Aritcles describing research and insights on different algorithmic strategies

Algorithmic Trading – A complete guide

What is algorithmic trading?

 

Algorithmic Trading in simple words is to use computer programs to automate the process of trading (buying and selling) financial instruments (stocks, FX pairs, Cryptocurrency, options). 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 algorithmic 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 algorithmic trading is 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. 

Algorithmic Trading Software

 

Based on the given use case like the size of orders, customizability and experience level there are options available for algorithmic 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 algorithmic trading software, usually, this software allows traders to apply their algorithmic 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 Algorithmic 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 algorithmic strategies determine the viability of the idea behind trading strategies.

Another vastly discussed advantage of algorithmic trading is risk diversification. Algorithmic trading allows traders to diversify themselves across man accounts, strategies or market 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 algorithmic 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 algorithmic 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 algorithmic trading itself 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 

 

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 algorithmic trading platforms for cryptocurrencies which can utilize the need for more sophisticated and institutional traders. 

 

Algorithmic Trading Trends:

 

On average 80% of the daily traders across the US are done by algorithmic trading and machines. Though the volume of the algorithmic 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 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. 

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 which 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 questions 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 earn real money 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 bots for trading makes life easier. It can save traders a lot of time but will give it earn real money? These types of solutions 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. I couldn’t find any information about latency, what is the maximum number of orders that can be sent  per second. You need to have significant capital resources to have access to this type of technology because it can give you an advantage on the market. Therefore, institutional investors (for example hedge funds) with large capital are able to achieve success in trading. But if you want to start trading with bots this is a good beginning to see how it works. It is best to make use of the free trial initially and confirm whether this is what you want before getting one of the paid plans as per your requirements. But no matter what, it is high time to get into the crypto market.

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.

 

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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.

VWAP Algorithm

VWAP

Description

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

The schedule is a heart of a strategy. To compute it strategy first should look in a historical data. As an input user defines n the Number of Intervals and delay between each two of them. This gives us a partition of period since Start Time until End Time into intervals I_1, \ldots I_n. Using the historical data strategy have to estimate how big fraction of a volume traded between Start Time and End Time is traded in each time interval – this values are denoted by u_j for each time interval I_j.

Notice that the following holds:

  • \sum_{j = 1}^{n} u_j = 1
  • u_j \geq 0 for each value of j \in \{1, 2, \ldots n\}

On a base of the above considerations strategy can estimate the size of a trade which should be traded in the end of each time interval to minimize market price strategy’s self-impact. For a i-th time interval it is defined as follows:

x_j = u_j \times Target~quantity.

Larger market participation does more impact on a market asset’s price weighted by volume, which is expressed by formula:

VWAP = \frac{\sum_i v_i p_i}{\sum_i v_i},

where i is every trade. Therefore strategy tries to keep steady market participation in each of intervals.

If, defined above, predictions of volume fractions in each interval are proper then the algorithm works perfectly, otherwise it can cause huge impact on a market price. To prevent this bad situation more advance versions of this algorithm takes into the account also actual volume and modify their schedule to fit the market conditions.

Market Data

Volume-Weighted Average Price known as VWAP is one the most basic and commonly used market indicators by traders around the world. In a book “Algorithmic & Trading DMA” we can read about VWAP that  “As a benchmark, it rapidly became ubiquitous since it gives a fair reflection of market conditions throughout the day and is simple to calculate. This led to algorithms that tracked the VWAP benchmark becoming extremely popular.”

  • Last trade
  • Best quote
  • Order book
  • Statistics
  • Historical market data

Parameters

PARAMETER NAME DESCRIPTION ESSENTIAL
Target Quantity Overall quantity to be realized by strategy Yes
Number of Intervals Number of intervals to be used Yes
Delay Table Delay times between following orders Yes
Start Time Time when strategy begins to submit orders Yes
End Time Time when strategy stops working No
Side Market side for orders Yes

Conditions

Open position

Side Defined by Side parameter
Amount Proper step size (x_j defined in the Description)
Price Last market price
Type Limit or Market Order

Strategy opens positions every time the delay value is reached.

Close position

Strategy does not close its opened positions.

Termination

Strategy ends when the declared orders’ quantity has been realized. By design around defined End Time.

Time frame

This strategy is dedicated to be used in short period of time like one day.

