Articles related to FinTech (Financial Technologies)

Machine Learning in Finance

It’s known that almost all industries are influenced or about to be influenced by the appliance of Artificial Intelligence. Perhaps operational efficiency is what makes Artificial Intelligence so attractive for business owners across different sectors. Operational efficiency could lead to the reduction of costs, increased performance, speed up some processes or increase the quality of services.

In this article, we would like to cover the appliance of Machine Learning across the financial industry by presenting interesting use cases and examples to structure this content. 

Artificial Intelligence is assisting financial institutions to drive new efficiencies and deliver new kinds of value. Autonomous Research predicts that Artificial Intelligence will represent $1 trillion in projected cost savings for the banking and financial services industry. By 2030, traditional financial institutions will save 22% of costs.

Let’s get something straight here and let’s define Machine Learning vs Artificial Intelligence. These two terms are always used side by side of each other, but they are different. With different, we mean that Machine Learning is a subset of Artificial Intelligence. Artificial Intelligence refers to create intelligent machines. Machine Learning refers to a system that can learn from experience. In this article, we may mention both but with the given simplistic definition you already know what we are referring to.

Machine Learning layers

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Challenges of applying Artificial Intelligence in finance

Before jumping into the use cases that we have gathered for this article, let’s take a look at the most common challenges of applying Artificial Intelligence in firms within the financial industry. 

As previously mentioned costs and budgeting required to automate some of the processes in finance could be one of the main important challenges financial firms face. Additionally, regulatory requirements sometimes could be also a burden. They are complex frameworks and the required research phase could be time-consuming and hiring regulatory consultants could be costly.

Perhaps another challenge these firms face is the lack of structured or sufficient data to process and train their data and test if models are efficient enough. Adding to that lack of in-house skills and knowledge as well as missing the development environment (lab) that data scientists can join and apply approaches of AI. 

Another challenge could also come from market maturity and retail readiness to use Artificial Intelligence-powered tools.  

Case 1. Fraud detection 

One of the very important appliances of Machine Learning in finance is fraud detection. With the advent of instant payment and global transfer services, the volume of payments and transfers has dramatically increased. So is a notable amount of transfers that aren’t with good intentions including money laundering. The estimated amount of money laundered globally in one year is 2-5% of global GDP, or $800 billion to $2trillion.  

An advantage that Machine Learning has brought to fraud detection is the amount of data that can be processed by machines with minimum or zero human intervention. The appliance comes with more accuracy in the detection of fraudulent activities. 

For instance, the credit card fraud detection problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be a fraud. This model is then used to identify whether a new transaction is fraudulent or not. The aim here is to detect fraudulent transactions while minimizing incorrect fraud classifications. Anomaly detection is a commonly used model for the credit card fraud detection problem, this is a technique used to identify unusual patterns that do not conform to expected behavior, called outliers. Anomaly detection or pattern recognition algorithms start with creating processes that find the hidden correlation between each user behavior and classify the likelihood of fraudulent activity.  

Discovering hidden and indirect correlations can be named as advantages that Machine Learning based algorithms bring compare to basic Rule-based fraud detection algorithms. Machine Learning-based algorithms also reduce the number of verification measures since the intelligent algorithms fit with the behavior analytics of users. A more automated approach to detection of fraud is also another asset for Fraud Detection algorithms applied by Machine Learning since they require less manual work to enumerate all possible detection rules. 

Case 2. Know Your Customer (KYC)

Improving the KYC process is one of the operational efficiency of artificial intelligence and machine learning algorithms that have been brought to the financial and banking industry. The appliance of Machine Learning on the KYC process is mostly implemented by traditional banks and neobanks. The main reason is the continuous evolvement of requirements from regulators. The due diligence required on customer registration that is required by regulators in banking is broad and complex. Machine Learning due diligence modules can be utilized to create robust automation and improve the process of KYC for institutions that are aiming to have an efficient retail onboarding. This will decrease the human intervention needed during the onboarding process and increases the accuracy as well as reducing the costs. 

Again neobanks are streamlining the KYC process with enhanced user interface and user experience. Simplifying and automating the KYC process can reduce the cost of onboarding and customer application process by 40% (source Thomson Reuters). 

One of Machine learning techniques used in the KYC process is the Facial Similarity check, which is to verify that the face in the picture is the same with that on the submitted document provided e.g. Identity Card. The customer will only be verified and pass the KYC process if the results of both Document and Facial Similarity checks are ‘clear’. If the result of any check is not ‘clear’, the customer has to submit all the photos again.

