by Michal Rozanski, CEO at Empirica
Most wealth managers are in deep denial about robo advice. They say they need human interaction in order to understand the nuances of financial lives of their customers. And their clients value the human touch. They’re wrong. Soon robo advice will be much more efficient than human advice ever was.
In this post, we will share the results of our analysis on the most important areas where the application of machine learning will have the greatest impact in taking wealth management to the next level.
What Artificial Intelligence is and why you should care
“Computers can only do what they are programmed to do.” Let us explain this is huge misconception, which was only valid because of limited processing power and memory capacity of computers. Most advanced programs which mimic specialized intelligences, known as expert systems, were indeed programmed around a set of rules based on the knowledge of specialists within the problem’s domain. There was no real intelligence there, only programmed rules. But there is another way to program computers, which makes them work more similarly to the functions of the human brain. It is based on showing the program examples of how certain problems can be solved and what results are expected. This way computers equipped with enough processing power, memory and storage are able to recognize objects in photographs, drive autonomous cars, recognize speech, or analyse any form of information which exhibits patterns.
We are entering the age where humans are outperformed by machines in activities related with reasoning based on the analysis of large amounts of information. Because of that finance and wealth management will be profoundly changed during the years to come.
Real advice – combining plans with execution
A great area for improvement in finance management is the combination of long term wealth building with the current financial situation of the customer as reflected by his bank account. For robo-advisors, an integration with bank API opens the door to an ocean of data which, after analysis, can dramatically improve the accuracy of advice provided to the customer.
By applying a machine learning capabilities to a customer’s monthly income and expenses data, wealth managers will gain a unique opportunity to combine two perspectives – the long term financial goals of their customers and their current spending patterns. Additionally, there is the potential of tax, mortgage, loans or credit card costs optimization, as well as using information on spending history to predict future expenditures.
By integrating data from social media, wealth management systems could detect major changes in one’s life situation, job, location, marital status or remuneration. This would allow for automated real time adjustments in investment strategies of on the finest level, which human advisors are simply unable to deliver.
New powerful tools in the wealth manager’s arsenal
Hedge funds that are basing their strategies on AI have provided better results over the last five years than the average (source Eurekahedge, more on hedge fund software). What is interesting is that the gap between AI and other strategies has been growing wider over the last two years, as advancements in machine learning accelerated.
The main applications of machine learning techniques in wealth management, can be categorized following cases:
- Making predictions on real-time information from sources such as market data, financial reports, news in different languages, and social media
- Analysis of historical financial data of companies to predict the company’s cash flow and important financial indicators based on the past performance of similar companies
- Analysis of management’s public statements and activity on social networks in order to track the integrity of their past words, actions and results
- Help in accurate portfolio diversification by looking for uncorrelated instruments which match requirements of the risk profile (see portfolio management software)
- Generation of investment strategies parametrized by goals such as expected risk profiles, asset categories, and timespan, resulting in sets of predictive models which may be applied in order to fulfill the assumptions
To give an example of machine learning accuracy, the algorithms for sentiment analysis and document classification are already on acceptable levels, well above 90%.
When it comes to the execution of the actual orders behind portfolio allocation and rebalancing strategies, many robo advisors are automating these processes passing generated orders to brokerage systems through algorithmic trading systems. The next step would be autonomous execution algorithms, that take under consideration the changing market situation and learn from incoming data, allowing for increased investment efficiency and reduced costs.
Machine learning can be applied to quantitative strategies like trend following, pattern recognition, mean reversion, and momentum, as well as the prediction and optimization of statistical arbitrage, and pairs trading. Additionally, there is a possibility to apply machine learning techniques in, already quite sophisticated, execution algorithms (aka trading bots) that help execute large orders by dividing them to thousands of smaller transactions without influencing the market while adjusting their aggressiveness to the market situation.
What’s interesting is that algorithms could also be trained to make use of rare events, like market crashes and properly react in milliseconds, already knowing the patterns of panic behaviour and shortages of liquidity provision.
