News about Empirica, algorithmic trading and software development.

Deribit – A derivative Cryptocurrency exchange

Introducing Deribit Cryptocurrency Exchange:

Deribit is a cryptocurrency derivatives exchange allowing traders to trade Bitcoin and Ethereum Options, Perpetual and futures. Deribit keeps its customers’ deposits in their cold storage with most of the funds stored in vaults with multiple banks safes. Though at this point in time, Deribit only accepts Bitcoin and no fiat currency as funds to deposit. 

 

Deribit Futures:

Futures on Deribit receive a cash settlement instead of physical Bitcoin, that means the buyer of the futures do not buy the actual Bitcoin and the seller won’t sell the actual BTC and what will be transferred is the transfer of profits and losses at the agreed settlement price of the contract on the expiration price. 

The minimum tick size to trade futures is 0.50 USD and all daily settlements happen at the coordinated universal time (UTC) 8 am. The expiration time is also at UTC 8 am at the end of each month. The size for the contracts at Deribit future exchange is 10 USD with initial margin starting at 1.0% with a linear increment of 0.5% per 100 BTC. The delivery price is the Time Weighted Average of Deribit BTC index measured in the half an hour before the UTC 8 am (7:30 am).

 

Deribit Perpetual

At Deribit exchange, Perpetual is a derivative very similar to Futures but with no fixed maturity and no exercise limit. The perpetual derivatives are to keep their price close to their underlying cryptocurrency price, which at Deribit exchange is referred to as “Deribit BTC Index”.

The Deribit Perpetual contract is 1 USD per Index Point with a contract size of 10 USD. The minimum tick size 0.50 USD and settlements are done at UTC 8 am. The contract size for trading Prepetuals in Deribit is 10 USD. As mentioned earlier there is no delivery/expiration when trading Prepetuals on Deribit. 

 

Deribit Options:

Options on Deribit are traded with what so-called the “European style”. This indicates that options cannot be exercised before expiration, but can only be exercised at expiration. Which this happens automatically on Deribit.  Options on Deribit are priced in Bitcoin and Ethereum and also viewable on USD.

BTC option deribit

Deribit Index:

There are 8 exchanges and the highest and lowest prices are taken out, and the remaining 6 are each at 16.67% accountable for creating an index in Deribit. 

Deribit Crypto Index

 

Market Making on Deribit:

Deribit does not include an in-house trading desk, therefore all active market makers are the third party Market Makers. The liquidity is provided by these parties and Deribit sees these services as a crucial point to their business. Based on the volume, designated market makers receive tailored agreement on fees. 

How Artificial Intelligence will revolutionize wealth management

by Michal Rozanski, CEO at Empirica

Most wealth managers are in deep denial about robo advice. They say they need human interaction in order to understand the nuances of financial lives of their customers. And their clients value the human touch. They’re wrong. Soon robo advice will be much more efficient than human advice ever was.

In this post, we will share the results of our analysis on the most important areas where the application of machine learning will have the greatest impact in taking wealth management to the next level.

What Artificial Intelligence is and why you should care

 “Computers can only do what they are programmed to do.” Let us explain this is huge misconception, which was only valid because of limited processing power and memory capacity of computers. Most advanced programs which mimic specialized intelligences, known as expert systems, were indeed programmed around a set of rules based on the knowledge of specialists within the problem’s domain. There was no real intelligence there, only programmed rules. But there is another way to program computers, which makes them work more similarly to the functions of the human brain. It is based on showing the program examples of how certain problems can be solved and what results are expected. This way computers equipped with enough processing power, memory and storage are able to recognize objects in photographs, drive autonomous cars, recognize speech, or analyse any form of information which exhibits patterns.

 

We are entering the age where humans are outperformed by machines in activities related with reasoning based on the analysis of large amounts of information. Because of that finance and wealth management will be profoundly changed during the years to come.

