run_po_job

Overview

run_po_job builds portfolio weights with skfolio.

Default: data_extractor_*.json — no ML needed
With forecasts: ml_engine_*.json / nn_engine_*.json — use predicted returns as μ (mu_mode: custom)

Ask for a method (equal weight, HRP, max Sharpe, DCC, …); skills fill the rest.

Parameters

string

required

Blob storage URL pointing to one of:

data_extractor_*.json — output of run_data_extraction (default path)
ml_engine_*.json — output of run_ml_job
nn_engine_*.json — output of run_dl_job

object

required

Portfolio optimization configuration.

Show config fields

object

required

Method and estimation knobs.

Show Optimization params fields

string

required

Optimization method (case-insensitive). Default when omitted: "maxsharpe".No-covariance (no covariance_mode / mu_mode knobs):

Value	Strategy
`"equal_weighted"`	Equal weight
`"inverse_volatility"`	Inverse volatility
`"random"`	Random portfolio

Covariance-based (support covariance_mode + mu_mode):

Value	Strategy
`"maxsharpe"`	Mean-Risk — maximize Sharpe
`"minvol"` / `"minimize_volatility"`	Mean-Risk — minimize volatility
`"hrp"`	Hierarchical Risk Parity
`"herc"`	Hierarchical Equal Risk Contribution
`"risk_budgeting"`	Risk Budgeting
`"max_diversification"`	Maximum Diversification
`"distributionally_robust_cvar"` / `"robust_cvar"`	Distributionally Robust CVaR

DCC aliases (auto-set covariance_mode to "dcc"):"dcc_maxsharpe", "dcc_minvol", "dcc_hrp", "dcc_herc", "dcc_risk_budgeting", "dcc_max_diversification", "dcc_robust_cvar"

Retired aliases — do not use: meanvariance, mean_variance, sharpe, maximize_sharpe, dcc_sharpe, MeanVariance, markowitz. Use "maxsharpe" / "dcc_maxsharpe" instead.

string

Covariance estimator for covariance-based methods. Default: "empirical". Values: "empirical", "ew", "ledoit_wolf", "oas", "denoise", "detone", "gerber", "shrunk", "graphical_lasso", "dcc". DCC aliases force "dcc".

string

Expected-return estimator: "empirical", "ew", or "custom" (ML/DL predictions as μ). Auto-default: "empirical" on data_extraction input; "custom" on ml/nn engine input. On data_extraction, "custom" is overridden to "empirical" (no ML predictions available).

number

Per-asset lower bound (0–1). Default: 0.

number

Per-asset upper bound (0–1). Default: 1.

integer

Rolling covariance window in trading days. Default: 40. On the no-ML path this is also the warmup: the first n days estimate covariance; everything after that is the evaluation window (you do not pass a separate testing-end date). For DCC, estimation still uses the full train sample; the window mainly controls that warmup split.

integer

Rebalance every N trading days. Default: 1 (daily). Example: 5 ≈ weekly, 21 ≈ monthly.

Without ML/DL: only sample start + end upstream. The evaluated period is everything after the first covariance_window days — there is no extra testing-end date. With ML/DL: still start + end of the sample; model training uses the train cut from extraction when present.

Returns

{
  "status": "Succeeded",
  "output_url": "https://stqsnpprod.blob.core.windows.net/data/portfolio_optimization_35c1b5239d23.json",
  "output_name": "portfolio_optimization_35c1b5239d23.json",
  "execution_name": "po-job-abc123xyz"
}

Field	Description
`status`	Job terminal status (`Succeeded`)
`output_url`	Full HTTPS URL to the output blob — pass to `run_trading_job`
`output_name`	Blob filename
`execution_name`	Job execution ID for audit/debugging

The blob contains Weights, Prices, and Meta (includes fallback flags if optimization falls back to equal weights).

Example — default path (data → PO)

{
  "input_url": "https://stqsnpprod.blob.core.windows.net/data/data_extractor_2b510101b9b7.json",
  "config": {
    "Optimization params": {
      "method": "equal_weighted",
      "rebalance_frequency": 21
    }
  }
}

Example — HRP with covariance mode

{
  "input_url": "https://stqsnpprod.blob.core.windows.net/data/data_extractor_2b510101b9b7.json",
  "config": {
    "Optimization params": {
      "method": "hrp",
      "covariance_mode": "empirical",
      "mu_mode": "empirical",
      "covariance_window": 40,
      "rebalance_frequency": 5
    }
  }
}

Example — ML-enhanced path

{
  "input_url": "https://stqsnpprod.blob.core.windows.net/data/ml_engine_7d4e2a91c0bf.json",
  "config": {
    "Optimization params": {
      "method": "maxsharpe",
      "mu_mode": "custom"
    }
  }
}

Resources

Resource	Value
Container Apps Job	`po-job`
Container name	`po-job`
Env vars injected	`INPUT_URL`, `CONFIG`
Output blob prefix	`portfolio_optimization_`

Next Step

Pass output_url to run_trading_job as input_url. Optional: run_plot_job, run_st_job, run_risk_job.

Overview

How To

Tools description

Overview

Parameters

Returns

Example — default path (data → PO)

Example — HRP with covariance mode

Example — ML-enhanced path

Resources

Next Step

​Overview

​Parameters

​Returns

​Example — default path (data → PO)

​Example — HRP with covariance mode

​Example — ML-enhanced path

​Resources

​Next Step

Overview

Parameters

Returns

Example — default path (data → PO)

Example — HRP with covariance mode

Example — ML-enhanced path

Resources

Next Step