qStudio now allow you to export to XLS.
This should solve most the text escape problems we have seen before. e.g. Double quotes, single quotes, scientific notation.
XLS Email will open outlook with the text location of the file as the first line in the message.
You should cut that line, click “Add Attachment”, then paste the line to quickly form an email.
It wasn’t possible to easily attach a file across windows, linux and mac.
The java core feature qStudio relied on for license key authentication has been removed in java 17. If you are using a version of qStudio lower than 2.53 you MUST upgrade this year. Download qStudio now.
Old license keys and old versions of qStudio cannot work on java 17+ as the core java library is removed.
qStudio 2.53+ released 2023-08-14 will accept both old and new license keys and work on java 8/9/11/17 …. everything. – Download it now.
All keys provided from 2024-01-01 have been using the new key format. These keys start with QSV3.
We really wish 11+ years ago we hadn’t chosen this particular library but what can you do 🙂
Existing enterprising customers may be issued an old key for exceptional circumstances. If absolutely required, get in touch.
We just launched a new sql documentation website: sqldock.com
to allow integration with Pulse / qStudio and docs more easily.
More updates on this integration will be announced shortly. 🙂
We have been working on version 2.0 of Pulse with a select group of advance users for weeks now. To give you a preview of one new feature, check out markers shown on the chart below. We have marker points, lines and areas.For example this will allow adding a news event to a line showing a stock price. This together with many other changes should be released soon as part of 2.0.
Pulse is specialized for real-time interactive data, as such it needs to be fast, very fast. When we first started building Pulse, we benchmarked all the grid components we could find and found that slick grid was just awesome, 60East did a fantastic writeup on how Slick grid compares to others. As we have added more features, e.g. column formatting, row formatting, sparklines…..it’s important to constantly monitor and test performance. We have:
Automated tests that check the visual output is correct.
Throughput tests to check we can process data fast enough
Manual tests to ensure subtle human interactions work.
Memory leak checks as our dashboards can be very long running.
Today I wanted to highlight how our throughput tests work by looking at our grid component.
HTML Table Throughput Testing
To test throughput we:
Use scenarios as close to our customers typical use cases as possible.
The most common query being a medium sized scrolling trade blotters with numerical/date formatting and row highlighting.
200 rows of data, scrolling 50 rows each update.
We use a subscription connection to replay and render 1000s of data points as fast as possible.
Video Demonstrating 21,781 rows being replayed as 435 snapshots taking 16 seconds = 27 Updates per second. (European TV updates at 25 FPS).
Update: After this video we continued making improvements and with a few days more work got to 40 FPS.
We then examine in detail where time is being spent. For example we:
Turn on/off all formatting, all rendering options.
Add/Remove columns
Change screen sizes
Change whether edit mode is on or table cells have been select (Off fact: selecting a cell makes the grid 30% slower to update)
Then we try to improve it!
Often this is looking at micro optimizations such as reducing the number of objects created. For example the analysis of how to format columns is only performed when columns change not when data is updated with the same schema. The really large wins tend to be optimizing for specific scenarios, e.g. a lot of our data is timestamped and received mostly in order. But those optimization are for a later post.
We just announced a unique event that gathers 4 of the newest, most advanced databases for Finance into 1 hour:
If you work on big data in Finance, this is your chance to get an overview of the rapidly changing database landscape. TimeStored will be organizing a free online presentation, each database company will present 10 minutes on what is unique to their solution. Bringing together the top new technologies together in one place.
We want to be the best finance streaming visualization solution. To achieve that, we can’t just use off the shelf parts, we have built our own market data order book visualization component from scratch, it’s only dependency is webgl. We call it DepthMap. It plots price levels over time, with the shading being the amount of liquidity at that level. It’s experimental right now but we are already receiving a lot of great feedback and ideas.
Faster Streaming Data
A lot of our users were capturing crypto data to a database, then polling that database. We want to remove that step so Pulse is faster and simpler. The first step is releasing our Binance Streaming Connection. In addition to our existing kdb streaming connection, we are trialling Websockets and Kafka. If this is something that interests you , please get in touch.
The below network diagram is intended as an outline of the skill set required for a financial software developer.
Note:
Most individuals should aim to have a strong core. Think of it like a pyramid, where the height is the strength of a skill. Core skills like general computing principles, probability, communication should be built “tall” and very strong. peripheral skills such as python/monitoring will be weaker. An individual will typically only learn 1 or 2 niche areas strongly (T shaped)
Notice the rough relative sizes of the areas. 55% computing, 10% math, 15% soft skills, 20% finance. This is intended to represent the rough allocation of effort.
If you only bring 95% techinical skills, you are going to waste time building the wrong thing, build something no one wants or build something useful but no one will know as you haven’t the soft skills to sell it.
The management branch on the bottom left is optional.
New keywords supported: in, distinct, inter, except, rank, sv, vs, sum, prd, xlog.
Improved compatibility and support of: null, avg, var, iasc, upper, lower, fills, fill, ^, sublist, prds, sums.
The added keywords in most cases will only support the most common types and arguments.
Mixed lists in particular are not handled well by most keywords but we will continue to improve.
 
