Historical Data

Historical Data API provides the candle data(Open, High, Low, Close) with timestamps for a given time period, for the given scrips, for a specifed candle duration.

Usage

Python

from nubra_python_sdk.marketdata.market_data import MarketData
from nubra_python_sdk.start_sdk import InitNubraSdk, NubraEnv

# Initialize the Nubra SDK client
# Use NubraEnv.UAT for testing or NubraEnv.PROD for production
nubra = InitNubraSdk(NubraEnv.UAT)  # or NubraEnv.PROD

##using totp login and .env file 
#nubra = InitNubraSdk(NubraEnv.UAT, totp_login= True ,env_creds = True)

# Initialize MarketData with the client
mdInstance = MarketData(nubra)
result = mdInstance.historical_data({
    "exchange": "NSE",
    "type": "STOCK",
    "values": ["ASIANPAINT", "TATAMOTORS"],
    "fields": ["close", "high", "low", "open", "cumulative_volume"],
    "startDate": "2025-04-19T11:01:57.000Z",
    "endDate": "2025-04-24T06:13:57.000Z",
    "interval": "1d",
    "intraDay": False, # If set to True, then startDate and endDate will be ignored, and the current date will be used.
    "realTime": False
})

Accessing Data

Python

# Access values only after checking data exists in any variable as based on request some fields may not have value
print(f"Market Time: {result.market_time}")
print(f"Message: {result.message}")

for data in result.result:
    print(f"Exchange: {data.exchange}")
    print(f"Type: {data.type}")
    for stock_data in data.values:
        for symbol, values in stock_data.items():
            print(f"\n{symbol}:")
            print(f"  Close: {[{'timestamp': v.timestamp, 'value': v.value} for v in values.close]}")
            print(f"  High: {[{'timestamp': v.timestamp, 'value': v.value} for v in values.high]}")
            print(f"  Low: {[{'timestamp': v.timestamp, 'value': v.value} for v in values.low]}")
            print(f"  Open: {[{'timestamp': v.timestamp, 'value': v.value} for v in values.open]}")
            print(f"  Volume: {[{'timestamp': v.timestamp, 'value': v.value} for v in values.cumulative_volume]}")

Request Parameters

Python

Attribute	Data Type	Description
exchange	String	Exchange name (e.g., NSE, BSE)
type	String	"STOCK", "INDEX" ,"OPT" , "FUT"
values	Union[List[str], str])	One or more instrument symbols e.g. ["ASIANPAINT","TATAMOTORS"],["HDFCBANK25MAY2380CE"],["NIFTY2550822400PE"]
fields	List[str]	One or more fields e.g.["open","high","low","close","tick_volume","cumulative_volume","cumulative_volume_premium","cumulative_oi","cumulative_call_oi","cumulative_put_oi","cumulative_fut_oi","l1bid","l1ask","theta","delta","gamma","vega","iv_bid","iv_ask","iv_mid","cumulative_volume_delta"]
startDate	str	Start date time format eg. "2025-04-19T11:01:57.000Z"
endDate	str	end date time format eg. "2025-04-24T06:13:57.000Z"
interval	str	Interval can be "1s","1m","2m","3m","5m","15m","30m","1h","1d","1w","1mt"
intraDay	bool	If True startDate is current date
realTime	bool	To be declared

Notes

Intervals less than 1 day → data for last 3 months

Intervals 1 day or more → data for up to 10 years (stocks)

1s interval special case
- Supported only for the previous 7 days
- startDate and endDate must fall on the same calendar day

(In PROD environment)

Response Structure

Python

# Response object structure
class MarketChartsResponse:
    market_time: str  # e.g. "2025-04-30T10:00:000Z"
    message: str      # e.g. "charts"
    result: List[ChartData]

class ChartData:
    exchange: str     # e.g. "NSE"
    type: str         # e.g. "STOCK"
    values: List[Dict[str, StockChart]]

