Articles related to software development and software tools aiding automated investment operations.

Who is moving FinTech forward in continental Europe? Thoughts after FinTech Forum on Tour.

By Michal Rozanski, CEO at Empirica.

In the very centre of Canary Wharf, London’s financial district, in a brand new EY building, a very interesting FinTech conference took place – FinTech Forum on Tour. The invitation-only conference targeted the most interesting startups from the investment area (InvestTech) from mainland Europe. The event had representative stakeholders from the entire financial ecosystem. As Efi Pylarinou noted – the regulator, the incumbents, the insurgents, and investors, were all represented.

 

Empirica was invited to present its flagship product – Algorithmic Trading Platform, which is a tool professional investors use for building, testing and executing of algorithmic strategies. However, it was amazing to see what is happening in other areas of the investment industry. There were a lot of interesting presentations of companies transforming the FinTech industry in the areas of asset and wealth management, social trading and analytics.

 

The conference was opened with a keynote speech by Anna Wallace from FCA. Anna talked about the mission of FCA’s Innovation Hub; that is to promote innovation and competition in the financial technology field and to ensure that rules and regulations are respected. Whilst listening to Anna it became clear to me what the real advantage of London holds in the race to become the global FinTech capital – London has Wall Street, Silicon Valley and the Government in one place – and what’s most important, they cooperate trying to push things forward in one direction.

 

FinTech Forum on Tour

 

Robo-advisory

A short look at the companies presenting themselves at the event leads to the conclusion that the hottest sector of FinTech right now is robo-advisory. It’s so hot, that one of the panellists noted it’s getting harder and harder to differentiate for robo-advisory startups. On FinTech on Tour this sector was represented by AdviseOnly from Italy, In2experience,  Niiio, Vaamo and Fincite – all from Germany. Ralf Heim from Fincite presented an interesting toolkit ‘algo as a service’ and white label robo-advisory solutions. Marko Modsching from niiio revealed the motivation of retail customers, that “they do not want to be rich, they do not want to be poor”. Scalable Capital stressed the role of risk management in its offering of robo advisory services.

 

Social analysis/Sentiment/ Big Data

The social or sentiment analysis area, keeps growing and gains traction. Every day there’s more data and more trust in the results of backtesting as that data builds up over the years. The social media space is gaining ground. Investment funds as well as FinTech startups are finding new ways to use sentiment data for trading. And, it’s inseparably related with the analysis of huge amounts of data, so technically the systems behind it? are not trivial.

Anders Bally gave an interesting presentation about how to deal with sentiment data and showed  how his company Sentifi is identifying and ranking financial market influencers in social channels, and what they discuss.

Sentitrade showed its sentiment engine for opinion mining that is using proprietary sentiment indicator and trend reversal signals. Sentitrade is concentrated on German-speaking markets.

 

Asset management

From the area of asset management an interesting pitch was given by Cashboard, offering alternative asset classes and preparing now for a  huge TV marketing campaign . StockPluse showed how to combine information derived from social networks and base investment decisions on the overall sentiment. United Signals allows for social investing by making it possible to trade by copying transactions of chosen trading gurus with a proven track record, all in an automated way. And, finally BondIT, an Israeli company, presented tools for fixed income portfolio construction, optimization and rebalancing with use of algorithms.

 

Bitcoin and Blockchain

An interesting remark was given   by one of the panelist: ‘we have nearly scratched the surface for what blockchain technology can be applied to in financial industry’. Looking at the latest news reports that are saying that big financial institutions are heavily investing in blockchain startups and their own research in this field, there is definitely something in it.

A company from this sector of FinTech – Crypto Facilities, represented by its CEO Timo Schaefer, showed  the functionalities of its bitcoin derivatives trading platform.

 

Other fields

Hervé Bonazzi, CEO of Scaled Risk, presented its technologically advanced Big Data platform for financial institutions for risk management, compliance, analytics and fraud detection. Using Hadoop under the hood and low latency processing. Ambitious as it sounds.

