Databento

In celebration of our launch anniversary, we’d like to share a glimpse into our journey with you! Take a peek into the origins of Databento, the incredible team behind it, and how we're disrupting the financial #data industry.

IEX Cloud will retire its financial data products on August 31, 2024. In response to the large number of users migrating over to Databento, we've prepared this guide to help users understand the differences and ease their transition. If you're an IEX Cloud user still seeking an alternative, Databento provides real-time and historical data from over 40 trading venues—including IEX itself—alongside a familiar suite of features. Discover how our coverage, pricing, and APIs can support your market data and corporate actions needs ➡️ https://to.dbn.to/3XIgIs4. #marketdata #api #equities #iex #databento

The new Databento API docs are so stunning and set the new bar on how market data APIs should look. We've also made many improvements to the content and added new tutorials over the past month. Check it out! ➡️ https://to.dbn.to/docs #marketdata #api #apis #equities #futures

Order book snapshots are available for CME live data! Now, users can get the latest order book state without replaying the entire week's trading session. Databento generates a snapshot every minute—just set schema="mbo" and snapshot=True when subscribing to the live stream using our Raw API or client libraries. You'll then receive one #snapshot per instrument to reconstruct the order book right before your session's start time. 🕑 We plan to roll out support for other datasets in the coming months. Learn more and see a code example at https://to.dbn.to/snapshot #orderbook #L3 #marketdataAPIs

Databento is mentioned on the front page of WatersTechnology today regarding the IEX Cloud shutdown. https://lnkd.in/gasSm_zj

Founded in 2001, the UC Berkeley Haas Master of Financial Engineering Program (Berkeley MFE) is the first financial engineering program at a U.S. business school, consistently ranking as the #1 MFE program year after year. The program prepares future leaders in finance, fintech, data science, and technology, leveraging market data for academic research and high-frequency trading coursework. After hearing feedback from students, UC Berkeley switched to Databento from an incumbent data provider due to its many data customizations, ease of use, and accessibility on students' laptops from any location. #marketdata #highfrequencytrading

We are thrilled to be recognized as one of the best products for PMs at the 2024 Q2 Product Awards in Fintech, hosted by Products That Count in partnership with Capgemini and Mighty Capital! 🎉 #productmanagement #2024productawards

"I hear a lot of great ideas, but they stop at the idea phase because the idea may risk failure. For me, the pinnacle of success is taking risks—albeit hopefully calculated risks." In this edition of Quants Worth Following, we spoke with Kyle Benton, Quantitative Trader at DV Trading LLC and General Partner at Lunar Capital. In addition, Kyle is a celebrity in the crypto space, taking big risks that generated quite a bit of buzz. We asked Kyle how he did it, what lessons he learned, and more: https://to.dbn.to/3VShRw5.

📋 Cheat sheet of errors in modern market data Exchange feeds are clean these days so long as (seqnum, channel ID) is gapless, so you're less likely to be concerned with outliers and inverted spreads like the old days. Here's better things to look out for in a modern electronic trading environment, arising from vendor normalization issues: 1️⃣ Loss of price precision. More likely for yen, fixed income and crypto instruments. 2️⃣ Loss of null semantics. When 0 or negative numbers are used to represent null, this could interfere with actual cases of negative prices, e.g. found in spreads, futures. 3️⃣ Coalescing. When the data is secretly conflated or aggregated for performance reasons. 4️⃣ Ex post cleaning. When the vendor corrects the data ex post, so the historical and real-time data are inconsistent. 5️⃣ Discarded or truncated timestamps. e.g. Includes tag 60 but not tag 52. Artificially truncated to second, millisecond, or microsecond precision. 6️⃣ Lossy schema. e.g. Only 5 levels of depth are shown when source feed provides 15. 7️⃣ Packet loss. This is hard to identify unless the vendor provides MBO/L3 data, sequence number and channel ID. 8️⃣ Irreversible or inaccessible symbology mappings. e.g. #Nasdaq, #NYSE CMS symbols are different and vendors often pick one. Futures data vendors often like to provide you with continuous symbology or limit symbology to common outrights, causing more obscure instruments like UDIs and UDSes to become completely inaccessible. #marketdata #api #apis #stocks #futures #trading #algotrading

We're proud to partner with Cybersyn to deliver market data on Snowflake. Cybersyn creates a real-time view of the world's economy and helps users access external data directly in their Snowflake instance—no ETL required. Databento powers the Nasdaq stock prices and trading volumes the company publishes to its Financial & Economics Essentials dataset daily. Our equities data is sourced directly from prop feeds, enabling much higher granularity than the SIPs and making our market data APIs the first choice for Cybersyn founder Alexander Izydorczyk. Databento is also easy to integrate with existing datasets, and our proprietary normalization eliminates the need for data cleaning throughout the process. Read the full story at https://to.dbn.to/3RwGV9j.