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++
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HTTP API
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Raw API
0.6.4 - TBD
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Data
2025-08-26
2025-08-05
2025-07-25
2025-07-06
2025-07-01
2025-06-27
2025-06-17
2025-06-10
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2024-10-22
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2024-07-05
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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
Historical data
Best bid, best offer, and midprice
Overview
In this example we will use the Historical client to retrieve the best bid and offer (BBO) and use these data to calculate the midprice. To do this we will request a subset of the entire order book data and then average the bid and ask prices to calculate the midprice.
Note that ask and offer are synonyms referring to the sell price.
TBBO schema
We'll use the TBBO schema to demonstrate this. This schema contains every trade execution event together with the best bid and offer immediately before the trade.
Example
import databento as db
# Create a historical client
client = db.Historical("YOUR_API_KEY")
# Request TBBO data
data = client.timeseries.get_range(
dataset="GLBX.MDP3",
start="2022-10-28T20:30:00",
end="2022-10-28T21:00:00",
symbols="ESH3",
stype_in="raw_symbol",
schema="tbbo",
)
# Convert to DataFrame
tbbo_data = data.to_df()
# Calculate the midprice for each row
tbbo_data["mid_px"] = tbbo_data[["ask_px_00", "bid_px_00"]].mean(axis=1)
# We now have a DataFrame that contains execution price, BBO, and midprice
print(tbbo_data[["symbol", "side", "price", "ask_px_00", "mid_px", "bid_px_00"]])
Result
symbol side price ask_px_00 mid_px bid_px_00
ts_recv
2022-10-28 20:30:59.047138053+00:00 ESH3 B 3955.25 3955.25 3955.000 3954.75
2022-10-28 20:37:53.112494436+00:00 ESH3 A 3955.00 3955.75 3955.375 3955.00
2022-10-28 20:39:05.152071481+00:00 ESH3 A 3955.00 3955.75 3955.375 3955.00
2022-10-28 20:51:37.650473254+00:00 ESH3 B 3955.50 3955.50 3955.125 3954.75
2022-10-28 20:52:36.842346377+00:00 ESH3 A 3956.00 3956.25 3956.125 3956.00
2022-10-28 20:52:57.780797186+00:00 ESH3 B 3955.00 3955.00 3954.750 3954.50
2022-10-28 20:57:28.474236098+00:00 ESH3 A 3954.75 3956.25 3955.500 3954.75
2022-10-28 20:59:15.075191111+00:00 ESH3 A 3953.75 3956.00 3954.875 3953.75
2022-10-28 20:59:34.607239899+00:00 ESH3 A 3954.50 3956.00 3955.250 3954.50