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 has been defined for them. Inputs could be based on the aimed strategy to take advantage of different market behaviours 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 which 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 modelling, 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 algorithms, 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 price 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 behaviours 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.

Pros and cons of algorithmic trading

Just like any other choice, there are pros and cons 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.

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