Explaining the markets
In wealth management systems, if portfolio valuations are provided to the customers in real time, then so should explanations of the market situation. Every time the customer logs in to the robo-advisor, she should see all required portfolio information with a summary of market information relevant to the content of her portfolio. This process includes the selection of proper articles or reports concerning companies from the investor portfolio, classification and summarization of negative or positive news, and delivering a brief overview.
Additionally, machine learning algorithms can be used to discover which articles are read by customers and present only those type of articles that were previously opened and read by the customer.
The result will be not only the increase in customer understanding but also, by providing engaging content to investors, the increase in their engagement and commitment to portfolio strategy and wealth management services.
Talking with robots
The ability to deliver precise explanations of the market situation in combination with conversational interfaces aided by voice recognition technology will enable robo-advisors to provide financial advice in a natural, conversational way.
Voice recognition is still under development, but it could be the final obstacle on they way to redesigning human-computer interaction. On the other hand, thanks to deep learning, chatbot technology and question answering systems are getting more reliable than ever. KAI, the chatbot platform of Kasisto, who has been trained in millions of investment and trade interactions, already handles 95 % of all customer queries for India’s digibank.
Decreasing customer churn with behavioral analysis
The ability to track all customer actions, analyzing them, finding common patterns in huge amounts of data, making predictions, and offering unique insights for fund managers delivers a powerful business tool not previously available to wealth managers. What if nervousness caused by portfolio results or market situation could be observed in user behaviour within the system? This information, combined with the results of investments and patterns of behaviour of other investors, can give a wealth manager the possibility to predict customer churn and react in advance.
When speaking with wealth management executives that are using our robo-advisory solutions, they indicate behavioural analysis as one of the most important advancements to their current processes. Customers leave not only when investment results are bad, but also when they are good if there is a fear that the results may not be repeated in the future. Therefore, the timely delivery of advice and explanations of market changes and the current portfolio situation are crucial.
The same model we used to solve the behavioral analysis problem has been proven to predict credit frauds in 93.07% of cases.
Other areas of applying machine learning in the processes supporting wealth management services could be:
- Security based on fraud detection which actively learns to recognize new threats
- Improving sales processes with recommendations of financial products chosen by similar customers
- Psychological profiling of customers to better understand their reactions in different investment situations
- Analysis and navigation of tax nuances
- Real estate valuation and advice
Implementing these AI functions in wealth management systems will be an important step towards the differentiation of the wealth managers on the market. Today’s wealth managers’ tool set will look completely different in five years. Choosing an open and innovative robo-advisory system that tackles these future challenges is crucial. Equally important will be wealth managers’ incorporation of data analytic processes and the use of this data to help their customers.
Artificial intelligence is poised to transform the wealth management industry. This intelligence will be built on modern wealth management software that combine data from different sources, process it, and transform it into relevant financial advice. The shift from data gathering systems to predictive ones that help wealth managers to understand the data, has already started. And wealth management is all about understanding the markets and the customers.
Volume is flawed metric of crypto exchanges liquidity. Because of wash trading practices of many crypto exchanges as well as token issuers, using trading volume as a basis of comparison is misleading. Many exchanges have problems attracting professional market makers and are trying to make shortcuts on the way to attract retail investors. Moreover attracting professional investors requires investments in crypto exchanges system development with stable and performant APIs so they could connect their algorithmic trading systems.]
There are more and more independent initiatives that are taking a closer look at what constitutes a high quality crypto exchange. Three major ones are Blockchain Transparency Institute, CryptoCompare Benchmark and Cointelligence Report. I also take a quick look at the Bitwise report for SEC from March 2019.
Blockchain Transparency Institute
BTI concentrates on analyzing crypto exchanges data feeds to spot wash trading mechanisms and provide the real volume metric which is cleaned out of suspicious activities.
BTI identified 17 of the CoinMarketCap Top 25 crypto exchanges to be over 99% wash traded. This one number alone shows the magnitude of the problem, as well as how volume is a false measure.