 

Calculations

 

Calculation of VWAP it’s relatively simple and it can be done even on piece of paper for small amount of data. In mathematical approach VWAP is represented by equation below:

 

wzor 1.jpg

 

where P is the price of i-th trade and V is the size related to i-th trade. In fact it takes five steps to calculate your first VWAP. First, only if we use intraday data for examination, we need to calculate typical price for our intervals. Then multiply the price by period’s volume and create running total of these values for future trades. Fourthly we create cumulative volume and in the end we divide cumulative multiplication of price and volume by running total of volume to obtain VWAP. Even simpler, VWAP is a turnover divided by total volume.

 

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 Software used by professional investors and services of development of trading algorithmsRobo Advisory softwarecrypto trading bots and trading software development services for companies from capital and cryptocurrency markets.

 

Let’s take a look at example results calculated using these five steps on 1-minute interval intraday Morgan Stanley’s data.

Time Close High Low Open Volume Typical Price Price*Volume Total PV Total Volume VWAP
09:30:00 38.90 38.96 38.90 38.96 69550 38.93 2707581.50 2707581.50 69550.00 38.930
09:31:00 38.94 38.97 38.86 38.92 27617 38.92 1074922.68 3782504.18 97167.00 38.928
09:32:00 38.91 38.96 38.91 38.94 11441 38.93 445398.13 4227902.31 108608.00 38.928
09:33:00 38.89 38.94 38.88 38.92 23587 38.91 917710.61 5145612.93 132195.00 38.924
09:34:00 38.90 38.94 38.90 38.90 10771 38.91 419099.61 5564712.54 142966.00 38.923
09:35:00 38.97 38.97 38.90 38.90 12721 38.93 495276.23 6059988.77 155687.00 38.924
09:36:00 38.92 38.96 38.92 38.96 16471 38.94 641384.86 6701373.63 172158.00 38.926
09:37:00 38.90 38.93 38.86 38.93 23788 38.91 925472.14 7626845.77 195946.00 38.923
09:38:00 38.90 38.92 38.89 38.89 9170 38.90 356701.54 7983547.30 205116.00 38.922
09:39:00 38.92 38.92 38.88 38.91 4644 38.91 180682.02 8164229.32 209760.00 38.922
09:40:00 38.90 38.92 38.88 38.91 4917 38.90 191283.59 8355512.92 214677.00 38.921

All calculations are pretty straightforward, but let us take a look at one interesting element. When you look at typical prices more than half of them (7/11) is below the last VWAP At the same time mean equals 38.917. So where does the difference come from? Volume is the culprit. In our case, period with higher typical price also has bigger Volume, thus bigger market impact and VWAP calculations indicate that.

 

Intraday or tick

 

The most classical VWAP approach is based on tick-by-tick data. But as the market grows and frequency of trades increases more resources are required to keep all calculations up-to-date. Nowadays it is nothing extraordinary for stock to have over hundred trades per minute (true or false?). When multiplied by minutes in a trading day and number of stocks it develops into numbers that might cause some performance troubles.

 

With help arrives intraday data, i.e. tick-by-tick data aggregated in time periods e.g. 1-minute, 5-minute or 15-minute that contains close, high, low and open price. As in VWAP calculations only one price is required we have to somehow average available prices. For this task exist typical price:  

 

typical

 

Also there is a second version of typical price that includes Open Price and it’s divided by 4.

 

Strategy

 

Most likely we can point out two different strategies of reading VWAP. First one used especially by short-term traders relies on waiting for VWAP to cross above the market price and then enter long position as they interpret price to be bullish. On the other hand are Institutions looking to sell at this moment because they consider it as good opportunity for that day’s price.

 

When the price goes below VWAP value, the trend seems to be down. Institutions recognize it as good moment to buy, but short-term trader will look to short that stock.   

 

Surely it’s basic approach to VWAP interpretation. For your strategy you would like to scrutinize e.g. influence of price deviation from VWAP value. You should consider that VWAP behaves differently based on period of trading day. It’s because of VWAP cumulative nature. VWAP value is very sensitive for price changes at the beginning of day, but insensitive at the end of trading day.

 

Big Fish

 

VWAP is surely commonly used between traders with strategies described above, but on the market there is a bunch of various indicators like VWAP that can suggest when to buy or sell shares. But there is other side of the fence.

