databento-bot

import databento as db


@singledispatch
def dbn_handler(_: db.DBNRecord):
    pass

@dbn_handler.register
def _(sym: db.SymbolMappingMsg):
    instrument_id = sym.instrument_id

databento-bot

import databento as db
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import coint
from sklearn.linear_model import LinearRegression

plt.style.use("ggplot")


# Instrument-specific configuration

databento-bot

# One-way, overall
#
# 0.973/1.093/3.042us (0/50/99p, 256 byte packets)


# Port-to-port (switch hop)
#  
# 680+/-20ns
#
# - Mellanox Spectrum SN2700 w/ Cumulus 5.7

databento-bot

# Extracts 1 day of OHLCV-1h data for XNAS.ITCH

import databento as db

client = db.Historical()

data = client.timeseries.get_range(
    dataset='XNAS.ITCH',
    schema='ohlcv-1h',
    symbols='ALL_SYMBOLS',

databento-bot

#!/usr/bin/env python
#
# replay_snapshot.py
#
# As of Databento's Python client v0.33.0, intraday snapshots are not available via
# the historical (HTTP) API, so you can use intraday replay to generate those snapshots
# on client side. This is the recommended method until these two features are released:
#
# If no bar for a symbol is received in the first second, there will be no entry in the
# DataFrame for that symbol.

databento-bot

#!/usr/bin/env python
#
# databento-extract-cme.py
#
# Fetch data of options on futures through Databento,
# using COMEX Gold (GC) and CBOT US 5-Year T-Note (ZF) and
# including weeklies, as an example

import databento as db
import itertools

databento-bot

# Reddit comment 2024-04-03

> See https://www.reddit.com/r/algotrading/comments/1bu59ql/comment/kxuil9a

There will always be some differences in the vendor’s infrastructure used to process real-time vs. historical.
It takes a bit of effort to make these as identical as possible. Non-exhaustive list:

The most common issue I’ve seen is that the vendor will retroactively clean and patch their historical data ex post in ways
that are not replicable in real-time. (The most obvious tell is if you report a data error and they tell you it’s patched
within the same day.) This is one area where Bloomberg is quite good despite doing it the “wrong” way - they have a

databento-bot

import datetime


import databento as db
import pandas as pd


def get_df_move(date: str = '2023-06-06'):
  
    end_dt = datetime.datetime.strptime(date+'T16:00', '%Y-%m-%dT%H:%M')

databento-bot

import databento as db


client = db.Historical()

# Heating oil, gasoline and crude futures on CME and ICE
# .FUT parent symbology notation fetches all expirations across 
# all outrights and multi-legged spreads etc.
CME_SYMBOLS = ['HO.FUT', 'RB.FUT', 'CL.FUT']
ICE_SYMBOLS = ['UHO.FUT', 'UHU.FUT', 'BRN.FUT']

databento-bot

import databento as db

client = db.Historical()
data = client.timeseries.get_range(
    dataset='XNAS.ITCH',
    schema='ohlcv-1m',
    symbols='ALL_SYMBOLS',
    start='20230606T00:00',
    end='20230606T14:30',
)