 

Real advice – combining plans with execution

A great area for improvement in finance management is the combination of long term wealth building with the current financial situation of the customer as reflected by his bank account. For robo-advisors, an integration with bank API opens the door to an ocean of data which, after analysis, can dramatically improve the accuracy of advice provided to the customer.

By applying a machine learning capabilities to a customer’s monthly income and expenses data, wealth managers will gain a unique opportunity to combine two perspectives – the long term financial goals of their customers and their current spending patterns. Additionally, there is the potential of tax, mortgage, loans or credit card costs optimization, as well as using information on spending history to predict future expenditures.

By integrating data from social media, wealth management systems could detect major changes in one’s life situation, job, location, marital status or remuneration. This would allow for automated real time adjustments in investment strategies of on the finest level, which human advisors are simply unable to deliver.

New powerful tools in the wealth manager’s arsenal

Hedge funds that are basing their strategies on AI have provided better results over the last five years than the average (source Eurekahedge, more on hedge fund software). What is interesting is that the gap between AI and other strategies has been growing wider over the last two years, as advancements in machine learning accelerated.

The main applications of machine learning techniques in wealth management, can be categorized following cases:

  •       Making predictions on real-time information from sources such as market data, financial reports, news in different languages, and social media
  •       Analysis of historical financial data of companies to predict the company’s cash flow and important financial indicators based on the past performance of similar companies
  •       Analysis of management’s public statements and activity on social networks in order   to track the integrity of their past words, actions and results
  •       Help in accurate portfolio diversification by looking for uncorrelated instruments which match requirements of the risk profile (see portfolio management software)
  •       Generation of investment strategies parametrized by goals such as expected risk profiles, asset categories, and timespan, resulting in sets of predictive models which may be applied in order to fulfill the assumptions

 To give an example of machine learning accuracy, the algorithms for sentiment analysis and document classification are already on acceptable levels, well above 90%.

Automated execution

When it comes to the execution of the actual orders behind portfolio allocation and rebalancing strategies, many robo advisors are automating these processes passing generated orders to brokerage systems through algorithmic trading systems. The next step would be autonomous execution algorithms, that take under consideration the changing market situation and learn from incoming data, allowing for increased investment efficiency and reduced costs.

Machine learning can be applied to quantitative strategies like trend following, pattern recognition, mean reversion, and momentum, as well as the prediction and optimization of statistical arbitrage, and pairs trading. Additionally, there is a possibility to apply machine learning techniques in, already quite sophisticated, execution algorithms (aka trading bots) that help execute large orders by dividing them to thousands of smaller transactions without influencing the market while adjusting their aggressiveness to the market situation.

What’s interesting is that algorithms could also be trained to make use of rare events, like market crashes and properly react in milliseconds, already knowing the patterns of panic behaviour and shortages of liquidity provision.

Explaining the markets

In wealth management systems, if portfolio valuations are provided to the customers in real time, then so should explanations of the market situation. Every time the customer logs in to the robo-advisor, she should see all required portfolio information with a summary of market information relevant to the content of her portfolio. This process includes the selection of proper articles or reports concerning companies from the investor portfolio, classification and summarization of negative or positive news, and delivering a brief overview.

Additionally, machine learning algorithms can be used to discover which articles are read by customers and present only those type of articles that were previously opened and read by the customer.

The result will be not only the increase in customer understanding but also, by providing engaging content to investors, the increase in their engagement and commitment to portfolio strategy and wealth management services.

 

Talking with robots

The ability to deliver precise explanations of the market situation in combination with conversational interfaces aided by voice recognition technology will enable robo-advisors to provide financial advice in a natural, conversational way.

Voice recognition is still under development, but it could be the final obstacle on they way to redesigning human-computer interaction. On the other hand, thanks to deep learning, chatbot technology and question answering systems are getting more reliable than ever. KAI, the chatbot platform of Kasisto, who has been trained in millions of investment and trade interactions, already handles 95 % of all customer queries for India’s digibank.