Case 3. Algorithmic Trading

The algorithmic trading with a technological infrastructure brought many advantages to the trading world e.g. the ability to trade in under a millisecond with the best prices available or the ability to simultaneously monitor and trade across multiple exchanges, and all with reducing the human error from trading. Algorithmic trading constitutes 50-70% of the equity market trades and 60% of futures trades in developed markets. 

Many hedge funds started to utilize Artificial Intelligence within the algorithmic trading world. It’s understandable that most of them do not disclose the details and mechanism of their approaches in applying Artificial Intelligence in their trading algorithms, but it’s understood that they use methods of Machine Learning and Deep Learning. There is also a wide appliance of sentiment analysis on the market in which the result can be used in trading. The main objective of applying sentiment algorithms is to obtain knowledge about the psychology of the market. 

Machine Learning is assisting the trading industry in order to leverage the market with fundamental and alternative data in order to research alpha factors. Supervised, unsupervised and reinforcement learning models are being utilized to enhance the processing of algorithmic trading strategies. Methods can be applied to optimize portfolio risk calculations and further improve the performance of the portfolios. 

Deep Learning models also have been widely applied in trading. Deep learning models with multiple layers have shown as a promising architecture that can be more suitable for predicting financial time series data. In a tested practice, the algorithm trains 5-layer Deep Learning Network on high-frequency data of Apple’s stock price, and their trading strategy based on the Deep Learning produces 81% successful trade and a 66% of directional accuracy.

Case 4. Chatbots and customer support

Reducing customer churn is perhaps one of the main criteria of financial institutions and banks. Generally, customers and especially millennials ones for neobanks do care about the customer service and support they receive. Chatbots and instant messaging apps could potentially increase communication quality between business and its customers. According to research by Juniper by the year 2023, the use of chatbots can reduce the operational costs for banking, retail, and healthcare business sector by $11 billion. 

The advantages that chatbots can bring to the industry are definitely increasing customer satisfaction and customer engagement rates. The speed of action and processing of many inquiries and threads at the same time could also be mentioned as a big advantage of chatbots and messaging systems. 

There are four types that Chatbots could be classified. Goal-based Chatbots, are designed for a particular task and set up to have a short conversation in order to complete a task given to them by a user. Perhaps this is the most common Chatbot. Goal-based Chatbots are deployed on websites to help visitors to answer their questions during their visit. 

The second type is knowledge-based Chatbots, these Chatbots use the underlying data sources or the amount of data they are trained on. Such data sources could be open-domain or cloud domain data. Usually, Knowledge-based Chatbots answer questions by providing the data and source of that data. 

The third category of Chatbots is Serviced-based. Such Chatbots are classified based on facilities provided to the customer. It could be personal or commercial information. Users of such Chatbots could place an order of a commercial good via the Chatbot. 

The fourth category is Response-generated Chatbots. Response Generated-based chatbots are developed based on what action they perform in response generation. The response models take input and output in natural language text. The dialogue manager is responsible for combining response models together. To generate a response, the dialogue manager follows three steps.

Types-of-chatbots

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Case 5. Automated Wealth Management with Robo Advisors

Wealth management is an industry and operation costs are a large burden for some of the firms. As wealth being transferred from one generation to a more tech-savvy one and considering the millennials are in their prime earning and spending years, the presence of automated and entirely digital investment advice tools can be expected. It’s expected that by the year 2022 the Robo Advisors revenue might reach 25 billion that is up from $1.7 billion from 2017, considering these tools are relatively cheaper to how the investment advice is being delivered traditionally, from 3% to 5% of assets managed to digital ones with 0.25% to 0.75%. 

There are practiced used cases where the processes of asset allocation modeling, portfolio construction, and optimization, as well as a portfolio recommendation systems, were bundled with Machine Learning techniques in order to enhance the current approaches. 

One example is the portfolio recommendation system that was designed to be implemented on top of a Robo Advisor and be utilized with the mean-variance optimization method was implemented using weighted linear regression. The model shows that adding a portfolio layer on top of the stock regression results is increasing the success rate (profit accuracy) up to 86.69% when success is calculated by the profitability of the recommendations. Moreover, it helps to reduce the risk by distributing the budget over a set of stocks and tries to minimize the reflection of the regression errors to the profit.

Robo Advisors come with many notable advantages, such a complete, online and real-time reporting dashboard to customers which can be checked on the go with mobile apps and dashboards. 

As previously mentioned they reduce the costs of operations for firms providing investment advice service and the fees that are clients charged. Robo Advisors are fully digital and they have online onboarding for clients which leads to expansion of client base for firms.

When can you apply AI (is your firm ready?)

There are few aspects to which we could measure the readiness of a firm to utilize Artificial Intelligence and Machine learning into their processes. A solid technological infrastructure is the most important element. An infrastructure that is put together to manage the whole lifecycle of data, from getting to cleaning to processing and feeding algorithms. The availability of the data can not be stressed more. 

