Articles related to algorithmic trading and software tools aiding automated investment operations.

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.

 

Algorithmic Trading – A complete guide

What is algorithmic trading?

 

Algorithmic Trading (also called quantitative or automated trading) in simple words describes the process of using computer programs to automate the process of trading (buying and selling) financial instruments (stocks, currencies, cryptocurrencies, derivatives). These computer programs are coded to trade based on the input that has been defined for them. Inputs could be based on the aimed strategy to take advantage of different market behaviors such as the specific change of a price could trigger the algorithms to make some specific trades, or other factors like volume, time or sophisticated algorithms that trade based on trading indicators.  

Algorithmic trading strategies and backtesting

 

Almost all trading ideas are first converted to a trading strategy and coded into an algorithm that then comes to life and ready for execution. Most algorithmic trading strategies are created on the basis of wide trading knowledge on the financial market combined with quantitative analysis and modeling, later the strategies are given to quants programmers who will convert the strategy to executable algorithms. 

 It is widely common to perform testing on trading strategies before they go live on the market, this practice is known as Backtesting. This is where the algorithm is being tested on historical data to check the algorithm and apply further modifications. 

The main idea behind Backtesting is to evaluate the performance of the algorithmic strategy to see if the strategy is behaving the way it was programmed and check the profitability of it using real market data. 

For more sophisticated algorithms and firms with more advanced tools, algorithmic strategies perform on what so-called paper trading, where the strategy performs virtual trading without committing any commercial value (trading without money). 

The most popular programming languages used to write automated trading strategies are JAVA, Python, and C++. Matlab is also a good tool with a wide range of analytic tools to plot and analyze algorithmic strategies.

Who uses algorithmic trading?

 

By far the most common fans of performing trades algorithmically are larger financial institutions as well as investment banks alongside Hedge Funds, pension funds, broker-dealer, market makers.

Some well-known algorithmic strategies

 

On a broad sense most commonly used algorithmic strategies are Momentum strategies, as the names indicate the algorithm start execution based on a given spike or given moment. The algorithm basically detects the moment (e.g spike) and executed by and sell order as to how it has been programmed. 

One another popular strategy is Mean-Reversion algorithmic strategy. This algorithm assumes that prices usually deviate back to its average. 

A more sophisticated type of algo trading is a market-making strategy, these algorithms are known as liquidity providers. Market Making strategies aim to supply buy and sell orders in order to fill the order book and make a certain instrument in a market more liquid. Market Making strategies are designed to capture the spread between buying and selling price and ultimately decrease the spread. 

Another advanced and complex algorithmic strategy is Arbitrage algorithms. These algorithms are designed to detect mispricing and spread inefficiencies among different markets. Basically, Arbitrage algorithms find the different prices among two different markets and buy or sell orders to take advantage of the price difference. 

Among big investment banks and hedge funds trading with high frequency is also a popular practice. A great deal of all trades executed globally is done with high-frequency trading. The main aim of high-frequency trading is to perform trades based on market behaviors as fast and as scalable as possible. Though, high-frequency trading requires solid and somewhat expensive infrastructure. Firms that would like to perform trading with high frequency need to collocate their servers that run the algorithm near the market they are executing to minimize the latency as much as possible.

Adaptive Shortfall

Adaptive Implementation Shortfall algorithm designed for reduction of market impact during executing large orders. It allows keeping trading plans with automatic reactions to price liquidity.

Basket Trading

Basket Orders is a strategy designed to automated parallel trading of many assets, balancing their share in the portfolio’s value.

Bollinger Band

Bollinger bands strategy is a trading algorithm that computes three bands – lower, middle and upper. When the middle band crosses one of the other from the proper side then some order is made.

CCI

Commodity channel index strategy is a trading algorithm which actions are dependent on the value of a CCI index which bases on average and variance of some number of last trades.

