For a full list of the OPRA venues, see the OPRA documentation.
Examples and tutorials
Options
Equity options: Introduction
Options on futures: Introduction
All options with a given underlying
Join options with underlying prices
US equity options volume by venue
Resample US equity options NBBO
Estimate implied volatility
Get symbols for 0DTE options
Calculate daily statistics for equity options
Historical data
Request a large number of symbols
Programmatic batch downloads
Best bid, best offer, and midprice
Custom OHLCV bars from trades
Join schemas on instrument ID
Plot a candlestick chart
Calculate VWAP and RSI
End-of-day pricing and portfolio valuation
Benchmark portfolio performance
Market halts, volatility interrupts, and price bands
Resample OHLCV from 1-minute to 5-minute
Algorithmic trading
A high-frequency liquidity-taking strategy
Build prediction models with machine learning
Execution slippage and markouts
Matching engine latencies
Using messaging rates as a proxy for implied volatility
Mean reversion and portfolio optimization
Pairs trading based on cointegration
Build a real-time stock screener
Core concepts
Venues and datasets
CME Globex MDP 3.0
Cboe BYX Depth
Cboe BYZ Depth
Cboe EDGA Depth
Cboe EDGX Depth
Databento US Equities Basic
Databento US Equities Mini
Databento US Equities Summary
Eurex Exchange
European Energy Exchange
ICE Endex iMpact
ICE Europe Commodities iMpact
ICE Europe Financials iMpact
ICE Futures US iMpact
IEX TOPS
MEMX Memoir
MIAX Depth of Market
Nasdaq Basic with NLS Plus
Nasdaq TotalView-ITCH
NYSE American Integrated
NYSE Arca Integrated
NYSE Texas Integrated
NYSE National Trades and BBO
NYSE Integrated
OPRA Pillar
Corporate actions
Adjustment factors
Security master
API Reference
Resources
Release notes
C++
0.42.0 - 2025-08-19
0.41.0 - 2025-08-12
0.40.0 - 2025-07-29
0.39.1 - 2025-07-22
0.39.0 - 2025-07-15
0.38.2 - 2025-07-01
0.38.1 - 2025-06-25
0.38.0 - 2025-06-10
0.37.1 - 2025-06-03
0.37.0 - 2025-06-03
0.36.0 - 2025-05-27
0.35.1 - 2025-05-20
0.35.0 - 2025-05-13
0.34.2 - 2025-05-06
0.34.1 - 2025-04-29
0.34.0 - 2025-04-22
0.33.0 - 2025-04-15
0.32.1 - 2025-04-07
0.32.0 - 2025-04-02
0.31.0 - 2025-03-18
0.30.0 - 2025-02-11
0.29.0 - 2025-02-04
0.28.0 - 2025-01-21
0.27.0 - 2025-01-07
0.26.0 - 2024-12-17
0.25.0 - 2024-11-12
0.24.0 - 2024-10-22
0.23.0 - 2024-09-25
0.22.0 - 2024-08-27
0.21.0 - 2024-07-30
0.20.1 - 2024-07-16
0.20.0 - 2024-07-09
0.19.1 - 2024-06-25
