What is algorithmic trading?
Algorithmic Trading (also called quantitative or automated trading) in simple words describes the process of using computer programs to automate the process of trading (buying and selling) financial instruments (stocks, currencies, cryptocurrencies, derivatives). These computer programs are coded to trade based on the input that has been defined for them. Inputs could be based on the aimed strategy to take advantage of different market behaviors such as the specific change of a price could trigger the algorithms to make some specific trades, or other factors like volume, time or sophisticated algorithms that trade based on trading indicators.
Algorithmic trading strategies and backtesting
Almost all trading ideas are first converted to a trading strategy and coded into an algorithm that then comes to life and ready for execution. Most algorithmic trading strategies are created on the basis of wide trading knowledge on the financial market combined with quantitative analysis and modeling, later the strategies are given to quants programmers who will convert the strategy to executable algorithms.
It is widely common to perform testing on trading strategies before they go live on the market, this practice is known as Backtesting. This is where the algorithm is being tested on historical data to check the algorithm and apply further modifications.
The main idea behind Backtesting is to evaluate the performance of the algorithmic strategy to see if the strategy is behaving the way it was programmed and check the profitability of it using real market data.
For more sophisticated algorithms and firms with more advanced tools, algorithmic strategies perform on what so-called paper trading, where the strategy performs virtual trading without committing any commercial value (trading without money).
The most popular programming languages used to write automated trading strategies are JAVA, Python, and C++. Matlab is also a good tool with a wide range of analytic tools to plot and analyze algorithmic strategies.
Who uses algorithmic trading?
By far the most common fans of performing trades algorithmically are larger financial institutions as well as investment banks alongside Hedge Funds, pension funds, broker-dealer, market makers.
Some well-known algorithmic strategies
On a broad sense most commonly used algorithmic strategies are Momentum strategies, as the names indicate the algorithm start execution based on a given spike or given moment. The algorithm basically detects the moment (e.g spike) and executed by and sell order as to how it has been programmed.
One another popular strategy is Mean-Reversion algorithmic strategy. This algorithm assumes that prices usually deviate back to its average.
A more sophisticated type of algo trading is a market-making strategy, these algorithms are known as liquidity providers. Market Making strategies aim to supply buy and sell orders in order to fill the order book and make a certain instrument in a market more liquid. Market Making strategies are designed to capture the spread between buying and selling price and ultimately decrease the spread.
Another advanced and complex algorithmic strategy is Arbitrage algorithms. These algorithms are designed to detect mispricing and spread inefficiencies among different markets. Basically, Arbitrage algorithms find the different prices among two different markets and buy or sell orders to take advantage of the price difference.
Among big investment banks and hedge funds trading with high frequency is also a popular practice. A great deal of all trades executed globally is done with high-frequency trading. The main aim of high-frequency trading is to perform trades based on market behaviors as fast and as scalable as possible. Though, high-frequency trading requires solid and somewhat expensive infrastructure. Firms that would like to perform trading with high frequency need to collocate their servers that run the algorithm near the market they are executing to minimize the latency as much as possible.
Adaptive Implementation Shortfall algorithm designed for reduction of market impact during executing large orders. It allows keeping trading plans with automatic reactions to price liquidity.
Basket Orders is a strategy designed to automated parallel trading of many assets, balancing their share in the portfolio’s value.
Bollinger bands strategy is a trading algorithm that computes three bands – lower, middle and upper. When the middle band crosses one of the other from the proper side then some order is made.
Commodity channel index strategy is a trading algorithm which actions are dependent on the value of a CCI index which bases on average and variance of some number of last trades.
MACD strategy is a trading algorithm which actions are dependent on two lines of MACD and the MACD Signal Line calculated with EMA.
The strategy is designed to reduce costs interrelated with the market impact of huge orders. It works until the demanded time and may take advantage of the auction on Market Close.
Parabolic SAR strategy is a trading algorithm whose role is to predict market trend change and trade assets in specific market conditions.
Percent of Volume (POV) is a trading algorithm based on volume used to the execution of bigger orders without excessive impact on the market price.
Relative strength index strategy is the trading algorithm which actions are dependent on the value of an RSI index which bases on average wins and losses of a strategy.
Slow Stochastic Oscillator Strategy is build to gain profit on buying/selling shares in specific market conditions.
Statistical Arbitrage (SA) is build to gain profit on simultaneously buying and selling two shares of two correlated instruments.
Time-Weighted Average Price (TWAP) is a trading algorithm based on the weighted average price used to the execution of bigger orders without excessive impact on the market price.
Volume-Weighted Average Price (VWAP) is a trading algorithm based on a pre-computed schedule that is used in the execution of a bigger order without an excessive impact on the market price.