MACD

MACD strategy is a trading algorithm which actions are dependent on two lines of MACD and the MACD Signal Line calculated with EMA.

Market Close

The strategy is designed to reduce costs interrelated with the market impact of huge orders. It works until the demanded time and may take advantage of the auction on Market Close.

Parabolic SAR

Parabolic SAR strategy is a trading algorithm whose role is to predict market trend change and trade assets in specific market conditions.

POV

Percent of Volume (POV) is a trading algorithm based on volume used to the execution of bigger orders without excessive impact on the market price.

RSI

Relative strength index strategy is the trading algorithm which actions are dependent on the value of an RSI index which bases on average wins and losses of a strategy.

Slow Stochastic Oscillator

Slow Stochastic Oscillator Strategy is build to gain profit on buying/selling shares in specific market conditions.

Statistical Arbitrage

Statistical Arbitrage (SA) is build to gain profit on simultaneously buying and selling two shares of two correlated instruments.

TWAP

Time-Weighted Average Price (TWAP) is a trading algorithm based on the weighted average price used to the execution of bigger orders without excessive impact on the market price.

VWAP

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

Williams %R

Williams %R strategy is a trading algorithm basing on trend change indicated by Williams %R oscillator. Oscillator leads the strategy to set a long or short position.

Smart Order Routing

Technically Smart Order Routing technology will search for available liquidy across given trading venues, and with mid-point matching will get the best possible chance of price improvements.

Triangular Arbitrage 

Triangular Arbitrage is used when a trader would like to use the opportunity of exploiting the arbitrage opportunity from three different FX currencies or Cryptocurrencies. Triangular Arbitrage happens when there are different rates within the trading venue/s.

Algorithmic Trading Software

 

Based on the given use case like the size of orders, customizability and experience level there are options available for algo trading software. Larger firms like hedge funds, investment banks or proprietary trading firms use rather more tailored custom-built and advanced tools. When it comes to more individual traders or quants with less capital to trade they will rather use more readymade algorithmic strategies, some on the cloud, some stand-alone. 

The most common features of algorithmic trading software are ways to analyze profit/loss of an algorithm on a live market data. There are different protocols available to get, process and send orders from software to market, such as TCP/IP, webhooks, FIX and etc. One important factor for this data processing from receiving to processing and pushing order is measured with latency. Latency is the time-delay introduced to the movement of data from points to points. Considering the changes in price in the market the lower obtained latency the better software reacts to market events hence a faster reaction. 

Backtesting is another useful feature that should be included in algo trading software, usually, this software allows traders to apply their automated trading strategies and test it with historical data to evaluate the profitability of their strategies. 

Pros and cons of algorithmic trading

 

Just like any other choice, there are pros and cons to using algorithmic trading strategies and automating the process of trading. Let’s get down with the pros. Based on many expert opinions in investments human emotions could be toxic and faulty when it comes to trading, one perhaps most acknowledged pros of quantitative Trading is taking away human emotions and errors out of trading.

Another huge advantage of algorithmic trading is the increase of speed in action of execution to the market as well as possibilities to test strategies using Backtesting and paper-trading in a simulated manner. Testing quantitative strategies determine the viability of the idea behind trading strategies.

Another vastly discussed advantage of quantitative trading is risk diversification. Algorithmic trading allows traders to diversify themselves across man accounts, strategies or markets at any given time. The act of diversification will spread the risk of different market instruments and hedge them against their losing positions. 

Making trading automatically using quant trading decreases the operational costs of performing large volumes of trade in a short period of time. 

There are also a few other advantages such as automation in the allocation of assets, keeping a consistent discipline in trading and faster execution.

Now let’s get on with some of the cons of using algorithmic trading. Perhaps one very discussed issue with using algorithmic trading is constant monitoring of the strategies which to some traders could be a bit stressful since the human control in automated trading is much less. Though it is widely common to have lost control features included in strategies and algorithmic trading software (automated and manual ones). 