Decreasing customer churn with behavioral analysis

The ability to track all customer actions, analyzing them, finding common patterns in huge amounts of data, making predictions, and offering unique insights for fund managers delivers a powerful business tool not previously available to wealth managers. What if nervousness caused by portfolio results or market situation could be observed in user behaviour within the system?  This information, combined with the results of investments and patterns of behaviour of other investors, can give a wealth manager the possibility to predict customer churn and react in advance.

When speaking with wealth management executives that are using our robo-advisory solutions, they indicate behavioural analysis as one of the most important advancements to their current processes. Customers leave not only when investment results are bad, but also when they are good if there is a fear that the results may not be repeated in the future. Therefore, the timely delivery of advice and explanations of market changes and the current portfolio situation are crucial.

The same model we used to solve the behavioral analysis problem has been proven to predict credit frauds in 93.07% of cases.

Summary

Other areas of applying machine learning in the processes supporting wealth management services could be:  

  •       Security based on fraud detection which actively learns to recognize new threats
  •       Improving sales processes with recommendations of financial products chosen by similar customers
  •       Psychological profiling of customers to better understand their reactions in different investment situations      
  •       Analysis and navigation of tax nuances   
  •       Real estate valuation and advice

Implementing these AI functions in wealth management systems will be an important step towards the differentiation of the wealth managers on the market. Today’s wealth managers’ tool set will look completely different in five years. Choosing an open and innovative robo-advisory system that tackles these future challenges is crucial. Equally important will be wealth managers’ incorporation of data analytic processes and the use of this data to help their customers.

Artificial intelligence is poised to transform the wealth management industry. This intelligence will be built on modern wealth management software that combine data from different sources, process it, and transform it into relevant financial advice. The shift from data gathering systems to predictive ones that help wealth managers to understand the data, has already started. And wealth management is all about understanding the markets and the customers.

 

 

Now Crypto. Lessons learned from over 10 years of developing trading software

By Michal Rozanski, CEO at Empirica.

Reading news about crypto 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 cryptocurrency and blockchain 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 kinds of systems for the financial markets for over 10 years we want to share a bit of our experience.

Read more on how Empirica delivers its trading software development services

“Software is eating the world”. I believe these words by Marc Andreessen. And now the time has come for financial markets, 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.

If crypto relies on software, and there is so much money flowing into crypto projects, what should be looked for when making a trading software project for cryptocurrency markets? Our trading software development projects for the capital and crypto markets 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.

 

  1. 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, software for market makers or trading bots, 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.

 

  1. 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.

 

  1. System’s performance.

For many crypto projects, especially those related with trading,  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 bitcoin 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.

 

  1. Stability, quality and level of service.

Trading understood as an exchange of value is all about the trust. And software in crypto 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 crypto sapce 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.

 

  1. The DevOps

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.

 

  1. The technology.

The range of technologies applied for crypto 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, crypto 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.

 

  1. 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, Xbox consoles 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?

Read more on how Empirica delivers its crypto software development services

 

Final words

Crypto 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 crypto projects, so learn our lessons, learn from others, learn your own and share it. This cycle of learning, doing and sharing will help the crypto community build great systems that change the rules of the game in the financial world!

 

 

Independent initiatives that analyze crypto exchanges liquidity and quality

Volume is flawed metric of crypto exchanges liquidity. Because of wash trading practices of many crypto exchanges as well as token issuers, using trading volume as a basis of comparison is misleading. Many exchanges have problems attracting professional market makers and are trying to make shortcuts on the way to attract retail investors. Moreover attracting professional investors requires investments in crypto exchanges system development with stable and performant APIs so they could connect their algorithmic trading systems.]

There are more and more independent initiatives that are taking a closer look at what constitutes a high quality crypto exchange. Three major ones are Blockchain Transparency Institute, CryptoCompare Benchmark and Cointelligence Report. I also take a quick look at the Bitwise report for SEC from March 2019.

