The days of busy stock exchange floors are almost gone. Recently, around 84 percent of trades have happened in NYSE and 60 percent in LSE through algorithmic trading. Inevitably, every trade will be using algorithmic trading. The global algorithmic trading market expects an annual compound growth rate of approximately 10.36 percent in 2018-2022. ML is expected to be leveraged in the algorithmic trading path shortly.
In order to set up any business organization, the team needs domain knowledge, skilled resources, technology, and infrastructure. Setting up algorithmic desk may vary according to the country where the business is set up. In order to set up one’s own algorithmic desk, the infrastructure requirements like collocation, hardware and software equipment, and network lines are essential.
The traders also build their investment strategy with a cloud-hosted instance using algorithmic trading. Computer programs speed up depending on the stock prices and specific market conditions. For a human trader, accuracy and speed should commute at equal rates. However, it is impossible for a manual trader. Plenty of algorithmic trading tools have been built in the market to help the traders. The tools have been compiled with complex programs that help in avoiding errors.
Algorithmic trading involves strategies like momentum, arbitrage, statistical arbitrage, and market making. Using momentum-based strategies, the trader can judge the time of the particular trend. In order to know which tools suit the trader, backtesting has been playing a vital role. Today, python and R have gained popularity in the backtesting and implementing trading strategies. Before implementing a strategy, the traders have to undergo a mock to give a demo of their strategy. But, the exchange doesn’t require testing each strategy. Instead, it is checked in the trading system and is granted access.
The multiple APIs and libraries like Pandas or Numpy in Python language will enable the traders to have multiple ideas. Traders use high-performance libraries in order to maintain the competitiveness over the equivalents. Using python also reduces the time taken for creating trading platforms.