For most individual traders having enough resources could be another disadvantage of Algorithmic trading. The automated trading reduces the cost of executing large orders but it could come expensive as it requires initial infrastructure such as the software cost or the server cost.

 

Pros Cons
Emotionless trading Needs for monitoring 
Less error Technological infrastructure 
Higher trading speed Programming skills required for updating strategies
Backtesting and paper trading
Risk diversification 
Lower operational costs 
Consistent trading discipline 

 

Performance

 

There are different performance results depending on the basis on which an algorithmic trading strategy is built. Though, as an example, an algorithmically managed fund in 2018 (during which S&P500 index was 19.42% high) SH capital partners posted 234.09% returns. Over the same period, Silver8 Partners and Global Advisors Bitcoin Investment Fund achieved 770.75% and 330.08% returns respectively.  Both of these algorithmic trading examples are automatically traded but differ on specific strategies – however, both attribute their success to their Automatic trading winning strategies and their rationale on digital assets.

Even most profitable algorithms with reasonable levels of volatility (eg. a Sharpe ratio of 2+ and max drawdowns of <5–10%) have a limited shelf life because any Algo that produces consistent greater-than-market returns will suffer from alpha decay (the erosion of edge due to others getting in on the action).

In order to provide a better view of performance statistics, we have prepared results from Reinsseance hedge fund, see the table below:

The are some perfromance statistic from the Reinsseance trading firm:

 

Year  Net return Management fee Performance fee Returns before fees Size of the fund Medallion trading profits 
1988 9.0% 5% 20% 16.3% $20 million $3 million 
1989 -0.4% 5% 20% 1.0% $20 million $0
1990 55.0% 5% 20% 77.8% $30 million  $23 million
1991 39.4% 5% 20% 54% $42 million $23
1992 33.6% 5% 20% 47% $74 million $35 million
1993 39.1% 5% 20% 53.9% $122 million $66 million
1994 70.7% 5% 20% 93.4% $276 million $258 million
1995 38.3% 5% 20% 52.9% $462 million $244 million
1996 31.5% 5% 20% 44.4% $637 million $283 million
1997 21.2% 5% 20% 31.5% $829 million $261 million 
1998 41.7% 5% 20% 57.1% $1.1 billion $261 million
1999 24.5% 5% 20% 35.6% $1.54 billion  $549 million
2000 98.5% 5% 20% 128.1% $1.9 billion 2,434 million
2001 33.0% 5% 36% 56.6% $3.8 billion 2,149 million
2002 25.8% 5% 44% 51.1% $5.24 billion 2.676 billion
2003 21.9% 5% 44% 44.1% $5.09 billion $2.245 billion
2004 24.9% 5% 44% 49.5% $5.2 billion $2.572 billion
2005 29.5% 5% 44% 57.7% %5.2 billion $2.572 billion
2006 44.3% 5% 44% 84.1% $5.5 billion $4.374 billion
2007 73.7% 5% 44% 136.6 $5.2 billion $7.104 billion
2008 82.4% 5% 44% 152.1% $5.2 billion  $7.911 billion
2009 39.0% 5% 44% 74.6% $5.2 billion $3.881 billion
2010 29.4% 5% 44%  57.7% $5.2 billion $3.881 billion
2011 37.0% 5% 44% 71.1% $10 billion $7.107 billion
2012 29.0% 5% 44% 56.8% $10 billion $5.679 billion
2013 46.9% 5% 44% 88.8% $10 billion $8..875 billion
2014 39.2% 5% 44% 75.0% $9.5 billion 7.125 billion
2015 36.0% 5% 44% 69.3% $9.5 billion $6.582 billion
2016 35.6% 5% 44% 68.6% $9.5 billion $6.514 billion
2017 45.0% 5% 44% 85.4% $10 billion $8.536 billion 
2018 40.0% 5% 44% 76.4% $10 billion 7.643 billion

Source: The man who solved the market, how Jim Simons launched the quant revolution, by Gregory Zuckerman

The Reinsurance hedge fund in total achieved 39.1% net returns, 66.1% average returns before fees and in total $104,530,000,000 total trading profits. 

