---name: trader-cloud-backtestdescription: 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 locallyallowed-tools: mcp__plugin_ruflo-core_ruflo__managed_agent_create mcp__plugin_ruflo-core_ruflo__managed_agent_prompt mcp__plugin_ruflo-core_ruflo__managed_agent_events mcp__plugin_ruflo-core_ruflo__managed_agent_status mcp__plugin_ruflo-core_ruflo__managed_agent_terminate mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__agentdb_pattern-store Bash Readargument-hint: <backtest|train|sweep> <strategy-or-model> --symbol <TICKER> [--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 --ignore-scripts 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 <name> --symbol <TICKER> --period <last 3 months> --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 <name> --symbol <TICKER> --period <range> --walk-forward --mc-paths <N>` (for training: `npx neural-trader --train --model <lstm|transformer|nbeats> --symbol <TICKER> --period <range>`; 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: <generous — minutes>
   })
   → { 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 + Ed25519 verify (ADR-126 Phase 4 fail-closed gate):

   • Build the SignedBacktestArtifact body from the cloud-returned metrics + params hash + runs hash. Sign it locally with signBacktestArtifact(body, privateKeyHex) from plugins/ruflo-neural-trader/src/signed-artifact.mjs (key resolution same as trader-backtest: RUFLO_WITNESS_KEY_PATH → verification/witness-key.json → degraded-unsigned warning).
   • Before storing OR promoting the artifact to a live strategy: call await verifyBacktestArtifact(artifact, trustedPublicKey) where trustedPublicKey is the pinned project-config Ed25519 public key (NOT the artifact.witnessPublicKey field — that's attacker-controllable; see CWE-347 / #1922). If verification returns false: REFUSE to promote — emit a loud error "[ERROR] ruflo-neural-trader: SignedBacktestArtifact signature INVALID against trusted key — refusing to promote to live strategy" and return early. This is the fail-closed gate per ADR-126.
   • On verify success: memory_store({ key: "backtest-<strategy>-<ts>", value: JSON.stringify(signedArtifact), namespace: "trading-backtests" }). The stored value carries witnessSignature + witnessPublicKey.
   • If Sharpe > 1.5: agentdb_pattern-store({ pattern: "profitable-<strategy-type>", data: "<params + results>" }).
   • 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 --ignore-scripts 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:"<metrics>", 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_…" }