0.19.0 - 2024-06-04
0.18.1 - 2024-05-22
0.18.0 - 2024-05-14
0.17.1 - 2024-04-08
0.17.0 - 2024-04-01
0.16.0 - 2024-03-01
0.15.0 - 2024-01-16
0.14.1 - 2023-12-18
0.14.0 - 2023-11-23
0.13.1 - 2023-10-23
0.13.0 - 2023-09-21
0.12.0 - 2023-08-24
0.11.0 - 2023-08-10
0.10.0 - 2023-07-20
0.9.1 - 2023-07-11
0.9.0 - 2023-06-13
0.8.0 - 2023-05-16
0.7.0 - 2023-04-28
0.6.1 - 2023-03-28
0.6.0 - 2023-03-24
0.5.0 - 2023-03-13
0.4.0 - 2023-03-02
0.3.0 - 2023-01-06
0.2.0 - 2022-12-01
0.1.0 - 2022-11-07
Python
0.63.1 - TBD
0.63.0 - 2025-09-02
0.62.0 - 2025-08-19
0.61.0 - 2025-08-12
0.60.0 - 2025-08-05
0.59.0 - 2025-07-15
0.58.0 - 2025-07-08
0.57.1 - 2025-06-17
0.57.0 - 2025-06-10
0.56.0 - 2025-06-03
0.55.1 - 2025-06-02
0.55.0 - 2025-05-29
0.54.0 - 2025-05-13
0.53.0 - 2025-04-29
0.52.0 - 2025-04-15
0.51.0 - 2025-04-08
0.50.0 - 2025-03-18
0.49.0 - 2025-03-04
0.48.0 - 2025-01-21
0.47.0 - 2024-12-17
0.46.0 - 2024-12-10
0.45.0 - 2024-11-12
0.44.1 - 2024-10-29
0.44.0 - 2024-10-22
0.43.1 - 2024-10-15
0.43.0 - 2024-10-09
0.42.0 - 2024-09-23
0.41.0 - 2024-09-03
0.40.0 - 2024-08-27
0.39.3 - 2024-08-20
0.39.2 - 2024-08-13
0.39.1 - 2024-08-13
0.39.0 - 2024-07-30
0.38.0 - 2024-07-23
0.37.0 - 2024-07-09
0.36.3 - 2024-07-02
0.36.2 - 2024-06-25
0.36.1 - 2024-06-18
0.36.0 - 2024-06-11
0.35.0 - 2024-06-04
0.34.1 - 2024-05-21
0.34.0 - 2024-05-14
0.33.0 - 2024-04-16
0.32.0 - 2024-04-04
0.31.1 - 2024-03-20
0.31.0 - 2024-03-05
0.30.0 - 2024-02-22
0.29.0 - 2024-02-13
0.28.0 - 2024-02-01
0.27.0 - 2024-01-23
0.26.0 - 2024-01-16
0.25.0 - 2024-01-09
0.24.1 - 2023-12-15
0.24.0 - 2023-11-23
0.23.1 - 2023-11-10
0.23.0 - 2023-10-26
0.22.1 - 2023-10-24
0.22.0 - 2023-10-23
0.21.0 - 2023-10-11
0.20.0 - 2023-09-21
0.19.1 - 2023-09-08
0.19.0 - 2023-08-25
0.18.1 - 2023-08-16
0.18.0 - 2023-08-14
0.17.0 - 2023-08-10
0.16.1 - 2023-08-03
0.16.0 - 2023-07-25
0.15.2 - 2023-07-19
0.15.1 - 2023-07-06
0.15.0 - 2023-07-05
0.14.1 - 2023-06-16
0.14.0 - 2023-06-14
0.13.0 - 2023-06-02
0.12.0 - 2023-05-01
0.11.0 - 2023-04-13
0.10.0 - 2023-04-07
0.9.0 - 2023-03-10
0.8.1 - 2023-03-05
0.8.0 - 2023-03-03
0.7.0 - 2023-01-10
0.6.0 - 2022-12-02
0.5.0 - 2022-11-07
0.4.0 - 2022-09-14
0.3.0 - 2022-08-30
HTTP API
0.35.0 - TBD
0.34.1 - 2025-06-17
0.34.0 - 2025-06-09
0.33.0 - 2024-12-10
0.32.0 - 2024-11-26
0.31.0 - 2024-11-12
0.30.0 - 2024-09-24
0.29.0 - 2024-09-03
0.28.0 - 2024-06-25
0.27.0 - 2024-06-04
0.26.0 - 2024-05-14
0.25.0 - 2024-03-26
0.24.0 - 2024-03-06