Algorithmic trading in Cryptocurrency

 

Unlike more mature instruments like stocks, options or CFDs, the Cryptocurrency market is quite volatile. Typically higher volatility leads to more frequent jumps in the price of instruments, higher and lower. Hence, some professional traders find this amusing and opportunistic to make the most of the profits.  

Generally, for Cryptocurrency traders, there are plenty of cloud-based solutions using trading bots, though for very professional and institutional traders this may not flexible enough. There are few automated trading platforms for cryptocurrencies which can utilize the need for more sophisticated and institutional traders. 

 

Quantitative Trading Trends

 

On average 80% of the daily traders across the US are done by algorithmic trading and machines. Though the volume of automated trading can change based on the volatility in the market. According to J.P. Morgan, fundamental discretionary traders are accounted for only 10% of trading volume in stocks. This is the traditional way of checking the companies business performance and their outlook before deciding whether to buy or sell a position. 

The growth in the number of algorithmic trading since last year comes close to 47% and there is 41% growth in the number of users executing their trades algorithmically. Mobile also plays an important role in the tools provided there is around 54% growth in trading FX algorithmically using mobile devices. 

New technologies, Artificial Intelligence, Machine Learning, Blockchain

 

According to another J.P. Morgan research, Artificial Intelligence and Machine learning are predicted to be the most influential for shaping the future of trading. Based on this analysis Artificial Intelligence and Machine Learning will influence the future of trading by 57% and 61% in the next three years.  

Interestingly this report states that Natural Language Processing alone will count to 5% of the change in the next 12 months and up to 9% in the next three years. 

J.P. Morgan’s report shows that around 68% of the traders believe that Artificial Intelligence and Machine Learning provide deep data analytics. Around 62% believe that Artificial Intelligence and Machine Learning optimize trade execution and 49% of traders believe that Artificial Intelligence and Machine Learning represents an opportunity to hone their trading decisions. 

The same report indicates that Blockchain within the next 12 months will influence the trading up to 9% and 19% within the next three years. Within the same report, the usage of mobile trading applications is to influence the trading market up to 28% within the next 12 months and 11% within the next 3 years. 

 

Market share

 

Morgan Stanley estimated in 2017 that algorithmic strategies have grown at 15% per year over the past six years and control about $1.5 trillion between hedge funds, mutual funds, and smart beta ETFs. Other reports suggest the quantitative hedge fund industry was about to exceed $1 trillion AUM, nearly doubling its size since 2010 amid outflows from traditional hedge funds. In contrast, total hedge fund industry capital hit $3.21 trillion according to the latest global Hedge Fund Research report.

Algorithmic crypto trading: market specifics and strategy development

By Marek Koza, Product Owner of Empirica’s Algo Trading Platform

Among trading professionals, interest in crypto-currency trading is steadily growing. At Empirica we see it by an increasing number of requests from trading companies, commonly associated with traditional markets, seeking algorithmic solutions for cryptocurrency trading or developing trading software with us from scratch. However, new crypto markets suffer from old and well-known problems. In this article, I try to indicate the main differences between traditional and crypto markets and take a closer look at a few algorithmic strategies (known as trading bots on crypto markets) that are currently effective in the crypto space. Differences between crypto and traditional markets constitute an interesting and deep subject in itself which is evolving quickly as
the pace of change in crypto is also quite fast. But here I only want to focus on algorithmic trading perspectives.