The regulatory compliance as mentioned in some of the cases above e.g. in KYC processes is a crucial process to be taken care of before applying Artificial Intelligence into processes. Audit trails, transparency, result supervision, and reporting mechanisms are some of the high-level requirements from financial authorities. 

Talents as resources from data engineers to data scientists specialized and familiar with financial processes is another important criteria before kick-starting with Artificial Intelligence projects. Their ability to understand the sector and ways to improve it should be taken into account in their hiring process. Eventually, they need to start training the existing data with an accuracy level as a requirement for the models used. 

How to start your machine learning project?

  • Start with a question 

Before anything starts you need to start with the question, what is it that we want to improve with our Machine Learning algorithms? This should specify and clarify the objective of the project. 

  • Understand your data

Not every question can be answered with any data. You need to have the right data for the right question. This is practiced by receiving, cleaning and processing data. Running exploratory analysis on your data and making sense of some of the summaries obtained could be the initial stage to which you will know that if your data has the potential to answer your questions. 

  • Modeling

Once you found clues in your data associated with your question it’s time to try to write algorithms to find patterns that leads to successful or unsuccessful journeys. Usually, data scientists do this by fitting the most suitable Machine Learning models into the data, find correlative and statistically significant patterns and try to test the accuracy. 

  • Evaluation

In the modeling section, we talked about training your data and once we have found the best models that suit the question, the answer and the data and now its time to evaluate, in other words, test your models. Data scientists will keep on testing the models with new data to see if their models do not only work for one dataset. 

  • Deployment

Once the fitting algorithms are certain that works, it’s time to deploy. Generally, this means deploying a code representation of the model into an operating system to score or categorize new unseen data as it arises and to create a mechanism for the use of that new information in the solution of the original business problem.  Importantly, the code representation must also include all the data preparation steps leading up to modeling so that the model will treat new raw data in the same manner as during model development.

 

Guide to Algorithmic Trading and Quant Funds’ Profitability

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!

 

 

Empirica among innovative companies at the Trading CEE conference

The “Trading CEE: Equities and Derivatives” conference is one of the most important financial industry related events in Central and Eastern Europe. The co-organizers of the event were the Warsaw Stock Exchange, the Global Investor Group and the National Depository for Securities. Michał Różański, CEO of Empirica took part in a panel devoted to the future of the fintech industry.

The Trading CEE was held in Warsaw’s Hilton hotel, where several hundred capital markets experts had the opportunity to talk about such important issues as the Mifid II regulation, or the scale of the fintech revolution in Poland and internationally.

They also discussed the decision made recently by FTSE Russell (the supplier of indices belonging to the London Stock Exchange group) to change the status of Poland from that of an Emerging Market into that of a Developed Market and considered the significance of this shift for the national economy.

Among many of the excellent speakers, we had the change to listen to Marek Dietl, President of the Warsaw Stock Exchange and Toby Webb, Head of EMEA Information Services FTSE Russell. The inaugural panel on the opportunities and threats facing investment markets in our region gathered such experts as Ales Ipavec, head of the stock exchange in Ljubljana, Richard Vegh from the Budapest Stock Exchange, Ivan Takev, head of the Bulgarian Stock Exchange and Head of International Sales of the Moscow Stock Exchange Tom O ‘ Brien.

Fintech Innovation Forum

The panel regarding the fintech industry was very popular among visitors, especially the topic of the development of tools based on artificial intelligence and their impact on investment markets in Poland. It was organized in such a way as to allow for 4 of the most promising Central & Eastern European companies in the modern financial technologies industry to present what they offer. One of the main participants of this part of the Trading CEE conference was Michał Różański, CEO and founder of Empirica, the fintech software house.

During his speech, he focused mainly on the presentation of innovations in the field of robo-advisors, which are already revolutionizing the global investment market.

– The robo-advisor platform is not only the future, but the present of wealth and asset management. Our Empirica Robo Advisor service stands out in the international market above all through its very high level of support for advisors in their work with the service’s users. All this is thanks to solutions in the field of AI analytics, which allows them to receive a full picture of the actions taken in the user profile and to quickly respond if these actions threaten the assets, which in the end also reduces the risk of losing the customer. Another important element of our consulting service is the fact that we have built it based on the strong foundations of our platform for Algo Trading. Thanks to it, our robo-solution has fully automated access to the data stream coming from the most important financial institutions at every stage of the Empirica Robo Advisor process. – explains Michał Różański.

