News about Empirica, algorithmic trading and software development.

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.

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.

 

 

A brief guide to cryptocurrency exchanges

With a rapidly growing interest among technologist as well as trader towards cryptocurrencies, we have been writing a series of posts about them. In this post we will be covering cryptocurrency exchanges and point out their characteristics, and hopefully at the end of this post you may get an idea on which crypotocurrency exchange to do your trades.

Generally there are many doubts and question marks around how reliable cryptocurrency exchanges are. There has been a lot of rumors and news also around governments getting involved and closing down cryptocurrency exchanges, we heard that in South Korea  the governments is going to raid the cryptocurrency exchanges operating in the country and shut them down. If you are curious about that story, one of the officials from the government called that an “unrealistic move”. nevertheless in recent times we have heard numerous speculations about cryptocurrency world which never came to life.

The purpose of this post is to assess the most known and used cryptocurrency exchanges. We have chosen arguably the top rated exchanges, basing on fees applied, how safe the exchange is, if liquidity in the exchange is high or not, the possible pairs and currencies to trade with  USD, Euros or crypto with crypto and so on. The list we have gathered is narrowed with qualities indicated above.

Coinbase

Coinbase is one the most known and used exchange for Cryptocurrencies with up to 10 million users. Coinbase was founded in 2012 and is California based Crypto exchange for cryptocurrencies like Bitcoin, Ethereum, Litcoin, Ripple and etc. After introducing GDAX, Coinbase also aimed more sophisticated traders with a more powerful tool. Coinbase is also available for mobile users. Fees charged are around 0.25%.

Bitfinex

Bitfinex is a Hong Kong based cryptocurrency exchange, specialized for trading Bitcoin and Altcoins. About fees, Bitfinex does have very low fees of 0.2% and for those who instead place trades in the order book will pay only 0.1%. Bitfinex is also available for traders to trade using mobile app. Bitfinex offers a variety of order types. For automating the trades Bitfinex also has provided an API feature for third-party softwares to integrate.

Coinmama

Coinmana is a well-known, Israeli based Bitcoin exchanges which traders could purchase Bitcoin using creadit/debit cards. The fees in Coinmama are about 6%, relatively high among other exchanges. Though Coinmama does not require traders to provide or upload their know your customer (KYC) documents.

Kraken

Kraken known as one of the largest Bitcoin exchanges. Kraken’s users can trade Bitcoin using Canadian dollars, US dollars, British Pounds and Japanese yen. Kraken is in Euro volume and liquidity. Kraken was founded in 211 by Jesse Powel, Kraken is also known for low transaction fees ranging from 0% to 0.26% depending on the account tier and the type of the transaction(buy/sell).

Gemini

Gemini is a US based exchange mainly focused on Bitcoin, US dollars and Ethereum. Gemini was founded in 2015 by Winklevoss twins (same brothers who claimed Mark Zuckerberg stole the idea of Facebook from them). Gemini’s users can deposit Bitcoin, Ether and make bank and wire transfer free of charge. In regard to trading fee, Gemini set to charge 0.25% for sellers and buyers. Gemini is referred to as the safest cryptocurrency exchange out there.

More on cryptocurrency exchanges:

Exchange

 

Estimated traffic

 

users

 

Fees

 

Tokens traded

 

Coinbase

 

109M

 

10.1M

 

0.25%

 

Bitcoin, Litecoin, Ethereum, Bitcoin Cash, Ethereum Classic

 

Bitterex

 

85M

 

5.6M

 

0.25%

 

Bitcon, Ubiq, Litecoin, Blackcoin, Dash, Ethereum, Gambit, Gridcoin

 

Bitfinex

 

 

36.5M

 

2.9M

 

0.20% Bitcoin, Ethereum, Ripple, Litecoin, Bitcoin Cash, EOS, NEO, Iota, Ethereum Classic, Monero, Dash, Zcash, OmiseGO and more
Kraken

 

22.6M

 

2.9M

 

0 to 0.26%

 

Bitcoin, Ethereum, Litecoin, Gnosis, EOS, Dogecoin, Tether, Melon, Zcash, Augur tokens, Iconomi, Stellar, Ethereum classic, Ripple, Monero, Dash