Analysis of financial data for company  valuations, Valutico presented a tool that’s using big data, AI and swarm intelligence. Dorothee Fuhrmann from Prophis Technologies (UK) presented a generic tool for financial institutions to derive value and insights from data, interestingly describing indirect exposures and a hidden transmission mechanism.

Stephen Dubois showed  what Xignite (US) has to offer to financial institutions and other FinTech startups in the area of real-time and historical data that is stored in the cloud and accessible by proprietary API.

 Qumram, in an energetic presentation delivered by Mathias Wegmueller, described technology for recording online sessions on web, mobile and social channels, allowing for the analysis of user behaviour and strengthening internal security policy.

 

Conclusion

London is the place to be for FinTech startups. No city in Europe gives such possibilities. Tax deductions for investors. Direct help from the UK regulator FCA. Great choice of incubators and bootcamps for startups. No place gives such a kick. Maybe Silicon Valley is the best place for finding investor for a startup, maybe the Wall Street is the centre of the financial world, but London is the place that combines both the tech and the finance. It has a real chance of becoming the FinTech capital of the world.

 

About organizators

The people responsible for creating both a great and professional atmosphere at the event were Samarth Shekhar and Michael Mellinghoff. Michael was a great mentor of mine who transformed my pitch from a long and quite boring list of functionalities of our product to something that was bearable for the audience. Michael let me thank you once more for the time and energy you have devoted to Empirica’s pitch!

 

And because the FinTech scene in our region is not well organized yet, I sincerely advise all FinTech startups from Central and Eastern Europe to attend cyclic events of FinTech Forum in Frankfurt organized by Techfluence professionals!

 
Read about our Lessons learned from FinTech software projects.

 

 

FinTech Companies

 

 

 

FinTech. Lessons learned from over 5 years of financial technology software projects.

By Michal Rozanski, CEO at Empirica.

 

Reading news about fintech we regularly see the big money inflow to new companies with a lot of potentially breakthrough ideas. But aside from the hype from the business side, there are sophisticated technical projects going on underneath. And for new fintech ideas to be successful, these projects have to end with the delivery of great software systems that scale and last. Because we have been building these kind of systems for the fintech area for over 5 years we want to share a bit of our experience.

 

fintech empirica

 

“Software is eating the world”. I believe these words by Marc Andreessen. And now the time has come for finance, as technology is transforming every corner of the financial sector. Algorithmic trading, which is our speciality, is a great example. Other examples include lending, payments, personal finance, crowdfunding, consumer banking and retail investments. Every part of the finance industry is experiencing rapid changes triggered by companies that propose new services with heavy use of software.
The best evidence that something is happening somewhere is to see where the money goes. Investments in fintech companies globally grew to $12 billion last year, which is a three times increase comparing to 2013, and five times during the last five years, according to the research reports by CBInsights.

If fintech relies on software, and there is so much money flowing into fintech projects, what should be looked for when making a fintech software project? Our outsourcing software projects for the fintech industry as well as building our own algorithmic trading platform has taught us a lot. Now we want to share our lessons learned from these projects.

 

1. The process – be agile.

Agile methodology is the essence of how software projects should be made. Short iterations. Frequent deliveries. Fast and constant feedback from users. Having a working product from early iterations, gives you the best understanding of where you are now, and where you should go.
It doesn’t matter if you outsource the team or build everything in-house; if your team is local or remote. Agile methodologies like Scrum or Kanban will help you build better software, lower the overall risk of the project and will help you show the business value sooner.

 

2. The team – hire the best.

A few words about productivity in software industry. The citation is from my favourite article by Robert Smallshire ‘Predictive Models of Development Teams and the Systems They Build’ : ‘… we know that on a small 10 000 line code base, the least productive developer will produce about 2000 lines of debugged and working code in a year, the most productive developer will produce about 29 000 lines of code in a year, and the typical (or average) developer will produce about 3200 lines of code in a year. Notice that the distribution is highly skewed toward the low productivity end, and the multiple between the typical and most productive developers corresponds to the fabled 10x programmer.’.
I don’t care what people say about lines of code as a metric of productivity. That’s only used here for illustration.
The skills of the people may not be that important when you are building relatively simple portals with some basic backend functionality. Or mobile apps. But if your business relies on sophisticated software for financial transactions processing, then the technical skills of those who build it make all the difference.