According to BTI Report crypto exchanges which are faking their volumes use a variety of different tactics to try and swindle investors. These tactics include buying twitter followers and likes, filling up fake order books, mirror wash trading the largest exchanges with real volume, and trying to disguise their wash trading using various bot settings to not affect price. On many of these exchanges trading high volumes closing the spread would make the volume plummet as the trading bots had no room to wash trade with themselves. Welcome to the wild wild west of no regulation and surveillance.
BTI finds that “all crypto exchanges combined are currently reporting around $50 Billion in daily volume on CMC. After removing all the wash traded volume via our algorithms the accurate number is around $4-5 Billion. About 88-92% of daily trading volume is fabricated depending on the day. Bitcoin’s daily trading volume is about 92% fabricated, which is in line with the space as a whole when comparing our findings to top data sites reporting wash traded volumes.”
And further “On our list of the top 40 largest exchanges with actual volume, Bitcoin’s volume is about 65% fabricated. Almost all of this fabricated volume comes from OKEx, Bibox, HitBTC, and Huobi. Of the top 25 tokens by market cap, Tron and Ethereum Classic are the highest wash traded tokens on our list at 85% fake volume each and coming in at #24 and #25 of the most wash traded tokens.”
Top 10 cryptocurrency exchanges according to real (not wash traded) volume by BTI
CryptoCompare’s Exchange Ranking methodology utilises a combination of 34 qualitative and quantitative metrics to assign a grade to over 100 active crypto exchanges. Metrics were categorised into several buckets ensuring that no one metric overly influences the overall exchange ranking. Each crypto exchange grade is derived from a broad due diligence check using qualitative data, followed by a market quality analysis that uses a combination of order book and transactional data.
Due diligence check comprises of 6 main categories that attempt to qualitatively rate each exchange on the basis of:
- Legal and regulatory metrics
- Calibre of investment
- Team and company quality
- Quality of data provision
- Trade surveillance
Although at Empirica we believe in numbers, I like the qualitative approach, as it’s also possible to prove a correlation of metric like number of employees and business size of the exchange, therefore proving this way it’s quality.
Another important factor is Market Quality. Crypto compare measures the market quality of each exchange using a combination of 5 metrics (derived from trade and order book data) that aim to measure the:
- Cost to trade,
- Market stability,
- Behaviour towards sentiment
- “Natural” trading behaviour
Exchanges were rated based on a combination of 9 of the most liquid BTC and ETH markets.
It’s worth taking a closer look how CryptoCompare report approaches Spread and Liquidity metrics:
“Generally, those exchanges which offer incentives to provide liquidity through either low or negative maker fees will achieve the tightest spreads. Due to the spread being calculated using the best bid and offer, it is misleading to use it as a sole gauge of liquidity and therefore as the market cost to trade; it must be used in conjunction with a depth
measurement to find the likely transaction price for any given size of transaction.”
Good point. And liquidity:
“Market depth is the total volume of orders in the order book. It provides an idea of how much it is possible to trade on crypto exchange, and how much the price is likely to move if large amounts are traded. An exchange with greater average depth is likely to be more stable (i.e flash crashes are much less likely) and allows trading of greater amounts at better prices.
We consider the depth up to 1% either side of the mid price.
Depth = E(depthUp+depthDown)/2
Where depthUp is the total volume that would be required to move the price by 1% upwards from the mid price, and
depthDown is the total volume that would be required to move the price by 1% downwards from the mid price.”
Top 10 crypto exchanges according CryptoCompare quality benchmark:
Cointelligence Rating System
Cointelligence is the most qualitative rating of crypto exchanges from the above. The methodology of the team was to manaully open accounts on all analyzed crypto exchanges and check from the user perspective the core aspects of beeing an exchange customer. The aspects cover:
Usability – covers KYC process, the quality of exchange website, extent of features and how easy it is to get a human answer from support staff.
Performance – functionalities and historical robustness of exchange matching engine, fees height, trading instruments like futures contracts and margin trading.
Team – analysis of the available information about management team behind the crypto exchange, especially business and technical experience of C-level staff, including person responsible for exchange’s security
Risk – information on past hacks, insurance status, account security layers but also regulatory status of cryptocurrency exchange. Based on the geographical location of the exchange headquarters and registration any potential run-ins with the local law or any sign of authorities involvement.
