Converting Nubra historical_data() to a pandas DataFrame: TimeSeriesPoint objects, nanosecond timestamps and odd volume
01:30 12 Oct 2026

I fetched 5-minute candles like this:

resp = market_data.historical_data({
    "exchange": "NSE", "type": "STOCK", "values": ["TATAMOTORS"],
    "fields": ["open", "high", "low", "close", "cumulative_volume"],
    "startDate": "2026-08-01T03:45:00.000Z", "endDate": "2026-08-05T10:00:00.000Z",
    "interval": "5m", "intraDay": False, "realTime": False,
})

pd.DataFrame(resp) gives garbage, values["close"] throws, and the volume column only ever increases. A 1-minute request going back a year also returns nothing. How should I parse this?

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