And this is the answer to the unasked question why we in Empirica are hiring only best developers.

We the tech founders tend to forget how important it is to have not only best developers but also the best specialists in the area which we want to market our product. If you are building an algo trading platform, you need quants. If you are building banking omnichannel system, you need bankers. Besides, especially in B2B world, you need someone who will speak to your customers in their language. Otherwise, your sales will suck.
And finally, unless you hire a subcontractor experienced in your industry, your developers will not understand the nuances of your area of finance.

 

3. The product – outsource or build in-house?

If you are seriously considering building a new team in-house, please read the points about performance and quality, and ask yourself the question – ‘Can I hire people who are able to build systems on required performance and stability levels?’. And these auxiliary questions – can you hire developers who really understand multithreading? Are you able to really check their abilities, hire them, and keep them with you? If yes, then you have a chance. If not, better go outsource.
And when deciding on outsourcing – do not outsource just to any IT company hoping they will take care. Find a company that makes systems similar to what you intend to build. Similar not only from a technical side but also from a business side.
Can outsourcing be made remotely without an unnecessary threat to the project? It depends on a few variables, but yes. Firstly, the skills mentioned above are crucial; not the place where people sleep. Secondly, there are many tools to help you make remote work as smooth as local work. Slack, trello, github, daily standups on Skype. Use it. Thirdly, find a team with proven experience in remote agile projects. And finally – the product owner will be the most important position for you to cover internally.

And one remark about a hidden cost of in-house development, inseparably related to the IT industry – staff turnover costs. Depending on the source of research, turnover rates for software developers are estimated at 25% to even 38%. That means that when constructing your in-house team, every fourth or even every third developer will not be with you in a year from now. Finding a good developer – takes months. Teaching a new developer and getting up to speed – another few months. When deciding on outsourcing, you are also outsourcing the cost and stress of staff turnover.

 

4. System’s performance.

For many fintech areas system’s performance is crucial. Not for all, but when it is important, it is really important. If you are building a lending portal, performance isn’t as crucial. Your customers are happy if they get a loan in a few days or weeks, so it doesn’t matter if their application is processed in 2 seconds or in 2 minutes. If you are building an algo trading operations or payments processing service, you measure time in milliseconds at best, but maybe even in nanoseconds. And then systems performance becomes a key input to the product map.
95% of developers don’t know how to program with performance in mind, because 95% of software projects don’t require these skills. Skills of thinking where bytes of memory go, when they will be cleaned up, which structure is more efficient for this kind of operation on this type of object. Or the nightmare of IT students – multithreading. I can count on my hands as to how many people I know who truly understand this topic.

 

5. Stability, quality and level of service.

Finance is all about the trust. And software in fintech usually processes financial transactions in someway.
Technology may change. Access channels may change. You may not have the word ‘bank’ in your company name, but you must have its level of service. No one in the world would allow someone to play with their money. Allowing the risk of technical failure may put you out of business. You don’t want to spare on technology. In the fintech sector there is no room for error.

You don’t achieve quality by putting 3 testers behind each developer. You achieve quality with processes of product development. And that’s what the next point is about.

 

6. The Dev Ops

The core idea behind DevOps is that the team is responsible for all the processes behind the development and continuous integration of the product. And it’s clear that agile processes and good development practices need frequent integrations. Non-functional requirements (stability and performance) need a lot of testing. All of this is an extra burden, requiring frequent builds and a lot of deployments on development and test machines. On top of that there are many functional requirements that need to be fulfilled and once built, kept tested and running.

On many larger projects the team is split into developers, testers, release managers and system administrators working in separate rooms. From a process perspective this is an unnecessary overhead. The good news is that this is more the bank’s way of doing business, rarely the fintech way. This separation of roles creates an artificial border when functionalities are complete from the developers’ point of view and when they are really done – tested, integrated, released, stable, ready for production. By putting all responsibilities in the hands of the project team you can achieve similar reliability and availability, with a faster time to the market. The team also communicates better and can focus its energy on the core business, rather than administration and firefighting.