 

Read more about our tool for measuring crypto exchange quality – Liquidity Analytics Dashboard

 

Blockchain Transparency Institute

BTI concentrates on analyzing crypto exchanges data feeds to spot wash trading mechanisms and provide the real volume metric which is cleaned out of suspicious activities.

BTI identified 17 of the CoinMarketCap Top 25 crypto exchanges to be over 99% wash traded. This one number alone shows the magnitude of the problem, as well as how volume is a false measure.

According to BTI Report crypto exchanges which are faking their volumes use a variety of different tactics to try and swindle investors. These tactics include buying twitter followers and likes, filling up fake order books, mirror wash trading the largest exchanges with real volume, and trying to disguise their wash trading using various bot settings to not affect price. On many of these exchanges trading high volumes closing the spread would make the volume plummet as the trading bots had no room to wash trade with themselves. Welcome to the wild wild west of no regulation and surveillance.

BTI finds that “all crypto exchanges combined are currently reporting around $50 Billion in daily volume on CMC. After removing all the wash traded volume via our algorithms the accurate number is around $4-5 Billion. About 88-92% of daily trading volume is fabricated depending on the day. Bitcoin’s daily trading volume is about 92% fabricated, which is in line with the space as a whole when comparing our findings to top data sites reporting wash traded volumes.” 

And further “On our list of the top 40 largest exchanges with actual volume, Bitcoin’s volume is about 65% fabricated. Almost all of this fabricated volume comes from OKEx, Bibox, HitBTC, and Huobi. Of the top 25 tokens by market cap, Tron and Ethereum Classic are the highest wash traded tokens on our list at 85% fake volume each and coming in at #24 and #25 of the most wash traded tokens.”

Top 10 cryptocurrency exchanges according to real (not wash traded) volume by BTI

  1. Binance 
  2. Kucoin
  3. Liquid
  4. Huobi
  5. Coinbase
  6. OKEx
  7. Bitfinex
  8. Upbit
  9. Kraken
  10. Bitstamp

CryptoCompare

CryptoCompare’s Exchange Ranking methodology utilises a combination of 34 qualitative and quantitative metrics to assign a grade to over 100 active crypto exchanges. Metrics were categorised into several buckets ensuring that no one metric overly influences the overall exchange ranking. Each crypto exchange grade is derived from a broad due diligence check using qualitative data, followed by a market quality analysis that uses a combination of order book and transactional data.

Due diligence check comprises of 6 main categories that attempt to qualitatively rate each exchange on the basis of:

  • Geography
  • Legal and regulatory metrics
  • Calibre of investment
  • Team and company quality
  • Quality of data provision
  • Trade surveillance

Although at Empirica we believe in numbers, I like the qualitative approach, as it’s also possible to prove a correlation of metric like number of employees and business size of the exchange, therefore proving this way it’s quality. 

Another important factor is Market Quality. Crypto compare measures the market quality of each exchange using a combination of 5 metrics (derived from trade and order book data) that aim to measure the:

  • Cost to trade, 
  • Liquidity, 
  • Market stability, 
  • Behaviour towards sentiment
  • “Natural” trading behaviour

Exchanges were rated based on a combination of 9 of the most liquid BTC and ETH markets.

It’s worth taking a closer look how CryptoCompare report approaches Spread and Liquidity metrics:

“Generally, those exchanges which offer incentives to provide liquidity through either low or negative maker fees will achieve the tightest spreads. Due to the spread being calculated using the best bid and offer, it is misleading to use it as a sole gauge of liquidity and therefore as the market cost to trade; it must be used in conjunction with a depth

measurement to find the likely transaction price for any given size of transaction.”

 

Good point. And liquidity:

“Market depth is the total volume of orders in the order book. It provides an idea of how much it is possible to trade on crypto exchange, and how much the price is likely to move if large amounts are traded. An exchange with greater average depth is likely to be more stable (i.e flash crashes are much less likely) and allows trading of greater amounts at better prices.

We consider the depth up to 1% either side of the mid price. 