This way Contelligence analyzed 85 crypto exchanges, but only 15 is rated with good quality mark, lead by Liquid and Gemini.
Top 10 cryptocurrency exchanges by Cointelligence by qualitative criteria
- Liquid (Quoine)
- Gibraltar Blockchain Exchange
Bitwise report for SEC
Bitwise analysis is based on detecting wash trading patterns in public marked data published by crypto exchanges. Out of 81 exchanges they have analyzed in March 2019 only 10 were identified as be free of wash trading practices. These exchanges are:
Bitwise identified that only 4,5% (about $275M daily) of officially reported volume (eg by the public sources like coinmarketcap) is the actual volume. The rest is wash traded.
The Bitcoin market is more orderly and efficient than is commonly understood. The 10 exchanges trade as a uniform, highly connected market. They form a singular price. Average deviations from the aggregate price for the ten exchanges is well within the expected arbitrage band when you account for exchange-level fees (~30 basis points), volatility and hedging costs. Arbitrage is operating well. Sustained deviations (defined as deviations >1% that last more than 100 seconds) appear as single white lines on the graph below. The graph demonstrates that the ten exchanges trade at a single unified price.
So although the message about the amount of wash traded volume is alarming, the report shows that the real crypto market is quite concentrated, ordered, efficient and well performing. The rest is just noise.
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By Marek Koza, Product Owner of Empirica’s Algo Trading Platform
Among trading professionals, interest in cryptocurrency 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 exciting 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.
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 and 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, restrict the possibility of algorithmic use, especially in the HFT field. This is crucial for market-making activities, which now require separate deals with trading venues.
As for market-making, we should notice an almost non-existent derivatives market in the crypto world. 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 the index derivatives (contracts that reflect 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 popular cryptocurrencies. Combining it with highly limited margin trading possibilities and none of the index derivatives (contracts that reflect market pricing), we see that many hedging strategies are almost impossible to execute and may only exist as a form of spot arbitrage.
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, cryptocurrency 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.
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, the 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.
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 probably will remain, one of the biggest challenges for cryptocurrency trading. Trading on bitcoin and Ethereum, and all other altcoins with smaller market capitalization, is split among over 200 different exchanges. Executing a larger volume of assets often requires seeking liquidity in 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 aggregate them internally. When an investment decision is made, the strategy splits the order among exchanges that offer the 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.
The days when simple cross-exchange 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 on 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 looks for differences among three currency pairs that 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 the 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 for using triangular arbitrage in the crypto space.
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 the exchange’s 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 price 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 in volatile crypto markets. This situation may happen in markets with a strong bias or when market maker is quoting wrong or delayed prices, which arbitrageurs will immediately exploit. 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 exciting subjects for future articles.
Read more about how we execute market making strategies for crypto exchanges
Fast-developing crypto markets are attracting many 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. To embrace the fast-changing crypto environment, one needs algorithmic trading systems with an open architecture that evolves alongside the market.
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 ($10.000 balance). Strategy performance results and statistics below.
Whether the profit is big or small I leave the answer to you. The rate of return is positive (+0,16$), 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.
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:
- 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
- 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.
- 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
- 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.
- Good exchange connections.
- 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.
- Easy to set up for beginners, making sure that newcomers can navigate the 3Commas bot and make trades without any hassles.
- 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.
- The free access offers a great trial so that users can make full use of the platform.
- A large number of exchange offers a wide array of information centres, making sure that your decision is well thought out with multiple inputs.
- The fact that users can refer and copy portfolios of successful traders.
Weak points of 3 Commas Bot Platform
- 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.
- 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.
- 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.
Read reviews on follwing bot platforms:
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).
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.
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.
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 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.
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.
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.
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.
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.