There is a lot of savings in time and cost in automation. And there are a lot of things that can be automated. Our DevOps processes have matured with our product, and now they are our most precious assets.

 

7. The technology.

The range of technologies applied for fintech software projects can be as wide as for any other industry. What technology makes best fit for the project depends, well, on the project. Some projects are really simple such as mobile or web application without complicated backend logic behind the system. So here technology will not be a challenge. Generally speaking, fintech projects can be some of the most challenging projects in the world. Here technologies applied can be the difference between success and failure. Need to process 10K transaction per second with a mean latency under 1/10th ms. You will need a proven technology, probably need to resign from standard application servers, and write a lot of stuff from scratch, to control the latency on every level of critical path.

Mobile, web, desktop? This is more of a business decision than technical. Some say the desktop is dead. Not in trading. If you sit whole day in front of the computer and you need to refer to more than one monitor, forget the mobile or web. As for your iPhone? This can be used as an additional channel, when you go to a lunch, to briefly check if the situation is under control.

 

8. The Culture.

After all these points up till now, you have a talented team, working as a well-oiled mechanism with agile processes, who know what to do and how to do it. Now you need to keep the spirits high through the next months or years of the project.
And it takes more than a cool office, table tennis, play station or Friday parties to build the right culture. Culture is about shared values. Culture is about a common story. With our fintech products or services we are often going against big institutions. We are often trying to disrupt the way their business used to work. We are small and want to change the world, going to war with the big and the powerful. Doesn’t it look to you like another variation of David and Goliath story? Don’t smile, this is one of the most effective stories. It unifies people and makes them go in the same direction with the strong feeling of purpose, a mission. This is something many startups in other non fintech branches can’t offer. If you are building the 10th online grocery store in your city, what can you tell your people about the mission?

 

Final words

Fintech software projects are usually technologically challenging. But that is just a risk that needs to be properly addressed with the right people and processes or with the right outsourcing partner. You shouldn’t outsource the responsibility of taking care of your customers or finding the right market fit for your product. But technology is something you can usually outsource and even expect significant added value after finding the right technology partner.
At Empirica we have taken part in many challenging fintech projects, so learn our lessons, learn from others, learn your own and share it. This cycle of learning, doing and sharing will help the fintech community build great systems that change the rules of the game in the financial world!

 

 

Free version of Algorithmic Trading Platform for retail investors

We have just released beta of Empirica – Algorithmic Trading Paltform for retail investors! It’s lifetime free for development, testing and optimizing of trading algorithms.

Our development team (exactly this team who implemented the entire system) also provides full support in algorithms development as well as connectivity to brokers. If you need help just contact us.

Among many features what is unique is our exchange simulation where you can influence market conditions under which you test your algorithms. No others software offers such a realistic level of simulation.

In paid versions we offer the execution of algorithms in robust server side architecture.

We strive for your feedback!

Best regards,

Michal Rozanski
Founder and CEO at Empirica
twitter: @MichalRoza
http://empirica.io


Empirica Trading Platform – http://empirica.io

Our platform implemented by large brokerage house!

Empirica has successfuly finished the implementation of its Algorithmic Trading Platform in one of the largest brokerage houses in Poland.

Brokerage house will use our software to:

  • aid its internal trading operations, like market making of derivatives on Warsaw Stock Exchange
  • offer functionalities of our platform to its institutional clients, which will be able to build, test and execute their own algorithmic trading strategies

Implementation included connecting of our software system directly to the system of Warsaw Stock Exchange (Universal Trading Platform delivered by NYSE Technologies), as well as the integration with transaction systems of brokerage house. Additionally we have fulfilled and successfuly passed tests regarding the highest security, stability and performance requirements.

This implementation is an important milestone for our system. The usage by team of market makers is a proof that our system is capable of performing high-throughput and low latency operations on level required by most sophisticated traders on the capital marketets.

 

Next release of our algorithmic platform. Version 1.3.4 – has code name “The Firebolt”.

