# Trader Cloud Backtest

> Ejecuta un job pesado de neural-trader (walk-forward largo, Monte-Carlo grande, barrido de parámetros o entrenamiento) en el runtime cloud de Anthropic Managed Agent en vez de en local.

Fuente: https://skillsagentes.com/skills/ruvnet/ruflo/trader-cloud-backtest
Markdown: https://skillsagentes.com/skills/ruvnet/ruflo/trader-cloud-backtest.md

Repositorio: https://github.com/ruvnet/ruflo
Autor: ruvnet
Licencia: MIT
Actualizado: el mes pasado
Coste de contexto: 41 tok instalada, 1.8k tok al activarse, 1.8k tok con todos los archivos del bundle
Bundle: 1 archivo, 7 KB
Permisos que pide: 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 read

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add ruvnet/ruflo --skill trader-cloud-backtest --agent claude-code
# Cursor
npx -y skills add ruvnet/ruflo --skill trader-cloud-backtest --agent cursor
# Codex
npx -y skills add ruvnet/ruflo --skill trader-cloud-backtest --agent codex
# Gemini CLI
npx -y skills add ruvnet/ruflo --skill trader-cloud-backtest --agent gemini
# Windsurf
npx -y skills add ruvnet/ruflo --skill trader-cloud-backtest --agent windsurf
# Cline
npx -y skills add ruvnet/ruflo --skill trader-cloud-backtest --agent cline
```

## Qué hace

- Crea un contenedor Managed Agent, instala neural-trader y lanza el job pesado (backtest, entrenamiento o sweep).
- Hace una prueba rápida y barata antes del job real para detectar errores de argumentos.
- Firma el artefacto resultante con Ed25519 y verifica la firma antes de promoverlo a estrategia real; si falla, se niega a promover.
- Termina el contenedor en cuanto tiene los resultados para no dejarlo facturando ocioso.

## Cuándo usarla

- El job es un walk-forward multianual, un Monte-Carlo grande, un barrido de parámetros o un entrenamiento de modelo (LSTM/Transformer/N-BEATS).

## Cuándo no

- Para una comprobación rápida o un backtest corto de menos de un minuto; usa trader-backtest en local.

## Qué la activa

- "Ejecuta un barrido de parámetros en la nube para esta estrategia"
- "Entrena un modelo LSTM en un Managed Agent"

## Antes de instalar

- Necesita ANTHROPIC_API_KEY (o CLAUDE_API_KEY) y acceso beta a Managed Agents.
- Necesita en el PATH: npx

## Archivos

- SKILL.md — 7 KB

## SKILL.md

Reproducido tal cual desde ruvnet/ruflo bajo MIT. Esta sección es el documento original y está en inglés.

# 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_…" }
```

## Dónde encaja

- Categoría: [Finanzas](https://skillsagentes.com/categorias/finanzas.md) — Contabilidad, modelado financiero y flujos de reportes.
- Creador: [ruvnet](https://skillsagentes.com/creators/ruvnet.md) — 275 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Harness Gepa](https://skillsagentes.com/skills/ruvnet/ruflo/harness-gepa.md): Inspecciona y audita genomas GEPA: carga y valida un genoma, renderiza el system prompt que compila, o clasifica los modos de fallo de una transcripción de ejecución.
- [Deepseek Reason](https://skillsagentes.com/skills/ruvnet/ruflo/deepseek-reason.md): Completion en modo razonamiento contra deepseek-reasoner (R1) de DeepSeek. Devuelve el chain-of-thought por separado de la respuesta final. Lee DEEPSEEK_API_KEY y degrada si falta o la API no responde.
- [Deepseek Chat](https://skillsagentes.com/skills/ruvnet/ruflo/deepseek-chat.md): Completion de un solo turno contra el modelo deepseek-chat de DeepSeek vía /v1/chat/completions. Lee DEEPSEEK_API_KEY y degrada con status:degraded si falta o la API no responde. Para tareas sin razonamiento.
- [Adr Index](https://skillsagentes.com/skills/ruvnet/ruflo/adr-index.md): Construye o reconstruye el índice de ADRs y su grafo de dependencias ejecutando scripts/import.mjs, en vez de cientos de llamadas MCP.
- [Agntcy Status](https://skillsagentes.com/skills/ruvnet/ruflo/agntcy-status.md): Muestra el estado de la integración AGNTCY/SLIM/CASA: si los paquetes están instalados, qué transporte está activo y si el enforcement de CASA está habilitado.

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