Williams %R strategy is a trading algorithm basing on trend change indicated by Williams %R oscillator. Oscillator leads the strategy to set a long or short position.
Technically Smart Order Routing technology will search for available liquidy across given trading venues, and with mid-point matching will get the best possible chance of price improvements.
Triangular Arbitrage is used when a trader would like to use the opportunity of exploiting the arbitrage opportunity from three different FX currencies or Cryptocurrencies. Triangular Arbitrage happens when there are different rates within the trading venue/s.
Algorithmic Trading Software
Based on the given use case like the size of orders, customizability and experience level there are options available for algo trading software. Larger firms like hedge funds, investment banks or proprietary trading firms use rather more tailored custom-built and advanced tools. When it comes to more individual traders or quants with less capital to trade they will rather use more readymade algorithmic strategies, some on the cloud, some stand-alone.
The most common features of algorithmic trading software are ways to analyze profit/loss of an algorithm on a live market data. There are different protocols available to get, process and send orders from software to market, such as TCP/IP, webhooks, FIX and etc. One important factor for this data processing from receiving to processing and pushing order is measured with latency. Latency is the time-delay introduced to the movement of data from points to points. Considering the changes in price in the market the lower obtained latency the better software reacts to market events hence a faster reaction.
Backtesting is another useful feature that should be included in algo trading software, usually, this software allows traders to apply their automated trading strategies and test it with historical data to evaluate the profitability of their strategies.
Pros and cons of algorithmic trading
Just like any other choice, there are pros and cons to using algorithmic trading strategies and automating the process of trading. Let’s get down with the pros. Based on many expert opinions in investments human emotions could be toxic and faulty when it comes to trading, one perhaps most acknowledged pros of quantitative Trading is taking away human emotions and errors out of trading.
Another huge advantage of algorithmic trading is the increase of speed in action of execution to the market as well as possibilities to test strategies using Backtesting and paper-trading in a simulated manner. Testing quantitative strategies determine the viability of the idea behind trading strategies.
Another vastly discussed advantage of quantitative trading is risk diversification. Algorithmic trading allows traders to diversify themselves across man accounts, strategies or markets at any given time. The act of diversification will spread the risk of different market instruments and hedge them against their losing positions.
Making trading automatically using quant trading decreases the operational costs of performing large volumes of trade in a short period of time.
There are also a few other advantages such as automation in the allocation of assets, keeping a consistent discipline in trading and faster execution.
Now let’s get on with some of the cons of using algorithmic trading. Perhaps one very discussed issue with using algorithmic trading is constant monitoring of the strategies which to some traders could be a bit stressful since the human control in automated trading is much less. Though it is widely common to have lost control features included in strategies and algorithmic trading software (automated and manual ones).
For most individual traders having enough resources could be another disadvantage of Algorithmic trading. The automated trading reduces the cost of executing large orders but it could come expensive as it requires initial infrastructure such as the software cost or the server cost.
|Emotionless trading||Needs for monitoring|
|Less error||Technological infrastructure|
|Higher trading speed||Programming skills required for updating strategies|
|Backtesting and paper trading|
|Lower operational costs|
|Consistent trading discipline|
There are different performance results depending on the basis on which an algorithmic trading strategy is built. Though, as an example, an algorithmically managed fund in 2018 (during which S&P500 index was 19.42% high) SH capital partners posted 234.09% returns. Over the same period, Silver8 Partners and Global Advisors Bitcoin Investment Fund achieved 770.75% and 330.08% returns respectively. Both of these algorithmic trading examples are automatically traded but differ on specific strategies – however, both attribute their success to their Automatic trading winning strategies and their rationale on digital assets.
Even most profitable algorithms with reasonable levels of volatility (eg. a Sharpe ratio of 2+ and max drawdowns of <5–10%) have a limited shelf life because any Algo that produces consistent greater-than-market returns will suffer from alpha decay (the erosion of edge due to others getting in on the action).
