Ruflo Neural Trader
插件 已验证 活跃Neural trading via npx neural-trader — self-learning strategies, Rust/NAPI backtesting, 112+ MCP tools, swarm coordination, and portfolio optimization
To enable users to build, test, and deploy sophisticated AI-driven trading strategies with robust backtesting and risk analysis capabilities.
功能
- Self-learning strategy development (LSTM, Transformer, N-BEATS)
- High-performance Rust/NAPI backtesting engine
- Cloud-based execution for heavy jobs
- Portfolio optimization and risk management
- 112+ MCP tools for integration
使用场景
- Developing and backtesting complex trading strategies.
- Running large-scale Monte Carlo simulations and parameter sweeps.
- Optimizing portfolio allocation based on risk targets.
- Executing live trades with swarm coordination.
非目标
- Providing real-time market data feeds.
- Acting as a direct broker interface (relies on external broker integrations).
- Performing general AI tasks outside of financial trading.
工作流
- Install plugin and dependencies
- Configure API keys if using cloud features
- Design or select a trading strategy
- Perform backtesting (local or cloud)
- Analyze risk and optimize portfolio
- Optionally, deploy for live trading
实践
- Strategy design
- Backtesting
- Risk management
- Portfolio optimization
- Cloud execution
先决条件
- npm install neural-trader
- ANTHROPIC_API_KEY (for cloud features)
Code Execution
- info:ValidationWhile commands and parameters are structured, explicit schema validation for all inputs and outputs is not readily apparent in the provided documentation.
- info:LoggingThe plugin interacts with managed agents and CLIs which may have their own logging, but explicit local audit logging for plugin actions is not detailed in the README.
安装
请先添加 Marketplace
/plugin marketplace add ruvnet/ruflo/plugin install ruflo-neural-trader@ruflo包含 7 个扩展
Skill (7)
Run a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation
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
Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy
Assess portfolio risk using npx neural-trader — VaR, CVaR, Sharpe, position sizing, circuit breaker status
Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals
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