QuantBook options: daily bars appear T+1; can’t get 0DTE — how to handle?

Here are detailed answers and best practices tailored for your scenario:

1. Is the T+1 timestamp for daily option bars by design? Any official guidance to map to trading day?

Yes, by design. In QuantConnect, daily bars are timestamped at the end of the period, which for US options is midnight UTC at the end of the trading day—so effectively, the bar for T (e.g., Feb 1st) has timestamp T+1 00:00 UTC (e.g., Feb 2nd 00:00 UTC). See official QuantConnect documentation on bar periods for more:

"Bars have a start and end time that represent the time period of which the bar aggregates data. LEAN passes the bar to your algorithm at the end time so that you don't receive the bar before it was actually available.”

How to map?

• To align bars to the actual trading day: shift the time column in your DataFrame back by one day.

2. Recommended method to get 0DTE (same-day time and expiry)?

Here is the most robust method, illustrated in full research-notebook-ready code:

from datetime import datetime, timedelta
import pandas as pd
from AlgorithmImports import *

qb = QuantBook()

start_date = datetime(2024, 1, 31)
end_date   = datetime(2024, 2, 3)        # ‘end’ is exclusive in history()

# 1. Add underlying + filter for all weeklies
aapl = qb.add_equity("AAPL",
                     data_normalization_mode=DataNormalizationMode.RAW).symbol

opt  = qb.add_option(aapl)
opt.set_filter(lambda u: u.include_weeklys())

# 2. Get daily history (bars stamped at next-day midnight UTC)
raw = qb.history(opt.symbol,
                 start_date - timedelta(days=20),
                 end_date,
                 Resolution.DAILY,
                 flatten=True)

# 3. Pandas post-processing
df = raw.reset_index()
# Shift timestamp back to "trading day"
df["time"] = (df["time"] - pd.Timedelta(days=1)).dt.normalize()
df["expiry"]   = df["symbol"].map(lambda s: pd.Timestamp(s.id.date))
df["strike"]   = df["symbol"].map(lambda s: s.id.strike_price)
df["put_call"] = df["symbol"].map(lambda s: "C"
                                  if s.id.option_right == OptionRight.CALL else "P")
cols = ["time", "expiry", "symbol", "put_call", "strike",
        "open", "high", "low", "close", "value", "volume",
        "openinterest", "impliedvolatility", "delta", "gamma",
        "theta", "vega", "rho", "underlying"]
df = df[cols]

# 4. Filter for 0-DTE rows (trading date == expiry date)
df_0dte = df[df["time"].dt.date == df["expiry"].dt.date]

# Example: 0-DTE for AAPL 185C expiring 2024-02-02
example = df_0dte[
    (df_0dte["strike"]   == 185) &
    (df_0dte["put_call"] == "C") &
    (df_0dte["expiry"]   == pd.Timestamp("2024-02-02"))
]
print("0-DTE rows for AAPL 185C 2024-02-02:\n", example)

# 5. Debugging: Why no 0-DTE row for a day?
exp_by_day = df.groupby(df["time"].dt.date)["expiry"].unique()
print("Expiries present per trading day:\n", exp_by_day)

# If a date’s list does not include itself, there truly are no 0-DTE contracts
# for that session in the data.

Why you may see no 0-DTE rows for a specific day:

• No contract with expiry that session (e.g., only Friday weekly, or no listed weekly for that day)
• Market holiday/early close
• No activity or bar data for that strike/expiry in the QC datalake
• The history window you requested does not include that expiry

Summary

• QC daily bars are always stamped at the end of the bar (next day at 00:00 UTC), so always shift by −1 day to get to the trading session.
• Filter for df['time'] == df['expiry'] to find 0DTE.
• Always check which expiries exist per session if 0DTE is missing.

References:

• QuantConnect Docs: Periods and Timestamps
• QC Docs: Option Filtering Example

Let me know if you'd like any modifications or further checks!

Sources: Equity Options - QuantConnect.com, Individual Contracts - QuantConnect.com, US Index Options - QuantConnect.com, Basic Template Options Daily Algorithm, Periods - QuantConnect.com

To find 0DTE, isnt it we should shift one day forward first?

df['time'] = df['time'] - pd.Timedelta(days=1)
df['time'] == df['expiry'] # To find 0DTE

But using this method, I can't find any 0DTE inside the dataframe. 

Alternatively, I was wondering if it is possible to find the Options OHLC and Greeks in minutes level. 

Please help. Thanks.