Find average spread for a symbol

import datetime as dt import databento as db import matplotlib.pyplot as plt import pandas as pd import seaborn as sns # Set parameters symbol = "SPY" prevol_date = dt.date(2025, 4, 1) # Tuesday before volatility spike postvol_date = dt.date(2025, 4, 8) # Tuesday after volatility spike # Regular Trading Hours (RTH) for US equities rth_start = dt.time(9, 30) rth_end = dt.time(16, 0) equity_datasets = [ "XNAS.ITCH", # Nasdaq "XBOS.ITCH", # Nasdaq BX "XPSX.ITCH", # Nasdaq PSX "XNYS.PILLAR", # NYSE "ARCX.PILLAR", # NYSE Arca "XASE.PILLAR", # NYSE American "XCHI.PILLAR", # NYSE Texas "XCIS.TRADESBBO", # NYSE National "MEMX.MEMOIR", # Members Exchange "EPRL.DOM", # MIAX Pearl "IEXG.TOPS", # IEX "BATS.PITCH", # Cboe BZX "BATY.PITCH", # Cboe BYX "EDGA.PITCH", # Cboe EDGA "EDGX.PITCH", # Cboe EDGX # Databento does not provide coverage for LTSE (MIC: LTSE) ] client = db.Historical(key="YOUR_API_KEY") venue_map = {x["dataset"]: x["venue"] for x in client.metadata.list_publishers()} def calculate_spread( dataset: str, symbol: str, date: dt.date, ) -> pd.DataFrame: """ Calculate spread size in basis points (bps) """ df = client.timeseries.get_range( dataset=dataset, symbols=symbol, schema="bbo-1s", # BBO-1s is required for this example start=date, ).to_df(tz="America/New_York") df = df.resample("1s").ffill().loc[rth_start:rth_end] # Calculate spread in basis points df["midpoint"] = (df["ask_px_00"] + df["bid_px_00"]) / 2 df["spread_abs"] = (df["ask_px_00"] - df["bid_px_00"]).clip(lower=0) df["spread_bps"] = df["spread_abs"] / df["midpoint"] * 1e4 df["Venue"] = venue_map[dataset] df["Date"] = df.index.date return df[["Venue", "Date", "spread_bps"]] df_list: list[pd.DataFrame] = [] for date in (prevol_date, postvol_date): for dataset in equity_datasets: df_list.append(calculate_spread(dataset, symbol, date)) df = pd.concat(df_list, ignore_index=True) plt.figure(figsize=(16, 8)) ax = sns.boxplot( data=df, x="Venue", y="spread_bps", hue="Date", order=df[df["Date"] == postvol_date].groupby("Venue")["spread_bps"].median().sort_values().index, gap=0.1, width=0.8, showfliers=False, ) # Set colors for each venue n_venues = len(equity_datasets) venue_colors = sns.color_palette("Set1", n_colors=n_venues) for i, patch in enumerate(ax.patches): patch.set_facecolor(venue_colors[i % n_venues]) # Color the post-volatility boxplots a lighter shade if i >= n_venues: patch.set_facecolor((*patch.get_facecolor()[:3], 0.4)) # Adjust legend prevol_patch, postvol_patch = ax.get_legend().get_patches() prevol_patch.set_facecolor(venue_colors[0]) postvol_patch.set_facecolor((venue_colors[0], 0.4)) plt.legend(handles=[prevol_patch, postvol_patch], labels=[f"{prevol_date}", f"{postvol_date}"]) plt.xticks(rotation=45) plt.ylabel("Spread size (bps)") plt.title(f"{symbol} spreads before and after April 2025 tariff crash") plt.tight_layout() plt.show()