Text representation in binary trading
SBE was one of the binary encodings looked at. Nowadays, a large proportion of trading decisions are made by algorithms, not humans in trading pits or traders clicking with a mouse. Message encoding must be up to the task, along with every other aspect of algorithmic applications. CME Group saw SBE text representation in binary trading an opportunity to speed up both its internal messages and the market data which it disseminates to the trading world.
Circumstances change in unforeseen ways, no matter text representation in binary trading brilliance of planners. What should you do about a protocol that is wildly successful for exchanging financial information around the world and yet is not fit-for-purpose for new use cases? FIX Protocol was created by the non-profit organisation 20 years ago to exchange trading information between US stock brokers.
It became a standard in the industry as its usage extended to more asset classes, including futures and options, fixed-income and other instruments. At the same time, it covered other facets of the text representation in binary trading beyond order routing, including market data dissemination, securities reference data and post-trade processing.
FIX was able to meet all these various scenarios because they were expansions of business semantics. The new challenge was high-performance trading - a technical challenge to deliver messages with latencies measured in microseconds and even nanoseconds. To be clear, the FIX Protocol is an industry standard, not a software implementation. Implementations need to follow the standard to interoperate, and there are numerous open-source and proprietary implementations of FIX.
However, the composition of a standard has a strong influence on whether high performance is attainable. Unfortunately, the original FIX protocol design created barriers that prevented achieving really low-latency performance. To see why Simple Binary Encoding is part of the text representation in binary trading to the low-latency challenge, let's first take a tour of the original FIX Protocol.
The first significant characteristic of the original FIX Protocol is that it is all-in-one. It specifies message encoding, rules for message exchange session establishment and check-pointing and business semantics, such as how order and execution quantities are managed.
In the terminology of the Open Systems Interconnection conceptual model OSIit is the application layer, the presentation layer and the session layer, all rolled up in one big ball of wax. The problem with a monolithic protocol is that text representation in binary trading is text representation in binary trading substitutability for a better solution offered at one layer.
Separation of layers is clearly desirable and technical standards recently created by the FIX Trading Community aim to do text representation in binary trading that. SBE was created to replace the presentation layer, no more and no less. The application layer of FIX that is familiar to thousands of users is retained. It is a requirement for SBE to be able to express existing FIX messages but also to make it possible to optimise them for performance.
A performance session layer is also under development but this is a discussion for another day. The second aspect of the original protocol that one needs to understand is why its message encoding is no longer fit-for-purpose. Text is acceptable for fields like an account identifier and the like, but numeric data, timestamps and so forth cannot be processed directly by computers.
To calculate a price or time difference, text must first be converted back into native binary types. Then, to put the data back in a response message, the computer must convert the binary types back to ASCII text. This is a waste of processing time, especially for time and calendar functions, and it has no place in a high-performance system.
It was designed for a high degree of optionality. For example, 'expiration date' is an optional field on an order message, because it is only required for derivatives, but not equities. FIX also has a concept of conditionally required fields. A stop price field is only required in an order message if the order type is stop or stop-limit, but is not required otherwise. Moreover, field order within a message is largely non-deterministic. Price and then quantity is as equally valid as quantity and then price.
Since the message layout is non-deterministic, it is necessary to convey metadata to identify each field in the message. The field tag is small for each field, but the combination of field tags, field delimiters and a verbose message header result in a typical message size of hundreds of bytes. All these characteristics resist performance optimisation. High variability defeats CPU optimisations such as branch prediction and memory pre-fetch.
Additionally, high variability of message layout in memory and large message size tends to cause cache misses that can stall a CPU. The variability also makes the encoding unsuitable for hardware implementations such as FPGA. The benefit is that the sender and receiver do not need to translate data representations but rather can directly use data right off the wire. The best kind of optimisation is elimination of unnecessary work.
More importantly, the SBE format is numeric, so time differences can be easily computed. In this example, the backing unsigned bit integer is a count of ticks in 20, days and 14 hours, 17 minutes and 22 seconds since the UNIX epoch.