Depth = E(depthUp+depthDown)/2

Where depthUp is the total volume that would be required to move the price by 1% upwards from the mid price, and

depthDown is the total volume that would be required to move the price by 1% downwards from the mid price.”

 

Top 10 crypto exchanges according CryptoCompare quality benchmark:

  1. Coinbase 
  2. Poloniex 
  3. Bitstamp 
  4. bitFlyer 
  5. Liquid
  6.  itBit 
  7. Kraken 
  8. Binance 
  9. Gemini 
  10. Bithumb 

 

Cointelligence Rating System

Cointelligence is the most qualitative rating of crypto exchanges from the above. The methodology of the team was to manaully open accounts on all analyzed crypto exchanges and check from the user perspective the core aspects of beeing an exchange customer. The aspects cover:

Usability – covers KYC process, the quality of exchange website, extent of features and how easy it is to get a human answer from support staff. 

Performance – functionalities and historical robustness of exchange matching engine, fees height, trading instruments like futures contracts and margin trading.

Team – analysis of the available information about management team behind the crypto exchange, especially business and technical experience of C-level staff, including person responsible for exchange’s security

Risk – information on past hacks, insurance status, account security layers but also regulatory status of cryptocurrency exchange. Based on the geographical location of the exchange headquarters and registration any potential run-ins with the local law or any sign of authorities involvement.

 

This way Contelligence analyzed 85 crypto exchanges, but only 15 is rated with good quality mark, lead by Liquid and Gemini. 

Top 10 cryptocurrency exchanges by Cointelligence by qualitative criteria 

  1. Liquid (Quoine)
  2. Gemini
  3. Binance
  4. Bitstamp
  5. Gibraltar Blockchain Exchange
  6. OKEx
  7. Bittrex
  8. itBit
  9. Kraken
  10. ABCC

Bitwise report for SEC

Bitwise analysis is based on detecting wash trading patterns in public marked data published by crypto exchanges. Out of 81 exchanges they have analyzed in March 2019 only 10 were identified as be free of wash trading practices. These exchanges are:

  1. Binance
  2. Bitfinex
  3. Kraken
  4. Bitstamp
  5. Coinbase
  6. bitFlyer
  7. Gemini
  8. itBit
  9. Bitrex
  10. Poloniex

Bitwise identified that only 4,5% (about $275M daily) of officially reported volume (eg by the public sources like coinmarketcap) is the actual volume. The rest is wash traded.

The Bitcoin market is more orderly and efficient than is commonly understood. The 10 exchanges trade as a uniform, highly connected market. They form a singular price. Average deviations from the aggregate price for the ten exchanges is well within the expected arbitrage band when you account for exchange-level fees (~30 basis points), volatility and hedging costs. Arbitrage is operating well. Sustained deviations (defined as deviations >1% that last more than 100 seconds) appear as single white lines on the graph below. The graph demonstrates that the ten exchanges trade at a single unified price.

So although the message about the amount of wash traded volume is alarming, the report shows that the real crypto market is quite concentrated, ordered, efficient and well performing. The rest is just noise.

 

 

Read more about our tool for measuring crypto exchange quality – Liquidity Analytics Dashboard

 

 

Blockchain meetup sponsored by Empirica, Wroclaw

Monday June 19th a beautiful sunny day in IT-friendly Wroclaw, tech start-ups and cryptocurrency enthusiast gather together at IT corner Tech meetup, sponsored by Empirica.

The event was planned to focus on key areas of current trends in Blockchain and Ethereum.

The event began with Mr Wojciech Rokosz, Ardeo CEO presentation. The session was dedicated to introduction to the economics of token. Explaining the new changes and updates we are and we will face in our economy with this huge entrance of virtual currencies.

The event later carried on with Mr Marek Kotewicz on introduction to Blockchain, Bitcoin and Ethereum. The session was summarizing the differences between Bitcoin and Ethereum.

