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

databento-bot

"""
This script demonstrates how to build a real-time scanner that detects significant 
price movements across all US stocks and ETFs using Databento's market data APIs.

Features:
- Handles entire US equities universe of ~9,000 symbols efficiently
- Median sub-ms feed delay to NY4/5 (WAN-shaped) and ~5s to start scanning
- Monitors all US stocks and ETFs for price movements exceeding a configurable threshold
- Compares current prices against previous day's closing prices
- Displays alerts when significant moves are detected

databento-bot

"""
OHLCV-1s data follows standard convention of most vendors and doesn't print on intervals
when there's no trade. This is the typical approach as there's no way to infer the type
of price interpolation or forward fill preferred. If you need prices printed uniformly
with forward filling or price interpolation, you can follow this example to do it on
client side.

See also: https://databento.com/docs/examples/basics-historical/tick-resampling/example
"""

databento-bot

// Condenses MEMX notices on https://info.memxtrading.com/category/alerts-notices/
const s=document.createElement("style");s.innerHTML="*{margin:0!important;padding:0!important;line-height:1!important;font-size:10px!important;letter-spacing:0!important}div,p,span,a,li,td,th{line-height:1!important;font-size:10px!important}table{border-spacing:0!important;border-collapse:collapse!important}",document.head.appendChild(s),(async()=>{const e=document.querySelector("main");if(!e)return void console.error("No <main> element found.");document.querySelectorAll("div.nav-links").forEach(t=>t.remove());for(let t=2;t<=91;t++){const n=`https://info.memxtrading.com/category/alerts-notices/page/${t}/`;try{const t=await fetch(n),o=await t.text(),i=new DOMParser().parseFromString(o,"text/html");i.querySelectorAll("hr, article").forEach(t=>e.appendChild(t.cloneNode(!0))),console.log(`Appended page ${t}`)}catch(a){console.error(`Error fetching page ${t}:`,a)}}})();

databento-bot

from enum import Enum

import databento as db
import pandas as pd


class Stat(Enum):
    SETTLEMENT_VOLUME = 6

client = db.Historical()

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.