 

Let’s say you want to buy 5 million shares of Morgan Stanley that is 37% of average daily volume in 2014. You cannot buy them at once, because that will impact significantly the market and the market will start to go against you. What you want to do is split the order in small pieces and execute them without impacting the market. Doing it by hand would be backbreaking, that’s what trading application has been made for.

 

Using trading application and VWAP Strategy, utilizing historical minute intraday files, you can easily generate average volume period profiles that will steadily buy proper number of shares without impacting the market.

 

Improve your VWAP

 

As we mentioned in previous paragraph there is a way to improve VWAP performance by creating volume profiles based on historical data. According to Kissel, Malamut and Glantz optimal trading strategy to meet VWAP benchmark can obtained by using equation:

 

wzor 3.jpg

 

where X is the total volume traded, uj is percentage of daily volume traded and xj is target quantity for each j-th period. Hence, VWAP can be calculated as below:

 

wzor 4

 

wherePj is the average price level in each period.

 

Read more on how we develop trading algorithms for capital and cryptocurrency markets

 

Summary

 

VWAP is really simple indicator although it can be interpreted in various ways depending on goal and approach of the trader. It is mainly used by mutual and pension funds, but also by short-term traders. Aside from buying/selling small amount of shares, VWAP might be used as strategy for trading  huge number of shares without impacting the market. “Simplicity leads to popularity.”

 

References

  1. Berkowitz, S., D. Logue, and E. Noser. “The Total Cost of Transactions on the NYSE.”Journal of Finance,41 (1988), pp.97-112.
  2. H. Kent Baker, Greg Filbeck. “Portfolio Theory of Management” (2013) , pp.421
  3. Barry Johnson “Algorithmic & Trading DMA – An introduction to direct access trading strategies” (2010), pp. 123-126

 

Bitcoin and Arbitrage: hand in hand

HFT – the good, the bad and the ugly

High Frequency Trading, known also as HFT, is a technology of market strategies execution. HFT is defined by technically simple and time costless algorithms that run on appropriate software optimized for data structures, level of memory usage and processor use, as well as suitable hardware, co-location and ultra low-latency data feeds.

 

Although HFT exists on the market for over 20 years, it has became one of the hottest topic during past few years. It is caused by several factors, such as May 6, 2010, “Flash crash”, latest poor situation on the market and Michael Lewis book – “Flash Boys”. Let’s look where all that fuss comes from.

 

The Bad

 

Among other things, the advantage over other market participants and ability to detect market inefficiencies is the reason why so many people critics HFT so much. Most common charges put on the table are:

 

  • Front Running – HFT companies use early access to incoming quotes to buy shares before other investors and then turn around and sell him just bought shares with slightly bigger price.
  • Quote Stuffing – Way of market manipulation by quick sending and withdrawing large number of orders. Because of speed of operations, it creates a false impression of the situation on the market that leads other participants to executing against phantom orders. Then there is nothing else to do, but to exploit favorable prices by HFT investors.
  • Spoofing – Another method for market manipulation by placing orders and then cancelling them for price increase/decrease. It is based on placing big order on the market to bait other investors, and when the market starts to react, quickly cancel it. Then new price allows to gain some profit by HFT investor.

 

But that’s just a tip of the iceberg. It can be often heard that there is lack of proper HFT regulations, exist false belief that there are Dark Pools without any regulations where HFT companies can hide their activity, and there is still active argument if HFT brings liquidity to the market or just useless volume.

 

The Ugly?

 

Bill Laswell once said “People are afraid of things they don’t understand. They don’t know how to relate. It threatens their security, their existence, their career, image.” That phrase perfectly fits to what is happening now on High Frequency Trading topic. When people would like to take a closer look on how exchanges work, probably, they would be less sceptic to High Frequency Trading.

 

Thus, on most, maybe even on all, exchanges exist two mechanism which can efficiently handle problem of quote stuffing and spoofing. First of them is limitation of number of messages per second that can be send from one client. For example on New York Stock Exchange there is a limit of 1000 messages/sec, so it means that if HFT company burst whole 1000 of messages in first half of the period, in second half it cannot send any message, so it’s cut out of the market. Other limitation used by exchanges is a limit of messages per trade. It hits even harder in quote stuffing and spoofing. In most of the cases limit is around 500 messages per trade and if someone exceed it then he should be prepared for fines. On top of it company that frequently break limits could be banned from exchange for some time.