 

Read more about our tool for market making strategies for crypto exchanges  – Liquidity Engine

 

LEGISLATION

First, there is a lack of regulations in terms of algorithmic usage. Creating DMA algorithms on traditional markets requires a great deal of additional work to meet reporting, measure standards as well as limitations rules provided by regulators (e.g., EU MiFIDII or US RegAT). In most countries crypto exchanges have yet to be covered by legal restrictions. Nevertheless, exchanges provide their own internal rules and technical limitations which, in a significant way, restricts the possibility of algorithmic use, especially in HFT field. This is crucial for market-making activities which now requires separated deals with trading venues.

 

DERIVATIVES

As for market-making, we should notice an almost non-existent derivatives market in the cryptoworld. Even if a few exchanges offer futures and options, they only apply to a few of the most popular cryptocurrencies. Combining it with highly limited margin trading possibilities and none of index derivatives (contracts which reflect wide market pricing), we see that many hedging strategies are almost impossible to execute and may only exist as a form of spot arbitrage.

 

 

 

 

 

 

 

 

 

 

 

 

As for market-making, we should notice an almost non-existent derivatives market in the cryptoworld. Even if a few exchanges offer futures and options, they only apply to a few of the most popular cryptocurrencies. Combining it with highly limited margin trading possibilities and none of index derivatives (contracts which reflect wide market pricing), we see that many hedging strategies are almost impossible to execute and may only exist as a form of spot arbitrage.

 

DECENTRALIZATION

The above-mentioned facts are slightly compensated for by the biggest advantage of blockchain currencies – fast and direct transfers around the world without banks intermediation. With cryptoexchange APIs mostly allowing automation of withdrawal requests, it opens up new possibilities for algorithmic asset allocation by much smaller firms than the biggest investment banks. This is important due to two things. Firstly, there is still no one-stop market brokerage solution we know from traditional markets. Secondly, cryptocurrencies trading is distributed among many exchanges around the world. It could therefore be tricky for liquidity seekers and heavy volume execution. It implies there is still much to do for execution algorithms such as smart order routing.

 

CONNECTIVITY

A smart order routing strategy GUI

Another difference is direct market access for algorithmic trading. While on traditional markets DMA is costly, cryptocurrency exchange systems provide open APIs for all their customers that may be used without upfront prerequisites. Although adopted protocols are usually easy to implement, they are often too simplistic. They do not usually offer advanced order types. Besides, order life-cycle status following is cumbersome and trading protocols differ among exchanges since each one requires its own implementation logic. That makes a costly technical difference compared to traditional markets with common standards, including FIX protocol.

 

MARKET DATA

Fast, precise and up-to-date data are crucial from an algorithmic trading perspective. When a trader develops algorithms for cryptocurrencies, she should be aware of a few differences. APIs provided by crypto-exchanges give easy access to time & sales or level II market data for everyone for free. Unfortunately, data protocols used in the crypto space are unreliable and trading venue systems often introduce glitches and disconnections. Moreover, not every exchange supports automatic updates and an algorithm has to issue a request every time it needs to check on the state of a market, which is difficult to reconcile with algorithmic strategies.

The APIs of most exchanges allow downloading of historical time & sale data, which is important in the algorithmic developing process. However, historical level II data are not offered by exchanges. We should also notice that despite being immature, the systems of crypto trading venues are evolving and becoming more and more professional. This forces trading systems to follow and adapt to these changes, which adds big costs to systems’ maintenance. In the following sections I overview a few trading algorithms that are currently popular among crypto algo traders because of the differences between traditional and crypto markets listed above.

 

SMART ORDER ROUTING

Liquidity is and most probably will remain, one of the biggest challenges for cryptocurrency trading. Trading on bitcoin and etherium and all other altcoins with smaller market capitalisation, is split among over 200 different exchanges. Executing a larger volume on any type of assets often requires seeking liquidity on more than one trading venue. To achieve that, cryptocurrency traders may apply smart order routing strategies. These follow limit order books for the same instrument from different exchanges and aggregates them internally. When an investment decision is made, the strategy splits the order among exchanges that offer best prices for the instrument. A well-designed strategy will also manage partially filled orders left in the order book in case some volume disappears before the order has arrived at the market. This strategy could be combined with other execution strategies such as TWAP or VWAP.

