run_ml_job

Overview

run_ml_job trains a classical machine learning model on feature-engineered data and produces per-ticker next-day log-return predictions for the ML-enhanced portfolio path. Supported libraries:

scikit-learn — linear, ridge, lasso, elastic net, quantile, random forest, …
XGBoost, LightGBM, CatBoost — gradient-boosting regressors

This stage is optional. The default QuantLab path is data → PO → trading. Use ML when you want predicted returns as μ (mu_mode: custom in run_po_job).

For XGBoost, LightGBM, and CatBoost, QuantLab uses the regressor APIs (e.g. XGBRegressor, LGBMRegressor, CatBoostRegressor) to predict continuous returns — not classifiers or clustering.

Hyperparameters — full library power

Pass any constructor argument the chosen model accepts via model_params. The worker forwards those kwargs into the library’s estimator __init__, so you can use the full API surface of sklearn / XGBoost / LightGBM / CatBoost (learning rate, depth, subsample, reg_alpha, l1_ratio, random_state, and so on). Examples:

"model": "xgboost",
"model_params": {
  "n_estimators": 300,
  "max_depth": 5,
  "learning_rate": 0.05,
  "subsample": 0.8,
  "colsample_bytree": 0.8,
  "random_state": 42
}

"model": "lightgbm",
"model_params": {
  "n_estimators": 400,
  "num_leaves": 63,
  "learning_rate": 0.03,
  "min_child_samples": 20
}

"model": "catboost",
"model_params": {
  "iterations": 500,
  "depth": 6,
  "learning_rate": 0.05,
  "random_seed": 42
}

In chat you can just say “XGBoost with max_depth 5 and 300 trees” — MCP skills fill in model_params.

Parameters

string

required

URL of feature_engine_*.json from run_feature_worker.

string

required

URL of data_extractor_*.json from run_data_extraction (train/test date alignment).

object

required

ML configuration.

Show config fields

object

required

Show ML params fields

string

required

Model key (case-insensitive):

Value	Backend
`"random_forest"` / `"rf"`	scikit-learn RandomForestRegressor
`"xgboost"` / `"xgb"`	XGBoost regressor
`"lightgbm"` / `"lgbm"`	LightGBM regressor
`"catboost"`	CatBoost regressor
`"linear"` / `"linear_regression"`	sklearn LinearRegression
`"ridge"`	sklearn Ridge
`"lasso"`	sklearn Lasso
`"elastic_net"`	sklearn ElasticNet
`"quantile"`	sklearn QuantileRegressor

Unknown names raise an error (no silent fallback).

object

Dict of hyperparameters for the model constructor — any kwargs supported by that library’s regressor API.

integer

Samples per CV test fold (walk-forward). Default: 15. Legacy alias: test_size.

integer

Number of CV splits. Default: 15.

object

Optional MAPIE conformal intervals (enabled, alpha, bound).

Returns

{
  "status": "Succeeded",
  "output_url": "https://stqsnpprod.blob.core.windows.net/data/ml_engine_7d4e2a91c0bf.json",
  "output_name": "ml_engine_7d4e2a91c0bf.json",
  "execution_name": "ml-worker-job-abc123xyz"
}

Pass output_url to run_po_job. Prefer run_dl_job if you want neural nets instead.

Example

{
  "feature_url": "https://stqsnpprod.blob.core.windows.net/data/feature_engine_a1c3e5f70912.json",
  "data_extractor_url": "https://stqsnpprod.blob.core.windows.net/data/data_extractor_2b510101b9b7.json",
  "config": {
    "ML params": {
      "model": "xgboost",
      "model_params": {
        "n_estimators": 200,
        "max_depth": 4,
        "learning_rate": 0.05,
        "random_state": 42
      },
      "cv_test_size": 15,
      "n_splits": 15,
      "intervals": {
        "enabled": false,
        "alpha": 0.99,
        "bound": "center"
      }
    }
  }
}

Resources

Resource	Value
Container Apps Job	`ml-worker-job`
Env vars injected	`FEATURE_URL`, `DATA_EXTRACTOR_URL`, `CONFIG`
Output blob prefix	`ml_engine_`

Overview

How To

Tools description

Overview

Hyperparameters — full library power

Parameters

Returns

Example

Resources

​Overview

​Hyperparameters — full library power

​Parameters

​Returns

​Example

​Resources

Overview

Hyperparameters — full library power

Parameters

Returns

Example

Resources