run_grid_job

Overview

run_grid_job runs the whole research pipeline once per parameter combination and returns a single artifact with the metrics of every variant side by side. It is the one tool that needs no upstream step: give it tickers and a date window and it downloads the prices itself. Pass a data_extractor_*.json link instead if you already have the data. Two pipelines:

short — data → portfolio construction → backtest. No features, no ML.
long — data → features → ML or deep learning → portfolio construction → backtest.

A sweep is heavy by design: every variant is a full backtest, and a large grid can run for up to two hours.

Parameters

object

required

Grid configuration. Pass {} to run the bundled defaults — short pipeline, one variant.

Show config fields

string

"short" (data → PO → backtest) or "long" (adds features and a forecasting model). Default: "short".

string

Which step is swept. "POgrid" for the short pipeline; "FeaturesGrid", "MLgrid" or "DeepLearningGrid" for the long one.

string

Long pipeline only: "ml", "dl" or "none".

object

Guard rails — max_axes and max_combinations. A sweep that exceeds them is refused before anything runs, so raise them deliberately rather than by accident.

object

Tickers and date window when the job downloads the data itself: Tickers (a list of symbols), Data_source, Learning_start, Testing_end.

object

Per-step settings. fixed_params holds what stays constant; every value in grid_params is a list, and their cartesian product forms the variants.

object

Per-variant HTML reports — equity curve, quantstats, bootstrap CI, riskfolio. On by default. A report that fails marks its variant failed, so switch them off for large sweeps.

string

Optional blob URL to a data_extractor_*.json file. When omitted, the job downloads the prices described in Data params.

Returns

{
  "status": "Succeeded",
  "output_url": "https://stqsnpprod.blob.core.windows.net/data/grid_engine_9a1b2c3d4e5f.json",
  "output_name": "grid_engine_9a1b2c3d4e5f.json",
  "execution_name": "grid-worker-abc123xyz"
}

The artifact holds run_id, pipeline_mode, n_ok / n_variants, and a variants array with the parameters, metrics and status of each variant. A variant whose backtest succeeded but whose HTML report failed has status report_error and keeps its metrics — read them rather than discarding the variant.

Example — sweep the rebalance frequency

{
  "config": {
    "block_params": {
      "POgrid": {
        "grid_config": {
          "grid_params": { "rebalance_frequency": [1, 5, 21] }
        }
      }
    }
  }
}

Example — explicit pipeline and data window

{
  "config": {
    "pipeline_mode": "short",
    "block_name": "POgrid",
    "returns_engine": "none",
    "grid_limits": { "max_axes": 3, "max_combinations": 100 },
    "Data params": {
      "Tickers": ["AAPL", "MSFT", "JPM"],
      "Data_source": "yahoo",
      "Learning_start": "2022-01-01",
      "Testing_end": "2025-01-01"
    },
    "block_params": {
      "POgrid": {
        "grid_config": {
          "fixed_params": { "methods": "equal_weighted" },
          "grid_params": { "rebalance_frequency": [1, 5, 21] }
        }
      }
    }
  }
}

Next Step

Compare the variants, then rerun the winning combination as a normal chain (run_po_job → run_trading_job) when you want charts and reports for it. Best variants are a search result, not evidence — confirm one on a window you did not sweep over.

Overview

How To

Tools description

Overview

Parameters

Returns

Example — sweep the rebalance frequency

Example — explicit pipeline and data window

Next Step

​Overview

​Parameters

​Returns

​Example — sweep the rebalance frequency

​Example — explicit pipeline and data window

​Next Step

Overview

Parameters

Returns

Example — sweep the rebalance frequency

Example — explicit pipeline and data window

Next Step