Available languages: [Python](https://databento.com/docs/examples/equities/closing-prices.python.md) · [C++](https://databento.com/docs/examples/equities/closing-prices.cpp.md) · [Rust](https://databento.com/docs/examples/equities/closing-prices.rust.md) · [HTTP](https://databento.com/docs/examples/equities/closing-prices.http.md) · [Raw](https://databento.com/docs/examples/equities/closing-prices.raw.md)

Source: https://databento.com/docs/examples/equities/closing-prices


# 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](https://databento.com/docs/datasets/equs-summary.md).

## Overview

We'll use the [historical client](https://databento.com/docs/api-historical/client.md) 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

```python
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

```plaintext
                           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   186.1900   183.4519   185.13  36044906   AMZN        0.000000
2024-10-01 00:00:00+00:00     35            90          11275   713.64   717.7600   698.5900   706.13   2813482   NFLX        0.000000
2024-10-01 00:00:00+00:00     35            90          16244   262.67   263.9800   248.5300   258.02  87397613   TSLA        0.000000
2024-10-02 00:00:00+00:00     35            90          16244   247.55   251.1585   241.5000   249.02  93983930   TSLA       -3.488102
2024-10-02 00:00:00+00:00     35            90            853   184.44   186.6000   184.0400   184.76  23704056   AMZN       -0.199860
...                          ...           ...            ...      ...        ...        ...      ...       ...    ...             ...
2025-09-29 00:00:00+00:00     35            90          16244   444.35   450.9800   439.5000   443.21  79491510   TSLA       71.773506
2025-09-29 00:00:00+00:00     35            90            853   220.08   222.6000   219.3000   222.17  44259177   AMZN       20.007562
2025-09-30 00:00:00+00:00     35            90            853   222.03   222.2400   217.8900   219.57  48396369   AMZN       18.603144
2025-09-30 00:00:00+00:00     35            90          11275  1206.41  1208.5000  1178.0000  1198.92   3830304   NFLX       69.787433
2025-09-30 00:00:00+00:00     35            90          16244   441.52   445.0000   433.1200   444.72  74357960   TSLA       72.358732

[750 rows x 10 columns]
```
> **See also**
>
> 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]($PLATFORM_HOSTNAME/docs/portal/live-data).

![Daily prices](https://databento.com/docs/closing-prices-0.svg)