News about Empirica, algorithmic trading and software development.

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). 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
  •       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. 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 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.

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 software platforms 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, 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.

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 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.

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 presented ‘ways of implementing Robo-Advisors’ at Fintech Trends

On behalf of Empirica, Hanif Nezhad talked about the different ways Robo Advisors could be implemented and the lessons we have learnt from firms which have digitalized their wealth management operations

He used our Robo-Advisory platform to showcase the different ways possible to apply Robo-Advisors. Although the list of choices firms may have before going Robo is long, he used the major options available, such as model portfolio vs discretionary portfolio management, different ways to perform rebalancing and Empirica’s recommendation engine.

At the end of his presentation he showed how at Empirica we can improve advisor-customer relationship using our built-in house machine learning algorithm for Robo-Advisors.

The event was followed with a panel session where the three speakers discussed variety of topic related to Robo-Advisors such as differences among Robo-Advisors, the Robo-Advisor business in Europe and Poland. The panelists also talked about ETFs and why Robo-Advisors are a big fan of them. After the panel audiences could approach the speakers and ask their questions. The event was concluded with networking session.

Empirica was a proud sponsor of the event.

Empirica will take part in Robo-Advisors – Open Mic Night run by Fintech Trends

On behalf of Empirica, Hanif Nezhad will talk about the different ways Robo Advisors can be implemented and the lessons we have learnt from firms which have digitalized their wealth management operations.

We are also a proud sponsor of the event, so make sure you come and visit us there.

Organizations will be provided free entry, but in order to make sure you will have a seat – register here.

Algorithmic Crypto Trading: market specifics and strategy development | Marek Koza, Product Owner @ Empirica S.A.

Empirica’s employees are industry’s experts with perennial experience gained during multiple complex projects executed for our clients. Our Product Owner Marek Koza wrote an article for FXAlgo News about differences between traditional and crypto markets and took a closer look at a few algorithmic strategies that are currently effective in the crypto space.

Link to the article: https://lnkd.in/dFQayjR

The evolution in ERC20 and the era of ERC223

The ever evolving Ethereum Blockchain brought to Cryptocurrency and ICO investors a new gift, the ERC223 a new standard for tokens created on Ethereum. Up until now the newly created tokens on Ethereum public Blockchain should have followed the ERC20 convention. This by all means was a huge success and relief for both token owners and the investors for that token. ERC20 makes sure the behavior of the token on Ethereum is standard with a defined common list of rules. The ERC223 is an improvement of ERC20 protocol, and is backwards compatible to ERC20, meaning every wallet and software that supports ERC20 does work with ERC223. So to get a better picture from this improvement, maybe its better to breakdown how these tokens are created and lets start with the ERC20 tokens.

ERC20 and how does such tokens are created? 

ERC20 protocol allows token owners and developers to create a token that complies with common, essential behavioral rules. The standard is now very popular, specially among ICO investors and their communities. Thanks to ERC20 investors can be certain that the following statement can be true if the token is ERC20:

  • Technically tokens can be accepted by almost all exchanges
  • Tokens are transferable, and all Ether wallets will automatically store the newly created tokens
  • Transactions using that token is done smoothly

A token is compliance with ERC20 if the developer of the token contract implement the following interfaces:

  • The token name with function name, it returns the name of the token.
  • The token symbol with function symbol, it returns the symbol that token will use.
  • The token decimal places, function that returns the unit8 decimals the token uses.
  • How much the owner want to start off with: function balanceOf, it returns the account balance.
  • The amount of tokens in circulation: function totalSupply, it returns the total token supply.
  • The transfer value: function transfer (address _to, unit256 _value), this function is in charge of the transfer events. the function should revert a transaction if the sending account _from does not have sufficient balance.
  • The transfer from: function transferFrom, this function is used for withdrawal workflow, it allows contracts on the Blockchain to transfer tokens on token holder behalf.
  • The crediting permission, function allowance (address _owner, address _spender), it returns the amount which the buyer (_spender) is allowed to withdraw from the owner (_owner).
  • The events: with function transfer (address indexed _from, address indexed _to, unit256 _value) its triggered when a token has been successfully transferred and function approval (address indexed _owner, address indexed _spender, unit 256 _value) this must trigger on any successful call.

 

What did ERC223 has added to ERC20 and what are the advantages?

Initially the idea of ERC223 came to play when the amount of lost tokens on Ethereum Blockchain went sky rocketing, this was due to lack of possibility to handle incoming transactions. Ethereum Blockchain is a leading network for number of lost tokens. Top 8 ERC20 contracts with losses will come up to approximately 3 million USD worth of tokens. how does this happen? once an ERC20 token is sent to a contract that is not designed to work with that ERC20 tokens, the contract will not reject the tokens because the contract does not recognize an incoming transaction. Consequently the token will get stuck the that contract balance. ERC223 will allow users to only send their tokens to either wallet or contracts with the same transfer function, this way it prevents the loosing of the token. ERC223 introduces the function transfer (address _to, unit _value, bytes _data). This function transfers tokens with invoking the function tokenFallback in _to, only if _to is a contract. This will allow the smart contract to actively handle sent tokens. Whereas when an ERC20 token is transferred, the token contract is not notifying the receiver that the transfer has occurred, to that end the address receiver has no possibility to handle the incoming transaction and therefore no way to reject not supported tokens.

A seamless token transfer is another advantage of ERC223 over ERC20. An ERC20 transaction between a regular (not a contract) and contract are two different transactions. There two functions need to be triggered, first the approve function on the token contract and latter the transferForm on the other contract (the receiver). ERC223 has addressed this more efficiently by allowing to use the same transfer function. ERC223 could be sent by only calling the transfer function on the token contract with no if the receiver is a regular address of a wallet or a contract. Due to this shortcut another advantage that ERC223 has is the gas cost, ERC233 consumes almost half as much as an ERC token.

So as discussed above ERC223 advantages over ERC20 comes down to the following points:

  • provides a possibility to prevent accidentally losing tokens
  • Allows users to transfer tokens anywhere (owned address or contract) using one function
  • allows contract developers to manage incoming transactions, contract developers could implement contract in a way that only works with some specific tokens incoming and handling them in a specific way which could also each tokens could be handled in a specific way.
  • ERC223 consumes almost half gas as ERC20

Currently is not possible to upgrade existing ERC20 token contract to ERC223, but if you are planning to create your own maybe its a good idea to go with ERC223.