New generation of users

Platforms from the robo-advisor category not only democratize investment opportunities, but also reduce the price of consultancy services. In an era of technological revolution, a millennial generation is slowly entering the capital market- people accustomed to continuous presence in the online world. Advisory platforms will enable it for them. Friendly user interfaces, notifications that they know from social media and an automated transaction system based on a personalized portfolio are already present in the fintech area. However, in order for these tools to function in such a complicated environment as the financial market, powerful computational engines based on artificial intelligence (AI) must be behind them. Empirica helps financial companies enter this world by providing an advisory platform that automates the asset management processes and is based on innovative solutions in the field of data processing. – adds the CEO of Empirica.

 

Empirica is a Wrocław-based company that offers 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.

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.

 

3Commas – A technical review

As we know, over the past several years, we have witnessed a real computer revolution. We have practically all available solutions replacing us with computers. These are already such advanced technologies that are already able to make a decision for us, and what’s more, they do it faster and more efficiently than man. It is particularly visible in trading, where several years ago all decisions were made by man. Now Traders are equipped in computer programs who are able to do all the work. However, the market is flooding with information on how many new programmes have been hiring by financial institutions recently. But what about us with retail traders? How should we deal with this situation? It remains for us either programming learning or uses trading bots (free/paid) from the Internet. There are really many of them when you looking for information on the web. That’s why I decided to check 3Commas in this short article. One of many users and additionally paid TradingBots. Let’s have a look at one of them – 3Commas. They were started in 2014, there are over 120,000 users currently being served with transaction volume in the tune of $60 million being handled every day, supported 23 exchanges- data from 3Commas website. You can trade on all exchanges from one single interface from 3commas’ window. Up to date, they support Bittrex, Bitfinex, Binance, KuCoin and Poloniex, Bitstamp, HitBTC, Cex, GDAX, OKEX, Huobi, YOBIT.

 

How well do 3commas trading bots work?

 

On the website, we can read that: “3commas is a cryptocurrency trading bot that provides a wide range of tools and services for users to choose from. It performs real-time market analysis using powerful algorithms for getting you the best trades possible”. Sounds interesting? Is this the right place to find a solution for retail traders? 3Commas offer a few types of trading bots: Simple, Composite, Short, Composite short. You can choose which one you want it depends on your individual approach to the market. At the moment available is almost 90 trading bots. Does quantity mean quality?       

Browsing information about bots, I wonder why the best strategies work only 30 days. How to trust this kind of bots with short history (just 30 days history)? How do I find out how it behaves with high market volatility? I don’t know. I couldn’t find this kind of information on the 3Commas website. For institutional investors or professional retail investors, this kind of question is fundamental. If you invest money you should know how much you can earn at what possibility of loss. That’s why it’s better for your wallet, to wait for a strategy with a long history to know what to expect.

Can I make a profit on real market with 3Commas?

 

Let’s see, how 3commas trading bots work. As a retail trader, I would like to try one of these 90 strategies. I choose for my example one “Simple Long Strategy” and I opened Paper Account. Pairs: USD_BTC, USDT_LINK, USD_LTC. Target profit 5%. On 3Commas website we can read the short description: “Simple Long Strategy gives you the possibility to make price increases”- information from 3Commas website. It looks simple to buy a lower price and sell higher price. The bot opens new deal according to one of the conditions that are available for selection during the creation. After that, it immediately puts a coin for sale. If the price rises and the order gets filled, the profit goal is achieved. In case of a price fall, the bot places safety orders below the purchase price every X%. Every filled safety order is averaging the buy price, and it makes possible to move the TakeProfit target lower and close the deal without losing profits in the first price bounce. 

My strategy has been worked for 14 days. Completed 15 orders and give me $0.16 profit ($10.000 balance). Strategy performance results and statistics below.  

3comma trading list

3comma statistics technical review

 

3 comma trading view technical review

 

Whether the profit is big or small I leave the answer to you. The rate of return is positive (+0,16$), therefore we should be satisfied (really  ?). My “New Bot” did not lose money. Of course, everything was happening on the real market but money was virtual. You should also know that is possible to change strategies parameters at any time and can adapt it to your current needs but I did not do that because left my 100% decisions to the bot. 

The main purpose of trading bots is to automate things which are either too complex, time-consuming, or difficult for users to carry out manually. Good trading bots can save a trader time and money by collecting data faster, placing orders faster and calculating next moves faster. In my case, I just set the parameters and Trading Bot did the rest but is it enough to tell that the strategy is good? Please rate it yourself.  Meanwhile on the market situation looks very interesting for my example (charts below). The market moved up, how I expected. As you can see from the charts below I could earn more money in this period of time. 