 

If we talk about front running, first thing we have to know is a fact that front running, in the dictionary meaning, is illegal action, and there are big fines for caught market participants who use it. Front running is using informations about new orders before they will go to the order book. Let’s say Broker gets new order with price limit to process, but before putting it to exchange, he will buy all available shares at better price than limit and then he execute client’s new order at limit getting extra profit. That’s highly not allowed and that’s not what HFT companies do.

 

All they do is tracking data feed, analyzing quotes, trades, statistics and basing on that information they try to predict what is going to happen in next seconds. Of course, they have advantage due to latency on data feed and so on, because of co-location, better connection and algorithms, but it’s still fair.

Hft-scalping-for-large-orders.svg

(source: Wikipedia)

 

HFT companies have to play on the same rules as other market participants, so they don’t have any special permits letting them do things not allowed for others. Same with Dark Pools, specially that they are regularly controlled by Finance Regulators.

 

The Good

 

First, we have to know that suppliers of liquidity, i.e. Market Makers and some investors use HFT. They place orders on both sides of the book, and all the time are exposed to sudden market movement against them. The sooner such investors will be able to respond to changes in the market, the more he will be willing to place orders and will accept the narrower spreads. For market makers the greatest threat is the inability to quickly respond to the changing market situation and the fact that someone else could realize their late orders.

 

System performance in this case is a risk management tool. Investments in the infrastructure, both a software and hardware (including co-location), are able to improve their situation in terms of risk profile. The increase in speed is then long-term positive qualitative impact on the entire market, because it leads to narrowing of the spread between bids and offers – that is, reduce the transaction costs for other market participants, and increase of the liquidity of the instruments.

 

HFT AND MARKET QUALITY

 

In April of 2012. IIROC (Investment Industry Regulatory Organization of Canada), the Canadian regulatory body, has changed fee structure based so far only on the volume of transactions, adding the tariffs and fees that also take into account the number of sent messages (new orders, modifications and cancellations). In result, introducing new fees made trading in the high frequencies more difficult. It was very clearly illustrated by data from the Canadian market.

 

Directly in the following months these fees caused a decrease in the number of messages sent by market participants by 30% and hit, as you might guess, precisely the institutions that use high-frequency trading, including market makers. The consequence for the whole market was increase in the average bid-ask spread by 9%.

NO PLACE FOR MISTAKES

 

When people talk about HFT, both enthusiast and critics, it is not rare to hear that HFT is risk free. Well, on the face of it, after analyzing how HFT works you would possibly agree with it, but there is a dangerous side of HFT that can be not so obvious and people often forgot about it. HFT algorithms works great if the code is well written, but what would happen if someone would run wrong, badly tested or incompatible code on a real market?

 

We don’t have to guess it, because it happened once and it failed spectacularly, it was a “Knightmare”. Week before unfortunate 1st of August Knight Capital started to upload new version of its proprietary software to eight of their servers. However Knight’s technicians didn’t copy the new code to one of eight servers. When the market started at 9:30 AM and all 8 server was run, the horror has begun. Old incompatible code messed up with the new one and Knight Capital initiated to lose over $170,000 every second.

(source: nanex.net)

It was going for 45 minutes before someone managed to turn off the system. For this period Knight Capital lost around $460 million and became bankrupt. That was valuable lesson for all market participants that there is no place for mistakes in HFT ecosystem, because even you can gain a lot of money fast, you can lose more even faster.

 

SUMMARY

 

HFT is a natural result of the evolution of financial markets and the development of technology. Companies that invest their own money in technology in order to take advantage of market inefficiencies deserve to profit like any other market participant.

 

HFT is not as black as is painted.

 

Aldridge, Irene (2013), High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems, 2nd edition, Wiley,

 

Basics of High Frequency Trading

Nanex’s High Frequency Trading Model (Sped Up)

Nanex released a video showing the results of half a second of worldwide high frequency trading with Johnson and Johnson stock. I simply sped up the footage to get a better feel of what it looked like. Blow Your Mind.

High frequency trading in action

CNN’s Maggie Lake gets a rare look inside the super-fast trading industry.

High Frequency Trading Explained (HFT)
Dave Fry, founder and publisher of ETF Digest, and Steve Hammer, founder of HFT Alert, discuss high frequency trading operations, fundamentals, the difference between algorithmic trading and high frequency trading, fluttering, latency and the role high frequency trading had in the May stock market flash crash in 2010.
 
