Available symbols

client.data.calibrated_data() lists the instruments that are already calibrated, and therefore simulatable, with their available dates, trading parameters and volume statistics. This is the list to drive a symbol picker from, and the place to find a valid cal_date before calling simulation.run(). For raw days that are not yet calibrated, see Available data.

Only calibrated days

The endpoint filters on pipeline state server-side, so there is no status or stage for you to check: if a date is in the response, you can simulate it. Both fields are reported for provenance and always read complete / model_calibration.

Filtering and paging

Called with no arguments it returns the whole catalog, which is large. Every filter narrows it server-side, and they combine with AND: symbol is an exact ticker; q is a case-insensitive substring over the ticker and company name ("angl" finds ANGLO AMERICAN); exchange and provider use the same spellings as simulation.run(); date keeps only symbols calibrated on that day and narrows each symbol's available_dates to it. A malformed date matches nothing (total 0) rather than raising.

Paging is by instrument, not by row, so a limit never cuts one instrument's dates in half. total is the match count before paging, so offset + len(symbols) < total means more pages remain.

What an instrument carries

Each entry in symbols is one instrument with every calibrated date for it. company_name is None for some providers, so fall back to the ticker. tick_size is the minimum price increment and lot_size the minimum order size; an execution algorithm's order_size is in lots, so multiply by lot_size to reason in shares. Each available_dates record has number_of_msgs, volume_traded and reference_price, and the two averages are means over exactly the dates shown.

{
  "total": 1,
  "limit": null,
  "offset": 0,
  "symbols": [
    {
      "symbol": "700.HK",
      "company_name": null,
      "provider": "omd",
      "exchange": "hkex_securities",
      "instrument_type": "equity",
      "currency": "HKD",
      "tick_size": 0.5,
      "lot_size": 100,
      "available_dates": [
        {
          "date": "2025-09-02",
          "status": "complete",
          "stage": "model_calibration",
          "number_of_msgs": 252812,
          "volume_traded": 21861323.0,
          "reference_price": 605.0
        }
      ],
      "avg_number_of_msgs": 252812.0,
      "avg_volume_traded": 21861323.0
    }
  ]
}

Examples

catalog = client.data.calibrated_data(limit=5)
print(catalog["total"], "calibrated instruments")

for sym in catalog["symbols"]:
    label = sym["company_name"] or sym["symbol"]
    dates = [d["date"] for d in sym["available_dates"]]
    print(f"{sym['symbol']} ({label}) on {sym['provider']}: {len(dates)} dates")

The newest calibrated date for one instrument is always safe to pass to run():

rows = client.data.calibrated_data(symbol="700.HK", provider="omd")["symbols"]
dates = [d["date"] for row in rows for d in row["available_dates"]]
print("Latest ready date:", max(dates) if dates else None)

# Which symbols are ready on a particular date?
ready = client.data.calibrated_data(date="2025-09-02")["symbols"]
print([s["symbol"] for s in ready])