Next implementations of our Algorithmic Trading Platform by customers don’t stop us from developing the platform itself. Working agile requires us to keep the pace in short and frequent iterations, which in case of product means frequent releases, keeping the whole product line stable.

A few iterations that we planned in our 1.3.4 release, code named by our developers ‘The Firebolt’, will include among others:

– even faster real-time replication of all server-side components in master-slave mode (for deployment in larger institutions)

– extended client side backtesting capabilities

– sophisticated charting of backtesting results and statistics

– multiscreen mode of client side application

– additional web-based server-side module for administration & management

 

For those curious about the release name and unfamiliar with Harry Potter, Firebolt is:

“The state-of-the-art racing broom. The Firebolt has unsurpassable balance and pinpoint precision. Aerodynamic perfection.”
—Harry Potter: Quidditch World Cup

“The Firebolt has an acceleration of 150 miles an hour in ten seconds and incorporates an unbreakable Braking Charm. Price upon request.”
—Harry reads about the features of the Firebolt.

Speed, precision, balance, perfection. These are the words that describe our software, therefore choosing the code name was kind of obvious :).

 

The Firebolt broom

HFT – the good, the bad and the ugly

High Frequency Trading, known also as HFT, is a technology of market strategies execution. HFT is defined by technically simple and time costless algorithms that run on appropriate software optimized for data structures, level of memory usage and processor use, as well as suitable hardware, co-location and ultra low-latency data feeds.

 

Although HFT exists on the market for over 20 years, it has became one of the hottest topic during past few years. It is caused by several factors, such as May 6, 2010, “Flash crash”, latest poor situation on the market and Michael Lewis book – “Flash Boys”. Let’s look where all that fuss comes from.

 

The Bad

 

Among other things, the advantage over other market participants and ability to detect market inefficiencies is the reason why so many people critics HFT so much. Most common charges put on the table are:

 

  • Front Running – HFT companies use early access to incoming quotes to buy shares before other investors and then turn around and sell him just bought shares with slightly bigger price.
  • Quote Stuffing – Way of market manipulation by quick sending and withdrawing large number of orders. Because of speed of operations, it creates a false impression of the situation on the market that leads other participants to executing against phantom orders. Then there is nothing else to do, but to exploit favorable prices by HFT investors.
  • Spoofing – Another method for market manipulation by placing orders and then cancelling them for price increase/decrease. It is based on placing big order on the market to bait other investors, and when the market starts to react, quickly cancel it. Then new price allows to gain some profit by HFT investor.

 

But that’s just a tip of the iceberg. It can be often heard that there is lack of proper HFT regulations, exist false belief that there are Dark Pools without any regulations where HFT companies can hide their activity, and there is still active argument if HFT brings liquidity to the market or just useless volume.

 

The Ugly?

 

Bill Laswell once said “People are afraid of things they don’t understand. They don’t know how to relate. It threatens their security, their existence, their career, image.” That phrase perfectly fits to what is happening now on High Frequency Trading topic. When people would like to take a closer look on how exchanges work, probably, they would be less sceptic to High Frequency Trading.

 

Thus, on most, maybe even on all, exchanges exist two mechanism which can efficiently handle problem of quote stuffing and spoofing. First of them is limitation of number of messages per second that can be send from one client. For example on New York Stock Exchange there is a limit of 1000 messages/sec, so it means that if HFT company burst whole 1000 of messages in first half of the period, in second half it cannot send any message, so it’s cut out of the market. Other limitation used by exchanges is a limit of messages per trade. It hits even harder in quote stuffing and spoofing. In most of the cases limit is around 500 messages per trade and if someone exceed it then he should be prepared for fines. On top of it company that frequently break limits could be banned from exchange for some time.

 

If we talk about front running, first thing we have to know is a fact that front running, in the dictionary meaning, is illegal action, and there are big fines for caught market participants who use it. Front running is using informations about new orders before they will go to the order book. Let’s say Broker gets new order with price limit to process, but before putting it to exchange, he will buy all available shares at better price than limit and then he execute client’s new order at limit getting extra profit. That’s highly not allowed and that’s not what HFT companies do.