TEDxNewWallStreet – Sean Gourley – High frequency trading and the new algorithmic ecosystem

Dr. Sean Gourley is the founder and CTO of Quid. He is a Physicist by training and has studied the mathematical patterns of war and terrorism. He is building tools to augment human intelligence.

Watch high-speed trading in action

Citadel Group, a high-frequency trading firm located in Chicago, trades more stocks each day than the floor of the NYSE.

Wild High Frequency Trading Algo Destroys eMini Futures

One of the scariest high frequency trading algos ran in the electronic S&P 500 futures (eMini) contract on January 14, 2008 starting at 2:01:11Eastern. During its 7 second reign, there were over 7,000 trades (52,000 contracts), and the price eventually oscillated within milliseconds, the equivalent of about 400 points in the Dow Jones Industrial Average!


HFT trading ideally must have the lowest possible info latency (time delays) and the maximum potential automation level. So participants prefer to trade in markets with high levels of integration and automation capacities in their trading platforms. These include NYSE NASDAQ, Direct Edge and BATS.
HFT is controlled by proprietary trading firms and spans across multiple securities, including equities, derivatives, index funds and ETFs, currencies and fixed income instruments.For HFT, participants want the following infrastructure in place:
– High speed computers, which need costly and regular hardware upgrades;
– Co-location.
– Real time data feeds, which must avert even the delay which could affect profits; and of a microsecond
– Computer algorithms, which are the heart of HFT and AT.

Benefits of HFT
– HFT is beneficial to traders, but does it help the total marketplace? Some market that is overall gains that HFT assistants cite contain:
– Bid-ask spreads have reduced due to HFT trading, making markets more efficient. Empiric evidence contains that after Canadian authorities in April 2012 imposed fees that deterred HFT, studies indicated that “the bid-ask spread rose by 9%,” possibly due to diminishing HFT trades. And thus facilitates the effects of market fragmentation.

– HFT assists in the price discovery and price formation process, as it is centered on a high number of orders (see related: How The Retail Investor Profits From High Frequency Trading.)

Basics of Machine Learning in Algorithmic Trading

Algorithmic Game Theory and Practice, Michael Kearns, University of Pennsylvania,

Traditional financial markets have undergone rapid technological change due to increased automation and the introduction of new mechanisms. Such changes have brought with them challenging new problems in algorithmic trading, many of which invite a machine learning approach. I will briefly survey several algorithmic trading problems, focusing on their novel ML and strategic aspects, including limiting market impact, dealing with censored data, and incorporating risk considerations.

 

 

Machine learning for algorithmic trading w/ Bert Mouler

Harnessing the power of machine learning for money making algo strategies with Bert Mouler

Practical Tips For Algorithmic Trading (Using Machine Learning)
Evgeny Mozgunov from Caltech won an algorithmic trading competition hosted by Quantiacs. Jenia used machine learning tools to write his trading algorithm that now trades an initial $1M investment. He is talking about his approach and his main learnings. Jenia’s algorithm currently has a live Sharpe Ratio of 2.66.
 
Machine Learning and Pattern Recognition for Algorithmic Forex and Stock Trading
This is a whole course (20 videos) on machine learning and algorithmic trading. In this series, you will be taught how to apply machine learning and pattern recognition principles to the field of stocks and forex. This is especially useful for people interested in quantitative analysis and algo trading. Even if you are not, the series will still be of great use to anyone interested in learning about machine learning and automatic pattern recognition, through a hands-on tutorial series.
 

The following frontier of the technological arms race in finance is artificial intelligence. Improvements in AI research have triggered massive curiosity about the sector, where some consider a a trading, learning and thinking computer will make even today’s superfast, ultra-complicated investment algorithms appear archaic — and potentially leave human fund managers redundant. Could the next generation’s Buffett be a super-algo?

Some of the world’s largest cash managers are betting on it. AI investing may sound as, although fantastical sci-fi writer William Gibson said The future is already here, it’s simply not evenly dispersed.” Bridgewater, the world’s biggest hedge fund group, poached the head of IBM’s artificial intelligence unit Watson in 2012, and Two Sigma and last year BlackRock, another rapidly growing hedge fund that uses quantitative models, hired two former top Google engineers. Headhunters say computer scientists are now the hottest property in finance.

The quantitative investment world plays down the prospect of machines arguing that human genius still plays an important part, pointing out that the prospect of artificial intelligence that is complete is still distant, and supplanting human fund managers. But the confident swagger of the cash management nerds is unmistakable. There are quasi-AI trading strategies working their magic and the future belongs to them, they predict.

