ricable Neural Trader
High-performance neural trading system with native HNSW vector search, SIMD-accelerated operations, and a complete NAPI API (178 functions). Provides CLI for strategy management, backtesting, model training, and real-time market analysis.
Quick Command Reference
| Task | Command |
|---|---|
| Show help | npx neural-trader@latest --help |
| Initialize | npx neural-trader@latest init |
| Train model | npx neural-trader@latest train |
| Backtest | npx neural-trader@latest backtest |
| Run strategy | npx neural-trader@latest run |
| Market data | npx neural-trader@latest data |
| HNSW search | npx neural-trader@latest search |
| Status | npx neural-trader@latest status |
| Benchmark | npx neural-trader@latest benchmark |
Installation
Install: npx neural-trader@latest
See Installation Guide for hub details.
Core Commands
init
Initialize neural trader workspace.
npx neural-trader@latest init [--template <name>] [--force]
train
Train neural trading model.
npx neural-trader@latest train [options]
Options: --data <path>, --epochs <n>, --model <type>, --output <path>
backtest
Run strategy backtesting.
npx neural-trader@latest backtest [options]
Options: --strategy <name>, --period <range>, --data <path>, --output <path>
run
Execute a trading strategy.
npx neural-trader@latest run [options]
Options: --strategy <name>, --mode <live|paper>, --risk <level>
search
HNSW vector similarity search for pattern matching.
npx neural-trader@latest search [options]
Options: --query <pattern>, --k <n>, --index <name>
benchmark
Performance benchmarking for HNSW and NAPI operations.
npx neural-trader@latest benchmark [options]
Programmatic API
import { NeuralTrader, HNSWIndex, Strategy } from 'neural-trader';
const trader = new NeuralTrader({ simd: true });
const index = new HNSWIndex({ dimensions: 128, m: 16 });
// Train on historical data
await trader.train({ data: './market-data.csv', epochs: 100 });
// Backtest strategy
const results = await trader.backtest({
strategy: 'momentum',
period: '2024-01-01..2024-12-31',
});
console.log(`Sharpe ratio: ${results.sharpeRatio}`);
RAN DDD Context
Bounded Context: RANO Optimization
References
- Command reference: See references/commands.md
- Full README
- npm