SBE integers are usually encoded little-endian on the text representation in binary trading, since that is conducive to Intel and compatible processors. But to be impartial, SBE also allows big-endian byte order to be specified in a message schema. One consequence of optimising for machine readability rather than human readability is that humans eventually need to read a log of FIX messages for trouble shooting or auditing, so SBE messages must be translated for human consumption.
However, visualisation does not impede trading performance since it is done after the fact or on demand. The second difference between encodings is that SBE keeps most metadata out-of-band rather than sending it on the wire with each message.
A message structure is defined by a template that must be disseminated to a peer, so it knows how to interpret a message. A message template contains a list of fields in the message along with each field's identifiers and data type. A message may contain nested structures, particularly repeating groups and arrays of blocks of fields.
The benefit of a template is that it controls text representation in binary trading deterministic message layout. Fields always appear in the message in the same order. Also, character data can have a defined length. However, it is possible for a designer to constrain usage to fixed-length fields and thus produce messages of fixed-length. Another potential benefit of deterministic field position is that an application that only needs to examine select fields, such as an order router, can count on direct access to those fields without having to crack the whole message.
Such deterministic message layout also lends itself to FPGA implementations. In-band transmission of a message contains a message header that identifies its template.
This supports mixing of message types in a stream. Templates may be narrowly tuned to a specific use case. The old way of designing FIX messages was to overload a message type with many potential uses while making the numerous alternatives into optional fields. The SBE way is to design a template for each narrow scenario, such as an equity order template distinct from a futures order.
Each template results in a highly optimised message layout. Aside from predictability, messages and repeating groups may even be forced to align with cache lines to prevent expensive cache misses. You may be thinking that XML is not a good choice for low-latency applications, but remember that a message schema is delivered out-of-band, not at run-time of message exchange. Most text representation in binary trading parse the XML only once and generate code for encoders and decoders or at least convert it to an intermediate binary representation.
A schema usually contains multiple message templates. It containsinstances and repeating groups, labelled with XML element. A field is a unit of semantic information, such as a price, quantity or trading symbol.
A field has a datatype, and its metadata has a name and a numeric ID. By using those traditional tags, business knowledge is retained, and firms interact using shared concepts. A repeating group is an array of field blocks. The number of entries in a repeating group is usually not known until run-time. For example, a market data message may contain multiple prices, corresponding to the number of levels in an order book. The number of levels is variable with the state of a market.
On the wire, each repeating group has a tiny header that gives the dimensions of the group, so a decoder can make sense of it. A wire format for a datatype may be shared by multiple messages and is expressed by reusable simple and composite types. A simple type, denoted by XML element, is backed by text representation in binary trading primitive datatype. It can be either a scalar or an array of scalars.
A composite type is a combination of two or more simple types, like a struct in the C language. The most common usage is to text representation in binary trading an exact decimal as a pair of integers, representing mantissa and exponent. Note that these are not two separate fields, but rather a small structure that backs a single field.
Remember that it is a field that carries semantic information. One feature of SBE is that fields and types can be set to constants in metadata. When constant, the value needs not to be sent on the wire since the constant value is known to both sender and receiver. In the composite encoding for a decimal, the exponent is sometimes set to a constant, so that only the mantissa needs to be transmitted on the wire. Also, the enumeration of code values is supported.
For example, order and execution messages may share an enumeration for order type; it needs to be defined only once and then can be referenced in multiple message templates. Any value sent on the wire that does not correspond to the set of valid values specified in the schema is invalid.
A bitset is sent on the text representation in binary trading as a native text representation in binary trading type of size 8 to 64 bits.
A text representation in binary trading encoder or decoder should stride just once through a message buffer. That is best practice. This access pattern takes advantage of memory pre-fetch in hardware. The system anticipates the next piece of memory to be accessed and fetches it ahead of a read or write operation. By not jumping back and forth in a buffer, cache misses are avoided. Another best practice is to build encoders and decoders as a flyweight pattern over message buffers.
