--- name: trader-cloud-backtest description: Run a heavy neural-trader job (long walk-forward, big Monte-Carlo, parameter sweep, model training) on the Anthropic Managed Agent cloud runtime instead of locally allowed-tools: mcp__claude-flow__managed_agent_create mcp__claude-flow__managed_agent_prompt mcp__claude-flow__managed_agent_events mcp__claude-flow__managed_agent_status mcp__claude-flow__managed_agent_terminate mcp__claude-flow__memory_store mcp__claude-flow__memory_retrieve mcp__claude-flow__memory_search mcp__claude-flow__agentdb_pattern-store Bash Read argument-hint: "

--symbol [--period 2020-2024] [--mc-paths 1000]" --- # Cloud backtest / train (neural-trader on a Managed Agent) Dispatch a **heavy** `neural-trader` job to an Anthropic Claude Managed Agent (cloud container) instead of running it locally. See project ADR-117 (recipe + cost rules) and ADR-115 (the `managed_agent_*` runtime). ## When to use this vs `trader-backtest` (local) | Job | Runtime | |---|---| | Quick sanity check; one short backtest (< ~1 min) | local — use the `trader-backtest` skill | | Multi-year **walk-forward**, big **Monte-Carlo** count, **parameter sweep** over a grid, or **model training** (LSTM/Transformer/N-BEATS) | **cloud — this skill** | Prereq: `ANTHROPIC_API_KEY` (or `CLAUDE_API_KEY`) + Managed Agents beta access. If `managed_agent_*` returns "needs ANTHROPIC_API_KEY", fall back to the local `trader-backtest` skill. ## Steps 1. **Estimate first.** From the job size, print an estimated cost (≈ container-minutes × rate + tokens) — a long sweep is a deliberate choice, not a default. 2. **Provision (or reuse) the container** — install neural-trader at container start so the agent doesn't reinstall mid-run: ``` managed_agent_create({ name: "nt-cloud", model: "claude-haiku-4-5-20251001", // orchestration only — the compute is the Rust engine, not the LM (ADR-026) system: "You operate the `neural-trader` CLI in this container. Run exactly the commands asked, report the metrics, write requested artifacts, then stop.", networking: "unrestricted", // or "restricted" pinned to your data host packages: { npm: ["neural-trader"] }, // add apt:["build-essential"] ONLY if there's no prebuilt NAPI binary for the arch (neural-trader ships prebuilds → usually omit) initScript: "npm install -g neural-trader >/dev/null 2>&1 || npx -y neural-trader --version >/dev/null 2>&1 || true" }) → { sessionId, agentId, environmentId } ``` For a **sweep**: create the environment once, run all configs in **one** `managed_agent_prompt` (one container), not N sessions. 3. **Pre-flight cheap.** Before a 1000-path / multi-year run, do a tiny smoke first (1 MC path, ~3 months) — catches a bad strategy name / symbol in seconds: ``` managed_agent_prompt({ sessionId, message: "Run `npx neural-trader --backtest --strategy --symbol --period --mc-paths 1`. Just confirm it ran and report the Sharpe. Then stop.", maxWaitMs: 60000 }) ``` If that fails, fix the args before the real run (and `managed_agent_terminate`). 4. **Run the real job:** ``` managed_agent_prompt({ sessionId, message: "Run `npx neural-trader --backtest --strategy --symbol --period --walk-forward --mc-paths ` (for training: `npx neural-trader --train --model --symbol --period `; for a sweep: loop the configs and run each). Report: total return, annualized return, Sharpe, Sortino, max drawdown, win rate, profit factor, # trades, 95% CVaR. Write the equity curve to /tmp/equity.csv and the trade log to /tmp/trades.csv. Then stop.", maxWaitMs: }) → { finished, status, stopReason, assistantText (the metrics), toolUses } ``` If `finished:false`, follow up with `managed_agent_events({ sessionId })` until idle. 5. **Pull artifacts (if needed):** `managed_agent_prompt({ sessionId, message: "cat /tmp/equity.csv" })` or `managed_agent_events` and read the tool_result. 6. **Ingest locally:** - `memory_store({ key: "backtest--", value: , namespace: "trading-backtests" })` - If Sharpe > 1.5: `agentdb_pattern-store({ pattern: "profitable-", data: "" })` - Record the run's container time + token cost to the `cost-tracking` namespace (per ADR-117 — cloud sessions bill until terminated). 7. **Terminate immediately** — results in hand: ``` managed_agent_terminate({ sessionId, environmentId }) → { sessionDeleted: true, environmentDeleted: true } ``` Never leave an idle billing container. (`ruflo doctor` / GC catches orphans — #1931.) ## Cost rules (don't skip) - Install once (`initScript`), reuse the environment, batch sweeps into one prompt, pre-flight cheap, terminate eagerly, use Haiku/Sonnet for the agent loop, estimate before kicking off. (ADR-117 §"Cost optimization".) - A cloud backtest that runs for an hour costs an hour of container time + the agent-loop tokens. Be deliberate. ## Quick example ``` managed_agent_create { "name":"nt-cloud", "model":"claude-haiku-4-5-20251001", "packages":{"npm":["neural-trader"]}, "initScript":"npm install -g neural-trader >/dev/null 2>&1 || true" } → { sessionId:"sesn_…", environmentId:"env_…" } managed_agent_prompt { "sessionId":"sesn_…", "message":"Run `npx neural-trader --backtest --strategy multi-indicator --symbol SPY --period 2020-2024 --walk-forward --mc-paths 1000`. Report Sharpe/Sortino/max-DD/win-rate/CVaR; write /tmp/equity.csv. Then stop.", "maxWaitMs":600000 } → { finished:true, status:"idle", assistantText:"", toolUses:[{bash:"npx neural-trader --backtest …"}] } # … memory_store the metrics, agentdb_pattern-store if Sharpe>1.5, record cost … managed_agent_terminate { "sessionId":"sesn_…", "environmentId":"env_…" } ```

trader-cloud-backtest

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