Read reviews on follwing bot platforms:
The ever-evolving Ethereum Blockchain brought to Cryptocurrency and ICO investors a new gift, the ERC223, a new standard for tokens created on Ethereum. Until now, the newly created tokens on Ethereum public Blockchain should have followed the ERC20 convention. This, by all means, was a huge success and relief for both token owners and investors of that token. ERC20 makes sure the behavior of the token on Ethereum is standard with a defined typical list of rules. The ERC223 is an improvement of the ERC20 protocol and is backward compatible with ERC20, meaning every wallet and software that supports ERC20 works with ERC223. So to get a better picture of this improvement, maybe it’s better to break down how these tokens are created, and let’s start with the ERC20 tokens.
ERC20 and how do such tokens are created?
ERC20 protocol allows token owners and developers to create a token that complies with common, essential behavioral rules. The standard is popular now, especially among ICO investors and their communities. Thanks to ERC20, investors can be certain that the following statement can be true if the token is ERC20:
- Technically tokens can be accepted by almost all exchanges
- Tokens are transferable, and all Ether wallets will automatically store the newly created tokens
- Transactions using that token are done smoothly
A token is in compliance with ERC20 if the developer of the token contract implements the following interfaces:
- The token name with the function returns the name of the token.
- The token symbol with function symbol, it returns the symbol that the token will use.
- The token decimal places a function that returns the unit8 decimals the token uses.
- How much the owner wants to start off with: function balanceOf, it returns the account balance.
- The number of tokens in circulation: function totalSupply, it returns the total token supply.
- The transfer value: function transfer (address _to, unit256 _value), this function is in charge of the transfer events. the function should revert a transaction if the sending account _from does not have sufficient balance.
- The transfer from: function transferFrom, this function is used for withdrawal workflow, it allows contracts on the Blockchain to transfer tokens on the token holder’s behalf.
- The crediting permission function allowance (address _owner, address _spender), it returns the amount which the buyer (_spender) is allowed to withdraw from the owner (_owner).
- The events: with function transfer (address indexed _from, address indexed _to, unit256 _value) its triggered when a token has been successfully transferred and function approval (address indexed _owner, address indexed _spender, unit 256 _value) this must trigger on any successful call.
What did ERC223 add to ERC20, and what are the advantages?
Initially, the idea of ERC223 came to play when the number of lost tokens on Ethereum Blockchain went skyrocketing, this was due to the lack of possibility to handle incoming transactions. Ethereum Blockchain is a leading network for a number of lost tokens. The top 8 ERC20 contracts with losses will come up to approximately 3 million USD worth of tokens. how does this happen? once an ERC20 token is sent to a contract that is not designed to work with that ERC20 token, the contract will not reject the tokens because the contract does not recognize an incoming transaction. Consequently, the token will get stuck in that contract balance. ERC223 will allow users to only send their tokens to either wallet or contracts with the same transfer function, preventing the token’s loss. ERC223 introduces the function transfer (address _to, unit _value, bytes _data). This function transfers tokens by invoking the function tokenFallback in _to, only if _to is a contract. This will allow the smart contract to handle sent tokens actively. Whereas when an ERC20 token is transferred, the token contract is not notifying the receiver that the transfer has occurred, to that end, the address receiver has no possibility to handle the incoming transaction and, therefore, no way to reject not supported tokens.
A seamless token transfer is another advantage of ERC223 over ERC20. An ERC20 transaction between a regular (not a contract) and a contract are two different transactions. These two functions need to be triggered, first, the approve function on the token contract and later the transferForm on the other contract (the receiver). ERC223 has addressed this more efficiently by allowing to use of the same transfer function. ERC223 could be sent by only calling the transfer function on the token contract with no if the receiver is a regular wallet address or a contract. Due to this shortcut, another advantage that ERC223 has is the gas cost, ERC233 consumes almost half as much as an ERC token.
So as discussed above, ERC223 advantages over ERC20 come down to the following points:
- provides a possibility to prevent accidentally losing tokens
- Allows users to transfer tokens anywhere (owned address or contract) using one function
- allows contract developers to manage incoming transactions, contract developers could implement contracts in a way that only works with some specific incoming tokens and handle them in a specific way which could also each token could be handled in a specific way.
- ERC223 consumes almost half gas as ERC20
Currently is not possible to upgrade the existing ERC20 token contract to ERC223, but if you are planning to create your own, maybe it’s a good idea to go with ERC223.
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