Cloud backtest / train (neural-trader on a Managed Agent)

neural-trader

managed_agent_*

When to use this vs

trader-backtest

(local)

trader-backtest

ANTHROPIC_API_KEY

CLAUDE_API_KEY

managed_agent_*

trader-backtest

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:

   • memory_store({ key: "backtest-<strategy>-<ts>", value: <metrics+params JSON>, namespace: "trading-backtests" })

   • 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_…" }