3comma trading review

3comma tradingview

You also need to know that 3Commas is not for free. They have four subscription plans: Junior from €0 (your total balance across all accounts is $750 and no bots), Starter €24 (without limits for trading, no bots), Advanced €41 (Simple bots), Pro €84 (Simple, Composite Bots). The interesting thing is that you don’t know how much you can earn but you immediately know how much you have to pay!! Profits are potential but costs are fixed.

How safe is 3Commas?

3Commas don’t go into too many details regarding the security protocols that they choose to employ, however, it’s worth remembering that you don’t actually hold any funds on the platform and your trading bots are not able to make withdrawals from your linked accounts.

Similar to other trading bot platforms, your trading bots connect with your exchange accounts via API and then proceed to carry out automated trades on your linked exchanges. While this process takes place, users aren’t required to make any cash/crypto transfers to external accounts and simply need to provide their API keys which are generated by their exchanges.

These keys provide the trading bots with restricted access to user accounts strictly to conduct trades and do not grant the bots with any withdrawal rights. This also means that if your account becomes compromised, and some hackers were able to gain control of your trading activity, they still wouldn’t be able to directly access your exchange accounts in order to make withdrawals. However, the standard personal security rules of crypto still apply, as they could still have a detrimental effect on the funds held in your exchange accounts. Hackers have been known to obtain API access to exchange accounts, and commander the bots to purchase high quantities of low-value coins that the hackers have already previously purchased. After artificially inflating both the demand and price of said coins, the hackers then sell off their personal holdings for a profit, leaving the compromised account owners holding funds in the low-value coins.

 

3Commas has made a positive impression. It is also worth mentioning about Key Features:

  1. Technology – Automated trading takes place via API integration with cryptocurrency exchanges and the bot works around the clock with any device and users can access their trading dashboard on desktop and laptop computers. The team have also developed mobile apps for both Android and iOS
  2. Tools – The platform provides a good range of trading tools and in addition to the automated bots and performance analytics, users are able to create, analyze and back-test crypto portfolios and monitor the best performing portfolios created by other users. In addition, users can engage in social trading and follow and copy the actions of other successful traders.
  3. Functionality- 3Commas utilises a web-based platform, and features an easy to use and intuitive user interface that includes a wide range of functions and detailed analytics. Users can make use of short, simple, composite, and composite short bots, and set stop loss and take profit targets, as well as customise their own trading strategies.

Strong points of 3 Commas Bot Platform

  1. Emotionless, fact-based trades make sure that decisions taken are taken entirely based on the ideal conditions with little room for doubt, instinct, and human error. This reduces the intensity of the decision-making process and helps to take logical and high-profit decisions.
  2. Good exchange connections.
  3. The Smart Trading option that makes use of ‘trailing take profit’ keeps the user away from a loss when trading. Since it is designed to stay in the loop and adapt itself to the market, it is an intelligent solution to make as much as possible with a trade.
  4. Easy to set up for beginners, making sure that newcomers can navigate the 3Commas bot and make trades without any hassles.
  5. A well-laid-out dashboard and visualization of data allow the users to keep track of everything that is happening while boosting their appeal and ease of use.
  6. The free access offers a great trial so that users can make full use of the platform.
  7. A large number of exchange offers a wide array of information centres, making sure that your decision is well thought out with multiple inputs.
  8. The fact that users can refer and copy portfolios of successful traders.

Weak points of 3 Commas Bot Platform

  1. Security protocols are not explained with great clarity, raising concerns about whether the trades are truly secure. Users can, of course, enable the 2-factor verification for additional security, but the fact that not much is said about it leaves room for concern.
  2. The plans change regularly and might prove to be a bit confusing to say the least with 3Comms’ paid plans, commission plans, and a mix of both.
  3. The balance has to be filled up for commission, which may be a hindrance for many users.

Using trading bots for trading makes life easier. It can save traders a lot of time but will give it earn real money? Popular trading bots available to individual investors (regardless of whether paid or free) have one basic problem, namely the speed of response to changing market conditions, as well as the speed of placing and sending orders. This is not their strength. You will not find any information about latency, what is the maximum number of orders that can be sent  per second. Using low latancy software will give you advantage on the market over retail bot users. Therefore, institutional investors have an edge on the market.

But retail bots are good place to start education on how automation on the markets can work. 

Cryptohopper – Technical Review 2019

There is quite a hot market for cryptocurrency trading platforms and algorithmic trading bots. New crypto traders and active traders from capital markets are pouring in funds into algorithmic strategies and bots to make the most out of the constant opportunistic cryptocurrency fluctuation. On the other hand trading platform, providers and investment bots are tailoring their strategies to be tuned well to different scenarios depending on the type of events occurring within the market.
Due to the expansion of development from these trading bots and their adaptability to different events, the process of choosing one has become quite challenging, even for those with technical and trading background, hence at Empirica, we decided to bring knowledge about professional crypto trading bots to interested readers and traders and our selected bot for this article is Cryptohopper.

