The prices seen in the EQUS.SUMMARY dataset are unadjusted for any splits or dividends. This data can be enriched with our corporate actions and adjustment factors datasets.
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
Convert DBN to other encoding formats
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 BZX 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++
0.43.1 - TBD
0.43.0 - 2025-10-22
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
0.65.0 - TBD
0.64.0 - 2025-09-30
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
0.7.0 - TBD
0.6.4 - 2025-09-28
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
0.35.0 - 2025-10-22
0.34.1 - 2025-09-30
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
2025-11-04
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
Equities
Get daily closing prices for equities
Official consolidated end-of-day summary data for all US equities is published by the Nasdaq NLS+ feed. This data is normalized into our Databento US Equities Summary dataset.
Overview
We'll use the historical client to request daily data for multiple equity symbols. For this example, we'll look at a few popular companies like AMZN, NFLX, and TSLA. We'll calculate the cumulative percent change for these symbols over time, then plot the data to compare their relative performance.
Example
import databento as db
import matplotlib.pyplot as plt
# Set parameters
dataset = "EQUS.SUMMARY"
symbols = ["AMZN", "NFLX", "TSLA"]
start = "2024-10-01"
end = "2025-10-01"
client = db.Historical("YOUR_API_KEY")
# Request OHLCV-1d data for the selected symbols
df = client.timeseries.get_range(
dataset=dataset,
symbols=symbols,
schema="ohlcv-1d",
start=start,
end=end,
).to_df()
# Calculate cumulative percent change over the request range
df["cum_pct_change"] = df.groupby("symbol")["close"].transform(lambda x: (x - x.iloc[0]) / x.iloc[0] * 100)
print(df)
# Plot the relative performance of these symbols
df.groupby("symbol")["cum_pct_change"].plot(
figsize=(12, 6),
legend=True,
xlabel="Date",
ylabel="Cumulative % Change",
title="Relative Performance\nOctober 2024 - October 2025",
)
plt.axhline(y=0, color="grey", linestyle="-", zorder=1)
plt.grid(True)
plt.tight_layout()
plt.show()
Results
rtype publisher_id instrument_id open high low close volume symbol cum_pct_change
ts_event
2024-10-01 00:00:00+00:00 35 90 853 184.90 186.19 183.4519 185.13 36044906 AMZN 0.000000
2024-10-01 00:00:00+00:00 35 90 11275 713.64 717.76 698.5900 706.13 2813482 NFLX 0.000000
2024-10-01 00:00:00+00:00 35 90 16244 262.67 263.98 248.5300 258.02 87397613 TSLA 0.000000
2024-10-02 00:00:00+00:00 35 90 853 184.44 186.60 184.0400 184.76 23704056 AMZN -0.199860
2024-10-02 00:00:00+00:00 35 90 11275 706.13 716.21 704.6878 711.09 1758167 NFLX 0.702420
... ... ... ... ... ... ... ... ... ... ...
2025-09-29 00:00:00+00:00 35 90 11275 1205.00 1224.49 1187.5400 1206.41 3033174 NFLX 70.848144
2025-09-29 00:00:00+00:00 35 90 16244 444.35 450.98 439.5000 443.21 79491510 TSLA 71.773506
2025-09-30 00:00:00+00:00 35 90 11275 1206.41 1208.50 1178.0000 1198.92 3830304 NFLX 69.787433
2025-09-30 00:00:00+00:00 35 90 16244 441.52 445.00 433.1200 444.72 74357960 TSLA 72.358732
2025-09-30 00:00:00+00:00 35 90 853 222.03 222.24 217.8900 219.57 48396369 AMZN 18.603144
[750 rows x 10 columns]
See also