading strategies → submits backtests to swym → evaluates results → feeds learnings back → repeats. Promising strategies are automatically validated on out-of-sample data to filter overfitting.

Quick start

export ANTHROPIC_API_KEY="sk-ant-..."

cargo run -- \
  --swym-url https://dev.swym.hanzalova.internal/api/v1 \
  --max-iterations 50 \
  --instruments binance_spot:BTCUSDC,binance_spot:ETHUSDC,binance_spot:SOLUSDC \
  --backtest-from 2025-01-01T00:00:00Z \
  --backtest-to 2025-10-01T00:00:00Z \
  --oos-from 2025-10-01T00:00:00Z \
  --oos-to 2026-03-01T00:00:00Z

How it works

1. Coverage check — verifies candle data exists for all instruments and finds common available intervals.

2. Strategy generation — sends the DSL schema + prior results to Claude, which produces a new strategy JSON each iteration.

3. In-sample backtest — submits the strategy against all instruments for the training period. Evaluates Sharpe ratio, profit factor, win rate, net PnL.

4. Out-of-sample validation — if any instrument shows Sharpe > threshold with enough trades, the strategy is re-tested on held-out data. Only strategies that pass both phases are saved as "validated".

5. Learning loop — all results (including failures) are fed back to Claude so it can learn from what works and what doesn't. The conversation is trimmed to avoid context exhaustion while the full results history is passed as structured text.

Configuration

All options are available as CLI flags and environment variables:

Flag	Env	Default	Description
--swym-url	SWYM_API_URL	https://dev.swym.hanzalova.internal/api/v1	Swym API base URL
--anthropic-key	ANTHROPIC_API_KEY	required	Anthropic API key
--model	CLAUDE_MODEL	claude-sonnet-4-20250514	Claude model
--max-iterations		50	Maximum search iterations
--min-sharpe		1.0	Minimum Sharpe for "promising"
--min-trades		10	Minimum trades for significance
--instruments		BTC,ETH,SOL vs USDC	Comma-separated exchange:SYMBOL
--backtest-from		2025-01-01	In-sample start
--backtest-to		2025-10-01	In-sample end
--oos-from		2025-10-01	Out-of-sample start
--oos-to		2026-03-01	Out-of-sample end
--initial-balance		10000	Starting USDC balance
--fees-percent		0.001	Fee per trade (0.1%)
--output-dir		./scout-results	Where to save strategies and reports

Output

scout-results/
├── strategy_001.json      # Every strategy attempted
├── strategy_002.json
├── ...
├── validated_017.json     # Strategies that passed OOS validation
├── validated_031.json     # (includes in-sample + OOS metrics)
└── best_strategy.json     # Highest avg Sharpe across instruments

Tips

• Start with Sonnet (claude-sonnet-4-20250514) for cost efficiency during exploration. Switch to Opus for refinement of promising strategies.

• 50 iterations is a reasonable starting point. The agent typically finds interesting patterns within 20-30 iterations if they exist.

• Watch the logs — the per-iteration summaries show you what the agent is learning in real time.

• Adjust dates to match your actual candle coverage. The agent checks coverage at startup and will fail fast if data is missing.

• The OOS validation threshold is intentionally relaxed (70% of in-sample Sharpe, half the trade count) because out-of-sample degradation is expected. Strategies that maintain edge through this filter are genuinely interesting.