In this review, we will cover relative features included in Cryptohopper trading platform. We analyze ways that traders can utilize Cryptohopper for their trades. We also take a look at their tool from a technical point of view (our team at Empirica has been focused on the institutional algorithmic trading platform and market making algorithms for almost 10 years). Later in the review, we will also take a look at options we believe Cryptohopper lacks. But first and foremost:

What is Cryptohopper?

Cryptohopper is an retail algorithmic trading platform with a series of configurable trading features (more on professional algorithmic trading platform). Cryptohopper’s platform is shaped around 5 key elements, which each have been developed further to meet the needs of traders. The 5 key elements are:

  • Mirror trading
    This feature allows investors to copy the trades of experienced and successful forex investors. Strategies are available through a marketplace, some free and some paid.
  • Paper trading
    A simulated trading practice to assess trading algorithms with real and live data.
  • Strategy designer
    A technical indicator assembler which lets traders design their strategies using the listed indicators. There are currently somewhere around 130 technical indicators provided by Cryptohopper.
  • Algorithmic trading
    An automated way of executing trading algorithms with a specified set of configuration.
  • Trailing stop
    It’s a feature designed to stop strategies to operate if a defined trigger has been pulled.

 

Which exchanges are supported by Cryptohopper?

There are in total of 10 exchanges that are supported by Cryptohopper. Exchanges are KuCoin, Bitvavo, Binance, Coinbase pro, Bitterex, Poloniex, Kraken, Huobi, Bitfinex and Binance.US. 

 

How can I trade with Cryptohopper?

Depending on your sophistication level and trading knowledge, Traders can utilize Cryptohopper platform to their use. There are two bots, the market-making and Arbitrage bots and there are also strategies that can be used to select a set of indicators to form a strategy.

 

Market Making Bot

The market making bot is designed for retail investors (check market making bot for professional users). It is designed to perform liquidity provision to the market of traders’ choice. The market making bot is a configurable algorithm that executes buy and/or sell (take and/or make) by placing a layered limit of buy and sell orders. 

To initiate using the market making bot, traders must go through the preliminary configurations. Starts with choosing an exchange and setting up the API keys. Even using the API the fund still will be located in the exchange and in order to trade on the exchange, traders need to generate an API key and then connect that to their Cryptohopper account. 

After the initial configuration, there is also a set of more advanced Market Making configuration. Market and Pricing is the second stage of Market Making setup at Cryptohopper. This stage includes configuration of the market and which pair trader is interested in. Then moving on to the strategy setup with market trends. Market trends are either uptrend, downtrend or it could stay as neutral. Additionally, the order sequence of buying and selling with a given sequence, the order layer which represents the tiered buy and sell orders that are going to be placed and the moving on to the amount constraints within layers (e.g. buy amount, higher ask and percentage lower bid).

Cryptohopper Trading Bot Review

 

The Cryptohopper Market Making bot is also equipped with an “Auto-cancel” functionality which based on the configuration determines when to open and close positions. There is also a time limit to trigger the cancel on the bot. Seemingly the most important feature of the Auto Cancel is the Cancel on the trend, which enables auto cancelling on the bot when the marker changes to a direction e.g. from neutral to a downtrend or from neutral to uptrend and etc. Cancellation on the bot could also be triggered with percentage change, this only happens if the market has a certain specified percentage change or within a given period. The auto-cancel feature also works with the depth limit, which Traders can set from a minimum of 1 to a maximum of 500. Additionally, Traders that are interested in Cryptohopper Market Making bot can set their “Stop-loss” settings. Stop-loss can be triggered in the event of a turn in the market. 

Cryptohopper market making bot also provides a revert and backlog feature, where it can move all the failed orders to the Traders’ backlog. Traders can also revert all their cancelled orders from the backlog if Traders decide to revert back a failed market maker orders and re-execute the orders. There are many more settings on reverting back orders that can be automated with configuration, to name of the settings, only revert if it will lead to a profit, or revert/not revert with market trends such as neutral trend, downtrend or uptrend.

In order to slow down the market making bot, Cryptohopper introduced the cool down feature, which the bots cooldowns by removing the order after a certain time has passed.

Cryptohopper has designed a dashboard with some widgets for Traders to monitor the market making bot in action. There is a trading view widget which is a visual representation of the current prices.

Cryptohopper Trading Bot Review

Among other widgets available on the Cryptohopper dashboard, there is the order book visualizations with the possibility of manual Market Making which enables buying and selling to be connected to each other and will input that order into the Market Making bot logs.

