Build a Pairs Trading Strategy in Python: A Step-by-Step GuidePairs trading is a form of statistical arbitrage that takes advantage of mean reversion or convergence in the prices of two instruments.Feb 10211Feb 10211
Easy, interactive financial charts in Python: Just 11 lines of code, no JavaScript requiredThis guide demonstrates how to display Databento’s market data with TradingView’s popular Lightweight Charts library, entirely in Python.Jan 28851Jan 28851
Introducing intraday PCAP deliveryMarket data PCAPs, how they’re used, and Databento’s intraday pcap delivery service.Jan 183Jan 183
How to build an order book (DoM) visualization using Databento, React, and RustBuild a web-based application to visualize market depth from L2 market data. Recommended tools and frameworks, code sample, and live demo.Sep 24, 202475Sep 24, 202475
Matching Engines in 3 Minutes: Terminology Guide for Traders and DevelopersFor algorithmic traders, researchers, developers. Learn about colocation, proximity hosting, wire protocols, DMA, matching algorithms.Sep 12, 202428Sep 12, 202428
Behind the scenes: How we built the world’s fastest cloud-based ticker plant at DatabentoA fast real-time market data feed — built with Rust, FPGAs, colocation, kernel bypass, layer 1 switching, zero-copy messaging, and more.Sep 9, 2024261Sep 9, 2024261
Getting futures tick sizes and notional tick values in Python with DatabentoLearn how to get futures tick sizes and notional tick values at scale with Python and Databento — almost 800,000 instruments in 10 seconds.Sep 9, 202410Sep 9, 202410
How to calculate VWAP in Python with Databento and pandasThis guide explains the VWAP, its formulation and use cases, and how you can compute it quickly in Python with data from Databento.Aug 29, 202425Aug 29, 202425
Writing a stock screener in Python with DatabentoScan over 600,000 instruments and compute ranking metrics like volume, average spread, depth, and largest pre-market movers in seconds.Aug 27, 202434Aug 27, 202434
An introduction to matching engines: A guide by DatabentoMatching engine architecture, gateways, latency, matching algorithms, port optimization, feed arbitration — how they affect your trading.Aug 26, 202439Aug 26, 202439
Low-latency tuning guide for Linux and trading systems“How to tune a server/Linux for low latency” is a question that gets asked a lot. In most cases,Aug 22, 2024Aug 22, 2024
Lies, damned lies, and latency — Behind Databento’s NTP time serviceShortly after Databento launched its real-time market data, we had a big problem: Some customers were reporting negative latencies when…Jul 2, 2024Jul 2, 2024
Top 6 things about daily market data that even institutional traders get wrongCommon mistakes when working with market data. And why nanoseconds matter even when you’re using daily settlement prices.Jun 14, 20241Jun 14, 20241
Analyzing private fills and canary orders with Databento’s high-precision market data timestampsOur latest case study on “faster-than-light” orders, based on a customer’s project.Jun 6, 2024Jun 6, 2024
How to use Polars with market data from DatabentoThe following article was written by our customer Nelson Griffiths, Engineering and Machine Learning Lead at Double River Investments, a…Apr 11, 20241Apr 11, 20241
Direct proprietary feeds vs SIPs: which is right for you?We frequently receive inquiries about the types of data provided by consolidated feeds from the Securities Information Processors (SIPs)…Apr 2, 2024Apr 2, 2024
How we serve real-time tick data of the entire US options market over the internet — on a single…Our US equity options feed effortlessly delivers real-time tick data from all 17 exchanges, timestamped to the nanosecond — streaming over…Mar 26, 20241Mar 26, 20241
Working with high-frequency market data: Data integrity and cleaningWe’re kicking off a multi-part series on using high-frequency market data. In part 1, we focus on data integrity and cleaning, sharing a…Mar 7, 2024Mar 7, 2024
Algorithmic trading guide: A high-frequency, liquidity-taking strategyIn an earlier guide, we showed you how to build an algorithmic trading strategy with a model-based (machine learning) alpha in Python with…Feb 16, 2024Feb 16, 2024
Building high-frequency trading signals in Python with Databento and sklearnSee how order book data from Databento can be used with machine learning models to develop algorithmic trading signals.Dec 20, 20232Dec 20, 20232