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Computers are machines that do stuff with information. They let you view, listen, create, and edit information in documents, images, videos, sound, spreadsheets and databases.
They let you text representation in binary trading and calculate text representation in binary trading numerical information; they let you send and receive information over networks.
To make computers easier to build and keep them reliable, everything is represented using just two text representation in binary trading. You may have seen these two values represented as 0 and 1, but on a computer they are represented by anything that can be in two states. For example, in memory a low or high voltage is used to store each 0 or 1. On a magnetic disk it's stored with magnetism whether a tiny spot on the disk is magnetised north or south.
The idea that everything stored and transmitted in our digital world is stored using just two values might seem somewhat fantastic, but here's an exercise that will give you a little experience using just black and white cards to represent numbers. In the following interactive, click on the last card on the right to reveal that it has one dot on it. Now click on the previous card, which should have two dots on it.
Before clicking on the next one, how many dots do you predict it will have? Carry on clicking on each card moving left, trying to guess how many dots each has.
Use the interactive online at http: The challenge for you now is to find a way to have exactly 22 dots showing the answer is in the spoiler below. Now try making up other numbers of dots, such as 11, 29 and Is there any number that can't be represented? To test this, try counting up from 0. You may have noticed that each card shows twice as many dots as the one to its right. This is an important pattern in data representation on computers. The number 22 requires the cards to be "white, black, white, white, black", 11 is "black, white, black, white, white", 29 is "white, white, white, black, white", and 19 is "white, black, black, black, white".
You should have found that any number from 0 to 31 can be represented with 5 cards. Each of the numbers could be communicated using just two words: For example, 22 dots is "white, black, white, white, black". Or you could decode "black, black, white, white, white" to the number 7. This is the basis of data representation - anything that can have two different states can represent anything on a digital device.
For example, a piece of computer memory could have the following voltages:. They are just using physical mechanisms such as high and low voltage, north or south polarity, and light or dark materials. The use of the two digits 0 and 1 is so common that some of the best known computer jargon is used for them. Since there are only two digits, the system is called binary.
The short word for a "binary digit" is made by text representation in binary trading the first two letters and the last letter a bit is just a digit that can have two values. Every file you save, every picture you make, every download, every digital recording, every web page is just a whole lot of bits.
These binary digits are what make digital technology digital! And the nature of these digits unlock a powerful world of storing and sharing a wealth text representation in binary trading information and entertainment. Computer scientists don't spend a lot of time reading bits themselves, but knowing how they are stored is really important because it affects the amount of space that data will use, the amount of time it takes to send the data to a friend as data that takes more space takes longer to send!
Understanding what the bits are doing enables you to work out how much space will be required to get high-quality colour, hard-to-crack secret codes, a unique ID for every device in the world, or text that uses more characters than the usual English alphabet.
This chapter is about some of the different methods that computers use to code different kinds of information in patterns of these bits, and how this affects the cost and quality of what we do on the computer, or even if something is feasible at all. To begin with, we'll look at Braille.
Braille is not actually a way that computers represent data, but is a great introduction to the topic. More than years ago a year-old French boy invented a system for representing text using combinations of flat and raised dots on paper so that they could be read by touch.
The system became very popular with people who had visual impairment as it provided a relatively fast and reliable way to "read" text without seeing it. Louis Braille's system is an early example of a "binary" representation of data there are only two symbols raised and flatand yet combinations of them can be text representation in binary trading to represent reference books and works of literature.
Each character in braille is represented with a cell of 6 dots. Each dot can either be raised or not raised. Different numbers and letters can be made text representation in binary trading using different patterns of raised and not raised dots. Let's work out how many different patterns can be made using the 6 dots in a Braille character. If text representation in binary trading used only 2 dots, there would be 4 patterns.
And text representation in binary trading 3 dots there would be 8 patterns. You may have noticed that there are twice as many patterns with 3 dots as there are with 2 dots. It turns out that every time you add an extra dot, that gives twice as many patterns, so with 4 dots there are 16 patterns, 5 dots has text representation in binary trading patterns, and 6 dots has 64 patterns.