 

All they do is tracking data feed, analyzing quotes, trades, statistics and basing on that information they try to predict what is going to happen in next seconds. Of course, they have advantage due to latency on data feed and so on, because of co-location, better connection and algorithms, but it’s still fair.

Hft-scalping-for-large-orders.svg

(source: Wikipedia)

 

HFT companies have to play on the same rules as other market participants, so they don’t have any special permits letting them do things not allowed for others. Same with Dark Pools, specially that they are regularly controlled by Finance Regulators.

 

The Good

 

First, we have to know that suppliers of liquidity, i.e. Market Makers and some investors use HFT. They place orders on both sides of the book, and all the time are exposed to sudden market movement against them. The sooner such investors will be able to respond to changes in the market, the more he will be willing to place orders and will accept the narrower spreads. For market makers the greatest threat is the inability to quickly respond to the changing market situation and the fact that someone else could realize their late orders.

 

System performance in this case is a risk management tool. Investments in the infrastructure, both a software and hardware (including co-location), are able to improve their situation in terms of risk profile. The increase in speed is then long-term positive qualitative impact on the entire market, because it leads to narrowing of the spread between bids and offers – that is, reduce the transaction costs for other market participants, and increase of the liquidity of the instruments.

 

HFT AND MARKET QUALITY

 

In April of 2012. IIROC (Investment Industry Regulatory Organization of Canada), the Canadian regulatory body, has changed fee structure based so far only on the volume of transactions, adding the tariffs and fees that also take into account the number of sent messages (new orders, modifications and cancellations). In result, introducing new fees made trading in the high frequencies more difficult. It was very clearly illustrated by data from the Canadian market.

 

Directly in the following months these fees caused a decrease in the number of messages sent by market participants by 30% and hit, as you might guess, precisely the institutions that use high-frequency trading, including market makers. The consequence for the whole market was increase in the average bid-ask spread by 9%.

NO PLACE FOR MISTAKES

 

When people talk about HFT, both enthusiast and critics, it is not rare to hear that HFT is risk free. Well, on the face of it, after analyzing how HFT works you would possibly agree with it, but there is a dangerous side of HFT that can be not so obvious and people often forgot about it. HFT algorithms works great if the code is well written, but what would happen if someone would run wrong, badly tested or incompatible code on a real market?

 

We don’t have to guess it, because it happened once and it failed spectacularly, it was a “Knightmare”. Week before unfortunate 1st of August Knight Capital started to upload new version of its proprietary software to eight of their servers. However Knight’s technicians didn’t copy the new code to one of eight servers. When the market started at 9:30 AM and all 8 server was run, the horror has begun. Old incompatible code messed up with the new one and Knight Capital initiated to lose over $170,000 every second.

(source: nanex.net)

It was going for 45 minutes before someone managed to turn off the system. For this period Knight Capital lost around $460 million and became bankrupt. That was valuable lesson for all market participants that there is no place for mistakes in HFT ecosystem, because even you can gain a lot of money fast, you can lose more even faster.

 

SUMMARY

 

HFT is a natural result of the evolution of financial markets and the development of technology. Companies that invest their own money in technology in order to take advantage of market inefficiencies deserve to profit like any other market participant.

 

HFT is not as black as is painted.

 

Aldridge, Irene (2013), High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems, 2nd edition, Wiley,

 

Warsaw Stock Exchange certifies our Trading Platform

 

Empirica’s Algorithmic Trading Platform has successfully passed the XDP protocol communication certification, issued by the Warsaw Stock Exchange.

From now on Empirica is officially listed as the ISV (Independent Software Vendor) for the Warsaw Stock Exchange.

WSE uses Universal Trading Platform delivered by NYSE Technologies. The same system is used by many other European and world stock exchanges. Fulfilment of technical criteria of Warsaw Stock Exchange makes certification for those markets only a formality for our platform.

Empirica founds IT Corner association to support local entrepreneurship!

Empirica, along with several other software companies based in Wroclaw, has founded the IT Corner association.

IT Corner aims at supporting the development of local IT enterprises, tightening the cooperation among small and medium size high-tech sector companies and developing project and product synergies between organization members.

