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

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()