databento-bot

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
get_atm_options_bbo.py
==============
Get BBO of ATM options for an underlying stock using Databento.

This example uses the "cbbo-1s" schema for 1-second granularity, but you can substitute
with "cmbp-1" or "cbbo-1m" for higher/lower granularity.

databento-bot

import databento as db

client = db.Historical("YOUR_API_KEY")

# Requesting a specific strike
data = client.timeseries.get_range(
   dataset="OPRA.PILLAR",
   schema="cmbp-1",
   stype_in="raw_symbol",
   symbols=["SPY   241115P00525000"],

databento-bot

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
eth_high_low.py
==============
Calculates daily high/low occurrences for ES futures outside regular US cash session hours
using Databento API.

Fetches second-by-second OHLCV data for ES continuous futures from Databento, 
filters to exclude regular US cash session (9:30 AM - 4:00 PM ET), then computes 

databento-bot

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
cme_fx_link.py
==============
Retrieve CME FX Link order book (MBO) data using the Databento API.
Databento API を使用して、CME FX Link のオーダーブック
（マーケット・バイ・オーダー）データを取得します。
"""
import databento as db

databento-bot

# Side-by-side comparison of pandas vs. polars syntax for reading CSV and filtering by symbol

import pandas as pd
import polars as pl

CSVFILE = "glbx-mdp3-20250716-20250815.ohlcv-1m.csv.zst"


# Using pandas
df = pd.read_csv(CSVFILE)

databento-bot

ts_recv,ts_event,rtype,publisher_id,instrument_id,action,side,price,size,flags,ts_in_delta,bid_px_00,ask_px_00,bid_sz_00,ask_sz_00,bid_pb_00,ask_pb_00,symbol
2023-03-28 09:30:00.067424405-04:00,2023-03-28 09:30:00.067217664-04:00,177,30,654313225,A,N,326.77,1,194,0,321.77,326.77,1,1,0,0,SPY   241220P00720000
2023-03-28 09:30:00.182559853-04:00,2023-03-28 09:30:00.182352128-04:00,177,30,654313225,A,N,,0,194,0,,,0,0,0,0,SPY   241220P00720000
2023-03-28 09:30:00.463387013-04:00,2023-03-28 09:30:00.463180032-04:00,177,30,654313225,A,N,329.22,1,194,0,319.22,329.22,1,1,0,0,SPY   241220P00720000
2023-03-28 09:30:00.536456272-04:00,2023-03-28 09:30:00.536249088-04:00,177,30,654313225,A,N,328.28,1,194,0,319.22,328.28,1,1,0,0,SPY   241220P00720000
2023-03-28 09:30:00.537369130-04:00,2023-03-28 09:30:00.537161984-04:00,177,30,654313225,A,N,318.28,1,194,0,318.28,328.28,1,1,0,0,SPY   241220P00720000
2023-03-28 09:30:00.661258449-04:00,2023-03-28 09:30:00.661050624-04:00,177,30,654313225,A,N,328.28,1,194,0,318.28,328.28,2,

databento-bot

"""
Databento follows the standard convention of only printing OHLCV records when 
trades actually occur within the interval. If no trade occurs within the interval,
no record is printed.

This approach is adopted by most data vendors for two key reasons:
1. Multiple interpolation strategies exist and the optimal choice depends on the 
   specific use case. Client-side interpolation keeps the strategy transparent.
2. This reduces storage and bandwidth requirements, especially for markets with 
   many illiquid instruments like options.

databento-bot

import pandas as pd
import databento as db


client = db.Historical()

data = client.timeseries.get_range(
    dataset="XNAS.ITCH",
    schema="ohlcv-1m",
    symbols="AAPL",

databento-bot

import databento as db
import pandas as pd


client = db.Historical()

def print_ng_summary_by_date(date_str: str):
    date = pd.to_datetime(date_str)
    weekday = date.weekday()

databento-bot

                                                               ts_event  rtype  publisher_id  instrument_id action side    price  size  channel_id       order_id  flags  ts_in_delta   sequence symbol
ts_recv
2025-04-25 13:30:00.002415517+00:00 2025-04-25 13:30:00.000435143+00:00    160             1       42003627      F    A  5510.25     2           8  6863196218407      0        14070  333790855  MESM5
2025-04-25 13:30:00.002415517+00:00 2025-04-25 13:30:00.000435143+00:00    160             1       42003627      F    A  5510.25     1           8  6863196218557      0        14070  333790855  MESM5
2025-04-25 13:30:00.002415517+00:00 2025-04-25 13:30:00.000435143+00:00    160             1       42003627      F    A  5510.50     1           8  6863196218273      0        14070  333790855  MESM5
2025-04-25 13:30:00.002495446+00:00 2025-04-25 13:30:00.000756343+00:00    160             1       42003627      F    A  5510.50     1           8  6863196218273      0        12976  333790857  MESM5
2025-04-25 13:30