Description of Automated Trading
Automated trading is a process through which you can execute a trading strategy with an algorithm. Algorithms is being used for trading in financial markets and because of that, trading does not depend on emotions which can make funds and financial institutions lose a lot of money especially when there is a rapid price drop.
Emotions can also cause panic and can be the reason for miscalculation, while algorithmic trading gives an opportunity to make a decision based off fear. Because many types of algo trading activities are described as high-frequency trading, a state of confusion may appear. High frequency trading is a form of automated trading which uses automated systems to interpret market patterns and signals. In order to create an algorithm we must have a trading strategy. Then, this strategy is turned into a computer program by a software expert.
The most common types of Automated Trading are:
- trend following strategies;
- arbitrage opportunities;
- mathematical model based strategies;
- trading range;
- volume weighted average price (VWAP);
- time weighted average price (TWAP);
- percentage of volume;
- implementation shortfall.
It is important to note that algorithms have no value if the strategies do not perform. Because of that, automated trading strategies are back tested using historical data. Only after a few weeks we can stabilize the algorithms when live trading begins because all systems have errors and some unpredictable issues. It is a hard work but will allow us to stabilize these algorithms and trade a larger amount of money.
The automated trading can be divided into two parts. The first part is meant to identify buying or selling opportunities meaning what to buy or sell and when to buy or sell. The second part determines the ways of executing the trades. The second type takes into consideration:
- what would the type of order that would use be;
- what would be the stop-loss;
- at what price the stop-loss would be executed
In automated trading algorithms can be used in different stages of the trade cycle and can be classified into pre-trade analytics, execution stage and post-trade analytics.