0.23.0 - 2024-02-15
0.22.0 - 2024-02-06
0.21.0 - 2024-01-30
0.20.0 - 2024-01-18
0.19.0 - 2023-10-17
0.18.0 - 2023-10-11
0.17.0 - 2023-10-04
0.16.0 - 2023-09-26
0.15.0 - 2023-09-19
0.14.0 - 2023-08-29
0.13.0 - 2023-08-23
0.12.0 - 2023-08-10
0.11.0 - 2023-07-25
0.10.0 - 2023-07-06
0.9.0 - 2023-06-01
0.8.0 - 2023-05-01
0.7.0 - 2023-04-07
0.6.0 - 2023-03-10
0.5.0 - 2023-03-03
0.4.0 - 2022-12-02
0.3.0 - 2022-08-30
0.2.0 - 2021-12-10
0.1.0 - 2021-08-30
Raw API
0.6.4 - TBD
0.6.3 - 2025-09-07
0.6.2 - 2025-08-02
0.6.1 - 2025-06-29
0.6.0 - 2025-05-24
0.5.6 - 2025-04-06
0.5.5 - 2024-12-01
0.5.4 - 2024-10-02
0.5.3 - 2024-10-02
0.5.1 - 2024-07-24
2024-07-20
2024-06-25
0.5.0 - 2024-05-25
0.4.6 - 2024-04-13
0.4.5 - 2024-03-25
0.4.4 - 2024-03-23
0.4.3 - 2024-02-13
0.4.2 - 2024-01-06
0.4.0 - 2023-11-08
0.3.0 - 2023-10-20
0.2.0 - 2023-07-23
0.1.0 - 2023-05-01
Rust
0.34.0 - 2025-09-23
0.33.1 - 2025-08-26
0.33.0 - 2025-08-19
0.32.0 - 2025-08-12
0.31.0 - 2025-07-30
0.30.0 - 2025-07-22
0.29.0 - 2025-07-15
0.28.0 - 2025-07-01
0.27.1 - 2025-06-25
0.27.0 - 2025-06-10
0.26.2 - 2025-06-03
0.26.1 - 2025-05-30
0.26.0 - 2025-05-28
0.25.0 - 2025-05-13
0.24.0 - 2025-04-22
0.23.0 - 2025-04-15
0.22.0 - 2025-04-01
0.21.0 - 2025-03-18
0.20.0 - 2025-02-12
0.19.0 - 2025-01-21
0.18.0 - 2025-01-08
0.17.0 - 2024-12-17
0.16.0 - 2024-11-12
0.15.0 - 2024-10-22
0.14.1 - 2024-10-08
0.14.0 - 2024-10-01
0.13.0 - 2024-09-25
0.12.1 - 2024-08-27
0.12.0 - 2024-07-30
0.11.4 - 2024-07-16
0.11.3 - 2024-07-09
0.11.2 - 2024-06-25
0.11.1 - 2024-06-11
0.11.0 - 2024-06-04
0.10.0 - 2024-05-22
0.9.1 - 2024-05-15
0.9.0 - 2024-05-14
0.8.0 - 2024-04-01
0.7.1 - 2024-03-05
0.7.0 - 2024-03-01
0.6.0 - 2024-01-16
0.5.0 - 2023-11-23
0.4.2 - 2023-10-23
0.4.1 - 2023-10-06
0.4.0 - 2023-09-21
0.3.0 - 2023-09-13
0.2.1 - 2023-08-25
0.2.0 - 2023-08-10
0.1.0 - 2023-08-02
Data
2025-09-23
2025-08-26
2025-08-05
2025-07-25
2025-07-06
2025-07-01
2025-06-27
2025-06-17
2025-06-10
2025-05-20
2025-05-07
2025-04-05
2025-04-01
2025-03-13
2025-02-26
2025-02-01
2025-01-15
2024-12-14
2024-12-03
2024-12-02
2024-10-22
2024-10-24
2024-07-05
2024-06-25
2024-06-18
2024-05-07
2024-01-18
2023-11-17
2023-10-04
2023-08-29
2023-07-23
2023-05-01
2023-04-28
2023-03-07
Collapse all
Examples and tutorials
Options
US equity options volume by venue
Overview
This example will use the Historical client to retrieve data from the entire US equity options market and compare volume across execution venues.