Empirica algorithmic trading platform front-end app (TradePad) for crypto-markets.

 

ARBITRAGE

The days when simple crossexchange arbitrage was profitable with manual execution are over. Nowadays price differences among exchanges for the most actively trading crypto-assets, are much smaller than a year ago and transactional and transfer costs (especially for fiat) still remain at a high level. Trading professionals are now focused towards using more sophisticated arbitrage algorithms such as maker-taker or triangular arbitrage. The former works by quoting a buy order on one exchange, based on VWAP for a particular amount of volume from another exchange (the same instrument) decreased by expected fees and return. A strategy is actively moving quoted order and if the passive gets executed, it sends a closing order to the other exchange. As the arbitrage is looking for bid-bid and ask-ask difference and maker fees are often lower, this type of arbitrage strategy is more cost-effective.

Triangular arbitrage may be executed on a single exchange because it is looking for differences among three currency pairs which are connected to each other. To illustrate, let us use this strategy with BTCUSD, ETHUSD and ETHBTC pairs. This strategy keeps following order books of these three instruments. The goal is to find the inefficient quoting and execute trades on three instruments simultaneously. To understand this process, we should notice that ratio between BTCUSD and ETHBTC should reflect the ETHUSD market rate. Contrary to some FX crosses, all cryptocurrency pairs are priced independently. This creates numerous possibilities of using triangular arbitrage in crypto space.

 

MARKET MAKING

Market making should be considered more as a type of business than as just a strategy. The main task of a market maker is to provide liquidity to markets by maintaining bid and ask orders to allow other market participants to trade any time they need. Since narrow spreads and adequate prices are among the biggest
factors of exchange’ attractiveness, market making services are in high demand. On the one hand, crypto exchanges have special offers for liquidity providers, but on the other hand, they require from new coins issuers a market maker before they start listing an altcoin.

These agreements are usually one source of market maker income. Another one is a spread – a difference between a buy and a sell prices provided to the other traders. The activity of a market maker is related to some risks. One of them is inventory imbalance – if a market maker buys much more than sells or sells much more than buys, she stays with an open long or short position and takes portfolio risk, especially on volatile crypto markets. This situation may happen in markets with a strong bias, or when market maker is quoting wrong or delayed prices, which will immediately be exploited by arbitrageurs. To avoid such situations, market makers apply algorithmic solutions such as different types of fair price calculations, trade-outs, hedging, trend and order-flow predictions, etc. Technology and math used in market making algorithms are an interesting subject for future articles.

 

Read more about our tool for market making strategies for crypto exchanges  – Liquidity Engine

 

SUMMARY

Fast developing crypto markets are attracting a growing number of participants, including more and more trading professionals from traditional markets. However, the crypto space has its own specificity such as high decentralization, maturing technology and market structure. Compared to other markets, these differences make some strategies more useful and profitable than others. Arbitrage – even simple cross-exchange is still very popular. Market making services are in high demand. Midsized and large orders involve execution algorithms like smart order routing. At the end of the day to embrace the fast changing crypto environment, one needs algorithmic trading systems with an open architecture that evolves alongside the market.

 

To see the original article click the button on the right.

 

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.

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%.

Read here more on our coinbase market making bot and coinbase trading bot.

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

Coinmama 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

About empirica

We are trading software company focused on developing the potential that cryptocurrencies bring to financial markets. Empirica is offering solutions such as Algorithmic Trading System used by professional investors, tools for cryptocurrency liquidity, robo advisory software, crypto trading bots and trading software development services for companies from capital and cryptocurrency markets.

Free version of Algorithmic Trading Platform for retail investors

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

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

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

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

We strive for your feedback!

Best regards,

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


Empirica Trading Platform – https://empirica.io