Algorithmic stock trading—aka “algo-trading”—uses machine learning algorithms to make stock market trades faster than a human could, calculating the best time, price, and amount to trade in an instant.
Algo-Trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. The trade, in theory, can generate profits at a speed and frequency that is impossible for a human trader. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders. Apart from profit opportunities for the trader, algo-trading renders markets more liquid and trading more systematic by ruling out the impact of human emotions on trading activities. Popular “algos” include Percentage of Volume, Pegged, VWAP (volume-weighted average price), TWAP (time-weighted average price), Implementation Shortfall, and Target Close.
Algo-trading provides a lot of benefits in several dimensions, allowing you to maximize profits, maximize human time, and improve accuracy and prevent errors.
Algo-trading enables you to maximize profits by executing trades at the best possible prices and under reduced transaction costs. Similarly, they reduce human labor by running simultaneous automated checks on multiple market conditions. Finally, because trade order placement is instantaneous and highly accurate at the desired levels, you can avoid significant price changes. Moreover, you can reduce human errors, especially those based on emotional and psychological factors.
There are many strategies of algo-trading, each one using different types of data. However, there are several common parameters, such as inter-day stock prices, stock opening and closing prices, high and low daily stock prices, 50- and 200-day moving averages, daily trading volume, and available historical data for back-testing. Of course, the internal data used depends on the complexity of rules implemented in the algorithm.
To improve your Algo-Trading model, it is essential to use Corporate Data and real-time stock data.
Index Fund Rebalancing
Algo-traders have the opportunity to make 20-80% basis points profits when index funds go through their periodic rebalancing.
News and Social Media
Twitter and Facebook in particular impact stock prices. For example, US President Trump’s tweets about trade war with China have affected international markets.
The algo-trading use case has several challenges. A major one is errant algorithms, where one faulty or errant algorithm can rack up millions in losses in a very short period due to the extremely high speed at which algorithmic trading takes place.
Additional risks and challenges include general system failures, faulty or delayed network connections, and, most dangerous of all, imperfect algorithms. Back-testing of algorithms is especially important when the algorithms move around so much money so quickly. And as the complexity of an algorithm increases, so too does the importance of quality checking and back-testing.
Algorithmic trading is quickly replacing manual (human) trading as people can readily see its advantages. For just one example, algo-trading machine learning tools were used to try to predict the behavior of the S&P 500 throughout the day. By running several models at one, the accuracy of market predictions rises—five models running at once predicted an Up day with over 85% accuracy.
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