IT Corner will pursue it goals by:

  • organization of IT events and conferences for larger audience
  • regular technological meet-ups targeted at employees of IT Corner companies
  • cooperate on larger IT projects with member companies
  • know-how and best-practices sharing among management of member companies
  • common presence on job fairs, IT events in Poland and abroad

First common events are already planned and will be officially announced soon!

it_corner_100x100-01

The list of founding members encloses over 10 software companies employing altogether over 200 people. Till end of the year IT Corner aims to double its size and establish its position as biggest and most active IT association in Wroclaw.

More on: IT Corner site

TWAP Strategy

Time-Weighted Average Price (TWAP) is another trading algorithm based on weighted average price and in compare to Volume-Weighted Average Price its calculations are even simplier. Also it’s one of the first execution algorithms and unlike most algorithms nowadays it’s passive execution algorithm that waits for proper market price to come, doesn’t chase it.

 

Calculations

 

As TWAP doesn’t bother about volume it’s extremely simple to obtain it. All it takes is to get Typical Price for every period bar using equation below and then calculate average of Typical Prices.

 

Typical Price = (Close+High+Low+Open)/4

 

Let’s just take a look at example results calculated on 1-minute interval intraday Morgan Stanley’s stock.

 

Time Close High Low Open Typical Price TWAP
09:30:00 38.90 38.96 38.90 38.96 38.93 38.930
09:31:00 38.94 38.97 38.86 38.92 38.92 38.926
09:32:00 38.91 38.96 38.91 38.94 38.93 38.928
09:33:00 38.89 38.94 38.88 38.92 38.91 38.922
09:34:00 38.90 38.94 38.90 38.90 38.91 38.920
09:35:00 38.97 38.97 38.90 38.90 38.93 38.922
09:36:00 38.92 38.96 38.92 38.96 38.94 38.925
09:37:00 38.90 38.93 38.86 38.93 38.91 38.922
09:38:00 38.90 38.92 38.89 38.89 38.90 38.920
09:39:00 38.92 38.92 38.88 38.91 38.91 38.918
09:40:00 38.90 38.92 38.88 38.91 38.90 38.917
09:41:00 38.84 38.89 38.82 38.89 38.86 38.912
09:42:00 38.87 38.87 38.84 38.84 38.86 38.908
09:43:00 38.85 38.89 38.84 38.89 38.87 38.905
09:44:00 38.81 38.85 38.80 38.85 38.83 38.900
09:45:00 38.69 38.80 38.67 38.80 38.74 38.890

 

Strategy

 

The most common use of TWAP is for distributing big orders throughout the trading day. For example let’s say you want to buy 100,000 shares of Morgan Stanley. Putting one such a big order would vastly impact the market and the price most likely would start to raise. To prevent that, investor can define time period in TWAP Strategy over which they want to buy shares. It will slice evenly big order into smaller ones and execute them over defined period.

 

TWAP could be used as alternative to VWAP, but because of itssimplicity we have to remember about some pitfalls. Even if we slice big orders, we do it evenly, thus there is a possibility to hit on low liquidity period when our splitted order will impact the market hard. That’s why it’s recommended to use TWAP over short periods or on stocks that are believed to not have any volume profile to follow.

 

Be random

 

There is also another threat coming directly from dividing big order evenly, namely, other traders or predatory algorithms. Obviously trading in such a predictable way can lead to situation where other traders or algorithms would look through our strategy and start to “game” us.

 

Barry Johnson in his book suggests adding some randomness to the strategy as a solution to the issue. He says that “We can use the linear nature of the target completion profile to adopt a more flexible trading approach. At any given time, we can determine the target quantity the order should have achieve just by looking up the corresponding value on the completion rate chart.”

 

In practice it means that when we have run 4-hour TWAP we don’t slice the order into evenly parts, but otherwise we focus on percentage completion. So for instance we would want to have 25% of the strategy completed by first hour, 50% by second and 75% by third. That gives a more freedom into size of orders, so we can be more random with it and hence less predictable for other traders on the market.

