Market Halts, Volatility Interrupts, and Price Bands

The article “Market Halts, Volatility Interrupts, and Price Bands” was originally published on Databento.

Venues implement different types of market integrity controls to ensure a fair and efficient market. Some of these controls will limit how far price can move in one session, as well as how much price can move in a short period of time. When these events are triggered, it can result in a temporary halt in trading or quoting.

Velocity Logic

CME Globex has Velocity Logic, which prevents price from moving too far, too fast. During Velocity Logic, an instrument will transition to a “Pre-Open” state. While this state is normally seen before the full session open for CME Globex, it is also seen during these events. At the end of the event, the instrument will transition back to an “Open” state.

Overview

On 2025-04-09, a tariff pause announcement was made that led to significantly increased volatility on CME Globex. Multiple Velocity Logic events were triggered in Equity Index futures following the announcement.

In this example, we’ll take a look at just one of the Velocity Logic events after the announcement. We’ll use the status schema to show the changes in market state during this event. Next, we’ll plot the BBO and trades from the MBP-1 schema to show how quoting and trading were affected. Additionally, we’ll use the statistics schema to show some exchange generated statistics.

import databento as db
import matplotlib.pyplot as plt

# Create historical client
client = db.Historical("YOUR_API_KEY")

# Set parameters
dataset = "GLBX.MDP3"
product = "NQ"
start = "2025-04-09T17:18:40"
end = "2025-04-09T17:18:55"

# Request status data and convert to DataFrame
status_data = client.timeseries.get_range(
    dataset=dataset,
    schema="status",
    start=start,
    end=end,
    symbols=f"{product}.v.0",
    stype_in="continuous",
)
status_df = status_data.to_df()

status_df = status_df[status_df["action"].isin([db.StatusAction.PRE_OPEN, db.StatusAction.TRADING])]
status_df["action"] = status_df["action"].map({
    db.StatusAction.PRE_OPEN: "Pre-Open",
    db.StatusAction.TRADING: "Open",
})

def add_status_vlines(ax):
    for status_change in status_df.iterrows():
        ax.axvline(status_change[0], color="dimgray", linestyle=":", linewidth=1.5)
        ax.text(
            status_change[0],
            ax.get_ylim()[1],
            status_change[1]["action"],
            color="dimgray",
            ha="center",
        )

# Request MBP-1 data and convert to DataFrame
mbp1_data = client.timeseries.get_range(
    dataset=dataset,
    schema="mbp-1",
    start=start,
    end=end,
    symbols=f"{product}.v.0",
    stype_in="continuous",
)
mbp1_df = mbp1_data.to_df()
mbp1_df = mbp1_df.rename(columns={"bid_px_00": "Bid", "ask_px_00": "Ask"})

# Request statistics data and convert to DataFrame
stats_data = client.timeseries.get_range(
    dataset=dataset,
    schema="statistics",
    start=start,
    end=end,
    symbols=f"{product}.v.0",
    stype_in="continuous",
)
stats_df = stats_data.to_df()
stats_df = stats_df[stats_df["stat_type"] == db.StatType.INDICATIVE_OPENING_PRICE]

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10.9, 10.9), sharey=True)

# Plot trades
trades_df = mbp1_df[mbp1_df["action"] == db.Action.TRADE]
trades_df["price"].plot(
    ax=ax1,
    style=".",
    markersize=5,
    color="C4",
    xlabel="Time (UTC)",
    ylabel="Price",
    label="Trades",
    title=f"{product} Trades",
)
add_status_vlines(ax1)
ax1.legend()

# Plot top of book data with Indicative Opening Price
mbp1_df[["Bid", "Ask"]].plot(ax=ax2, drawstyle="steps-post")
add_status_vlines(ax2)

stats_df["price"].plot(
    ax=ax2,
    style=".",
    markersize=10,
    color="C2",
    xlabel="Time (UTC)",
    ylabel="Price",
    label="IOP",
    title=f"{product} Top of Book",
)
ax2.legend()

plt.tight_layout()
plt.show()

In this first plot, you’ll see how trading activity will temporarily halt when the market transitions to a Pre-Open state during Velocity Logic.

The second plot takes a look at the BBO. While quoting is still allowed during these events, the bid-ask spread can become crossed.

During these Pre-Open states, the exchange publishes a statistic called the Indicative Opening Price, also known as the “IOP”. This statistic, which can be updated multiple times during a Pre-Open state, provides the most probable price that trading will resume at when the market transitions back to an Open state.

When the market transitions back to an Open state, the bid-ask spread will uncross, and trading will resume.

Source: Databento