 

Okex

 

 

3.5M

 

350K

 

0.20% to 0.25% CommerceBlock, Revain, Bitcoin, Chatcoin, Gifto, Zipper, Ethereum, Zencash and more
Gdax

 

46M

 

4.5M

 

0.25%

 

Bitcoin, Bitcoin Cash, Litecoin, Ethereum
CEX

 

10.8M

 

1.6m

 

3.9%

 

Bitcoin, Ethereum, Bitcoin Cash, Litcoin
Gemini

 

3.4M

 

111K

 

0.25%

 

Bitcoin, Ethereum
Coinmama

 

999K

 

33.4K

 

6%

 

Bitcoin, Ethereum

 

Bitcoin and Arbitrage: hand in hand

How to use Sharpe Ratio?

The Sharpe Ratio is well known measure of portfolio performance. It is a ratio which allows to compare various portfolios and allows to measure theirs profitability. The mathematical definition is described as a division between expected rate of return subtracted by risk free rate of return and standard deviation of rates of return [6].

In practice the ratio is calculated annually:

sharpesratio

 

The ratio describes how much excess return you are receiving for the extra volatility that you endure for holding a riskier asset ([1]). In simple words, the higher Sharpe Ratio is, the portfolio is more profitable.

 

Why it is so popular?

 

Most traders are still using the Sharpe Ratio because of its simplicity and ease of interpretation. It was proposed by Sharpe in 1966 and became credible to most users due to Nobel Prize in Economics for his works on Capital Asset Pricing Model ([5]).

 

Properties of the Sharpe Ratio

 

With reference to [1], the ratio has several properties. It is immune to manipulation by leverage. It can be interpreted as a T-statistics to test the hypothesis (see [8]) that the return on the portfolio is equal to the risk free rate of return.  The higher ratio is consistent with a higher probability that the portfolio return will exceed the risk- free return. The investor who is using the ratio has a utility (see [7]) whose only arguments are expectation and variance of returns.

 

Forgotten assumptions

 

Unfortunately, most traders forget that the Sharpe Ratio doesn’t apply for every set of data. The main assumption of the ratio is that the distribution of the returns is normal. The financial market should be frictionless (without financial costs) and the risk free rate of return should be constant and identical for lending and borrowing. Also data used for computation should contain initial capital of the portfolio.

Consequences of the ratio

Despite the fact that it is really hard to obtain normality of returns on every possible data, the Sharpe Ratio has other disadvantages which are represented from [1].

The ratio does not quantify the value added and it’s only a ranking criterion. It has hard interpretation when the value is negative. The Sharpe ratio does not make any distinction between upside risk and downside risk.

In case of aggregation of portfolios, the correlation between volatilities is not included in the ratio. It is suitable for investors who invest in only one fund.

It doesn’t refer to a benchmark. The choice of risk free rate is rather important, though the impact is rather week. The result highly depends on the initial capital of the portfolio.

The sampling error of standard deviation is embedded in the values of the ratio. By using standard deviation of returns, the Sharpe measure puts both positive and negative variations from the average on the same level. But most investors are only afraid of negative variations.

As a consequence, the Sharpe Ratio leads to inappropriate results on particular sets of data.

 

When the ratio fails

 

To be aware of how important are the assumptions, consider the following example [*]:

In the case when the expected return is negative, the Sharpe Ratio gives inappropriate answer. The Sharpe Ratio from Instrument A should be greater than the Sharpe Ratio from Instrument B.

INVESTMENT A SHARPE RATIO
TOTAL RETURN -10.00% -1.5
RISK FREE RATE 5.00% NUMERATOR -15.00%
STANDATD DEVIATION 10.00% DENOMINATOR 10.00%
INVESTMENT B SHARPE RATIO
TOTAL RETURN -10.00% -0.92
RISK FREE RATE 5.00% NUMERATOR -15.00%
STANDATD DEVIATION 16.25% DENOMINATOR 16.25%

 

Solution to Negative Returns

 

In the case of negative returns, the Israelsen modification of a Sharpe Ratio gives appropriate results. This ratio could be used as a verifying measure of a standard Sharpe Ratio (the values are meaningless but the order remains accurate).