class StockChart:
    open: Optional[List[TimeSeriesPoint]] 
    high: Optional[List[TimeSeriesPoint]]
    low: Optional[List[TimeSeriesPoint]] 
    close: Optional[List[TimeSeriesPoint]] 
    tick_volume: Optional[List[TimeSeriesPoint]] 
    theta: Optional[List[TickPoint]] 
    delta: Optional[List[TickPoint]] 
    gamma: Optional[List[TickPoint]] 
    vega: Optional[List[TickPoint]] 
    iv_bid: Optional[List[TickPoint]] 
    iv_ask: Optional[List[TickPoint]] 
    iv_mid: Optional[List[TickPoint]] 
    cumulative_volume: Optional[List[TimeSeriesPoint]] 
    cumulative_volume_delta: Optional[List[TickPoint]] 
    cumulative_volume_premium: Optional[List[TimeSeriesPoint]] 
    cumulative_oi: Optional[List[TimeSeriesPoint]] 
    cumulative_call_oi: Optional[List[TimeSeriesPoint]] 
    cumulative_put_oi : Optional[List[TimeSeriesPoint]] 
    cumulative_fut_oi: Optional[List[TimeSeriesPoint]] 
    l1bid: Optional[List[TimeSeriesPoint]] 
    l1ask: Optional[List[TimeSeriesPoint]] 

class Tickpoint:
    timestamp: Optional[int] 
    value: Optional[float] 

class TimeSeriesPoint:
    timestamp: int       # Timestamp in Epoch
    value: int         # Value at the timestamp

Response attributes

Python

Attribute	Description
market_time	Current market time in ISO format
message	Response message type (e.g. "charts")
result	List of DataResult objects containing the historical data
result[].exchange	Exchange name (e.g. "NSE")
result[].type	Security type (e.g. "STOCK", "INDEX")
result[].values	List of dictionaries containing data for each symbol
result[].values[].{symbol}	SymbolData object containing OHLCV data for specific symbol
result[].values[].{symbol}.{field}	List of DataPoint objects for each requested field
result[].values[].{symbol}.{field}[].timestamp	Timestamp in nanoseconds
result[].values[].{symbol}.{field}[].value	Value for the field at given timestamp

Historical Data — Examples

Below are ready-to-use examples showing how to query Historical Data for different Asset Types

Expired Options DataIndices DataStocks Data

from nubra_python_sdk.marketdata.market_data import MarketData
from nubra_python_sdk.start_sdk import InitNubraSdk, NubraEnv
import pandas as pd
pd.set_option('display.max_columns', None)     # Show all columns
pd.set_option('display.max_rows', 1000)         # Limit to 100 rows
pd.set_option('display.max_colwidth', None)    # Show full cell contents
pd.set_option('display.width', 0)

# -------------------------------
# Initialize Nubra SDK
# -------------------------------
nubra = InitNubraSdk(NubraEnv.PROD, env_creds=True)
mdInstance = MarketData(nubra)

# -------------------------------
# Fetch historical data
# -------------------------------
instruments = ['NIFTY2611326000CE','NIFTY25D2326000CE']

response = mdInstance.historical_data({
    "exchange": "NSE",
    "type": "OPT",
    "values": instruments,
    "fields": ["open", "high", "low", "close", "cumulative_volume","theta","delta","gamma","vega","iv_mid",'cumulative_oi'],
    "startDate": "2025-12-01T11:01:57.000Z",
    "endDate": "2026-01-14T06:13:57.000Z",
    "interval": "1d",
    "intraDay": False,
    "realTime": False
})

# -------------------------------
# Helper function
# -------------------------------
def tsp_list_to_series(tsp_list):
    return pd.Series(
        data=[p.value for p in tsp_list],
        index=pd.to_datetime([p.timestamp for p in tsp_list], unit="ns")
    )

# -------------------------------
# Convert ALL instruments dynamically
# -------------------------------
dfs = {}

for instrument_dict in response.result[0].values:
    for symbol, stock_chart in instrument_dict.items():

        df = pd.DataFrame({
            "open": tsp_list_to_series(stock_chart.open),
            "high": tsp_list_to_series(stock_chart.high),
            "low": tsp_list_to_series(stock_chart.low),
            "close": tsp_list_to_series(stock_chart.close),
            "volume": tsp_list_to_series(stock_chart.cumulative_volume),
            "theta": tsp_list_to_series(stock_chart.theta),
            "delta": tsp_list_to_series(stock_chart.delta),
            "gamma": tsp_list_to_series(stock_chart.gamma),
            "vega": tsp_list_to_series(stock_chart.vega),
            "iv_mid": tsp_list_to_series(stock_chart.iv_mid),
            "cumulative_oi": tsp_list_to_series(stock_chart.cumulative_oi),
        })

        df.sort_index(inplace=True)
        df["symbol"] = symbol  # optional
        dfs[symbol] = df

