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trader-train

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Original

English
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Translation

Chinese
Train neural prediction models using neural-trader's ML engine.
Steps:
  1. Ensure neural-trader is available: 
    npm ls neural-trader 2>/dev/null || npm install neural-trader
  2. Train the specified model:
    bash
    npx neural-trader --model lstm --symbol TICKER --confidence 0.95
npx neural-trader --model transformer --symbol TICKER --predict
npx neural-trader --model nbeats --symbol TICKER --decompose
  3. Review training output: loss curves, validation metrics, prediction accuracy
  4. Generate predictions with confidence intervals:
    bash
    npx neural-trader --model MODEL --symbol TICKER --predict --horizon 5d
  5. Compare model performance across types:
    bash
    npx neural-trader --model-compare --symbol TICKER --models "lstm,transformer,nbeats"
  6. Store model results: 
    mcp__claude-flow__memory_store({ key: "model-MODEL-TICKER-DATE", value: "TRAINING_RESULTS", namespace: "trading-models" })
  7. Train SONA on model outcomes: 
    mcp__claude-flow__neural_train({ patternType: "trading-model", epochs: 10 })
使用neural-trader的ML引擎训练神经预测模型。
步骤：
  1. 确保neural-trader可用： 
    npm ls neural-trader 2>/dev/null || npm install neural-trader
  2. 训练指定模型：
    bash
    npx neural-trader --model lstm --symbol TICKER --confidence 0.95
npx neural-trader --model transformer --symbol TICKER --predict
npx neural-trader --model nbeats --symbol TICKER --decompose
  3. 查看训练输出：损失曲线、验证指标、预测准确率
  4. 生成带置信区间的预测结果：
    bash
    npx neural-trader --model MODEL --symbol TICKER --predict --horizon 5d
  5. 对比不同类型模型的性能：
    bash
    npx neural-trader --model-compare --symbol TICKER --models "lstm,transformer,nbeats"
  6. 存储模型结果： 
    mcp__claude-flow__memory_store({ key: "model-MODEL-TICKER-DATE", value: "TRAINING_RESULTS", namespace: "trading-models" })
  7. 基于模型结果训练SONA： 
    mcp__claude-flow__neural_train({ patternType: "trading-model", epochs: 10 })