The third and last part of the event was conducted with Mr Tomek Drwga, Blockchain meetup organizer,  diving deeper into smart contracts and programming ( introduction to Solidity) for Ethereum.

The event ended with open discussion between the audience and speakers, and visitors were served with beverages.

Empirica is a Wrocław-based company that supports many local IT initiatives. Empirica is offering solutions such as an Algo Trading Software implemented by major institutional investors in Poland, market makers software, wealth management system framework, cryptocurrency trading bots and trading software development services for companies from capital and cryptocurrency markets.

 

The WEALTHTECH Book: The FinTech Handbook for Investors, Entrepreneurs and Finance Visionaries

CEO at Empirica S.A. was a Co-Author of The WealthTech Book published in March 2018 by Wiley. He wrote a brilliant article on Robo-Advisors, which was placed in Chapter 67 – link to the release below ↓ https://lnkd.in/dUmfPt4

New York Intensive Business Journey | Consensus 2019

During our last visit in New York, we held multiple business meetings with our partners and potential clients, which led to kick-start some new, exciting algo-trading projects.

We had been spreading word about our flagship products – Algorithmic Trading Engine, Liquidity Engine and the newborn baby – Liquidity Analytics Dashboard for crypto markets. Making use of every spare hour, we participated in different industry events connected with crypto trading and blockchain.

You might have met Empirica’s Vice-President and Co-Funder Piotr Stawiński on conferences and meetups such as NYC Crypto Mondays, various Blockchain Week events or Consensus 2019.

Empirica is a Poland based company that supports many local IT initiatives. Empirica is offering solutions such as an Algo Trading Software implemented by major institutional investors in Poland, market makers software, wealth management system framework, cryptocurrency trading bots and trading software development services for companies from capital and cryptocurrency markets.

Hummingbot

Hummingbot short review 2019

Hummingbot is a software client that allows users to create and customize automated, algorithmic trading bots for making markets on both centralized and decentralized crypto asset exchanges.

Hummingbot was launched to the public on April 04, 2019. Its main features include a command line-based user interface, trading strategy configuration and trade execution. 

Functionality HummingBot
Installation Self-driven with Docker images. Needs to know Docker if one wants to use a server. No Docker support in case of problems. 
User Experience – GUI All done from config files and a command line. No straightway to visualise the current strategy output. More than one instance hard to manage. 
User Experience – Parameters modification Modification of the strategy’s parameters requires a restart of the strategy.
Reference price  A midpoint. 
Inventory management Automatic size adjustments, manual pricing adjustments. 
Risk management No parameters related to risk management. 
Performance – Technology behind Written in Python, scripting programming language (one of the slowest)
Performance – Price validation Time interval based orders price validation
Connectors Centralised:  Binance, Coinbase, Huobi Global, Bittrex International.

Decentralised: DDEX, IDEX, Radar Relay, 0x Relayers

Summary Humming Bot is set for individuals acting as market-makers on illiquid markets. 

 

Vendor website: hummingbot.io

 

VWAP Algorithm

VWAP

Description

Volume-Weighted Average Price (VWAP) is a trading algorithm based on pre-computed schedule which is used in an execution of a bigger order without excessive impact on the market price.

The schedule is a heart of a strategy. To compute it strategy first should look in a historical data. As an input user defines n the Number of Intervals and delay between each two of them. This gives us a partition of period since Start Time until End Time into intervals I_1, \ldots I_n. Using the historical data strategy have to estimate how big fraction of a volume traded between Start Time and End Time is traded in each time interval – this values are denoted by u_j for each time interval I_j.

Notice that the following holds:

  • \sum_{j = 1}^{n} u_j = 1
  • u_j \geq 0 for each value of j \in \{1, 2, \ldots n\}

On a base of the above considerations strategy can estimate the size of a trade which should be traded in the end of each time interval to minimize market price strategy’s self-impact. For a i-th time interval it is defined as follows:

x_j = u_j \times Target~quantity.