 

TWAP vs VWAP

 

As both indicators use same mechanism, i.e. weighted average price, it’s common to compare them. Despite that VWAP’s nature is more complex and includes volume in its calculations, on  instruments with low turnover TWAP and VWAP values can be close. On the other hand when a session starts to be more volatile both indicators will diverge.

 

 

On a table below there are TWAP and VWAP calculated for whole trading day. As we can see at the beginning of the trading day the difference is less than a cent, but on close the difference raised up to 2 cents. It happened because during the day there were some small volume trades for lower price that didn’t affected VWAP, but did TWAP.

 

Time Close High Low Open TWAP VWAP
09:44:00 38.81 38.85 38.80 38.85 38.900 38.904
09:45:00 38.69 38.80 38.67 38.80 38.890 38.887
15:57:00 38.70 38.70 38.68 38.69 38.666 38.686
15:58:00 38.71 38.72 38.68 38.70 38.666 38.686

 

Summary

 

TWAP Strategy is another great tool for executing big orders without impacting the market too hard. Like everything it has its own pros and cons and it’s up to us to select if TWAP will be the best strategy to use for our case or maybe we should consider using VWAP or other strategy.

 

References

  1. H. Kent Baker, Greg Filbeck. “Portfolio Theory of Management” (2013) , pp.421
  2. Barry Johnson “Algorithmic & Trading DMA – An introduction to direct access trading strategies” (2010), pp. 123-126

 

 

Basics of High Frequency Trading

Nanex’s High Frequency Trading Model (Sped Up)

Nanex released a video showing the results of half a second of worldwide high frequency trading with Johnson and Johnson stock. I simply sped up the footage to get a better feel of what it looked like. Blow Your Mind.

High frequency trading in action

CNN’s Maggie Lake gets a rare look inside the super-fast trading industry.

High Frequency Trading Explained (HFT)
Dave Fry, founder and publisher of ETF Digest, and Steve Hammer, founder of HFT Alert, discuss high frequency trading operations, fundamentals, the difference between algorithmic trading and high frequency trading, fluttering, latency and the role high frequency trading had in the May stock market flash crash in 2010.
 
TEDxNewWallStreet – Sean Gourley – High frequency trading and the new algorithmic ecosystem

Dr. Sean Gourley is the founder and CTO of Quid. He is a Physicist by training and has studied the mathematical patterns of war and terrorism. He is building tools to augment human intelligence.

Watch high-speed trading in action

Citadel Group, a high-frequency trading firm located in Chicago, trades more stocks each day than the floor of the NYSE.

Wild High Frequency Trading Algo Destroys eMini Futures

One of the scariest high frequency trading algos ran in the electronic S&P 500 futures (eMini) contract on January 14, 2008 starting at 2:01:11Eastern. During its 7 second reign, there were over 7,000 trades (52,000 contracts), and the price eventually oscillated within milliseconds, the equivalent of about 400 points in the Dow Jones Industrial Average!


HFT trading ideally must have the lowest possible info latency (time delays) and the maximum potential automation level. So participants prefer to trade in markets with high levels of integration and automation capacities in their trading platforms. These include NYSE NASDAQ, Direct Edge and BATS.
HFT is controlled by proprietary trading firms and spans across multiple securities, including equities, derivatives, index funds and ETFs, currencies and fixed income instruments.For HFT, participants want the following infrastructure in place:
– High speed computers, which need costly and regular hardware upgrades;
– Co-location.
– Real time data feeds, which must avert even the delay which could affect profits; and of a microsecond
– Computer algorithms, which are the heart of HFT and AT.

Benefits of HFT
– HFT is beneficial to traders, but does it help the total marketplace? Some market that is overall gains that HFT assistants cite contain:
– Bid-ask spreads have reduced due to HFT trading, making markets more efficient. Empiric evidence contains that after Canadian authorities in April 2012 imposed fees that deterred HFT, studies indicated that “the bid-ask spread rose by 9%,” possibly due to diminishing HFT trades. And thus facilitates the effects of market fragmentation.

– HFT assists in the price discovery and price formation process, as it is centered on a high number of orders (see related: How The Retail Investor Profits From High Frequency Trading.)