Cryptohoppe also has created an inventory for all failed trades to be stored in a place called backlog. In order for Traders to be able to use the Cryptohopper Market Making bot they need to be subscribed to the “hero hopper Pro” package, which costs a monthly subscription fee of 99$.

 

The Arbitrage bot:

The Arbitrage bot of Cryptohopper is designed to capitalize from changes across different markets. The bot allows to trade discrepancies in the market, taking advantage in market price between the same pairs on different exchanges.

Just like the market making bot, the Arbitrage bot also requires a pre-setup procedure to get going with the bot. The procedure starts with setting up the maximum open time of all buy orders, which determined the number of minutes a buy order remains open before the order is cancelled. Following that, there is the maximum open time of all sell orders which does the same thing but for all sell orders.

The setting up procedure then takes traders to exchange setting, where traders should specify two exchanges that would like to perform their arbitrage. Afterwards, they will set the percentage sell amount, which it should use to create the amounts which are being traded and then the Arbitrage amount per market which how much of trade at a time should take place.

In case interested trader would like to utilize exchange specific configuration, they can set minimum profit that they would like arbitrage with. Additionally, there are options to have the maximum open time of the Arbitrage. Traders are also given the possibility to simultaneous arbitrages which determine the maximum number of simultaneous or concurrent arbitrages. Furthermore, they set rate on buy and sells which specify the amount the Arbitrage should check. 

The Arbitrage dashboard also includes a backlog where all failed trades will be stored. The dashboard also has the latest Arbitrage trades that were both successful and failed. There are also other widgets inside the Arbitrage dashboard, e.g. exchange arbitrage dashboard results, the last five trades and market Arbitrage results.

Strategies:

Traders using Cryptohopper platform could create a trading strategy with a collection of indicators they have selected. These are the indicators to buy and sell trades. Cryptohopper has created a strategy designer feature where traders create and custom their strategies. There are three ways trades can utilize a strategy. First is to use Market Strategies, these are strategies bought on the Marketplace (we cover features of Marketplace later in this article). Strategies bought from Marketplace which could also be automatically be updated every time the seller of the strategy makes changes on the strategy. Second is built-in strategies, Cyrptohopper offers a set of built-in strategies that are offered free of charge. These are rather basic strategies such as uptrend strategies, buy the dip strategies, Bollinger strategies and etc. Third and last is My strategies, these are custom made strategies that traders built.

Strategy designer:

Strategy designer is a place where traders can personalize their technical analysis setting. There are given a set of indicators where traders can find and configure a wide selection of trading indicators. Traders on cryptohopper can decide on the chart period, buy and sell signals and candle period when selecting an indicator. With candle patterns, traders can directly respond to price movements from the chart data of an exchange. 

Furthermore, traders can design their strategies by adding a JSON code, this section is designed for more technical and programmer traders. These traders could also modify existing strategies. Once strategies are configured and up and running, Cryptohopper strategy dashboard allows traders to monitor their strategies. 

Cryptohopper Marketplace:

The marketplace is a section within the strategy creation process. This unit is designed solely for social and mirror trading. This is where traders with a usually lower level of experience and knowledge in trading can browse already created strategies and use it for their funds to invest with.

There is a set of strategies and templates available in the marketplace. Each template and strategy has a corresponding base-currency and exchange. Therefore templates can be chosen based on traders preferences. Additionally, all templates have information about their ratings, total downloads, modifications and recentness. 

The marketplace also consists of Signalers. All signals in the marketplace correspond to an exchange. A trader can configure their trading using only signals. The Signal configuration could limit orders. The setting also allows traders to take profit with a given percentage set.

 

Strategy statistics:

Cryptohopper provides traders with a set of statistics in order for traders to be able to monitor the performance of their strategies and trades. There is a variety of ways provided in the statistics dashboard to see how trades and strategies are performing. 

The time period for all buy and sell order, allocation of funds based on currency, open positions and base currency reserved. Traders can view their profit stats basing on currency invested on, base currency returns, the base currency gained/lost in current positions and trading fees paid. 

Profit based on sell triggers is another statistic available for traders to monitor profit related to percentage profit, trailing stop loss and auto close within time. Traders can also view profits based on buy triggers that we generated by strategies, signalers, trailing stop buy.

Cryptohopper Academy

For traders who would like to be familiar with Cryptohopper as a trading platform, there is a tutorial-like instruction supported by Cryptohopper itself and other instructors can also use this academy portal to provide education knowledge to interested traders.