Larger market participation does more impact on a market asset’s price weighted by volume, which is expressed by formula:

VWAP = \frac{\sum_i v_i p_i}{\sum_i v_i},

where i is every trade. Therefore strategy tries to keep steady market participation in each of intervals.

If, defined above, predictions of volume fractions in each interval are proper then the algorithm works perfectly, otherwise it can cause huge impact on a market price. To prevent this bad situation more advance versions of this algorithm takes into the account also actual volume and modify their schedule to fit the market conditions.

Market Data

Volume-Weighted Average Price known as VWAP is one the most basic and commonly used market indicators by traders around the world. In a book “Algorithmic & Trading DMA” we can read about VWAP that  “As a benchmark, it rapidly became ubiquitous since it gives a fair reflection of market conditions throughout the day and is simple to calculate. This led to algorithms that tracked the VWAP benchmark becoming extremely popular.”

  • Last trade
  • Best quote
  • Order book
  • Statistics
  • Historical market data

Parameters

PARAMETER NAME DESCRIPTION ESSENTIAL
Target Quantity Overall quantity to be realized by strategy Yes
Number of Intervals Number of intervals to be used Yes
Delay Table Delay times between following orders Yes
Start Time Time when strategy begins to submit orders Yes
End Time Time when strategy stops working No
Side Market side for orders Yes

Conditions

Open position

Side Defined by Side parameter
Amount Proper step size (x_j defined in the Description)
Price Last market price
Type Limit or Market Order

Strategy opens positions every time the delay value is reached.

Close position

Strategy does not close its opened positions.

Termination

Strategy ends when the declared orders’ quantity has been realized. By design around defined End Time.

Time frame

This strategy is dedicated to be used in short period of time like one day.

 

Calculations

 

Calculation of VWAP it’s relatively simple and it can be done even on piece of paper for small amount of data. In mathematical approach VWAP is represented by equation below:

 

wzor 1.jpg

 

where P is the price of i-th trade and V is the size related to i-th trade. In fact it takes five steps to calculate your first VWAP. First, only if we use intraday data for examination, we need to calculate typical price for our intervals. Then multiply the price by period’s volume and create running total of these values for future trades. Fourthly we create cumulative volume and in the end we divide cumulative multiplication of price and volume by running total of volume to obtain VWAP. Even simpler, VWAP is a turnover divided by total volume.

 

About empirica

We are trading software company focused on developing the potential that cryptocurrencies bring to financial markets. Empirica is offering solutions such as Algorithmic Trading Software used by professional investors and services of development of trading algorithmsRobo Advisory softwarecrypto trading bots and trading software development services for companies from capital and cryptocurrency markets.

 

Let’s take a look at example results calculated using these five steps on 1-minute interval intraday Morgan Stanley’s data.

Time Close High Low Open Volume Typical Price Price*Volume Total PV Total Volume VWAP
09:30:00 38.90 38.96 38.90 38.96 69550 38.93 2707581.50 2707581.50 69550.00 38.930
09:31:00 38.94 38.97 38.86 38.92 27617 38.92 1074922.68 3782504.18 97167.00 38.928
09:32:00 38.91 38.96 38.91 38.94 11441 38.93 445398.13 4227902.31 108608.00 38.928
09:33:00 38.89 38.94 38.88 38.92 23587 38.91 917710.61 5145612.93 132195.00 38.924
09:34:00 38.90 38.94 38.90 38.90 10771 38.91 419099.61 5564712.54 142966.00 38.923
09:35:00 38.97 38.97 38.90 38.90 12721 38.93 495276.23 6059988.77 155687.00 38.924
09:36:00 38.92 38.96 38.92 38.96 16471 38.94 641384.86 6701373.63 172158.00 38.926
09:37:00 38.90 38.93 38.86 38.93 23788 38.91 925472.14 7626845.77 195946.00 38.923
09:38:00 38.90 38.92 38.89 38.89 9170 38.90 356701.54 7983547.30 205116.00 38.922
09:39:00 38.92 38.92 38.88 38.91 4644 38.91 180682.02 8164229.32 209760.00 38.922
09:40:00 38.90 38.92 38.88 38.91 4917 38.90 191283.59 8355512.92 214677.00 38.921

All calculations are pretty straightforward, but let us take a look at one interesting element. When you look at typical prices more than half of them (7/11) is below the last VWAP At the same time mean equals 38.917. So where does the difference come from? Volume is the culprit. In our case, period with higher typical price also has bigger Volume, thus bigger market impact and VWAP calculations indicate that.