We'll use the OHLCV-1m schema and look at data over a 30-minute window.
This example could be modified to look at the entire session by changing the start
and end
variables, or by using the OHLCV-1d schema.
The OPRA datafeed provides consolidated options data for the 18 US equity options venues.
The publisher_id
field indicates which venue the record is associated with.
Using this information, we will create a table and plot similar to the data found in Cboe's Market Volume Summary report.
See also
Example
import databento as db
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
# Set parameters
dataset = "OPRA.PILLAR"
start = "2025-03-26T14:00"
end = "2025-03-26T14:30"
# Create a historical client
client = db.Historical(key="$YOUR_API_KEY")
# Request OHLCV-1m data for ALL_SYMBOLS and convert to DataFrame
df = client.timeseries.get_range(
dataset=dataset,
schema="ohlcv-1m",
symbols="ALL_SYMBOLS",
start=start,
end=end,
).to_df()
# Aggregate volume by venue
df = df.groupby("publisher_id", as_index=False)["volume"].sum()
df["percentage"] = df["volume"] / df["volume"].sum() * 100
# Rename publisher_id and columns for readability
publishers = client.metadata.list_publishers()
venue_name_map = {x["publisher_id"]: db.Publisher.from_int(x["publisher_id"]).venue.description for x in publishers}
df["publisher_id"] = df["publisher_id"].map(venue_name_map)
df = df.rename(columns={"publisher_id": "Venue", "volume": "Volume", "percentage": "% of Total"})
# Calculate volume by group and sort
df["Group"] = df["Venue"].str.split(" ").str[0]
df["Group Volume"] = df.groupby("Group")["Volume"].transform("sum")
df = df.sort_values(by=["Group Volume", "Volume"], ascending=[False, False])
df = df[["Group", "Venue", "Volume", "% of Total"]]
print(df.set_index(["Group", "Venue"]).to_string(float_format="%0.2f"))
# Now, let's plot this data
fig, ax = plt.subplots(figsize=(14, 6))
group_handles = [] # Legend handles
for group_idx, group in enumerate(df["Group"].unique()):
group_data = df[df["Group"] == group]
# Create handle for plot legend
group_pct = group_data["% of Total"].sum()
group_handles.append(Patch(facecolor=f"C{group_idx}", label=f"{group}: {group_pct:.2f}%"))
last_venue_pct = 0
for i, (_, venue_data) in enumerate(group_data.iterrows()):
venue_pct = venue_data["% of Total"] # Used as height for each venue section
# Add section for venue
bar = ax.bar(
x=group,
height=venue_pct,
bottom=last_venue_pct,
color=f"C{group_idx}",
alpha=1.0 - i * 0.15,
)
# Add the venue label
ax.text(
x=group_idx,
y=last_venue_pct + venue_pct / 2,
s=" ".join(venue_data["Venue"].split(" ")[:2]),
ha="center",
va="center",
)
last_venue_pct += venue_pct
ax.legend(handles=group_handles)
ax.set_title("U.S. Options Market Volume")
ax.set_ylabel("% of Total Volume")
plt.tight_layout()
plt.show()
Volume % of Total
Group Venue
Cboe Cboe Options 954511 14.47
Cboe EDGX Options 381901 5.79
Cboe BZX Options 261275 3.96
Cboe C2 Options 152628 2.31
Nasdaq Nasdaq PHLX 435190 6.60
Nasdaq ISE 381951 5.79
Nasdaq Options 327166 4.96
Nasdaq GEMX 272005 4.12
Nasdaq MRX 182480 2.77
Nasdaq BX Options 105090 1.59
NYSE NYSE American Options 865360 13.12
NYSE Arca Options 664865 10.08
MIAX MIAX Options 447819 6.79
MIAX Emerald 230339 3.49
MIAX Pearl 201093 3.05
MIAX Sapphire 124162 1.88
BOX BOX Options 386105 5.85
MEMX MEMX Options 222973 3.38