# -------------------------------
# Example usage
# -------------------------------
print(f"{instruments[0]} Historical data with greeks")
print(dfs[instruments[0]].head())
print('\n')
print(f"{instruments[1]} Historical data with greeks")
print(dfs[instruments[1]].head())

Explanation

• Replace the instruments using expired options names of the last 3 months

import pandas as pd
from nubra_python_sdk.marketdata.market_data import MarketData
from nubra_python_sdk.start_sdk import InitNubraSdk, NubraEnv

# -------------------------------
# Initialize Nubra SDK
# -------------------------------
nubra = InitNubraSdk(NubraEnv.PROD, env_creds=True)
mdInstance = MarketData(nubra)

# -------------------------------
# Fetch historical data
# -------------------------------
instruments = ["NIFTY"]

response = mdInstance.historical_data({
    "exchange": "NSE",
    "type": "INDEX",
    "values": instruments,
    "fields": ["open", "high", "low", "close", "cumulative_volume"],
    "startDate": "2016-02-01T11:01:57.000Z",
    "endDate": "2026-02-04T06:18:57.000Z",
    "interval": "1mt",
    "intraDay": False,
    "realTime": False
})

# -------------------------------
# Helper function (values only)
# -------------------------------
def tsp_values(tsp_list):
    return [p.value for p in tsp_list]

# -------------------------------
# Convert response to DataFrames
# -------------------------------
dfs = {}

for instrument_dict in response.result[0].values:
    for symbol, stock_chart in instrument_dict.items():

        # Create timestamp index ONCE
        index = pd.to_datetime(
            [p.timestamp for p in stock_chart.open],
            unit="ns"
        )

        df = pd.DataFrame(
            {
                "open": tsp_values(stock_chart.open),
                "high": tsp_values(stock_chart.high),
                "low": tsp_values(stock_chart.low),
                "close": tsp_values(stock_chart.close),
                "volume": tsp_values(stock_chart.cumulative_volume),
            },
            index=index
        )

        df.sort_index(inplace=True)

        # -------------------------------
        # Scale price columns (paise → rupees)
        # Only non-null values are affected
        # -------------------------------
        price_cols = ["open", "high", "low", "close"]
        df[price_cols] = df[price_cols].div(100)

        df["symbol"] = symbol  # optional
        dfs[symbol] = df

# -------------------------------
# Example usage
# -------------------------------
print(dfs["NIFTY"])

Explanation

• Change or add indices to the instruments list

import pandas as pd
from nubra_python_sdk.marketdata.market_data import MarketData
from nubra_python_sdk.start_sdk import InitNubraSdk, NubraEnv

# -------------------------------
# Initialize Nubra SDK
# -------------------------------
nubra = InitNubraSdk(NubraEnv.PROD, env_creds=True)
mdInstance = MarketData(nubra)

# -------------------------------
# Fetch historical data
# -------------------------------
instruments = ["RELIANCE"]

response = mdInstance.historical_data({
    "exchange": "NSE",
    "type": "STOCK",
    "values": instruments,
    "fields": ["open", "high", "low", "close", "cumulative_volume"],
    "startDate": "2026-02-05T03:30:00.000Z",
    "endDate": "2026-02-05T11:30:00.000Z",
    "interval": "3m",
    "intraDay": False,
    "realTime": False
})

# -------------------------------
# Helper function
# -------------------------------
def tsp_list_to_series(tsp_list):
    idx = pd.to_datetime(
        [p.timestamp for p in tsp_list],
        unit="ns",
        utc=True  # tells pandas this is UTC
    ).tz_convert("Asia/Kolkata")  # convert to IST

    return pd.Series(
        data=[p.value for p in tsp_list],
        index=idx
    )


# -------------------------------
# Convert ALL instruments dynamically
# -------------------------------
dfs = {}

for instrument_dict in response.result[0].values:
    for symbol, stock_chart in instrument_dict.items():

        df = pd.DataFrame({
            "open": tsp_list_to_series(stock_chart.open),
            "high": tsp_list_to_series(stock_chart.high),
            "low": tsp_list_to_series(stock_chart.low),
            "close": tsp_list_to_series(stock_chart.close),
            "volume": tsp_list_to_series(stock_chart.cumulative_volume),
        })

        df.sort_index(inplace=True)
        df["symbol"] = symbol  # optional
        dfs[symbol] = df

# -------------------------------
# Example usage
# -------------------------------
print(dfs["RELIANCE"])