 

Our take:

Cryptohopper has done a decent job working out a tool that traders would feel comfortable doing their trades. We really liked the interface and how they have designed a user journey that would fit a different type of traders with different level of expertise. The wording in the platform is well explanatory and hints around important features. Though, as a solution provider for professional crypto market makers, we believe the assessment of market trends are done manually by users and very sensitive to human error. The market making bot has a low ability to manage more market at once and needs of content human supervision.

Check our take on how trading bots for professional crypocurrency traders are build and designed.

Do traditional exchanges see Blockchain as an opportunity?

Distributed ledgers technology also known as Blockchain, offers a new way to data management and sharing that is being used to propose solving many inefficiencies affecting the financial industry. Technology experts, Fintech start-ups, banks and market infrastructure providers are working on underlying technologies and its potential use in the industry. However the journey of such transformation may take long. In this post we will focus on the benefits and architectural changes Blockchain could bring to capital market, and some example from such appliances across exchanges around the world.

The potential benefits of Blockchain technologies could cover different process within different stages in capital markets. In order to expose why capital markets would pursue to Blockchain technologies its worth taking a look at the benefits across pre-trade, trade, post-trade and security servicing.

Pre-trade:

Blockchain technology will establish more transparency on verification of holdings. Additionally it reduces the credit exposure and making Know-your-customer way simpler.

Trade:

For this stage, Blockchain technologies provide a more secure, real-time transaction matching and a prompt irrevocable settlement. Blockchain could also help automating the reporting and more transparent supervision for market authorities, we could add higher standards for anti-money laundering.

Post-trade:

In this regard it eliminates the demand for central clearing for real time cash transactions, reducing collateral requirements. Blockchain technology enables quicker novation and effective post-trade processing.

Securities and custody servicing:

Distributed asset ledgers with flat accounting structures could remove some of the role which custodians and sub-custodians play today. Custodians’ function might change to that of a ‘keeper of the keys’, managing holdings data and ensuring automatic securities servicing operations are done correctly. To that end we could also add advantages such as common reference data, simplification of fun servicing, accounting, allocation and administration.

Nasdaq has become the forefront of blockchain revolution, they have and are currently involved with many blockchain jobs. To name these endeavors, it started with Nasdaq Linq blockchain ledger technology. Linq is the primary platform in a recognized financial services firm to show how asset trading could be managed digitally through the usage of blockchain-based platforms. Nasdaq has continued more to blockchain, showing that, it is working to develop a trial utilizing the Nasdaq OMX Tallinn Stock Exchange in Estonia which will discover blockchain technology being used as a way to reduce obstacles preventing investors by engaging in shareholder voting. The intention is to boost efficiency in the processing of purchases and sales of fund units and also to make a device ledger — a place which currently is primarily characterized by manual patterns, longterm cycles and newspaper driven processes.

Read more about Nasdaq activities in Blockchain here.

London Stock Exchange developed to simplify the tracking and management of shareholding information, the new system plans to make a distributed shared registry comprising a list of all shareholder trades, helping to open up new opportunities for investing and trading.

Read more about LSE and IBM activities in Blockchain here.

Australian Securities Exchange (ASX), is all about the replacement of this system that underpins post-trade procedures of Australia’s money equity marketplace, known as CHESS (the Clearing House Electronic Subregister System). ASX is working on a prototype of a post-trade platform for the cash equity market using Blockchain. This initial phase of work was completed in mid-2016. In December 2017 ASX completed its own analysis and assessment of the technology which included:

  • Comprehensive functional testing of the critical clearing and settlement functions currently performed by CHESS
  • Comprehensive non-functional testing (scalability, security and performance requirements) for a replacement system when deployed in a permissioned private network
  • A broad industry engagement process to capture users input on the desired features and functions of a replacement solution
  • Third party security reviews of the Digital Asset DLT based system.

Read more about ASX procedure here.

The Korea Exchange (KRX), South Korea’s sole securities market operator, has established a new service where equity shares of startup businesses may be traded on the open marketplace. The Coinstack platform will offer record and authentication options for your KSM by checking against client references which have already been provided to the platform by Korean banks such as JB Bank, KISA, Lottecard, Paygate in addition to others.

Deutsche Börse Group has developed a theory for riskless transfer of commercial bank funding through an infrastructure based on distributed ledger technology. By combining blockchain technology using its proven post-trade infrastructure, Deutsche Börse aims to achieve efficiencies while at exactly the same time investigating possible new business opportunities enabled by this technology.

Read more about Deutsche Börse Group activities in Blockchain here.

Japan Exchange Group: IBM had teamed up with Japan Exchange Group, which works the Tokyo market, to begin experimenting with blockchain technology for clearing and other operations. IBM says it expect the technology will reduce the cost, complexity and speed of settlement and trading procedures.

About us

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