Handling tick sizes

Overview

In this example we will use the Historical client to retrieve instrument definitions and top-of-book data for two futures contracts during non-US equity cash session hours. We will then calculate the mean spread and normalize the values to a discrete number of price increments or "ticks" using the min_price_increment field from the definition schema. Finally, we will plot the changes in mean spread.

The futures we will use for this example are British Pound Futures (6B) and Euro FX Futures (6E). We will use continuous contract symbology to request the second highest contracts by open interest, which should have a more dynamic mean spread.

Definition schema

The definition schema contains instrument definitions and properties. In this example, we are interested in the min_price_increment field, which will allow us to normalize a spread to a discrete number of increments.

Mean spread

The mean spread is the average difference between the bid and ask price of an instrument. The mean spread is a metric which helps traders understand the liquidity and transaction costs associated with a particular trade. Illiquid and volatile instruments tend to have a larger mean spread. Such instruments are more likely to experience price slippage, where the actual price of a trade moves or "slips" away from the expected price.

Dependencies

For plotting, this example will use the matplotlib package.

This dependency can be installed with the following:

$ pip install matplotlib

Example

Python

      
    
import databento as db
import pandas as pd
from matplotlib import pyplot as plt

# Get symbols for 6B and 6E futures with the second highest open interest
symbols = ["6B.n.1", "6E.n.1"]

# Define a start and end time for this example
dataset = "GLBX.MDP3"
start = pd.Timestamp("2024-04-07T21:00:00", tz="US/Eastern")
end = pd.Timestamp("2024-04-08T04:00:00", tz="US/Eastern")

# First, create a historical client
client = db.Historical(key="$YOUR_API_KEY")

# Next, retrieve the instrument definitions for our symbols
definitions_data = client.timeseries.get_range(
    dataset=dataset,
    schema="definition",
    symbols=symbols,
    stype_in="continuous",
    start=start.date(),
)

# And extract the min_price_increment field
definitions_df = definitions_data.to_df()
tick_sizes = definitions_df[
    ["raw_symbol", "instrument_id", "min_price_increment"]
].set_index(
    "instrument_id",
)

# Print the min_price_increment for each symbol
print(tick_sizes)

# Then, request top-of-book updates for our symbols overnight
mbp_data = client.timeseries.get_range(
    dataset=dataset,
    schema="MBP-1",
    symbols=symbols,
    stype_in="continuous",
    start=start,
    end=end,
)

# And convert to a DataFrame and join with our tick size
mbp_df = mbp_data.to_df(tz="US/Eastern")
mbp_df = mbp_df.join(tick_sizes, on="instrument_id")
mbp_df = mbp_df[["raw_symbol", "ask_px_00", "bid_px_00", "min_price_increment"]]

# Now, calculate the spread in number of ticks
mbp_df["spread"] = (mbp_df["ask_px_00"] - mbp_df["bid_px_00"]) / mbp_df[
    "min_price_increment"
]

# Finally, plot the mean spread for each symbol
for symbol, data in mbp_df.groupby("raw_symbol"):
    # Average this data using a 5-minute rolling window
    mean_spread = (
        data["spread"]
        .ewm(
            times=data.index,
            halflife=pd.Timedelta(minutes=5),
        )
        .mean()
        .reset_index()
    )

    plt.plot(
        "ts_recv",
        "spread",
        data=mean_spread,
        label=symbol,
    )

plt.legend()
plt.title(f"Overnight Mean Spread - {start.date()}")
plt.ylabel("Mean Spread (ticks)")
plt.xlabel("Time (UTC)")
plt.show()

Result

Python

      
    
              raw_symbol  min_price_increment
instrument_id
200251              6BU4              0.00010
201330              6EU4              0.00005

Handling tick sizes