Can you come up with an explanation as to why this doubling of the number of patterns occurs? The reason that the number of patterns doubles with each extra dot is that with, say, 3 dots you have 8 patterns, so with 4 dots you can use all the 3-dot patterns with the 4th dot flat, and all of them with it raised. This gives 16 4-dot text representation in binary trading. And then, you can do the same with one more dot to bring it up to 5 dots.
This process can be repeated infinitely. So, Braille, with its 6 dots, can make 64 patterns. That's enough for all text representation in binary trading letters of the alphabet, and other symbols too, such as digits and text representation in binary trading. The reason we're looking at Braille in this chapter is because it is a text representation in binary trading using bits.
That is, it contains 2 different values raised and not raised and contains sequences of these to represent different patterns. The letter m, for example, could be written aswhere "1" means raised dot, and "0" means not raised dot assuming we're reading from left to right and then down. This is the same as how we sometimes use 1's and 0's to show how text representation in binary trading computer is representing data.
Braille also illustrates why binary representation is so popular. It would be possible to have three kinds of dot: A skilled braille reader could distinguish them, and with three values per dot, you would only need 4 dots to represent 64 patterns. The trouble is that you would need more accurate devices to create the dots, and people would need to be more accurate at sensing them. If a page was squashed, text representation in binary trading very slightly, it could leave the information unreadable.
Digital devices almost always use two values binary for similar reasons: Using ten digits like we do in our every day decimal counting system would obviously be too challenging. Why are digital systems so hung up on only using two digits?
After all, you could do text representation in binary trading the same things with a 10 digit system? As it happens, people have tried to build decimal-based computers, but it's just too hard. Recording a digit between 0 and 9 involves having accurate equipment for reading voltage levels, magnetisation or reflections, and it's a lot easier just to check if it's mainly one way or the other. Watch the video online at https: In this section, we will look at how computers represent numbers.
To begin with, we'll revise how the base number system that we use every day works, and then look at binary, which is base After text representation in binary trading, we'll look at some other charactertistics of numbers that computers must deal with, such as negative numbers and numbers with decimal points. The number system that humans normally use is in base 10 also known as decimal.
It's worth revising quickly, text representation in binary trading binary numbers use the same ideas as decimal numbers, just with fewer digits! In decimal, the value of each digit in a number depends on its place in the number. Each place value in a number is worth 10 times more than the place value to its right, i. Also, there are 10 different digits 0,1,2,3,4,5,6,7,8,9 that can be at each of those place values.
If you were only able to use one digit to represent a number, then the largest number would be 9. After that, you need a second digit, which goes to the left, giving you the next ten numbers 10, 11, It's because we have 10 digits that each one is worth 10 times as much as the one to its right. For example, if you want to write the number in expanded form you might have written it as:. Remember that any number to the power of 0 is 1.
All this probably sounds really obvious, but it is worth thinking about consciously, because binary numbers have the same properties. As discussed earlier, computers can only store information using bits, which only have 2 possible states. This means that they cannot represent base 10 numbers using digits 0 to 9, the way we write down numbers in decimal.
Instead, they must represent numbers using just 2 digits -- 0 and 1. Binary works in a very similar way to Decimal, even though it might not initially seem that way. Because there are only 2 digits, this means that each digit is 2 times the value of the one immediately to the right.
The base 10 decimal system is sometimes called denary, which is more consistent with the the name binary for the base 2 system. The word "denary" also refers to the Roman denarius coin, which was text representation in binary trading ten asses an "as" was a copper or bronze coin.
The interactive below illustrates how this binary number system represents numbers. Have a play around with it to text representation in binary trading what patterns you can see.
To ensure you are understanding correctly how to use the interactive, verify that when you enter the binary number it shows that the decimal representation is 45, that when you enter it shows that the decimal representation is 32, and when you enter it shows text representation in binary trading decimal representation is What is the largest number you can make with the interactive?
What is the smallest? Are there any numbers with more than one representation?