databento-bot

import databento as db
import pandas as pd


client = db.Historical()


def rank_by_volume(top=500):
    """
    Returns instrument IDs of instruments that traded most, in descending rank

databento-bot

databento-bot

#!/usr/bin/env python

# Building high-frequency trading signals in Python with Databento and sklearn
#
# This is a simple example that demonstrates how to build high-frequency trading signals in Python,
# using order book and market depth data from [Databento](https://databento.com) together with
# machine learning models from [sklearn](https://scikit-learn.org/).

import databento as db
import numpy as np

databento-bot

import databento as db


client = db.Historical()

data = client.timeseries.get_range(
    dataset='GLBX.MDP3',
    schema='definition',
    symbols='ALL_SYMBOLS',
    start='2023-12-27',

databento-bot

#!/usr/bin/env python

# Gets official open interest and volume from CME options data using Databento
#
# CME has different parent symbols like E[1-4][A-D], EW, ES, LO[1-4][A-D], LO
# Databento provides these as E1A.OPT, E2A.OPT, ..., respectively.
# 
# However, this can be tedious to fetch one at a time. Another way to fetch
# all options of interest is to do it yourself using `ALL_SYMBOLS` and
# take advantage of the 'group' column in the instrument definitions.

databento-bot

import databento as db
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


DATE = pd.Timestamp(year=2023, month=6, day=22, tz='US/Eastern')
NUM_TIME_SAMPLES = 1000
SYMBOL = 'NVDA'
WINDOW_LIMITS_US = 120 * 1e6

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',
)

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 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

# 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