Artificial intelligence and video that is financePlay
The time will come that no human investment manager will manage to defeat the computer.” Or, as Agent Smith put it succinctly in The Matrix: “Never send a human to do a machine’s occupation.”

Yin Luo first learned to code after an used Apple II computer was brought back to China by his dad from a business trip to West Germany in 1985, when he was 11. But there were no games to purchase his home town in Heilongjiang province, in Yichun, so he made a crude variation of Tanks, where the player shoots down randomly created aeroplanes and taught himself to program.
It was arduous work. The computer’s lack of memory meant it crashed the program coding grew not too simple. He’d no floppy discs, so he learnt how to save the info on cassette tapes. “I just really needed something to play with,” Mr Luo recalls.

But the expertise paid off. Now, he is part of a growing tribe of brainiacs on Wall Street investigating the bleeding edge of computer science.

A network of 20 Linux servers is needed to run the hyper-sensible “ adaptive style turning” that was linear model, which is founded on a “machine learning” algorithm.

Machine learning is a branch of AI a diffuse term that is certainly frequently misused or misunderstood. While many people comprehend AI to mean sentient computers like the archvillains SkyNet in the Terminator films or HAL 9000 in 2001: A Space Odyssey, in practice everyday tools including Google’s language translation service, Netflix’s film recommendation engine or Apple’s Siri virtual assistant install basic forms of AI.

Quants have long used increasingly powerful computers to crunch numbers and uncover statistical signs of money-making opportunities, but machine learning goes a step further.

It can learn the difference between apples and bananas and sort out them, or perhaps instruct a computer how to play and quickly master a game like Super Mario from scratch. Machine learning can also be unleashed on “unstructured data”, such as for example jumbled amounts but also pictures and videos which can be typically not easy for a computer to comprehend.

More powerful computers mean that it are now able to be applied to financial markets, although the technique is old. “It’s a very bright area,” Mr Luo says. “Artificial intelligence is able to help you find designs an individual would never see. That may give you an enormous advantage.”

But that is not the only advantage of machine learning.

When marketplaces undergo what industry participants call a regime change that is “ ” and trusted strategies no more use, one of the classic challenges for quants is that their models can often prove worthless — or worse. Algorithmic trading strategies that print cash one day can blow the next up.

A machine learning algorithm adjusting to what works in markets that day, will autonomously develop and search for new patterns.
That means they can be used by asset managers as something to develop trade and strategies by itself, or perhaps to enhance their investment process, maybe by screening for patterns undetectable by people.

For Nick Granger, a fund manager at Man AHL, a quant hedge fund, that is the advantage that is critical. “You see it creating intuitive trading strategies in the bottom up, changing styles according to what works,” he says. “ We have been using machine learning for the past few years and have an interest in investing it in more.”

Nonetheless, machine learning has pitfalls. One of the largest challenges for quants is a phenomenon called “overfitting”, when an algorithm that is coded or exceedingly complicated finds false signals or specious correlations in the noise of data. For instance, a blog called “Spurious Correlations” notes that margarine consumption is linked to Nick Cage pictures to swimming pool drownings, and divorce rates in Maine.

When confronted by actual markets even if your model functions well in testing it can fail. Also, new data can be changed by the trading algorithm, says Osman Ali, a quant at Goldman Sachs’ asset management arm. “ you’re not affecting the weather, but if you deal marketplaces they are being affected by you If you crunch weather data.”

Nor can the most complex AI think as creatively as an individual, especially in a crisis. Brad Betts, a former Nasa computer scientist now working at BlackRock’s “ active equity” arm that is scientific, emphasizes the 2009 emergency plane landing on the Hudson river by Chesley Sullenberger of when machine is trumped by man as an example.

Truly, some quants remain sceptical that machine learning — AI or more broadly — is a holy grail for investment. Many see it just as a fresh, sophisticated gizmo to supplement their present toolkit, but others claim it truly is mainly a case of intelligent marketing rather than something genuinely ground-breaking.

They do’t constantly work, although “People are always desperate to find new ways to earn money in financial markets. He points out the human brain is uniquely adept at pattern recognition, “whether it really is love, a triangle or a face. Investment management is totally amenable to being addressed by computers designed to see patterns, but I’m not going to rush to use the latest hot algo to do so.”