In order to provide a better view of performance statistics, we have prepared results from Reinsseance hedge fund, see the table below:
The are some perfromance statistic from the Reinsseance trading firm:
|Year||Net return||Management fee||Performance fee||Returns before fees||Size of the fund||Medallion trading profits|
|1988||9.0%||5%||20%||16.3%||$20 million||$3 million|
|1990||55.0%||5%||20%||77.8%||$30 million||$23 million|
|1992||33.6%||5%||20%||47%||$74 million||$35 million|
|1993||39.1%||5%||20%||53.9%||$122 million||$66 million|
|1994||70.7%||5%||20%||93.4%||$276 million||$258 million|
|1995||38.3%||5%||20%||52.9%||$462 million||$244 million|
|1996||31.5%||5%||20%||44.4%||$637 million||$283 million|
|1997||21.2%||5%||20%||31.5%||$829 million||$261 million|
|1998||41.7%||5%||20%||57.1%||$1.1 billion||$261 million|
|1999||24.5%||5%||20%||35.6%||$1.54 billion||$549 million|
|2000||98.5%||5%||20%||128.1%||$1.9 billion||2,434 million|
|2001||33.0%||5%||36%||56.6%||$3.8 billion||2,149 million|
|2002||25.8%||5%||44%||51.1%||$5.24 billion||2.676 billion|
|2003||21.9%||5%||44%||44.1%||$5.09 billion||$2.245 billion|
|2004||24.9%||5%||44%||49.5%||$5.2 billion||$2.572 billion|
|2005||29.5%||5%||44%||57.7%||%5.2 billion||$2.572 billion|
|2006||44.3%||5%||44%||84.1%||$5.5 billion||$4.374 billion|
|2007||73.7%||5%||44%||136.6||$5.2 billion||$7.104 billion|
|2008||82.4%||5%||44%||152.1%||$5.2 billion||$7.911 billion|
|2009||39.0%||5%||44%||74.6%||$5.2 billion||$3.881 billion|
|2010||29.4%||5%||44%||57.7%||$5.2 billion||$3.881 billion|
|2011||37.0%||5%||44%||71.1%||$10 billion||$7.107 billion|
|2012||29.0%||5%||44%||56.8%||$10 billion||$5.679 billion|
|2013||46.9%||5%||44%||88.8%||$10 billion||$8..875 billion|
|2014||39.2%||5%||44%||75.0%||$9.5 billion||7.125 billion|
|2015||36.0%||5%||44%||69.3%||$9.5 billion||$6.582 billion|
|2016||35.6%||5%||44%||68.6%||$9.5 billion||$6.514 billion|
|2017||45.0%||5%||44%||85.4%||$10 billion||$8.536 billion|
|2018||40.0%||5%||44%||76.4%||$10 billion||7.643 billion|
Source: The man who solved the market, how Jim Simons launched the quant revolution, by Gregory Zuckerman
The Reinsurance hedge fund in total achieved 39.1% net returns, 66.1% average returns before fees and in total $104,530,000,000 total trading profits.
Algorithmic trading in Cryptocurrency
Unlike more mature instruments like stocks, options or CFDs, the Cryptocurrency market is quite volatile. Typically higher volatility leads to more frequent jumps in the price of instruments, higher and lower. Hence, some professional traders find this amusing and opportunistic to make the most of the profits.
Generally, for Cryptocurrency traders, there are plenty of cloud-based solutions using trading bots, though for very professional and institutional traders this may not flexible enough. There are few automated trading platforms for cryptocurrencies which can utilize the need for more sophisticated and institutional traders.
Quantitative Trading Trends
On average 80% of the daily traders across the US are done by algorithmic trading and machines. Though the volume of automated trading can change based on the volatility in the market. According to J.P. Morgan, fundamental discretionary traders are accounted for only 10% of trading volume in stocks. This is the traditional way of checking the companies business performance and their outlook before deciding whether to buy or sell a position.
The growth in the number of algorithmic trading since last year comes close to 47% and there is 41% growth in the number of users executing their trades algorithmically. Mobile also plays an important role in the tools provided there is around 54% growth in trading FX algorithmically using mobile devices.
New technologies, Artificial Intelligence, Machine Learning, Blockchain
According to another J.P. Morgan research, Artificial Intelligence and Machine learning are predicted to be the most influential for shaping the future of trading. Based on this analysis Artificial Intelligence and Machine Learning will influence the future of trading by 57% and 61% in the next three years.
Interestingly this report states that Natural Language Processing alone will count to 5% of the change in the next 12 months and up to 9% in the next three years.
J.P. Morgan’s report shows that around 68% of the traders believe that Artificial Intelligence and Machine Learning provide deep data analytics. Around 62% believe that Artificial Intelligence and Machine Learning optimize trade execution and 49% of traders believe that Artificial Intelligence and Machine Learning represents an opportunity to hone their trading decisions.
The same report indicates that Blockchain within the next 12 months will influence the trading up to 9% and 19% within the next three years. Within the same report, the usage of mobile trading applications is to influence the trading market up to 28% within the next 12 months and 11% within the next 3 years.
Morgan Stanley estimated in 2017 that algorithmic strategies have grown at 15% per year over the past six years and control about $1.5 trillion between hedge funds, mutual funds, and smart beta ETFs. Other reports suggest the quantitative hedge fund industry was about to exceed $1 trillion AUM, nearly doubling its size since 2010 amid outflows from traditional hedge funds. In contrast, total hedge fund industry capital hit $3.21 trillion according to the latest global Hedge Fund Research report.