 

Intraday or tick

 

The most classical VWAP approach is based on tick-by-tick data. But as the market grows and frequency of trades increases more resources are required to keep all calculations up-to-date. Nowadays it is nothing extraordinary for stock to have over hundred trades per minute (true or false?). When multiplied by minutes in a trading day and number of stocks it develops into numbers that might cause some performance troubles.

 

With help arrives intraday data, i.e. tick-by-tick data aggregated in time periods e.g. 1-minute, 5-minute or 15-minute that contains close, high, low and open price. As in VWAP calculations only one price is required we have to somehow average available prices. For this task exist typical price:  

 

typical

 

Also there is a second version of typical price that includes Open Price and it’s divided by 4.

 

Strategy

 

Most likely we can point out two different strategies of reading VWAP. First one used especially by short-term traders relies on waiting for VWAP to cross above the market price and then enter long position as they interpret price to be bullish. On the other hand are Institutions looking to sell at this moment because they consider it as good opportunity for that day’s price.

 

When the price goes below VWAP value, the trend seems to be down. Institutions recognize it as good moment to buy, but short-term trader will look to short that stock.   

 

Surely it’s basic approach to VWAP interpretation. For your strategy you would like to scrutinize e.g. influence of price deviation from VWAP value. You should consider that VWAP behaves differently based on period of trading day. It’s because of VWAP cumulative nature. VWAP value is very sensitive for price changes at the beginning of day, but insensitive at the end of trading day.

 

Big Fish

 

VWAP is surely commonly used between traders with strategies described above, but on the market there is a bunch of various indicators like VWAP that can suggest when to buy or sell shares. But there is other side of the fence.

 

Let’s say you want to buy 5 million shares of Morgan Stanley that is 37% of average daily volume in 2014. You cannot buy them at once, because that will impact significantly the market and the market will start to go against you. What you want to do is split the order in small pieces and execute them without impacting the market. Doing it by hand would be backbreaking, that’s what trading application has been made for.

 

Using trading application and VWAP Strategy, utilizing historical minute intraday files, you can easily generate average volume period profiles that will steadily buy proper number of shares without impacting the market.

 

Improve your VWAP

 

As we mentioned in previous paragraph there is a way to improve VWAP performance by creating volume profiles based on historical data. According to Kissel, Malamut and Glantz optimal trading strategy to meet VWAP benchmark can obtained by using equation:

 

wzor 3.jpg

 

where X is the total volume traded, uj is percentage of daily volume traded and xj is target quantity for each j-th period. Hence, VWAP can be calculated as below:

 

wzor 4

 

wherePj is the average price level in each period.

 

Read more on how we develop trading algorithms for capital and cryptocurrency markets

 

Summary

 

VWAP is really simple indicator although it can be interpreted in various ways depending on goal and approach of the trader. It is mainly used by mutual and pension funds, but also by short-term traders. Aside from buying/selling small amount of shares, VWAP might be used as strategy for trading  huge number of shares without impacting the market. “Simplicity leads to popularity.”

 

References

  1. Berkowitz, S., D. Logue, and E. Noser. “The Total Cost of Transactions on the NYSE.”Journal of Finance,41 (1988), pp.97-112.
  2. H. Kent Baker, Greg Filbeck. “Portfolio Theory of Management” (2013) , pp.421
  3. Barry Johnson “Algorithmic & Trading DMA – An introduction to direct access trading strategies” (2010), pp. 123-126

 

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