Join options with underlying prices

import databento as db
import pandas as pd

# Create a historical client
client = db.Historical(key="$YOUR_API_KEY")

# Set parameters
dataset = "GLBX.MDP3"
symbol = "CL.c.0"
start = "2023-04-03T13:00:00"
end = "2023-04-03T13:10:00"

# Get symbol mapping for the continuous contract
symbol_map = client.symbology.resolve(
    dataset=dataset,
    symbols=symbol,
    stype_in="continuous",
    stype_out="instrument_id",
    start_date="2023-04-03",
)

# Get instrument ID for the front month future
front_month_id = int(symbol_map["result"][symbol][0]["s"])

# Get all option definitions, starting from Sunday
def_data = client.timeseries.get_range(
    dataset=dataset,
    schema="definition",
    symbols=["LO.OPT"],
    stype_in="parent",
    start="2023-04-02",
)

# Convert to DataFrame
def_df = def_data.to_df()

# Filter for options on the front month future
opt_def_df = def_df[
    (def_df["user_defined_instrument"] == db.UserDefinedInstrument.NO) &
    (def_df["instrument_class"].isin((db.InstrumentClass.CALL, db.InstrumentClass.PUT))) &
    (def_df["underlying_id"] == front_month_id)
]

# Get trades data for options on the front month future
opt_trades_df = client.timeseries.get_range(
    dataset=dataset,
    schema="trades",
    symbols=opt_def_df["instrument_id"].to_list(),
    stype_in="instrument_id",
    start=start,
    end=end,
).to_df()

# Get MBP-1 data for the front month future
fut_mbp_df = client.timeseries.get_range(
    dataset=dataset,
    schema="mbp-1",
    symbols=front_month_id,
    stype_in="instrument_id",
    start=start,
    end=end,
).to_df()
fut_mbp_df = fut_mbp_df[["bid_px_00", "ask_px_00"]]
fut_mbp_df = fut_mbp_df.sort_index()

# Join options with their definitions
opt_df = opt_trades_df.merge(
    opt_def_df,
    on="instrument_id",
    how="inner",
    suffixes=("", "_def"),
).set_index("ts_event")
opt_df = opt_df.sort_index()

# Join most recent underlying bid/ask with options trades
df = pd.merge_asof(
    opt_df,
    fut_mbp_df,
    left_index=True,
    right_index=True,
    direction="backward",
)

# Rename the columns
df = df.rename(
    columns={
        "ask_px_00": "underlying_ask",
        "bid_px_00": "underlying_bid",
    },
)

print(df[["strike_price", "instrument_class", "price", "underlying_bid", "underlying_ask"]].head())

                                     strike_price instrument_class  price  underlying_bid  underlying_ask
ts_event
2023-04-03 13:00:05.464491127+00:00          75.5                P   0.81           80.24           80.25
2023-04-03 13:00:10.275423815+00:00          80.0                P   2.26           80.22           80.24
2023-04-03 13:00:30.917505675+00:00          72.0                P   0.34           80.19           80.20
2023-04-03 13:00:30.927198369+00:00          72.0                P   0.34           80.18           80.19
2023-04-03 13:01:15.044222777+00:00          75.0                P   0.75           80.01           80.03