The Israelsen modification of the Sharpe Ratio from a previous example provides that Instrument A is greater than the Sharpe Ratio from Instrument B.

INVESTMENT A SHARPE RATIO ISRAELSEN
TOTAL RETURN -10.00% -0.015
RISK FREE RATE 5.00% NUMERATOR -15.00%
STANDATD DEVIATION 10.00% DENOMINATOR 10.00%
INVESTMENT B SHARPE RATIO ISRAELSEN
TOTAL RETURN -10.00% -0.024
RISK FREE RATE 5.00% NUMERATOR -15.00%
STANDATD DEVIATION 16.25% DENOMINATOR 16.25%

As a conclusion, in the case when the negative returns occurred, the investor should also check the value of the Israelsen modification before making the decision.

 

Avoiding invalid distribution

 

The most dangerous usage of the ratio is when the distribution of the returns isn’t normal. It seems that the best idea is to check normality of the returns before use of the Sharpe Ratio. There are several statistical test that can validate data’s distribution such us: Sharpiro Wilk, Anderson Darling, Cramer von Misses, Dagostino Pearson, Jarque Berra, Kolmogorov Smirnov, Kolmogorov Lilliefors, Shapiro Francia (see [9]).

 

Generalizing Sharpe Ratio

 

On the other hand there are different extensions of the Sharpe Ratio, which gives more accurate results by extending different assumptions. There are plenty of them described in [1], but only these which contain skewness and kurtosis will be presented.

Skewness describes asymmetry of the probability distribution. If there are more extreme returns extending to the right tail of a distribution it is said to be positively skewed and if they are more returns extending to the left it is said to be negatively skewed [2].

Kurtosis provides additional information about the shape of a return distribution. Formally it measures the weight of returns in the tails of the distribution relative to standard deviation but is more often associated as a measure of flatness or peakedness of the return distribution [2].

Skewness and Kustosis are normalized forms of 3rd and 4th central moments respectively (see [10]). Its combination with the Sharpe Ratio allows to approximate the ratio when the distribution is not normal. This method leads to the proposition from [4], where skewness divided by kurtosis is added to the Sharpe Ratio which results a new performance measure.

In the case when the owner of the portfolio has different preference (different utility – see [7]), there are other ratios to measure the performance. The Adjusted for Skewness Sharpe Ratios, gives the opportunity to measure the performance using hyperbolic absolute risk aversion (HARA), constant relative risk aversion (CARA) or constant absolute risk aversion (CRRA). More details can be found in [3].

 

Conclusion

 

The Sharpe Ratio is a standard measure for portfolio performance. Due to simplicity and ease of interpretation, it is one of the most popular indexes. Unfortunately, most users forget the assumptions what results inappropriate outcome. They should consider checking the distribution of the returns or validating the results with other performance measures before making a decision on the market.

 

References

 

[1] Cogneau P., Hübner G., The 101 ways to measure portfolio performance, Université de Liège

[2] Bacon C., How Sharp is The Sharpe Ratio? – Risk-Adjusted Performance Measures, StatPro Group

[3] Zakamouline V., Koekebakker S., Portfolio Performance Evaluation with Generalized

Sharpe Ratios: Beyond the Mean and Variance, University of Agder, 2008

[4] Watanabe Y., Is Sharpe Ratio Still Effective?, Journal of Performance Measurement, 2006

[*] The original example is taken from the site:

http://allaboutalpha.com/blog/2009/09/02/alternative-viewpoints-using-the-modified-sharpe-information-ratios/

[5] http://www.investopedia.com/articles/07/sharpe_ratio.asp

[6] https://en.wikipedia.org/wiki/Sharpe_ratio

[7] https://en.wikipedia.org/wiki/Utility

[8] https://en.wikipedia.org/wiki/Statistical_hypothesis_testing

[9] https://en.wikipedia.org/wiki/Normality_test

[10] https://en.wikipedia.org/wiki/Central_moment

 

VWAP Algorithm

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.”

 

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.

 

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.

 

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

 

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